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Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter

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  • Liu, Zhentong
  • He, Hongwen

Abstract

This paper presents an effective model-based sensor fault detection and isolation (FDI) scheme for a series battery pack with low computational effort. The large number of current and voltage sensors in the battery pack, make it of high computational complexity. The major purpose of sensor FDI is to guarantee the healthy operations of the battery management system (BMS), and thus to prevent the battery from over-charge and over-discharge. In the voltage sensors fault scenarios, the most possibly being over-charged and over-discharged cells are these two cells with the maximum and minimum voltage respectively. Within the proposed scheme, these two cells are monitored in real time to diagnose the pack current sensor fault, or a voltage sensor fault of these two cells, while the rest cells are monitored offline with a long time interval, guaranteeing other voltage sensors working normally. For the scheme implementation, adaptive extended Kalman filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. Then the residuals are evaluated by a statistical inference method that determines the presence of the fault. Finally, the effectiveness of the proposed sensor FDI scheme is experimentally validated with a series battery pack under the UDDS driving cycles.

Suggested Citation

  • Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:2033-2044
    DOI: 10.1016/j.apenergy.2015.10.168
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    References listed on IDEAS

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    1. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    2. Zhentong Liu & Hongwen He, 2015. "Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles," Energies, MDPI, vol. 8(7), pages 1-19, June.
    3. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    4. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai & Xie, Jing & Zhang, Xu, 2015. "A novel active equalization method for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 36-42.
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