Simulation of Cathode Catalyst Durability Under Fuel Cell Vehicle Operation - the Effect of Temperature
Notice bibliographique
Résumé
Fuel cell vehicles (FCVs) emerge to be promising candidates to produce clean power for the transportation sector in order to tackle climate change issues. Although the commercialization of polymer electrolyte membrane fuel cells (PEMFCs) has progressed in the past few decades, there are still obstacles to address, including high cost and limited hydrogen infrastructure. For heavy duty transportation applications, the PEMFC durability is also not yet proven, and extrapolating from lab data to real-world field operating conditions remains a significant challenge [1]. In this work, the cathode catalyst degradation in PEMFCs is studied to make an estimation of fuel cell durability in the FCV application. The well-known Butler-Volmer approach is utilized to model platinum dissolution and redeposition, platinum oxidation and platinum ion formation during the fuel cell operation [2]. First, the model is calibrated with the results presented by Ferreira et al. [3] at 80℃; the calibrated model is then validated with Kocha’s results [4] to demonstrate the model’s capability of generating reliable results at different temperatures. In order to realistically predict the cathode degradation pattern and lifetime, the real-life fuel cell operating conditions should be obtained. Therefore, a drive cycle recorded based on a real-life transit bus operation in the city of Victoria is utilized to calculate the input fuel cell voltage profile. The procedure to calculate fuel cell operating voltage profile using the drive cycle has been thoroughly explained by Ahmadi and Kjeang [5]. This procedure employs Newton’s second law to calculate the required cell power density considering the air flow drag force counteracting the vehicle thrust. Finally, polarization curves representing the fuel cell performance are used to calculate the fuel cell voltage profile with the determined required cell power density. Polarization curves measured under a range of temperatures are employed to adequately reflect the effect of temperature on the fuel cell performance. The cell active area is considered to be 500 cm2 and the stack is assumed to contain 225 cells, representing a nominal stack power of 74 kW at 80℃. The fuel cell operation and degradation are simulated at three different temperatures: 60, 70, and 80℃. The change of remaining electrochemically active surface area (ECSA) with time is calculated as the output of the model. Temperature highly affects the electrochemical reactions of platinum dissolution in several ways. First, it affects the Tafel slope in the platinum dissolution reaction. Second, it substantially impacts on platinum degradation reaction rate constant [6]. The Arrhenius approach is taken in this study to apply the effect of temperature on the reaction rate constants. Fuel cell voltage loss over time is determined by assuming simple Tafel kinetics. The cathode lifetime is calculated at 0.6 A/cm2 and is estimated by considering 10% voltage drop as the failure criterion. Fig. 1 shows the simulated change of remaining ECSA over time and the resulting fuel cell lifetime for the three different cell temperatures. The results indicate that the cathode lifetime increases 168% when the cell temperature drops from 80℃ to 60℃ due to a significantly lower platinum degradation rate at 60℃. The results show relatively low cathode catalyst lifetimes for a transit bus. The reason is attributed to the bus drive cycle used as the model input. The drive cycle contains a great portion of deceleration and idling time which leads to a fuel cell operation close to open circuit voltage (OCV), thus experiencing a high degradation rate. In this regard, the present modeling framework could become a useful tool for fuel cell and FCV developers to predict lifetime for new products and systems prior to commercial release. Scientifically, the present model can also be used to further explore key influencing factors on fuel cell durability and develop more durable cell, stack, and system designs for a targeted FCV application. Acknowledgements This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, and Simon Fraser University Community Trust Endowment Fund. References [1] S. D. Knights, K. M. Colbow, J. St-Pierre and D. P. Wilkinson, Journal of power sources. 127 (2004) 127-134. [2] H. A. Baroody and E. Kjeang, Journal of The Electrochemical Society. 168 (2021) 044524. [3] P. Ferreira, Y. Shao-Horn, D. Morgan, R. Makharia, S. Kocha and H. Gasteiger, Journal of the Electrochemical Society. 152 (2005) A2256. [4] S. S. Kocha, Academic Press Oxford. (2012) 89-214. [5] P. Ahmadi and E. Kjeang, International Journal of Energy Research. 41 (2017) 714-727. [6] A. Kregar, G. Tavcar, A. Kravos and T. Katrasnik, Applied Energy. 263 (2020) 114547. Figure 1
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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.
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