Electric Vehicle Battery Management System: A Comprehensive

Review

Gowri Sankar P. A.

, Shanmuga Priya S. R.

, Karthikumar K.

and Suba S.

Department of Electrical and Electronics Engineering, Knowledge Institute of Technology, Salem, Tamil Nadu, India

Department of Embedded System Technology, Knowledge Institute of Technology, Salem, Tamil Nadu, India

Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and

Technology, Chennai, Tamil Nadu, India

Mahendra Engineering College, Namakkal, Tamil Nadu, India

Keywords: Electric Vehicle, Battery Management System, Battery Charging, Batter Technology, Battery Mechanism.

Abstract: The Battery Management System (BMS) plays a critical role in enhancing the performance, safety, and

longevity of Electric Vehicles (EVs). The BMS continuously monitors various parameters such as current,

temperature, and overall battery health, ensuring that the battery operates within optimal conditions. The BMS

collects real-time data from sensors embedded within the EV, providing insights into battery status, charge

levels, and temperature fluctuations. By tracking these parameters, the BMS prevents overcharging,

overheating, and deep discharging, all of which can negatively impact battery life and performance. In this

article, we present an extensive literature review about the recent three year what are the advantages,

techniques, mechanism, AI/ML algorithm used for the Battery Management System (BMS) is presented. This

review paper plays a vital role for the research who pursuing his research especially in the BMS for advancing

sustainable transportation.

1 INTRODUCTION

Electric vehicles offer significant advantages to the

environment, cheaper to run, and offer a smoother,

quieter ride. Electric vehicles are eco-friendly, cost-

effective, and provide a smoother, quieter driving

experience. Recent EV trends include rapid sales

growth, tech innovations, and a focus on

sustainability. Electric vehicle sales increased by 27%

in 2024 relative to the previous year. Battery aging

significantly impacts fuel economy, drivability, and

electric range (Anselma, P. G, et, al 2022)

Demonstrated effectiveness through simulations and

experiments, showing improved fuel cell durability

and reduced hydrogen consumption across various

driving conditions (Yuan, H,et, al, 2022) Develop a

comprehensive methodology for estimating the

duration of lithium battery packs of electric vehicles

(EVs) (Ceraolo, M, et, al, 2024)Energy management

strategy using model-based reinforcement learning

(MBRL) and fuel cell electric vehicles (FCEVs) Lee,

H., & Cha, S. W. (2021). Efficient energy use, battery

life reduction due to charge cycles, and cybersecurity

threats. Novel taxonomy for battery optimization,

demand-side management, revenue maximization,

and machine learning applications (Colucci, R, et, al,

2024) Rolling resistance increases significantly with

speed. Aerodynamic drag is influenced by gradient.

Proposed a hybrid Pneumatic-Liquid Thermal

Management System to battery temperature control

(Agrawal, A, et, al, 2023) Classifies EMS

methodologies into rule-based, dynamic

optimization-based, and learning-based strategies.

Emphasizes lifetime optimization of fuel cell systems

and batteries (Rudolf, T, et, al, 2021)

Examines the critical role of the Battery

Management Systems (BMS) in battery-powered

UAVs. Identifies nine key areas categorized into:

Charging and discharging strategies, Battery state

estimation (SOC, SOH, RUL), System components

and safety issues (Jiao, S, et, al, 2023) Developing the

Model Predictive Control (MPC) based Energy

Management System (EMS) for series hybrid electric

agricultural tractor. Achieved 7.2% fuel reduction,

improved battery state of health (SoH), and better

thermal management relative to the conventional

rule-based EMS (Curiel-Olivares, G, et, al, 2023).

Historical safety concerns with Li-ion batteries across

402

A., G. S. P., R., S. P. S., K., K. and S., S.

Electric Vehicle Battery Management System: A Comprehensive Review.

