基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计

被引:79
作者
魏克新
陈峭岩
机构
[1] 天津大学电气与自动化工程学院
关键词
荷电状态; 健康状态; 自适应无迹卡尔曼滤波器; 电动汽车; 锂离子动力电池;
D O I
10.13334/j.0258-8013.pcsee.2014.03.016
中图分类号
TM912 [蓄电池];
学科分类号
摘要
应用传统的无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计电动汽车锂离子动力电池核电状态(state of charge,SOC)时,常会出现由于电池模型参数不准确而造成估计误差增大的问题,该文采用了自适应无迹卡尔曼滤波(adaptive unscented Kalman filter,AUKF)算法解决该问题。AUKF算法是一种循环迭代算法,可以实时估计电池模型中的欧姆内阻,提高电池模型准确性,从而提高电池SOC估计精度。另外,电池的欧姆内阻可以表征电池的健康状态(state of health,SOH),因此还可以根据电池的欧姆内阻估计出电池的SOH。在设定工况下对电池做充放电实验,实验分析表明,与UKF相比,AUKF提高了电池SOC估计的精度,并能准确的估计出电池的欧姆内阻。
引用
收藏
页码:445 / 452
页数:8
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