Review

A Comprehensive Review on Lithium-Ion Battery Lifetime

Prediction and Aging Mechanism Analysis
Seyed Saeed Madani 1, * , Yasmin Shabeer 1 , François Allard 2 , Michael Fowler 1 , Carlos Ziebert 3 ,
Zuolu Wang 4 , Satyam Panchal 1 , Hicham Chaoui 5 , Saad Mekhilef 6 , Shi Xue Dou 7,8 , Khay See 8
and Kaveh Khalilpour 9

1 Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;

yshabeer@uwaterloo.ca (Y.S.); mfowler@uwaterloo.ca (M.F.); satyam.panchal@uwaterloo.ca (S.P.)
2 Centre Énergie, Matériaux et Télécommunications (EMT), Institut National de la Recherche
Scientifique (INRS), Varennes, QC J3X 1P7, Canada; francois.allard@inrs.ca
3 Institute of Applied Materials-Applied Materials Physics, Karlsruhe Institute of Technology,
76131 Karlsruhe, Germany; carlos.ziebert@kit.edu
4 Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK;
z.wang3@hud.ac.uk
5 Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada; hichamchaoui@cunet.carleton.ca
6 School of Science, Computing and Engineering Technologies, Swinburne University of Technology,
Melbourne, VIC 3122, Australia; smekhilef@swin.edu.au
7 Institute for Superconducting & Electronic Materials (ISEM), Australian Institute for Innovative
Materials (AIIM), University of Wollongong, Wollongong, NSW 2500, Australia; shi@uow.edu.au
8 Institute of Energy Materials Science, University of Shanghai for Science and Technology,
Shanghai 200093, China; kwsee@uow.edu.au
9 Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia;
kaveh.khalilpour@uts.edu.au
* Correspondence: ssmadani@uwaterloo.ca

Abstract: Lithium-ion batteries experience degradation with each cycle, and while aging-related
deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial
to slowing it down. The aging processes in these batteries are complex and influenced by factors
such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors
Academic Editor: Pascal Venet including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations
or extreme temperature conditions play a significant role in accelerating degradation, making
Received: 13 February 2025
Revised: 10 March 2025 them central to aging analysis. Battery aging directly impacts power, energy density, and reliabil-
Accepted: 20 March 2025 ity, presenting a substantial challenge to extending battery lifespan across diverse applications.
Published: 26 March 2025 This paper provides a comprehensive review of methods for modeling and analyzing battery
Citation: Madani, S.S.; Shabeer, Y.; aging, focusing on essential indicators for assessing the health status of lithium-ion batteries.
Allard, F.; Fowler, M.; Ziebert, C.; It examines the principles of battery lifespan modeling, which are vital for applications such
Wang, Z.; Panchal, S.; Chaoui, H.; as portable electronics, electric vehicles, and grid energy storage systems. This work aims
Mekhilef, S.; Dou, S.X.; et al. A
to advance battery technology and promote sustainable resource use by understanding the
Comprehensive Review on
variables influencing battery durability. Synthesizing a wide array of studies on battery aging,
Lithium-Ion Battery Lifetime
Prediction and Aging Mechanism the review identifies gaps in current methodologies and highlights innovative approaches for
Analysis. Batteries 2025, 11, 127. accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage
https://doi.org/10.3390/ advanced algorithms to improve predictive model precision, ultimately driving enhancements
batteries11040127 in battery performance and supporting their integration into various systems, from electric
Copyright: © 2025 by the authors. vehicles to renewable energy infrastructures.
Licensee MDPI, Basel, Switzerland.
This article is an open access article Keywords: lithium-ion batteries; battery lifetime modeling; aging mechanisms; state of
distributed under the terms and health (SOH); RUL; degradation factors; battery health assessment; battery durability;
conditions of the Creative Commons
predictive model precision; temperature fluctuation
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).

Batteries 2025, 11, 127 https://doi.org/10.3390/batteries11040127

Batteries 2025, 11, 127 2 of 68

1. Introduction
As the world moves towards sustainable energy systems and decarbonization, lithium-
ion batteries (LIBs) play a crucial role in supporting clean energy solutions, facilitating
the shift to electric mobility and renewable energy storage. While there have been notable
advancements in LIB technology, much of the current research tends to neglect the interac-
tions among various aging factors, resulting in a lack of comprehensive models that address
all relevant degradation mechanisms. The aging processes of LIBs in electric cars, LIBs in
stationary energy storage systems, and their integration into grid systems are the main
topics of this paper. “Grid integration” refers to the use of LIBs to store energy harvested
from renewable sources, such as wind and solar, and then the use of that energy to balance
and stabilize the grid. Conversely, non-mobile energy storage devices, such as microgrids
that are independent of renewable energy sources and industrial and residential energy
storage, are referred to as “stationary applications”. Data-oriented and model-oriented
methods were greatly used to examine the aging processes in EVs and stationary storage
systems. Data-driven approaches to battery aging modeling usually use an experimental
dataset to estimate battery aging and performance, whereas model-driven approaches
usually estimate the electrochemical processes occurring inside the battery [1–3].
While aging factors in EVs are critical, similar challenges impact the broader applica-
tion of LIBs in energy storage systems, including temperature extremes and infrastructure
limitations. The emergence of EVs has been seen as a viable solution to combat global
carbon emissions. However, their widespread adoption faces significant challenges, includ-
ing range anxiety and battery degradation [4]. The range of an EV heavily relies on the
energy density and specific energy of the battery, while battery degradation is influenced
by driving behavior and environmental conditions. The deployment of LIBs in EV appli-
cations is complicated by a number of additional issues in addition to range anxiety and
battery deterioration. Because the infrastructure of fast charging is not universal and its
thermal management is sophisticated, charging time remains an issue. The high energy
density of LIBs raises safety concerns, particularly regarding thermal runaway. Further-
more, supply chain risks are introduced, and battery production costs are impacted by the
balance of vital raw materials like nickel, cobalt, and lithium. Additionally, the energy
needed for battery recycling and disposal raises environmental concerns. Furthermore,
LIB performance is significantly impacted by temperature extremes: hot weather leads
to aging, while cold weather reduces efficiency. The viability and sustainability of EVs
will suffer if these problems are not resolved [5–8]. Accurate battery lifetime prediction is
not only crucial for EV performance but also impacts the reliability and cost-efficiency of
renewable energy storage systems, military technology, and off-grid applications in remote
areas where replacement access is limited. In this context, understanding battery lifetime
prediction and aging mechanisms becomes essential for optimizing battery technology in a
way that enhances EV viability [9,10].
While EVs face challenges related to battery performance and sustainability, similar issues
arise in grid applications, where LIBs also encounter unique aging and operational concerns
due to their stationary use and varying environmental factors. The value of lithium extends
beyond its use in electric vehicles. It is also necessary for power integration and moving energy
storage. Energy reliability is ensured by the widespread use of backup power systems, energy
load shifting, and microgrid operations in the commercial, industrial, and residential sectors. In
addition to improving energy dispatch efficiency, LIBs linked into the grid also aid in stabilizing
voltage variations brought on by sporadic renewable energy sources like wind and solar. The
aging processes in these applications of LIBs, however, differ from those in EVs. The aging
process of the calendar is much more harmful because these batteries cycle slowly and remain
unused for extended periods of time. Problems including capacity fade, electrode cracking, and
Batteries 2025, 11, 127 3 of 68

lithium plating prevent grid storage batteries from cycling quickly. These issues are made more
difficult by the fact that, unlike EV aging models, fixed and grid storage systems have to take
into consideration seasonal variations, yearly energy flow, and shifting load patterns over time.
For battery-powered technologies to be practical and sustainable in multipurpose energy storage
systems, these issues require immediate consideration. In grid-scale applications, data-driven
and model-based approaches are essential for enhancing the longevity and dependability of
LIBs. By accurately predicting battery behavior, these techniques assist in controlling energy
flow, preserving grid stability, and guaranteeing long-term performance even in the face of load
and seasonal fluctuations [11,12].
Building on the challenges faced by LIBs in grid applications, reutilizing EV batteries for
second-life applications offers potential solutions, but it also brings new concerns related to
aging, efficiency, and thermal management. Reutilizing EV batteries presents an opportunity to
use their remaining energy capacity and extend their lifespan. Second-life applications for these
batteries typically require lower power and energy density than EVs. However, the technical
and economic feasibility of these systems still faces uncertainties. It is crucial to understand
the aging and lifespan of reused batteries to facilitate their development [13]. Such battery
reuse contributes to environmental sustainability and offers economic and resource-saving ben-
efits, particularly when supported by accurate aging predictions and cost-effective assessment
strategies. Accurately estimating battery life often requires lengthy and costly testing processes.
To address this, efficient methods need to be explored to minimize testing requirements by
leveraging existing knowledge of aging patterns from different battery chemistries [14]. These
predictions hold significance for industries where reliable battery life forecasting directly impacts
operational efficiency, including hybrid vehicles and remote monitoring systems, and even
critical applications such as defense and aerospace. Heat generation and thermal transport are
crucial factors affecting LIBs’ performance, aging, and safety. Elevated battery temperatures sig-
nificantly accelerate aging. Managing temperature and aging during battery operation presents
a complex challenge spanning multiple scales, from the micro/nanoscale within individual
material layers to large integrated LIB packs [15,16]. The multi-scale approach to tempera-
ture management is vital for extending battery life and critical for ensuring battery safety and
reliability in both primary and second-life applications.
The fire and explosion risks associated with LIBs pose significant concerns for their use
and transportation in aircraft. Therefore, studying thermal safety issues specific to flight
conditions is crucial. Lithium-ion batteries are prone to overcharging, which can lead to
thermal runaway and potentially dangerous situations. Inconsistent battery performance,
charging devices, or failures in the battery management system (BMS) can contribute to
such incidents [17]. Addressing these safety challenges is crucial for expanding lithium-ion
battery use across sectors that have stringent safety standards, including aviation and mili-
tary. In recent years, there has been growing confidence among stakeholders that end-of-life
batteries can be repurposed for less demanding applications, such as stationary energy
storage, providing new value in the electric grid and transportation sectors. Assessing the
feasibility of second-life battery applications from economic and technological perspectives
becomes imperative in this context. This paper acknowledges the significance of LIBs
in various applications. However, it also highlights the limitation of battery aging as a
challenge that needs to be addressed. By addressing the complexities of aging and incorpo-
rating them into battery design and management strategies, it becomes possible to improve
the overall performance and lifespan of lithium-ion batteries in different applications [18].
Battery aging directly influences the feasibility and maintenance planning of these systems.
For example, reliable lifetime prediction in second-life batteries could support energy
storage for grid stability, backup power for critical infrastructure, and electrification in
remote communities. Understanding the economic feasibility of second-life applications
 possible to improve the overall performance and lifespan of lithium-ion batteries in dif-
ferent applications [18]. Battery aging directly influences the feasibility and maintenance
planning of these systems. For example, reliable lifetime prediction in second-life batteries
Batteries 2025, 11, 127 4 of 68
could support energy storage for grid stability, backup power for critical infrastructure,
and electrification in remote communities. Understanding the economic feasibility of sec-
ond-life applications could greatly enhance resource efficiency in sectors such as energy
could greatly enhance resource efficiency in sectors such as energy management for hybrid
management for hybrid vehicles or maintenance planning for isolated regions.
vehicles or maintenance planning for isolated regions.
Data-driven approaches for estimating the state of LIBs primarily employ machine
Data-driven approaches for estimating the state of LIBs primarily employ machine
learning and deep learning techniques to predict essential parameters such as state of
learning and deep learning techniques to predict essential parameters such as state of
charge (SOC), state of health (SOH), and future performance or lifespan. Unlike model-
charge (SOC), state of health (SOH), and future performance or lifespan. Unlike model-
based methods, these approaches bypass complex electrochemical models and instead de-
based methods, these approaches bypass complex electrochemical models and instead
rive insights from experimental data. They typically require extensive datasets to ensure
derive insights from experimental data. They typically require extensive datasets to ensure
accurate and reliable predictions, as illustrated in Figure 1 [19–23]. Data-driven ap-
accurate and reliable predictions, as illustrated in Figure 1 [19–23]. Data-driven approaches,
proaches, therefore, enable scalable solutions for battery lifecycle management, which are
therefore, enable scalable solutions for battery lifecycle management, which are essential
essential for applications requiring real-time and long-term reliability [24].
for applications requiring real-time and long-term reliability [24].

Figure Flowchartofofdata-driven
1. Flowchart
Figure 1. data-driven state
state estimation
estimation based
based on the
on the Liion
Liion battery
battery modified
modified version
version from
from [19–23].
[19–23].

Due to their accuracy in predicting a battery’s state of charge (SOC), state of health (SOH),
Due to their accuracy in predicting a battery’s state of charge (SOC), state of health
and prognostics or life expectancy, data-driven methods for evaluating the state of LIBs (Lithium-
(SOH), and prognostics or life expectancy, data-driven methods for evaluating the state
Ion Batteries) have grown in popularity. Model-based prediction attempts are predicated on
of LIBs (Lithium-Ion Batteries) have grown in popularity. Model-based prediction at-
intricate electrochemical models that replicate battery function. In contrast to these, data-
tempts are predicated on intricate electrochemical models that replicate battery function.
driven approaches apply deep learning (DL) and machine learning (ML) models directly to the
In contrast to these, data-driven approaches apply deep learning (DL) and machine learn-
experimental data. This presents the chance to extract insightful information with consistent
ing (ML) models directly to the experimental data. This presents the chance to extract in-
accuracy from large amounts of data. Real-time and long-term sustainable applications require
sightful information with consistent accuracy from large amounts of data. Real-time and
sophisticated battery lifecycle management solutions, which are supported by data-driven
long-term sustainable applications require sophisticated battery lifecycle management so-
approaches. Nevertheless, in certain situations, other methods, such as model-based ones,
lutions, which are supported by data-driven approaches. Nevertheless, in certain situa-
are still helpful, particularly where precise physical modelling is needed. These techniques
tions, other methods, such as model-based ones, are still helpful, particularly where pre-
provide insight into the internal electrochemical processes of batteries, despite the fact that
cise physical modelling is needed. These techniques provide insight into the internal elec-
they are frequently more expensive and complex. Additionally, there are now integrated
trochemical processes of batteries, despite the fact that they are frequently more expensive
approaches that combine data-driven and model-based methodologies in an effort to strike a
and complex. Additionally, there are now integrated approaches that combine data-
balance between prediction accuracy and computation time. This study examines the benefits
driven and model-based methodologies in an effort to strike a balance between prediction
and limitations of data-driven methods in comparison to other conventional approaches, as
accuracy and computation time. This study examines the benefits and limitations of data-
well as how they can aid in the development of battery management systems for broader
driven methods in comparison to other conventional approaches, as well as how they can
applications. A thorough grasp of battery aging mechanisms and internal processes can be
gained using model-based approaches, which are based on intricate electrochemical simulations.
On the other hand, data-driven approaches are more scalable and require less computing power
than these computationally costly approaches, which may also be less effective in real-time
applications [25–29].
Monitoring a battery’s SOH has become a critical challenge in the field of hybrid
electric vehicles (HEVs) and EVs, as it significantly impacts vehicle performance and
lifespan. This poses challenges in real-world scenarios where batteries are expected to
 Batteries 2025, 11, 127 5 of 68

deliver reliable and consistent performance over extended periods. To optimize battery
designs, it is crucial to have a deep understanding of aging behavior. Empirical and
semiempirical models are frequently used to estimate battery aging. However, it is essential
to recognize that these models may introduce errors if they do not consider the limitations
and interdependencies among various stress factors that contribute to aging. Neglecting
these factors can lead to inaccurate estimations, and can potentially impact the performance
and reliability of batteries [19–23]. This underscores the importance of robust models that
incorporate multiple aging factors, which help ensure that battery performance meets
application-specific requirements over time.
In [30], Wang et al. focused on investigating the degradation of a LiFePO4 (LFP)
battery resulting from cycling and developing cycle-life models. The researchers collected
extensive data on cell lifespan through a comprehensive cycle-test matrix. This matrix
incorporated three key parameters: temperature (−30 to +60 ◦ C), depth of discharge (DOD)
(10–90%), and discharge rate (C-rate) ranging from C/2 to 10 C (with 1 C equivalent to 2 A).
The experimental findings revealed that, at lower C-rates, the battery’s capacity loss was
primarily influenced by time and temperature, with the impact of DOD being relatively
less significant. However, the charge/discharge rate had a more pronounced effect on
capacity loss at higher C-rates. To establish a life model, the researchers utilized a power
law equation that linked capacity loss to either time or charge throughput. Additionally,
the temperature effect was accounted for using an Arrhenius correlation. By allowing the
model parameters to vary with C-rates, the researchers observed that the model effectively
represented a wide range of life cycle data. Finally, the paper discusses ongoing efforts to
develop a comprehensive battery life model considering Ah throughput (time), C-rate, and
temperature. Figure 2 shows the test matrix for a cycle-life model for graphite-LiFePO4
Batteries 2025, 11, x FOR PEER REVIEW 6 of 74
cells. This study exemplifies the nuanced impact of aging factors on battery performance,
reinforcing the need for a comprehensive review of current predictive models and their
effectiveness across varying conditions.

Figure 2. Cycle−life model test matrix for graphite−LiFePO cells [30].

Figure 2. Cycle−life model test matrix for graphite−LiFePO
4
4 cells [30].

Mechanical stress in Nickel Manganese Cobalt (NMC) particles is a significant factor

inMechanical
the aging ofstress in Nickel
LIBs using NMCManganese Cobalt (NMC)
cathodes. Repeated particles
expansion is a significant
and contraction duringfactor
in the aging of LIBs using NMC cathodes. Repeated expansion and contraction during
charge/discharge cycles lead to the formation of microcracks, which gradually degrade
the material. This degradation reduces the amount of active material and disrupts electri-
cal contact within the electrode, ultimately diminishing the battery’s capacity and effi-
 Batteries 2025, 11, 127 6 of 68

charge/discharge cycles lead to the formation of microcracks, which gradually degrade the
material. This degradation reduces the amount of active material and disrupts electrical
contact within the electrode, ultimately diminishing the battery’s capacity and efficiency
over time. Moreover, these cracks allow for electrolyte penetration into the particles,
initiating side reactions that further accelerate degradation. The resulting thermal hotspots
worsen these effects, hastening capacity loss and overall battery aging. Extending battery
lifespan requires optimized particle design and stress-mitigating materials to address these
issues effectively [31–34].
Lithium-ion battery aging is driven by Solid Electrolyte Interphase (SEI) degradation,
high voltage, temperature, and poor charging/storage conditions, leading to capacity loss
and increased resistance. The quality of electrolyte and electrode materials also impacts aging.
Mitigating strategies like optimizing charge cycles and improving thermal management can
extend battery life. It highlights key areas of focus, including SEI formation, temperature effects,
and charge cycles, offering insights into ongoing efforts to mitigate aging and improve battery
longevity [35]. Aging is the gradual degradation of the battery cell’s performance parameters.
Negative electrodes in batteries are commonly composed of materials such as graphite, carbon,
titanate, or silicon. Graphite plays a crucial role in battery aging and safety. Upon the initial
charge of a battery, a SEI forms between the electrolyte and the electrode, shielding the electrode
from corrosion. This SEI is typically stable and helps extend the lifespan of lithium-ion batteries
by minimizing capacity loss. However, over time, factors like high voltage, temperature, or
improper charging can cause the SEI to degrade. This degradation can lead to gas formation,
cracking, and increased electrode impedance, ultimately diminishing the battery’s performance.
A high state of charge along with conditions such as high temperatures or overcharging can
Batteries 2025, 11, x FOR PEER REVIEWaccelerate the breakdown of the SEI. Conversely, low temperatures can hinder lithium diffusion, 7 of 74
resulting in lithium plating. These effects contribute to the loss of cyclable lithium and reduce
the battery’s efficiency. While the SEI provides protection, it becomes unstable when the battery
operates beyond
phenomena the electrolyte’s
occurring at the SEI.electrochemical
These processes stability range,both
can occur leading to further
during capacity loss
the operation of
and
the electrolyte
battery breakdown
and while it is in[35]. Figure 3 illustrates all these phenomena occurring at the SEI.
storage.
These processes can occur both during the operation of the battery and while it is in storage.

Illustrationofofthe
Figure3.3.Illustration
Figure theaging
agingeffects
effectson
onthe
thebattery’s
battery’snegative
negativeelectrode:
electrode:the
thedecrease
decreaseinincapacity
capacity
and the growth of the SEI layer, modified version
and the growth of the SEI layer, modified version from [35]. from [35].

