relation: http://repositorio.unini.edu.mx/id/eprint/14934/ canonical: http://repositorio.unini.edu.mx/id/eprint/14934/ title: A deep learning approach to optimize remaining useful life prediction for Li-ion batteries creator: Iftikhar, Mahrukh creator: Shoaib, Muhammad creator: Altaf, Ayesha creator: Iqbal, Faiza creator: Gracia Villar, Santos creator: Dzul López, Luis Alonso creator: Ashraf, Imran subject: Ingeniería description: 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. date: 2024-10 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_nc_nd_4 identifier: http://repositorio.unini.edu.mx/id/eprint/14934/1/s41598-024-77427-1.pdf identifier: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Iftikhar, Mahrukh; Shoaib, Muhammad; Altaf, Ayesha; Iqbal, Faiza; Gracia Villar, Santos; Dzul López, Luis Alonso y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR (2024) A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Scientific Reports, 14 (1). ISSN 2045-2322 relation: http://doi.org/10.1038/s41598-024-77427-1 relation: doi:10.1038/s41598-024-77427-1 language: en