基于RLS和CKF算法的铅酸蓄电池荷电状态估计

State of charge estimation of lead acid battery based on RLS and CKF algorithm

针对铅酸蓄电池荷电状态(SOC)估计不够精准的问题,提出了基于等效电路模型的递推最小二乘法(RLS)结合容积卡尔曼滤波算法(CKF)的电池SOC估计方法。推导铅酸蓄电池离散空间方程,通过实验得到电池的SOC-OCV拟合曲线,并采用递推最小二乘法进行模型的参数辨识,建立仿真模型对比扩展卡尔曼滤波算法(EKF)和CKF算法对铅酸蓄电池SOC估算的精准度,结果表明CKF算法对SOC的估计效果更佳。

To solve the problem of inaccurate SOC estimation of lead-acid batteries,a recursive least squares(RLS)method based on equivalent circuit model and cubature Kalman filter(CKF)method for SOC estimation of batteries were proposed.The discrete space equation of lead-acid battery was derived,SOC-OCV fitting curve of the battery was obtained by experiment,and parameters of the model were identified by recursive least squares method.The simulation model was established to compare the accuracy of extended Kalman filter(EKF)algorithm with that of CKF algorithm for SOC estimation of lead-acid battery.The simulation results prove that CKF algorithm is better for SOC estimation.