Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models-Reference-Cited by-同舟云学术

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Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models

Published:2022-11 Issue: Volume:326 Page:119975
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Liu Ze,Xu Sichuan,Zhao Honghui,Wang Yupeng

Funder

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference64 articles.

1. Batteries and fuel cells for emerging electric vehicle markets;Cano;Nat Energy,2018

2. Designed experiments to characterize PEMFC material properties and performance;Flick;Appl Energy,2014

3. Degradation pattern prediction of a polymer electrolyte membrane fuel cell stack with series reliability structure via durability data of single cells;Bae;Appl Energy,2014

4. A review of polymer electrolyte membrane fuel cell durability test protocols;Yuan;J Power Sources,2011

5. Review on hydrogen fuel cell condition monitoring and prediction methods;Lin;Int J Hydrogen Energy,2019

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1. Multi-scenario long-term degradation prediction of PEMFC based on generative inference informer model;Applied Energy;2025-01

2. Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer;Renewable Energy;2024-11

3. An enhanced salp swarm algorithm with chaotic mapping and dynamic learning for optimizing purge process of proton exchange membrane fuel cell systems;Energy;2024-11

4. An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction;Energy;2024-11

5. Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization;Scientific Reports;2024-09-13

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