%0 Journal Article
%@ 2045-2322
%A Iftikhar, Mahrukh
%A Shoaib, Muhammad
%A Altaf, Ayesha
%A Iqbal, Faiza
%A Gracia Villar, Santos
%A Dzul López, Luis Alonso
%A Ashraf, Imran
%D 2024
%F uneatlantico:14934
%J Scientific Reports
%K Energy efficiency; Li-ion batteries; Deep learning; AccuCell prodigy; Remaining useful life
%N 1
%T A deep learning approach to optimize remaining useful life prediction for Li-ion batteries
%U http://repositorio.uneatlantico.es/id/eprint/14934/
%V 14
%X 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.