Predicting Platinum Dissolution and Performance Degradation under Drive Cycle Operation of Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
A protocol is presented that allows for fuel cell performance degradation to be determined based on a vehicle drive cycle. Four stages are outlined beginning with the conversion of vehicle velocity data to a cell voltage profile. The amount of platinum dissolved in the system and oxide coverage on platinum particles are simultaneously calculated by considering several degradation mechanisms including Ostwald ripening and platinum particles loss to the membrane. The platinum loss is used to determine the Electrochemically Active Surface Area (ECSA) loss in the catalyst layer. The voltage loss due to platinum degradation is then determined from the ECSA data. The results show that longer times at higher upper potential limits lead to more platinum degradation and thus performance loss as expected. Accelerated Stress Test data is reproduced within the acceptable error. The model is applied to real-world data from a vehicle drive cycle showing that the model simplifications and assumptions outlined are reasonable and prove predictive capabilities. Although more experimental data would be beneficial to fully validate the model, the present work provides a complete, physics-based catalyst degradation model that can be integrated with performance models to predict durability and optimize future system designs and operating conditions. This paper is part of the JES Focus Issue on Proton Exchange Membrane Fuel Cell and Proton Exchange Membrane Water Electrolyzer Durability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".