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A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

Author

Listed:
  • Quanqing Yu

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China
    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Changjiang Wan

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China)

  • Junfu Li

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China)

  • Rui Xiong

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Zeyu Chen

    (School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

Abstract

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

Suggested Citation

  • Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:829-:d:493903
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Zhang, Kaimei & Wang, Shaohua & Shi, Dehua & Yin, Chunfang & Shi, Yupeng & Huang, Huanming, 2024. "Research on fault diagnosis of multi-mode electromechanical compound transmission system for hybrid electric vehicle based on global analytical redundancy relations," Energy, Elsevier, vol. 310(C).
    3. Grzegorz Karoń, 2022. "Safe and Effective Smart Urban Transportation—Energy Flow in Electric (EV) and Hybrid Electric Vehicles (HEV)," Energies, MDPI, vol. 15(18), pages 1-8, September.
    4. 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).
    5. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).

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