Data on Machine Learning regenerated Lithium-ion battery impedance

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

DATA IN BRIEF, vol.45, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45
  • Publication Date: 2022
  • Doi Number: 10.1016/j.dib.2022.108698
  • Journal Name: DATA IN BRIEF
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, BIOSIS, CAB Abstracts, Directory of Open Access Journals
  • Keywords: Machine Learning (ML) on Li-ion batteries, Co-modeling approach, Electrochemical Impedance Spectroscopy, (EIS) for Li-ion batteries, Regeration of impedance for Li-ion batteries
  • Eskisehir Osmangazi University Affiliated: Yes

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. (C) 2022 The Author(s). Published by Elsevier Inc.