A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles

Bosong Zou, Lisheng Zhang (), Xiaoqing Xue, Rui Tan, Pengchang Jiang, Bin Ma (), Zehua Song () and Wei Hua
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Bosong Zou: College of Communication Engineering, Jilin University, Changchun 130022, China
Lisheng Zhang: School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Xiaoqing Xue: Beijing Saimo Technology Co., Ltd., Beijing 100097, China
Rui Tan: Warwick Electrochemical Engineering Group, WMG, Energy Innovation Centre, University of Warwick, Warwick CV4 7AL, UK
Pengchang Jiang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Bin Ma: College of Communication Engineering, Jilin University, Changchun 130022, China
Zehua Song: School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Wei Hua: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2023, vol. 16, issue 14, 1-19

Abstract: The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

Keywords: electric vehicles; lithium-ion batteries; battery faults; fault diagnosis methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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