Editorial

Performance Analysis and Simulation of Electric Vehicles

Automotive Engineering and Transports Department, Technical University of Cluj Napoca, Bdul. Muncii 103–105,
400114 Cluj Napoca, Romania; florin.mariasiu@auto.utcluj.ro

1. Introduction
As electric vehicles offer a cleaner, more efficient, and more cost-effective mode of
transport, already being a choice for the future of the transport sector, the analysis of the
performance of electric vehicles (EV) is a dynamic research area that must come up with
pertinent solutions and with optimization strategies for functional and operational aspects
(energy efficiency, autonomy, powertrain performance, and overall vehicle dynamics) [1,2].
Energy efficiency is crucial for expanding the range of electric vehicles, with necessary
research involving the optimization of powertrain components, including the electric motor,
battery, and power electronics.
At the same time, it must be emphasized that the energy efficiency of an EV is also
determined/linked to the efficiency of the battery thermal management system. It is well
known that efficient battery management systems (BMSs) are essential for monitoring
and optimizing the battery’s operational performance [3], ensuring safety by reducing
fire risks, and last but not least, extending the battery’s lifespan and operation within the
required parameters.
The particular design and dynamics characteristics of electric vehicles must be taken
into account through studies on vehicle maneuverability, stability and comfort, aerodynam-
ics, and chassis design [4–7]. All of these research directions and topics have a direct and
important role in improving the performance of electric vehicles.
Due to their development and accuracy, a multitude of modeling and simulation
tools are currently widely used in engineering to model and analyze the performance
of electric vehicles. These simulations help to immediately understand the impact of
different parameters on the overall performance and efficiency of the electric vehicle
through dynamic modeling processes, simulating standard driving cycles and performing
complex simulations related to components/subsystems/propulsion systems, aerodynamic
Received: 19 February 2025 efficiency, and thermal dynamics. It can be stated that engineering modeling and simulation
Accepted: 23 April 2025
processes are the engine of the study for the continuous improvement of electric vehicles,
Published: 6 May 2025
making them more efficient, reliable, and suitable as a solution for large-scale adoption in
Citation: Mariasiu, F. Performance
the field of transportation.
Analysis and Simulation of Electric
Vehicles. Energies 2025, 18, 2365.
Some of these previously presented aspects have been addressed and presented as
https://doi.org/10.3390/en18092365 results of research in this Special Issue, in areas related to the performance of hybrid electric
vehicles, operational safety by reducing/eliminating fire risks due to batteries, optimization
Copyright: © 2025 by the author.
Licensee MDPI, Basel, Switzerland.
by increasing transmission efficiency, control and command of power flow, and analysis of
This article is an open access article thermal management systems of electric vehicle batteries.
distributed under the terms and
conditions of the Creative Commons 2. An Overview of the Published Articles
Attribution (CC BY) license
One of the most significant challenges in the development of HEV performance is the
(https://creativecommons.org/
complexity of the hybrid control system, which must know when to operate the electric
licenses/by/4.0/).