Structure of the Review

Given the complexity and diversity of factors impacting battery aging and lifetime
prediction, a comprehensive review is essential to synthesize current approaches, address
knowledge gaps, and identify trends that could drive future advancements in battery
technology. This paper provides a structured review of these complex aging mechanisms,
the multitude of predictive models developed internationally, and the implications of
Batteries 2025, 11, 127 7 of 68

Structure of the Review

Given the complexity and diversity of factors impacting battery aging and lifetime
prediction, a comprehensive review is essential to synthesize current approaches, address
knowledge gaps, and identify trends that could drive future advancements in battery
technology. This paper provides a structured review of these complex aging mechanisms,
the multitude of predictive models developed internationally, and the implications of these
insights on the advancement of battery technology. The following sections present the
detailed framework of this review, beginning with an analysis of aging mechanisms and
progressing to data-driven prediction models and their applications. Section 2 introduces
aging in lithium-ion batteries, with a focus on calendar and cycle aging processes, fol-
lowed by Section 3, we examine various modeling approaches, highlighting physics-based,
empirical, and electrochemical models, along with the importance of validation through
case studies. Section 4 delves into factors affecting battery aging, such as temperature,
SOC, DOD, and electrolyte composition, while Section 5 presents lifetime modeling spe-
cific to NCA cathodes, covering degradation mechanisms, mathematical models like the
Doyle–Fuller–Newman (DFN) model, and experimental validation. Section 6 discusses key
indicators used to quantify battery health, including SOH, End of Life (EOL), and RUL.
In Section 7 aging estimation models, contrasting electrochemical models and equivalent
circuit-based approaches are presented. Section 8 addresses both the prospects and chal-
lenges associated with LIB lifetime modeling, emphasizing its potential for performance
enhancement, cost reduction, sustainability, and safety. Section 9 evaluates precise models
for describing aging, including calendar aging, using single-particle models, the DFN
model, and empirical approaches. In Section 10, we analyze the role of lithium-ion battery
materials and their impact on aging, exploring developments in electrode materials and
essential lifetime modeling components. Section 11 provides a comparative analysis of
different lithium-ion chemistries, followed by Section 12, which outlines future research di-
rections in lithium-ion battery aging. Finally, Section 13 offers a comprehensive discussion
synthesizing insights from across the paper followed by the conclusions in Section 14.

2. Aging of Lithium-Ion Batteries

In the context of Li-ion batteries, aging refers to the gradual decline of the battery’s
performance characteristics over time due to regular usage and charging/discharging
cycles. Calendar aging specifically relates to the gradual deterioration of the Li-ion battery’s
performance parameters during periods of storage or inactivity, taking place under various
environmental conditions. Even when not actively used, the battery undergoes chemical
changes that can impact its overall performance. Cycling aging, on the other hand, refers to
the gradual degradation of the Li-ion battery’s performance parameters occurring during
the repetitive charging and discharging cycles it experiences during regular operation.
Lifetime represents the duration for which the Li-ion battery can be effectively operated
until its performance parameters reach predefined threshold values. Once the battery’s
performance degrades to the specified levels, it may be considered at the end of its usable
life, with reduced efficiency and capacity that might no longer meet practical application
requirements [36]. The aging of lithium-ion batteries is summarized in Figure 4. To
differentiate between “power capability decay” and “internal resistance increment”, it
should be noted that the former refers to a decrease in the battery’s ability to supply power
over time, while the latter specifically refers to an increase in resistance inside the battery
brought on by electrolyte and electrode degradation. Deterioration of power capability
is largely caused by internal resistance, although it is also influenced by capacity loss,
structural deterioration, and thermal effects. Therefore, although while an increase in
internal resistance is a significant indicator of power capability decay, power capability
 over time, while the latter specifically refers to an increase in resistance inside the battery
brought on by electrolyte and electrode degradation. Deterioration of power capability is
Batteries 2025, 11, 127
largely caused by internal resistance, although it is also influenced by capacity loss, struc-
8 of 68
tural deterioration, and thermal effects. Therefore, although while an increase in internal
resistance is a significant indicator of power capability decay, power capability decay en-
compasses a variety aofvariety
decay encompasses degradation mechanisms
of degradation in additionintoaddition
mechanisms an increase in increase
to an internal re-
in
sistance,resistance,
internal therefore the two terms
therefore are terms
the two not entirely
are notinterchangeable [37].
entirely interchangeable [37].

Figure 4.
Figure 4. Aging of lithium-ion
lithium-ion batteries.
batteries.

Battery agingin
Battery aging inlithium-ion
lithium-ionsystems
systems is influenced
is influenced by aby a combination
combination of chemical,
of chemical, ther-
thermal,
mal, andand mechanical
mechanical factors,
factors, which
which cancandiffer
differsignificantly
significantlybetween
betweencalendar
calendar and
and cycle
cycle
aging
aging processes.
processes. Understanding
Understanding these
these influences
influences is is crucial
crucial for
for optimizing
optimizing performance
performance inin
various
various applications.
applications.

2.1. Calendar Aging

Lithium-ion batteries experience performance degradation even when not in use,
such as when electric or hybrid vehicles remain parked. This phenomenon, known as
calendar aging, occurs over time and significantly impacts the overall lifespan of lithium-
ion batteries in electric vehicles. As a result, understanding and predicting calendar aging
is crucial for developing long-lasting electric vehicles [38].
Battery degradation plays a vital role in quality management and customer satisfaction
across applications such as electric scooters and EVs. The overall health of a battery directly
influences its value and depreciation, making it essential to monitor battery performance
throughout its lifespan. This includes both cycle aging, which results from usage, and
calendar aging, which occurs in stationary conditions. By effectively managing both
aspects of battery degradation, manufacturers can optimize battery performance and
extend durability [39–43].
A novel approach to studying calendar aging involves radiolysis, a process using
ionizing radiation to accelerate the degradation of battery electrolytes. Souid et al. [44]
demonstrated that radiolysis can simulate long-term calendar aging up to 30 times faster
than natural aging. Using electrochemical impedance spectroscopy (EIS), researchers
analyzed symmetrical coin cells with various electrolyte additives, finding that irradiation
produces degradation compounds similar to those formed during natural aging. Low
radiation doses increased cell resistance by affecting both the solid electrolyte interphase
(SEI) layer and charge transfer, while higher doses altered the SEI composition depending
on the additive used. Notably, vinylene carbonate (VC) and fluoroethylene carbonate (FEC)
enhanced SEI formation, creating more uniform layers than those observed in natural
Batteries 2025, 11, 127 9 of 68

aging. These findings suggest that radiolysis could serve as a valuable tool for rapidly
screening electrolyte additives, aiding in the identification of compounds that improve
battery lifespan.
In a separate study, Plattard et al. [45] aimed to improve the reliability of lithium-ion
batteries by developing accurate aging models. Their research focused on a weighted
ampere-hour throughput model, incorporating temperature, current intensity, and state of
charge (SOC) as key stress factors influencing aging. To refine this model, they utilized Incre-
mental Capacity Analysis (ICA), which evaluates dQ/dV as a function of voltage to assess
a cell’s state of health and detect material degradation. When applied to NMC/graphite
cells, ICA revealed two peaks: one linked to cycling-induced aging and another poten-
tially associated with calendar aging. However, distinguishing between the two remains
challenging since both mechanisms overlap. A key trend observed was that one ICA peak
correlated with initial capacity loss (first 10%), primarily driven by lithium inventory loss
(LLI). Further research is required to explore degradation at later stages, where loss of
active material (LAM) becomes more pronounced.

2.2. Cycle Aging

Cycle aging in lithium-ion batteries results in gradual performance degradation due to
repeated charge and discharge cycles. Key factors influencing this process include electrode
material degradation, mechanical stress, thermal effects, and electrolyte decomposition.
High current rates and deep discharges accelerate capacity loss, while environmental con-
ditions, such as humidity, further contribute to aging. To mitigate these effects and extend
battery lifespan, optimized charging protocols and advanced materials are employed [46].
Table 1 presents a comparative summary of lithium-ion battery cycle aging studies.
Electrolyte additives and pulse charging techniques play crucial roles in enhancing
lithium-ion battery performance, efficiency, and longevity. Electrolyte additives improve the
stability of the solid electrolyte interphase (SEI) layer, minimize undesirable side reactions,
and slow electrolyte degradation. These benefits contribute to better battery performance,
particularly under varying environmental conditions such as low temperatures. Pulse
charging, which applies intermittent rather than continuous charging currents, helps
manage heat generation, reduce internal component stress, and improve ion diffusion.
By mitigating capacity fade and increasing cycle life, these strategies collectively enhance
battery efficiency and longevity [47–53].
Understanding degradation processes in lithium-ion cells is crucial for battery-
powered applications. Degradation arises from temperature variations, current load,
SOC operating range, and cycle depth, leading to reduced capacity and increased inter-
nal resistance. Laboratory aging studies provide insights into these influences, enabling
prediction of cell degradation under specific conditions and identification of detrimental
operating practices [54,55] However, most studies focus on steady-state conditions, such as
constant-current profiles, while dynamic influences like variable current and temperature
profiles remain underexplored. Addressing this gap by incorporating real-world dynamic
conditions into aging studies can improve the accuracy of degradation assessments and
optimize battery performance strategies [56,57].
Battery aging is categorized into calendar aging, driven by factors like temperature and
SOC, and cycle aging, influenced by charge/discharge current rates and cut-off voltages. While
studies confirm that higher current rates accelerate aging, research on mitigating these effects
remains ongoing [58,59]. At high SOC levels, particularly above 4 V in NMC cathodes, increased
electrochemical activity accelerates degradation. This leads to electrolyte oxidation, SEI layer
instability, cobalt leaching, thermal instability, and increased internal resistance, all contributing
to reduced capacity retention and battery lifespan [60–62].
Batteries 2025, 11, 127 10 of 68

Accurate lithium-ion battery lifetime prediction requires considering both calendar

and cycle aging. Future research should focus on developing accelerated aging tests that
simulate real-world operational conditions, particularly for EV applications. Incorporating
dynamic conditions and comprehensive offline training data can enhance battery lifetime
predictions, aiding successful market integration.
Ecker et al. [63] developed a lifetime prediction approach for NMC/graphite lithium-
ion batteries by analyzing temperature and SOC effects on impedance rise and capacity
loss. Using experimental data, they parameterized a semi-empirical aging model integrated
with an impedance-based electro-thermal model, allowing evaluation of drive cycles and
battery management strategies. This approach improves lifetime prediction accuracy and
provides insights for optimizing vehicle battery design.
Gu et al. [64] introduced a data-driven grey model for predicting life-ending indices
in lithium-ion batteries. Unlike traditional aging models that require detailed mechanistic
knowledge, the grey model relies on limited test data and employs a smoothing method
to enhance accuracy. Applied to phosphate iron and manganese oxide lithium-ion batter-
ies, the model demonstrated effectiveness in reducing the number of cycles needed for
operational evaluations in EVs.
Schmalstieg et al. [65] conducted calendar life and cycle aging tests to evaluate the
effects of voltage, temperature, cycle depth, and mean SOC on capacity loss and resistance
increase. Their results informed a mathematical aging model, which was integrated with
an impedance-based electro-thermal model. This approach enabled the simulation and
optimization of different drive cycles and battery management strategies, considering
seasonal temperature variations for diverse applications.
Stroe et al. [66] proposed a three-stage methodology for accelerated lifetime testing in
wind power applications. By collecting capacity fade and power degradation data, they
developed a performance degradation model incorporating both calendar and cycle aging.
Validation under normal operating conditions confirmed the model’s reliability, offering
insights for optimizing battery selection, operation, and maintenance strategies.
Sandia National Laboratories developed accelerated life test protocols for high-power
lithium-ion cells in hybrid EV applications. Aging experiments on 18,650-size cells revealed
power loss, capacity fade, and increased cathode interfacial impedance. Inductive models
describing power fade, capacity loss, and impedance rise enabled precise lifetime predic-
tions under varied conditions, contributing to the development of reliable lithium-ion cells
for hybrid EVs [67].
Stroe et al. [68] conducted accelerated aging tests on EV batteries, simulating real-
world driving conditions using the Worldwide Harmonized Light Vehicles Test Cycle
(WLTC) and representative temperature profiles. Focusing on NMC batteries, the study
investigated capacity fade and internal resistance increase, enhancing understanding of
degradation mechanisms and improving battery management strategies. These findings
support more effective BMS designs, contributing to EV performance improvements and
wider adoption.
Takei et al. [69] developed testing methods to estimate lithium-ion battery lifespan
efficiently. Their experiments on LiCoO2 /hard carbon cells revealed that most degradation
reactions occur above 4 V. They demonstrated that while straight-line approximations
can extrapolate limited cycle data, early short-cycle data introduces significant errors.
Accelerated aging tests under high charge rates and elevated temperatures confirmed rapid
degradation, emphasizing the importance of voltage control and stress factor analysis for
accurate lifespan prediction and effective battery management strategies.
Batteries 2025, 11, 127 11 of 68

Table 1. Comparative summary of lithium-ion battery cycle aging studies.

Degradation
Focus Area Methodology Battery Type Key Findings Ref.
Model
Experimental investigation
and parameterization of
Lifetime prediction Accurate lifetime prediction
semi-empirical aging model Semi-empirical
of high-power NMC/Graphite based on drive cycles and [30,63,70]
coupled with aging model
batteries management strategies
impedance-based
electrical-thermal model
Phosphate Iron & Efficient lifetime prediction
Cycle-life Data-driven grey model with
Manganese Oxide Grey model for with fewer cycles, without
prediction using smoothing methods to predict [64,71]
Lithium-ion life-end prediction needing detailed aging
the grey model cycle life
batteries mechanism knowledge
Mathematical equations Comprehensive aging model
Impedance-based
Calendar and cycle derived from voltage, for optimizing different drive
- electrical-thermal [65,72]
aging tests temperature, cycle depth, and cycles and battery
model
SOC management strategies
Accelerated Accelerated aging conditions Optimized battery selection,
Performance-
lifetime testing for and performance-degradation operation, and maintenance
- degradation [66,73,74]
wind power model validation with for improved performance
lifetime model
applications mission profiles and longevity
Establishment of
Inductive models
life test protocols Aging experiments on Accurate lifetime predictions
High-power for power fade,
for high-power 18,650-size cells; developed under various operating [22,37,67]
lithium-ion cells capacity loss, and
lithium-ion cells in inductive models conditions
impedance rise
HEV applications
Accelerated aging tests using
Aging tests on Insights into capacity fade
standardized driving cycle
NMC batteries for NMC - and internal resistance [68,75]
(WLTC) and temperature
EV applications increase in NMC batteries
profiles
High-voltage testing, Highlighted degradation
Lifespan estimation
accelerated aging tests with LiCoO2 /Hard above 4 V, importance of
of lithium-ion - [69]
high charge rates and carbon cells avoiding high voltage for
batteries
elevated temperatures lifespan prediction

3. Modeling the Lifespan and Aging of LIBs

LIBs are prevalent in our modern world, powering various devices, from smartphones
to electric vehicles. It is vital to comprehend and accurately forecast the lifespan of these
batteries to optimize their performance, reduce expenses, and ensure the sustainability of
energy storage systems. This chapter explores the intricate science behind modeling the
longevity of lithium-ion batteries, drawing from extensive scientific research and references
to offer a comprehensive overview.

3.1. Mechanisms of Battery Aging

The duration of LIBs’ usability is primarily dictated by the gradual deterioration of
their essential components.

3.1.1. Electrochemical Breakdown

An anode solid electrolyte interphase (SEI) is created during the battery’s charge and
discharge cycles by electrochemical deterioration, which over time may reduce the battery’s
capacity. Researchers now have a better understanding of how SEIs occur and how they
impact battery life thanks to the substantial amount of work that has been done [76,77].

3.1.2. Lithium Deposition

Lee et al. [78] addressed the commercialization challenges of LIBs with Li metal
anodes, primarily caused by unpredictable dendrite growth and performance degradation.
While prior studies have examined factors influencing dendrite formation, their complex
Batteries 2025, 11, 127 12 of 68

interactions remain unclear. To bridge this gap, the authors developed a multiscale model
that combines a kinetic Monte Carlo model for microscopic dendrite formation with a
macroscopic electrochemical model. This framework tracks macroscopic variables (e.g.,
current density, Li-ion concentration, voltage, and state of charge), analyzes their impact
on dendrite growth, and optimizes LIB operation to mitigate dendrite formation.

3.1.3. Thermal and Mechanical Stress

Thermal modeling and heat generation are crucial for managing lithium-ion battery
safety and performance. As heat from internal resistance and reactions increases with higher
charge rates, it risks uneven temperatures and accelerated aging. Modeling techniques
predict hotspots, aiding thermal management strategies to prevent overheating. Compres-
sive loads can also deform internal structures, raising resistance and causing micro-cracks,
which further accelerate aging. Micro-crack formation is linked to the charge/discharge
depth, which causes volume expansion of cathode active materials. Thermal modeling
in lithium-ion batteries spans from simple lumped parameter models, assuming uniform
temperature, to more advanced finite element and electrochemical-thermal coupled mod-
els, which consider spatial and electrochemical variations. Heat generation is influenced
by charge/discharge rates, state of charge, ambient temperature, and battery structure.
Effective thermal management, using either passive (conductive materials) or active cool-
ing (liquid or air cooling), is vital for preventing overheating and ensuring longevity,
particularly in high-power applications such as electric vehicles [79–84].
In EV applications, lithium-ion batteries experience compressive loads from thermal
expansion, vibrations, and potential impacts, which can affect mechanical integrity and
performance. These forces may deform battery components, accelerate electrochemical
degradation, and, in severe cases, cause thermal runaway. Characterization methods
include mechanical compression testing, finite element modeling, and in situ stress mon-
itoring to understand how compressive loads impact safety and longevity. To mitigate
these effects, battery pack designs often incorporate high-strength materials and protective
structures, which help ensure the battery’s resilience and reliability under operational
stresses [85]. LIBs face safety risks throughout their lifecycle, particularly related to internal
short circuit (ISC) triggering, ISC modes, and subsequent TR. For aged LIBs, ISC triggering
delays with declining SOH, and “soft” ISC modes occur more frequently, likely due to
changes in current collectors’ mechanical properties. This delay and soft ISC process result
in milder TR events, as aged cells show lower temperature rises and peak temperatures. In
thermal front propagation (TFP) within cells, the TR velocity stabilizes away from the initial
heat source, with the energy release rate being inversely proportional to cell dimensions.
Anisotropic cells exhibit ellipsoid-shaped TFP with long and thin cells releasing less energy
than shorter, thicker ones. These insights advance thermal hazard modeling and inform
design strategies for safer, next-generation LIBs [86–88]. The longevity of lithium-ion cells
during high-voltage cycling can be enhanced by choosing specific electrolyte additives, as
demonstrated in tests on NMC442/graphite and cobalt-free NMC640/graphite cells under
accelerated degradation conditions (4.4 V at 40 ◦ C). Promising additives effectively reduced
degradation by minimizing the dissolution of transition metals (primarily manganese)
from the positive electrode, which otherwise leads to increased cell impedance through
the formation of a rock salt layer on NMC particles. Additionally, blending LiFePO4 (LFP)
with NMC640 in a 90% to 10% ratio notably improved stability in high-temperature cycling
and reduced iron deposition on the anode, achieving a synergistic performance boost over
cells with pure LFP. Cells with high-nickel positive electrodes (LiNi0.95 Mn0.04 Co0.01 O2 ) also
showed stable cycling at lower cut-off voltages (4.04 V or ~75% SOC), as higher voltages
(4.18 V) raised positive electrode impedance due to parasitic electrolyte reactions. Adding
Batteries 2025, 11, 127 13 of 68

1 wt% lithium difluorophosphate to a 1.2 M LiPF6 electrolyte composed of fluoroethylene

carbonate and ethyl methyl carbonate effectively curbed impedance growth, thereby ex-
tending cycle life without compromising electrode particle structure. These insights inform
electrolyte and material selection for high-energy, long-lasting LIBs designed for rigorous
cycling conditions [45]. Galushkin et al. [89–91] demonstrated that the probability of TR
in commercial lithium-ion cells, specifically the 18,650 types, increases with the number
of charge/discharge cycles and the cells’ SOC. Experiments conducted in an accelerat-
ing rate calorimeter (ARC) revealed a significant decrease in the initiation temperature
of exothermic reactions, leading to TRand increased released energy as the number of
charge/discharge cycles increased. Further ARC experiments, along with gas analysis,
indicated the accumulation of hydrogen during cycling in the anode graphite. It was
confirmed that the recombination of atomic hydrogen released from the graphite anode is a
powerful exothermic reaction, leading to increased released energy and decreased initiation
temperature of TR. Thus, TR initiation in lithium-ion cells is attributed to this recombination
reaction of accumulated atomic hydrogen in the anode graphite during cycling. A possible
mechanism for initiating TR is proposed based on the experimental findings.

3.2. Battery Aging and Modeling Approaches

The challenge in simulating and optimizing battery lifetime lies in balancing accuracy
with computational efficiency. While ECMs are commonly used in the automotive industry
for their efficiency, they rely on empirical relations for aging extensions, limiting their
applicability. The choice of a precise model to depict the aging of lithium-ion batteries is
contingent on various factors, including the unique battery chemistry and usage pattern.
Researchers frequently employ a combination of empirical models, electrochemical models,
and statistical models to make accurate forecasts about battery aging. The selection of the
most suitable model should align with the available data and the particular application [92].
Recent scientific literature in this field has shown an increased utilization of advanced
modeling techniques, machine learning, and data-driven methodologies to enhance the
precision of battery aging predictions. These models are customized for different battery
chemistries and provide valuable insights into the aging process, contributing to developing
more robust battery management strategies [93]. To sum up, lithium-ion battery aging is
influenced by many factors, with temperature, cycling rate, and depth of discharge ranking
among the most crucial. Accurate models are continuously advancing, harnessing advanced
techniques to gain a better understanding and projection of battery aging, facilitating the
creation of longer-lasting and more dependable energy storage solutions [94–96].
Numerous calendar aging models exist for LIBs, and their complexity can differ de-
pending on specific applications and the required level of detail. Understanding and
modeling battery aging is crucial to ensure cost optimization and safety. Calendar aging
analysis involves a periodic cycle of aging and cell characterization. So far, the influence
of characterization on calendar aging results has been considered insignificant. How-
ever, different studies use varying characterization measurements, particularly in capacity
measurement, which could affect capacity and resistance change in calendar aging [97]. Re-
searchers employ various modeling methodologies to predict the longevity of lithium-ion
batteries accurately. These models aim to capture the intricate interplay of degradation
mechanisms over time. Some commonly used modeling techniques encompass:

3.2.1. Physics-Based Models

Physics-based models replicate the behavior of lithium-ion cells by incorporating
fundamental electrochemical processes. These models consider critical variables such as
electrode composition, temperature effects, mechanical expansion, and current flow. They
Batteries 2025, 11, 127 14 of 68

typically rely on partial differential equations (PDEs) to describe the internal dynamics of
the battery, including ion transport, reaction kinetics, and thermal behavior [98].
Jianing et al. [99] proposed an improved approach for modeling the micro-health
parameters of LiFePO4 batteries by analyzing negative electrode materials and electrolytes.
Their method simplifies complex liquid and solid diffusion processes using a pseudo-two-
dimensional (P2D) model, refining liquid-phase diffusion boundary conditions to enhance
electrolyte concentration predictions. A terminal voltage model, employing lumped pa-
rameters and nonlinear optimization, enables efficient identification of battery health
characteristics. Experimental validation under 1 C constant-current charging demonstrated
improved accuracy and reliability compared to conventional techniques.

