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Data on Machine Learning regenerated Lithium-ion battery impedance.

Selcuk Temiz1, Hasan Kurban2,3, Salim Erol4

  • 1Department of Physics, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.

Data in Brief
|November 25, 2022
PubMed
Summary
This summary is machine-generated.

This study uses a novel "co-modelling" approach to synthetically generate electrochemical impedance spectroscopy data for lithium-ion batteries. This method efficiently predicts battery performance across various charge states using minimal experimental data.

Keywords:
Co-modeling approachElectrochemical Impedance Spectroscopy (EIS) for Li-ion batteriesMachine Learning (ML) on Li-ion batteriesRegeration of impedance for Li-ion batteries

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Battery Technology

Background:

  • Electrochemical impedance spectroscopy (EIS) is crucial for characterizing battery performance.
  • Acquiring comprehensive EIS data across all states of charge (SoC) is experimentally intensive.
  • Accurate impedance data is vital for battery management systems and performance modeling.

Purpose of the Study:

  • To develop and validate a novel method for synthetically generating EIS data for Li-ion batteries.
  • To reduce the experimental effort required for comprehensive battery impedance characterization.
  • To assess the efficacy of a "co-modelling" framework using limited experimental data.

Main Methods:

  • Experimental EIS measurements were performed on commercial Li-ion coin cells at various SoC.
  • A
  • co-modelling
  • approach was employed, building an ensemble of weighted models from a small seed dataset.
  • Synthetic data generation was achieved by combining disparate, non-correlative models.

Main Results:

  • The study successfully computed regenerated-impedance spectra from experimental EIS data.
  • The
  • Cooperative Model Framework
  • demonstrated the ability to synthetically generate realistic impedance data.
  • The generated data accurately reflects battery behavior across a range of charge states.

Conclusions:

  • The proposed
  • co-modelling
  • framework effectively generates synthetic EIS data for Li-ion batteries.
  • This approach significantly reduces the need for extensive experimental data acquisition.
  • The method holds promise for efficient battery performance assessment and modeling.