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An Early Micro Internal Short Circuit Fault Diagnosis Method Based on Accumulated Correlation Coefficient for Lithium-Ion Battery Pack

Author

Listed:
  • Juntao Wang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Zhengye Yang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Shihao Wang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Hui Yang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Mingzhe Du

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Jifeng Song

    (Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China)

Abstract

Early micro internal short circuit (ISC) fault diagnosis is crucial for the safe and reliable operation of lithium-ion batteries. In order to solve the problem that the early micro ISC fault is difficult to identify due to its weak fault characteristics, this paper proposes a fault diagnosis method based on the accumulated correlation coefficient. Specifically, the method uses the accumulated voltage value within the time window as the input feature, constructs an adjustment factor based on the distance difference of the accumulated voltage value to amplify the difference between the fault voltage correlation coefficient and the normal voltage correlation coefficient, and finally achieves the purpose of highlighting the faulty cell. The effectiveness and diagnostic capability of the proposed method are verified in experiments of short circuit faults of different severity. The results show that the proposed method can effectively identify and locate early micro ISC faults within 200 s, and improve the diagnostic capability up to 0.02 C short-circuit severity. In addition, a multi-level diagnostic warning mechanism can be established according to the decrease of the fault voltage correlation coefficient, so as to measure the severity of the fault and track the fault evolution process.

Suggested Citation

  • Juntao Wang & Zhengye Yang & Shihao Wang & Hui Yang & Mingzhe Du & Jifeng Song, 2024. "An Early Micro Internal Short Circuit Fault Diagnosis Method Based on Accumulated Correlation Coefficient for Lithium-Ion Battery Pack," Energies, MDPI, vol. 17(23), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6071-:d:1535218
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    References listed on IDEAS

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    1. Minhwan Seo & Taedong Goh & Minjun Park & Sang Woo Kim, 2018. "Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell," Energies, MDPI, vol. 11(7), pages 1-18, June.
    2. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    3. 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.
    4. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    5. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
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