最小二乘算法优化及其在锂离子电池参数辨识中的应用  

Optimization of Least Squares Method and Its Application in Parameter Identification of Lithium-Ion Battery Model

作　　者：范兴明[1] 封浩 张鑫[1] Fan Xingming;Feng Hao;Zhang Xin(Department of Electrical Engineering&Automation Guilin University of Electronic and Technology,Guilin 541004 China)

机构地区：[1]桂林电子科技大学机电工程学院,桂林541004

出　　处：《电工技术学报》2024年第5期1577-1588,共12页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金项目(61741126);广西自然科学基金项目(2022GXNSFAA035533)资助。

摘　　要：传统最小二乘法(LS)用于锂离子电池模型在线参数辨识精度低,通过带遗忘因子递推最小二乘算法能够有效地提高辨识精度,但固定的遗忘因子影响模型动态特性。遗忘因子的自适应处理能提高算法对动态系统的参数辨识能力,而目前的自适应方法容易忽略模型参数的稳定性,同时方法待定系数范围较大且难以确认。为了得到高精度且稳定性良好的模型参数,该文设计了一种精度和稳定性兼优且更简单的自适应遗忘因子递推最小二乘(AFFRLS)改进方法,并与其他AFFRLS、可变遗忘因子递推最小二乘(VFFRLS)进行仿真对比分析。结果表明,改进的AFFRLS能够在模型精度和参数稳定性取得更好的平衡,且对不同的在线工况具有良好的适用性。Offline and online methods are used to identify model parameters,but the model dynamic characteristic obtained by the online method is better.The recursive least squares method is simple and often used for online parameter identification of lithium-ion battery models.However,the least square method(RLS)has a low identification accuracy.Thus,the forgetting factor recursive least square method was proposed to improve the accuracy of parameter identification.To improve the dynamic identification ability,the variable forgetting factor least square(VFFRLS)method and adaptive forgetting factor recursive least square(AFFRLS)method appear.Yet the current adaptive methods tend to ignore the stability of model parameters,and the undetermined coefficient range of this method is large and difficult to confirm.The model parameter changes drastically,and it is easy to cause the divergence of the algorithm.This paper proposes a simpler AFFRLS method without an undetermined coefficient to address these issues.And it takes into account the accuracy and stability of the model.Firstly,based on dynamic stress testing(DST)and Federal City Operating Conditions(FUDS)data,the FFRLS method with fixed forgetting factor value is simulated and analyzed,and the influence trend of different forgetting factors on the accuracy and stability of model parameters is obtained.Secondly,the proposed AFFRLS method is compared with other AFFRLS and VFFRLS,and the stability and accuracy of the identification parameters are analyzed.Finally,the error tracking ability and convergence speed of the three adaptive methods are analyzed,and the adaptive performance of the proposed AFFRLS to DST and FUDS conditions are analyzed.The FFRLS simulation results with fixed forgetting factor(λ)value show that whenλvalue decreases,the algorithm has better tracking ability for time-varying parameters,the convergence speed is accelerated,and the identification accuracy is effectively improved.However,when theλvalue decreases,the parameter changes drastically,and the s

关 键 词：锂离子电池模型 参数辨识 最小二乘法 自适应遗忘因子 

分 类 号：TM912[电气工程—电力电子与电力传动]