Understanding Fuel-Cell Impedance Spectra Via Numerical Modeling: A Single-Phase Water-Management Study
Notice bibliographique
Résumé
Cost of hydrogen-fueled proton-exchange-membrane fuel cells (PEMFCs) remains a challenge for the global commercialization of the technology and needs to be reduced to meet the U.S. Department of Energy goals [1]. Cost-reduction can be achieved by incorporating efficient water management, which helps keep the electrolyte hydrated while avoiding cell flooding by product water. Dynamic liquid-water accumulation and electrolyte hydration can be studied via transient polarization [2-6] and ohmic-resistance [2,4-7] curves, in which they induce hysteresis. Analyzing the polarization and resistance data may, however, be challenging due to the overlapping transients of various physical processes occurring in the cell during the voltage and current sweeps. Electrochemical impedance spectroscopy (EIS) allows the study of the dynamic processes of different time scales separately in the frequency domain and has been widely used for fuel-cell characterization [8-11]. Fuel-cell impedance spectra often exhibit an inductive behavior at low frequencies, which has been shown to be affected by electrolyte hydration [10,12-14]. A recent experimental study [9] showed that the strength of this inductive behavior depends on the anode and cathode RH. Understanding the low-frequency inductance may help improve the water-management strategies for fuel cells. However, this inductive phenomenon has not been investigated in detail with a comprehensive numerical model. In this work, a transient 2D PEMFC model is developed in the open-source fuel-cell modeling software OpenFCST [17] with a thorough validation through dynamic polarization, resistance, and impedance-spectroscopy data at various operating conditions. Unlike other physical EIS models [13-16], the presented model does not rely on parameter fitting to reproduce the experimental impedance spectra. The model also takes the finite-rate water absorption/desorption kinetics of the electrolyte into account, which allows to investigate the effect of the interfacial transport of water on fuel-cell impedance and performance. With the presented model, water-management signatures are identified in fuel-cell impedance spectra under single-phase conditions that help elucidate electrolyte-hydration dynamics. The trends in the simulated effect of anode and cathode RH on the fuel-cell inductance are in agreement with experiments in [9], which verifies the model’s ability to correctly predict the inductive behavior. The low-frequency inductive behavior of fuel cells is shown to be related to the interfacial transport of water in the electrolyte and linked to the electrolyte-hydration and proton-conductivity dynamics in the membrane and in the catalyst layers. Additionally, an ohmic-resistance breakdown is performed with the model using the ohmic-heating-based approach proposed by Secanell et al. [18]. The high-frequency resistance obtained through EIS is shown to be comprised of the membrane resistance and the electronic resistance of other MEA components, but not the protonic resistance of the catalyst layers. Figure: Single-phase water-transport signatures in a fuel-cell-impedance spectrum. References [1] D. Papageorgopoulos. Fuel Cell R&D Overview. U.S. Department of Energy; 2019. [2] C. Ziegler et al. J. Electrochem. Soc. 152.8 (2005): A1555-A1567. [3] H. Yu and C. Ziegler. J. Electrochem. Soc. 153.3 (2006): A570-A575. [4] D. Gerteisen et al. J. Power Sources 187.1 (2009): 165-181. [5] L. Hao et al. J. Power Sources 177.2 (2008): 404-411. [6] J. Hou. Int. J. Hydrogen Energ. 36.12 (2011): 7199-7206. [7] A. Goshtasbi et al. J. Electrochem. Soc. 166.7 (2019): F3154-F3179. [8] D. Malevich et al. J. Electrochem. Soc. 156.2 (2009): B216-B224. [9] A. Schiefer et al. EFCF 2019: Low-Temperature Fuel Cells, Electrolisers, & H2 Processing, Chapter 03 (2019): 179–189. [10] I. Pivac and F. Barbir. J. Power Sources 326 (2016): 112-119. [11] S. M. R. Niya and M. Hoorfar. J. Power Sources 240 (2013): 281-293. [12] B. P. Setzler and T. F. Fuller. J. Electrochem. Soc. 162.6 (2015): F519-F530. [13] C. Bao and W. G. Bessler. J. Power Sources 278 (2015): 675-682. [14] G. A. Futter et al. J. Power Sources 391 (2018): 148-161. [15] A. Baricci et al. Fuel Cells 14.6 (2014): 926-937. [16] J. R. Vang et al. ECS Transactions 68.3 (2015): 13-34. [17] M. Secanell et al. ECS Transactions 64.3 (2014): 655-680. [18] M. Secanell et al. ECS Transactions 69.17 (2015): 157-187. 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 | 0,001 |
| 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 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».