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- Title
Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries.
- Authors
Mamo, Tadele; Fu-Kwun Wang
- Abstract
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.
- Subjects
RECURRENT neural networks; LITHIUM-ion batteries; STANDARD deviations; ARTIFICIAL neural networks; DIFFERENTIAL evolution
- Publication
Batteries, 2021, Vol 7, Issue 4, p1
- ISSN
2313-0105
- Publication type
Article
- DOI
10.3390/batteries7040066