电动汽车动力电池SOC估计

SOC estimation of power batteries for an electric vehicle

为准确评估电动汽车锂离子电池荷电状态(state of charge,SOC),构建二阶电阻电容等效电路模型,通过递推最小二乘法识别等效电路模型参数,采用开路电压放电试验获取动态应力测试(dynamic stress test,DST)工况下开路电压与SOC之间的函数关系,在DST工况下对比分析开路电压法、卡尔曼滤波法、扩展卡尔曼滤波法估算的SOC及误差。结果表明:卡尔曼滤波及扩展卡尔曼滤波2种算法与开路电压法SOC估算结果吻合性较好;卡尔曼滤波法最大SOC估计误差为0.017,扩展卡尔曼滤波法最大SOC估计误差为0.013,均满足SOC估计误差不得超过0.050的标准要求,但扩展卡尔曼滤波算法精度更高。

In order to accurately evaluate the state of charge(SOC)of lithium-ion batteries for an electric vehicle,a second-order resistance capacitance equivalent circuit model is constructed.The parameters of the equivalent circuit model are identified using recursive least squares method,and the functional relationship between the open circuit voltage and SOC under dynamic stress test(DST)conditions is obtained through open circuit voltage discharge test.The SOC is estimated by open circuit voltage method,Kalman filter method,and extended Kalman filter method,and their errors are compared and analyzed under DST condition.The results show that the Kalman filter and extended Kalman filter algorithms are in good agreement with the SOC estimation results of the open circuit voltage method.The maximum SOC estimation error of the Kalman filtering method is 0.017,and the maximum SOC estimation error of the extended Kalman filtering method is 0.013,both of which meet the standard requirement of SOC estimation error not exceeding 0.050,however,the accuracy of the extended Kalman filtering algorithm is higher.