Designing graded fuel cell electrodes for proton exchange membrane (PEM) fuel cells with recurrent neural network (RNN) approaches

Lei, Hanhui; Lü, Xing; Jiang, H.; Wang, Yucheng; Xu, Ben Bin; Xuan, Jin; Liu, Xiaoteng

doi:10.1016/j.ces.2022.118350

Cited by 14 publications

(2 citation statements)

References 61 publications

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“…Likewise, CL and ML have also been implemented in the field of GDL for feature extraction [296][297][298], optimization [290][291][292][293][294][295][296][297][298][299][300][301], and degradation [302,303], though they particularly do not consider the degradation of GDL. Mahdaviara et al [297] implemented 2D and 3D U-net deep learning models for multiphase segmentation of images from high-resolution X-ray tomography (micro-CT).…”

Section: In the Field Of Gdlmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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Section: In the Field Of Gdlmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…The design of novel porous electrode architecture and the study of mass/ion transfer within the structures are the core issues in developing fuel cells and other electrochemical energy electrolysers [75]. Currently, electrochemical reactors suffer from gas diffusion through the electrode, insufficient ion transfer within the electrolyte/flow channel, or difficult electron transfer between the gas diffusion layer and catalyst layer [50].…”

Section: Fuel Cell Type Electrochemical Reactormentioning

confidence: 99%

Ice-Templated Method to Promote Electrochemical Energy Storage and Conversion: A Review

Wang

Zheng

et al. 2023

Energies

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The ice−templated method (ITM) has drawn significant attention to the improvement of the electrochemical properties of various materials. The ITM approach is relatively straightforward and can produce hierarchically porous structures that exhibit superior performance in mass transfer, and the unique morphology has been shown to significantly enhance electrochemical performance, making it a promising method for energy storage and conversion applications. In this review, we aim to present an overview of the ITM and its applications in the electrochemical energy storage and conversion field. The fundamental principles underlying the ITM will be discussed, as well as the factors that influence the morphology and properties of the resulting structures. We will then proceed to comprehensively explore the applications of ITM in the fabrication of high−performance electrodes for supercapacitors, batteries, and fuel cells. We intend to find the key advances in the use of ITM and evaluate its potential to overcome the existing challenges in the development of efficient energy storage and conversion systems.

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Sustainable Aviation: A Review in Intelligent Health Management for Decarbonized Aerospace Propulsion

Xiao,

Zhang,

et al. 2024

Lecture Notes in Electrical Engineering

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Designing graded fuel cell electrodes for proton exchange membrane (PEM) fuel cells with recurrent neural network (RNN) approaches

Cited by 14 publications

References 61 publications

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Ice-Templated Method to Promote Electrochemical Energy Storage and Conversion: A Review

Sustainable Aviation: A Review in Intelligent Health Management for Decarbonized Aerospace Propulsion

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