Energies 2025, 18, 2365 https://doi.org/10.3390/en18092365

motor and optimal power delivery. In addition, gear shifting becomes a difficult problem
in optimization, a problem that plays a key role in the energy efficiency of the propulsion
system. In this regard, Filho, R.H.Q. et al. proposed the implementation and use of
Artificial Intelligence tools (Contribution 1). A genetic algorithm (GA) was used as a
machine learning-based control strategy to determine the torque split and the engaged
gear for each driving condition of an MHEV operation, with the aim of optimizing the
fuel consumption. A quasi-static vehicle model was developed in Matlab/Simulink and
tested for the FTP75 and HWFET driving cycles. The simulation results indicate that
the control decisions made using the genetic algorithm are qualitatively consistent for
all operating conditions, with the potential to be used as a control strategy outside the
simulation environment.
The challenges related to the power and efficiency of electric motors (as an integral
part of the powertrain of an electric vehicle) require the development of multi-speed trans-
missions for electric commercial vehicles. In this regard, Kim, J. et al. (in Contribution 2),
present the development possibilities in domains that were approached through the model-
ing and computer simulation methods of a four-speed transmission with a synchronizer. A
transmission shift map was developed and the verification of the increase in power and
efficiency was carried out by comparing the proposed solution with the existing product
on the automotive market.
Another approach related to the performance of electric vehicles is also related to the
reduction/elimination of the risk of fire caused by overheating the electric vehicle battery or
by a short circuit due to road accidents. The efficiency of using auxetic structures to dissipate
the impact between the electric vehicle chassis and the battery case was analyzed in order
to reduce the risk of battery damage and maintain the safety of the vehicle occupants
(Contribution 3). The modeling of the resistance structure was based on a particular shape
based on a re-entrant auxetic model, and the simulations were performed at an impact
velocity of 10 m/s with a rigid pole. The results obtained by Scurtu, Szabo, and Gheres
highlighted the fact that, by using auxetic structures in the construction of the battery case,
the effect of the impact can be mitigated, with a decrease in the number of damaged cells of
up to 35.2%.
The aim of Pavković, D. et al.’s work was to design, develop, and analyze the effective-
ness of a vibration damping system for the belt transmission in the front accessory drive of a
mild hybrid powertrain, which was analyzed in Contribution 4 using computer simulation
methods. The simulation results highlighted the attenuation of vibrations related to the
operation of the belt through active control techniques (vibration magnitude reduced by
three to five times during the engine starting phase), with a positive effect on a 30% gain in
acceleration during vehicle launch.
Specific performance modeling and simulation techniques have also been applied to
electric aerial vehicles, by Krznar, M. et al. in Contribution 5. This topic was approached
by designing a control system and verifying the proposed solution by simulating a hybrid
electric propulsion topology suitable for power flow control in unmanned aerial vehicles
(UAVs). The general control system features a proportional–integral–derivative (PID)
feedback control of the thermal thruster rotation speed using an estimator, and the voltage
and current of the active rectifier of the BLDC generator are controlled by proportional–
integral (PI) feedback controllers, augmented by feed-forward load compensators based on
the estimator. The general design of the control system was based on the choice and use of
an optimal damping criterion, which gave the analytical expressions for the control and
estimator parameters.
The dynamic growth of electric vehicle use in recent years has led to a potential increase
in the risk of fire and hazards associated with high-energy batteries used in the construction
Energies 2025, 18, 2365 3 of 4

of electric vehicles (Li-ion technology), and this has been considered by Brzezinska D. and
Bryant P. Contribution 6 presents the general fire risks for electric vehicles and possible
protection strategies against fires due to the faulty operation/exploitation of batteries.
Through analysis methods and computer simulation processes, CFD simulations were
performed to predict smoke dispersion and temperature distribution during an EV fire in
an indoor parking lot. The presented case study demonstrates how the use of these tools
predicts the conditions for the safe evacuation of people and the conditions for firefighting
in the event of a fire caused by electric vehicles.
Starting from the premise presented above, namely that one of the important systems
in the construction of an electric vehicle is the battery thermal management system (with
the role of optimizing the operation of the battery in terms of performance and lifespan),
Contribution 7 critically analyzes the studies and research carried out to date related to
the type, design, and operating principles of battery thermal management systems (with
an emphasis on cooling technologies). The advantages and disadvantages of individual
components and the functional constructive solutions of existing BTMs were extensively
investigated based on the adaptability of these systems to different Li-ion battery shapes.
The study provides necessary and important information and the authors (Buidin T.I.C.
and Mariasiu F.) propose future research directions for those interested in this topic, in
order to increase the efficiency of battery thermal management systems and the overall
efficiency of the electric vehicle.

3. Conclusions
The approaches in the research carried out and published in the Special Issue “Perfor-
mance Analysis and Simulation of Electric Vehicles” showcases a wide array of innovative
approaches, each addressing various challenges in the development of electric vehicles
(EVs). These diverse methodologies encompass advanced simulation techniques, battery
management systems, powertrain optimization, and energy efficiency improvements. For
instance, several studies utilized finite element analysis (FEA) to model the structural be-
havior of EV batteries, while others employed machine learning algorithms and computer
simulation models to predict energy consumption and battery performance and optimize
charging cycles, contributing to the overall reliability and performance of EVs (battery
and hybrid).
Looking ahead, advancements in autonomous driving systems and their implications
for EV performance and safety are present promising areas for future research. Continued
interdisciplinary collaboration will be essential to address these emerging challenges and
drive the next generation of electric vehicle technologies.
The editor extends their heartfelt gratitude to all contributing authors, whose efforts
have significantly enriched this Special Issue. The success of this publication is evident from
the impressive number of views, downloads, and citations it has garnered, reflecting its
impact and relevance in the scientific community. This collective work not only highlights
the complexity of EV technology, but also proposes practical solutions to advance the field,
paving the way for future innovations in electric mobility.

