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. ; 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 ...


    Access

    Download


    Export, share and cite



    Title :

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



    Publication date :

    2023-01-01


    Remarks:

    Rieger , L H , Flores , E , Nielsen , K F , Norby , P , Ayerbe , E , Winther , O , Vegge , T & Bhowmik , A 2023 , ' Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory ' , Digital Discovery , vol. 2 , no. 1 , pp. 112-122 . https://doi.org/10.1039/d2dd00067a



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629





    Explainable Machine Learning and Uncertainty Quantification in Robotic Lunar Surface Traversal

    Cleghern, Zach / Rudd, Keith / Thornton, Griffin et al. | AIAA | 2023


    Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles

    Zhou, Weitao / Cao, Zhong / Xu, Yunkang et al. | IEEE | 2022


    Comparison of traffic accident injury severity prediction models with explainable machine learning

    Cicek, Elif / Akin, Murat / Uysal, Furkan et al. | Taylor & Francis Verlag | 2023