Equivalent modeling and parameter identification of power lithium-ion batteries

Dan Deng, Jialu Qiao, Jun Qi, Shunli Wang, Siyu Jin, Xianyong Xiao, Xueyi Hao, Yunlong Shang

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

1 Citation (Scopus)

Abstract

The equivalent modeling and parameter identification of power lithium-ion batteries are important basis for describing the working characteristics of power lithium-ion batteries. To improve the estimation accuracy of the state of charge of a lithium battery in a complex working environment, this chapter studies and analyzes different battery modeling methods and determines the optimal order of the Thevenin equivalent circuit model and constructs an improved Thevenin equivalent circuit model. By analyzing different parameter identification methods, the Forgetting Factor Recursive Extended Least Squares algorithm is proposed to identify the model parameters online. The experimental results show that the algorithm can better identify the parameters of the high-power lithium-ion battery model by taking into account the influence of historical data when the current data is saturated and the influence of noise.

Original languageEnglish
Title of host publicationState Estimation Strategies in Lithium-ion Battery Management Systems
Number of pages30
PublisherElsevier
Publication date2023
Pages95-124
Chapter6
ISBN (Print)9780443161612
ISBN (Electronic)9780443161605
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc. All rights reserved.

Keywords

  • Akaike information criterion
  • forgetting factor recursive extended least squares algorithm
  • full-parameter online identification
  • Power lithium-ion battery
  • Thevenin equivalent circuit model

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