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A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction
Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address t...
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| Main Authors: | , , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
MDPI AG
2020-02-01
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| Series: | Energies |
| Assuntos: | |
| Acceso en liña: | https://www.mdpi.com/1996-1073/13/3/752 |
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