Data-Driven Transfer-Stacking-Based State of Health Estimation for Lithium-Ion Batteries-Reference-Cited by-同舟云学术

Data-Driven Transfer-Stacking-Based State of Health Estimation for Lithium-Ion Batteries

Published:2024-01 Issue:1 Volume:71 Page:604-614
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Wu Ji¹^ORCID,Cui Xuchen¹^ORCID,Meng Jinhao²^ORCID,Peng Jichang³^ORCID,Lin Mingqiang⁴^ORCID

Affiliation:

1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China

2. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, China

3. Smart Grid Research Institute, Nanjing Institute of Technology, Nanjing, China

4. Quanzhou Institute of Equipment Manufacturing, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Jinjiang, China

Funder

National Natural Science Foundation of China

Youth Science and Technology Talents Support Program

Anhui Association for Science and Technology

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/10184152/10054606.pdf?arnumber=10054606

Reference29 articles.

1. Small sample state of health estimation based on weighted Gaussian process regression

2. A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning

3. A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries

4. State-of-health Estimation for Lithium-ion Batteries Based on Decoupled Dynamic Characteristic of Constant-voltage Charging Current

5. Battery health estimation with degradation pattern recognition and transfer learning

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