Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator

Huo, Da; Hall, Carrie M.

doi:10.1016/j.egyai.2023.100289

Cited by 18 publications

(1 citation statement)

References 24 publications

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“…m air (t)= ρ air * Q air (t) (10) where ρ air is the density of air in kg/m 3 at Standard Temperature and Pressure (STP) and Q air (t) is the volumetric air flow rate in m 3 /s. For an arbitrary fan rotational speed, Q air (t) is determined by [21] Q air (t)= w(t) Q nom w nom (11) where w(t) denotes the arbitrary fan speed in revolutions per minute (rpm), Q nom is the nominal volumetric flow rate of the fan in m 3 /s, and w nom is the nominal fan speed in rpm. Q nom and w nom are determined in a manner as described by [22].…”

Section: Air Flow Modelmentioning

confidence: 99%

A Control-Oriented Model for Predicting Variations in Membrane Water Content of an Open-Cathode Proton Exchange Membrane Fuel Cell

Adunyah,

Gawli,

Hall

2024

Energies

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Proton exchange membrane (PEM) fuel cells have emerged as a viable alternative energy production source for stationary and transportation applications. Reliable and sustainable fuel cell operation requires effective water management. Membrane water content can vary along the stack during transients which can lead to losses in fuel cell performance. To control these variations, a model that predicts the internal humidity dynamics of the stack is needed. In this study, a control-oriented model for predicting membrane water content variation was developed and implemented in MATLAB/Simulink. A lumped parameter model was initially developed and then further discretized into smaller control volumes to track humidity distribution along the stack. To validate the model’s predictions, the predicted results were compared to computer simulation results from GT-Suite. The root mean square error (RMSE) between the model’s prediction and GT-Suite’s simulation results was found to be within 1.5 membrane water content for all cases, demonstrating the model’s capability to capture the variation in membrane water content along the stack. The developed model will be useful for real-time control of membrane water content distribution in PEM fuel cells.

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Section: Air Flow Modelmentioning

confidence: 99%

A Control-Oriented Model for Predicting Variations in Membrane Water Content of an Open-Cathode Proton Exchange Membrane Fuel Cell

Adunyah,

Gawli,

Hall

2024

Energies

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Ice Nucleation Mechanisms on Platinum Surfaces in PEM Fuel Cells: Effects of Surface Morphology and Wettability

Wang,

Fan,

et al. 2024

Advanced Science

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Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL.

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Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

Gong,

Wang,

Benbouzid

et al. 2024

Energy and AI

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Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator

Cited by 18 publications

References 24 publications

A Control-Oriented Model for Predicting Variations in Membrane Water Content of an Open-Cathode Proton Exchange Membrane Fuel Cell

A Control-Oriented Model for Predicting Variations in Membrane Water Content of an Open-Cathode Proton Exchange Membrane Fuel Cell

Ice Nucleation Mechanisms on Platinum Surfaces in PEM Fuel Cells: Effects of Surface Morphology and Wettability

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

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