Hu Longzhou, Hu Rong, Ma Zengsheng, Jiang Wenjuan
School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
Materials (Basel). 2022 Dec 7;15(24):8744. doi: 10.3390/ma15248744.
The accurate and rapid estimation of the state of charge (SOC) is important and difficult in lithium battery management systems. In this paper, an adaptive infinite Kalman filter (AUKF) was used to estimate the state of charge for a 18650 LiNiMnCoO/graphite lithium-ion battery, and its performance was systematically evaluated under large initial errors, wide temperature ranges, and different drive cycles. In addition, three other Kalman filter algorithms on the predicted SOC of LIB were compared under different work conditions, and the accuracy and convergence time of different models were compared. The results showed that the convergence time of the AUKF algorithms was one order of magnitude smaller than that of the other three methods, and the mean absolute error was only less than 50% of the other methods. The present work can be used to help other researchers select an appropriate strategy for the SOC online estimation of lithium-ion cells under different applicable conditions.
在锂电池管理系统中，准确快速地估算荷电状态（SOC）既重要又困难。本文采用自适应扩展卡尔曼滤波器（AUKF）对18650型LiNiMnCoO/石墨锂离子电池的荷电状态进行估算，并在大初始误差、宽温度范围和不同驱动循环下系统地评估了其性能。此外，在不同工作条件下比较了其他三种关于锂离子电池预测SOC的卡尔曼滤波算法，并比较了不同模型的准确性和收敛时间。结果表明，AUKF算法的收敛时间比其他三种方法小一个数量级，平均绝对误差仅不到其他方法的50%。本研究可帮助其他研究人员在不同适用条件下为锂离子电池的SOC在线估算选择合适的策略。
