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

Autor: Mahrukh Iftikhar, Muhammad Shoaib, Ayesha Altaf, Faiza Iqbal, Santos Gracia Villar, Luis Alonso Dzul Lopez, Imran Ashraf
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-77427-1
Popis: 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.
Databáze: Directory of Open Access Journals
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