| Autor: |
Zraibi Brahim, Mansouri Mohamed, Okar Chafik |
| Jazyk: |
English<br />French |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 297, p 01043 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202129701043 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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