Data-Driven Lithium-Ion Battery Degradation Evaluation Under Overcharge Cycling Conditions

Yiwen Zhao, Zhenpo Wang, Zhenyu Sun*, Peng Liu*, Dingsong Cui, Junjun Deng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Accurately assessing degradation and detecting abnormalities of overcharged lithium-ion batteries is critical to ensure the health and safe adoption of electric vehicles. This article proposed a data-driven lithium-ion battery degradation evaluation framework. First, a multilevel overcharge cycling experiment was conducted. Second, the battery degradation behaviors and features were analyzed and extracted using incremental capacity analysis and Pearson correlation coefficient. Above all, a data-driven lithium-ion battery degradation evaluation method based on machine learning and model integration method was developed. The proposed integrated model was compared with other state-of-the-art methods and reached a mean squared error of 1.26 × 10-4. Finally, based on prediction results, rate of degradation was calculated and classified to different degrees, and overcharged cells can be effectively identified. Moreover, to verify the feasibility of the proposed overall framework, this article carried out an experiment by connecting overcharge-induced degraded cell and fresh cells in series to simulate the real-world battery assembly and function of battery management systems. Based on the proposed scheme, the overcharged batteries in the battery series can be detected efficiently likewise.

Original languageEnglish
Pages (from-to)10138-10150
Number of pages13
JournalIEEE Transactions on Power Electronics
Volume38
Issue number8
DOIs
Publication statusPublished - 1 Aug 2023

Keywords

  • Degradation evaluation
  • electric vehicle (EV)
  • incremental capacity analysis (ICA)
  • model integration
  • overcharge cycling

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