DOI: 10.5220/0013914000004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

402-412

ISBN: 978-989-758-777-1

various applications. Lack of customer safety

consideration; corrective actions insufficient to

address root causes. Suggestions for improved safety

measures in future EV battery implementations

(Aalund, R, et, al 2021). The article explores the

electrification of road freight in India, focusing on

battery-electric trucks (BETs) and their potential to

address health, ecological and energy security issues

related to conventional transportation. It presents the

usage of energy simulation study tailored to Indian

conditions, analysing key components such as

powertrain systems, battery technologies, and

charging infrastructure. The findings aim to inform

future research on the feasibility and practicality of

BETs in India, ultimately supporting the transition

towards sustainable transportation solutions

(Madichetty, S, et, al, 2022)

Utilizes LSTM for real-time velocity prediction

and a neural network-optimized rule-based energy

management strategy. Prolonged battery lifespan by

26.85%. Reduced total energy losses by 22.25%.

Improved efficiency and energy throughput for

supercapacitors (Udeogu, C. U, et, al, 2022). The

proposed system significantly reduces peak load

demand and operational costs in real distribution

networks. Utilizes real load patterns, various EV

types, and financial analysis to validate performance

against prediction-based techniques (Das, N, et, al,

2023). Utilizes MATLAB/Simulink for system

configuration and Mixed Integer Programming (MIP)

for cost calculations. Two test cases demonstrate the

model's effectiveness in both regulated and

deregulated environments. Findings indicate notable

economic savings and efficient battery state of charge

(SOC) management. The framework is applicable to

various operational environments, enhancing the

feasibility of OLEV systems (Nisar, F, et, al, 2021).

Developed a battery model using real-world data;

integrated into an optimization scheme. Advanced

thermal models are crucial in charging power > 7 Kw.

Ignoring battery aging can underestimate operating

costs by up to 30%. Effective Vehicle-to-Grid (V2G)

services require consideration of battery aging costs

and dynamic electricity tariffs (Schwenk, K, et, al,

2021). Figure 1 shows Battery Management System

(BMS) for electric vehicles, where sensors attached

to the battery module measure current, voltage, and

temperature. This data is then processed by the BMS,

which calculates and communicates state of charge

(SOC), state of health (SOH), thermal management,

and power optimization to a display unit, ensuring the

safety and optimal performance of the battery pack.

Figure 1: Battery management system (BMS).

2 BATTERY MANAGEMENT

SYSTEM

2.1 Literature Review

Research on the electric vehicle charging safety

warning system (Diao, X, et, al, 2023). This paper

discusses the model predicts voltage changes during

charging, dynamically adjusts warning thresholds,

and effectively identifies abnormal charging data,

enhancing safety and reducing risks of fire incidents.

In this paper, the limitations of the study regarding

data acquisition for EV charging, is founded upon the

limitations in data acquisition concerning the State of

Health (SOH) of the battery cell and the lack of

complete life cycle EV charging data. Additionally,

the collected charging information lacked any fault

data, which could affect the robustness of the early

warning model. Further research is suggested to

explore these areas in greater depth.

Machine learning-based on the battery

management system for the electric vehicle

(Duraisamy, T, 2021). This paper focuses on

improving battery management systems (BMS) for

electric vehicles through an optimal cell balancing

mechanism. It employs machine learning algorithms

to select balancing resistors based on factors such as

cell imbalance, balancing time, and temperature,

resulting in enhanced balancing speed, reduced power

loss, and better thermal management compared to

traditional methods. The effectiveness of the

suggested mechanism is evaluated using BPNN,

RBNN, and LSTM models, showcasing superior

accuracy and efficiency. The main categories of the

machine learning methods used in battery

Electric Vehicle Battery Management System: A Comprehensive Review

403

management system applications are categorized into

supervised learning, unsupervised learning, semi-

supervised learning and reinforcement learning.

Review of cloud-based lithium-ion battery

management systems for the electric vehicle (Ismail,

M., & Ahmed, R. 2024). The Cloud computing

enhances BMS efficiency and reliability. Identified

research gaps include online learning, connectivity,

and security. Future work should integrate recent

cloud advancements to improve BMS functionality.

Battery Management Systems (BMS) face challenges

such as limited onboard computational resources,

which restrict the use of accurate state estimation

techniques and lead to energy inefficiencies.

