Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data. (25th August 2022)

• Record Type: Journal Article
• Authors: Temiz, Selcuk; Kurban, Hasan; Erol, Salim; Dalkilic, Mehmet M.
• Is Part Of: Journal of energy storage. Volume 52:Part C(2022)
• Publication Date: 2022-08-25
• Subjects: Li-ion batteries -- Electrochemical impedance spectroscopy -- Machine learning -- Regression -- Cooperative learning; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105022
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: 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. Graphical abstract: Highlights: Impedance-driven phase shift analysis at various SoC of Li-ion battery. A novel Machine Learning framework to model Li-ion battery impedance. A detailed comparison of our framework with the traditional Machine Learning algorithms over EIS data