Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

Laura Hannemose Rieger, Eibar Flores, Kristian Frellesen Nielsen, Poul Norby, Elixabete Ayerbe, Ole Winther, Tejs Vegge, Arghya Bhowmik*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Enhancing cell lifetime is a vital criterion in battery design and development. Because lifetime evaluation requires prolonged cycling experiments, early prediction of cell aging can significantly accelerate both the autonomous discovery of better battery chemistries and their development into production. We demonstrate an early prediction model with reliable uncertainty estimates, which utilizes an arbitrary number of initial cycles to predict the whole battery degradation trajectory. Our autoregressive model achieves an RMSE of 106 cycles and a MAPE of 10.6% when predicting the cell's end of life (EOL). Beyond being a black box, we show evidence through an explainability analysis that our deep model learns the interplay between multiple cell degradation mechanisms. The learned patterns align with existing chemical insights into the rationale for early EOL despite not being trained for this or having received prior chemical knowledge. Our model will enable accelerated battery development via uncertainty-guided truncation of cell cycle experiments once the predictions are reliable.
Original languageEnglish
JournalDigital Discovery
Volume2
Issue number1
Pages (from-to)112-122
Number of pages11
ISSN2635-098x
DOIs
Publication statusPublished - 2023

Bibliographical note

The authors acknowledge the European Union's Horizon 2020 research and innovation program under grant agreement No 957189 (BIG-MAP) and No 957213 (BATTERY2030PLUS).

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