[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 …

Degradation mechanisms of proton exchange membrane fuel cell under typical automotive operating conditions

P Ren, P Pei, Y Li, Z Wu, D Chen, S Huang - Progress in Energy and …, 2020 - Elsevier
The proton exchange membrane (PEM) fuel cell is an ideal automotive power source with
great potential, owing to its high efficiency and zero emissions. However, the durability and …

A review on lifetime prediction of proton exchange membrane fuel cells system

Z Hua, Z Zheng, E Pahon, MC Péra, F Gao - Journal of Power Sources, 2022 - Elsevier
The proton exchange membrane fuel cells (PEMFC) system is a promising eco-friendly
power converter device in a wide range of applications, especially in the transportation area …

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 …

Data-driven proton exchange membrane fuel cell degradation predication through deep learning method

R Ma, T Yang, E Breaz, Z Li, P Briois, F Gao - Applied energy, 2018 - Elsevier
Proton exchange membrane fuel cells (PEMFCs) is one of the principal candidates to take
part of the worldwide future clean and renewable energy solution. However, fuel cells are …

Machine learning toward advanced energy storage devices and systems

T Gao, W Lu - Iscience, 2021 - cell.com
Technology advancement demands energy storage devices (ESD) and systems (ESS) with
better performance, longer life, higher reliability, and smarter management strategy …

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

S Meraghni, LS Terrissa, M Yue, J Ma, S Jemei… - International journal of …, 2021 - Elsevier
Prognostics and health management of proton exchange membrane fuel cell (PEMFC)
systems have driven increasing research attention in recent years as the durability of …

Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning

W He, T Liu, W Ming, Z Li, J Du, X Li, X Guo… - … and Sustainable Energy …, 2024 - Elsevier
Hydrogen fuel cells are promising power sources that directly transform the chemical energy
produced by the chemical reaction of hydrogen and oxygen into electrical energy. However …

Performance analysis of a degraded PEM fuel cell stack for hydrogen passenger vehicles based on machine learning algorithms in real driving conditions

M Raeesi, S Changizian, P Ahmadi… - Energy Conversion and …, 2021 - Elsevier
Fuel cell degradation is one of the main challenges of hydrogen fuel cell vehicles, which can
be solved by robust prediction techniques like machine learning. In this research, a specific …

Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application

J Zuo, H Lv, D Zhou, Q Xue, L **, W Zhou, D Yang… - Applied Energy, 2021 - Elsevier
Currently, the larger-scaled commercialization of fuel cell technology is considerably
impeded by the limited durability of fuel cells. Prognostics and health management (PHM) is …