基于无迹变换强跟踪滤波的锂离子电池SOC估计

State of Charge Estimation of Lithium-Ion Battery Based on Unscented Transformation Strong Tracking Filter

针对车用锂离子动力电池,以Thevenin等效电路模型为基础,基于复合脉冲功率循环实验进行模型参数辨识,运用无迹变换强跟踪滤波(unscented transformation strong tracking filter,UTSTF)算法进行电池SOC(state of charge)估算,并与常用的扩展卡尔曼滤波(extended kalman filter,EKF)算法和无迹卡尔曼滤波(unscented kalman filter,UKF)算法进行对比研究,验证了该算法的有效性。实验结果表明,与UKF和EKF相比,该UTSTF算法具有更好的精度和鲁棒性,最大误差小于1.5%。

Lithium-ion power batteries in electric vehicles were studied in this paper.The battery model was established based on a Thevenin equivalent circuit.The model parameters were identified based on the hybrid pulse power characteristic(HPPC)test result.Then,an unscented transformation strong tracking filter(UTSTF)algorithm was adopted to estimate the SOC of the lithium-ion batteries.The experiments were carried out to compare the proposed method with the commonly used extended Kalman filter(EKF)or the unscented Kalman filter(UKF).The experimental results show that the UTSTF has better accuracyand robustness on the SOC estimation than that of the EKF and UKF.