Deep Learning-Enhanced Parameter Extraction for Equivalent Circuit Modeling in Electrochemical Impedance Spectroscopy

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Reliable and automatic parameter extraction in equivalent circuit modeling of electrochemical impedance spectroscopy (EIS) could be a challenge as the common circuit fitting method, complex nonlinear least-squares (CNLS), heavily depends on the initial guesses. To prevent the adjustment of the initial guess that demands extra time and experience, we propose employing a deep learning-based convolutional neural network (CNN) to perform the pre-fitting of the measured impedance spectrum. This approach not only facilitates the convergence dynamics of CNLS but also manifests a notable enhancement in parameter extraction fidelity, especially when benchmarked against conventional methodologies. The improvement of 25% in fitting success rate is demonstrated on an open-source impedance dataset by comparing to CNLS with random initials and the traditional stochastic methods including differential evolution and simulated annealing. Thus, we believe the proposed pre-fitting method can provide a useful tool for reliable parameter extraction with the uncertainty minimized to explore the underlying mechanism from EIS and automate this process for the analysis of a large amount of data.
Original languageEnglish
Title of host publication2023 IEEE Nordic Circuits and Systems Conference, NorCAS 2023 - Proceedings
EditorsJari Nurmi, Peeter Ellervee, Peter Koch, Farshad Moradi, Ming Shen
Publication dateNov 2023
Article number10305482
ISBN (Print)979-8-3503-3758-7
ISBN (Electronic)979-8-3503-3757-0
Publication statusPublished - Nov 2023
Event2023 IEEE Nordic Circuits and Systems Conference - Aalborg University, Aalborg, Denmark
Duration: 31 Oct 20231 Nov 2023


Conference2023 IEEE Nordic Circuits and Systems Conference
LocationAalborg University
Internet address


  • Electrochemical Impedance Spectroscopy
  • Equivalent Circuit
  • Deep Learning
  • Convolutional Neural Network


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