Determining the Impact of Dynamic Load Conditions on Interfacial Liquid Water Accumulation in Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers Using Synchrotron X-Ray Radiography
Notice bibliographique
Résumé
Automakers around the world are investing in polymer electrolyte membrane (PEM) fuel cells for the next generation drivetrains for their low emission automobiles. For PEM fuel cell vehicles to become commercially competitive, fuel cell efficiency and durability must increase. However, in order to advance the performance of PEM fuel cells, the dynamic behaviour of liquid water transport must be taken into consideration, particularly because fuel cell cars will be driven with dynamic load (power draw) conditions1. The relationship between PEM fuel cell performance and dynamic load must be well understood so that next generation fuel cells can be tailored for optimal dynamic operation. Effective water management is key to obtaining the optimal performance of fuel cells2,3. It has also been shown that the transient response of two-phase flows of water is significantly longer compared to the electrochemical response1. Therefore, the changes in two-phase flows in the fuel cell can have a larger impact on the dynamic performance of fuel cells4,5. Liquid water evolves in the porous layers of the fuel cell until it reaches a steady state for the current density of operation6. However, there is a scarcity of experimental observations focused on GDL liquid water saturation as a function of time with changes in operational current density. In this work, the transient response of the cell potential and the dynamic change in water saturation of the gas diffusion layer (GDL) are investigated. The current density of the cell was increased from 0 A/cm2 to prescribed values at various rates of increasing current density (ramp rates), while the response of the cell potential was concurrently measured. Figure 1 shows the transient response of the cell potential due to a change in the current density from 0 to 1.2 A/cm2 at various rates of increase. With a step change in the current density, the voltage fluctuated for a few minutes before the potential of the cell dropped, leading to performance failure that was indicative of flooding. However, when a ramp was applied to gradually reach the same current density as the step change, the cell was able to maintain a steady potential. This shows that the ramp rate of increasing current density has a direct impact on the cell performance and its transient response. In addition, X-ray radiographic evidence of water in the porous layers of the PEM fuel cell during transient operation will be presented. The dynamic water evolution at the microporous layer (MPL)|catalyst layer interface and the MPL|GDL interface are highly influenced by the rate of increasing current density. The cell performance was correlated to the time-dependent water evolution patterns in order to identify the interfacial liquid water accumulation as a function of changing current density. References: 1. R. Banerjee and S. G. Kandlikar, Int. J. Hydrog. Energy, 40, 3990–4010 (2015). 2. S. G. Kandlikar, Heat Transf. Eng., 29, 575–587 (2008). 3. J. P. Owejan, J. J. Gagliardo, J. M. Sergi, S. G. Kandlikar, and T. A. Trabold, Int. J. Hydrog. Energy, 34, 3436–3444 (2009). 4. R. Banerjee and S. G. Kandlikar, Int. J. Hydrog. Energy, 39, 19079–19086 (2014). 5. R. Banerjee and S. G. Kandlikar, J. Power Sources, 268, 194–203 (2014). 6. Y. Wang and C.-Y. Wang, J. Electrochem. Soc., 154, B636 (2007). Figure 1
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Comment cette classification a été obtenuedéplier
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 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».