Remaining life prediction of lithium-ion batteries based on health management: A review-Reference-Cited by-同舟云学术

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Remaining life prediction of lithium-ion batteries based on health management: A review

Published:2023-01 Issue: Volume:57 Page:106193
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Song Kai,Hu Die,Tong Yao,Yue Xiaoguang

Funder

Changchun Normal University

Publisher

Elsevier BV

Subject

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

Reference159 articles.

1. Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries

2. Fundamental degradation mechanisms of layered oxide Li-ion battery cathode materials: Methodology, insights and novel approaches

3. Degradation mechanisms in Li-ion batteries: a state-of-the-art review

4. Revealing SEI Morphology: In-Depth Analysis of a Modeling Approach

Cited by 51 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 novel sequential estimation framework for battery state of health and remaining useful life based on sparse and limited data;Journal of Energy Storage;2024-10

2. A model-data-fusion method for real-time continuous remaining useful life prediction of lithium batteries;Measurement;2024-10

3. Probabilistic remaining useful life prediction without lifetime labels: A Bayesian deep learning and stochastic process fusion method;Reliability Engineering & System Safety;2024-10

4. State of Health Estimation for Lithium-Ion Batteries Based on Multi-Scale Frequency Feature and Time-Domain Feature Fusion Method;Journal of Electrochemical Energy Conversion and Storage;2024-09-11

5. A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteries;Journal of Energy Storage;2024-09

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