文章摘要
熊瑞,孙逢春,何洪文.自适应卡尔曼滤波器在车用锂离子动力电池SOC估计上的应用[J].高技术通讯(中文),2012,22(2):198~204
自适应卡尔曼滤波器在车用锂离子动力电池SOC估计上的应用
State of charge estimation of lithium ion batteries in electric vehicles based on an adaptive extended Kalman filter
  修订日期:2010-10-08
DOI:
中文关键词: 自适应扩展卡尔曼滤波(AEKF), 荷电状态(SOC), 参数辨识, 电池模型, 锂离子电池, 电动汽车
英文关键词: adaptive extended Kalman filter (AEKF), state of charge (SOC), parameter identification, battery model, lithium ion power battery, electric vehicles
基金项目:863计划(2008AA11A124,2011AA112304,2011AA11A228,2011AA1290)资助项目
作者单位
熊瑞 北京理工大学电动车辆国家工程实验室 
孙逢春 北京理工大学电动车辆国家工程实验室 
何洪文 北京理工大学电动车辆国家工程实验室 
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中文摘要:
      进行了用自适应扩展卡尔曼滤波(AEKF)算法估计电动车用锂离子动力电池的荷电状态(SOC)的研究。基于混合脉冲功率特性(HPPC)试验,利用遗传优化算法改进Thevenin电路模型参数辨识方法,且从充放电两个方向来获得模型参数,然后在动态应力测试(DST)工况下对改进的模型进行仿真验证分析,基于改进的模型和联邦城市行驶工况(FUDS),应用AEKF算法开展SOC估计研究。仿真和台架试验结果对比表明,改进的Thevenin电路模型和AEKF算法均具有较高的精度,最大估算误差分别为1.70%和2.53%;同时AEKF算法具有较好的鲁棒性,可以有效地解决初始估算不准和累计误差的问题。
英文摘要:
      An adaptive extended Kalman filter (AEKF) algorithm was adopted to estimate the state of charge (SOC) of lithium ion batteries in electric vehicles. Based on the hybrid pulse power characterization (HPPC) test, an improved Thevenin battery model was achieved by using the genetic algorithm to optimize the parameter identification method and identify the model parameters from the charge direction and the discharge direction respectively. In addition, the improved model was verified under the dynamic stress test cycle. Further, an AEKF algorithm was adopted to design the approach for estimation of SOC of lithium ion batteries. Finally, the proposed method was verified by the simulation experiment and the bench test under the federal urban driving schedules. It is shown that the improved Thevenin model and the proposed SOC estimation method all have the high accuracy and their maximum errors are 1.70% and 2.53% respectively, and the AEKF algorithm is of robustness and it can efficiently solve the problems of cumulate error and inaccurate initial SOC estimation.
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