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Optimized XGBoost framework for RUL prediction of lithium-ion batteries using multi health indicators

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Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy

Published online on

Abstract

Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Ahead of Print.
Lithium-ion batteries are central to the growth of electric vehicles (EVs), providing high energy density, long life, and efficiency for sustainable transportation. Nevertheless, information on the Remaining Useful Life (RUL) of battery remains a ...