基于改进的 AEKF 铅酸电池 SOC 在线估计

为了提高铅酸电池在随机工况下荷电状态(SOC)估计精度，减小误差变化对估计精度的影响。针对自适应扩展卡尔曼滤波中误差新息序列长度固定选取的局限性，本文提出一种改进的自适应扩展卡尔曼滤波算法估计SOC。通过似然估计来监测协方差匹配算法中的误差新息序列分布变化时刻，根据误差新息的分布变化来自适应调整新息序列长度，进而降低估计SOC时的误差。首先通过带遗忘因子的递推最小二乘法(FFRLS)辨识获得等效模型参数，其模型平均误差电压为13.63 mV,然后在随机工况实验下发现，改进后的算法在估计SOC时的RMSE和MAE性能上精度分别提高了14.44%和17.26%,结果表明改进后的算法拥有更好的稳定性和精度。

Online SOC estimation based on improved AEKF lead-acid battery

In order to improve the state of charge (SOC) estimation accuracy of lead-acid battery under random conditions, reduce the influence of error variation on estimation accuracy. Aiming at the limitation of fixed length selection of error innovation sequence in adaptive extended Kalman filter, an improved adaptive extended Kalman filter algorithm is proposed to estimate SOC. The likelihood estimation is used to monitor the distribution change time of the error innovation sequence in the covariance matching algorithm, and the length of the innovation sequence is adaptively adjusted according to the distribution change of the error innovation, thereby reducing the error when estimating SOC. Firstly, the equivalent model parameters are identified by the recursive least squares method with forgetting factor ( FFRLS ), the average error voltage of the model is 13. 63 mV. Then, in the random condition experiment, it is found that the improved algorithm improves the accuracy of RMSE and MAE performance by 14. 44% and 17. 26% respectively when estimating SOC. The results show that the improved algorithm has better stability and accuracy.