基于粒子滤波方法的LiFePO_4电池SOC估计

SOC Estimation of LiFePO_4 Power Battery Based on Particle Filter

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。

Measuring SOC（ state of charge） of LiFePO_4 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.