Simulation of Cathode Catalyst Durability Under Fuel Cell Vehicle Operation - the Effect of Temperature
Bibliographic record
Abstract
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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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".