Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data

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Temiz S., Kurban H., Erol S., Dalkilic M. M.

JOURNAL OF ENERGY STORAGE, cilt.52, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.est.2022.105022
  • Dergi Adı: JOURNAL OF ENERGY STORAGE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Anahtar Kelimeler: Li-ion batteries, Electrochemical impedance spectroscopy, Machine learning, Regression, Cooperative learning, CHARGE, STATE, MODELS
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

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.