Single-Particle Model (SPM)

To replicate the behavior of a single particle in a LIB, a mathematical model known
as the SPM was developed. Calendar aging can be investigated by observing the changes
in the particle’s characteristics over time. The battery’s aging process can be examined
holistically by extending this model to include additional components [100].

Doyle–Fuller–Newman (DFN) Model

Single Particle Models (SPMs) offer a simpler electrochemical approach, potentially
suitable for automotive use. Despite their distinct development paths, there’s a discus-
sion about connecting EEMs and SPMs. A new empirical aging model called SPM-EEM,
derived from simplified SPM aging relations, was proposed by Rechkemmer et al. [100]
and compared to existing models, particularly tailored to LiMn2 O4 (LMO) cell chemistry.
SPM-EEM demonstrates promising initial results for enhancing accuracy. Although SPMs
are somewhat more predictable than EEMs, their complexity often hinders implementation
on control devices. Thus, enhancing the predictability of EEMs is crucial for improving
aging estimation and optimization in automotive applications.

3.2.2. Empirical Models

Zhang et al. [101] addressed the challenge of increasing longevity in LIB technology by
proposing a health-conscious advanced BMS. The system incorporates monitoring and control
algorithms to extend battery lifespan while preserving performance. Central to these algorithms
are real-time battery capacity estimates. The paper introduces an online capacity estimation
scheme for LIBs, which relies on two key innovations: (1) utilizing thermal dynamics for capacity
estimation and (2) developing a hierarchical estimation algorithm with guaranteed convergence
properties. This algorithm comprises two stages working sequentially. The first stage estimates
battery core temperature and heat generation using a two-state thermal model, while the second
stage utilizes these estimates to determine state-of-charge and capacity. Numerical simulations
and experimental data are presented to demonstrate the effectiveness of the proposed capacity
estimation scheme [102,103]. Krupp et al. [97] quantified, for the first time, the impact of
characterization using periodic measurements. It reveals a significant influence, manifesting as
capacity increase and resistance decrease, attributed to an increase in active electrode surface
area due to characterization. Consequently, cell characterization emerges as a potential source
for capacity increase in calendar aging. The study suggests that future capacity measurements
should use small currents below 1 C to mitigate the influence of characterization on results.
Moreover, a method for correcting the characterization effect is proposed.

3.2.3. Electrochemical Models

The electrochemical battery model is valued for its physical interpretability but re-
quires precise parameter settings for accuracy. Conventional parameter identification
methods are often time-intensive and may compromise interpretability. Chang et al. [104]
Batteries 2025, 11, 127 15 of 68

addressed these challenges by developing a framework that: (1) incorporates lithium plat-
ing and SEI film growth into the electrochemical model, (2) employs sensitivity analysis
using voltage and impedance characteristics to efficiently identify key parameters, and
(3) integrates machine learning with optimization in a two-step parameter identification
process to improve initialization and prevent convergence issues. This approach enhances
accuracy while maintaining efficiency and physical interpretability.

Electrochemical Impedance Spectroscopy (EIS)

EIS serves as a valuable non-invasive method for characterizing Lithium-ion cells,
enabling the identification and monitoring of cell degradation processes within a short
testing period. Iurilli et al. [105] reviewed and compiles existing studies that utilize EIS
spectra for characterizing Li-ion cell degradation or developing ECMs. The objectives
include highlighting the impact of various aging test conditions on EIS spectra, establishing
correlations between EIS spectra changes and underlying degradation mechanisms, and
outlining options for formulating ECMs from EIS spectra of aged cells. Following a com-
prehensive analysis of the current state-of-the-art, the review offers a critical examination
to discuss the connections between degradation mechanisms and the most dependable
approaches for modeling them.

3.2.4. Volume Change Models

The development of high-capacity lithium-ion batteries faces challenges due to the
large volumetric changes in anode materials during electrochemical cycling, leading to
degradation and reduced cycle life. Coupled electrochemical-mechanical models, which
account for the interaction between electrochemical processes and mechanical stresses,
have been developed to understand and mitigate these issues. These models highlight the
importance of considering particle size distributions, as non-uniform particle utilization
significantly impacts the rate-dependent volume changes at the electrode and cell levels.
Strategies such as surface coatings and porosity adjustments can help alleviate degradation.
Future work will focus on improving the understanding of delithiation effects, SEI growth,
and the interplay between active materials, as well as refining models for automotive
applications to predict the impact of volume changes on battery fatigue and stress [106,107].
By combining mechanical and electrochemical models, the behavior of blended silicon
oxide-graphite anodes shows a notable tradeoff in volume expansion during cell operation.
Although silicon oxide accounts for just 20% of the anode’s capacity, it contributes to
half of the total volume change in the cell, underscoring its significant effect on the cell’s
mechanical performance. This modeling framework enables virtual assessment of design
changes at both the cell and pack levels, offering insights into balancing energy density and
managing volume expansion. Integrating this model with aging predictions will provide
better estimations of irreversible volume changes, supporting more accurate long-term
performance and lifespan evaluations. Future work will focus on refining the model to
account for rate-dependent volume changes and investigating microstructure modeling to
differentiate between dimensional and porosity-related volume changes, further improving
volume expansion predictions [108].

3.2.5. Combined Multiphysics- and Data-Based Models

Silva et al. [109] presented a comprehensive overview of recent aging modeling meth-
ods, using a multiscale approach that explores aging at the particle, cell, and battery pack
levels, and identifies future research opportunities in LIB aging modeling. They also reviewed
battery testing strategies to demonstrate how numerical aging models are validated, offering
a holistic modeling framework. Additionally, they proposed a combined multiphysics- and
data-based modeling approach to achieve accurate, computationally efficient LIB aging simu-
Batteries 2025, 11, 127 16 of 68

lations. Figure 5 illustrates the typical progression of a LIB over its lifetime, emphasizing key
aging events. In Stage I, manufacturing conditions play a crucial role, with early-cycle capacity
increases followed by declines due to SEI layer formation. In Stage II, aging processes such as
SEI growth, electrode cracking, dissolution, and electrolyte breakdown occur at a constant rate.
Stage III sees a rapid, non-linear decrease in state of health (SOH), mainly due to lithium
Batteries 2025, 11, x FOR PEER REVIEW 17 of plating.
74
While these stages are defined by specific mechanisms in this paper, real-world applications
experience variable paths depending on usage requirements.

Figure 5. Common progression of state of health and aging processes throughout the battery’s
Figure 5. Modified
lifespan. Common version
progression
fromof state of health and aging processes throughout the battery’s
[109].
lifespan. Modified version from [109].
4. Factors Affecting Lithium-Ion Battery Aging
4. Factors Affecting
Because Lithium-Ion
they power Battery
everything from Agingto electric cars, LIBs have become
cellphones
essential to our
Because theydaily
power lives. However,
everything fromLi-ion batteries
cellphones age likecars,
to electric anyLIBs
other energy
have storage
become
essential to our
technology, daily
which lives.
can haveHowever, Li-ion
a big impact onbatteries
how well agethey
likework
any other energy
and how storage
long they last.
technology,
Several whichvariables
external can havecan a big impactthe
impact onlifespan
how wellofthey
LIBs.work andvariables
These how longmust
they be
last.
taken
Several external variables can impact the
into account when formulating lifespan models: lifespan of LIBs. These variables must be taken
into account when formulating lifespan models:
4.1. Operating Conditions
4.1. Operating Conditions including operating temperature, charging and discharging rates,
Several variables,
Several drain,
and battery variables, including
influence operating
battery temperature,
lifespan. charging and
High temperatures, discharging
rapid charging,rates,
and full
and battery drain, influence battery lifespan. High temperatures, rapid charging, and
discharges can significantly accelerate degradation [110]. Crawford et al. [111] investigated full
discharges can significantly accelerate degradation [110]. Crawford et al. [111] investi-
the potential of LIBs in stabilizing the electrical grid as renewable energy sources like solar
gatedwind
and the potential of LIBs
become more in stabilizing
integrated. Theythe electrical
tested grid as renewable
two commercial Li-ion energy sources
batteries, one with
like solar and wind become more integrated. They tested two commercial Li-ion batteries,
NCA chemistry and the other with LFP chemistry, under grid duty cycles designed for
one with NCA chemistry and the other with LFP chemistry, under grid duty cycles de-
signed for frequency regulation (FR) and peak shaving (PS), both with and without EV
drive cycles. The study compares the lifecycle performance of the two battery chemistries
based on metrics such as capacity, round-trip efficiency, resistance, charge/discharge en-
ergy, and total used energy. It finds that LFP chemistry offers better stability for energy-
Batteries 2025, 11, 127 17 of 68

frequency regulation (FR) and peak shaving (PS), both with and without EV drive cycles.
The
Batteries 2025, 11, x FOR PEER REVIEW study compares the lifecycle performance of the two battery chemistries18based of 74 on

metrics such as capacity, round-trip efficiency, resistance, charge/discharge energy, and

total used energy. It finds that LFP chemistry offers better stability for energy-intensive PS
studied while
services, conditions.
NCA These
chemistryresults provide
is more insights
suitable for FRinto selecting,
services underdeploying, operating,
the studied conditions.
and analyzing
These the costs
results provide of Li-ion
insights intobatteries for deploying,
selecting, different grid applications.
operating, and analyzing the costs
of Li-ion batteries for different grid applications.
4.2. Cycling Frequency
4.2. Cycling Frequency
Elmahallawy et al. [112] emphasized the environmental impact of gasoline consump-
tion Elmahallawy
and the importance of reducing
et al. [112] emphasizedfuel usage through the adoption
the environmental impact of ofgasoline
hybrid electric
consump-
vehicles
tion and (HEVs) and EVs powered
the importance of reducingby renewable
fuel usageenergy
throughsources. It highlights
the adoption the concern
of hybrid electric
regarding the degradation of EV batteries over time, which can compromise
vehicles (HEVs) and EVs powered by renewable energy sources. It highlights the concern both perfor-
mance andthe
regarding safety. Assessingofand
degradation EV predicting battery
batteries over time,health,
whichreferred to as SOH, is
can compromise crucial
both perfor-
for ensuring EV safety. While various techniques exist for estimating and predicting SOH,
mance and safety. Assessing and predicting battery health, referred to as SOH, is crucial
they may not cover all degradation scenarios. The paper focuses on Li-ion EV batteries
for ensuring EV safety. While various techniques exist for estimating and predicting SOH,
and aims to (1) present Li-ion battery models, (2) discuss factors causing degradation and
they may not cover all degradation scenarios. The paper focuses on Li-ion EV batteries
safety issues, (3) review SOH estimation and prediction techniques, and (4) provide rec-
and aims to (1) present Li-ion battery models, (2) discuss factors causing degradation
ommendations for improving battery lifetime estimation. Overall, it aims to serve as a
and safety issues, (3) review SOH estimation and prediction techniques, and (4) provide
valuable resource for researchers in the battery community to enhance EV battery safety.
recommendations for improving battery lifetime estimation. Overall, it aims to serve as a
The factors contributing to Li-ion battery aging and the resulting degradation effects are
valuable resource
illustrated in Figurefor6.researchers in the battery community to enhance EV battery safety.
The factors contributing to Li-ion battery aging and the resulting degradation effects are
illustrated in Figure 6.

Figure 6. Representation of Li-ion battery aging factors and their associated degradation effects,
Figure 6.version
modified Representation of Li-ion battery aging factors and their associated degradation effects,
from [112].
modified version from [112].
Batteries 2025, 11, 127 18 of 68

Feng et al. [113] proposed an integrated state-of-charge (SOC) estimation algorithm

combining an advanced ampere-hour counting method with a multistate open-circuit
voltage (OCV) method. While the ampere-hour counting method is widely used, it can be
affected by temperature and current, leading to SOC estimation errors. To address this, the
enhanced method adjusts available capacity and coulombic efficiency based on temperature.
Additionally, battery SOCs at different temperatures are converted considering capacity loss.
The OCV method addresses errors from current sensors and initial SOC estimation, using
rated and non-rated OCV-SOCs to estimate initial SOCs. The method was validated through
constant- and alternated-temperature tests, demonstrating accurate SOC estimation across
various ambient temperatures.
Multi-scale modeling, combining micro- and macro-level perspectives, provides a
comprehensive understanding of LIB lifespan by capturing degradation mechanisms at
different structural levels. Several case studies have highlighted the practical value of
battery lifespan modeling. For example, electric vehicle manufacturers use these mod-
els to predict battery degradation and optimize charging strategies, enhancing lifespan
and efficiency [114]. However, existing research has not focused on predicting capac-
ity degradation paths for entire battery packs, creating a gap in real-world applications.
To address this, Chen et al. [115] introduced the MMRNet model, a multi-horizon time
series forecasting approach comprising MOSUM, flash-MUSE attention, and RNN core
modules. The model leverages domain knowledge to extract features from large battery
aging datasets. MOSUM and flash multi-scale attention effectively capture capacity curve
mutations and trends. Dynamic dropout training, transposition linear architecture, residual
connections, and module stacking improve model generalization and accuracy. Experi-
mental results show that MMRNet outperforms six baseline time series models, offering
effective prediction of battery degradation trajectories with significant implications for
condition monitoring and EV safety. Table 2 offers a comparative analysis of lithium-ion
battery aging modeling methodologies.

Table 2. Comparative analysis of lithium-ion battery aging modeling methodologies.

Focus/Contribution Methodology Key Findings Ref.

Formation of solid-electrolyte
Investigates the electrochemical breakdown and
interface (SEI) and its impact on Literature review [76,116]
its effects on battery lifespan.
capacity
Provides insights into the mechanisms of SEI
Impact of SEI on battery lifespan Experimental study [77,117–119]
formation and its role in degradation.
Multiscale modeling (kinetic Proposes a model to track dendrite growth and
Dendrite growth in Li metal anodes Monte Carlo and electrochemical its influence on performance, emphasizing the [78,120,121]
model) complexity of dendrite formation.
Shows the correlation between charge/discharge
Thermal runaway in commercial Accelerating rate calorimeter
cycles and increased risk of thermal runaway, [91]
lithium-ion cells (ARC) experiments
elucidating the underlying exothermic reactions.
Highlights the significance of fundamental
Physics-based modeling for lifespan Partial differential equations
electrochemical principles in predicting battery [98,118,122]
prediction (PDEs)
behavior.
Introduces a dual-stage estimation algorithm for
Advanced battery management Online capacity estimation
real-time capacity estimation, enhancing battery [101,123,124]
system for longevity scheme with thermal dynamics
longevity.
Proposes a systematic approach for parameter
Parameter identification in Sensitivity analysis and machine
identification, balancing accuracy and [104,125–127]
electrochemical models learning
interpretability in electrochemical models.
Impact of operating conditions on Discusses how temperature, charging rates, and
Literature review [110,128,129]
battery longevity depth of discharge affect degradation rates.
Batteries 2025, 11, 127 19 of 68

Table 2. Cont.

Focus/Contribution Methodology Key Findings Ref.

Compares lifecycle performance of NCA and
Performance comparison of Li-ion
Experimental testing LFP chemistries in grid applications, [112,130,131]
battery chemistries
highlighting suitability for different services.
Addresses degradation and safety issues in EV
State of health (SOH) estimation for Literature review and
batteries, focusing on SOH estimation [132–135]
EV batteries recommendations
techniques.
Proposes an integrated algorithm for accurate
SOC estimation algorithm Combined ampere-hour counting
SOC estimation across varying temperatures, [113,136–139]
development and OCV methods
enhancing battery management.
Demonstrates real-world application of lifespan
Practical utility of battery lifespan
Case study models in forecasting degradation and [114,140–143]
modeling in EVs
improving charging strategies.
Develop a data-driven model that effectively
Multi-horizon forecasting model for MMRNet (MOSUM, flash-MUSE
predicts degradation trajectories, enhancing [144–147]
battery capacity degradation attention, RNN)
condition monitoring in EVs.

4.3. Temperature
Zhang et al. [148] examined the effects of ambient temperature, charge/discharge
rate, and cut-off voltage on capacity degradation and internal resistance growth in com-
mercial LIBs. Results highlight the charging rate as the most influential factor, especially
in low-temperature aging. Understanding these risks can inform strategies to prevent
safety incidents and optimize battery performance in energy storage and electric vehicle
applications, including vehicle-to-grid interactions.

4.4. State of Charge (SoC)

State-of-charge (SOC), which represents the amount of energy stored in the battery
relative to its capacity, plays a key role in battery aging. Operating a Li-ion battery at
extreme SOCs accelerates aging. Ramadass et al. [149] showed that maintaining a high SOC
leads to increased capacity degradation due to side reactions, while low SOCs can promote
copper dendrite formation, causing internal short circuits. Proper charge and discharge
management is essential for extending LIB lifespan.
Accurate SOC estimation is crucial for battery safety, and several techniques are used,
including machine learning, voltage-based methods, and Coulomb counting. However,
temperature and measurement errors can affect accuracy. As SOC is highly influenced by
temperature, parameters are often evaluated together, though challenges remain due to
battery manufacturing variations and environmental factors. Zhang et al. [150] introduced
a method using ultrasonic reflection waves to measure battery states, considering the effects
of SOC and temperature on the ultrasonic signal. Sliding windows help optimize feature
selection, and the MD-MTD technique generates virtual samples to address sparse data.
The approach, verified in constant-current discharge mode, showed adequate accuracy,
though real-world operating conditions may impact its reliability. Future studies could use
ICP-OES to measure lithium content at the anode or cathode during SOC and temperature
changes in lithium iron phosphate batteries.
Yao et al. [151] reviewed the use of lithium-ion power batteries in transportation,
highlighting safety concerns from inaccurate battery health state estimation and prediction.
The paper discusses degradation mechanisms and critical definitions of state-of-health
(SOH), evaluating model-based, data-driven, and fusion technology methods for SOH esti-
mation and prediction. It also assesses the strengths and weaknesses of current techniques
and suggests future research may focus on innovative feature extraction, multi-algorithm
coupling, and integration with cloud platforms to improve SOH estimation and prediction.
Batteries 2025, 11, 127 20 of 68

4.5. Cycling Rate

Sun et al. [152] focused on investigating the characteristics of Li[Ni0.85 CoxMn0.15−x ]O2
cathodes synthesized through a coprecipitation method, with varying Mn to Co ratios
(x = 0–0.15). These cathodes exhibit similar discharge capacities around 206 mAh g−1
at room temperature and 213.8 mAh g−1 at 55 ◦ C between 2.7 and 4.3 V at a 0.2 C rate.
However, their cyclability, thermal stability, and rate capability vary based on the Mn
and Co ratio. Among the evaluated cathodes, Li[Ni0.85 Co0.05 Mn0.10 ]O2 shows the most
promising electrochemical properties, with high rate capacity (approximately 163 mAh g−1
at 5 C rate) at 25 ◦ C and good thermal stability (main exothermic temperature of 233.7 ◦ C
and relatively low heat evolution of 857.3 J g−1 ).
Ouyang et al. [153] investigated the capacity fading behavior of large format lithium-
ion batteries with a LiyNi1/3 Co1/3 Mn1/3 O2 + LiyMn2 O4 composite cathode under over-
charge conditions. It employs a prognostic/mechanistic model and ICA to understand the
capacity degradation mechanism. The overcharge process is divided into four stages: Stage
I has no obvious capacity degradation until the battery is overcharged to 120% SOC. In
Stage II, lithium deposition leads to LLI LAM in the LiyMn2 O4 of the composite cathode.
Increased internal resistance indicates the thickening of the SEI film. Stage III sees LAM in
both the cathode and anode as the battery is overcharged beyond 140% SOC, accompanied
by battery swelling due to electrolyte oxidation. In Stage IV, the battery ruptures due to an
internal short circuit, instantly releasing stored energy. Pinholes on the separator surface
are observed in batteries overcharged to 150% SOC or more.
Choi et al. [154] highlighted the growing importance of analytical methods for assess-
ing the condition of secondary batteries, with a particular focus on EIS that is noted for
its convenience, speed, accuracy, and cost-effectiveness. However, interpreting EIS data
requires understanding the entire electrochemical system to extract meaningful insights.
The review emphasizes constructing a physically sound circuit model tailored to the battery
cell system’s characteristics. By doing so, the circuit elements representing various aspects
of the cell’s behavior can be identified. These elements include bulk resistance (Rb), charge
transfer resistance (Rct), interface layer resistance (RSEI), and diffusion process (W). The
review further discusses the relationship between these resistances and battery parameters,
such as SOC, SOH.
The capacity of lithium-ion batteries diminishes over cycles due to various mechanisms
stemming from unwanted side reactions. These reactions, occurring during overcharge or
over-discharge, lead to electrolyte decomposition, formation of passive films, dissolution
of active materials, and other phenomena. Unfortunately, existing mathematical models for
lithium-ion batteries lack the incorporation of these capacity loss mechanisms. As a result,
Arora et al. [155] fall short in predicting cell performance during cycling and under abusive
conditions. They reviewed current literature on capacity fade mechanisms and proposed
avenues for integrating these mechanisms into advanced lithium-ion battery models, high-
lighting the requisite information and directions for future research. A comparison of the
factors influencing the aging of lithium-ion batteries is presented in Table 3.
Beyond thermal and operational factors, external environmental conditions such as
humidity and atmospheric pressure play a role in battery aging, particularly in applications
within extreme climates or high-altitude regions. The most significant factors for lithium-
ion battery aging include temperature, cycling rate, and depth of discharge (DOD), which
are often regarded as the most pivotal elements impacting aging. Elevated temperatures
considerably expedite chemical reactions and intensify the aging process, while frequent
cycling and deep discharges place a substantial mechanical and electrochemical load on
the battery. Meta-analyses have consistently identified temperature and DOD as leading
contributors to battery aging, underscoring the critical need for precise control of these
Batteries 2025, 11, 127 21 of 68

variables in real-world applications. To address this, it’s imperative to better understand

and mitigate battery aging effects by leveraging predictive aging modeling methods. The
significance of these factors can vary depending on the specific battery chemistry, usage
scenario, and application.

