基于扩展卡尔曼滤波的动力电池SOC估算

Power Battery SOC Estimation Based on Extended Kalman Filter

卡尔曼滤波(KF)是基于最小方差估计的一种最优估计方法,适用于线性系统,而车载动力电池在实际运行过程中具有较强的非线性特征。通过对卡尔曼滤波进行改进得到扩展卡尔曼滤波(EKF),可以较好地解决这一问题。文章以三元锂电池为研究对象,建立一阶RC等效电路模型作为电池的基础模型,在锂电池充放电的试验数据基础上,利用MATLAB进行拟合得到电压与电池荷电状态(SOC)的关系曲线OVC-SOC,利用最小二乘法进行参数辨识,再利用EKF算法对动力电池SOC进行实时估算。

Kalman filter(KF)is an optimal estimation method based on minimum variance estimation,which is suitable for linear systems.However,on-board power batteries have strong nonlinear characteristics during actual operation.The estimation kalman filter(EKF)is obtained by improving the estimation kalman filter.This problem can be solved satisfactorily.Taking ternary lithium battery as the research object,this paper establishes a first-order RC equivalent circuit model as the basic battery model.Based on the experimental data of charge and discharge of lithium battery,the relationship curve of voltage and state of charge(SOC),OVC-SOC,is obtained by using MATLAB fitting.The least square method is used for parameter identification,and the EKF algorithm is used for real-time estimation of power battery SOC.