双卡尔曼滤波法估计电动汽车电池健康状态  

Estimation state of health of electric vehicle battery by dual Kalman filter

作　　者：邓涛[1,2] 罗卫兴 李志飞[1] 罗俊林[1] DENG Tao1,2, LUO Wei-xing1, LI Zhi-fei1, LUO Jun-lin1(1. School of Mechatronics ＆ Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China ; 2. Chongqing Key Laboratory of System Integration and Control for Urban Rail Transit Vehicle, Chongqing Jiaotong University, Chongqing 400074, Chin)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074 [2]重庆交通大学城市轨道交通车辆系统集成与控制重庆市重点实验室,重庆400074

出　　处：《电池》2018年第2期95-99,共5页Battery Bimonthly

基　　金：国家自然科学基金(51305473);中国博士后科学基金(2014M552317);重庆市博士后研究人员科研项目(xm2014032)

摘　　要：选用戴维南等效电路模型作为基础电池模型,标定荷电状态(SOC)-开路电压(OCV)曲线,利用指数拟合法拟合等效电路模型中的电阻-电容电路(RC)参数,提出基于安时积分法使用拓展卡尔曼滤波法估计SOC,以及基于容量法使用卡尔曼滤波估计电池健康状态(SOH),建立双卡尔曼滤波SOH估算方法。随机电流激励仿真结果表明:该方法的估计值与真实值变化趋势一致,且估计误差控制在1%以内。SOH估算实验结果表明:在开始阶段,SOH估计值与真实值有一定的偏差,之后变化趋势一致,误差可控制在1%以内。Thevenin equivalent circuit model was selected as the basic battery model, the state of charge （SOC） and open circuit voltage （OCV） curve were calibrated. The resistance-capacitance circuit（RC） parameters in the equivalent circuit model were fitted by exponential fitting. The extended Kalman filter method was proposed to estimate the SOC based on the time-integral method, and the Kalman filter was also adopted to estimate the state of health （SOH） based on the capacity method. The dual extended Kalman filter method for SOH estimation was established. The simulation results under random current excitation condition showed that the estimated value obtained by dual extended Kalman filter was consistent with the real value,the estimated error was within 1%. The test results of SOH estimation experiment showed that the estimated value was different from the real value at the beginning state, the following change trend could be consistent and the error could be controlled within 1%.

关 键 词：电动汽车 锂离子电池 健康状态(SOH) 双卡尔曼滤波 

分 类 号：TM912.9[电气工程—电力电子与电力传动]