A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Iftikhar, Mahrukh and Shoaib, Muhammad and Altaf, Ayesha and Iqbal, Faiza and Gracia Villar, Santos and Dzul López, Luis Alonso and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, UNSPECIFIED (2024) A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Scientific Reports, 14 (1). ISSN 2045-2322

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Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Item Type:	Article
Uncontrolled Keywords:	Energy efficiency; Li-ion batteries; Deep learning; AccuCell prodigy; Remaining useful life
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	30 Oct 2024 15:52
Last Modified:	30 Oct 2024 15:52
URI:	http://repositorio.funiber.org/id/eprint/14934

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