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

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dc.authoridTemiz, Selcuk/0000-0002-6649-6251
dc.authoridKURBAN, HASAN/0000-0003-3142-2866
dc.authoridErol, Salim/0000-0002-7219-6642
dc.contributor.authorTemiz, Selcuk
dc.contributor.authorKurban, Hasan
dc.contributor.authorErol, Salim
dc.contributor.authorDalkilic, Mehmet M.
dc.date.accessioned2024-12-24T19:27:03Z
dc.date.available2024-12-24T19:27:03Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractThe 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.
dc.description.sponsorshipScientific Research Foun-dation at Eskisehir Osmangazi University, Turkey [2017-1911]
dc.description.sponsorshipFunding information S. Erol acknowledges the support from the Scientific Research Foun-dation at Eskisehir Osmangazi University, Turkey under grant number 2017-1911.
dc.identifier.doi10.1016/j.est.2022.105022
dc.identifier.issn2352-152X
dc.identifier.issn2352-1538
dc.identifier.scopus2-s2.0-85131744114
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.est.2022.105022
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6482
dc.identifier.volume52
dc.identifier.wosWOS:000814756200008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Energy Storage
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectLi-ion batteries
dc.subjectElectrochemical impedance spectroscopy
dc.subjectMachine learning
dc.subjectRegression
dc.subjectCooperative learning
dc.titleRegeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data
dc.typeArticle

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