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Article

Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

by 1,*,† and 2,†
1
Department of Mechanical Engineering, Mettu University, Mettu P.O. Box 318, Ethiopia
2
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Burçak Ebin and Martina Petranikova
Batteries 2021, 7(4), 66; https://doi.org/10.3390/batteries7040066
Received: 3 August 2021 / Revised: 1 September 2021 / Accepted: 30 September 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Circular Battery Technologies)
Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life. View Full-Text
Keywords: lithium-ion battery; capacity degradation; long short-term memory; attention mechanism lithium-ion battery; capacity degradation; long short-term memory; attention mechanism
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MDPI and ACS Style

Mamo, T.; Wang, F.-K. Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries. Batteries 2021, 7, 66. https://doi.org/10.3390/batteries7040066

AMA Style

Mamo T, Wang F-K. Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries. Batteries. 2021; 7(4):66. https://doi.org/10.3390/batteries7040066

Chicago/Turabian Style

Mamo, Tadele, and Fu-Kwun Wang. 2021. "Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries" Batteries 7, no. 4: 66. https://doi.org/10.3390/batteries7040066

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