Physics-Dominated Neural Network for Spatiotemporal Modeling of Battery Thermal Process-Reference-Cited by-同舟云学术

Physics-Dominated Neural Network for Spatiotemporal Modeling of Battery Thermal Process

Published:2024-01 Issue:1 Volume:20 Page:452-460
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Deng Hai-Peng¹^ORCID,He Yan-Bo²,Wang Bing-Chuan²^ORCID,Li Han-Xiong³^ORCID

Affiliation:

1. State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering, Central South University, Hunan, China

2. School of Automation, Central South University, Changsha, China

3. Department of Advanced Design and Systems Engineering, City University of Hong Kong, Hong Kong, China

Funder

General Research Fund

Research Grants Council of Hong Kong

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10355676/10098951.pdf?arnumber=10098951

Reference32 articles.

1. Data-driven prediction of battery cycle life before capacity degradation

2. Thermal management evaluation of Li-ion battery employing multiple phase change materials integrated thin heat sinks for hybrid electric vehicles

3. Locally Weighted Principal Component Analysis-Based Multimode Modeling for Complex Distributed Parameter Systems

4. Deep Extreme Learning Machines Based Two-Phase Spatiotemporal Modeling for Distributed Parameter Systems

5. Spatial-Construction-Based Abnormality Detection and Localization for Distributed Parameter Systems

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