Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

被引：12

作者：

Bhushan, Neha ^{[1
]}

Mekhilef, Saad ^{[1
,2
]}

Tey, Kok Soon ^{[3
]}

Shaaban, Mohamed ^{[1
]}

Seyedmahmoudian, Mehdi ^{[2
]}

Stojcevski, Alex ^{[2
]}

机构：

[1] Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia

[2] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Hawthorn, Vic 3122, Australia

[3] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia

来源：

SUSTAINABILITY | 2022年 / 14卷 / 23期

关键词：

lithium-ion battery; battery management system (BMS); electrical vehicle (EV); battery charging; battery modeling; states estimation and fault diagnosis; LITHIUM-ION BATTERY; STATE-OF-CHARGE; EQUIVALENT-CIRCUIT MODELS; UNSCENTED KALMAN FILTER; NEURAL-NETWORK; FAULT-DIAGNOSIS; GAUSSIAN PROCESS; SOC ESTIMATION; CYCLE LIFE; THE-ART;

D O I：

10.3390/su142315912

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.

引用

页数：31

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