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Record W4285398173 · doi:10.1149/ma2022-01412456mtgabs

Simulation of Cathode Catalyst Durability Under Fuel Cell Vehicle Operation - the Effect of Fuel Cell Stack Size

2022· article· en· W4285398173 on OpenAlexaffabout
Mohammad Shojayian, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsProton exchange membrane fuel cellDurabilityStack (abstract data type)CathodeProcess engineeringElectrolyteAutomotive engineeringMembrane electrode assemblyMaterials scienceDuty cycleHydrogen fuelAnodeEnvironmental scienceVoltageComputer scienceChemical engineeringNuclear engineeringFuel cellsEngineeringElectrical engineeringChemistryComposite materialElectrode

Abstract

fetched live from OpenAlex

The concerns regarding climate change have made the researchers seek a clean alternative for the fossil fuel vehicles. Fuel cell vehicles (FCVs) are considered to be promising candidates owing to their efficient energy conversion and zero-carbon emission. However, a number of obstacles such as high cost and limited hydrogen infrastructure have made the FCVs commercialization process challenging. Polymer electrolyte membrane fuel cells (PEMFCs) have been proven promising for transportation applications. 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 PEMFC is studied to estimate the effect of stack size on fuel cell durability in the FCV application. Platinum dissolution and redeposition, platinum oxidation and platinum ion formation during the fuel cell operation are modeled using the Butler-Volmer approach presented in [2]. 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 based on the methodology presented by Ahmadi and Kjeang [3]. According to this methodology, the required cell power density is calculated using Newton’s second law considering the air flow drag force as a counteracting force against the vehicle movement. Then, the required voltage cycle is obtained by employing a polarization curve characterizing the fuel cell performance. Finally, the change of remaining electrochemically active surface area (ECSA) with time is calculated as the output of the model. The fuel cell is assumed to operate at 80 ℃ and the cell active area is considered to be 500 cm2. Simple Tafel kinetics is then used to determine the fuel cell voltage loss. A 10% voltage drop at 0.6 A/cm2 is considered as the failure criterion for the cathode lifetime. Moreover, the effect of the fuel cell stack size is studied. By increasing fuel cell stack size, the required cell power density drops, leading to a decrease in the voltage cycle amplitude while the voltage cycle period remains the same. According to the empirical kinetic rate equation, the catalyst degradation exponentially increases with increasing the voltage. Therefore, a higher degradation rate is observed for a catalyst operating on a voltage cycle with a lower amplitude while the period and the upper potential limit (UPL) are maintained the same, causing a significant platinum ion generation. Fig. 1 shows the change of remaining ECSA over time and resulting fuel cell lifetime for three stack sizes which are represented by the stack nominal powers. The results show that the fuel cell lifetime will be roughly doubled when the stack size is reduced by half. Stack sizing is thus an important consideration for fuel cell durability in the FCV application. In this regard, predicting fuel cell lifetime is a crucial step in commercializing FCVs. The present modeling framework could be utilized by FCV developers to predict lifetime for new products instead of carrying out time-consuming lifetime experiments. The factors influencing fuel cell durability can also be investigated using the present model framework to develop durables cells and stacks 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, "Aging mechanisms and lifetime of PEFC and DMFC," Journal of power sources, vol. 127, no. 1-2, pp. 127-134, 2004. [2] H. A. Baroody and E. Kjeang, "Predicting Platinum Dissolution and Performance Degradation under Drive Cycle Operation of Polymer Electrolyte Fuel Cells," Journal of The Electrochemical Society, vol. 168, no. 4, p. 044524, 2021. [3] P. Ahmadi and E. Kjeang, "Realistic simulation of fuel economy and life cycle metrics for hydrogen fuel cell vehicles," International Journal of Energy Research, vol. 41, no. 5, pp. 714-727, 2017. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.215
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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