基于改进DAEKF的锂电池SOC和SOH联合估计

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。

Joint estimation of lithium battery SOC and SOH based on improved DAEKF

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.