Additionally, BMS cannot be updated remotely,

making it difficult to adapt to changes in battery

behaviour due to aging and preventing manufacturers

from offering new features. These limitations hinder

the overall performance and reliability of BMS in

electric vehicles.

Energy management in hybrid electric and hybrid

energy storage system vehicles (Maghfiroh, H, 2024).

This paper emphasizes the environmental benefits of

HEVs and HESS EVs while discussing various types

of FLC and their practical applications. Additionally,

the review analyses the advantages and challenges

associated with FLC EMS and outlines future

research directions in this field. The benefits of using

Fuzzy Logic Controllers in managing energy

consumption in hybrid vehicles, Fuzzy Logic

Controllers (FLC) in effective energy management in

hybrid vehicles brings numerous advantages, such as

enhanced adaptability to varying driving conditions,

leading to enhanced fuel efficiency and reduced

power consumption. They outperform traditional

control methods, such as Proportional-Integral (PI)

and Sliding Mode Control (SMC), in areas like

voltage regulation and energy management.

Additionally, FLCs can be combined with other

methods to address limitations and optimize

performance, contributing to more efficient and

sustainable transportation solutions.

Development of fuzzy logic and ANFIS control

for the energy management in electric vehicle

(Suhail, M., et, al, 2024) This study centres on the

development of the fuzzy logic and Adaptive Neuro-

Fuzzy Inference System (ANFIS) controllers for

managing energy consumption in hybrid electric

vehicles (HEVs). The primary goal is to improve the

state of charge (SOC) of battery to enhance vehicle

autonomy and efficiency. Results indicate that the

ANFIS controller outperforms the fuzzy logic

controller in maintaining higher SOC levels,

suggesting better energy management strategies for

HEVs. The ANFIS controller improves the SOC

profile of hybrid electric vehicles by utilizing precise

fuzzy modelling and real-time data to adaptively

control the battery charging process. It analyses input

variables such as state of charge (SOC) and engine

speed to adjust the forward gain, optimizing the

quantity of generated torque utilized for the battery

charging. This leads to a smoother SOC curve and an

increased SOC levels by the conclusion of the drive

cycle, enhancing overall energy efficiency and

performance.

Prediction of the battery state using the digital

twin framework (Jafari, S, et, al, 2022). The

methodology employs Extreme Gradient Boost

(XGBoost) and Extended Kalman Filter (EKF) to

achieve accurate battery state estimation. The

findings indicate that the DT model significantly

improves the reliability, optimization, and accuracy

of battery management, ultimately extending battery

life through effective monitoring and predictive

maintenance. The main types of battery cell models

discussed in the paper are Equivalent Circuit Models

(ECM), electrochemical models, and machine

learning models. Each model boasts unique strengths

and limitations, playing different roles in the battery

system digital twin. ECMs are particularly used for

accurately monitoring the battery cell, state of charge

(SOC), and state of health (SOH).

Online data-driven efficient energy management

of the hybrid electric vehicle (Lee, H, et, al, 2020):

The proposed framework is designed to learn driving

conditions and adapt control policies in real-time,

resulting in simulation outcomes that demonstrate

near-optimal fuel economy, surpassing conventional

rule-based strategies. This work enhances the

understanding of HEV control and offers a robust,

explainable approach to energy management, with

future efforts aimed at experimental validation and

balancing computational efficiency with fuel

economy performance. The proposed Q-learning

algorithm enhances fuel economy in hybrid electric

vehicles by utilizing a model-based approach that

learns from real-time driving data to optimize control

policies. It effectively separates the internal

powertrain dynamics from external driving

conditions, allowing for a more tailored and efficient

energy management strategy. Furthermore, the

algorithm's ability to update the vehicle state

approximation model through interactions helps

refine decision-making, leading to improved fuel

efficiency over time.

