Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Paul Gasper, Nils Collath, Holger Hesse, Andreas Jossen, Kandler Smith

Research output: Contribution to journalArticlepeer-review

18 Scopus Citations

Abstract

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

Original languageAmerican English
Article number080518
Number of pages20
JournalJournal of the Electrochemical Society
Volume169
Issue number8
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.

NREL Publication Number

  • NREL/JA-5700-82648

Keywords

  • battery
  • battery degradation
  • battery lifetime
  • battery modeling
  • battery reliability
  • lifetime uncertainty
  • lithium-ion
  • lithium-iron-phosphate
  • machine-learning

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