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

Author

Listed:
  • Bosong Zou

    (College of Communication Engineering, Jilin University, Changchun 130022, China
    China Software Testing Center, Beijing 100038, 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)

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.

Suggested Citation

  • Bosong Zou & Lisheng Zhang & Xiaoqing Xue & Rui Tan & Pengchang Jiang & Bin Ma & Zehua Song & Wei Hua, 2023. "A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5507-:d:1198517
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    References listed on IDEAS

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    2. Tessa Krause & Daniel Nusko & Luciana Pitta Bauermann & Matthias Vetter & Marcel Schäfer & Carlo Holly, 2024. "Methods for Quantifying Expansion in Lithium-Ion Battery Cells Resulting from Cycling: A Review," Energies, MDPI, vol. 17(7), pages 1-40, March.

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