Battery management techniques for an electric

vehicle traction system (Abdelaal, A. S, et, al, 2022):

This paper focuses on the implementation of Battery

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Energy Management (BEM) techniques in electric

vehicle (EV) traction systems, specifically utilizing

conventional Fuzzy Logic Controller (FLC) and the

Model Predictive Control (MPC) alongside a

Cascaded FLC (CSFLC) to enhance battery longevity

and minimize current fluctuations. The findings

suggest that the CSFLC technique improved battery

lifetime by 5.6% during the New European Driving

Cycle (NEDC) & 6.1% during the US06 cycle, while

also demonstrating lower battery current

consumption compared to the traditional FLC

approach. Overall, the study highlights the efficiency

of advanced control strategies in optimizing energy

use for EV traction systems. The Fuzzy MPC (FMPC)

technique contributes to battery energy management

in EVs by generating a reference current signal for

motor speed regulation while dynamically adjusting

the input weight in the MPC depending on the

battery's state of charge (SOC) and its variations. This

approach minimizes battery energy consumption and

degradation by optimizing the current signal in real-

time, leading to extended battery runtime and

lifetime. Additionally, FMPC exhibits lower

computational effort compared to traditional

methods, enhancing overall system efficiency.

Energy management system for hybrid renewable

energy (Karmaker, A, et, al, 2023). The document

discusses the operation and maintenance costs

(Co&m) associated using Electric Vehicle Charging

Stations (EVCS) and highlights the payback period

(PBP) for charging station owners, which is relatively

short, indicating profitability. It emphasizes the use of

a SIMULINK model to optimize power generation

and charging costs, resulting in a 74.67% reduction in

energy costs compared to flat rate tariffs.

Additionally, the integration of hybrid renewable

resources significantly lowers greenhouse gas

emissions. The integration of renewable resources in

EV charging stations leads to a significant reduction

in charging costs, especially during off-peak hours,

with savings of up to 74.67% compared to

conventional rates. Additionally, it results in a CO2

emission reduction of up to 54.86% when 84% of the

energy is sourced from renewables, thereby

enhancing environmental sustainability. Overall,

increased renewable utilization decreases both

charging costs and greenhouse gas emissions.

Development of optimal power-distribution-

management algorithm (Lee, H, et, al, 2021): This

research is dedicated to developing an ideal allocation

of power control algorithm for the 4WD electric

vehicles to enhance improving battery efficiency and

driving range. Simulations demonstrated

improvements in battery efficiency improved by

0.2% under the specified conditions urban driving

and 2.52% on highways compared to a comparison

model, with increased driving efficiency at high-

speed and high-torque ranges. Future efforts will aim

to refine the power allocation ratio contingent on

actual driving behaviour and environmental factors to

further improve the performance of the 4WD electric

vehicles. The study employed urban and simulations

of highway driving based on EPA standards for

analysing battery performance. It utilized dynamic

programming and Pontryagin’s minimum principle

for optimizing power distribution, and

MATLAB/Simulink for modelling the vehicle's

dynamic characteristics. Performance comparisons

were conducted between the proposed optimal power

distribution algorithm and a comparison model to

assess battery efficiency and power consumption.

Energy management of the hybrid electric

vehicles by sequential programming (Ghandriz, T, et,

al, 2021): The document discusses a sequential

programming and gear optimization algorithm for

hybrid powertrains, focusing on minimizing energy

consumption by selecting optimal gears based on

vehicle speed and force. It outlines the constraints and

equations governing the system, including power

balance among various components like internal

combustion engine (ICE) and electric motor (EM).

Additionally, it highlights the importance of

preventing frequent gear shifts to enhance fuel

efficiency and overall performance. The study

utilized sequential linear programming (SLP) and

compared it with sequential quadratic programming

(SQP) to address the predictive control problem. It

also mentioned the application of dynamic

programming (DP) and Pontryagin’s minimum

principle (PMP) as model-based solution methods for

optimal control. These methods were employed to

address the challenges of real-time predictive energy

management for the hybrid electric vehicles.

