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Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Author

Listed:
  • Kaizhi Liang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles, Beijing 100081, China)

  • Zhaosheng Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles, Beijing 100081, China)

  • Peng Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles, Beijing 100081, China)

  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles, Beijing 100081, China)

  • Shangfeng Jiang

    (Zhengzhou Yutong Bus Co., Ltd., Henan 450016, China)

Abstract

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

Suggested Citation

  • Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4772-:d:297883
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    References listed on IDEAS

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    2. Dominik Dvorak & Daniele Basciotti & Imre Gellai, 2020. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-18, October.
    3. Yan Ding & Zhe Ji & Peng Liu & Zhiqiang Wu & Gang Li & Dingsong Cui & Yizhong Wu & Sha Xu, 2021. "Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles," Energies, MDPI, vol. 14(23), pages 1-13, November.
    4. Lorentz Jäntschi, 2020. "Detecting Extreme Values with Order Statistics in Samples from Continuous Distributions," Mathematics, MDPI, vol. 8(2), pages 1-21, February.
    5. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.

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