基于RLS和UKF算法的锂离子电池荷电状态估计

State of charge estimation of lithium ion battery based on RLS and UKF algorithm of composite model

针对锂离子电池荷电状态(SOC)估计不够精准的问题,提出了采用二阶RC等效电路模型结合递推最小二乘法(RLS)和无迹卡尔曼滤波算法(UKF)的SOC估计方法。推导了二阶RC的锂离子电池离散状态空间方程和观测方程,通过实验得到了电池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,通过仿真对比了自适应扩展卡尔曼滤波算法(AEKF)和UKF算法对模型的适应性,从而证明了UKF算法对SOC的估计效果更好。

In order to solve the problem of imprecise SOC estimation of Li-ion battery,a new SOC estimation method based on the second-order RC equivalent circuit model,RLS(recursive least square)and UKF is proposed.Firstly,the discrete state space equation and observation equation of the second-order RC are derived.Secondly,the SOC-OCV fitting curve of the battery is obtained through experiments and the RLS method is used to identify the parameters of the model.Finally,the adaptability of the adaptive extended Kalman filtering algorithm(AEKF)and UKF algorithm to the model is compared through simulation,which proves that UKF algorithm has better estimation ability to SOC.