Driving cycle recognition for the hybrid electric

vehicles (Chen, D, et, al, 2022): This paper focuses

on developing an adaptive equivalent consumption

minimization strategy (A-ECMS) for the hybrid

electric vehicles (HEVs) by employing driving cycle

recognition. The authors utilize a learning vector

quantization (LVQ) neural network, achieving a

recognition accuracy of 98%. The results demonstrate

that A-ECMS enhances fuel economy by 3.8% in the

New European Driving Cycle (NEDC) and 3.6% in

the China Heavy-duty Truck Cycle (CHTC-LT)

compared to traditional logic-based energy

management strategies. The primary goal of the study

on A-ECMS for hybrid electric vehicles (HEVs) is to

create an adaptive equivalent consumption

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minimization strategy that optimizes fuel

consumption by recognizing driving cycles, thereby

improving energy management and overall fuel

economy.

Comparative performance of the machine

learning algorithm for predicting electric vehicles

energy consumption (Ullah, I, et, al, 2022): The

research assesses a range of machine learning (ML)

frameworks for forecasting electric vehicle (EV)

energy usage, utilizing data derived from 38,362 trips

in Aichi Prefecture, Japan. Advanced ML models,

specifically XGBOOST and Light GBM,

demonstrated superior prediction accuracy compared

to conventional frameworks such as multiple linear

regression (MLR) and artificial neural networks

(ANN). Key factors impacting energy consumption

include trip distance, heater and A/C usage, and road

gradient, with Light GBM achieving the best

performance, reflected by an R² of 0.98, highlighting

its effectiveness in this domain. The dataset was split

into two sections: 80% for training and 20% for

testing. This division is crucial for evaluating the

efficacy of the proposed machine learning algorithms.

Additionally, a 10-fold cross-validation method was

utilized to improve the robustness and efficacy of the

prediction models.

A real-time energy management strategy for the

hybrid electric vehicles (Lee, W, et, al, 2021) It

highlights the potential of HEVs to enhance fuel

efficiency significantly while addressing the

challenges of transitioning to zero-emission vehicles.

The suggested approach showcases enhanced

performance compared to existing adaptive Energy

Consumption Management Strategies (ECMS),

achieving fuel efficiency improvements of 0.5% to

1.5% across different driving cycles. Future driving

information is crucial in the proposed control strategy

as it allows the intelligent control part to estimate the

optimal costate in real-time, enhancing decision-

making for energy management. By predicting

factors such as vehicle power demand, speed, and

acceleration, the strategy can adjust the costate to

optimize the distribution of energy between fuel and

electricity, leading to improved fuel efficiency. This

predictive capability enables the vehicle to

proactively manage its energy resources, ensuring

better performance under varying driving conditions.

Battery management system of electric vehicle

using an artificial neural network (Afzal, M, et, al,

2024) This paper presents an innovative Battery

Management System (BMS) that combines artificial

neural networks (ANN) and fuzzy logic. This new

system features decentralized control and

communication-free operation, leading to improved

reliability, a 15% increase in energy efficiency, and a

20% enhancement in battery life. The BMS was

validated through simulations and experimental

prototypes utilizing a 100kWh lithium-ion battery

pack, representing a substantial advancement in

electric vehicle battery management. The key

innovations in the new Battery Management System

(BMS) for electric vehicles include the application of

artificial neural networks (ANN) and fuzzy logic for

decentralized control and communication-free

operation. It features adaptive virtual admittance for

even load sharing, leading to improved reliability, a

15% increase in energy efficiency, and a 20%

enhancement in battery life.

Energy modelling for the electric vehicles

building on real driving cycles Mądziel, M. (2024).

The study analyses real driving cycles across varying

temperatures, yielding a summer model with an R² of

0.86 and MSE of 1.4, and a winter model with an R²

of 0.89 and MSE of 2.8. The findings are intended to

assist city planners in optimizing charging

infrastructure and enhancing the understanding of EV

energy behaviour in different environmental

conditions. The neural network method performs

comparably to gradient boosting in predicting energy

values for electric vehicles, with superior validation

results, particularly for the test set. While the random

forest technique shows slightly better performance,

the neural network method is recognized as the best

due to its lower error rates and effective predictions.

Overall, the neural network method is favoured for its

simplicity and satisfactory results in energy

consumption modelling.

AI models for energy efficiency in hybrid and

electric vehicles Mądziel, M., & Campisi, T. (2024).

