[HTML][HTML] Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology

Y Wang, B Seo, B Wang, N Zamel, K Jiao, XC Adroher - Energy and AI, 2020 - Elsevier
Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that directly
convert the chemical energy stored in fuel into electrical energy with a practical conversion …

A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring

W Ming, P Sun, Z Zhang, W Qiu, J Du, X Li… - International Journal of …, 2023 - Elsevier
A fuel cell is a power generation device that directly converts chemical energy into electrical
energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle …

[HTML][HTML] Application of machine learning in optimizing proton exchange membrane fuel cells: a review

R Ding, S Zhang, Y Chen, Z Rui, K Hua, Y Wu, X Li… - Energy and AI, 2022 - Elsevier
Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for
hydrogen energy are crucial for achieving an eco-friendly society, but their cost and …

Modeling and temperature control of a water-cooled PEMFC system using intelligent algorithms

JH Chen, P He, SJ Cai, ZH He, HN Zhu, ZY Yu… - Applied Energy, 2024 - Elsevier
Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source
owing to its low operating temperature, high energy efficiency, high power density, and …

[HTML][HTML] Advances of machine learning in multi-energy district communities‒mechanisms, applications and perspectives

Y Zhou - Energy and AI, 2022 - Elsevier
Energy paradigm transition towards the carbon neutrality requires combined and continuous
efforts in cleaner power production, advanced energy storages, flexible district energy …

[HTML][HTML] Machine learning modeling for proton exchange membrane fuel cell performance

A Legala, J Zhao, X Li - Energy and AI, 2022 - Elsevier
Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change
mitigation, and a fast and accurate model is necessary for its control and operation in …

Review on hydrogen fuel cell condition monitoring and prediction methods

RH Lin, XN **, PN Wang, BD Wu, SM Tian - International Journal of …, 2019 - Elsevier
A hydrogen fuel cell combines oxygen and hydrogen to generate electricity, which becomes
a promising power source. The conditions of the fuel cell, such as health status, and faults …

An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of …

C Zhang, H Hu, J Ji, K Liu, X **a, MS Nazir, T Peng - Applied Energy, 2023 - Elsevier
Accurate prediction of the future degradation trend (FDT) and remaining useful life (RUL) of
proton exchange membrane fuel cell (PEMFC) is crucial in the prognosis and health …

Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm

K Chen, A Badji, S Laghrouche, A Djerdir - Applied Energy, 2022 - Elsevier
Degradation and cost are the main factors affecting the commercial applications of Polymer
Electrolyte Membrane Fuel Cells (PEMFC). This paper proposes a novel degradation …

Fullerene applications in fuel cells: A review

J Coro, M Suarez, LSR Silva, KIB Eguiluz… - International Journal of …, 2016 - Elsevier
High-area carbon particles are regularly used as catalyst supports in fuel cells electrodes.
The carbon material used as support has a strong influence on the properties of supported …