基于ICDKF的锂电池SOC在线估计  

Online-SOC estimation of Li-ion battery based on ICDKF

作　　者：程泽[1] 张秋艳[1] 刘继光[1] 

机构地区：[1]天津大学,天津300072

出　　处：《电源技术》2015年第3期518-520,共3页Chinese Journal of Power Sources

摘　　要：动态的实时估计锂离子电池荷电状态(state of charge,SOC)是锂离子电池管理系统研究的关键技术。针对扩展卡尔曼滤波(EKF)估计SOC误差大的不足,基于二阶RC等效电路模型,提出了一种基于迭代中心差分卡尔曼滤波(ICDKF)算法的磷酸铁锂电池SOC估计方法。利用Matlab进行了仿真,并与扩展卡尔曼滤波和中心差分卡尔曼滤波(CDKF)算法进行了效果对比,从仿真结果可以看出,该SOC算法有效地降低了估计误差,与EKF相比,具有更好的滤波估计精度。The real-timely estimation of the SOC（state of charge） was the key technology in Li-ion battery management system. To overcome the error of the SOC estimation of Extended Kalman filter（EKF）, a new estimation method based on iterative Center Difference Kalman Filter（ICDKF） was applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments were made to compare the new filter with the EKF and Center Difference Kalman Filter（CDKF）. The simulation results demonstrate that the using new filter algorithm ICDKF has higher filtering accuracy under the same conditions.

关 键 词：锂电池 荷电状态 迭代 中心差分卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]