The model demonstrates high accuracy in forecasting

energy usage based on vehicle velocity and

acceleration, which can significantly aid in

optimizing charging infrastructure and energy

management. The findings support sustainable

transport policies and provide valuable insights for

decision-making among EV users. This study

contributes to intelligent optimizing energy usage in

electric vehicles (EVs) by accurately predicting

energy consumption based on driving conditions,

such as velocity and acceleration. This anticipatory

feature enables better strategic planning regarding the

deployment of charging stations and the

incorporation of renewable energy sources into the

grid. Additionally, it enhances the understanding of

vehicle operation for users, ultimately supporting

environmental protection and optimizing energy use

goals.

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Role of generative artificial intelligence in

internet of the electric vehicles (Zhang, H, et, al,

2024): The paper explores GAN-based methods,

especially WGAN-GP, for addressing uncertainties in

EV charging load analysis using data from 32 stations

in Zhejiang. It highlights the limited comparative

studies with other Generative AI methods and

introduces CopulaGAN for generating diverse

vehicle types. These approaches aim to improve EV

charging behaviour generation and data augmentation

for better scheduling. The purpose of the WGAN-GP

approach in EV charging load analysis is to tackle

spatial-temporal uncertainty by generating realistic

EV charging scenarios without relying on uniform

probability assumptions across charging stations. It

aims to explore load dynamics and improve the

accuracy of load forecasting at various nodes in the

distribution network. This method enhances the

understanding of EV charging behaviours and their

impact on the power grid.

Artificial intelligence for the electric vehicle

energy systems integration (Hua, W, et, al, 2023):

The paper discusses the incorporation of electric

vehicles (EVs) into energy systems through the

application of artificial intelligence (AI). It highlights

challenges such as battery production, charging

infrastructure, and grid demand, while reviewing AI's

role in optimizing EV integration, including range

prediction and load management. Additionally, it

identifies limitations like gaps in real-world

validation and consumer trust, and suggests future

research directions focusing on advancements in AI

algorithms, explainability, and peer-to-peer energy

trading. The main technical challenges faced by

electric vehicles (EVs) include battery technology

issues such as capacity, range, charging efficiency,

lifespan, and cost. Additionally, developing sufficient

charging infrastructure and decarbonizing the battery

supply chain are significant hurdles. Public opinion

and high costs also impact EV adoption.

Recent AI applications in electrical vehicles for

sustainability (Reddy, K, et, al, 2024): The paper

discusses the function of artificial intelligence (AI) in

electric vehicles (EVs) to enhance sustainability by

improving vehicle control, energy management, and

battery design. It notes significant reductions in

greenhouse gas emissions but highlights challenges

like data security and regulatory issues. The authors

emphasize the necessity for future research to tackle

these challenges and improve infrastructure for AI in

EVs. AI contributes the energy management of the

electric vehicles (EVs) by optimizing charging

schedules, predicting energy usage, and enhancing

battery management systems. It utilizes algorithms

for range prediction, smart charging, and grid

integration, which help reduce peak grid loads and

improve overall efficiency. Additionally, AI enables

real-time monitoring and control of energy

consumption, ultimately maximizing driving range

and minimizing operating costs. Table 1 show the

Authors, Features, Mechanism Used, Advantages and

Disadvantages.

Table 1: Authors, features, mechanism used, advantages and disadvantages.

Reference

Paper

Year Features

Control

Technique

Advantages Disadvantages

Xiaohong Diao,

et. al.

2023

- Early

warning

system for EV

charging safety

- Prediction of

voltage

changes

Adaptive

Long Short-

Term

Memory (A-

LSTM)

algorithm

- Accurate

prediction of

voltage changes -

Dynamic

adjustment of

warning

thresholds

- Real-time

warnings

- Requires

extensive

historical

charging data

- Potential for

false alarms if

data is not

accurate

Thiruvonasundari

et. al.

