Modelling Hydrogen Refuelling for Light-Duty Passenger Vehicles
Bibliographic record
Abstract
Due to the rising concerns over environmental issues and the pressing need to reduce carbon emissions, hydrogen (H2) has gained significant attention as a clean, reliable and sustainable vehicle energy carrier, which is produced from renewable sources.Hydrogen Refuelling Stations (HRSs) are considered a crucial infrastructure for supporting Fuel Cell Electric Vehicles (FCEVs).Nonetheless, a significant obstacle to the commercialization of FCEVs is to store highly flammable hydrogen gas efficiently and securely.Numerous techniques for storing H2 have been devised, however, compressed H2 storage tanks, due to their lightweight and effectiveness, are the most used technique for storing H2 in automobiles.In the present study, a thermodynamic model of HRS was developed to examine the effects of different refuelling parameters such as H2 supply temperature and Average Pressure Ramp Rate (APRR on light-duty FSEVs fueling performance, and State of Charge (SOC) for 70 MPa, 99-liter type IV H2 cylinder.Compared to other refuelling parameters, it was observed that the hydrogen supply temperature has a significant effect on the final tank temperature and SOC.Simulation results show that an increase in H2 supply temperature from -40℃ to 20℃ causes an increase of 65.2% in end gas temperature (68.9℃ to 113.8℃) and 9.3% lower SOC (92.4% to 84.6%) respectively.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".