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

Bosong Zou, Lisheng Zhang, Xiaoqing Xue, Rui Tan Orcid Logo, Pengchang Jiang Orcid Logo, Bin Ma, Zehua Song, Wei Hua Orcid Logo

Energies, Volume: 16, Issue: 14, Start page: 5507

Swansea University Author: Rui Tan Orcid Logo

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DOI (Published version): 10.3390/en16145507

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 f...

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Published in: Energies
ISSN: 1996-1073
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa67799
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spelling v2 67799 2024-09-25 A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles 774c33a0a76a9152ca86a156b5ae26ff 0009-0001-9278-7327 Rui Tan Rui Tan true false 2024-09-25 EAAS 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. Journal Article Energies 16 14 5507 MDPI AG 1996-1073 electric vehicles; lithium-ion batteries; battery faults; fault diagnosis methods 20 7 2023 2023-07-20 10.3390/en16145507 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Another institution paid the OA fee This research received no external funding. 2024-10-18T12:02:45.7965984 2024-09-25T21:27:50.4894839 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemical Engineering Bosong Zou 1 Lisheng Zhang 2 Xiaoqing Xue 3 Rui Tan 0009-0001-9278-7327 4 Pengchang Jiang 0009-0004-6815-1785 5 Bin Ma 6 Zehua Song 7 Wei Hua 0000-0002-2047-9712 8 67799__32635__5164c06be2834590ae60f1d5fcb53c02.pdf 67799.VoR.pdf 2024-10-18T11:49:07.0034516 Output 2707279 application/pdf Version of Record true © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
spellingShingle A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
Rui Tan
title_short A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
title_full A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
title_fullStr A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
title_full_unstemmed A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
title_sort A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
author_id_str_mv 774c33a0a76a9152ca86a156b5ae26ff
author_id_fullname_str_mv 774c33a0a76a9152ca86a156b5ae26ff_***_Rui Tan
author Rui Tan
author2 Bosong Zou
Lisheng Zhang
Xiaoqing Xue
Rui Tan
Pengchang Jiang
Bin Ma
Zehua Song
Wei Hua
format Journal article
container_title Energies
container_volume 16
container_issue 14
container_start_page 5507
publishDate 2023
institution Swansea University
issn 1996-1073
doi_str_mv 10.3390/en16145507
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemical Engineering
document_store_str 1
active_str 0
description 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.
published_date 2023-07-20T12:02:44Z
_version_ 1813249381596397568
score 11.033506

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