A Reversible Degradation Adaptive Avoidance-Driven Echo State Network: Application to Fuel Cell Prognostics-Reference-Cited by-同舟云学术

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A Reversible Degradation Adaptive Avoidance-Driven Echo State Network: Application to Fuel Cell Prognostics

Published:2024-05-10 Issue: Volume: Page:2303-2308
ISSN:
Container-title:2024 IEEE 7th International Electrical and Energy Conference (CIEEC)
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Author:

Zhang Shuang¹,Wang Chu¹,Chen Zhiwen¹,Lv Zhigang¹,Li Xiaoyan¹,Di Ruohai¹,Li Zhongliang²,Wang Peng³

Affiliation:

1. School of Electronic Information Engineering, Xi'an Technological University,Xi'an,China

2. University of Franche-Comte,FEMTO-ST (CNRS 6174) / FCLAB (CNRS 3539),Belfort,France

3. Office of Development and Planning, Xi'an Technological University,Xi'an,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10582916/10582978/10583806.pdf?arnumber=10583806

Reference17 articles.

1. Recent progress and challenges of multi-stack fuel cell systems: Fault detection and reconfiguration, energy management strategies, and applications

2. A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load

3. Degradations analysis and aging modeling for health assessment and prognostics of PEMFC

4. Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

5. The challenges in reliable determination of degradation rates and lifetime in polymer electrolyte membrane fuel cells

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