TY  - JOUR
IS  - 1
KW  - Energy efficiency; Li-ion batteries; Deep learning; AccuCell prodigy; Remaining useful life
VL  - 14
UR  - http://doi.org/10.1038/s41598-024-77427-1
Y1  - 2024/10//
JF  - Scientific Reports
SN  - 2045-2322
TI  - A deep learning approach to optimize remaining useful life prediction for Li-ion batteries
AV  - public
A1  - Iftikhar, Mahrukh
A1  - Shoaib, Muhammad
A1  - Altaf, Ayesha
A1  - Iqbal, Faiza
A1  - Gracia Villar, Santos
A1  - Dzul López, Luis Alonso
A1  - Ashraf, Imran
ID  - uninimx14934
N2  - 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.
ER  -