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Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

Author

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  • Chuanxue Song

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Yulong Shao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Shixin Song

    (School of Mechanical Science and Engineering, Jilin University; Changchun 130022, China)

  • Silun Peng

    (Department of Automotive Engineering, College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Fang Zhou

    (Department of Automotive Engineering, College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Cheng Chang

    (Department of Automotive Engineering, College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Da Wang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

Abstract

To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

Suggested Citation

  • Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Fang Zhou & Cheng Chang & Da Wang, 2017. "Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering," Energies, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:714-:d:98997
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    References listed on IDEAS

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    Cited by:

    1. Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
    2. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    3. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.

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