Battery state of charge estimation using machine learning and electrochemical impedance spectroscopy measurements

Buchicchio, F. Bianconi, F. Smeraldi , A. De Angelis, F. Santoni, P. Carbone, in Science Talks, vol. 4, pp 100100, 2022

Abstract

Efficient energy management in battery-powered devices requires reliable estimation of the battery state of charge. We developed a data-driven state-of-charge estimation method based on machine learning and electrochemical impedance spectroscopy. Several states-of-charge models were trained and tested using an original measurement dataset from a set of commercial Samsung ICR18650-26 J lithium-Ion batteries. The implications of the curse of dimensionality for this task have been analyzed, and the effectiveness of different feature reduction techniques to avoid classification model overfitting was investigated.

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Keywords:

Lithium-Ion batteries; Electrochemical impedance spectroscopy; State of charge; Machine learning

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