基于自适应CKF的锂离子电池SOC估算

扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)算法估算电池荷电状态(SOC)依赖等效模型参数的准确性,估算精度低。容积卡尔曼滤波(CKF)算法的滤波性能良好。利用自适应CKF(ACKF)算法估算电池SOC,自适应调节过程噪声协方差和量测噪声协方差,提高估算SOC的精度。对锂离子电池建立二阶RC等效电路模型,在不同工况下进行充放电,用卡尔曼滤波算法在线辨识等效模型的参数,ACKF算法实时估算SOC。ACKF算法估算SOC的鲁棒性较强,精度在1.5%以内。

State of charge estimation of Li-ion battery based on adaptive CKF

The estimation accuracy of extended Kalman filter(EKF)and unscented Kalman filter(UKF)estimating battery state of charge(SOC)was related to the accuracy of equivalent model parameters,the estimation accuracy was low.Cubature Kalman filter(CKF)algorithm had good filtering performance.Adaptive CKF(ACKF)algorithm was used to estimate the battery SOC,adaptively adjust the process noise covariance and measure the noise covariance to improve the accuracy of estimation.The second order RC equivalent circuit model for Li-ion battery was established.When charged-discharged under different working conditions,the parameters of equivalent model were identified online by Kalman filter algorithm,SOC was real time estimated by ACKF algorithm.ACKF algorithm had strong robustness when used to estimate SOC,the estimation accuracy was less than 1.5%.