A data and physical model joint driven method for lithium-ion battery remaining useful life prediction under complex dynamic conditions-Reference-Cited by-同舟云学术

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A data and physical model joint driven method for lithium-ion battery remaining useful life prediction under complex dynamic conditions

Published:2024-02 Issue: Volume:79 Page:110065
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Ren Yi,Tang Ting,Xia Quan,Zhang Kun,Tian Jun,Hu Daozhong,Yang Dezhen,Sun Bo,Feng Qiang,Qian Cheng

Funder

China Postdoctoral Science Foundation

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference53 articles.

1. State of health estimation with attentional long short-term memory network for lithium-ion batteries;Lin;Energy,2023

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

3. Life prediction of lithium-ion batteries based on stacked denoising autoencoders;Xu;Reliab. Eng. Syst. Saf.,2021

4. Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life;Xue;Energy,2022

5. Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries;Chen;Energy,2022

Cited by 4 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. A capacity fade reliability model for lithium-ion battery packs based on real-vehicle data;Energy;2024-10

2. Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability;Energy;2024-10

3. Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm;Energy;2024-09

4. High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework;Ionics;2024-08-03

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