Impact of Micro-Porous Layer Properties on Fuel Cell Performance
Notice bibliographique
Résumé
Fuel cell engines, in general, and polymer electrolyte fuel cells, in particular, are potential substitutes for internal combustion engines in vehicles. Considering the current stage of fuel cell performance, the technology is gaining competiveness and is not far from being mature. Decades of research and development in this field have led to the existence of high power density stacks with acceptable durability. The power generation inside the fuel cell is facilitated by a membrane electrode assembly (MEA) which contains a catalyst coated membrane (CCM) and two gas diffusion layers (GDLs) exposed to hydrogen and oxygen on opposite sides. One of the major performance enhancing additions to the MEA architecture is the micro-porous layer (MPL), which is a thin layer of carbon nano-particles mixed with hydrophobic agent (e.g., PTFE) coated on the macro-porous GDL substrate. While the main role of the GDL is to provide reactant transport from the flow channels to the reaction sites in the catalyst layer, the underlying motivation of adding the thin MPL layer is to create a smooth transition between the macro-pores in the substrate and the nano-pores of the adjacent catalyst layer. It has been shown experimentally that the presence of the MPL can enhance the fuel cell performance under typical operating conditions; however, the fundamental understanding of its direct and indirect performance benefits is still an active field of research. Reliable, intrinsic MPL properties are difficult to measure experimentally, provided that the thin, delicate MPL material is not available as a discrete object and therefore requires a physical support upon which it is coated. Most commonly to date, MPL properties have been extracted from properties measured on full GDLs using established methods. However, due to the coupling and overlap of the macro-porous and micro-porous parts, such estimates are relatively crude. Numerical modeling of the structure and properties can therefore be useful to improve the fundamental understanding of the MPL material. More importantly, linking MPL structure and properties to fuel cell performance will ultimately close the design cycle for development of new materials and MEA concepts with enhanced performance. The present work focuses on examining the sensitivity of the fuel cell performance on MPL properties. To achieve this goal, a steady state three-dimensional two-phase flow numerical model is developed to simulate the fuel cell performance. The results of the simulations demonstrate a weak dependence of fuel cell performance on the electrical and thermal conductivity of the MPL within the considered range of analysis, i.e., between 0.05 to 0.25 W m-1 K-1 for the thermal conductivity and between 500 to 2500 S m-1 for the electrical conductivity. The effective diffusivity of the MPL, on the other hand, causes substantial changes in the fuel cell performance. Notably, the nano-scale pores of the MPL are sufficiently small for the mass transport to be dominated by Knudsen diffusion, which is intimately linked to the internal morphology of the material and also depends on the nature of the diffusing species. It is observed that when the effective diffusivity is decreased, the cell voltage drops considerably, as depicted in Figure 1. This shows the significance of MPL effective diffusivity and its impact on fuel cell performance. The effect is particularly critical at high current densities, which is a relevant operating range for automotive fuel cell technologies. MPL materials with sufficiently large pore sizes and porosity that support an effective diffusivity of at least 0.20 are recommended to avoid performance trade-offs. Acknowledgments: The research was supported by Mercedes-Benz Canada, Fuel Cell Division and the Natural Sciences and Engineering Research Council of Canada.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».