Anode-Design Strategies for Improved Performance of Polymer-Electrolyte Fuel Cells with Ultra-Thin Electrodes

Steinbach, Andrew J.; Allen, Jeffrey S.; Borup, Rodney L.; Hussey, Daniel S.; Jacobson, David L.; Комлев, А. А.; Kwong, Anthony; MacDonald, James C.; Mukundan, Rangachary; Pejsa, Matt J.; Roos, Michael; Santamaria, Anthony D.; Sieracki, James M.; Spernjak, Dusan; Zenyuk, Iryna V.; Weber, Adam Z.

doi:10.1016/j.joule.2018.03.022

Cited by 56 publications

(37 citation statements)

References 53 publications

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“…The complex interactions between the layers of the PEM fuel cell still requires significant experimental and theoretical investigations. There are a number of studies that have been devoted to developing numerical models for analysing the influence of some of the GDL parameters on the PEMFC performance, namely thickness [13,[25][26][27], porosity [14][15][16][17]28], permeability [29][30][31][32] or contact angle [13,[33][34]. Notably, the porosity of the GDL has a tremendous impact on the performance of the PEM fuel cell.…”

Section: Introductionmentioning

confidence: 99%

PEM fuel cell performance improvement through numerical optimization of the parameters of the porous layers

Carcadea

Ismail

Ingham

et al. 2020

International Journal of Hydrogen Energy

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A numerical model for a PEM fuel cell has been developed and used to investigate the effect of some of the key parameters of the porous layers of the fuel cell (GDL and MPL) on its performance. The model is comprehensive as it is three-dimensional, multiphase and non-isothermal and it has been well-validated with the experimental data of a 5 cm² active area-fuel cell with/without MPLs. As a result of the reduced mass transport resistance of the gaseous and liquid flow, a better performance was achieved when the GDL thickness was decreased. For the same reason, the fuel cell was shown to be significantly improved with increasing the GDL porosity by a factor of 2 and the consumption of oxygen doubled when increasing the porosity from 0.40 to 0.78. Compared to the conventional constant-porosity GDL, the graded-porosity (gradually decreasing from the flow channel to the catalyst layer) GDL was found to enhance the fuel cell performance and this is due to the better liquid water rejection. The incorporation of a realistic value for the contact resistance between the GDL and the bipolar plate slightly decreases the performance of the fuel cell. Also the results show that the addition of the MPL to the GDL is crucially important as it assists in the humidifying of the electrolyte membrane, thus improving the overall performance of the fuel cell. Finally, realistically increasing the MPL contact angle has led to a positive influence on the fuel cell performance.

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Section: Introductionmentioning

confidence: 99%

PEM fuel cell performance improvement through numerical optimization of the parameters of the porous layers

Carcadea

Ismail

Ingham

et al. 2020

International Journal of Hydrogen Energy

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show abstract

“…In this regard, Cheng et al 51 employed synchrotron X-ray microdiffraction to spatially resolve and quantify the degradation of Pt catalyst by mapping the size of Pt particle. In addition, direct evidence of liquid water accumulation at the anode leading to severe ionomer swelling, performance loss, and cell drying due to undesirably low water content in the cathode was obtained using operando neutron imaging and operando micro-X-ray-computed tomography 52,53 .…”

Section: Low-pgm Electrocatalysts and Triple-phase Boundary Designmentioning

confidence: 99%

Advancements in cathode catalyst and cathode layer design for proton exchange membrane fuel cells

et al. 2021

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Proton exchange membrane fuel cells have been recently developed at an increasing pace as clean energy conversion devices for stationary and transport sector applications. High platinum cathode loadings contribute significantly to costs. This is why improved catalyst and support materials as well as catalyst layer design are critically needed. Recent advances in nanotechnologies and material sciences have led to the discoveries of several highly promising families of materials. These include platinum-based alloys with shape-selected nanostructures, platinum-group-metal-free catalysts such as metal-nitrogen-doped carbon materials and modification of the carbon support to control surface properties and ionomer/catalyst interactions. Furthermore, the development of advanced characterization techniques allows a deeper understanding of the catalyst evolution under different conditions. This review focuses on all these recent developments and it closes with a discussion of future research directions in the field.

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“…As an efficient and clean energy conversion device, proton exchange membrane fuel cell (PEMFC) is frequently highlighted in the worldwide energy applications in recent years [1][2][3]. PEMFC technology made remarkable progress in the past decade, and its commercialization is also gradually realized [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling

Wang

Xie

Xuan

et al. 2020

Energy Conversion and Management

141

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Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation-resource efficiency: the *Revised Manuscript with no changes marked Click here to view linked References calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic Algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems.

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Anode-Design Strategies for Improved Performance of Polymer-Electrolyte Fuel Cells with Ultra-Thin Electrodes

Cited by 56 publications

References 53 publications

PEM fuel cell performance improvement through numerical optimization of the parameters of the porous layers

PEM fuel cell performance improvement through numerical optimization of the parameters of the porous layers

Advancements in cathode catalyst and cathode layer design for proton exchange membrane fuel cells

AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling

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