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Automotive Engineering ›› 2020, Vol. 42 ›› Issue (8): 1008-1015.doi: 10.19562/j.chinasae.qcgc.2020.08.003

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Lithium Battery SOC Estimation Based on Internal Resistance Power Consumption

Jin Bowen1,2, Qiao Huimin3, Pan Tianhong1, Chen Shan2   

  1. 1. Electrical Engineering and Automation, Anhui University, Hefei 230601;
    2. Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013;
    3. Mechanical and Transportation Engineering, Hunan University, Changsha 410082
  • Received:2019-11-19 Online:2020-08-25 Published:2020-09-24

Abstract: Aiming at the problem of estimating the remaining capacity of lithium battery under different operation situations, a calculation method of remaining battery capacity based on internal resistance power discharge strategy and power integration is proposed. The first-order Thevenin equivalent circuit model of the battery is selected, the internal parameters of the battery are determined by discharge experiments, and the variable parameter model of the battery is established. According to the different operation requirements of the battery, the battery discharge current is controlled by power, to stabilize battery capacity and improve the robustness of the ampere-hour integral algorithm under stable discharge conditions. The temperature, high frequency fluctuation current and SOH of the battery are introduced into integral item to measure the capacity consumption rate of the battery, and the remaining capacity of the battery is estimated by power integration algorithm. The integration algorithm is combined with EKF to weaken the influence of integral error on estimation accuracy. The experimental bench is set up and the discharge condition of the lithium battery is designed with the corresponding discharge strategy and calculation method adopted. The results show that the method proposed can effectively enhance the estimation accuracy of battery remaining capacity.

Key words: lithium battery, variable parameter model, remaining capacity, power integration algorithm, EKF algorithm, robustness