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...

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Detalles Bibliográficos
Autores principales: Ali, Muhammad Umair, Kallu, Karam Dad, Masood, Haris, Niazi, Kamran Ali Khan, Alvi, Muhammad Junaid, Ghafoor, Usman, Zafar, Amad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
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

Internet

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

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