Machine learning for battery systems applications: Progress, challenges, and opportunities-Reference-Cited by-同舟云学术

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Machine learning for battery systems applications: Progress, challenges, and opportunities

Published:2024-05 Issue: Volume:601 Page:234272
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

Nozarijouybari Zahra^ORCID,Fathy Hosam K.

Funder

University of Maryland

Publisher

Elsevier BV

Reference158 articles.

1. Perspective—combining physics and machine learning to predict battery lifetime;Aykol;J. Electrochem. Soc.,2021

2. An enhanced single-particle model using a physics-informed neural network considering electrolyte dynamics for lithium-ion batteries;Xue;Batteries,2023

3. Integrating physics-based modeling with machine learning for lithium-ion batteries;Tu;Appl. Energy,2023

4. A physics-inspired machine learning nonlinear model of Li-ion batteries;Ahmadzadeh,2023

5. Machine learning: an advanced platform for materials development and state prediction in lithium-ion batteries;Lv;Adv. Mater.,2022

Cited by 9 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 discharge energy prediction for lithium-ion batteries over broad current ranges: A machine learning approach;Applied Energy;2024-12

2. Artificial intelligence-driven real-world battery diagnostics;Energy and AI;2024-12

3. Enhancing battery pack safety against cone impact using machine learning techniques and Gaussian noise;Process Safety and Environmental Protection;2024-11

4. Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter;Energy;2024-10

5. Identifying freshness of various chilled pork cuts using rapid imaging analysis;Journal of the Science of Food and Agriculture;2024-09-09

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