基于粒子滤波方法的LiFePO4电池SOC估计-《集美大学学报（自然科学版）》

文章信息/Info

作者:: 肖国红1; 弓清忠1 ; 王云超1; 宋君亮2 ; 陈上生2; （1.集美大学机械与能源工程学院,福建厦门 361021;2.厦门宝龙工业有限公司,福建厦门 361021）

Author(s):: XIAO Guohong1; GONG Qingzhong1; WANG Yunchao1; SONG Junliang2; CHEN Shangsheng2; (1.School of Mechanical and Energy Engineering,Jimei University,Xiamen 361021，China;2.Xiamen Powerlong Industrial Co.Ltd.,Xiamen 361021,China)

摘要:: 为了准确获取磷酸铁锂电池的荷电状态（state of charge，SOC），针对直接测量法和扩展卡尔曼滤波方法(extended kalman filter，EKF)估计SOC存在的不足，在分析电池的充放电过程和电池的Thevenin等效电路模型基础上，基于粒子滤波算法（particle filter，PF）对电池的SOC进行了估计。实验结果表明，PF方法比EKF方法的准确度提高了5%，采用PF算法估计SOC更加准确有效，在实际应用中更有价值。

Abstract:: Measuring SOC(state of charge)of LiFePO4 battery is the key problem to the battery management.Due to the disadvantages of the direct measurement method and EKF(extended kalman filter)method applied in the SOC estimation,the Particle Filter algorithm was used for estimating SOC on the basis of analysing the charge/discharge process and thevenin equivalent circuit model of battery.Experimental results indicate that the accuracy of PF method is 5% higher than that of EKF method.Moreover,PF algorithm is more accurate and effective than EKF method for SOC estimation,and more valuable in practical application.