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Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model
A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571724/ https://www.ncbi.nlm.nih.gov/pubmed/34765915 http://dx.doi.org/10.1016/j.isci.2021.103286 |