Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
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 …

[HTML][HTML] Artificial intelligence and machine learning in energy systems: A bibliographic perspective

A Entezari, A Aslani, R Zahedi, Y Noorollahi - Energy Strategy Reviews, 2023 - Elsevier
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 …

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 …

[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 …

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 …

[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 …

Continuum modeling of porous electrodes for electrochemical synthesis

JC Bui, EW Lees, LM Pant, IV Zenyuk, AT Bell… - Chemical …, 2022 - ACS Publications
Electrochemical synthesis possesses substantial promise to utilize renewable energy
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 …

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 …