Project Report on
Energy Management And Control Strategies For
EVs Considering The Battery Degradation
Submitted in Partial Fulfillment of the Requirements for the Award of the
Degree
BACHELOR OF TECHNOLOGY
In
ELECTRICAL AND ELECTRONICS
ENGINEERING
Submitted by
T. Keerthana Lakshmi (N200403)
A. Gunwanth (N200641)
M. Rithvik (N200828)
B. Laxmi Prasad (N200012)
Under the Esteemed Guidance of
Mr.M.Chiranjeevi,
Asst.Professor, IIIT-N
DEPARTMENT OF ELECTRICAL AND ELECTRONICS
ENGINEERING
RAJIV GANDHI UNIVERSITY OF KNOWLEDGE
TECHNOLOGIES A.P
RGUKT-NUZVID
December-2024
DEPARTMENT OF ELECTRICAL AND ELECTRONICS
ENGINEERING
BONAFIDE CERTIFICATE
This is to certify that the project report “ Efficiency Improvement of EV using Python
in Hybrid Energy Storage System ” submitted by T. Keerthana Lakshmi(N2200403), A.
Gunwanth(N200641),M. Rithvik (N200828),B. Laxmi Prasad (N200012) in partial ful-
fillment of the requirement for the award of Bachelor of Technology in Electrical and
Electronics Engineering is a record of bonafide project work carried out under my super-
vision.
Mr.M.Chiranjeevi Mrs.shravani Kanaka Kumari
Asst.Professor Head of the Department
Dept of EEE Dept of EEE
RGUKT-NUZVID RGUKT-NUZVID
Approval Sheet
This report entitled “Efficiency Improvement of EV using Python in Hybrid Energy Stor-
age System” by T. Keerthana Lakshmi(N2200403), A. Gunwanth(N200641),M. Rithvik
(N200828),B. Laxmi Prasad (N200012) is approved by Mr.M.Chiranjeevi, Asst.Professor
for the partial fulfillment of the requirements for the award of the degree of “Bachelor of
Technology” in Electrical and Electronics Engineering.
Examiner: Supervisor:
Date:
Place:
2
Declaration
We declare that this written submission represents our ideas in our own words and where
others’ ideas and words have been included, We have adequately cited and referenced
the original sources. We also declare that We have adhered to all principles of aca-
demic honesty and integrity and have not misrepresented or fabricated or falsified any
idea/data/fact/source in our submission.We understand that any violation of the above
will be cause for disciplinary action by the Institute and can also evoke penal action from
the sources which have thus not been properly cited or from whom proper permission has
not been taken when needed
Signatures:
1.
2.
3.
4.
Names (Regd No.):
1.
2.
3.
4.
Date:
3
Acknowledgement
We are highly indebted to Mr.M.Chiranjeevi, Asst.Professorr for his guidance and con-
stant supervision as well as for providing necessary information regarding the project.
We owe our gratitude to our Head of the Department, Mrs.shravani Kanaka Kumari, and
our Dean, Mr.N.Ratnakar, for their encouragement and support throughout the project.
With sincere regards,
T. Keerthana Lakshmi (N200403)
A. Gunwanth (N200641)
M. Rithvik (N200828)
B. Laxmi Prasad (N200012)
4
Table of Contents
1 Introduction 6
1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1 Adaptive Double Kalman Filter Method in Multi-Type Energy
Storage System: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.2 Adaptive Optimization Operation of Electric Vehicle Energy Re-
plenishment Stations : . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.3 Adaptive-Energy-Sharing-Based Energy Management Strategy of
Hybrid Sources in Electric Vehicles: . . . . . . . . . . . . . . . . . 8
1.4.4 S Energy Management and Control in Multiple Storage Energy
Units (Battery–Supercapacitor) of Fuel Cell Electric Vehicles: . . 8
1.4.5 Optimal Energy Management and Control Strategies for Electric
Vehicles: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5
Chapter 1
Introduction
1.1 Abstract
The goal of this project is to increase the efficiency of electric cars (EVs) by managing
hybrid energy storage systems (HESS) with RBF networks and Python programming.
Batteries and supercapacitors are used in hybrid storage systems to deliver both con-
tinuous energy and short-term power spikes. None the less, minimizing energy loss and
increasing battery life depends on effectively controlling these systems. The project builds
an intelligent system that can forecast and modify energy consumption in real time based
on driving circumstances and the dtatus of the energy storage components using RBF
networks, a form of machine learning model. These models will be constructed in Python
utilizing well-known libraries for calculations and data analysis, such as NumPy and
Scikit-learn.
1.2 Objective
Optimize Energy Distribution
Extend Battery Lifespan
Adapt Yo Driving Conditions
Enhance Vehicle Performance
1.3 Introduction
The report explores methods to enhance the efficiency and longevity of electric vehicles
(EVs). EVs, as sustainable alternatives to traditional internal combustion engine vehicles,
significantly contribute to reducing greenhouse gas emissions. However, their efficiency
heavily relies on effective energy management, which optimizes electrical energy usage
within the vehicle’s systems. This becomes challenging due to the variability in driving
conditions, such as urban versus highway environments, and battery degradation over
time. Addressing these challenges is crucial for maintaining vehicle performance and
extending battery life.
