基于迭代扩展卡尔曼滤波的锂离子电池SOC估算

SOC estimation of lithium ion battery based on iterative extended Kalman filter

基于电池的二阶RC等效电路模型,建立了状态空间方程式,并通过离线参数辨识确定了模型的参数值。为了能够提高扩展卡尔曼滤波算法的估算精度,采用了迭代扩展卡尔曼滤波算法,对状态更新过程进行多次迭代。基于NEDC工况试验数据对算法的电池荷电状态(SOC)估算精度进行验证。迭代扩展卡尔曼滤波对测量噪声具有更好的鲁棒性,尤其是在噪声协方差较大时,能够以更快的速度收敛至真实值。

A state space equation was established based on the second order RC equivalent circuit model of the battery,and the parameters of the model were determined by off-line parameter identification approach.In order to improve the estimation accuracy of the extended Kalman filter algorithm,the iterative extended Kalman filter algorithm is used to iterate the state update process for many times.The state of charge(SOC)estimation accuracy of the algorithm is verified based on the New European Driving Cycle(NEDC)test.The results show that the iterative extended Kalman filter has better robustness to the measurement noise,especially when the noise covariance is large,it can converge to the real value faster.