基于EKF算法的动力锂离子电池SOC估计

SOC Estimation of Power Lithium-ion Battery Based on EKF Algorithm

为了解决新能源汽车中动力锂离子电池荷电状态(SOC)难以精确估计与预测的问题,首先选用二阶RC等效电路模型对锂离子电池进行建模,运用MATLAB/cftool工具箱和遗忘因子递推最小二乘法(FFRLS)两种方法对锂电池模型参数进行辨识。其次,通过扩展卡尔曼滤波(EKF)算法来提高SOC的估计精度。最后,搭建了电池管理系统(BMS)实验平台,在美国联邦城市驾驶工况(FUDS)下仿真和实验。仿真和实验结果表明FFRLS方法相较于利用cftool工具箱的参数辨识方法能够提高锂离子电池模型精度,且SOC估计精度高、收敛速度快,验证了锂离子电池二阶RC等效电路模型结合EKF算法估计锂离子电池SOC的准确性和有效性。

In order to solve the problem of difficult to accurately estimate and predict the state of charge(SOC)of power lithium-ion batteries in new energy vehicles,a second-order RC equivalent circuit model is chosen to model lithium-ion battery firstly.MATLAB/cftool toolbox and the forgetting factor recursive least squares(FFRLS)are used to identify the parameters of the lithium-ion battery model.Secondly,the SOC estimation accuracy is improved by the extended Kalman filter(EKF)algorithm.Finally,the battery management system(BMS)experimental platform is built.The simulations and experiments are conducted under the federal urban driving schedule(FUDS)in the United States.The results show that the FFRLS method can improve the lithium-ion battery model accuracy compared with the parameter identification method fitted by the cftool toolbox,and the SOC estimation accuracy is high and the convergence speed is fast,which can verify the accuracy and effectiveness of the second-order RC equivalent circuit model of lithium-ion batteries combined with the EKF algorithm to estimate the SOC of lithium-ion batteries.