Early Prediction of Lithium-Ion Battery Cycle Life by Machine Learning Methods

Ankan Mitra, Rong Pan

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

Abstract

For a lithium-ion battery, its cycle life is defined as the number of full charge cycles that a battery can undergo until its full charge capacity falls below 80% of the design capacity. In a recent study by Severson et al. [1], a large set of lithium-ion battery cycle life experiments were conducted and analyzed, and early cycle data were used to predict battery lives without any prior assumptions on degradation mechanism. In this paper, we reexamine the data and suggest additional features to the model, which also use early cycle data (up to the first 100 cycles), for a better battery cycle life prediction. We suggest an ensemble machine learning method that combines several classifiers such as the k-nearest neighbor classifier, neural networks, support vector machines and decision tree-based classifiers for classifying batteries to low or high lifetime category. It is found that our ensemble approach provides more robust predictions. For predicting cycle life, a support vector regression model is suggested, and we compare it with an elastic net-based regression model. It is found that SVR outperforms elastic net in terms of percentage prediction error.

Original languageEnglish (US)
Title of host publication68th Annual Reliability and Maintainability Symposium, RAMS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665424325
DOIs
StatePublished - 2022
Externally publishedYes
Event68th Annual Reliability and Maintainability Symposium, RAMS 2022 - Tucson, United States
Duration: Jan 24 2022Jan 27 2022

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
Volume2022-January
ISSN (Print)0149-144X

Conference

Conference68th Annual Reliability and Maintainability Symposium, RAMS 2022
Country/TerritoryUnited States
CityTucson
Period1/24/221/27/22

Keywords

  • degradation
  • ensemble machine learning
  • feature selection
  • reliability prediction
  • support vector regression

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications

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