BatteryDEV/AutoECM
Machine learning appoaches for the classification of Equivalent Circuit Models based on Electrochemical Impedance Spectroscopy data
This project helps battery scientists and materials engineers automatically classify electrochemical impedance spectroscopy (EIS) data to identify the underlying equivalent circuit model (ECM). You input your EIS measurements, and it outputs the predicted ECM, simplifying the analysis of battery performance and degradation. It's designed for researchers and engineers working with battery characterization data.
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Use this if you need to quickly and accurately classify electrochemical impedance spectra to determine the equivalent circuit model for battery analysis.
Not ideal if you are looking for a general-purpose machine learning library or if your primary focus is on simulating complex battery models rather than classifying existing data.
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48
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8
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 05, 2024
Commits (30d)
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