Table 3. Comparison of factors affecting lithium-ion battery aging.

Factor Key Findings Methodology Ref.

Ambient temperature and charge rate Experimental analysis of commercial LIBs to
Temperature significantly affect capacity degradation, assess the impact of temperature and charging [149,156,157]
especially in low temperatures. conditions.
High SoC accelerates degradation through side
reactions, while low SoC promotes dendrite Empirical study on how different SoC levels
State of Charge (SoC) [149,158–160]
formation. Effective change management is impact battery aging.
essential for longevity.
Reviews methods for estimating state of health Literature review synthesizing findings on SOH
State of Health (SOH) and highlights degradation mechanisms estimation and degradation mechanisms from [151,161]
in lithium-ion batteries. various sources.
Investigates Li[Ni0.85 CoxMn0.15−x ]O2 cathodes, Synthesis and characterization of cathodes with
Cycling Rate showing varying cyclability and thermal evaluation of electrochemical properties under [152,162]
stability based on composition. different conditions.
Examines capacity fading under overcharge, Mechanistic modeling and incremental capacity
Depth of Charge identifying stages of degradation, including analysis to study overcharge effects in large [153,163,164]
lithium deposition and active material loss. format LIBs.
Highlights the role of electrochemical impedance
Analytical review of EIS techniques, focusing on
Electrolyte spectroscopy (EIS) in assessing battery health,
developing circuit models to represent [154,165,166]
Composition emphasizing circuit model construction for
battery behavior.
data interpretation.
Discusses side reactions causing capacity fade Literature review summarizing capacity fade
Calendar Aging and identifies gaps in predictive models for mechanisms and suggesting improvements for [155,167,168]
battery performance under stress. predictive battery modeling.

4.6. Environmental Factors

Wang et al.’s study [169] showed that when LIBs are subjected to saline conditions,
humidity accelerates the aging process to its greatest degree. The primary aging factor
resulting from continuous SEI film breakdown and restoration procedures is lithium in-
ventory loss (LLI). According to the findings of the structural study, there is observable
cathode degradation, indications of material cracking, and an increase in impedance rating
as capacity falls. In order to control humidity-triggered degradation rates, new design
strategies and prediction models should be created.
To investigate the thermal safety of cycling-aged LIBs under various pressure settings,
Xie et al. [170] used a dynamic pressure chamber. The time it takes for thermal runaway
to start and happen at lower temperatures is shortened by higher cycle aging and lower
ambient pressure. Shorter times between gas release and ignite are the result of aging
and decreased operating pressure. Battery safety is adversely affected by three factors:
pressure imbalances, side chemical reactions, and cathode structural failure. According to
the study, for 18,650 LIBs in flight systems, measuring voltage changes offers better early
notice capabilities than smoke monitoring systems.

5. Multi-Factor Interactions in Battery Aging

Temperature, SOC, DOD, and charge rate are some of the factors that affect how
batteries naturally age. High SOC levels and elevated temperatures combine to promote
lithium plating and electrolyte breakdown, while deep cycling at elevated temperatures
results in increased mechanical stress and active material loss. Fast charging and high
state of charge levels increase the risk of material plating and cause the internal resistance
to heat up more. In addition to machine learning techniques that capture non-linear
Batteries 2025, 11, 127 22 of 68

dependencies, effective battery lifetime prediction models employ semi-empirical and

Arrhenius-based formulations with exponential or power-law correlations. Performance
models are improved and management systems for battery lifetime systems are optimized
by laboratory research and experimental testing under real-world aging situations [72].

5.1. Combined Effects of Temperature and SOC

The aging behavior of Samsung INR21700-50E lithium-ion battery cells (Samsung SDI
Co., Ltd., Suwon-si, Republic of Korea.) was investigated by Florian et al. [171] using a
combination of calendar and cycle aging experiments. The study involved 279 cells under
71 experimental circumstances over the course of a year, or 250 years of data collecting.
Various experimental techniques were used throughout the research project’s stages: Stage 2
used model-based parameter individual optimum experimental design, while Stage 1 used
non-model-based experimental design. Better insight into degradation was made possible
by these two methods. By facilitating the calibration of performance models and the
investigation of unknown aging mechanisms, this dataset accomplishes three goals.
In order to reconcile the manufacturer’s stated battery lifespan from marketing claims
with actual usage performance, Sai Krishna et al. [172] developed a sophisticated cycle
testing process. By taking into consideration temperature, charging and discharging rules,
and rest periods, this approach employs 1000 distinct test cycles to examine realistic driving
patterns. Important findings on battery deterioration were revealed by experimental data,
which showed that capacity decline was caused by both SEI layer expansion and lithium
plate development. Research indicates that whereas cycle time is the primary determi-
nant of SEI formation, lithium plating responds to rest period duration independent of
charge/discharge speed. While the development of an SEI layer improves the durability of
lithium plating across cycles, dimensional and time-related variations in operating temper-
atures are important factors that accelerate capacity fading rates. The study demonstrates
that safe operating temperatures affect battery lifetime, but it also calls for more investiga-
tion into how to replicate actual driving habits and develop innovative charging techniques
to improve management systems.

5.2. Impact of Temperature and Discharge Rate

In order to determine how temperature and discharge rate affect cycle-life performance,
the authors Yao et al. [173] assessed lithium-ion pouch cells using graphite anode cells
and LiCoO2 /LiNi0.8 Co0.15 Al0.05 O2 blended cathode cells. These cells are less affected by
discharge rate, but exposure to low temperatures causes increasing degradation, mostly
in the LiCoO2 component in the cathode area. At discharge rates as high as 5 C, the
cells show the ability to function for 3000–5000 cycles before their capacity drops by 20%.
Although low temperatures accelerate aging by increasing charge transfer impedance in
aged cathodes, rising temperatures accelerate the production of SEI layers and electrolyte
breakdown. By design, the pouch cell distribution lessens the effects of rapid discharge
rates while lowering mechanical and thermal stress. This work demonstrates why it is still
crucial to monitor the cathode’s condition in low-temperature applications.

5.3. DOD and Capacity Degradation

Guoqing et al.’s study [174] examined the aging processes of lithium-ion batteries by
examining several operational characteristics, such as ambient temperature, DOD, and
SOC range. The report describes four main degradation processes, including as particle
breakage, active material reduction (LAM), lithium (Li) plate creation, and solid-electrolyte
interface (SEI) expansion. The investigation reveals that while temperature extremes
accelerate degradation, ideal battery operation temperature ranges between 25 and 35 ◦ C
effectively slow down the aging process. The study found that there is constructive feedback
Batteries 2025, 11, 127 23 of 68

between particle cracking and LAM, and destructive feedback between Li plating and SEI
growth. Low temperatures accelerate the rate of capacity degradation because Li plating
intensifies, whereas high temperatures decrease Li plating but accelerate SEI development.
Blocking anode pores results in electrolyte potential gradients, which causes nonlinear
capacitance degradation.

6. Lifetime Modeling of Lithium-Ion Batteries with Different Cathode

6.1. (Nickel Cobalt Aluminum) NCA
Torregrosa et al. [175] addressed lithium-ion battery calendar aging, proposing a semi-
empirical model with physically interpretable parameters. It incorporates temperature and
state of charge dependencies through a pre-exponential factor and a power law coefficient
calibrated via a two-step optimization process. This model accurately predicts capacity
loss across various conditions and chemistries over 50 years. Findings indicate that NMC
chemistry offers superior longevity under specific temperatures and state of charge condi-
tions, which is crucial for automotive and deep-space applications. LFP chemistry shows
resilience at higher temperatures and state of charge levels.
Zhang et al. [176] investigated the degradation of lithium-ion battery cells containing
nickel–cobalt-aluminum-oxide electrodes due to cyclic overcharging. It suggests non-
destructive methods for detecting overcharging-induced degradation. The study finds that
battery capacity decreases notably with increased overcharge depth and cycles, particularly
in the initial three cycles and when the charging termination voltage is set to 5 V. The
tolerance to overcharge also diminishes with cyclic overcharging. The authors employ
electrochemical impedance spectroscopy, incremental capacity, and differential voltage
analysis to diagnose cell degradation. Three main degradation modes are identified: loss
of lithium inventory, loss of active materials, and a unique increase in the third peak
on incremental capacity curves, indicative of overcharging degradation in batteries with
NCA cathodes.
NCA (LiNiCoAlO2 ) LIBs, commonly used in electric vehicles and power grid ap-
plications, are prone to degradation, raising safety concerns. To monitor changes in the
battery’s state of health, it’s essential to analyze degradation under various stress condi-
tions. While methods like EIS, GITT, and ICA have been used individually, there are few
studies combining them to assess NCA battery degradation [177].
High temperatures can hasten NCA cathode deterioration, which lowers capacity and
shortens cycle life. To reduce this degradation mechanism, efficient thermal management
techniques are essential [178].
Mathematical models play a key role in forecasting the lifespan of NCA cathodes
in Lithium-Ion Batteries by modeling degradation processes like capacity loss, cycling
effects, and chemical reactions. They factor in variables such as voltage, temperature, and
impedance to predict battery behavior. These models are vital for enhancing battery design
and extending performance [179].
An electrochemical model called the DFN model is used to explain how NCA cathodes
behave during cycles of charge and discharge. A number of variables are considered,
including as SOC, temperature, cycling rate, and DOD [180].
Hu et al. [181] emphasized the critical importance of ensuring the reliability of recharge-
able LIBs due to the potential for significant economic losses and safety hazards resulting
from battery failures. To address this concern, the study introduces a methodological
framework for the quantitative analysis of degradation mechanisms in LIBs while they
are in operation. The framework involves two main phases: (1) offline characterization
of half-cell differential voltage behavior to collect precise voltage and capacity data, and
(2) online (on-board) analysis of degradation mechanisms using recursive Bayesian filtering
Batteries 2025, 11, 127 24 of 68

to estimate degradation parameters based on full-cell voltage behavior. These parameters

quantify the extent of degradation from different mechanisms. Simulation results using
LiCoO2 /graphite Li-ion cells demonstrate the effectiveness of the proposed framework in
the online estimation of degradation parameters [182,183].
Reduced cyclable lithium inventory is the main source of capacity fade, while anodic
side reactions such as electrolyte decrease and SEI growth have been found to be the main
culprits. The impacts of anodic and cathodic side reactions during storage are shown by
coulomb tracking, while differential voltage analysis (DVA) indicates a change in electrode
balance that is highly correlated with anode potential. Reversible self-discharge can happen
with high SoCs, which could lead to a misconception of slower aging. The lowest graphite
potential should be avoided in order to limit aging and prolong battery life. Cells should
be stored at low temperatures and lower SoCs [184].
Data-driven models, founded on machine learning and artificial intelligence tech-
niques, are emerging as potent tools for predicting NCA cathode lifespan. These models
can capture intricate patterns and dependencies within extensive datasets [185]. Lifetime
modeling for NCA cathodes in LIBs may differ from other chemistries, such as NMC or LFP,
due to distinctions in material attributes, charge and discharge characteristics, thermal sta-
bility, and the effects of degradation mechanisms [186]. Material Properties: NCA cathodes
typically have a higher cobalt content than NMC cathodes, making them more costly but
potentially offering greater energy densities. LFP cathodes possess a different olivine crystal
structure than the spinel structure of NCA and NMC, impacting ion transport and overall
performance [187]. Charge/Discharge Characteristics: NCA and NMC cathodes are com-
monly employed in applications prioritizing high energy density and power, like electric
vehicles, due to their higher specific capacity. In contrast, LFP has higher structural stability
and potential for longer cycle life, albeit with a slightly lower energy density [188]. Thermal
Stability: Distinct cathode materials show varied thermal stability. For instance, NCA tends
to be less thermally stable than LFP, influencing operating temperature ranges and safety
considerations [189]. Degradation Mechanisms: Each cathode chemistry has unique degra-
dation mechanisms that can influence lifetime modeling. For example, NCA and NMC
may experience capacity fade due to side reactions at the electrode-electrolyte interface,
while LFP might be more susceptible to mechanical degradation due to its brittleness [110].
Performance vs. Cycle Life: The selection of cathode material can impact the trade-off
between performance and cycle life. NCA and NMC may offer higher specific energy but
have a shorter cycle life than LFP [190]. Overall, lifetime modeling for NCA cathodes must
consider these differences and variations in properties and performance compared to other
cathode chemistries. This involves understanding the kinetics of electrochemical processes,
capacity fading mechanisms, thermal stability, and other factors influencing long-term
battery performance [191–193]. Transfer learning approaches, which adapt models trained
on related chemistries or systems, may help overcome data limitations, particularly in
predicting NCA cathode behavior under varied operating conditions.
Accurate lifespan modeling relies on experimental data and validation. Researchers
conduct accelerated aging tests under extreme conditions to gather valuable data for
calibrating models. These tests involve subjecting batteries to high temperatures, rapid
charge/discharge rates, and other stressors. Validation of lifespan models entails comparing
model predictions with real-world battery performance data. This step ensures that the
models accurately depict the degradation behavior of NCA cathodes in practical scenarios.
Modeling the lifespan of NCA cathodes in lithium-ion batteries is a multidisciplinary
endeavor that integrates elements of electrochemistry, materials science, and mathematical
modeling. Precise models are indispensable for optimizing battery design management
strategies and guaranteeing the long-term performance and safety of LIBs. As research
Batteries 2025, 11, 127 25 of 68

progresses in this domain, enhancements in the durability of NCA cathodes will contribute
to more dependable and long-lasting lithium-ion battery technologies [194].

6.2. Lithium Cobalt Oxide (LCO)

Two major issues must be resolved in order to increase the energy density and func-
tionality of lithium cobalt oxide (LCO)-based batteries for 3 C electronics: interfacial
instability with electrolytes and structural deterioration, which includes phase transitions,
cobalt dissolution, and oxygen evolution. Techniques including coating, doping, and elec-
trolyte optimization have worked well; coating improves surface protection, while doping
increases structural stability. To improve cycling stability, particularly at higher cut-off
voltages above 4.5 V, future developments should concentrate on integrating sophisticated
characterization techniques, creating innovative electrolytes, and combining numerous
modification tactics [195].

6.3. Lithium Iron Phosphate (LFP)

Lithium iron phosphate (LFP) battery deterioration is primarily influenced by en-
vironmental temperature (T) and charging voltage limit (Vchg), with charging current
(Ichg), discharging current (Idis), and discharging voltage limit (Vdis) following closely
behind. Higher charging voltage limits speed up the degradation of accessible lithium
and positive electrode materials, while higher charging current speeds up the degradation
of negative electrode materials. Future studies on the aging mechanisms of lithium-ion
batteries are also essential for enhancing battery management techniques and aging test
optimization [196].

6.4. Nickel Cobalt Aluminum (NCA)

NCA batteries’ capacity fade is accelerated by high-rate pulse discharge; after 400 cy-
cles, when pulse-discharge cell’s capacity fade was 20.5%, whereas the high-rate continuous
discharge’s was 5.68% and the control cell’s was 0%. EIS demonstrates that the degradation
under high-rate pulse discharge is associated with increased passive layer development
and higher charge transfer resistance. Using additional methods such as material analysis
and three-electrode EIS, more research is required to ascertain if the observed degradation
is due to the peak current or the pulsed character of the discharge [197].

6.5. Lithium Manganese Oxide (LMO)

A semi-empirical life model was created by Wang et al. [198] to explain both calendar-
life and cycle-life losses. In graphite/composite metal oxide cells, the DOD, temperature,
and rate have the biggest effects on capacity loss. According to the differential voltage
approach, lithium loss happens more quickly than active material loss. The cycle-life loss
is linearly dependent on time or charge throughput and exponentially dependent on rate,
while the calendar-life loss is controlled by an Arrhenius correlation for temperature effects
and a square root of time relation.
It can be concluded that the main reasons for capacity fading in lithium-manganese
oxide and lithium-nickel-cobalt mixed oxide batteries include structural and mechanical
changes that occur during cycling, as well as adverse reactivity with the electrolyte. Doped
materials have helped to lessen these problems, but more research is still needed to fully
understand concerns like manganese dissolution and electrolyte interactions. The cathode
chemistry (NCA, LCO, LFP, LMO) affects the temperature, charging voltage, current rates,
and discharge depth, among other aspects that affect the aging and deterioration of LIBs.
Enhancing battery performance, longevity, and safety requires an understanding of these
systems. Although semi-empirical and data-driven models shed light on degradation
processes, more study is required to improve thermal and structural stability, optimize
 these systems. Although semi-empirical and data-driven models shed light on degrada-
tion processes, more study is required to improve thermal and structural stability, opti-
Batteries 2025, 11, 127
mize battery management strategies, and improve testing techniques to anticipate and 26 of 68

lessen capacity fade. These advancements will eventually result in more dependable and
long-lasting battery technologies
battery managementfor cutting-edge
strategies, applications.
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capacity fade. These advancements will eventually result in more dependable and long-
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As batteries are increasingly integrated into complex systems such as aircraft and
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7.1. State of Health
vehicles (BEVs) due to their superior performance characteristics. However, these batteries
undergowidespread
LIBs have gained aging and performance degradation
use as the primary over time, influenced
power source forby external
batteryand internal
electric
factors. Evaluating the SOH of LIBs is crucial to ensure their longevity and support
vehicles (BEVs) due to their superior performance characteristics. However, these
safe driving in BEVs. While various SOH prediction methods exist, many are primarily
tested under simulated environments and face challenges when implemented in real-world
industrial production settings [201]. Tian, Huixin, et al. [202] investigated the elements
contributing to the aging of LIB, presented a classification-oriented method to predict the
SOH, and evaluated the pros and cons of each approach. Ultimately, they offered practical
recommendations and solutions tailored to the specific demands of industrial production.
Mawonou et al. [203] introduced two innovative indicators for evaluating the aging of
LIBs to improve the existing diagnosis-based state of health (DB-SOH) solutions. These
indicators, known as charging event-based (CDB-SOH) and driving event-based (DDB-
Batteries 2025, 11, 127 27 of 68

SOH) indicators, utilize data collected during charging and driving activities, incorporating
variables like distance, speed, temperature, charging power, and more. Both indicators
offer reliable assessments of the state of health with a significantly reduced estimation error.
Additionally, the researchers proposed a data-driven battery aging prediction model that
utilizes the random forest (RF) algorithm, considering real-world user behavior and ambient
conditions. This model achieved an estimation error of only 1.27%. Finally, a method for
ranking the factors contributing to battery aging was proposed, and the obtained ranking
aligns with known causes of aging in existing literature. This ranking can be applied to
mitigate the rapid aging of lithium-ion batteries in electrified vehicle applications.
Feng et al. [204] presented the probability density function (PDF) method as a means
of evaluating the SOH of electric storage batteries. The PDF method was compared
to other techniques, such as cyclic voltammogram (CV), ICA, and differential voltage
analysis (DVA), revealing their mathematical agreement. When applied to LiFePO4 and
LiMn2 O4 batteries and coin cells, the PDF method produced results similar to those of
the ICA/DVA methods, demonstrating its effectiveness. Durability tests conducted on
commercial batteries using the PDF method indicated a reduction in peak height as battery
capacity declined, facilitating the development of an algorithm for online SOH assessment.
Zhang et al. [205] studied the aging behavior of a 15P4S battery module using
a specific cycle protocol. They employed various evaluation methods, including EIS,
charge/discharge curves, ICA, and average Fréchet distance (AFD). The findings indicated
that certain internal resistances increased as the module aged, and the combined value of
two resistances served as a health factor for evaluating the offline SOH. The characteristic
peak height observed on the ICA curves offered a quick assessment of the module’s SOH.
The AFD method demonstrated high accuracy in estimating the SOH, surpassing ICA
regarding online module evaluation.
Eddahech et al. [206] investigated the kinetics of the constant current–constant voltage
(CC-CV) charging process at 1 C and focused explicitly on the voltage regulation kinetics
during the CV step. The CV step is considered crucial in assessing the battery SOH,
particularly concerning calendar aging. The study compared the aging behavior of four
different battery technologies and observed variations in battery behavior throughout
the aging process. In the case of lithium–nickel–manganese–cobalt-oxide, lithium–nickel–
cobalt–aluminum-oxide, and lithium-ion–manganese batteries, the current during the
CV charging phase proved valuable in determining the SOH. However, for lithium-iron-
phosphate batteries, a simple calculation of the duration of the CV step showed high
accuracy compared to traditional capacity measurements.
Tang et al. [207] presented an innovative algorithm for estimating the SOH of LIBs
through incremental capacity analysis. The algorithm leverages regional capacity and volt-
age data to develop a precise SOH model with a high goodness of fit. Notably, this method
is computationally efficient, resilient to noise, and does not necessitate the direct derivation
of characteristic parameters. It accurately estimates SOH without relying on the state of
charge and impedance measurements, which are commonly utilized in other approaches.
Weng et al. [208] tackled the challenge of capacity degradation in lithium-ion batteries,
specifically in the context of EVs and plug-in hybrid electric vehicles (PHEVs). They pro-
posed a monitoring scheme for battery SOH based on partial charging data to track capacity
loss during on-board operations. ICA was employed to identify a robust signature asso-
ciated with battery aging. Several algorithms, including support vector regression (SVR),
were developed and evaluated for on-board SOH monitoring. The SVR model consistently
delivered accurate identification results, capable of predicting capacity degradation in other
cells within a 1% error margin [209,210].
Batteries 2025, 11, 127 28 of 68

Zhang et al. [211] presented a thorough overview of the most recent advancements
in impedance spectroscopy measurement technology and its application for estimating
the health state of LIBs. The paper discusses the benefits and constraints associated with
this approach and highlights potential future directions. This review article addresses a
significant gap in the field and contributes to the continued progress of this technology.
Feng et al. [212] conducted a study to examine the thermal behavior and heat accu-
mulation of commercial lithium-ion batteries under various SOH during overcharging.
The findings revealed that thermal runaway, caused by separator melting and subsequent
internal short circuits, was the triggering factor regardless of the SOH. The safety of the
batteries decreased after aging, as evidenced by temperature, voltage, and duration changes
leading up to thermal runaway. This decrease in safety was primarily attributed to the loss
of lithium ions and changes in negative capacity following high-rate overcharging. The
SOH did not significantly influence the contribution of heat from side reactions to thermal
runaway. The study provides valuable insights into the risks associated with overcharging
lithium-ion batteries and recommends safety measures to be implemented within 3 min of
reaching a voltage inflection point.

