基于改进DAEKF的锂电池SOC和SOH联合估计

Joint estimation of lithium battery SOC and SOHbased on improved DAEKF

为提高锂离子荷电状态(state of charge,SOC)及健康状态(state of health,SOH)的精度,提出改进双自适应扩展卡尔曼滤波(dual adaptive extended Kalman filter,DAEKF)算法。基于二阶RC模型,建立空间状态方程;选取电池容量作为SOH的表征量,在双扩展卡尔曼滤波算法基础上引入改进的Sage-Husa自适应算法,实现系统协方差矩阵的实时更新;为降低系统计算量,进一步加入多时间尺度理论进行优化。实验结果表明,提出的算法能较准确地估计锂电池的SOC与SOH,SOC的平均误差为0.58%,SOH最大估计误差为0.8%,该算法正确有效。

In order to improve the accuracy of lithium-ion state of charge(SOC)and state of health(SOH),an improved dual adaptive extended Kalman filter(DAEKF)algorithm is proposed.Based on the second-order RC model,the spatial equation of state is established.The battery capacity is selected as the characterization of SOH,and the improved Sage-Husa adaptive algorithm is introduced based on the double extended Kalman filter algorithm to realize the real-time update of the system covariance matrix.To reduce the computational complexity of the system,the multi-time scale theory is further added for optimization.The experimental results show that the proposed algorithm can accurately estimate the SOC and SOH of lithium batteries,with an average error of 0.58%for SOC and a maximum estimation error of 0.8%for SOH.The algorithm is correct and effective.