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Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters

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  • Zhang, Zhendong
  • Kong, Xiangdong
  • Zheng, Yuejiu
  • Zhou, Long
  • Lai, Xin

Abstract

The fast diagnosis of micro-short circuit cells is crucial for the safety of battery packs. Based on the difference between the “median cell” and other cells in a battery pack, we propose a method that can identify micro-short circuit cells under dynamic conditions in real time. We model cell differences and analyze the model from the perspective of its low frequency variation characteristics. We find that approximate open-circuit voltage differences can be obtained when terminal voltage differences are passed through low-pass filters. Then approximate electric quantity differences can be obtained by utilizing the open-circuit voltage differences and the data smoothing function of low-pass filters. For onboard applications of diagnosis method, the recursive least square is adopted to estimate micro-short circuit currents and resistances utilizing the change of electric quantity differences. We verify and analyze the feasibility of the diagnosis method by using simulation data when the cells in a battery pack have temperature, state of charge, capacity, and internal resistance inconsistency, respectively. Finally, the effectiveness of the diagnosis method is further verified by the triggering experiments of micro-short circuits for real battery packs.

Suggested Citation

  • Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:1013-1024
    DOI: 10.1016/j.energy.2018.10.160
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    Cited by:

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    5. Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    6. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
    7. 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.
    8. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).

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