7.2. EOL
The limited lifespan of EV batteries is attributed to their declining capacity and
power capabilities over time. However, there is a lack of understanding regarding the
EOL value chains, their interdependencies, and the dynamic conditions that influence
decision-making and monetary outcomes for EOL options [213]. The increasing global
usage of LIBs necessitates effective management of their EOL. While numerous studies
have focused on managing their EOL within closed-loop supply chains, safety has been
largely overlooked [214]. The EV industry has grown significantly, with major players now
manufacturing EVs worldwide. As the number of EVs continues to rise, it becomes crucial
to establish well-defined EOL strategies for the batteries removed from these vehicles,
aligning with efforts to make the automotive industry more environmentally friendly [215].
E-mobility, particularly electric cars, has experienced rapid growth driven by LIB technol-
ogy advancements. However, LIBs degrade over time and usage, resulting in diminished
performance. With the increasing adoption of EVs, a substantial volume of retired LIB
packs that no longer meet the performance requirements for powering an EV will soon
be available. Various EOL options, including recycling and recovery processes, are being
developed to address this challenge [216].
Daigle et al. [217] utilized an electrochemistry-based model to examine the variations
in key parameters of batteries throughout the aging process. By developing models that
capture the aging effects on these parameters, the authors could (i) accurately forecast the
end-of-discharge for aged batteries and (ii) predict the EOL of a battery based on anticipated
usage patterns. To validate their approach, they conducted experiments using randomized
discharge profiles. The results demonstrated the efficacy of their models in accurately
predicting battery behavior and showcased their potential for practical implementation
in BMS.
Stroe et al. [218] conducted a study comparing the performance of a Lithium-ion
battery at the beginning of its life (BOL) and at two higher levels of degradation. The
research involved measuring the capacity, internal resistance, and open circuit voltage
of a high-power 13 Ah battery under various temperature conditions, C-rates, and SOC
levels. Two cells underwent aging processes, resulting in 40% and 60% capacity losses,
respectively. These degraded cells were characterized in a similar manner to the fresh cell at
BOL. The results indicated significant changes in battery performance parameters, such as
temperature, C-rate, and SOC, between the fresh and highly degraded cells. These findings
Batteries 2025, 11, 127 29 of 68

underscore the impact of degradation on battery performance and emphasize the need to
consider these variations in real-world applications.
Rohr et al. [219] created a dynamic model for EOL batteries to examine the economic
aspects of battery value chains and estimate residual values. This model addresses a
research gap and employs cost-benefit and net present-value approaches. The findings
from a survey conducted in Germany indicate an economic potential for all EOL strategies,
namely Recycling, Remanufacturing, and Second-Life. Second-Life already proves to be
economically feasible, while the economic viability of Remanufacturing and Recycling
depends on the quantity of discarded batteries. Recycling is expected to reach a break-even
point within 5 to 10 years. The model provides flexibility for future parameter surveys to
evaluate the impact of evolving battery characteristics on EOL value chains. It is a valuable
tool for devising strategies concerning the end-of-life of electric vehicle batteries.
Chen et al. [220] highlighted the significance of these research inquiries in materials
science, supply chain management, and fire protection engineering. The study emphasizes
the importance of addressing safety considerations in managing EOL of LIBs. By doing
so, the research aims to contribute to the establishment of a comprehensive and secure
approach for handling LIBs at the conclusion of their life cycle.
Kupper et al. [221] introduced an electrochemical model for a lithium iron phos-
phate/graphite (LFP/C6) cell that encompasses various aging mechanisms, including
SEI formation, SEI breaking, and electrode dry-out. To address inadequate electrolyte
penetration, the model incorporates an activity-saturation relationship. A time-upscaling
methodology is utilized to make long-term aging predictions. The model demonstrates
accurate predictions of both calendric and cyclic aging, aligning with experimental data.
Valuable insights are obtained concerning the impact of temperature, cycling depth, and
average state of charge on capacity loss. Additionally, the model captures the non-linear
aging behavior observed towards the end of the battery’s life, commonly referred to as
“sudden death”. This study provides a comprehensive understanding of aging mechanisms
and capacity loss in LFP/C6 cells.
Ramoni et al. [222] underscored the importance of conducting thorough research
to tackle the diverse challenges related to the remanufacturing of EV batteries. These
challenges encompass areas such as comprehending battery degradation, optimizing re-
manufacturing procedures, devising effective testing and quality control techniques, and
exploring viable business models for remanufactured EV batteries. By addressing these
research concerns and advocating for remanufacturing as a feasible EOL option, the paper
seeks to contribute to establishing a sustainable and efficient EOL strategy for EV batteries.
Santhira Sekeran et al. [223] addressed two key research questions related to battery
life estimation. Firstly, they tackled the issue of incomplete battery cell testing data by
proposing the application of survival analysis. This statistical technique can handle cen-
sored data and estimate the remaining lifespan of cells that have not yet reached the EOL
threshold. By implementing survival analysis, the researchers aimed to overcome the
challenge posed by limited testing data availability. Secondly, the study focused on the
reusability of prediction models trained on one battery cell chemistry to predict EOL for
a different chemistry using transfer learning. They developed a workflow that enables
training a prediction model for one chemistry and subsequently reusing it to improve the
prediction accuracy for a different chemistry. This approach reduces the need for extensive
testing and enhances efficiency in battery life estimation. The work presented by Santhira
Sekeran et al. [223] contributes to the development of effective methods for battery life
estimation by addressing the challenges associated with incomplete data and leveraging
transfer learning techniques across different battery cell chemistries.
Batteries 2025, 11, 127 30 of 68

Using historical data, Kandasamy et al. [224] conducted a study focused on the proac-
tive identification of EOL for LIBs. The research employed multiple machine learning
(ML) methods to predict the EOL of batteries, aiming to forecast the EOL at least 30 cycles
in advance. Such predictions aim to facilitate timely maintenance and effective battery
replacement. The paper thoroughly analyzes the performance of different ML methods
to enhance the accuracy of EOL predictions. Furthermore, it establishes a correlation be-
tween the predictions and data obtained from a practical BMS, thereby demonstrating the
practical applicability of the approach. By leveraging ML techniques and historical data,
this study contributes to the proactive management of stationary battery systems (SBSs) by
providing timely EOL predictions. The findings underscore the potential for improving
battery maintenance strategies and optimizing battery replacement schedules to ensure the
reliable and efficient operation of SBSs.
Zhu et al. [225] offered valuable insights into the feasibility of repurposing retired
LIBs for second-life applications. The study examined the viability of utilizing these
retired batteries by considering both economic factors and technological considerations.
Through an analysis informed by industry reports and technical literature, the research
provided a comprehensive perspective on the potential benefits and challenges associated
with repurposing retired LIBs for second-life applications. This evaluation contributes
to a better understanding of the feasibility and practicality of implementing second-life
battery strategies.

7.3. RUL
Accurately predicting future capacities and RUL of batteries while managing uncer-
tainty is challenging in battery health diagnosis and management. To address this challenge,
advanced machine learning techniques can be applied to achieve effective predictions of
future capacities and RUL for LIBs, while also providing reliable uncertainty quantifica-
tion [226]. The accurate prediction of RUL and the diagnosis of SOH are essential for
ensuring the safety, durability, and cost-effectiveness of energy storage systems that rely
on Li-ion batteries. However, the prediction of RUL and the diagnosis of SOH present
significant challenges due to the complex aging mechanisms inherent in batteries [227,228].
Maximizing the longevity of Li-ion batteries is a major concern, and Intelligent BMS plays
a critical role in achieving this goal while maintaining performance. To optimize battery
performance and minimize degradation, accurate information on the battery’s RUL is
required. However, accurately predicting RUL remains a challenging task [229].
Liu et al. [230] proposed a data-driven approach for predicting battery capacity and
estimating RUL. The study utilized empirical mode decomposition (EMD) to decompose
capacity data into intrinsic mode functions (IMFs) and a residual component. The residual
component was estimated using a long short-term memory (LSTM) submodel to capture
long-term dependencies, while the IMFs were fitted using a Gaussian process regression
(GPR) submodel to quantify uncertainty. Comparative evaluations with other models
demonstrated that the combined LSTM + GPR model outperformed alternative approaches,
providing accurate forecasts for both short-term and long-term battery capacity. The
proposed approach also exhibited good adaptability and reliable uncertainty quantification
for battery health diagnosis, including RUL prediction. This study significantly contributes
to battery health diagnosis and management by presenting an effective solution for capacity
prediction and RUL estimation, while effectively managing uncertainty.
Wei et al. [231] proposed a comprehensive method for predicting the RUL and estimat-
ing the SOH of batteries. Their approach involved developing a battery SOH state-space
model based on SVR. The model incorporated representative features extracted from a
CC and CV protocol. The state variable of the model was battery capacity, and the output
Batteries 2025, 11, 127 31 of 68

variables were estimated impedance values, considering the correlation between capacity
and charge transfer resistance plus electrolyte resistance. A particle filter was employed to
mitigate measurement noise and enhance the accuracy and robustness of SOH estimation.
Experimental tests were conducted to validate the method, and the results demonstrated
accurate and reliable SOH estimation and RUL prediction. This approach contributes to
advancing BMS for energy storage applications by providing a comprehensive framework
for RUL prediction and SOH estimation.
Zhang et al. [232] presented a novel online scheme for estimating Li-ion batteries’
RUL from a thermal perspective. The approach leverages thermal dynamics to predict
the RUL and incorporates a hierarchical estimation algorithm with provable convergence
properties. The algorithm has three stages that estimate core temperature, SOC, battery
capacity, and capacity fade aging model. Sliding mode observers and nonlinear least-
squares algorithms are employed to design the estimators for each stage. Simulation results
demonstrate the effectiveness of the proposed scheme, showcasing accurate and reliable
RUL predictions. This paper makes a valuable contribution to the field by introducing an
innovative approach to RUL estimation that considers thermal dynamics. The proposed
scheme can potentially enhance BMS and optimize battery performance.
Zhang et al. [233] introduced a novel fusion technique for predicting LIBs’ RUL LIBs.
The technique reduces the training data requirement while maintaining high prediction ac-
curacy. The study also introduces a validation and verification framework, which provides
a robust approach for evaluating the prediction performance. This framework ensures
that the predictions are reliable and can be effectively assessed. The results of the study
demonstrate the effectiveness of the proposed fusion technique in accurately predicting
battery failure and estimating the RUL. This highlights the technique’s potential to enhance
BMS by improving RUL predictions and optimizing battery maintenance strategies.
Zhang et al. [234] introduced practical methods for battery health diagnosis and RUL
prediction. The study focused on analyzing the charging voltage curve of batteries using a
feature extraction-based approach to estimate the battery’s SOH. Additionally, the authors
identified different aging stages in the battery’s lifespan to predict its RUL. The proposed
methods were validated using data from acceleration aging tests conducted on multiple
battery cells under various current rates. The capacity estimates achieved high accuracy,
with less than 1% estimation errors in most cycles. RUL prediction was also rapid, even
when subjected to dynamic current rates, with prediction errors remaining below 10 cycles
for most cycles after 300 cycles. The results demonstrated the effectiveness of the proposed
methods in accurately estimating battery capacity and predicting RUL. These approaches
provide practical and efficient battery health diagnosis and RUL prediction solutions.
They enable informed decision-making and optimize battery performance in a wide range
of applications.
Zhang et al. [235] introduced a novel approach to predicting LIBs’ RUL. The method
utilizes the Box-Cox transformation and Monte Carlo simulation to achieve accurate and
efficient RUL predictions. Unlike traditional approaches, this method does not rely on
offline training data, which offers advantages in terms of flexibility and adaptability. The
proposed approach has the potential to significantly reduce the acceleration aging test
time for lithium-ion batteries. Providing reliable RUL predictions without the need for
extensive offline training contributes to the development of more practical and effective
BMS for EVs. Overall, Zhang et al.’s approach presents an innovative solution for RUL
prediction in lithium-ion batteries, offering improved efficiency and applicability for BMS
in the EV industry.
Xu et al. [236] introduced a novel method for predicting LIBs’ RUL under varying
temperature conditions. Their approach combines a stochastic degradation rate model, an
Batteries 2025, 11, 127 32 of 68

aging model based on the Wiener process, and a two-step estimation method that integrates
maximum likelihood estimation (MLE) with a genetic algorithm (GA). By incorporating
these elements, the proposed method enhances accuracy and reduces uncertainty in RUL
prediction compared to existing approaches. A case study demonstrated the improved
performance of the method, showcasing its ability to provide more reliable predictions.
The method presented by Xu et al. offers a comprehensive solution for RUL prediction by
considering stochastic degradation, parameter estimation, and online parameter update.
This contributes to a better understanding and managing battery health, enabling more
informed decision-making in various applications.
Wang et al. [237] introduced a model-free method for predicting the RUL of LIBs in
EVs. Their approach utilizes the discrete wavelet transform (DWT) to incorporate real
operational factors and overcome the limitations of previous methods that relied on specific
models or identification techniques. The proposed method offers several advantages,
including flexibility and adaptability to different battery systems and operating conditions.
It considers dynamic stress tests and accounts for the non-stationary behavior of batteries,
enhancing the accuracy of RUL predictions. Experimental tests conducted on a commercial
battery with various aging levels validated the accuracy of the method in predicting RUL.
The results demonstrated the effectiveness of the model-free approach in enhancing BMS
for EVs, ensuring safety and reliability throughout the battery’s lifespan. Overall, Wang
et al.’s approach contributes to the field by providing a practical and efficient method for
RUL prediction that takes into account real operational factors and avoids the need for
specific models or identification methods.
Qiu et al. [238] conducted a study on predicting batteries’ RUL. They extracted eight
aging features from battery data and examined their correlation using gray relation analysis.
To create an RUL prediction model, the researchers employed the improved gray wolf
constrained optimization algorithm to determine coefficients for combining kernel functions
in the multi-kernel relevance vector machine. The validation of the proposed model was
carried out using NASA’s battery dataset. The results showed that the improved model
outperformed single-kernel and other multi-kernel models. It achieved high accuracy
in RUL prediction, with a prediction error of less than 10 cycles and a mean absolute
error (MAE) of less than 0.05. The study demonstrated that the improved model has
superior long-term prediction capability and robustness in RUL prediction, indicating its
effectiveness in accurately estimating the RUL of batteries.
Sadabadi et al. [239] developed an algorithm for predicting the RUL of LIBs using
an enhanced single particle model (eSPM) and vehicle charging data. The researchers
collected experimental aging data and estimated eSPM parameters associated with bat-
tery aging. They established a correlation between these parameters and the battery’s
SOH, from which they derived a composite SOH metric. The RUL predictor, based on
a particle filter (PF) algorithm, utilized the evolution of the SOH metric to estimate the
RUL of the battery. The algorithm was validated using experimental data, demonstrating
its feasibility for predicting SOH and RUL using readily available charging data from
electric or hybrid vehicles. This approach holds significant implications for battery health
monitoring and management in the automotive industry. By leveraging the eSPM model
and vehicle charging data, the algorithm provides a valuable tool for assessing the battery
RUL and facilitating informed decision-making in battery maintenance and replacement
strategies [240].
Table 4 offers an overview of major findings and approaches for measuring battery
health indicators. Recent advancements in health monitoring leverage sensor-based data
collection and machine learning to assess battery health in real time, enhancing accuracy in
SOH, EOL, and RUL predictions.
Batteries 2025, 11, 127 33 of 68

Table 4. Summary of key findings and methodologies in indicators to quantify the health level of
the battery.

Key Findings Methodology/Approach Ref.

Analyzed the effect of environmental conditions on LIB aging. Systematic examination of various environmental stressors. [202]
Presented a mechanistic model to understand the degradation
Developed model based on electrochemical and thermal behavior. [203]
processes in LIBs.
Examined how cycling frequency affects the aging of LIBs. Long-term cycling tests under varying frequencies. [204]
Addressed the challenges in predicting battery life due to aging
Discussed statistical approaches to enhance prediction accuracy. [205]
variability.
Reviewed different techniques for estimation of battery state of Evaluated methods including impedance spectroscopy and
[206]
health (SOH). machine learning.
Highlighted the importance of second-life applications for used
Comprehensive analysis of potential repurposing strategies. [207]
LIBs.
Discussed the economic implications of LIB recycling and
Cost-benefit analysis of different end-of-life strategies. [208]
remanufacturing.
Examined the role of battery management systems (BMS) in
Analysis of BMS features that optimize battery usage and longevity. [211]
enhancing battery life.
Reviewed advancements in battery diagnostics and health
Summarized recent innovations in diagnostic technologies. [212]
monitoring.
Investigated the relationship between battery composition and
Comparative study of different battery chemistries. [213]
aging behavior.
Analyzed the life cycle assessment (LCA) of lithium-ion batteries. Environmental impact analysis of LIBs throughout their lifecycle. [214]
Explored the safety challenges associated with aging LIBs. Discussed failure modes and safety measures. [215]
Reviewed regulatory frameworks affecting LIB end-of-life Analysis of current regulations and their impact on LIB
[216]
strategies. management.
Evaluated innovative recycling techniques for LIBs. Investigated advanced recycling processes and technologies. [217]
Presented a framework for sustainable battery management
Developed best practices for battery use and end-of-life strategies. [218]
practices.
Created a dynamic model for end-of-life (EoL) batteries, addressing Cost-benefit and net present value approaches; survey conducted in
[219]
economic aspects and estimating residual values. Germany.
Highlighted the significance of safety considerations in managing Focused on materials science, supply chain management, and
[220]
end-of-life lithium-ion batteries (LIBs). fire-protection engineering.
Introduced an electrochemical model for LFP/C6 cells, covering Utilized a time-upscaling methodology to make long-term
[221]
aging mechanisms and predicting capacity loss. predictions.
Emphasized the need for research on remanufacturing challenges Addressed battery degradation, optimization of procedures, and
[222]
related to EV batteries. viable business models.
Tackled incomplete battery testing data through survival analysis Developed a workflow for reusing prediction models across
[223]
and transfer learning. different battery chemistries.
Focused on proactive identification of EoL for lithium-ion batteries Employed multiple machine learning methods to predict EoL at
[241]
using historical data. least 30 cycles in advance.
Explored the feasibility of repurposing retired lithium-ion batteries Analysis based on economic factors and technological
[225]
for second-life applications. considerations.
Proposed a data-driven approach for battery capacity prediction Empirical mode decomposition (EMD) and long short-term
[230]
and RUL estimation. memory (LSTM) models were used.
Developed a SOH state-space model for predicting RUL and Incorporated features from a constant-current and constant-voltage
[231]
estimating SOH of batteries. protocol.
Presented a novel online scheme for estimating RUL of Li-ion Thermal dynamics and a hierarchical estimation algorithm were
[232]
batteries from a thermal perspective. used.
Introduced a fusion technique for RUL prediction of lithium-ion Created a validation framework for evaluating prediction
[233]
batteries. performance.
Analyzed charging voltage curves for battery health diagnosis and
Validated methods using data from acceleration aging tests. [234]
RUL prediction.
Utilized Box-Cox transformation and Monte Carlo simulation for Offered advantages in flexibility and adaptability without offline
[235]
RUL prediction. training data.
 Batteries 2025, 11, 127 34 of 68

Table 4. Cont.

Key Findings Methodology/Approach Ref.

Combined stochastic degradation rate model and aging model for Integrated maximum likelihood estimation with a genetic
[236]
RUL prediction. algorithm.
Introduced a model-free method for RUL prediction considering
Used discrete wavelet transform (DWT) for enhanced accuracy. [237]
real operational factors.
Employed improved gray wolf optimization algorithm for kernel
Developed an RUL prediction model using gray relation analysis. [238]
functions in multi-kernel relevance vector machine.
Developed an algorithm for RUL prediction using an enhanced Utilized vehicle charging data to estimate parameters and derive a
[239]
single particle model (eSPM). composite SOH metric.

