首页> 外文期刊>Energies >Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering
【24h】

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

机译:基于鲁棒扩展卡尔曼滤波的混合动力汽车锂离子电池充电状态估计

获取原文
获取外文期刊封面目录资料

摘要

A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.
机译:提出了一种鲁棒的扩展卡尔曼滤波器(EKF)作为估计混合动力汽车(HEV)中使用的锂离子电池的充电状态(SOC)的方法。使用电池的等效电路模型,包括其电动势(EMF)磁滞特性和极化特性。分析了鲁棒的EKF增益系数对SOC估计的影响,并确定了一个优化的增益系数以抑制电池端电压的波动。给出了实验和仿真结果。将使用标准EKF的SOC估计值与建议的鲁棒EKF算法进行比较,以证明后者用于SOC估计的准确性和精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号