不同温度下基于FFRLS-AEKF的锂电池SOC估计

SOC ESTIMATION OF LITHIUM BATTERIES AT DIFFERENT TEMPERATURES BASED ON FFRLS-AEKF

荷电状态(SOC)是电池管理系统的重要指标。针对不同环境温度对于SOC估计的影响,分别建立基于温度影响的二阶RC等效电路模型与电池可用容量模型。在此基础上,采用带有遗忘因子的递归最小二乘法(FFRLS)对模型参数进行在线识别,同时结合改进的自适应扩展卡尔曼滤波算法(AEKF)实现SOC在线联合估计,以其闭环反馈系统通过迭代来保障估计的准确性。实验结果表明,该方法在不同的环境温度下都具有较高的精度,且最大误差小于1.2%,平均绝对误差小于0.6%,均方根误差小于0.5%。

State of Charge(SOC)is an important index of battery management system.According to the influence of different temperatures on the SOC estimation,a second-order RC equivalent circuit model and battery capacity model based on temperature effects were established respectively.On the basis,the proposed method implemented the online parameter identification by the forgetting-factor recursive least square method(FFRLS),carried out the SOC estimation by the adaptive extended Kalman filter(AEKF)and ensured the estimation accuracy by the iteration of closed loop feedback system.The experimental results show that this method has higher accuracy at different ambient temperatures,the maximum error is less than 1.2%,the average absolute error is less than 0.6%,and the root mean square error is less than 0.5%.