7.4. Aging Estimation

Modeling lithium-ion batteries simulates electrochemical, thermal, and electrical
behaviors to predict performance, aging, and efficiency. Techniques such as equivalent
circuits and electrochemical models help understand charge dynamics and degradation.
SOC and SOH are key for optimizing energy use and lifespan, while SOX estimation
enhances performance monitoring. System-level modeling integrates these factors to
predict aging and optimize battery management in real-world conditions [241–245]. LIB
aging modeling methods can generally be divided into electrochemical, statistical, and
machine learning approaches, with each focusing on specific aging factors like capacity
fade, resistance increase, and thermal effects. Figure 8 demonstrates a classification of aging
models for lithium-ion batteries. Recent research has introduced machine learning-based
models that enhance the accuracy of RUL predictions by incorporating real-time data and
Batteries 2025, 11, x FOR PEER REVIEW accounting for operational variability. Additionally, emerging technologies, including 36 of 74
solid-state batteries and new electrolyte formulations, offer the potential to slow down
aging processes and extend battery life, opening up promising avenues for future research.

Figure 8. A8. classification

Figure A classificationofofaging
agingmodels forlithium-ion
models for lithium-ionbatteries.
batteries.

Figure
Figure 9 illustratesdifferent
9 illustrates different aging
agingestimation methods
estimation for lithium-ion
methods batteries.
for lithium-ion Aging Ag-
batteries.
estimation of lithium-ion batteries predicts their RUL and performance degradation using
ing estimation of lithium-ion batteries predicts their RUL and performance degradation
modeling approaches like electrochemical and empirical models. Key indicators include
using modeling approaches like electrochemical and empirical models. Key indicators in-
clude SOC, SOH, and cycle count, which correlate with capacity fade. Techniques such
ICA and EIS help evaluate the effects of aging. Considering temperature and usage pat-
terns is essential for accurate aging predictions and effective battery management [246]. A
 Batteries 2025, 11, 127 35 of 68

PEER REVIEW 37 of 74
SOC, SOH, and cycle count, which correlate with capacity fade. Techniques such ICA
and EIS help evaluate the effects of aging. Considering temperature and usage patterns
is essential for accurate aging predictions and effective battery management [246]. A
comparison of research studies on lithium-ion battery aging and degradation estimation is
offered in Table 5.

Figure 9. Different aging estimation

Figure 9. Differentmethods for lithium-ion
aging estimation batteries.batteries.
methods for lithium-ion

Having a comprehensive understanding of battery degradation and aging in an in

Having a comprehensive
operando settingunderstanding of battery
is crucial for designing degradation
effective and aging
BMS and ensuring the safeinuseanand in
operando setting is crucial
optimizingforthedesigning
manufacturing effective BMS
of LIBs in and ensuring
large-scale applications. the
EISsafe use and op-
is a nondestructive
technique that enables the investigation of electrode kinetic processes occurring within
timizing the manufacturing of LIBs in large-scale applications. EIS is a nondestructive
batteries across different time domains. This method reveals information about charge
technique that enables thereactions,
transfer investigation
interfacial of electrode
resistance, kinetic
and mass processes
diffusions. occurring
EIS has emerged within
as a powerful
batteries across different time
diagnostic anddomains.
predictive toolThis method
in battery aging reveals
research. Itinformation about
provides significant charge
insights into
the changes occurring in internal electrochemical processes
transfer reactions, interfacial resistance, and mass diffusions. EIS has emerged as a pow- by correlating the evolution of
impedance with degradation mechanisms. By analyzing the impedance spectra, researchers
erful diagnostic andcan predictive
gain importanttoolknowledge
in battery aboutaging research.mechanisms
the degradation It provides significant
affecting in-
the battery’s
sights into the changes occurring
performance in internal
and longevity [247].electrochemical
Figure 10 illustrates aprocesses
lifetime modelbycomparison
correlating the
of LIBs.
Using EIS, researchers can unravel the intricate
evolution of impedance with degradation mechanisms. By analyzing the impedance spec- electrochemical processes within
batteries during their operation. This knowledge is essential for improving battery designs,
tra, researchers can gain important knowledge about the degradation mechanisms affect-
optimizing manufacturing processes, and developing effective strategies for prolonging
ing the battery’s performance
the lifespan ofand
LIBslongevity
in real-world [247]. Figure[248].
applications 10 illustrates
Narayanrao et a lifetime model
al. [249] proposed
comparison of LIBs. and validated a phenomenological model to investigate the degradation of lithium-ion
cells caused by charge/discharge cyclic fatigue. The model considers factors such as SEI
formation, fractures, and the isolation of electrode material, which lead to capacity loss and
Batteries 2025, 11, 127 36 of 68
Batteries 2025, 11, x FOR PEER REVIEW 38 of 74

increased electronic resistance. The study incorporates these phenomena into Newman’s
Porous Composite Electrode framework, implementing the model in COMSOL.

Figure
Figure10.
10.Lifetime
Lifetimemodel
modelcomparison.
comparison.

Horstkoetter
Using et al. [250]
EIS, researchers canconducted
unravel the anintricate
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electrochemical processes the impact
within of
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current
teries gradients,
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knowledge in current per time
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and developing and its effect on battery
strategies aging. The
for prolonging
study
the revealed
lifespan thatin
of LIBs current gradients
real-world play a significant
applications role in battery
[248]. Narayanrao et al. degradation.
[249] proposed Higher
and
current gradients were found to lead to a larger degradation
validated a phenomenological model to investigate the degradation of lithium-ion cells rate, and even moderate gra-
dients contributed
caused to this trend.
by charge/discharge Thefatigue.
cyclic results The indicated
modela linear
considersrelationship
factors such between as SEIcurrent
for-
gradient and degradation rate. These findings underscore the importance
mation, fractures, and the isolation of electrode material, which lead to capacity loss and of considering
dynamic electronic
increased influencesresistance.
and steady-state
The study conditions
incorporatesfor a these
comprehensive
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Porous aging. By recognizing the impact implementing
Electrode framework, of discharging the dynamics,
model in strategies
COMSOL. can be devel-
opedHorstkoetter
to mitigate degradation, enhance battery
et al. [250] conducted performance,
an experimental studyandtoextend
examine battery longevity.
the impact of
The research by Horstkoetter et al. emphasizes the need to consider
current gradients, specifically the change in current per time interval, on battery degrada- not only the average
current
tion. Theirbutresearch
also thefocused
dynamicon changes in current
discharging over time
dynamics and itsforeffect
a more onaccurate assessment
battery aging. The
of battery degradation and effective battery management strategies.
study revealed that current gradients play a significant role in battery degradation. Higher
currentWeng et al. [251]
gradients weredeveloped
found to leada semi-empirical model describing
to a larger degradation rate, andSEIeven
growth processgra-
moderate dur-
ing lithium-ion
dients contributed battery formation
to this trend. The cycling andindicated
results aging. The model relationship
a linear combines a full-cell
between model,
cur-
a SEI growth kinetics model, and a representation of cell expansion.
rent gradient and degradation rate. These findings underscore the importance of consid- Experimental trends
observed
ering dynamicin a pouch cell,and
influences including first-cycle
steady-state efficiency
conditions for aand SEI layer thickness
comprehensive changes,
understanding
of battery aging. By recognizing the impact of discharging dynamics, strategies can SEI
were successfully reproduced using the model. The model’s capability to simulate be
growth and multi-component reactions makes it valuable for studying
developed to mitigate degradation, enhance battery performance, and extend battery lon- solvent and additive
consumption
gevity. in industrial
The research battery manufacturing.
by Horstkoetter et al. emphasizes It serves
the needas atobridge
considerbetween
not only electro-
the
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average understanding
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dynamicapplication,
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accurate
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and effectiveThis contributes to improved
battery management battery formation
strategies.
strategies,
Weng electrolyte
et al. [251] optimization, and enhanced battery
developed a semi-empirical longevity and
model describing SEIperformance.
growth process
Langner et al. [252] investigated corrosion
during lithium-ion battery formation cycling and aging. The model in LIBs with LiNi 0.6 Co Mn0.2 O2 a(NMC)
combines
0.2 full-cellas
the cathode
model, a SEImaterial. Moisture-induced
growth kinetics model, andwater accumulation
a representation of on
cellthe NMC surface
expansion. increased
Experimental
pH, leading to corrosion of the carrier foil. Corrosion extent
trends observed in a pouch cell, including first-cycle efficiency and SEI layer thicknesswas quantified by measuring
the relative
changes, werearea of holes in the
successfully aluminumusing
reproduced foil, influenced
the model.by Theexposure
model’s duration
capability andtoambient
simu-
humidity. Analysis techniques detected lithium, aluminum,
late SEI growth and multi-component reactions makes it valuable for studying solvent sulfur, and oxygen in the
corrosion
and additiveproducts, revealing
consumption in local degradation
industrial batteryinmanufacturing.
the NMC layer. ItThese serves insights contribute
as a bridge be-
to strategies for mitigating corrosion and improving the longevity
tween electrochemical understanding and practical application, aiding in predicting for- and performance of
NMC-containing
mation protocols and batteries.
electrolyte effects on SEI passivation. This contributes to improved
battery formation strategies, electrolyte optimization, and enhanced battery longevity and
performance.
Batteries 2025, 11, 127 37 of 68

Yan et al. [253] investigated the formation and evolution of SEI film during the initial
lithium intercalation into graphite electrodes in lithium-ion batteries. They developed a
model that describes the SEI film formation as a precipitation process with nucleation and
growth phases. The model, based on classical nucleation theory, explains the observed
two-layer structure of the SEI film. The inner layer, close to the graphite electrode, is thin
and compact with inorganic species such as LiF and Li2 O. The outer layer, farther from the
graphite, is thicker and porous, composed mainly of organic compounds. Understanding
the mechanisms and structure of the SEI film is crucial for improving battery performance,
stability, and longevity. The findings contribute to developing strategies for optimizing SEI
formation and controlling its properties to enhance lithium-ion battery performance.
Yan et al. [254] developed three phenomenological models to predict mechanical phe-
nomena during Li-ion intercalation in batteries. These models estimate the forces induced by
intercalation, predict dynamic swelling behavior, and determine the swelling shape on the
battery surface. Incorporating these models into BMS can enhance understanding, control, and
performance of Li-ion batteries, contributing to improved safety and lifespan. These models
simplify the measurement and correlation of mechanical aspects, advancing battery technology.
Li et al. [255] investigated diffusion-induced stress in elastoplastic hollow spherical silicon
electrodes using analytical modeling and molecular simulations. The research showed that
controlling electrode parameters and achieving low yield strength reduced diffusion-induced
stress. It identified concentration and stress gradients, particularly at the interface and interior
of the electrode. Molecular dynamics simulations revealed plastic deformation in these regions.
The findings emphasize the importance of managing mechanical stress to enhance the lifespan
of lithium-ion batteries and provide valuable insights for designing more durable electrode
materials. Understanding diffusion-induced stress in these electrodes helps develop strategies
to improve battery performance and longevity.
Li et al. [256] highlighted the application of atomistic modeling in discovering and de-
signing materials for lithium-ion batteries. It emphasizes how atomistic modeling provides
insights into material mechanisms, predicts properties, and guides material design, particularly
in processes like lithium-ion diffusion and intercalation reactions. Collaboration between ex-
perimentalists and computational researchers is essential for advancing the field. The review
demonstrates how atomistic modeling accelerates the development of electrode and electrolyte
materials, contributing to improved battery performance.
Tröltzsch et al. [257] presented a method that addresses challenges in using impedance
spectroscopy to characterize aging effects in portable secondary batteries online. They
developed a composite electrode model and a hybrid parameter estimation method to
represent and analyze battery aging mechanisms accurately. Experimental validation
demonstrated the approach’s effectiveness in capturing changes during battery aging.
The method’s potential to enhance the understanding and monitoring of battery aging
contributes to developing more reliable and durable energy storage systems.
Monem et al. [258] investigated the impact of three charging methodologies (CC,
CC-CV, and CC-CVNP) on the lifetime of high-power LiFePO4 batteries. Their study
demonstrated that the CC-CVNP charging method, with low amplitude and fewer nega-
tive pulses, resulted in reduced capacity degradation and improved battery performance.
This technique minimized impedance-related aging mechanisms, such as concentration
polarization resistance and diffusion time constant. The findings offer insights for design-
ing optimized charging systems to extend the lifetime and enhance the performance of
high-power LiFePO4 batteries in practical applications.
Guo et al. [259] introduced the universal voltage protocol (UVP) as an innovative charging
technique for lithium-ion batteries. The UVP aims to improve charging efficiency and cycle
life while requiring less adaptation to changing battery conditions than conventional CC-
Batteries 2025, 11, 127 38 of 68

CV methods. The paper presents a mathematical formulation of the UVP, demonstrating

its effectiveness through comparisons with optimized varying current profiles. The UVP
offers adaptability, charging efficiency, and cycle-life advantages, making it a promising option
for practical implementation in battery charging systems. Its utilization can lead to better
performance and longer cycle life for lithium-ion batteries, contributing to advancements in
battery technology.
Prasad et al. [260] investigated key aging parameters in lithium-ion battery models for
SOH estimation, explicitly focusing on power and energy fade caused by impedance rise and
capacity loss. They proposed using cell resistance and solid phase diffusion time of Li+ species
as simplified aging parameters, which exhibit consistent variations with battery age. The study
also developed estimation techniques using voltage and current data from fresh and aged cells.
These findings enhance the understanding of battery degradation and provide insights for
accurate SOH estimation, enabling informed decisions regarding battery usage and replacement
based on monitoring the identified aging parameters.
Xia et al. [261] conducted a study on the variations of equivalent circuit model (ECM)
parameters in lithium-ion batteries at different SOH levels. The researchers developed an
ECM by fitting it to experimentally measured EIS data. The accuracy of the ECM model was
then validated using EIS data collected during an accelerated aging experiment. The study
found that as battery health deteriorated, certain ECM parameters, such as the series resistor,
increased, indicating an increase in internal resistance. On the other hand, capacitance
components decreased with decreasing battery health. These findings emphasize the
potential of the ECM model in estimating battery state-of-health and its application in BMS.
Monitoring and analyzing the variations in ECM parameters makes it possible to assess
battery performance, predict RUL, and optimize battery management strategies. The study
contributes to understanding battery aging and highlights the importance of utilizing ECM
models in battery health estimation and management. It provides valuable insights for
developing more accurate and reliable battery diagnostics and prognostics techniques,
ultimately enhancing the performance and longevity of lithium-ion batteries [262,263].
Gong et al. [264] conducted a study to improve Li-ion battery modeling and state esti-
mation for electric vehicle applications by addressing uncertainties related to temperature
and aging. The researchers introduced an equivalent circuit battery model and utilized an
Adaptive Extended Kalman Filter (AEKF) algorithm to accurately estimate SOC. The study
further focused on understanding the temperature-dependent performance of LIBs through
EIS tests. Compensation functions were derived to account for the temperature effects on
battery behavior. Battery aging mechanisms were investigated using ICA for SOH esti-
mation and a bias correction modeling method to account for aging-induced inaccuracies.
The study also addressed the inconsistency in parallel-connected battery packs, where
varying levels of battery aging can lead to current differences among parallel-connected
cells. Simulation and experimental results demonstrated the impact of aging and SOC
on current differences in parallel-connected cells. The contributions of this work include
the development of analytical compensation functions, the utilization of ICA-based SOH
estimation, and the introduction of modeling methods to optimize battery management
in electric vehicles, considering temperature and aging uncertainties. This study provides
valuable insights and methodologies to enhance Li-ion battery modeling, state estima-
tion, and battery management strategies for electric vehicle applications, considering the
complex factors of temperature variations and battery aging.
Stiaszny et al. [265] conducted a detailed analysis of a commercially available lithium-
ion battery to understand its decline in capacity. The study revealed the depletion of
cyclable lithium and the formation of a SEI layer as key aging processes, aided by impedance
spectroscopy and the distribution of relaxation times (DRT) analysis. The research provides
Batteries 2025, 11, 127 39 of 68

valuable insights into battery aging mechanisms and highlights the importance of SEI
formation and its influence on capacity decline [266].
Hu et al. [267] conducted a comprehensive analysis of impedance techniques for
studying degradation and aging in Li-ion batteries, summarizing variations EIS techniques
and discussing modeling approaches. The paper elaborates on classical EIS and dynamic
EIS methods, their underlying principles, and data validation. The authors highlight
the potential of EIS in understanding battery aging and degradation mechanisms while
acknowledging challenges in data complexity, accurate modeling, and interpretation of
impedance spectra. This analysis provides a valuable resource for researchers in the field,
outlining future directions and challenges in Li-ion battery aging studies.
De Sutter et al. [268] investigated the fractional differential model (FDM) as an alternative
to the first-order RC battery model for NMC cells. The FDM improved simulation accuracy,
especially in the low SOC range, with up to an 85% improvement, effectively capturing nonlinear
battery behavior and offering valuable insights for accurate battery modeling and characterization.
From both economic and technical perspectives, developing models to predict the lifespan
of lithium-ion batteries is essential, particularly for evaluating the economic viability of energy
storage systems in wind power plants (WPPs). While LIB prices are decreasing due to advance-
ments in portable electronics and automotive industries, they remain costly for energy storage.
Accurate lifetime data are crucial during project planning to assess economic feasibility and
optimize battery utilization. Given the complexity of LIB performance degradation, EEC-based
performance degradation models provide a balance between fast but less accurate pure-lifetime
models and highly accurate but complex electrochemical lifetime models [269,270].

Table 5. Comparison of research studies on lithium-ion battery aging and degradation estimation.

Study Focus Key Findings Ref.

EIS and Battery Aging Discusses the importance of EIS in understanding degradation mechanisms. [247]
Phenomenological Model Investigates degradation due to cyclic fatigue, including SEI formation. [249]
Current Gradients Examines the impact of current gradients on battery degradation. [250]
SEI Growth Model Describes SEI growth during cycling and aging. [251]
Corrosion in Batteries Investigates corrosion processes in Li-ion batteries. [252]
SEI Film Formation Models the formation of the SEI film during lithium intercalation. [253]
Mechanical Models Develops models to predict mechanical phenomena during intercalation. [254]
Diffusion-Induced Stress Studies stress distributions in silicon electrodes. [255]
Atomistic Modeling Highlights the role of atomistic modeling in material design. [256]
Impedance Spectroscopy Proposes a method for online characterization of battery aging. [257]
Charging Methodologies Investigates the impact of different charging methods on battery life. [258]
Universal Voltage Protocol Introduces a new charging technique for improved efficiency. [259]
Aging Parameters Explores aging parameters for SOH estimation in Li-ion batteries. [260]
ECM Parameters Analyzes variations in ECM parameters at different SOH levels. [261]
State Estimation Investigates aging uncertainties in battery modeling for EV applications. [264]
Capacity Decline Analyzes aging mechanisms using impedance spectroscopy. [265]
Impedance Techniques Reviews various impedance techniques for aging studies. [267]
Fractional Differential Model Examines a new battery model for NMC cells. [268]
Economic and Technical Models Discusses the importance of lifespan prediction for economic viability. [269]

Modeling the lifespan of LIBs is a critical aspect of battery management and design, es-
pecially in applications where long-term reliability and performance are vital, such as EVs
and renewable energy storage systems. To predict a lithium-ion battery’s longevity, it is es-
sential to comprehend the factors contributing to its deterioration and employ mathematical
Batteries 2025, 11, 127 40 of 68

models to estimate how these factors impact the battery’s capacity and performance over time.
Hybrid modeling approaches that combine electrochemical and empirical techniques have
gained attention for their ability to balance accuracy and computational efficiency, especially in
applications with limited data. ECMs provide a simple method for estimating battery health,
while statistical approaches like Weibull distributions and machine learning models offer greater
adaptability in capturing intricate aging patterns. A sensitivity analysis of crucial factors such
as temperature, charge/discharge rates, and cycling depth indicates that temperature plays
the most significant role in accelerating capacity loss in lithium-ion batteries. Validating these
models with real-world data from electric vehicle fleets or grid storage systems is crucial to
evaluate their relevance and ensure their reliability across various operational conditions.

8. Prospects and Challenges of Lifetime Modeling of LIBs

The widespread adoption of LIBs in various applications, including EVs, renewable energy
storage, and consumer electronics, emphasizes the critical need for accurate and dependable
lifespan modeling. A comprehensive grasp of the factors influencing LIB longevity is crucial for
optimizing their performance, reducing production expenses, and minimizing environmental
impact. In this part, we delve deeper into the possibilities and obstacles surrounding modeling
LIB lifetimes, backed by scientific references and ongoing research.

8.1. Prospects
8.1.1. Enhanced Battery Performance
Accurate lifetime modeling enhances battery performance by identifying ideal operating
settings and usage trends. Variables including temperature, charge/discharge rates, and battery
state of charge have a significant impact on battery degradation. Manufacturers can develop
more efficient BMS to extend battery longevity by measuring these aspects [270,271].

8.1.2. Cost Reduction

Effective lifespan modeling can lead to significant cost reductions by improving bat-
tery design and material selection. Research is focused on creating improved electrode,
electrolyte, and separator materials to extend cycle life and capacity retention. Addition-
ally, accurate lifetime projections save production costs by removing the need for overly
complicated engineering [272].

8.1.3. Sustainability and Environmental Impact

Extending the life of Li-ion batteries contributes to sustainability objectives by reducing
waste and battery manufacture. This reduction in environmental effect, which includes
mining, production, and recycling, lowers the carbon footprint associated with energy
storage devices [273].

