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

Neha Bhushan (), Saad Mekhilef (), Kok Soon Tey, Mohamed Shaaban, Mehdi Seyedmahmoudian and Alex Stojcevski
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Neha Bhushan: Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Saad Mekhilef: Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Kok Soon Tey: Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Mohamed Shaaban: Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Mehdi Seyedmahmoudian: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Alex Stojcevski: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

Sustainability, 2022, vol. 14, issue 23, 1-31

Abstract: 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.

Keywords: lithium-ion battery; battery management system (BMS); electrical vehicle (EV); battery charging; battery modeling; states estimation and fault diagnosis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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