基于改进自适应无迹卡尔曼滤波算法的锂电池荷电状态估计  

SOC Estimation of Lithium Battery Based on Improved AUKF Algorithm

作　　者：张海涛[1] 刘新天[1] Zhang Haitao;Liu Xintian(Hefei University of Technology,Hefei 230009)

机构地区：[1]合肥工业大学,合肥230009

出　　处：《汽车工程师》2023年第11期12-18,共7页Automotive Engineer

摘　　要：针对变窗口自适应无迹卡尔曼滤波(AUKF)算法在窗口改变时窗口长度发生突变,窗口序列数据急剧减少,导致状态估计误差增大,稳定性和精确度下降的问题,基于二阶RC等效电路模型,并采用遗忘递推最小二乘(FFRLS)算法进行参数辨识,结合改进后的变窗口AUKF算法估计锂电池荷电状态(SOC)。在城市道路循环(UDDS)工况下进行试验验证,并与无迹卡尔曼滤波(UKF)、自适应无迹卡尔曼滤波(AUKF)及变窗口AUKF算法进行对比,结果表明,改进后的变窗口AUKF算法将平均误差控制在0.38%以内,具有更高的精确性和收敛性。In view of the problem that the variable window Adaptive Unscented Kalman Filter(AUKF) algorithm has a large mutation when the window changes,and the window sequence data decreases sharply,resulting in the increase of error,stability and accuracy decline in state estimation,this paper uses the Forgetting Factor Recursive Least Square(FFRLS)algorithm to identify parameters based on the second-order RC equivalent circuit model,combined with the improved variable window AUKF algorithm to estimate the State of Charge(SOC) of lithium battery,it is verified by the Urban Dynamometer Driving Schedule(UDDS) cycle test,and compared with Unscented Kalman Filter(UKF),AUKF and variable window AUKF algorithms.The test results show that the improved AUKF algorithm can control the average error within 0.38%,with higher accuracy and convergence.

关 键 词：无迹卡尔曼滤波 荷电状态 变窗口噪声估计器 自适应滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]