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A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack

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  • Zhang, Shuzhi
  • Jiang, Shiyong
  • Wang, Hongxia
  • Zhang, Xiongwen

Abstract

Considering the limitations in existing correlation coefficient-based, entropy-based and big data analysis-based voltage sensor fault diagnosis methods, we develop a novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack in this paper. Firstly, a “ohmic resistance”-based selection method is periodically performed to artificially divide all in-pack cells into “representative cell” and non-representative cells. Secondly, during the “representative cell”-based battery pack state-of-charge (SOC) and cell SOC inconsistence estimation process, the measurement innovation (MI) between measured and estimated voltage of the “representative cell” and non-representative cells is generated in micro time-scale and macro time-scale, respectively. Regarding the “representative cell”, the faulty voltage sensor is immediately detected at the moment of the voltage sensor fault occurrence by catching the abnormal MI. As for the non-representative cells, through analyzing the discreteness degree of generated MI under faultless and faulty voltage sensors, an abnormal variance-based voltage sensor fault diagnosis method and an abnormal variance contribution-based fault location method are developed. The validation results through three sophisticated cases demonstrate that this method can rapidly catch the abnormal features for further voltage sensor fault diagnosis with low complexity and satisfactory robustness even though there exist certain faulty current.

Suggested Citation

  • Zhang, Shuzhi & Jiang, Shiyong & Wang, Hongxia & Zhang, Xiongwen, 2022. "A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s030626192200856x
    DOI: 10.1016/j.apenergy.2022.119541
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    References listed on IDEAS

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    Cited by:

    1. Tongrui Zhang & Ran Li & Yongqin Zhou, 2023. "Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine," Energies, MDPI, vol. 16(21), pages 1-17, October.
    2. Xu, Yiming & Ge, Xiaohua & Shen, Weixiang, 2024. "Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles," Applied Energy, Elsevier, vol. 362(C).
    3. Xin Liu & Haihong Huang & Wenjing Chang & Yongqi Cao & Yuhang Wang, 2024. "Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection," Energies, MDPI, vol. 17(20), pages 1-15, October.
    4. Shen, Dongxu & Yang, Dazhi & Lyu, Chao & Ma, Jingyan & Hinds, Gareth & Sun, Qingmin & Du, Limei & Wang, Lixin, 2024. "Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features," Energy, Elsevier, vol. 290(C).
    5. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).

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