Parameters identification of lithium battery based on forgetting factor recursive least square algorithm with improved initial value
王文 1史华泽 1岳雨霏 1黎隆基 1吴传平 2童宇轩1
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作者信息
1. 长沙理工大学电气与信息工程学院,湖南长沙 410114
2. 国网湖南省电力有限公司防灾减灾中心,湖南长沙 410007
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摘要
锂电池荷电状态(state of charge,SOC)的准确估计依赖于精确的锂电池模型参数.在采用带遗忘因子的递推最小二乘法(forgetting factor recursive least square,FFRLS)对锂电池等效电路模型进行参数辨识时,迭代初始值选取不当会造成辨识精度低、收敛速度慢的问题.为此,将电路分析法与FFRLS相结合,提出基于改进初值带遗忘因子的递推最小二乘法(improved initial value-FFRLS,IIV-FFRLS).首先,通过离线辨识得到各荷电状态点对应的等效电路模型参数并进行多项式拟合;然后,利用初始开路电压(open circuit voltage,OCV)和OCV-SOC曲线获得初始SOC,代入参数拟合函数得到初始参数;最后,将初始参数带入递推公式得到IIV-FFRLS迭代初始值.对4种锂电池工况进行参数辨识,结果表明:与传统方法相比,IIV-FFRLS的平均相对误差、收敛时间分别减小58％、23％以上;IIV-FFRLS具有更高的辨识精度与更快的收敛速度.
Abstract
Accurate estimation of the state of charge (SOC) of lithium-ion batteries relies on precise model parameters. When using the forgetting factor recursive least square (FFRLS) algorithm for parameter identification of the equivalent circuit model of lithium-ion batteries,improper selection of initial iterative values can lead to low identification accuracy and slow convergence speed. To address this issue,circuit analysis is combined with the FFRLS algorithm,and then an improved initial value-FFRLS (IIV-FFRLS) algorithm is proposed. Firstly,offline identification is performed to obtain the equivalent circuit model parameters corresponding to various SOC points,which are then fitted using a polynomial function. Secondly,the initial SOC is obtained using the initial open circuit voltage (OCV) and the OCV-SOC curve,which is then substituted into the parameter fitting function to obtain the initial parameters. Finally,these initial parameters are used in the recursive formula to obtain the initial iterative values for the IIV-FFRLS algorithm. Parameter identification is performed for four operating conditions of lithium-ion batteries,and the results show that compared with traditional methods,the IIV-FFRLS algorithm reduces the average relative error by more than 58％ and the convergence time by more than 23％. The IIV-FFRLS algorithm exhibits higher identification accuracy and faster convergence speed.
关键词
锂离子电池/参数辨识/带遗忘因子的递推最小二乘算法/迭代初始值
Key words
lithium-ion battery/parameter identification/forgetting factor recursive least square algorithm/initial value of iteration
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