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A comprehensive review on energy management strategies of hybrid energy storage systems for electric vehicles

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Abstract

The development of electric vehicles represents a significant breakthrough in the dispute over pollution and the inadequate supply of fuel. The reliability of the battery technology, the amount of driving range it can provide, and the amount of time it takes to charge an electric vehicle are all constraints. The eradication of these constraints is possible through the combination of energy storage systems. The hybrid energy storage system is potentially a significant development since it combines the advantages that are traditionally associated with batteries and supercapacitors. When compared to conventional energy storage systems for electric vehicles, hybrid energy storage systems offer improvements in terms of energy density, operating temperature, power density, and driving range. Thus, the review paper explores the different architectures of a hybrid energy storage system, which include passive, semi-active, or active controlled hybrid energy storage systems. Further, the effectiveness of hybrid energy storage systems based on the different architectures and operating modes was examined. Also, this work presents control modes of energy management strategies based on rules and optimization based strategies. Further, this review paper provides the effects of driving cycles and thermal behavior on the performance of hybrid energy storage systems. From this extensive review, based on simulation and experimental results, it is concluded that the battery parameters and energy management strategy for a hybrid energy storage system are the prime factors for the battery’s charging and discharging time, state of charge, state of health, energy consumption, and safety of the electric vehicle.

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Abbreviations

A-ECMS:

Adaptive equivalent consumption minimization strategy

AC:

Alternating current

ANN:

Artificial neural network

ARTEMIS:

Artemis drive cycle

CATC:

China automotive test cycle

CBDC:

Chinese bus driving cycle

DC:

Direct current

DNN:

Deep neural network

DP:

Dynamic programming

DPR:

Driving pattern recognition

ECE 15:

Urban driving cycle

EMS:

Energy management strategy

ESS:

Energy storage system

EV:

Electric vehicle

EWMA:

Exponential weighted moving average

FTP75:

Federal test procedure

GA:

Genetic algorithms

GHG:

Greenhouse gas

HESS:

Hybrid energy storage system

HIL:

Hardware-in-the-loop

HWDC:

Highway driving cycle

HWFET:

The highway fuel economy test

IUDC:

Indian urban driving cycle

Japan 10–15:

Japanese 10–15 mode

LA92:

“Unified” dynamometer driving schedule

Li-ion:

Lithium-ion

LPF:

Low-pass filter

LSTM:

Long short-term memory

MANHATTAN:

Manhattan drive cycle

MDP:

Markov decision process

MFB:

Math function-based

MIC:

Multi-input converter

MPC:

Model predictive control

NEDC:

New european driving cycle

Ni-MH:

Nickel-metal hydride

NN:

Neural network

NYCC:

New York city cycle

PbA:

Lead-acid

PD:

Proportional derivative

PI:

Proportional integrator

PID:

Proportional integral derivative

PMP:

Pontryagin’s minimum principle

PSO:

Particle swarm optimization

RCP:

Rapid control prototype

RFOSMC:

Robust fractional-order sliding mode control

RMS:

Root mean square

RUL:

Remaining useful life

SOC:

State of charge

SOH:

State of health

SOP:

State of power

SRT:

Single ratio transmission

SS-IFS:

Squirrel search with improved food storage

Th strategy:

Threshold strategy

UDDS:

Urban dynamometer driving cycle

UDIT:

Uninterrupted dual input transmission

US06:

High-speed, steady-state driving cycle

VRLA:

Valve-regulated lead acid battery

WLTC:

Worldwide harmonized light-duty vehicles test cycles

WLTP:

Worldwide harmonized light vehicles test procedure

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Acknowledgements

The authors would like to thank Vellore Institute of Technology, Vellore, Tamil Nadu, India, for providing research facilities.

Funding

There is no funding for the above research work.

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Authors and Affiliations

  1. School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, India

    N. Kumaresan

  2. Automotive Research Center, Vellore Institute of Technology, Vellore, 632014, India

    A. Rammohan

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  1. N. Kumaresan
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  2. A. Rammohan
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Correspondence to A. Rammohan.

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Kumaresan, N., Rammohan, A. A comprehensive review on energy management strategies of hybrid energy storage systems for electric vehicles. J Braz. Soc. Mech. Sci. Eng. 46, 146 (2024). https://doi.org/10.1007/s40430-024-04736-x

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