Data on Machine Learning regenerated Lithium-ion battery impedance

Selcuk Temiz*, Hasan Kurban, Salim Erol, Mehmet M. Dalkilic

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number108698
JournalData in Brief
Volume45
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

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

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