Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Thelen, Adam; Huan, Xun; Paulson, Noah; Onori, Simona; Hu, Zhen; Hu, Chao

doi:10.1038/s44296-024-00011-1

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Journal Article · Mon Jun 03 00:00:00 EDT 2024 · npj Materials Sustainability

DOI:https://doi.org/10.1038/s44296-024-00011-1· OSTI ID:2370016

Thelen, Adam; Huan, Xun; Paulson, Noah; Onori, Simona; Hu, Zhen; Hu, Chao

Abstract

Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0021397

OSTI ID:: 2370016

Journal Information:: npj Materials Sustainability, Journal Name: npj Materials Sustainability Journal Issue: 1 Vol. 2; ISSN 2948-1775

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

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Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

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