@article{uniromana14934,
           title = {A deep learning approach to optimize remaining useful life prediction for Li-ion batteries},
           month = {Octubre},
         journal = {Scientific Reports},
          volume = {14},
          number = {1},
            year = {2024},
          author = {Mahrukh Iftikhar and Muhammad Shoaib and Ayesha Altaf and Faiza Iqbal and Santos Gracia Villar and Luis Alonso Dzul L{\'o}pez and Imran Ashraf},
        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.},
             url = {http://repositorio.uniromana.edu.do/id/eprint/14934/}
}