基于UKPF算法的锂离子电池SOC估算

SOC estimation of lithium ion battery based on UKPF algorithm

电池在工作时电流变化剧烈,使用传统无迹卡尔曼算法(UKF)估算电池荷电状态(SOC)时有较大误差。为了提高SOC估算精度,基于锂离子电池混合噪声模型,利用粒子滤波算法对无迹卡尔曼的滤波进行修正,得到无迹卡尔曼粒子滤波算法(UKPF),并用该方法来估算锂离子电池的SOC。实验结果表明,UKPF算法SOC的估算误差小于2.1%,明显优于UKF和PF算法。

The current of the battery changes dramatically when it is working,so there is a big error when using the traditional unscented Kalman filter(UKF)to estimate the state of charge(SOC)of the battery.In order to improve the accuracy of SOC estimation,based on the mixed noise model of lithium ion battery,the particle filter algorithm is used to modify the unscented Kalman filter,and the unscented Kalman particle filter algorithm(UKPF)is obtained,which is used to estimate the lithium ion battery SOC.The experimental results show that the estimation error of the UKPF algorithm SOC is less than 2.1%,which is obviously better than the UKF and PF algorithms.