8.1.4. Safety
Zhang et al. [274] examined thermal runaway (TR) behavior in batteries, highlighting
challenges due to complex chemical reactions and degradation mechanisms. It focuses on aged
18,650 cells with lithium nickel cobalt manganese oxide cathodes under typical usage scenarios.
EIS reveals increased impedance in aged cells, affecting electrochemical properties. TR tests
show aging mechanisms significantly impact safety, with lower onset temperatures and shorter
delay times observed in cells subjected to low-temperature cycling. This research underscores
the importance of understanding degradation effects on battery safety, contributing insights
into TR behavior under various conditions. Feng et al. [275] discussed the safety challenges
limiting the widespread adoption of lithium-ion batteries in electric vehicles. It highlights the
urgency of improving battery safety alongside increasing energy density. The main focus is on
understanding thermal runaway, a critical safety issue. The research reviewed the mechanisms
Batteries 2025, 11, 127 41 of 68

leading to thermal runaway, including mechanical, electrical, and thermal abuse, typically
featuring internal short circuits. It proposes a novel energy release diagram to quantify reaction
kinetics during thermal runaway, clarifying the relationship between internal short circuits
and thermal runaway. Finally, it suggests a three-level protection concept to mitigate thermal
runaway hazards, including passive defense, enhancing material thermal stability, and reducing
secondary hazards like thermal runaway propagation.

8.2. Challenges
A significant challenge in lifetime modeling is the absence of standardized protocols,
which hinders consistent cross-comparison and reduces the reliability of model predictions
across different applications.

8.2.1. Complex Degradation Mechanisms

Doyle et al. [276] delved into using mathematical modeling and computer simulations
to understand the performance of lithium/polymer batteries. It discusses a comprehensive
model of these systems, focusing on the underlying assumptions. The model parameters are
classified based on whether they relate to transport, thermodynamics, or design-adjustable
properties, and the experiments needed to measure these parameters are outlined. Furthermore,
the research explored the role of mathematical modeling in the battery design process. It
presents dimensionless groups and simple correlations that aid in characterizing primary
system limitations. Additionally, it reviews a general approach for determining optimal values
of system parameters, such as electrode thicknesses. Degradation in Li-ion cells results from
various physical and chemical processes that impact the different cell components, including
the electrodes, electrolyte, separator, and current collectors. Figure 11 highlights some of the
most frequently reported degradation mechanisms in Li-ion cells. The diverse causes, rates,
and interactions of these mechanisms make them highly complex to model. As a result, most
physics-based
Batteries 2025, 11, x FOR PEER REVIEW models concentrate on the most significant mechanisms, such 44 ofas74the formation
and growth of SEI or the loss of electronic contact due to particle cracking [277–283].

FigureFigure 11. Degradationmechanisms

11. Degradation mechanisms in in
Li-ion cells.cells.
Li-ion Modified version from
Modified [277].from [277].
version

8.2.2. Lack of Standardization

One significant challenge is the absence of standardized testing protocols and data
reporting for Li-ion batteries. This inconsistency makes it more challenging to compare
and assess life-time models across various studies and businesses. Continuous effort is
needed to establish uniform testing protocols and data-sharing protocols [284].
Batteries 2025, 11, 127 42 of 68

8.2.2. Lack of Standardization

One significant challenge is the absence of standardized testing protocols and data
reporting for Li-ion batteries. This inconsistency makes it more challenging to compare and
assess life-time models across various studies and businesses. Continuous effort is needed
to establish uniform testing protocols and data-sharing protocols [284].

8.2.3. Limited Long-Term Data

Shchurov et al. [285] discussed the challenges of extending the service life of LIBs and
presented research methods for understanding LIB degradation. Factors affecting battery
lifespan, such as charging/discharging currents and temperature, and the role of BMS are
examined. The analysis also covers various operating cycles of electric transport and their
impact on LIB degradation, offering recommendations for engineers and designers.

8.2.4. Predicting Real-World Conditions

LIBs operate in diverse and dynamic real-world conditions, making accurately pre-
dicting their lifespan challenging. Models need to account for variations in temperature,
charge/discharge profiles, and other external factors that affect battery performance. Real-
istic modeling of these conditions remains an active area of research [286]. Lifetime modeling
of Li-ion batteries offers significant potential for enhancing performance, reducing costs, and
promoting sustainability. However, addressing the challenges associated with complex degrada-
tion mechanisms, standardization, limited long-term data, and predicting real-world conditions
is crucial to realizing these prospects. Ongoing research, collaboration among stakeholders,
and advancements in modeling techniques will be pivotal in overcoming these challenges and
ensuring the longevity and reliability of LIBs across an expanding spectrum of applications [287].
Machine learning techniques, such as reinforcement learning, offer adaptive solutions
by optimizing charge/discharge cycles in real time, thus contributing to more accurate
lifespan predictions for LIBs. Table 6 offers a comparison of key studies for precise models
for describing lithium-ion battery aging.

Table 6. Comparison of key studies for precise models for describing lithium-ion battery aging.

Focus Methods Key Findings Ref.

Comprehensive review of aging modeling at
Overview of aging modeling Multiscale approach, testing
particle, cell, and pack levels; proposed a [109]
methods strategies
multiphysics and data-based framework.
Identified significant influence of cell characterization
Impact of characterization on Periodic measurements, empirical
on capacity and resistance; recommended small [97]
calendar aging analysis
currents for accurate measurements.
Discussed the correlation between EIS spectra
Non-invasive characterization Electrochemical Impedance
changes and degradation mechanisms; [100]
using EIS Spectroscopy (EIS)
emphasized modeling degradation through ECMs.
Proposed SPM-EEM for LiMn2 O4 chemistry,
Development of the SPM-EEM Single-Particle Model (SPM),
showing enhanced predictability compared to [100]
model empirical aging model
traditional EEMs.
Established a more complex framework for Doyle–Fuller–
Electrochemical approach to
Mathematical modeling understanding aging processes, though complexity Newman (DFN)
battery aging
limits control applications. Model

9. Lithium-Ion Battery Material and Aging

Lithium-ion battery material significantly influences aging mechanisms and perfor-
mance, with common anode materials like graphite and silicon, and cathode materials
such as lithium cobalt oxide (LCO) and lithium iron phosphate (LFP). Aging is affected by
factors like SEI formation, lithium plating, and electrolyte stability. Elevated temperatures
accelerate degradation, while innovations in nanostructured electrodes and electrolyte
Batteries 2025, 11, 127 43 of 68

additives aim to enhance longevity. Understanding these relationships is crucial for devel-
oping durable and efficient batteries [288]. Table 7 offers a comparison of research studies
on lithium-ion battery material and aging. LIBs have transformed the landscape of portable
electronic devices, electric vehicles, and grid energy storage due to their remarkable energy
density and extended life cycles [289–291]. The effectiveness of LIBs relies on various
factors, including the selection of electrode materials and their crystalline structures [292].
Chen [293] introduced various carbon-based anode materials for LIBs. It briefly discusses
commonly used carbon anode materials and highlights methods to improve their performance,
particularly focusing on silicon carbon anode materials and metal oxide/carbon matrix compos-
ites. In the case of silicon carbon anode materials, techniques like electrostatic electrospinning
and carbon-silicon nanotubes are highlighted as promising for large-scale development. For
metal/metal oxide composites, utilizing sodium chloride particles as templates to manufacture
Fe3O4/C composites is noted for its advantages. Overall, by enhancing the electrochemical
performance of LIBs through improved carbon anode materials, the study suggests the potential
for batteries with better performance, broader applications, and higher safety standards.
Gao et al. [294] introduced a facile dual-temperature zone heating strategy to fabricate
high-purity fibrous phosphorus (FP) with a unique lamellar structure, which, when combined
with graphite (G) into an FP-G composite anode, exhibits superior rate performance and cycle
stability in LIBs. This approach presents a promising avenue for enhancing LIB electrode
materials, leveraging FP’s unique properties for improved battery performance and extending
their application potential.
Rahman et al. [295] proposed a novel approach utilizing experimental nonlinear fre-
quency response analysis (NFRA) measurements to identify LIB aging history, achieving
accurate quantification of degradation modes such as SEI growth, lithium plating, and
LAM without prior knowledge of the cell’s duty. Combining experimental and simulation
approaches, the analysis demonstrates NFRA’s potential as a powerful tool for aging diag-
nosis, emphasizing the importance of correlating NFRA at multiple open circuit voltages
(OCVs) and frequencies for comprehensive characterization, thereby enhancing battery
management strategies and extending lifespan for various applications, while suggesting
avenues for further research to improve analysis robustness and testing conditions.
Mikheenkova et al. [296] Synchrotron X-ray diffraction (XRD) radiography was used
to investigate aging heterogeneity in lithium-ion cells with NMC811 and graphite elec-
trodes after ∼2800 cycles. The study revealed degradation near the positive electrode tab,
particularly affecting the NMC material. Principal component analysis identified areas of
degradation, largely due to lithium plating. Electrochemical characterization highlights
the value of a complementary approach, demonstrating the potential of non-destructive
techniques for studying large prismatic cells and advancing battery research and industry.
Sulfide solid-state electrolytes (SSEs) show promise for all-solid-state batteries (SSBs) due to
their high ionic conductivity and safety benefits. However, challenges like interfacial instability
with high-capacity cathodes such as NMC811 hinder their practical use. Issues like oxidation insta-
bility, CEI formation, and volumetric changes contribute to performance degradation. New testing
protocols and strategies, including optimizing catholytes and exploring interfacial protection, aim
to address these challenges. Simulations on electrolyte aging offer insights into factors affect-
ing capacity degradation. Combining experimental studies, advanced modeling, and material
optimization is key to improving SSB performance and driving commercial viability [297–299].

9.1. Development of Symmetrical Electrode Materials

Xing Li et al. [300] emphasized the growing demand for flexible energy storage
solutions, highlighting flexible LIBs as having potential for wearable electronics due to
their high energy density, mechanical elasticity, and stable electrochemical performance.
Batteries 2025, 11, 127 44 of 68

Specifically focusing on flexible electrode materials, carbon nanomaterials like carbon

nanotubes and graphene are explored for their excellent mechanical flexibility, large surface
area, and high conductivity. The study identified challenges these materials face and
anticipates future directions in electrode material development, offering valuable insights
for future lithium battery research and development.

9.2. Structure of Electrode Materials and Lithium-Ion Battery Aging

LIBs depend on the structured materials of their electrodes, such as layered and spinel
structures in cathodes and graphite anodes, to enable efficient lithium-ion movement. Over
time, aging mechanisms such as mechanical stress, SEI layer formation, lithium plating, and
electrolyte breakdown deteriorate these materials, causing capacity reduction, increased
internal resistance, and self-discharge. Key factors in aging include chemical reactions
during calendar aging and mechanical wear during cycle aging. Strategies like optimizing
electrode materials, managing temperatures, using advanced electrolytes, and applying
protective coatings are employed to extend battery life and improve performance [288].
Electrolyte composition plays a pivotal role in stabilizing high-energy-density LIBs, as
advanced electrolyte formulations can mitigate decomposition and extend cycle life.

9.2.1. Lithium-Ion Diffusion Rate

Electrode material aging in LIBs leads to decreased capacity and increased resistance,
affecting overall performance. This complex phenomenon arises from multiple interacting
factors. Lin et al. [301] explained the capacity and power fading mechanisms for metallic
oxide-based cathodes and carbon-based anodes under cycling and storage conditions.
For cathodes, mechanical stress from lithium-ion insertion/extraction causes structural
disorder, and metal dissolution from the cathode leads to deposition on the anode. Anode
aging is primarily due to the loss of recyclable lithium ions from the SEI growth and
mechanical fatigue from diffusion-induced stress. Aging is influenced by electrochemical
behavior during cycling and storage and involves structural/morphological changes and
side reactions exacerbated by decomposition products and impurities in the electrolyte.

9.2.2. Thermal Stability

Yannick et al. [302] investigated the safety properties of LIB cells by examining thermally
induced reactions of active materials. Thermal profiles of anodes and cathodes at various SOCs
after electrochemical aging were determined using methods to measure temperature-related
mass loss, m/z detection, and heat flow, followed by pyrolysis-GC-MS analysis. Key findings
include intense decomposition and reactions of lithiated anodes with binder molecules, confir-
mation of thermally induced de-lithiation, and phase changes in de-lithiated cathodes detectable
via oxygen release. The decomposition properties of the solid electrolyte interphase vary with
SOC, showing SOC-dependent behavior with oligocarbonates at 100% SOC and CO2 at high
temperatures for 0% SOC. This study provides a set of methods for thermal profiling to identify
reactions that affect the safety of LIBs. Understanding degradation mechanisms, gathering
data, modeling mathematically, and taking into account many elements that affect battery aging
are all part of the complicated process of calculating the lifespan of LIBs. Accurate lifetime
prediction is essential for maximizing battery-powered systems’ efficiency and economy [303].

9.3. Degradation Mechanisms

Degradation of LIBs includes electrolyte breakdown, SEI development, lithium plating,
and electrode material loss, all of which reduce capacity and performance. Mechanical
stress and thermal aging exacerbate these processes even further. Lifetime is also impacted
by gas production and cathode degradation. Accurate lifetime models under different
settings take these mechanisms into account [304].
Batteries 2025, 11, 127 45 of 68

9.4. Data Collection and Mathematical Models

Xiaodong et al. [305] addressed the increasing use of lithium-ion batteries in EVs and
energy storage stations (ESSs) in the context of carbon neutrality. It highlights how harsh
conditions like vehicle-to-grid (V2G) interactions, peak–valley regulation, and frequency
regulation accelerate battery degradation, making the development of long-life batteries
crucial. It reviews urgent requirements, degradation mechanisms, design methods for
suppressing these mechanisms, durability modeling, and management approaches for long-
life batteries. The paper emphasizes the significance and urgency of developing durable
batteries, discusses design methods to suppress degradation, elaborates on degradation
modeling and advanced management strategies, and offers insights into overcoming
challenges and seizing future opportunities for the practical application of long-life LIBs.
Binelo et al. [306] introduced a parametrization methodology employing the Genetic
Algorithm meta-heuristic for estimating parameters in the Chen and Rincón-Mora model,
used to mathematically model the lifetime of lithium-ion polymer batteries in mobile de-
vices. This approach is compared with the conventional method based on visual analysis
of pulsed discharge curves. Experimental data from a platform test are utilized for both
parametrization procedures and model validation. Results indicate that the Genetic Algo-
rithm method achieves superior efficacy, exhibiting lower mean error, and is more efficient
and less subjective than the conventional approach.

9.5. Uncertainty Analysis

Rohr et al. [307] addressed the economic challenges of reusing LIBs from EVs, which
are affected by increasing inner resistance and capacity degradation over time. To aid
decision-making regarding the use of these batteries post-removal, the article proposes a
method for predicting their RUL while accounting for uncertainties. Key risks identified
in lifetime prediction include non-linear capacity changes, increasing cell spreading, and
critical limit exceedances such as deep discharge events. The article advocates for a separate
investigation of linear cell aging and uncertainties up to the battery pack level to establish
correlations between operational conditions and failure distribution.

9.6. Analyzing Cell Aging Under Various Duty Cycles

Preger et al. [308] investigated the performance of three lithium-ion battery types
(LiFePO4, LiNix Coy Al1−x−y O2 , and LiNix Mny Co1−x−y O2 ) across varying discharge rates,
DOD, and temperatures. Despite adherence to manufacturer’s guidelines, it highlights
that varied cycling conditions significantly impact cell degradation, with 80% capacity loss
varying by thousands of hours and cycle counts per cell chemistry. Comparisons are made
with previous studies to outline common trends and performance standard deviation.
Kim et al. [309] focused on assessing four commercially available cylindrical cells,
each with a distinct Li-ion chemistry, for their performance under standardized testing
protocols designed by the U.S. Department of Energy Office of Electricity (DOE-OE) for
grid services like frequency regulation, peak shaving, and EV drive cycles. The 15-month
investigation compares various parameters, including capacity retention, resistance, OCV,
cyclic voltammetry (CV), (dQ/dV, differential voltage (dV/dQ), and AC impedance.
Dubarry et al. [310] explored the performance of a lithium titanate-based battery energy
storage system over a seven-year period in an isolated island grid setting. They demonstrated
that the modules’ capacity loss remains below 10% even after seven years of operation, and the
overall battery performance remains within expected specifications. Based on their findings,
they projected the battery’s full lifespan on the grid, indicating it should easily exceed 15 years.
They also identify certain inaccuracies in the current online capacity estimation method, which
pose challenges in effectively monitoring the system’s performance.
Batteries 2025, 11, 127 46 of 68

Rosewater et al. [311] detailed the rationale behind establishing a duty-cycle for
frequency regulation. A year’s worth of publicly available utility frequency regulation
control signal data were analyzed, revealing that signal standard deviation could quantify
its aggressiveness. Two representative two-hour-long signals, mirroring average and
aggressive scenarios, were selected and combined into a 24-h duty cycle. The article
reviews the duty cycle’s implications and its impact on the energy storage industry.
Crawford et al. [312] developed a pseudo-2D model using COMSOL Multiphysics®
to simulate lithium-ion battery degradation during peak shaving grid service. The model
accounts for SEI layer formation, breakdown, and cathode dissolution, with high accu-
racy in simulations of commercial cells. Two models were created: a global model for
all chemistries and individual models for each, showing close agreement. A simplified
model and a statistics-based model, predicting degradation from current, voltage, and
anode expansion, were also developed. These models support an efficient BMS combining
machine learning and physics-based algorithms. The reviewed papers focus on battery
technology, energy storage systems, and degradation under various cycling conditions.
They emphasize the need for accurate models, control systems, and standardized testing to
optimize battery performance and lifespan in grid applications, highlighting the importance
of collaboration and addressing technical challenges in Li-ion BESS and V2G operations.

Table 7. Comparison of research studies on lithium-ion battery material and aging.

Study Focus Key Findings Methodologies/Approaches Implications/Contributions Ref.

Discussed various carbon anode materials Review of carbon-based
Enhanced electrochemical
Carbon-based and methods to improve performance, anodes, highlighting
performance and wider [293]
Anode Materials focusing on silicon carbon anodes and electrostatic electrospinning
applications for LIBs.
metal oxide/carbon composites. and templates for composites.
Introduced a dual-temperature zone heating Demonstrated enhanced rate
Phosphorus- Composite fabrication and
strategy to fabricate fibrous phosphorus for performance and cycle [294]
Graphite Composite performance testing.
improved battery performance. stability of LIBs.
Nonlinear Experimental NFRA Highlighted the importance of
Proposed NFRA to quantify battery aging
Frequency Response measurements combined with NFRA for aging diagnosis and [294]
modes without prior knowledge of cell’s duty.
Analysis simulations. battery management.
Aging Investigated aging heterogeneity in Synchrotron XRD radiography Showcased potential for
Heterogeneity NMC811 and graphite cells after extensive and electrochemical non-destructive techniques in [296]
Investigation cycling. characterization. battery research.
Emphasized the potential of flexible LIBs Identified challenges and
Flexible Lithium-Ion Review of flexible electrode
for wearable electronics and explored future directions for electrode [300]
Batteries materials.
carbon nanomaterials. development.
Explained aging mechanisms for cathodes Provided insights into
Capacity and Power Analysis of aging mechanisms
and anodes, focusing on structural disorder performance degradation in [301]
Fading Mechanisms under cycling and storage.
and SEI growth. LIBs.
Various thermal profiling
Thermal Stability Investigated thermal stability and reactions Identified thermal reactions
methods, including [302]
Analysis of active materials in LIBs. affecting LIB safety.
pyrolysis-GC-MS analysis.
Advanced understanding of
Reviewed degradation mechanisms, data Comprehensive review and
Lifetime Modeling battery lifespan and [305]
collection, and modeling for long-life LIBs. analysis of methodologies.
degradation management.
Parameter Introduced a Genetic Algorithm Comparison of Improved parameter
Estimation for methodology for estimating parameters in parametrization estimation for battery lifespan [306]
Lifetime Models battery lifetime modeling. methodologies. models.
Uncertainty in Proposed a method for predicting
Analytical and predictive Aimed to aid decision-making
Battery Lifetime remaining battery life, considering [307]
modeling. for reused batteries in EVs.
Prediction uncertainties.
[308]
Analyzed the performance of various LIB Highlighted significant [309]
Performance under Experimental performance
types under different discharge rates and impacts of cycling conditions [310],
Varying Conditions evaluation and comparison.
conditions. on degradation. [311],
[312]
Batteries 2025, 11, 127 47 of 68

10. Limitations of Data-Driven Methods in Battery Life Prediction

10.1. Data Quality, Preprocessing, Overfitting and Underfitting
Due to its efficient handling of handcrafted features, the Random Forest Regressor
demonstrated superiority in life prediction challenges for lithium-ion batteries with limited
information. The successful operation of these models was found to be largely dependent
on feature extraction, particularly when using data from sources of variance. Although deep
learning exhibits promise, its limited performance with the available data sets indicates that
more data and better network architecture are required to produce more accurate forecasts.
Currently, the most effective machine learning techniques for predicting battery life in these
situations use handcrafted features [313].

10.2. Insufficient Data for Training

An in-depth analysis of data-driven methods for predicting rechargeable battery
longevity in Cyber-Physical Systems (CPS) was presented by Yang et al. [314] The au-
thors examine every stage of battery lifespan prediction, from gathering data to feature
engineering and preprocessing to modelling. While addressing the implementation chal-
lenges within CPS, the authors look at real-world applications of these strategies in various
contexts. The operational challenges of battery duration forecasting in CPS networks
are described in this study, along with strategies for improving battery predictions via
CPS networks. The authors commit to fostering scholarly investigations and real-world
implementations that will foster the long-term advancement of data-driven energy stor-
age methods.

