Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra
Name
Schaeffer_2023_J._Electrochem._Soc._170_060512.pdf
Description
Published version
Size
11.02 MB
Format
Adobe PDF
Checksum (MD5)
16ced25b6617afe7bbae798cd4baaaee
Author(s)
Schaeffer, Joachim
Gasper, Paul
Garcia-Tamayo, Esteban
Gasper, Raymond
Adachi, Masaki
Pablo Gaviria-Cardona, Juan
Montoya-Bedoya, Simon
Bhutani, Anoushka
Schiek, Andrew
Goodall, Rhys
Date Issued
June 1, 2023
Journal
Journal of The Electrochemical Society
Publisher
The Electrochemical Society
Citation
Joachim Schaeffer et al 2023 J. Electrochem. Soc. 170 060512
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1149/1945-7111/acd8fb
