基于联合EKF-UKF算法的锂电池SOC预估研究  

SOC estimation of lithium battery based on joint EKF-UKF algorithm

作　　者：海涛[1] 范攀龙 王钧 HAI Tao;FAN Panlong;WANG Jun(College of Electrical Engineering,Guangxi University,Nanning Guangxi 530004,China;Hualan Design(Group)Limited,Nanning Guangxi 530004,China)

机构地区：[1]广西大学电气工程学院,广西南宁530004 [2]华蓝设计(集团)有限公司,广西南宁530004

出　　处：《电源技术》2023年第11期1424-1428,共5页Chinese Journal of Power Sources

基　　金：广西重点研发计划(桂科AB22035037);国家自然科学基金项目(51867003)。

摘　　要：电池储能在当前能源结构中有着至关重要的作用,其荷电状态(state of charge,SOC)的准确预估是电池管理系统工作的前提。通过在MATLAB/Simulink中建立电池的Thevenin模型,并经过实验获得SOC与开路电压之间的关系,对扩展卡尔曼滤波(extended Kalman filter,EKF)算法、无迹卡尔曼滤波(unscented Kalman filter,UKF)算法进行了分析与模型的搭建,在实际工况输入下进行了SOC预估并与真实值进行了对比分析。最后,依据对EKF、UKF算法仿真结果的分析,提出了联合EKF-UKF算法,即初始阶段采用收敛速度快的EKF算法,之后采用误差低的UKF算法求取SOC值,仿真结果表明该算法能够有效提升SOC预估过程中稳定性。Battery energy storage plays a vital role in the current energy structure,and the accurate estimation of the state of charge(SOC)is the premise of the battery management system.By establishing the Thevenin model of the battery in MATLAB/Simulink,and obtaining the relationship between SOC and open circuit voltage through experiments,the extended Kalman filter(EKF)algorithm and the unscented Kalman filter(UKF)algorithm were analyzed and modeled.SOC estimates were made under the actual operating conditions input and compared with the true values.Finally,based on the analysis of the simulation results of EKF and UKF algorithms,the joint EKF-UKF algorithm was proposed,the EKF algorithm with fast convergence speed was used in the initial stage and then the low error UKF algorithm was used to obtain the SOC value.The simulation results show that the algorithm can effectively improve the stability of the SOC estimation process.

关 键 词：电池储能 荷电状态(SOC) 联合扩展卡尔曼滤波-无迹卡尔曼滤波(EKF-UKF) 

分 类 号：TM912[电气工程—电力电子与电力传动]