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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory. / Rieger, Laura Hannemose; Flores, Eibar; Nielsen, Kristian Frellesen; Norby, Poul; Ayerbe, Elixabete; Winther, Ole; Vegge, Tejs; Bhowmik, Arghya.

In: Digital Discovery, Vol. 2, No. 1, 2023, p. 112-122.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Rieger, LH, Flores, E, Nielsen, KF, 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

APA

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, 2(1), 112-122. https://doi.org/10.1039/d2dd00067a

Vancouver

Rieger LH, Flores E, Nielsen KF, Norby P, Ayerbe E, Winther O et al. Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory. Digital Discovery. 2023;2(1):112-122. https://doi.org/10.1039/d2dd00067a

Author

Rieger, Laura Hannemose ; Flores, Eibar ; Nielsen, Kristian Frellesen ; Norby, Poul ; Ayerbe, Elixabete ; Winther, Ole ; Vegge, Tejs ; Bhowmik, Arghya. / Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory. In: Digital Discovery. 2023 ; Vol. 2, No. 1. pp. 112-122.

Bibtex

@article{203a5f0a0d984b5582a0a243f3cbf57d,

title = "Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory",

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.",

author = "Rieger, {Laura Hannemose} and Eibar Flores and Nielsen, {Kristian Frellesen} and Poul Norby and Elixabete Ayerbe and Ole Winther and Tejs Vegge and Arghya Bhowmik",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by the Royal Society of Chemistry.",

year = "2023",

doi = "10.1039/d2dd00067a",

language = "English",

volume = "2",

pages = "112--122",

journal = "Digital Discovery",

issn = "2635-098X",

publisher = "Royal Society of Chemistry",

number = "1",

}

RIS

TY - JOUR

T1 - Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

AU - Rieger, Laura Hannemose

AU - Flores, Eibar

AU - Nielsen, Kristian Frellesen

AU - Norby, Poul

AU - Ayerbe, Elixabete

AU - Winther, Ole

AU - Vegge, Tejs

AU - Bhowmik, Arghya

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1039/d2dd00067a

DO - 10.1039/d2dd00067a

M3 - Journal article

AN - SCOPUS:85152765251

VL - 2

SP - 112

EP - 122

JO - Digital Discovery

JF - Digital Discovery

SN - 2635-098X

IS - 1

ER -

ID: 365812970

Department of Biology