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A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

Author

Listed:
  • Xinwei Cong

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Caiping Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Jiuchun Jiang

    (Sunwoda Co., Ltd., Shenzhen 518100, China
    School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 432200, China)

  • Weige Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Yan Jiang

    (Sunwoda Co., Ltd., Shenzhen 518100, China
    School of Electric Power, South China University of Technology, Guangzhou 510006, China)

  • Linjing Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

Abstract

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

Suggested Citation

  • Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1221-:d:504700
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    References listed on IDEAS

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    Cited by:

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    2. Rüther, Tom & Plank, Christian & Schamel, Maximilian & Danzer, Michael A., 2023. "Detection of inhomogeneities in serially connected lithium-ion batteries," Applied Energy, Elsevier, vol. 332(C).
    3. Ben Qi & Jingang Liang & Jiejuan Tong, 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective," Energies, MDPI, vol. 16(4), pages 1-27, February.
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    5. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.

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