2021

- Optimal cell

balancing for

EV batteries

- Improved

balancing time

and power loss

mana

ement

Machine

Learning

(ML)

algorithms

(BPNN,

RBNN,

LSTM

)

- Enhanced

battery run time

and lifespan

- Optimized

power loss

management

- Requires

accurate data

for effective

balancing

Implementation

com

lexit

Electric Vehicle Battery Management System: A Comprehensive Review

407

Mohanad Ismail,

et. al.

2024

- Integration

with cloud

computing

- Enhanced

data analysis

and monitoring

- Real-time

battery

management

Cloud-based

data

processing

and analytics

- Improved

battery

performance and

lifespan

- Enhanced

predictive

maintenance

- Better resource

utilization

- Dependency

on internet

connectivity

- Potential data

privacy

concerns

Implementation

complexit

Hari Maghfiroh,

et. al.

2024

- Efficient

energy

utilization in

hybrid electric

and hybrid

energy storage

system

vehicles

- Fuzzy logic

controller

(FLC)

Fuzzy Logic

Controller

(FLC)

- Efficient energy

storage and

power flow

regulation

- Improved

performance and

stability

- Complexity in

designing and

modelling

- Requires

accurate rule

definition

Mohammad

Suhail, et. al.

2021

- Progressive

fuzzy logic

- Adaptive

Neuro-Fuzzy

Inference

System

(ANFIS)

- Efficient

energy

utilization for

plug-in hybrid

electric

vehicles

Fuzzy Logic

Controller

(FLC) and

ANFIS

- Improved

battery

performance

- Enhanced

energy

management

- Better fuel

efficiency

- Complexity in

designing and

modelling

- Requires

accurate rule

definition

Sadiqa Jafari, et.

al.

2022

- Digital Twin

framework

- State of

Health (SOH)

& State of

Charge (SOC)

prediction

Extreme

Gradient

Boost

(XGBoost)

and Extended

Kalman Filter

(EKF)

- Enhanced

situational

awareness

- Accurate SOH

and SOC

estimation

- Improved

battery

maintenance

- Requires

accurate data

for effective

predictions

- Complexity in

implementation

Heeyun Lee, et.

al.

2020

- Online data-

driven energy

management

- Model-based

Q-learning

Model-Based

Q-Learning

- Adaptive to

driving

environment

- Near optimal

control solution

- Improved fuel

econom

- Requires

accurate model

of vehicle

dynamics

- Complexity in

implementation

Ahmed Sayed

Abdelaal, et. al.

2022

- Two battery

energy

management

(BEM)

techniques

- Indirect field-

oriented (IFO)

induction

motor (IM)

drive syste

- Cascaded

Fuzzy Logic

Controllers

(CSFLC)

- Fuzzy

Tuned Model

Predictive

Control

(FMPC)

- Regulates

motor speed

- Minimizes

battery pack state

of charge (SOC)

reduction and

state of health

(SOH)

degradation

- Requires

accurate battery

information

- Higher

computational

burden for

CSFLC

compared to

FMPC

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- Prolongs

battery runtime

and lifetime

Ashish Kumar

Karmaker, et. al.

2023

- Hybrid solar

and biogas-

based EV

charging

station

- Fuzzy

inference

system

Fuzzy Logic

Controller

(FLC)

- Optimizes real-

time charging

costs

- Enhances

renewable energy

utilization

- Reduces

greenhouse gas

emissions

- Requires

accurate data

for effective

management

- Dependency

on renewable

energy sources

Hae-Sol Lee, et.

2021

- Optimal

power

distribution for

4WD EVs

- Improved

battery

efficiency

Power-

distribution-

management

optimization

algorithm

- Increased

vehicle battery

range

- Reduced power

loss

- Enhanced

driving energy

efficienc

- Requires

accurate vehicle

driving

condition data

- Complexity in

implementation

Toheed

Ghandriz, et. al.

2021

- Real-time

predictive

energy

management

- Optimal

control

strategy

- Sequential

linear

rogramming

Model

Predictive

Control

(MPC) and

Sequential

Linear

Programming

- Reduced fuel

consumption

- Optimal power

split between

vehicle power

sources and

brakes

- Enhanced

vehicle

erformance

- Requires high-

fidelity vehicle

model for

accurate

predictions

- Complexity in

real-time

implementation

Dongdong Chen,

Tie Wang, et. al.

