E3S Web Conf.
Volume 297, 2021The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
|Number of page(s)||8|
|Published online||22 September 2021|
Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries
1 Hassan First University of Settat, National School of Applied Sciences of Berrechid, Laboratory LAMSAD, Morocco
2 Carleton University, Ottawa, Canada
* Corresponding author: email@example.com
The prediction lifetime of a Lithium-ion battery is able to be utilized as an early warning system to prevent the battery’s failure that makes it very significant for assuring safety and reliability. This paper represents a benchmark study that compares its RUL prediction results of single and hybrid methods with similar articles. We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining useful life (RUL) of Lithium-ion battery. We selected three statistical indicators (MAE, R², and RMSE) to assess the results of performance prediction. Experimental validation is performed using the lithium-ion battery dataset from the NASA and results reveal that the effectiveness of the suggested hybrid method in reducing the prediction error and in achieving better RUL prediction performance compared to the other algorithms.
Key words: Lithium-ion batteries / machine learning / remaining useful life / long short-term memory / convolutional neural network
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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