基于RTS-IEKPF算法的锂电池SOC估算  

Lithium battery SOC estimation based on RTS-IEKPF algorithm

作　　者：吴铁洲[1] 杜炘宇 吴麟章[1] WU Tiezhou;DU Xinyu;WU Linzhang(Hubei Key Laboratory for High.efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室

出　　处：《现代电子技术》2019年第18期84-89,共6页Modern Electronics Technique

基　　金：国家自然科学基金资助项目(51677058)~~

摘　　要：针对瞬间大电流充放电使电池非线性加剧,使用迭代扩展卡尔曼滤波算法(IEKF)估算电池荷电状态(SOC)时会有较大误差.为了减小误差,进一步提高SOC的估算精度,提出一种基于锂电池复合电化学模型的融合RTS最优平滑的迭代扩展卡尔曼粒子滤波算法(RTS-IEKPF).该方法利用RTS(Rauch-Tung-Streibel)最优平滑算法与IEKF算法结合生成粒子滤波的建议分布,得到RTS-IEKPF,并用该方法来估算锂电池的SOC.实验结果表明,RTS-IEKPF算法SOC的估算精度优于PF,IEKF和IEKPF算法SOC的估算精度.In view of the battery non-linearity aggravation caused by instantaneous high current charging and discharging, if the iterated extended Kalman filter(IEKF)algorithm is used to estimate the battery′ s state of charge(SOC),a great error may appear. A rauch-tung-streibel(RTS) iterated extended Kalman particle filter(RTS-IEKPF) algorithm integrated with the RTS optimal smoothness based on lithium battery composite electrochemical model is proposed to reduce the estimation error and improve the estimation accuracy of SOC further more. In this method,the suggested distribution of particle filtering is generated by combining the RTS optimal smoothing algorithm and IEKF algorithm,and RTS-IEKPF is obtained. The SOC of lithium bat-tery is estimated by means of this method. The experimental results show that the estimation accuracy of SOC of RTS-IEKPF algorithm is better than that of PF,IEKF and IEKPF algorithms.

关 键 词：锂电池 SOC估算 RTS-IEKPF 粒子滤波 最优平滑 实验验证 

分 类 号：TN245.34[电子电信—物理电子学] TM912[电气工程—电力电子与电力传动]