Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data
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Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion battery characterization is an extensively recognized non-destructive procedure for both in-situ and ex-situ analyses. In an impedance measurement for a rechargeable battery, the oscillating current with an accompanying phase angle is the response for a potential perturbation. The proper evaluation of phase angle as a crucial impedance parameter, provides critical understanding of the status of the battery. Although fast and simple, impedance data is difficult to interpret. Using a novel data-centric Machine Learning framework (co-modeling) we demonstrate how to impute experimental data quickly, precisely, and inexpensively that agrees with wholly experimentally generated data. In particular, we predict the phase angle with 99.9% accuracy by training the minimal empirical impedance data. This approach demonstrates a potentially burgeoning field of Machine Learning experimental data imputation and the consequence of faster diagnostic and study of batteries.
Açıklama
Anahtar Kelimeler
Li-ion batteries, Electrochemical impedance spectroscopy, Machine learning, Regression, Cooperative learning
Kaynak
Journal of Energy Storage
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
52
