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Battery state of charge estimation using machine learning and electrochemical impedance spectroscopy measurements
AbstractEfficient 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. Keywords:Lithium-Ion batteries; Electrochemical impedance spectroscopy; State of charge; Machine learning |
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