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Predicting future capacity of lithium-ion batteries using transfer learning method

  • Published:2023-11 Issue: Volume:71 Page:108120
  • ISSN:2352-152X
  • Container-title:Journal of Energy Storage
  • language:en
  • Short-container-title:Journal of Energy Storage

Author:

Chou Jia-Hong,Wang Fu-Kwun,Lo Shih-Che

Funder

National Science and Technology Council

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference28 articles.

1. Online estimation of power capacity with noise effect attenuation for lithium-ion battery;Wei;IEEE Trans. Ind. Electron.,2018

2. Critical review of state of health estimation methods of li-ion batteries for real applications;Berecibar;Renew. Sust. Energ. Rev.,2016

3. A brief review on key technologies in the battery management system of electric vehicles;Liu;Front. Mech. Eng.,2019

4. SOH prediction of lithium battery based on IC curve feature and BP neural network;Wen;Energy,2022

5. State-of-health estimation for lithium-ion batteries based on partial charging segment and stacking model fusion;Xu;Energy Sci. Eng.,2023
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