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Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

journal contribution
posted on 2024-02-08, 15:00 authored by Chuanxin Fan, Kailong Liu, Yaxing Ren, Qiao Peng

 Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of electric vehicles and energy storage systems in the grid. The design of battery state estimation and control algorithms in battery management systems is usually based on battery models, which interpret crucial battery dynamics through the utilization of mathematical functions. Therefore, the investigation of battery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery research. Characterization methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this field. This review has undertaken an analysis and discussion of characterization methods, with a particular focus on the motivation of battery system identification. Specifically, this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical models. The aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries, with the intention of promoting the advancement of efficient battery technology for real-world applications.

History

School affiliated with

  • School of Engineering (Research Outputs)
  • College of Health and Science (Research Outputs)

Publication Title

Journal of Energy Chemistry

Volume

92

Pages/Article Number

738-758

Publisher

Elsevier

eISSN

2095-4956

Date Submitted

2023-12-10

Date Accepted

2024-01-05

Date of First Publication

2024-02-01

Date of Final Publication

2024-05-01

Open Access Status

  • Not Open Access

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