Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

220 Scopus Citations
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Author(s)

  • Dong Wang
  • Qiang Miao
  • Michael Pecht

Detail(s)

Original languageEnglish
Pages (from-to)253-264
Journal / PublicationJournal of Power Sources
Volume239
Online published2 Apr 2013
Publication statusPublished - 1 Oct 2013

Abstract

Lithium-ion batteries are widely used as power sources in commercial products, such as laptops, electric vehicles (EVs) and unmanned aerial vehicles (UAVs). In order to ensure a continuous power supply, the functionality and reliability of lithium-ion batteries have received considerable attention. In this paper, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries. This capacity prognostic method consists of a relevance vector machine and a conditional three-parameter capacity degradation model. The relevance vector machine is used to derive the relevance vectors that can be used to find the representative training vectors containing the cycles of the relevance vectors and the predictive values at the cycles of the relevance vectors. The conditional three-parameter capacity degradation model is developed to fit the predictive values at the cycles of the relevance vectors. Extrapolation of the conditional three-parameter capacity degradation model to a failure threshold is used to estimate the remaining useful life of lithium-ion batteries. Three instance studies were conducted to validate the developed method. The results show that the developed method is able to predict the future health condition of lithium-ion batteries. © 2013 Elsevier B.V. All rights reserved.

Research Area(s)

  • Battery capacity degradation model, Lithium-ion batteries, Relevance vector machine, Remaining useful life, State of health