基于FA-EKF的锂电池参数辨识与SOC估计  

Parameter Identification and SOC Estimation of Lithium Battery Based on FA-EKF

作　　者：李晓晨 杨帆 纳春宁[1] LI Xiao-chen;YANG Fan;NA Chun-ning(School of Electronic and Electrical Engineering,Ningxia University,Yinchuan 750021,China;State Grid Ningxia Electric Power Co.,Ltd.Zhongwei Power Supply Company,Zhongwei 755099,China;China Tower Co.,Ltd.Wuzhong Branch,Wuzhong 751100,China)

机构地区：[1]宁夏大学电子与电气工程学院,宁夏银川750021 [2]国网宁夏电力有限公司中卫供电公司,宁夏中卫755099 [3]中国铁塔股份有限公司吴忠市分公司,宁夏吴忠751100

出　　处：《电工电气》2024年第12期9-14,共6页Electrotechnics Electric

摘　　要：随着新能源汽车的广泛使用,锂电池的荷电状态(SOC)成为电池管理系统的研究热点。针对扩展卡尔曼滤波(EKF)的锂电池的荷电状态估计中传统参数辨识法易受初始条件影响,而陷入局部最优的问题,提出了适用于非线性系统的萤火虫算法(FA)参数辨识,并与遗传算法(GA)的参数辨识结果比较,结合扩展卡尔曼滤波法,实现两种参数辨识结果的荷电状态估计。采用MATLAB/Simulink软件搭建EKF模型,仿真结果表明,相对于GA-EKF,所提出的FA-EKF参数辨识与SOC估计精度更高。With the widespread use of new energy vehicles,the state of charge(SOC)of lithium batteries has become a research hotspot in battery management systems.In response to the issue that the traditional parameter identification method in the extended Kalman filter(EKF)estimation of lithium battery SOC is susceptible to the influence of initial conditions and can fall into local optima,a firefly algorithm(FA)parameter identification method suitable for nonlinear systems is proposed.The results are compared with those of the genetic algorithm(GA)parameter identification,and combined with the extended Kalman filter method,to achieve SOC estimation for both parameter identification results.The EKF model is built using MATLAB/Simulink software,and simulation results show that the proposed FA-EKF parameter identi-fication and SOC estimation have higher accuracy compared to GA-EKF.

关 键 词：锂电池 新能源汽车 参数辨识 荷电状态估计 扩展卡尔曼滤波 萤火虫算法 遗传算法 

分 类 号：TM761[电气工程—电力系统及自动化] TM912