基于RLS-EKF联合算法的锂电池SOC估算

SOC estimation of lithium battery based on RLS-EKF joint algorithm

针对锂电池的荷电状态(SOC)估算问题,给出了一种RLS-EKF联合算法。该联合算法在二阶RC等效电路模型基础上,采用递推最小二乘算法(RLS)和扩展卡尔曼滤波算法(EKF)对电池模型参数与SOC进行在线联合估算。在恒流放电工况、动态压力测试工况(DST)和纯电动乘用车用能量型蓄电池主放电工况下验证了该联合算法对锂电池SOC的准确实时估算,SOC估算误差低于传统单个EKF算法估算误差。

Aiming at the estimation of the state of charge(SOC)of lithium batteries,a joint RLS-EKF algorithm was presented.Based on the second-order RC equivalent circuit model,the recursive least squares(RLS)and extended Kalman filter algorithm(EKF)were used to jointly estimate the battery model parameters and SOC online.Under the constant-current discharge conditions,dynamic pressure test conditions(DST),and the main discharge conditions of energy-type batteries for pure electric passenger vehicles,the accurate real-time estimation of the SOC of lithium batteries was verified.The error of SOC estimation was lower than the traditional single EKF algorithm estimate error.