Approaches for the Modeling of PBI/H3PO4 Based HT-PEM Fuel Cells

Siegel, C.; Lang, Sebastian; Fontes, Ed; Beckhaus, Peter

doi:10.1007/978-3-319-17082-4_18

Cited by 2 publications

(3 citation statements)

References 207 publications

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“…where K H 2 p -the permeability coefficient of hydrogen in the membrane; ν H 2 -the stoichiometry coefficient; n H 2 e -the number of electrons; P H 2 cat,j -the partial pressure of hydrogen in the anode catalyst layer. Siegel et al [25] have provided empirical correlations for calculating the permeability coefficient in an HT-MEA.…”

Section: Electrode Currentmentioning

confidence: 99%

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A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell

Danilov,

Kolb,

Cremers

2024

Energies

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A dynamic tanks-in-series model has been developed for the coupled heat, mass, and charge transfer processes in a high-temperature proton exchange membrane fuel cell. The semi-empirical model includes the heat and mass balance equations in the gas channels and the membrane electrode assembly together with the charge balance at the electrode/membrane interfaces. The outputs of the tanks-in-series model are the concentration, the temperature, and the current density with a step change from tank to tank. The dynamic non-isothermal model is capable of predicting both the transient and steady-state behavior of the fuel cell and reproducing impedance data under harmonic perturbations of the cell potential together with a comprehensive interpretation of experimental data.

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Section: Electrode Currentmentioning

confidence: 99%

“…For the potentiostatic mode, the cell voltage (E cell ) is an input parameter. In their studies, Olapade et al [25,26] presented the following empirical correlation for the ionic conductivity of the PBI membrane:…”

Section: Electrolyte Currentmentioning

confidence: 99%

A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell

Danilov,

Kolb,

Cremers

2024

Energies

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“…For example, a peak power of 1.7 W cm -2 has been achieved for HT-PEMFCs using hydrogen and oxygen [5,6,7,8], while a HT-PEM ECHP has shown 1 A cm -2 at 55 mV [7]. A central advantage of HT-PEM fuel cells and hydrogen pumps is their ability to tolerate carbon monoxide (CO) in the hydrogen stream as CO adsorption at temperatures of 200 °C or above is diminished [9]. Over 90% of all hydrogen is derived from steam methane reforming (SMR) that leads to mixtures of hydrogen and CO. Electrochemical processes that can tolerate CO allow the use of low-cost hydrogen because hydrogen from SMR is about 2x to 3x cheaper than hydrogen from water electrolysis.…”

Section: Introductionmentioning

confidence: 99%

PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Briceno-Mena,

Arges,

Romagnoli

2021

Preprint

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Widespread adoption of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs) requires models and computational tools that provide accurate scale-up and optimization. Knowledge-based modeling has limitations as it is time consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling on the other hand, is easier to implement, but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are uniquely combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFC was used to generate simulated data (887,735 points) and used to pretrain a neural network source model. Furthermore, the source model developed for HT-PEMFCs was successfully applied to HT-PEM ECHPs -a different electrochemical system that utilizes similar materials to the fuel cell. Experimental datasets from both HT-PEMFCs and HT-PEM ECHPs with different materials and operating conditions (~50 points each) were used to train 8 target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs and between 6.38 and 8.46% for HT-PEM ECHPs).

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Approaches for the Modeling of PBI/H3PO4 Based HT-PEM Fuel Cells

Cited by 2 publications

References 207 publications

A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell

A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell

PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

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