The study provides a detailed overview of EV types, including Battery Electric Ve-
hicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Hybrid Electric Vehicles
6
(HEVs). BEVs rely solely on electric power from onboard batteries, while PHEVs com-
bine electric drivetrains with internal combustion engines. HEVs utilize a combination
of internal combustion engines and electric motors. The rising adoption of EVs is at-
tributed to advancements in battery technology, government incentives, and increased
environmental awareness, underscoring the importance of energy management strategies
tailored to these vehicles.
Effective energy management involves dynamically allocating power to meet the de-
mands of propulsion and auxiliary systems while minimizing energy losses. However, this
is complicated by the aging of batteries, which leads to capacity fade and other forms
of degradation, impacting overall performance. The document emphasizes the need for
innovative strategies that not only optimize energy use but also mitigate battery degra-
dation, ensuring the reliability and longevity of EVs. Addressing this dual challenge
forms the core motivation for the research, as traditional approaches often overlook the
real-world dynamics of driving conditions and long-term battery health.
To address these challenges, the document explores advanced methodologies for en-
ergy management, such as Model Predictive Control (MPC), Reinforcement Learning
(RL), and Genetic Algorithms (GAs). These approaches enable real-time optimization
of energy usage while considering battery health and driving conditions. Additionally,
predictive modeling techniques, including physics-based and data-driven approaches, are
discussed for forecasting battery degradation. The integration of real-time data analytics
is highlighted as a critical factor in enhancing the adaptability of EV energy management
systems, allowing them to respond to dynamic driving environments.
The analysis of driving conditions reveals that urban driving, with frequent stops and
low-speed operations, demands more energy but can benefit from regenerative braking
and idle stop-start systems. In contrast, highway driving, characterized by steady-state
speeds, has lower energy consumption per unit distance, though factors like aerodynamic
drag and rolling resistance play significant roles. Mixed driving scenarios necessitate
adaptive control strategies to transition seamlessly between different environments, max-
imizing efficiency and energy recovery during deceleration phases.
The study concludes that the proposed strategies significantly enhance energy ef-
ficiency and reduce battery wear, leading to an extended driving range and improved
battery life. Future research directions include real-world testing and further refine-
ment of control algorithms to improve their applicability under varied driving conditions.
This research addresses critical challenges in EV energy management, offering advanced
strategies that consider the complexities of real-world driving scenarios and the long-term
dynamics of battery degradation.
1.4 Literature Survey
1.4.1 Adaptive Double Kalman Filter Method in Multi-Type
Energy Storage System:
The authors propose an innovative control strategy using an adaptive double Kalman
filter that manages the state of charge (SOC) in a hybrid energy storage system (HESS).
This method optimizes the performance of lithium batteries and supercapacitors by dy-
namically regulating power output, enhancing system efficiency, and reducing the energy
storage capacity required to meet grid-connection standards. The study concludes that
this adaptive approach effectively reduces fluctuations within the specified criteria, con-
tributing to greater system stability. By integrating SOC adjustments with real-time
smoothing, the proposed strategy addresses both economic and technical demands for
renewable energy integration, promoting a more reliable and efficient wind energy supply
for power grids.
1.4.2 Adaptive Optimization Operation of Electric Vehicle En-
ergy Replenishment Stations :
The authors propose an adaptive optimization model that integrates real-time data on
battery health and degradation patterns, allowing for more effective management of en-
ergy storage resources. In conclusion, the study demonstrates that the proposed opti-
mization approach significantly enhances the operational efficiency of EV energy replen-
ishment stations. By factoring in battery degradation, the model not only improves the
longetivity and reliability of energy storage systems but also contributes to cost-effective
energy management. The findings offer valuable insights for the future development of
EV infrastructure, ensuring that energy replenishment stations can meet the growing
demand while maintaining optimal performance and sustainability.
1.4.3 Adaptive-Energy-Sharing-Based Energy Management Strat-
egy of Hybrid Sources in Electric Vehicles:
This paper propose an Intelligent Hybrid-Source Energy Management Strategy (IHSEMS)
that efficiently manages energy distribution among these sources, improving efficiency,
reducing stress on batteries, and extending battery lifespan under different driving and
environmental conditions. The IHSEMS approach demonstrates notable improvements
in performance, cost savings, and battery longevity for HEVs. By effectively handling
energy fluctuations and peak demands, this strategy reduces battery wear, enhances en-
ergy efficiency, and minimizes operational costs, offering a robust solution for sustainable
transportation technology.
1.4.4 S Energy Management and Control in Multiple Storage
Energy Units (Battery–Supercapacitor) of Fuel Cell Elec-
tric Vehicles:
It propose a setup where the supercapacitor assists the battery during high power de-
mands, such as acceleration, and regenerative braking, which reduces stress on the battery
.They also employ fuzzy logic control and PI control strategies to optimize energy distri-
bution. The supercapacitor helps in minimizing current ripples and voltage fluctuations,
which results in lower heat generation and smoother motor operation. This configura-
tion extends the cruising range of the vehicle and stabilizes the battery’s state of charge,
proving that this energy management approach can be successfully applied to hybrid and
electric vehicles for enhanced efficiency and reduced energy consumption .
1.4.5 Optimal Energy Management and Control Strategies for
Electric Vehicles:
The paper explores strategies to improve energy management in electric vehicles (EVs),
focusing on adapting to different driving conditions and reducing battery wear. The con-
clusion confirms that the proposed energy management approach—using a mix of rule
based and optimization methods—successfully boosts EV range and extends battery life.
Future research is suggested to test these strategies in real-world conditions, aiming to
make EVs more reliable and efficient for sustainable transportation.
