改进的PNGV模型估算全钒液流电池荷电状态  

SOC Estimation of Vanadium Redox Flow Battery Based on Improved PNGV Model

作　　者：孙成才 罗承双 黄艳国 

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《河南科技大学学报（自然科学版）》2018年第4期39-44,共6页Journal of Henan University of Science And Technology:Natural Science

基　　金：国家自然科学基金项目(61463020)

摘　　要：针对全钒液流电池(VRB)充放电时,循环泵产生的支路电流对荷电状态(SOC)估算有影响的问题,提出了一种基于无迹卡尔曼滤波的全钒液流电池SOC估算方法。通过改进的新一代车辆伙伴关系(PNGV)等效电路模型,在考虑了电池堆极化、支路电流分流和温度对电池内阻影响的情况下,建立了VRB仿真模型。采用无迹卡尔曼滤波(UKF)算法和扩展卡尔曼滤波(EKF)算法对电池SOC分别进行估算,并与试验测量值进行对比分析。仿真结果表明:UKF算法比EKF算法更接近试验测量值,其估算误差不超过±0.02。In view of the problem that the branch current generated by the circulation pump had an impact on the estimation of state of charge( SOC) during charging and discharging of vanadium redox flow battery( VRB),the estimation method of SOC for VRB based on unscented Kalman filtering( UKF) was proposed. By establishing the improved equivalent circuit model of the partnership for a new generation of vehicles( PNGV),the VRB simulation model was established considering the influence of cell stack polarization,branch current shunting and temperature on battery internal resistance. The UKF algorithm and the extend Kalman filtering( EKF) algorithm were used to estimate the battery SOC separately and compared with the experimental measurements. The simulation results show that the UKF algorithm is closer to the experimental measurement than the EKF algorithm,and the estimation error does not exceed ± 0. 02.

关 键 词：全钒液流电池 荷电状态 无迹卡尔曼滤波 等效电路模型 

分 类 号：TM912[电气工程—电力电子与电力传动]