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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".