Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
[HTML][HTML] Artificial intelligence and machine learning in energy systems: A bibliographic perspective
Economic development and the comfort-loving nature of human beings in recent years have
resulted in increased energy demand. Since energy resources are scarce and should be …
resulted in increased energy demand. Since energy resources are scarce and should be …
A review on lifetime prediction of proton exchange membrane fuel cells system
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 …
power converter device in a wide range of applications, especially in the transportation area …
[HTML][HTML] Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
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 …
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 …
energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle …
Development and application of fuel cells in the automobile industry
Y Luo, Y Wu, B Li, T Mo, Y Li, SP Feng, J Qu… - Journal of Energy …, 2021 - Elsevier
The automotive industry consumes a large amount of fossil fuels consequently exacerbating
the global environmental and energy crisis and fuel cell electric vehicles (FCEVs) are …
the global environmental and energy crisis and fuel cell electric vehicles (FCEVs) are …
[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 …
hydrogen energy are crucial for achieving an eco-friendly society, but their cost and …
Continuum modeling of porous electrodes for electrochemical synthesis
Electrochemical synthesis possesses substantial promise to utilize renewable energy
sources to power the conversion of abundant feedstocks to value-added commodity …
sources to power the conversion of abundant feedstocks to value-added commodity …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions
XC Zhang, JG Gong, FZ Xuan - International Journal of Fatigue, 2021 - Elsevier
Deep learning is a particular kind of machine learning, which achieves great power and
flexibility by a nested hierarchy of concepts. A general life prediction method for components …
flexibility by a nested hierarchy of concepts. A general life prediction method for components …