4.7 Article

Automatic initialization of a Complex Nonlinear Least Squares algorithm for impedance battery frequential identification

期刊

JOURNAL OF ENERGY STORAGE
卷 73, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.est.2023.109149

关键词

Fractional models; Electrochemical impedance spectroscopy; CNLS algorithm; Automatic initialization of optimization; algorithm

向作者/读者索取更多资源

This paper discusses the identification of battery impedance parameters using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling in the frequency domain. It proposes an automatic initialization method for a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements to accurately estimate impedance parameters for different diffusion scenarios.
This paper deals with the identification of battery impedance parameters in the frequency domain using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling. Unlike other classical models used for frequency identification algorithms, fractional modeling allows to perform simulations also in the time domain offering interesting perspectives for identification in the time domain in future works. The objective of the paper is to propose an automatic initialization of a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements in order to accurately estimate impedance parameters whatever the kind of diffusion involved: Finite Length Warburg (FLW) or Finite Space Warburg (FSW) diffusion. Fractional models are used to approximate non-integer orders and are based on a simplified Randles equivalent circuit. When no sufficient low frequency measurements are available, diffusion can be modeled thanks to a single fractional integrator whose order is close to 0.5 (Warburg zone). Otherwise, dedicated fractional models are proposed to take into account FLW or FSW diffusion. The method is validated using not only simulation data but also EIS measurements performed on a commercial 3.5 Ah Li-ion cell and open source experimental data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据