On-line remaining useful life prediction of lithium-ion batteries based on the optimized gray model GM(1,1)

Dong Zhou, Long Xue, Yijia Song, Jiayu Chen

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries’ health indicator (HI) for quantifying degradation and predicting the RUL. However, the capacity and internal resistance are too difficult to measure on-line due to the batteries’ internal state variables being inaccessible to sensors under operational conditions. In addition, measuring these variables requires accurate measurement devices, which can be expensive, and have limited applicability in practice. In this paper, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation models and RUL prediction. Moreover, the Box–Cox transformation is applied to improve the correlation between the extracted HI and the battery’s real capacity. Then, Pearson and Spearman correlation analyses are utilized to assess the similarity between the real capacity and the estimated capacity derived from the HI. An optimized gray model GM(1,1) is employed to predict the RUL based on the presented HI. The experimental results show that the proposed method is effective and accurate for battery degradation modeling and RUL prediction.

Original languageEnglish
Article number21
JournalBatteries
Volume3
Issue number3
DOIs
StatePublished - Sep 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • 1)
  • Health indicator
  • Lithium-ion battery
  • On-line
  • Optimized gray model GM(1
  • Remaining useful life prediction

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