Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Gasper, Paul; Collath, Nils; Hesse, Holger C.; Jossen, Andreas; Smith, Kandler

doi:10.1149/1945-7111/ac86a8

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Journal Article · Wed Aug 24 00:00:00 EDT 2022 · Journal of the Electrochemical Society

DOI:https://doi.org/10.1149/1945-7111/ac86a8· OSTI ID:1887564

^[1]; Collath, Nils ^[2]; Hesse, Holger C. ^[3]; ^[2]; ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Technical Univ. of Munich (Germany)
Technical Univ. of Munich (Germany); Kempten University of Applied Sciences (Germany)

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1887564

Report Number(s):: NREL/JA-5700-82648; MainId:83421; UUID:f06bd7c0-785b-4618-a1c1-b509c3627ed5; MainAdminID:65440

Journal Information:: Journal of the Electrochemical Society, Journal Name: Journal of the Electrochemical Society Journal Issue: 8 Vol. 169; ISSN 0013-4651

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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