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Current sensor fault diagnosis method based on an improved equivalent circuit battery model

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  • Yu, Quanqing
  • Dai, Lei
  • Xiong, Rui
  • Chen, Zeyu
  • Zhang, Xin
  • Shen, Weixiang

Abstract

Battery management systems (BMSs) are very important to ensure the safety of electric vehicles. The normal operation of BMSs is highly dependent on the accuracy of battery sensors. The present fault diagnosis efficiency of current sensors is much lower than that of voltage sensors due to model limitations in conventional methods. In this paper, a fault diagnosis method based on an improved model with voltage as input and current as output (VICO) is proposed to detect current sensor faults, where the least squares method combined with the unscented Kalman filter is used to estimate the fault current of current sensor. By comparing the estimated fault current with the diagnosis threshold, the fast fault diagnosis of current sensor is realized. The proposed method is verified under different operating conditions and compared with the methods based on state of charge and open-circuit voltage residuals. To highlight the importance of the proposed method, the influence and possible causes of minor faults and temperature on diagnosis are analyzed. The experimental results show that the method can detect the fault of the current sensor more accurately and quickly compared with the conventional methods, and has the ability to detect minor faults and adaptability under different operating conditions and temperatures.

Suggested Citation

  • Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000691
    DOI: 10.1016/j.apenergy.2022.118588
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    Cited by:

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    3. Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
    4. Tang, Aihua & Huang, Yukun & Liu, Shangmei & Yu, Quanqing & Shen, Weixiang & Xiong, Rui, 2023. "A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models," Applied Energy, Elsevier, vol. 348(C).
    5. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    6. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    7. 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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