https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1131&context=usdoepub

A review of polymer electrolyte membrane fuel cells: Technology, applications,and needs on fundamental research

3.8.2. Temperature Distribution Measurements 4749 3.8.3. Two-Phase Visualization 4750 3.8.4. Experimental Validation 4751 3.9. Modeling the Catalyst Layer at Pore Level 4751 3.10. Summary and Outlook 4752 4. Direct Methanol Fuel Cells 4753 4.1. Technical Challenges 4754 4.1.1. Methanol Oxidation Kinetics 4754 4.1.2. Methanol Crossover 4755 4.1.3. Water Management 4755 4.1.4. Heat Management 4756 4.2. DMFC Modeling 4756 4.2.1. Needs for Modeling 4756 4.2.2. DMFC Models 4756 4.3. Experimental Diagnostics 4757 4.4. Model Validation 4758 4.5. Summary and Outlook 4760 5. Solid Oxide Fuel Cells 4760 5.1. SOFC Models 4761 5.2. Summary and Outlook 4762 6. Closing Remarks 4763 7. Acknowledgments 4763 8. References 4763

Fundamental models for fuel cell engineering.

Water transport in polymer electrolyte membrane fuel cells

In this review, we have examined the different models for polymer-electrolyte fuel cells operating with hydrogen. The major focus has been on transport of the various species within the fuel cell. The different regions of the fuel cell were examined, and their modeling methodologies and equations were elucidated. In particular, the 1-D fuel-cell sandwich was discussed thoroughly because it is the most important part of the fuel-cell assembly. Models that included other effects such as temperature gradients and transport in other directions besides through the fuel-cell sandwich were also discussed. Models were not directly compared to each other; instead they were broken down into their constitutive parts. The reason for this is that validation of the models is usually accomplished by comparison of simulation to experimental polarization data (e.g., Figure 3). However, other data can also be used such as the net flux of water through the membrane. In fitting these data, the models vary not only in their complexity and treatments but also in their number and kind of fitting parameters. This is one reason it is hard to justify one approach over another by just looking at the modeling results. In general, it seems reasonable that the more complex models, which are based on physical arguments and do not contain many fitting parameters, are perhaps closest to reality. Of course, this assumes that they fit the experimental data and observations. This last point has been overlooked in the validation of many models. For example, a model may fit the data very well for certain operating conditions, but if it does not at least predict the correct trend when one of those conditions is changed, then the model is shown to be valid only within a certain operating range. This review has highlighted the important effects that should be modeled. These include two-phase flow of liquid water and gas in the fuel-cell sandwich, a robust membrane model that accounts for the different membrane transport modes, nonisothermal effects, especially in the directions perpendicular to the sandwich, and multidimensional effects such as changing gas composition along the channel, among others. For any model, a balance must be struck between the complexity required to describe the physical reality and the additional costs of such complexity. In other words, while more complex models more accurately describe the physics of the transport processes, they are more computationally costly and may have so many unknown parameters that their results are not as meaningful. Hopefully, this review has shown and broken down for the reader the vast complexities of transport within polymer-electrolyte fuel cells and the various ways they have been and can be modeled.

Modeling transport in polymer-electrolyte fuel cells.

aLawrence Berkeley National Laboratory, Berkeley, California 94720, USA bLos Alamos National Laboratory, Los Alamos, New Mexico 87545, USA cUnited Technologies Research Center, East Hartford, Connecticut 06118, USA dSchool of Mechanical and Systems Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom eChemical and Biomolecular Engineering Department, University of California, Berkeley, California 94720, USA fFuel Cell Research and Development, General Motors, Pontiac, Michigan 48340, USA gBallard Power Systems, Burnaby, British Columbia V5J 5J8, Canada hFuel Cell Research Centre, Queens University, Kingston, Ontario K7L 3N6, Canada iDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA jDepartment of Mechanical Aerospace and Biomedical Engineering, University of Tennessee at Knoxville, Knoxville, Tennessee 37996, USA kDepartment of Mechanical Engineering Technology, SUNY Alfred State College, Alfred, New York 14802, USA lDepartment of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 2G, Canada

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A Critical Review of Modeling Transport Phenomena in Polymer-Electrolyte Fuel Cells

In this part of the paper, we present a model to treat formation and transport of liquid water in proton exchange membrane ~PEM! fuel cells ~FCs! in three-dimensional ~3-D! geometry. The performance of modern-day PEM FCs at high current density are largely dictated by the effective management of liquid water. In the first part of this paper, a rigorous model was presented to model PEM FCs using a computational fluid dynamic technique. It was found that under the assumption of no liquid water formation, the model consistently overpredicted measured polarization behavior. In the model presented here, the phase change process is modeled as an equilibrium process, while the transport of liquid water is governed by pressure, surface tension, gravity and electro-osmotic drag. Results show that the inclusion of liquid water transport greatly enhances the predictive capability of the model and is necessary to match experimental data at high current density.

James Vernon Cole

Papers

Rigorous 3-d mathematical modeling of PEM fuel cells - II. Model predictions with liquid water transport