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This document presents a method for estimating the state of charge (SOC) of batteries using electrochemical impedance spectroscopy (EIS) data and machine learning techniques. The proposed approach demonstrates over 93% accuracy in SOC classification by utilizing an equivalent circuit model and a low-complexity machine learning algorithm, validated through experimental data from lithium-ion cells. The study highlights the advantages of combining model-driven and data-driven methods for efficient SOC estimati

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vqgilcyxzlfgihwdyy

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EN