Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Summary: 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...

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Autores principales: Muhammad Umair Ali, Karam Dad Kallu, Haris Masood, Kamran Ali Khan Niazi, Muhammad Junaid Alvi, Usman Ghafoor, Amad Zafar
Formato: article
Lenguaje:EN
Publicado: Elsevier 2021
Materias:
Computer systems organization
Energy engineering
Energy systems
Science
Q
Acceso en línea:https://doaj.org/article/3cc3c92c679641f5826c32922126220d
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https://doaj.org/article/3cc3c92c679641f5826c32922126220d

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