EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

Zhaoyang Zhao

doi:10.1016/j.electacta.2022.140350

EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

2022-06-01

31

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AI

DOI：10.1016/j.electacta.2022.140350

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Abstract

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Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge-the slowest step usually is vital in the whole electrochemical process. Twenty global optimization algorithms (GOA)s were assessed on simulated and experimental impedance spectra belonging to nine different ECMs, which proved that GOAs obtained nearly the same identification accuracy as the artificial identification under no interference of obvious abnormal points. ML combining with GOA provides a new possibility to automatically process EIS.

Keywords:

Electrochemical impedance spectroscopy

Equivalent circuit model

Interpretable machine learning

Classification

Global optimization algorithm

Parameter identification

EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

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