2022

- Adaptive

Equivalent

Consumption

Minimization

Strategy

(ECMS)

- Driving cycle

reco

nition

Neural

networks and

optimization

algorithms

- Improved fuel

economy

- Enhanced

adaptability to

driving

conditions

- Better energy

mana

ement

- Requires

accurate driving

cycle data

- Complexity in

implementation

Irfan Ullah et. al.

2022

- Comparative

performance of

ML algorithms

- Prediction of

EV energy

consumption

Extreme

Gradient

Boosting

(XGBoost)

and Light

Gradient

Boosting

Machine

(LightGBM)

- Higher

accuracy in

prediction

- Better

performance

compared to

traditional

models

- Enhanced

sustainabilit

- Necessitates

substantial data

for training

purposes

- Complexity in

implementation

Woong Lee, et.

al.

2021

- Real-time

intelligent

energy

management

Reinforcement

Deep Q-

Networks

(DQN)

- Improved

energy efficiency

- Optimal control

parameter

determination

- Requires

extensive

training data

- Complexity in

implementation

Electric Vehicle Battery Management System: A Comprehensive Review

409

learning (Deep

Q-Networks)

- Enhanced

vehicle

erformance

Muhammad

Zeshan Afzal, et.

al.

2023

- ANN-based

adaptive droop

control theory

- Improved

load

distribution

- Enhanced

battery

performance

Artificial

Neural

Network

(ANN) and

Fuzzy Logic

- Decentralized

control

architecture

Communication-

free capability

- Improved

reliability and

efficiency

- Requires

accurate SOC

data

- Complexity in

implementation

Maksymilian

Mądziel et. al.

2024

- AI-based

energy

modelling

- Real driving

cycles

- Microscale

analysis

Neural

Networks

- High precision

in energy

consumption

prediction

- Rapid

generation of

results

- Creation of

energy maps

- Necessitates

substantial data

for training

purposes

- Complexity in

implementation

Maksymilian

Mądziel, et. al.

2024

- AI-based

energy

efficiency

models

- Real driving

cycles

- Microscale

analysis

Deep Neural

Network

(DNN)

- High accuracy

in energy

consumption

prediction

- Versatility in

application

- Useful for

transport policy

lanning

- Necessitates

substantial data

for training

purposes

- Complexity in

implementation

Hanwen Zhang,

Dusit Niyato, et.

al.

2024

- Generative

AI in IoEV

- Applications

across multiple

layers

Generative AI

techniques

- Enhanced

charging

management

- Improved

cyber-attack

prevention

- Versatile

applications

across different

layers

- Requires

extensive data

for training

- Complexity in

implementation

Weiqi Hua,

Daniel Mullen,

et. al.

2024

- AI for EV

energy systems

integration

- Addressing

integration

challenges

Various AI

techniques

- Enhanced

integration of

EVs into energy

systems

- Improved

energy

management

- Support for

global Net Zero

transition

- Necessitates

substantial data

for training

purposes

- Complexity in

implementation

K. Balaji Nanda,

et. al.

2024

- AI

applications in

EVs

Sustainable

transportation

solutions

Various AI

techniques

- Improved

vehicle control

- Enhanced

energy

management

- Reduced

greenhouse gas

emissions

- Requires

extensive data

for training

- Complexity in

implementation

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

410

3 CONCLUSIONS

This paper's primary goal is to provide an in-depth

analysis of the battery management systems that are

already in use for different types of electric vehicles.

The review article summarizes the various methods,

algorithm proposed for the BMS and provide a clear

knowledge for the unconfiguring researchers and

methods for the new BMS. One of the fundamental

components of electrical energy storage systems is

the BMS. The components, topology, operation, and

functionality of BMS for energy storage systems are

all covered in detail in this study. Although the BMS

can have a variety of configurations depending on the

application, its fundamental operating objective and

safety feature never change. The research offers BMS

suggestions for the present market and battery

technologies.

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