A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

• Published in: Scientific reports 2024-10, Vol.14 (1), p.25838-14, Article 25838
• Main Authors: Iftikhar, Mahrukh, Shoaib, Muhammad, Altaf, Ayesha, Iqbal, Faiza, Villar, Santos Gracia, Dzul Lopez, Luis Alonso, Ashraf, Imran
• Format: Article
• Language: English
• Subjects: 639/705/117; 704/158; AccuCell prodigy; Deep learning; Electric vehicles; Energy efficiency; Humanities and Social Sciences; Li-ion batteries; Lithium; Long short-term memory; Mean square errors; multidisciplinary; Predictions; Prodigies; Remaining useful life; Science; Science (multidisciplinary); Useful life
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-77427-1

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.