基于复合模型动力蓄电池SOC估算  

SOC estimation of power storage battery based on compound model

作　　者：高顺荣 谢慕君[1] 

机构地区：[1]长春工业大学电气与电子工程学院,吉林长春130012

出　　处：《长春工业大学学报》2017年第6期560-565,共6页Journal of Changchun University of Technology

基　　金：吉林省科技计划基金资助项目(20150203003sf)

摘　　要：采用安时计量法、开路电压法和卡尔曼滤波法相结合估计磷酸铁锂电池初始荷电状态(SOC)。为了修正电池容量特性对SOC的影响,在安时计量法中引入等效电流系数和温度系数,对修正后的安时计量法离散化处理,建立了电池的非线性复合模型的状态方程;结合Shepherd模型、Unnewehr universal模型和Nernst模型获得复合模型的观测方程。建立了基于电池复合模型的扩展卡尔曼滤波法(EKF)来估计SOC。通过Matlab进行了实验研究,经过与实验室测量出的SOC数据对比,EKF收敛很快,设计的估算方法可行实用。Ah counting method combined with open circuit voltage method and Kalman filter is used to estimate the state of charge(SOC) of Lithium Iron Phosphate(LiFePO_4) battery.To correct the influence of the battery capacity,equivalent current coefficient and temperature coefficient are introduced into the Ah counting method to modify the discretized Ah counting for establishing the state equation of the nonlinear composite model of the battery.With Shepherd,Unnewehr universal and Nernst models,Extended Kalman filter(EKF)based on battery compound model is applied to predict the SOC.Experimental results compared with that of the simulation indicate that the EKF is feasible with fast convergence.

关 键 词：磷酸铁锂电池 复合模型 安时计量法 开路电压法 扩展卡尔曼滤波法 

分 类 号：TM912.1[电气工程—电力电子与电力传动] TP202.7[自动化与计算机技术—检测技术与自动化装置]