A Novel Transfer Learning Framework Combining Attention Mechanisms and Random Forest Regression for State of Health Estimation of Lithium-Ion Battery With Different Formulations-Reference-Cited by-同舟云学术

A Novel Transfer Learning Framework Combining Attention Mechanisms and Random Forest Regression for State of Health Estimation of Lithium-Ion Battery With Different Formulations

Published:2024-07 Issue:4 Volume:60 Page:5726-5736
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Lu Dong¹^ORCID,Cui Naxin¹^ORCID,Li Changlong¹^ORCID

Affiliation:

1. School of Control Science and Engineering, Shandong University, Jinan, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province, China

Special Funds of the Central Government Guiding Local Science and Technology Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/28/10604662/10490215.pdf?arnumber=10490215

Reference48 articles.

1. Design and optimization of lithium-ion battery as an efficient energy storage device for electric vehicles: A comprehensive review

2. Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

3. Lithium-ion battery lifetime extension: A review of derating methods

4. A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

5. Sensitivity Analysis and Joint Estimation of Parameters and States for All-Solid-State Batteries

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