An enhanced copula-based method for battery capacity prognosis considering insufficient training data sets

Zhimin Xi, Xiangxue Zhao

Research output: Contribution to conferencePaperpeer-review

Abstract

Data-driven based prognostics typically requires sufficient run-to-failure training data in order to gain knowledge of the degradation characteristic of engineering components or products without understanding the fundamental degradation mechanisms. With insufficient training data, however, the model learned from the training data may be inaccurate, which could result in large prediction errors for the remaining useful life (RUL) of many actual test units. This paper proposes an enhanced copula-based prognosis method to address the limitations for insufficient training data. Effectiveness of the proposed method is demonstrated using the capacity degradation data from four lithium-ion batteries.

Original languageEnglish (US)
Pages1306-1311
Number of pages6
StatePublished - 2018
Event2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018 - Orlando, United States
Duration: May 19 2018May 22 2018

Other

Other2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018
Country/TerritoryUnited States
CityOrlando
Period5/19/185/22/18

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Keywords

  • Copula-based prognostics
  • Data-driven prognostics
  • Insufficient training data
  • Lithium-ion battery
  • Remaining useful life

Fingerprint

Dive into the research topics of 'An enhanced copula-based method for battery capacity prognosis considering insufficient training data sets'. Together they form a unique fingerprint.

Cite this