Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Joachim Schaeffer, Paul Gasper, Esteban Garcia-Tamayo, Raymond Gasper, Masaki Adachi, Juan Pablo Gaviria-Cardona, Simon Montoya-Bedoya, Anoushka Bhutani, Andrew Schiek, Rhys Goodall, Rolf Findelsen, Richard Braatz, Simon Engelke

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations


Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

Original languageAmerican English
Article number060512
Number of pages13
JournalJournal of the Electrochemical Society
Issue number6
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.

NREL Publication Number

  • NREL/JA-5700-86260


  • Batteries
  • Classification
  • Electrochemical Impedance Spectroscopy
  • Equivalent Circuit Model
  • Hackathon
  • Machine Learning
  • Open Data


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