Journal article
Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory
Autonomous Materials Discovery, Department of Energy Conversion and Storage, Technical University of Denmark1
Department of Energy Conversion and Storage, Technical University of Denmark2
Technical University of Denmark3
Imaging and Structural Analysis, Department of Energy Conversion and Storage, Technical University of Denmark4
CIDETEC5
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark6
Department of Applied Mathematics and Computer Science, Technical University of Denmark7
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.
Language: | English |
---|---|
Year: | 2023 |
Pages: | 112-122 |
ISSN: | 2635098x |
Types: | Journal article |
DOI: | 10.1039/D2DD00067A |
ORCIDs: | Rieger, Laura Hannemose , Norby, Poul , Winther, Ole , Vegge, Tejs and Bhowmik, Arghya |