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

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. We present an interpretable uncertainty-aware machine learning model to predict battery degradation trajectories. Using LSTM Recurrent Neural Networks, we reach an RMSE of 106 and MAPE of 10.6%.