基于无迹卡尔曼滤波的并联型电池系统SOC估计研究

Research on SOC estimation of parallel-connection battery system based on unscented kalman filter

为准确估计由多个电池单体构成的并联型电池系统的荷电状态(SOC),以SOC与电池极化电压为系统状态变量,提出基于无迹卡尔曼滤波法的并联型电池系统荷电状态估计算法,建立电池系统SOC估计平台,在恒流和脉冲两种工况下,通过UKF算法与EKF算法的对比分析,证明了采用UKF算法进行并联型电池系统SOC估计的结果更准确、鲁棒性更强。

To accurately estimate the state of charge（SOC）of parallel-connection battery system（PBS）which consists of many cells,the method based on unscented Kalman filter（UKF）was presented.The polarization voltage and SOC of PBS were selected as system state variables.The simulation platform was built to estimate SOC of PBS,and the effectiveness and robustness of the method were verified by the comparison between UKF and EKF under constant current and pulse current conditions.