Remaining Useful Life Estimation for Lithium-Ion Batteries using Physics-Informed Neural Networks-Reference-Cited by-同舟云学术

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Remaining Useful Life Estimation for Lithium-Ion Batteries using Physics-Informed Neural Networks

Published:2024-06-17 Issue: Volume: Page:67-73
ISSN:
Container-title:2024 IEEE International Conference on Prognostics and Health Management (ICPHM)
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Author:

Pugalenthi Karkulali¹,Park Hyunseok²,Hussain Shaista³,Raghavan Nagarajan⁴

Affiliation:

1. Singapore Institute of Manufacuting Technology Agency for Science, Technology and Research,Singapore

2. Hanyang University,Department of Information Systems,Seoul

3. Institute of High-Performance Computing (IHPC),Agency for Science, Technology and Research,Singapore

4. Singapore University of Technology and Design,Engineering Product Development Pillar,Singapore

Funder

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10625768/10626308/10627352.pdf?arnumber=10627352

Reference13 articles.

1. Hybrid Particle Filter Trained Neural Network for Prognosis of Lithium-Ion Batteries

2. Predicting Lumen Degradation of Light Emitting Diodes Using Hybrid Particle Filter Trained Neural Networks

3. fPINNs: Fractional Physics-Informed Neural Networks

4. Physics-informed neural networks for high-speed flows

5. Physics-Informed Neural Networks for Heat Transfer Problems

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