Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework...

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Bibliographic Details
Main Authors: Chao Li, Yigang Kong, Changjiang Wang, Xueliang Wang, Min Wang, Yulong Wang
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Energies
Subjects:
lithium-ion batteries
informer
empirical mode decomposition
relevance reconstruction
thermal runaway
temperature prediction
Online Access:https://www.mdpi.com/1996-1073/17/19/5001

Internet

https://www.mdpi.com/1996-1073/17/19/5001

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