%N 1
%D 2024
%K Energy efficiency; Li-ion batteries; Deep learning; AccuCell prodigy; Remaining useful life
%V 14
%L unic14934
%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.
%R doi:10.1038/s41598-024-77427-1
%A Mahrukh Iftikhar
%A Muhammad Shoaib
%A Ayesha Altaf
%A Faiza Iqbal
%A Santos Gracia Villar
%A Luis Alonso Dzul López
%A Imran Ashraf
%T A deep learning approach to optimize remaining useful life prediction for Li-ion batteries
%J Scientific Reports