eprintid: 14934 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/49/34 datestamp: 2024-10-30 23:30:09 lastmod: 2024-10-30 23:30:10 status_changed: 2024-10-30 23:30:09 type: article metadata_visibility: show creators_name: Iftikhar, Mahrukh creators_name: Shoaib, Muhammad creators_name: Altaf, Ayesha creators_name: Iqbal, Faiza creators_name: Gracia Villar, Santos creators_name: Dzul López, Luis Alonso creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: luis.dzul@uneatlantico.es creators_id: title: A deep learning approach to optimize remaining useful life prediction for Li-ion batteries ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Energy efficiency; Li-ion batteries; Deep learning; AccuCell prodigy; Remaining useful life 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. date: 2024-10 publication: Scientific Reports volume: 14 number: 1 id_number: doi:10.1038/s41598-024-77427-1 refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-024-77427-1 access: open language: en citation: 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 document_url: http://repositorio.unini.edu.mx/id/eprint/14934/1/s41598-024-77427-1.pdf