Simulation of fuel cell membrane durability under vehicle operation
Bibliographic record
Abstract
In this work, a statistical fuel cell chemo-mechanical membrane degradation model is developed based on the ionomer fibrillar morphology as a framework for use-level membrane durability prediction for fuel cell electric vehicles . The mechanical and chemical degradation modes are separately calibrated with pressure differential-accelerated mechanical stress tests and accelerated membrane durability tests, respectively. Finite element simulations are used to estimate the initial stress distribution across the membrane, while the genetic algorithm with the least squares method is employed to calibrate the model parameters with experimental results, thus reaching a good agreement. Next, the validated model is utilized for a case study of fuel cell electric transit bus operation in the city of Victoria, B.C., Canada. The initial cell voltage profile is obtained using a dynamic fuel cell power system model applied to the transit bus drive cycle recorded during real-world operation. According to the model predictions, reducing the stack nominal power from 396 to 132 kW results in a 148% membrane lifetime enhancement, whereas decreasing the cell temperature from 90 to 70°C results in an 11-fold increase in the membrane lifetime under simulated transit bus operation, thereby exceeding the 25,000-h lifetime target for this application.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".