Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique

Zhicheng Xu, Jun Wang*, Qi Fan, Peter D. Lund, Jie Hong

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The accuracy of the state of charge (SoC) estimation is of great importance to the operational safety of a battery pack, especially for secondary applications with retired batteries. Here, a novel approach combining Sigma-point Kalman filter and machine learning technique based on an equivalent circuit model is proposed to improve the state of charge estimation accuracy of a reused battery pack (LiFePO4) by abating the negative effect of the hysteresis phenomenon. Compared to traditional estimation methods, this approach can reduce the root mean square error by up to 8.3%. The maximum estimation error for three experimental tests is only 0.016 being within acceptable range and demonstrating the effectiveness of the proposed approach.

Original languageEnglish
Article number101678
JournalJournal of Energy Storage
Volume32
DOIs
Publication statusPublished - Dec 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Battery
  • Equivalent circuit model
  • Hysteresis phenomenon
  • Machine learning
  • Sigma-point Kalman filter
  • State of charge

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