Temiz, SelcukKurban, HasanErol, SalimDalkilic, Mehmet M.2024-12-242024-12-2420222352-152X2352-1538https://doi.org/10.1016/j.est.2022.105022https://hdl.handle.net/20.500.12604/6482The 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.eninfo:eu-repo/semantics/closedAccessLi-ion batteriesElectrochemical impedance spectroscopyMachine learningRegressionCooperative learningArticle52Q1WOS:000814756200008Q12-s2.0-8513174411410.1016/j.est.2022.105022