Conflicts of Interest: The author declares no conflicts of interest.

List of Contributions
1. Filho, R.H.Q.; Ruiz, R.P.M.; Fernandes, E.d.M.; Filho, R.B.; Pimenta, F.C. Development of
a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild
Hybrid Electrified Vehicle’s Electrified Propulsion System. Energies 2024, 17, 2015. https:
//doi.org/10.3390/en17092015
Energies 2025, 18, 2365 4 of 4

2. Kim, J.; Lee, Y.; Jin, H.; Park, S.; Hwang, S.-H. Development of Shift Map for Electric Commercial
Vehicle and Comparison Verification of Pneumatic 4-Speed AMT and 4-Speed Transmission
with Synchronizer in Simulation. Energies 2024, 17, 1038. https://doi.org/10.3390/en17051038
3. Scurtu, L.I.; Szabo, I.; Gheres, M. Numerical Analysis of Crashworthiness on Electric Vehicle’s
Battery Case with Auxetic Structure. Energies 2023, 16, 5849. https://doi.org/10.3390/en16155849
4. Pavković, D.; Cipek, M.; Plavac, F.; Karlušić, J.; Krznar, M. Internal Combustion Engine Starting
and Torque Boosting Control System Design with Vibration Active Damping Features for a P0
Mild Hybrid Vehicle Configuration. Energies 2022, 15, 1311. https://doi.org/10.3390/en15041311
5. Krznar, M.; Pavković, D.; Cipek, M.; Benić, J. Modeling, Controller Design and Simulation
Groundwork on Multirotor Unmanned Aerial Vehicle Hybrid Power Unit. Energies 2021, 14,
7125. https://doi.org/10.3390/en14217125
6. Brzezinska, D.; Bryant, P. Performance-Based Analysis in Evaluation of Safety in Car Parks under
Electric Vehicle Fire Conditions. Energies 2022, 15, 649. https://doi.org/10.3390/en15020649
7. Buidin, T.I.C.; Mariasiu, F. Battery Thermal Management Systems: Current Status and Design
Approach of Cooling Technologies. Energies 2021, 14, 4879. https://doi.org/10.3390/en14164879

References
1. Hassan, M.R.M.; Mossa, M.A.; Dousoky, G.M. Evaluation of Electric Dynamic Performance of an Electric Vehicle System Using
Different Control Techniques. Electronics 2021, 10, 2586. [CrossRef]
2. Yilmaz, M. Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and
analysis in plug-in electric vehicle applications. Renew. Sustain. Energy Rev. 2015, 52, 80–99. [CrossRef]
3. Nyamathulla, S.; Dhanamjayulu, C. A review of battery energy storage systems and advanced battery management system for
different applications: Challenges and recommendations. J. Energy Storage 2024, 86, 111179. [CrossRef]
4. Zamzam, O.; Ramzy, A.A.; Abdelaziz, M.; Elnady, T.; El-Wahab, A.A.A. Structural performance evaluation of electric vehicle
chassis under static and dynamic loads. Sci. Rep. 2025, 15, 5168. [CrossRef] [PubMed]
5. Luque, P.; Mántaras, D.A.; Maradona, Á.; Roces, J.; Sánchez, L.; Castejón, L.; Malón, H. Multi-Objective Evolutionary Design of
an Electric Vehicle Chassis. Sensors 2020, 20, 3633. [CrossRef] [PubMed]
6. Beigmoradi, S. Harmonizing aerodynamic efficiency, stability, and acoustic excellence: Multi-objective optimization for electric
vehicle rear-end design. Multiscale Multidiscip. Model. Exp. Des. 2025, 8, 56. [CrossRef]
7. Sun, P.; Trigell, A.S.; Drugge, L.; Jerrelind, J. Energy efficiency and stability of electric vehicles utilising direct yaw moment control.
Veh. Syst. Dyn. 2020, 60, 930–950. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.