Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning-Reference-Cited by-同舟云学术

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Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning

Published:2024-09 Issue: Volume:249 Page:110166
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Song Dengwei,Cheng Yujie,Zhou An,Lu Chen,Chong Jin,Ma Jian^ORCID

Publisher

Elsevier BV

Reference48 articles.

1. Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms;Wei;Reliab Eng Syst Safe,2023

2. Multivariate stacked bidirectional long short term memory for lithium-ion battery health management;Ardeshiri;Reliab Eng Syst Safe,2022

3. A hybrid data-driven method for rapid prediction of lithium-ion battery capacity;He;Reliab Eng Syst Safe,2022

4. A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery;Tang;Reliab Eng Syst Safe,2022

5. Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method;Ma;Reliab Eng Syst Safe,2023

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model;Reliability Engineering & System Safety;2024-12

2. Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries;Energy and AI;2024-09

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