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Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O 2 Battery

Author

Listed:
  • Haobin Jiang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xijia Chen

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yifu Liu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Qian Zhao

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Huanhuan Li

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Biao Chen

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O 2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

Suggested Citation

  • Haobin Jiang & Xijia Chen & Yifu Liu & Qian Zhao & Huanhuan Li & Biao Chen, 2021. "Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O 2 Battery," Energies, MDPI, vol. 14(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:324-:d:477293
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    References listed on IDEAS

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