10.3. Generalization Issues

Using a “capacity matrix” representation of electrochemical cycle data, Attia et al. [315]
developed simple yet accurate data estimation algorithms to forecast battery lifespan. Ac-
cording to benchmarking experiments on a previously published dataset, univariate and
multivariate statistical learning techniques are equivalent to deep learning models, par-
ticularly in terms of generalization. Deep learning models perform on par with ridge
regression, elastic net, and partial least squares regression (PLSR). Studies suggest that
feature engineering creates useful models and that a capacity matrix is a possible standard-
ized format for storing battery cycle data. Novel creative feature extraction techniques,
comprehensive testing across battery chemistries and usage conditions for these techniques,
and other goals should be the three main topics of future study.

10.4. Data Imbalance

Li et al.’s [316] study offered a cutting-edge method for forecasting smartphone battery
life using machine learning and copious amounts of usage data. The technique that uses
the concordance index to fill in missing data is beneficial for survival analysis. According
to the study, precise battery predictions are made possible by combining status monitoring,
sensor data, application tracking, and analysis of current system performance. The results
show that predictions now achieve an average reduction of 33 min, allowing users to
better manage their smartphone usage. Predictive features are based on user activity
patterns and battery discharge records, and tree-based analytical models outperform linear
regression techniques. The technique is applicable to prediction systems in electric cars
and wearable technology. In order to improve forecast accuracy levels, future studies will
employ sophisticated machine learning algorithms and incorporate more data dimensions
into the analysis.
Batteries 2025, 11, 127 48 of 68

10.5. Domain Knowledge Integration

In order to improve the performance of EV batteries, Naresh et al. [317] investigated
predictive machine learning (ML) techniques by assessing the states of charge (SoC), health
(SoH), function (SoF), and remaining usable life (RUL). Proactive maintenance functions,
real-time monitoring, and efficient energy use are all made possible by the method’s
use of several supervised and unsupervised deep learning algorithms to forecast battery
behavior. The study discusses challenges with data collection, model development, and
battery prediction accuracy while evaluating EV battery performance optimization using
an operation research model. Solid-state and lithium–sulfur batteries will be used to
adapt to new battery technologies in the future, and IoT sensors will be included for
real-time monitoring and explainable AI system verification for regulatory requirements.
This study demonstrates the significance of controlling energy use and grid connection
processes, predicting product lifespan, and advocating for sustainable business practices
that maximize battery recycling and restoration.

11. Comparison of Lithium-Ion Battery Chemistries

Cathode expansion, lithium anode dendrite growth, and electrolyte breakdown are
some of the mechanisms that cause advanced energy storage system Li–S batteries to
age. Various aging processes impact material waste, structural degradation, increased
internal resistance, and safety risks, all of which contribute to a shorter battery life and
worse performance during operation. Research on developing sulfur-host materials is also
ongoing, including the development of protective anode coatings and stable electrolyte
enhancements. Real-time diagnostic methods and computational models that assist de-
signers in creating long-lasting sulfur batteries make it possible to comprehend battery
aging [318–320]. The techniques used to create lithium-ion batteries exhibit significant
differences in terms of energy density levels, cycle life, thermal stability, and safety require-
ments. Although the NCA and NMC chemical families achieve high energy densities, they
continue to perform poorly in terms of life cycle and thermal stability. Although these char-
acteristics limit their energy storage capacity, lithium iron phosphate (LiFePO4 ) and lithium
titanate (LTO) provide excellent safety performance, long cycle life, and heat-resistance
qualities for power storage systems [321]. Solid-State, Li–S, and Lithium–Air (Li–Air)
battery technologies offer greater energy density and safety features, but they still need
to be further developed because of their existing cycle performance and manufacturing
cost limits. Future electric car ranges will be revolutionized by Li–S and Li–Air batteries,
and solid-state batteries will combine safety and maximum efficiency for future use after
technological obstacles are resolved [322]. Table 8 summarizes a comparison of lithium-ion
battery chemistries and their aging behavior.

Table 8. Comparison of lithium-ion battery chemistries.

No. of
Chemistry Energy Heat Power Safety Price
Application Charge/Discharge Aging Factors Ref.
Type (Wh/kg) Resistance Output Measures Point
Cycles
Volume
Lithium Iron
EVs, Energy Highly Stable Expansion of
Phosphate 90–160 2000–5000 Moderate Safe Moderate [323–326]
Storage at High Temps Electrodes, SEI
(LFP)
Development
Nickel
Improved Electrolyte
Cobalt Needs Better
EVs 200–260 500–1000 High with Battery Higher Breakdown, [327–330]
Aluminum Cooling
Management SEI Growth
(NCA)
Cathode
Electric
Sensitive to Moderate- Degradation,
Tools, Power NMC 150–220 1000–2000 High Moderate [331–333]
High Temps High SEI Layer
Storage
Expansion
Batteries 2025, 11, 127 49 of 68

Table 8. Cont.

No. of
Chemistry Energy Heat Power Safety Price
Application Charge/Discharge Aging Factors Ref.
Type (Wh/kg) Resistance Output Measures Point
Cycles
SEI Growth,
High Power,
Lithium Lithium
Fast Stable at All Extremely
Titanate 80–120 7000–20,000 Very High Higher Plating, [334–337]
Charging Temps Safe
(LTO) Electrolyte
EVs
Aging
Lithium
Portable Overheating
Cobalt Oxide 150–240 500–1000 Moderate Moderate High SEI Expansion [338–340]
Electronics Issues
(LCO)
Next-Gen Reduced Side
EVs, Solid-State 300+ Non- Extremely Reactions, Solid
>10,000 High High [341–345]
Consumer Lithium-Ion (Potential) Flammable Safe Electrolyte
Electronics Interface
Lithium
Stable at Low
Aerospace, Lithium– Low to Degradation,
400–600 100–500 and Moderate Moderate Moderate [346–352]
EVs Sulfur (Li–S) Moderate Electrolyte
Temps
Decomposition
EVs, Energy 1000+
Lithium–Air Susceptible to Lithium
Storage (Theoreti- 100–500 cycles High Low High [353–357]
(Li–Air) Contamination Degradation
Systems cal)

12. Future Research Directions for Lithium-Ion Battery Aging

Research into the aging of LIBs requires more stable material development, ideal ther-
mal management and charging techniques, and effective recycling systems. The application
of efficient technologies that reverse material degradation across a variety of environmen-
tal circumstances defines the base lifespan of batteries. Accurate precision in diagnostic
and predictive modeling technology allows for better management of LIBs through better
forecasting of aging processes. Combining novel materials with sophisticated production
processes leads to improved system functionality. Research on recycling methods and
secondary product usage systems must be prioritized in order to accomplish sustainable
product disposal from an ecological and financial standpoint. Table 9 summarizes future
research directions for lithium-ion battery aging. Three main areas are the focus of research
on lithium-ion battery aging: producing materials that reduce the impacts of aging, creating
machine learning algorithms for health assessment, and enhancing battery monitoring
systems using cutting-edge methods. To achieve sustainability, a number of improvements
in secondary battery applications, recycling systems, and heat management systems must
be put into practice. Improved electrode design, sophisticated electrochemical modeling
techniques that increase accuracy, and mechanistic research on aging mechanisms under
use settings will all help batteries reach their full potential. It is necessary to fully com-
prehend interfacial phenomena and evaluate the environmental impact using a variety
of charging simulations based on multi-scale models in order to select the appropriate
battery technology development [358]. The main area of research is the development of
sustainable LIB materials that decompose slowly and are likely to be reused. With the
help of new battery types, industrial operations can continue economically and sustainably
while reducing the need for replacements and protecting the environment.

Table 9. Future research directions for lithium-ion battery aging

Research Focus Goals Methods Potential Impact

Enhance comprehension of
Understanding
degradation processes at the Advanced characterization methods More precise modeling
Aging Mechanisms
molecular level
Investigate battery performance in
Aging Under Low-temperature experiments, fast Enhanced performance in varied
extreme temperatures and
Extreme Conditions charging tests, thermal assessments climates; safer charging
rapid charging
Batteries 2025, 11, 127 50 of 68

Table 9. Cont.

Research Focus Goals Methods Potential Impact

Innovative anode/cathode designs,
Novel Materials & Create new, stable battery materials Improved longevity, enhanced safety,
electrolyte additives, solid-state
Electrolytes and safer electrolyte options increased energy density
advancements
Aging in Real-World Examine battery aging during Machine learning applied to varied Enhanced predictive maintenance;
Conditions practical use duty cycle evaluations greater battery reliability
Electrode-Electrolyte Interface coatings, solid-electrolyte Reduced degradation; extended cycle
Stabilize battery interfaces
Interface Stability interface research life
Self-healing materials, thermal
Aging Mitigation Develop technologies to reduce Longer battery lifespan;
management techniques, adaptive
Strategies aging rates enhanced safety
charging methods
Recycling and
Optimize battery reuse and Second-life performance assessments, Lower environmental impact;
Second-Life
recycling efforts sustainable recycling methodologies cost-effective resource recovery
Applications
Next-Generation Enables higher energy densities with
Investigate aging in new chemistries Research on air contamination
Battery Research novel materials
Safety and Failure Prevent catastrophic events like Comprehensive analysis of thermal Safer batteries; mitigated risks in
Mechanisms thermal runaway events, electrolyte instability high-stress situations
Standardized Aging Establish new testing methodologies Standardization of aging tests, Enhanced comparability among
Protocols for consistent outcomes comprehensive data reporting studies; more reliable forecasting

13. Discussion
Table 10 offers a comparison of aging behavior and performance degradation across
LIB chemistries. LIBs have emerged as essential components in the clean energy landscape
due to their compact size, high energy density, and suitability for various applications. In
particular, Stationary Battery Systems (SBSs), which leverage LIBs, play a crucial role in
power distribution networks worldwide, enabling functions such as peak load management,
load shifting, voltage regulation, and power quality improvement. These applications
underscore the critical importance of effective health management for LIBs to ensure long-
term performance and reliability. This study explored various methodologies used to
estimate LIB aging, with a focus on the most widely examined degradation mechanisms,
such as capacity fade and internal resistance increase. However, existing research often
tends to address these degradation factors in isolation, despite the fact that both play
significant roles in the overall performance of LIBs, especially in the context of EVs and
clean energy systems. Although each method reviewed has its merits, there are notable
limitations that must be addressed. For instance, detailed data analysis from single battery
cells or large datasets from vehicle applications can provide valuable insights under specific
conditions, yet challenges such as reproducibility in chemical studies and the need for
diverse datasets to capture all interactions effectively persist.
For EV users, ensuring a consistent range and reliable power output over the lifespan
of the battery is paramount. Accurate battery SOH assessment is essential for evaluating
battery performance and predicting longevity. Despite significant advances in lifetime mod-
eling, there are still substantial challenges in developing a model-based evaluation system,
primarily due to the limited availability of comprehensive data samples. The necessity for
extensive battery testing to establish degradation relationships across a range of operating
conditions remains a resource-intensive and time-consuming process. Thus, efforts are
urgently required to advance our understanding of battery aging mechanisms and the
development of reliable techniques to assess the SOH of LIBs. Innovative approaches
that overcome the current limitations—such as those related to data availability and the
resource demands of traditional testing methods—must be explored. Such advancements
would facilitate the development of more efficient and accurate systems for evaluating
battery aging, ultimately leading to improved battery performance and increased user
Batteries 2025, 11, 127 51 of 68

confidence in EV technology. For EVs, an ideal method for estimating battery aging should
strike a balance between flexibility and simplicity, providing quick results using easily
obtainable variables without the need for complex measurements. Real-time models, such
as equivalent circuit models or statistical approaches, offer practical advantages, though
they are often less accurate compared to direct measurements. Nonetheless, the high
accuracy achieved through direct measurements highlights the trade-off between ease of
use and precision in battery health assessments.

Table 10. Aging behavior and degradation of li-ion batteries.

Battery Capacity Resistance

Aging Behavior Aging Mechanism Additional Observations
Chemistry Degradation Increase
Moderate Loss of lithium inventory & Best for long life services,
LFP Low aging, long life Minimal loss
increase anode material Umax 3.65 V
Degrades at 60 ◦ C, Drastic loss at high High resistance Loss of active material & Performance vs. cycle life
NCA
high voltage temp/volt at 60 ◦ C lithium inventory compromise
Degrades at 60 ◦ C, Drastic loss at high High resistance Loss of active material & Sensitive to high temp,
NMC
high voltage temp/volt at 60 ◦ C lithium inventory manganese dissolution
Lower resistance Less extreme aging
LCO Mixed degradation Less capacity loss Loss of active material
increase compared to others
High degradation at High degradation at Least resistance Minimal loss of anode Sensitive to high voltage,
LTO
high voltages high voltages increase material high cycle life
No degradation at Significant Increase in resistance &
Almost no Sensitive to high temp,
LMO 50 ◦ C, moderate at resistance possible loss of active
degradation at 50 ◦ C manganese dissolution
higher increase material
Highest Loss of active material,
Overall Higher degradation High degradation at LTO: less resistance
resistance for lithium inventory, &
Aging at high temp/voltage 60 ◦ C increase, LFP: high long life
NMC & NCA resistance increase

Table 11 provides a comprehensive summary of the key aspects of LIB aging mech-
anisms, contributing factors, and mitigation strategies. Battery aging is a multifaceted
process influenced by a variety of factors, including usage patterns, environmental con-
ditions (such as temperature), and charging profiles. The inherent complexity of aging
dynamics presents significant challenges in achieving accurate characterization, particu-
larly due to the interplay of multiple interdependent factors. One specific area of concern is
voltage imbalance in lithium-ion battery packs, often attributed to varying self-discharge
rates among individual cells. There is a notable lack of detailed research on the variability
of self-discharge currents and their potential implications for battery packs composed
of series-connected cells. Given the crucial role that voltage balance plays in the overall
performance and longevity of battery packs, further investigation into this phenomenon is
required to understand its impact on both performance degradation and the aging process.
Developing an ideal method for estimating battery aging in electric vehicles requires a
careful balancing of several factors, including flexibility, measurement complexity, accu-
racy, and precision. While real-time calculations based on easily obtainable variables can
provide timely insights into battery health, achieving an accurate and reliable assessment
necessitates a deeper understanding of the complex interactions underlying battery aging.
Recognizing these complexities is crucial for the accurate prediction of battery performance
and the optimization of vehicle operation over the course of the battery’s lifetime. In
conclusion, LIBs are indispensable for the advancement of clean energy technologies and
electric mobility. Their high energy density and compact size make them the preferred
choice for numerous applications, with accurate assessment and prediction of aging playing
a central role in ensuring optimal performance, longevity, and reliability, particularly in
electric vehicles, where users expect consistent performance throughout the battery’s lifecy-
cle. Although current methodologies for aging estimation have made significant strides,
Batteries 2025, 11, 127 52 of 68

they also exhibit inherent limitations that must be overcome. The development of more
reliable techniques for assessing the SOH of LIBs, including innovative approaches that
address data availability and resource constraints, is critical for the continued advancement
of battery technologies. The ideal method for estimating battery aging should balance
flexibility, ease of use, and precision while accounting for the complex and interdependent
factors that drive battery degradation. A comprehensive understanding of these factors will
lead to more accurate predictions of battery health, ultimately contributing to the reliability
and efficiency of electric vehicles and other clean energy systems. Uncertainties in battery
aging predictions, such as variations in cell manufacturing and usage conditions, can result
in substantial discrepancies in predicted outcomes. This emphasizes the importance of
developing robust, adaptive models that can address these uncertainties, leading to more
dependable predictions of battery health and performance.

Table 11. Key aspects of lithium-ion battery aging mechanisms, factors, and mitigation strategies.

Aspect Key Insights & Implications

Lithium plating: can result in higher resistance, capacity loss, and potentially safety
problems.
SEI growth: lowers the ion’s mobility and increases resistance.
Electrolyte degradation: decreases the electrolyte’s function and raises pressure.
Aging Mechanisms
Cathode degradation: lowers efficiency and energy density.
Mechanical stress: reduces performance by causing cracks and the loss of active material.
Lithium consumption: traps lithium, increasing self-discharge and reducing the amount of
charge that may be released.
Lithium plating: can lead to increased resistance, loss of capacity, and possibly safety issues.
SEI growth: raises resistance and decreases ion mobility.
Electrolyte degradation: increases pressure and reduces the electrolyte’s action.
Factors Influencing
Cathode degradation: reduces energy density and efficiency.
Aging
Mechanical stress: causes loss of active material, which lowers performance.
Lithium consumption: reduces the amount of charge that may be released and increases
self-discharge by trapping lithium.
EIS: detects resistance variations and SEI accumulation.
Diagnostic Methods
Post-cycle analysis: checks for material failure and mechanical damage.
Thermal management: prevents SEI and plating by controlling temperatures.
BMS: minimizes overcharging or discharging by optimizing SOC, voltage, and current.
Charging protocols: use more intelligent charging techniques to prolong battery life and
Mitigation Strategies
lessen stress.
Material innovation: creates superior materials to increase energy density, stability,
and safety.

14. Conclusions
LIBs have established themselves as a cornerstone of contemporary energy storage
solutions, offering high energy density and compact design. Their widespread use spans
diverse applications, particularly in SBS systems, which are integral to modern power
distribution networks. In these systems, LIBs facilitate essential functions such as peak
load management, load shifting, voltage regulation, and power quality enhancement, thus
underscoring the importance of efficient health management strategies for maintaining
system performance and longevity. This review has critically examined the current state
of research on battery aging, focusing on the methodologies used to estimate and predict
the degradation of LIBs. Although significant progress has been made, most studies ad-
dress specific aging aspects, such as capacity fade and internal resistance increase. These
factors, while important, cannot be fully understood in isolation, as they jointly impact the
Batteries 2025, 11, 127 53 of 68

performance and reliability of EVs and energy storage systems. The review highlights that
the existing methods provide valuable insights but are not without limitations. Challenges
such as data reproducibility and the need for extensive, diverse datasets remain signifi-
cant barriers to developing comprehensive aging models. Also, more effort is needed to
understand the mechanisms and predict chemomechanical degradation, which is multi-
physics and complex. Accurately assessing the health of LIBs is paramount for ensuring
the consistent performance and extended lifespan of EVs, where maintaining a stable
range and reliable power output over time is crucial. Despite several proposed solutions,
developing an efficient, model-based system for lifetime estimation remains hindered by
data constraints and the resource-intensive nature of battery testing. To address these
issues, future research must focus on innovative approaches to overcome these limitations,
enabling more precise and scalable methods for estimating battery aging. Ideally, a robust
battery aging estimation method should be adaptable, simple to implement, and capable
of providing rapid assessments using readily accessible operational parameters. While
real-time prediction methods offer operational advantages, it is critical to recognize that
direct measurement techniques, although more complex to implement, provide the highest
accuracy and reliability. Striking a balance between practical usability and precision is
essential to optimizing battery health management strategies and ensuring the sustained
performance of LIBs in real-world applications. Battery aging is a multifaceted process,
influenced by various factors such as usage patterns, environmental conditions, and charg-
ing profiles. The complex interplay of these factors makes it challenging to accurately
predict the degradation behavior of LIBs. Additionally, issues such as voltage imbalance
within battery packs—resulting from variations in self-discharge rates among individual
cells—remain underexplored and warrant further investigation. As the adoption of LIBs
continues to grow, particularly in the context of EVs and large-scale energy storage systems,
it is critical to monitor SOH of these batteries in real time. Ensuring high precision in SOH
estimation will be crucial for maintaining the stability and reliability of energy storage
systems and optimizing the performance of lithium-ion-based technologies.
This paper has provided a comprehensive review of the current methodologies for
modeling the lifespan of LIBs, emphasizing the gaps in existing approaches and presenting a
novel framework that integrates various methods and algorithms. The findings underscore
significant improvements in the predictive accuracy of aging models, offering a pathway for
advancing battery technology. Future work should focus on refining these methodologies
and applying them to specific applications, including electric vehicles and renewable
energy systems, to enhance battery performance, safety, and reliability. Ultimately, a
deeper understanding of the factors influencing battery aging and the development of
more accurate estimation models will be crucial for driving the future of sustainable
energy systems.
Future research should focus on developing adaptive aging models incorporating real-
time data from various sources, such as vehicle performance metrics and environmental
conditions, to improve predictive accuracy. Enhancing battery aging prediction models
will not only boost the performance and lifespan of electric vehicles but also support
the sustainability of energy systems by reducing the frequency of battery replacements.
Moreover, extending the lifespan of lithium-ion batteries will significantly minimize the
environmental impact linked to battery production and disposal, promoting more sustain-
able energy solutions worldwide. The findings of this study highlight the essential role of
precise battery aging predictions in ensuring the long-term reliability and performance of
clean energy technologies.
Batteries 2025, 11, 127 54 of 68

Author Contributions: S.S.M. proposed the idea of the paper; S.S.M. and Y.S. wrote the paper; M.F.,
F.A., S.P., C.Z., H.C., S.M., S.X.D., K.S., K.K. and Z.W. provided suggestions on the content and
structure of the paper and reviewed the draft manuscripts. All authors have read and agreed to the
published version of the manuscript.

Funding: This study was undertaken as part of the HELIOS Project (https://www.helios-h202
0project.eu/project) and HELIOS received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 963646. Its content only reflects the authors’
views, and the European Commission is not responsible for any use that may be made of the
information it contains. In addition this research was partly funded by the Helmholtz Association,
grant number FE.5341.0118.0012, in the programme Materials and Technologies for the Energy
Transition (MTET). We want to express our gratitude for the funding.

Acknowledgments: This work contributes to the research performed at CELEST (Center of Electro-
chemical Energy Storage Ulm-Karlsruhe).

Conflicts of Interest: The authors declare no conflicts of interest.

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