A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack-Reference-Cited by-同舟云学术

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A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack

Published:2024-06 Issue:6 Volume:20 Page:8089-8101
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Yuchen¹^ORCID,Xiong Can¹^ORCID,Ju Changjiang¹^ORCID,Yang Genke¹^ORCID,Chen Yu-wang²^ORCID,Yu Xiaotian³^ORCID

Affiliation:

1. Ningbo Artificial Intelligence Institute, Department of Automation, Shanghai Jiao Tong University, Shanghai, China

2. Manchester Business School, The University of Manchester, Manchester, U.K.

3. Ningbo Artificial Intelligence Institute, Ningbo, China

Funder

China National R&D Key Research Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10550081/10462637.pdf?arnumber=10462637

Reference47 articles.

1. The development of stationary battery storage systems in Germanystatus 2020;Figgener,2021

2. A Meta-learning Based Multimodal Neural Network for Multistep Ahead Battery Thermal Runaway Forecasting

3. Sensorless Temperature Estimation of Lithium-Ion Battery Based on Broadband Impedance Measurements

4. Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions

5. Real-Time Estimation of 2-D Temperature Distribution in Lithium-Ion Pouch Cells

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