基于改进无迹卡尔曼滤波法的大容量电池储能系统SOC预测  

State-of-Charge Estimation of Large Scale Battery Energy Storage System Based on Improved Unscented Kalman Filter

作　　者：赵泽昆 张喜林[2] 张斌 韩晓娟[1] 

机构地区：[1]华北电力大学控制与计算机工程学院,北京市102206 [2]国网吉林省电力有限公司长春供电公司,长春市130000 [3]国网冀北三河市供电有限公司,河北省三河市065200

出　　处：《电力建设》2016年第9期50-55,共6页Electric Power Construction

基　　金：国家自然科学基金项目(51577065);国家电网公司科技项目(KY-SG-2016-204-JLDKY)~~

摘　　要：大容量电池储能系统的荷电状态(state of charge,SOC)是电池管理系统(battery management system,BMS)的重要参数,必须准确预测,由于电池单体存在较强的差异性,传统的SOC预测技术很难达到准确预测的效果。针对上述问题,提出基于改进无迹卡尔曼滤波法(unscented Kalman filter,UKF)的大容量电池储能系统SOC预测方法,利用遗传算法(genetic algorithm,GA)优化无迹卡尔曼滤波的滤波参数,进一步提高SOC的预测精度。在设定工况下对串联型电池储能系统进行仿真实验,仿真结果表明该文提出的改进无迹卡尔曼滤波方法可以获得有效可靠的SOC预测结果,具有良好的工程应用前景。Accurate and reliable state of charge （SOC） estimation for large capacity battery energy storage system （LCBESS） is necessary for the battery management system （BMS）. Since the difference between batteries, it is difficult to obtain ideal prediction results by traditional methods. To solve the above problem, this paper proposes the prediction method of SOC for LCBESS based on improved unscented Kalman filter （UKF）, which adopts the genetic algorithm （GA） method to optimize the filter coefficients of UKF, in order to further improve the prediction accuracy of SOC. This paper simulates the series battery energy storage system under setting conditions. The simulation results show that the proposed improved unscented Kalman filter method can obtain the effective and reliable prediction results of SOC, which has a broad prospect of engineering application.

关 键 词：遗传算法 无迹卡尔曼滤波(UKF) 荷电状态预测 等效电路 

分 类 号：TM912[电气工程—电力电子与电力传动]