Rastegarparnah, A.; Asif, M.E.; Stolkin, R. Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries. Batteries2024, 10, 106.
Rastegarparnah, A.; Asif, M.E.; Stolkin, R. Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries. Batteries 2024, 10, 106.
Rastegarparnah, A.; Asif, M.E.; Stolkin, R. Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries. Batteries2024, 10, 106.
Rastegarparnah, A.; Asif, M.E.; Stolkin, R. Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries. Batteries 2024, 10, 106.
Abstract
With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessment of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainability goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the Convolutional-LSTM-Deep Neural Network (CLDNN) model and the Transformer-LSTM (Temporal-Transformer) model have emerged as standout Remaining Useful Life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable Mean Absolute Error (MAE) of 84.012 and a Mean Absolute Percentage Error (MAPE) of 25.676. Similarly, the Temporal-Transformer model exhibited notable performance with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved through the application of Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were benchmarked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.
Keywords
Deep Learning; Remaining Useful Life; Lithium-Ion Batteries; Battery Management Systems; Recycling and Reuse; Battery Degradation
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
