电工技术学报  2020, Vol. 35 Issue (4): 698-707    DOI: 10.19595/j.cnki.1000-6753.tces.181497
电工理论与新技术 |
基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法
刘芳1,2, 马杰1, 苏卫星1, 窦汝振3, 林辉4
1. 天津工业大学计算机科学与技术学院 天津 300387;
2. 天津清源电动车辆有限责任公司 天津 300462;
3. 中国汽车技术研究中心 天津 300300;
4. 东软睿驰汽车技术有限公司 汽车电子研究院 沈阳 110179
State of Charge Estimation Method of Electric Vehicle Power Battery Life Cycle Based on Auto Regression Extended Kalman Filter
Liu Fang1,2, Ma Jie1, Su Weixing1, Dou Ruzhen3, Lin Hui4
1. School of Computer Science & Technology TianGong University Tianjin 300387 China;
2. Tianjin Qingyuan Electric Vehicle Limited Liability Company Tianjin 300462 China;
3. China Automotive Technology & Research Center Tianjin 300300 China;
4. Neusoft Reach Automotive Technology Co. Ltd Automotive Research Institute Shenyang 110179 China
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摘要 该文针对传统扩展卡尔曼滤波(EKF)算法对电池数学模型精确的高度依赖与动态电池模型难以精确获得之间的矛盾问题,提出一种完全数据驱动的基于改进EKF算法的动力电池全生命周期荷电状态(SOC)估计方法。该方法为数据驱动的SOC估计方法和基于模型的SOC估计方法的良好结合,其优点在于:一方面抑制数据驱动方法存在累积误差的问题,并保留其良好的动态特性;另一方面改善基于模型的算法过度依赖电池模型的缺点,并保留其很好的鲁棒特性。该方法的创新之处在于将等效电路中难以获知的一部分视为以电池电流为输入,以内部电压为输出,以电池内部阻抗为时变参数的黑箱系统,并加以动态在线辨识,获得实时的动力电池真实状态,从而保证电池模型的准确性和动态性,真正实现动力电池全生命周期的SOC估算。仿真结果表明,该方法具有较好的鲁棒性和实用性。
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刘芳
马杰
苏卫星
窦汝振
林辉
关键词 电池荷电状态扩展卡尔曼滤波算法自回归模型电动汽车动力电池电池管理系统    
Abstract:The traditional extended Kalman filter(EKF) algorithm relies heavily on battery mathematic model and the dynamic battery model is difficult to obtain accurately. This paper proposes a completely data driven state of charge(SOC) estimation method based on improved EKF algorithm for battery life cycle. This method is a good combination of data-based SOC estimation method and model-based SOC estimation method. On the one hand, the SOC calculation method proposed in this paper can avoid the problem that data-driven SOC estimation method cannot eliminate the accumulated error and retain the data-driven algorithm good dynamic characteristics. On the other hand, it can improve the shortcoming of the model-based SOC estimation method that relies heavily on battery model while preserving the robustness. The hard-to-know part of the equivalent circuit is regarded as a black-box system, where the battery current is taken as input, the internal voltage as output and the internal impedance of the battery as time-varying parameter. The dynamic online identification is carried out to obtain the real state of the power battery, thereby ensuring the accuracy and dynamics of the battery model and realizing the battery life cycle SOC estimation. Simulation results show that the proposed method has good robustness and practicability.
Key wordsState of charge    extended Kalman filter    auto regressive model    electric vehicles    power battery    battery management system   
收稿日期: 2018-08-31      出版日期: 2020-02-28
PACS: TM835.4  
基金资助:天津市教委科研计划资助项目(2017KJ094)
通讯作者: 苏卫星 男,1980年生,博士,教授,硕士生导师,研究方向为复杂数据分析。E-mail: satelliteer@126.com   
作者简介: 刘 芳 女,1983年生,博士,副教授,硕士生导师,研究方向为电动汽车BMS状态估计。E-mail: 15900201597@163.com
引用本文:   
刘芳, 马杰, 苏卫星, 窦汝振, 林辉. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法[J]. 电工技术学报, 2020, 35(4): 698-707. Liu Fang, Ma Jie, Su Weixing, Dou Ruzhen, Lin Hui. State of Charge Estimation Method of Electric Vehicle Power Battery Life Cycle Based on Auto Regression Extended Kalman Filter. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707.
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