Fuel-Cell Performance and Stability during Liquid-Water Removal Cycles
Bibliographic record
Abstract
Water management is crucial for achieving high-performance proton-exchange-membrane fuel cells (PEMFCs), as it helps keep the electrolyte hydrated while avoiding performance degradation and cell shutdown due to liquid-water accumulation. According to ex-situ measurements [1-3], accumulation of liquid water in gas-diffusion layers (GDLs) of PEMFCs is a transient process that can be accompanied with oscillations in capillary pressure and saturation. To understand how liquid-water accumulation and drainage impact PEMFC performance hysteresis and stability, a transient cell-level model that accounts for electrode structure and composition and is computationally efficient is needed. A number of volume-averaged models that describe the electrode structure through pore-size distribution have been developed in the past [4-7], but they are steady-state and thus cannot predict dynamic PEMFC performance. Existing transient models have also not been used to analyze cyclic liquid-water accumulation [8-10]. In this work, a transient two-phase 2D PEMFC model is developed in the open-source fuel-cell modeling software OpenFCST [11] and applied to analyze PEMFC performance hysteresis and stability during liquid-water accumulation and drainage cycles. The model accounts for the electrode structure through a mixed-wettability pore-size-distribution framework and incorporates a novel dynamic boundary condition to describe the experimentally observed cyclic liquid-water accumulation. Results of the numerical simulations are compared to transient current-density and resistance data at two polarization scan rates and during voltage steps measured with an in-house single-channel cell at multiple operating conditions. This work demonstrates how high breakthrough pressure and rapid liquid-water removal from GDLs may cause highly unstable fuel-cell operation with strong hysteresis and oscillations in the polarization curve (such as those in the figure below) that closely resemble experimental reports [12]. Numerical simulations also reveal the existence of a scan rate that maximizes polarization hysteresis due to a match between the time scale of GDL flooding and of a complete voltage sweep. Fast-scan polarization sweeps are shown most suitable for detecting catalyst-layer flooding that depends on its wettability and occurs within single seconds in contrast to GDL flooding that takes hundreds of seconds. Overall, this work brings more attention to the transient analysis of fuel-cell performance under wet conditions. Figure: Polarization-curve oscillations caused by cyclic liquid-water removal from the cathode GDL. Similar fluctuations have been experimentally observed in [12]. Transient graphs show the dynamics of current density and cathode GDL saturation. Operating conditions are 60 °C, 90% RH, 1.5 atm; scan rate is 0.5 mV/s. References J. T. Gostick et al., J. Electrochem. Soc. 157.4 (2010) C. Quesnel et al., J. Phys. Chem. C 119.40 (2015) D. Ziegler, B.Sc. thesis, Hochschule Mannheim / University of Alberta (2020) A. Z. Weber et al., J. Electrochem. Soc. 151.10 (2004) M. Eikerling, J. Electrochem. Soc. 153.3 (2006) V. Mulone and K. Karan, Int. J. Hydrog. Energy 38.1 (2013) Zhou et al., J. Electrochem. Soc. 164.6 (2017) R. J. Balliet and J. Newman, J. Electrochem. Soc. 158.8 (2011) I. V. Zenyuk et al., J. Electrochem. Soc. 163.7 (2016) A. Goshtasbi et al., J. Electrochem. Soc. 166.7 (2019) M. Secanell et al. ECS Transactions 64.3 (2014) C. Ziegler and D. Gerteisen, J. Power Sources 188.1 (2009) Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".