基于双卡尔曼滤波的电池SOC估算

Battery SOC estimation based on double Kalman filter

为有效解决储能电池中剩余电量的管理问题,提出基于双卡尔曼滤波的电池荷电状态(state of charge,SOC)估算研究方法。分析二阶戴维南电池等效模型,获得其状态空间方程和输出方程,利用泰勒公式对其进行线性化处理,对比分析锂离子电池的离线参数辨识和在线参数辨识结果,结合协同滤波算法进一步提升卡尔曼滤波算法的辨识精度。在Matlab环境下编写基于双卡尔曼滤波算法的SOC估算以及验证程序,在算法初值准确和有误差两种情况下进行验证,并与其它算法进行比较,验证了双卡尔曼滤波算法精度高,收敛性好。

To solve the problem of remaining power management in the battery management system of electric vehicles effectively,a research method of battery SOC estimation based on dual Kalman filtering was proposed.The second-order Thevenin battery equivalent model was analyzed,the state space equation and output equation were obtained and Taylor’s formula was used to linearize the state equation of the battery.By comparing and analyzing the results of offline parameter identification and online parameter identification of lithium-ion batteries,combined with the collaborative filtering algorithm,the online identification accuracy of Kalman filter realization parameters was further improved.The SOC estimation and verification program based on the dual Kalman filter algorithm was written in the MATLAB environment,and the battery voltage,current and SOC values were verified under the conditions of accuracy and error in the initial value of the algorithm.By comparing the experimental results with other algorithms,it is verified that the dual Kalman filter algorithm has high accuracy,good convergence,and high application value.