摘要
实现对锂电池的荷电状态(state ofcharge,SOC)的准确估算对电动汽车电池管理系统具有重要意义。采用了二阶RC等效电路模型对电池进行精确建模,并分别利用离线参数辨识和带遗忘因子的递推最小二乘法的在线参数辨识方法对等效电路中的参数进行辨识,在确保模型精度满足要求后,利用扩展卡尔曼滤波(extended Kalman filter,EKF)算法来实现对电池SOC的准确估算。以美国联邦城市运行工况(federal urbandriving schedule,FUDS)和城市道路循环工况(urban dynamometerdrivingschedule,UDDS)进行仿真实验,并将实验中标准SOC值与离线辨识和在线辨识的SOC估计值进行对比分析。实验结果表明,在FUDS工况和UDDS工况下利用EKF算法估算SOC的平均误差都在2.5%以下,且在线参数辨识模型比离线参数辨识模型的平均误差分别降低了0.7%和0.9%。证明了EKF算法能实现对电池SOC的准确估算,且在线参数辨识方法下的电池模型具有更高的估算精度。
It is crucial for the battery management system of electric vehicles to accurately estimate the state of charge(SOC)of lithium batteries.The offline parameter identification and the online parameter identification approaches of recursive least squares with forgetting factor are employed to identify the parameters in the equivalent circuit,where the second-order RC equivalent circuit model is employed to accurately model the battery,and,after ensuring the accuracy of the model meets the requirements,the extended Kalman filter(EKF)algorithm is employed to accurately estimate the battery's SOC.The simulation experiment was conducted with the federal urban driving schedule(FUDS)and urban dynamometer driving schedule(UDDS),and the standard SOC value in the experiment is compared with the SOC estimation values of offline identification and online identification.The experimental findings demonstrate that the average error of SOC estimation using the EKF algorithm under FUDS and UDDS conditions is less than 2.5%,and the average error of the online parameter identification model decreased by 0.7%and 0.9%than offline parameter identification model,respectively.The battery model under the online parameter identification approach is shown to have higher estimation accuracy and it is proved that the EKF algorithm can realize an accurate estimation of battery SOC.
作者
刘志聪
张彦会
LIU Zhicong;ZHANG Yanhui(School of Mechanical and Automotive Engineering,Guangxi University of Science and Technology,Liuzhou 545616,Guangxi,China)
出处
《储能科学与技术》
CAS
CSCD
北大核心
2022年第11期3613-3622,共10页
Energy Storage Science and Technology
基金
广西科技大学研究生教育创新计划项目(GKYC202212)
柳州市科技计划项目(2019DH10603)。
