Data on Machine Learning regenerated Lithium-ion battery impedance

Selcuk Temiz; Hasan Kurban; Salim Erol; Mehmet M Dalkilic

doi:10.1016/j.dib.2022.108698

Data on Machine Learning regenerated Lithium-ion battery impedance

Data Brief. 2022 Oct 28:45:108698. doi: 10.1016/j.dib.2022.108698. eCollection 2022 Dec.

Authors

Selcuk Temiz¹, Hasan Kurban^{2

3}, Salim Erol⁴, Mehmet M Dalkilic³

Affiliations

¹ Department of Physics, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.
² Applied Data Science Department, San Jose State University, San Jose, CA, 95192, US.
³ Computer Science Department, Indiana University, Bloomington, 47405, IN, US.
⁴ Department of Chemical Engineering, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.

Abstract

This paper describes and provides the data on the regenerated-impedance spectra that is computed from experimental results of electrochemical impedance spectroscopy measurements taken from a commercial Li-ion battery. The empirical impedance data of secondary coin type Li-ion batteries were collected in different states of charge ranging from empty to full state of charge configurations. This approach utilizes only a small seed (ex grano) experimental data set to first build an ensemble of weighted disparate models selected based on performance and non-correlative criteria ("co-modelling") then second to generate what would be the remaining experimental data synthetically. The "Cooperative Model Framework" demonstrates the efficacy of this approach by assessing the synthetically generated data.

Keywords: Co-modeling approach; Electrochemical Impedance Spectroscopy (EIS) for Li-ion batteries; Machine Learning (ML) on Li-ion batteries; Regeration of impedance for Li-ion batteries.