Data-driven prognostics for batteries subject to hard failure

Qiang Zhou, Jianing Man, Junbo Son

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter considers data-driven remaining useful life (RUL) of a battery prediction, which is typically made on the basis of projecting the trajectory of the system's health indicator, often called the degradation signal. Two most commonly used health indicators of batteries are capacity and internal resistance, while other health-dependent variables such as battery self-discharge rate may also be considered. By analyzing the evolution paths of the health indicating variables/degradation signals, it is possible to infer not only the current but also the future health status of the unit being studied. The chapter introduces a method specifically developed for battery RUL prediction under hard failure. In this method, a joint modeling scheme is used to take into consideration both the degradation data and the time-to-failure data. To better assess the performance of the prognostic algorithm, alternative interval prediction, the maximum power interval (MPI), is introduced as opposed to confidence intervals and mean/median-based intervals.

Original languageEnglish (US)
Title of host publicationAdvances in Battery Manufacturing, Services, and Management Systems
PublisherWiley-IEEE Press
Pages233-254
Number of pages22
ISBN (Electronic)9781119060741
ISBN (Print)9781119056492
DOIs
StatePublished - Oct 3 2016
Externally publishedYes

Keywords

  • Battery's failure
  • Data-driven prognostics
  • Degradation signal-based RUL prediction
  • Health indicating variables/degradation signals
  • Maximum power interval
  • Self-discharge rate
  • Time-to-failure data

ASJC Scopus subject areas

  • Engineering(all)

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