A comparison study of machine learning enabled filtering methods for battery management

Sara Kohtz, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prognostics and health management has become a prominent field for the analyses of dynamic system degradation. Specifically, methods for forecasting remaining useful life have been studied extensively, including some hybrid approaches that have indicated successful results. Mainly, a combination of machine learning and filtering techniques have shown to be the most effective. Currently, there exists a need to determine an optimal general method for remaining useful life estimation in complex systems. This paper focuses on a comparison between successful hybrid approaches. The methods are applied to modeling capacity degradation in lithium-ion batteries, with the NASA dataset utilized for this study.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020
EditorsIndranil Roychoudhury, Jose R. Celaya, Abhinav Saxena
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263059
DOIs
StatePublished - Jun 2020
Event2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 - Detroit, United States
Duration: Jun 8 2020Jun 10 2020

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Volume2020-June
ISSN (Print)2325-0178

Conference

Conference2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020
CountryUnited States
CityDetroit
Period6/8/206/10/20

Keywords

  • Battery
  • Filtering
  • Health management
  • Machine Learning
  • Prognostics
  • State of Charge
  • State of Health

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management

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