关键词: 电池荷电状态, 二阶扩展卡尔曼滤波, 可变遗忘因子最小二乘法, 改进粒子群算法, 参数识别

Abstract: To improve the accuracy of battery SOC estimation， a higher order EKF algorithm was used to estimate SOC. Firstly， the first-order Thevenin equivalent circuit model（ECM） of lithium-ion battery was established， and the function relationship between open circuit voltage（OCV） and SOC was expressed by spline function. In order to more accurately identify the ECM parameters， a new kind of with VFFRLS algorithm was proposed for on-line identification of model parameters. Since the accuracy of the VFFRLS solution depended on the setting of the initial values of the algorithm， the improved particle swarm optimization algorithm was used to obtain the initial parameters of ECM， which helped to obtain more accurate initial values of VFFRLS. Finally， the second-order EKF was employed to estimate the SOC of the batterys to improve the estimation accuracy. Two different datasets were used to demonstrate the universality of second-order EKF estimation SOC. The experimental results indicate that the mean absolute error（MAE） of second-order EKF is within 1% when estimating SOC under different working conditions， which proves the effectiveness of the proposed method. 

Key words:  , state of charge（SOC）, second-order extended Kalman filter（EKF）, variable forgetting factor recursive least square（VFFRLS）, improved particle swarm optimization, parameter identification