Rapid health estimation of in-service battery packs based on limited labels and domain adaptation-Reference-Cited by-同舟云学术

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Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

Published:2024-02 Issue: Volume:89 Page:345-354
ISSN:2095-4956
Container-title:Journal of Energy Chemistry
language:en
Short-container-title:Journal of Energy Chemistry

Author:

Deng Zhongwei,Xu Le,Liu Hongao,Hu Xiaosong,Wang Bing,Zhou Jingjing

Funder

China Postdoctoral Science Foundation

National Key Research and Development Program of China

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Electrochemistry,Energy (miscellaneous),Energy Engineering and Power Technology,Fuel Technology

Reference39 articles.

1. The role of energy infrastructure in shaping early adoption of electric and gasoline cars

2. Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage

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

4. Battery prognostics and health management for electric vehicles under industry 4.0

5. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

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1. Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy;Reliability Engineering & System Safety;2024-12

2. Mechanisms for the evolution of cell-to-cell variations and their impacts on fast-charging performance within a lithium-ion battery pack;Journal of Energy Chemistry;2024-12

3. Refined lithium-ion battery state of health estimation with charging segment adjustment;Applied Energy;2024-12

4. A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges;Journal of Energy Chemistry;2024-10

5. Multi-stage deep sorting strategy for retired batteries based on the clustering of static and dynamic features;Journal of Energy Storage;2024-10

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