考虑老化的修正EKF算法估计锂电池SOC

Modified EKF Algorithm Considering Aging to Estimate the SOC of Lithium-ion Battery

锂电池的荷电状态(State of Charge,SOC)作为电池管理系统(BMS)的基本参数之一,对其进行准确的估计是BMS可靠性和准确性的基础。为了提升SOC的估算精度,提出了一种考虑老化的锂电池SOC估算方法。选择戴维南二阶模型作为锂电池的等效模型,依据实际数据进行参数辨识并验证。然后,考虑到电池老化对模型参数和实际容量的影响,加入总容量校准和遗忘因子改进扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法,使用改进后的EKF算法精确估计电池的SOC。实验结果表明,在EKF算法基础上加入容量校准和模型老化的遗传因子后SOC的估算精度大大提升。

The State of Charge(SOC)of lithium battery is one of the basic parameters of battery management system(BMS).The accurate estimation of SOC is the basis of BMS reliability and accuracy.In order to improve the estimation accuracy of SOC,an estimation method considering aging of lithium battery SOC was proposed.The Thevenin second-order model was selected as the equivalent model of lithium battery,and the parameters were identified and verified according to the actual data.Then,considering the influence of battery aging on model parameters and actual capacity,the improved Extended Kalman Filter(EKF)algorithm was added with total capacity calibration and forgetting factor,and the improved EKF algorithm was used to accurately estimate the SOC of the battery.Experimental results show that the accuracy of SOC estimation is greatly improved by adding capacity calibration and model aging genetic factors on the basis of EKF.