Lithium-ion batteries play a crucial role in vehicle electrification to meet the goals of reducing fossil fuels. However, they deteriorate over time and it is thus needed to predict their rate of decay. The indicator of the State of Health (SOH) expresses the remaining total capacity of the cell in comparison with its initial total capacity. For an accurate prediction of the SOH, various methods have been proposed in the literature. In this article, a comparative study of seven different techniques is presented. Furthermore, a feature selection analysis for the most promising ones has been proposed.
Comparative study of machine learning techniques for the state of health estimation of Li-Ion batteries
Eleftheriadis, P.;Leva, S.;Gangi, M.;
2022-01-01
Abstract
Lithium-ion batteries play a crucial role in vehicle electrification to meet the goals of reducing fossil fuels. However, they deteriorate over time and it is thus needed to predict their rate of decay. The indicator of the State of Health (SOH) expresses the remaining total capacity of the cell in comparison with its initial total capacity. For an accurate prediction of the SOH, various methods have been proposed in the literature. In this article, a comparative study of seven different techniques is presented. Furthermore, a feature selection analysis for the most promising ones has been proposed.File in questo prodotto:
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