Mathematical Model for the Placement of Hydrogen Refueling Stations to Support Future Fuel Cell Trucks
Bibliographic record
Abstract
Fuel cell- and electric-powered trucks are promising technologies for zero-emission heavy-duty transportation. Recently, Fuel Cell Trucks (FCT) have gained wider acceptance as the technology of choice for long-distance trips due to their lighter weight and shorter fueling time than electric-powered trucks. Broader adoption of Fuel Cell Trucks (FCT) requires planning strategies for locating future hydrogen refueling stations (HRS), especially for fleets that transport freight along intercity and inter-country highways. Existing mathematical models of HRS placement often focus on inner-city layouts, which make them inadequate when studying the intercity and intercountry FCT operation scale of FCT. Furthermore, the same models rarely consider decentralized hydrogen production from renewable energy sources, essential for decarbonizing the transportation sector. This paper proposes a mathematical model to guide the planning of the hydrogen infrastructure to support future long-haul FCTs. First, the model uses Geographic Information System (GIS) data to determine the HRS’s optimal number and location placement. Then, the model categorizes and compares potential hydrogen production sources, including off-site delivery and on-site solar-to-hydrogen production. The proposed model is illustrated through a case study of the west coastal area of the United States (from Baja California, Mexico to British Colombia, Canada). Different geospatial scenarios were tested, ranging from the current operational distance of FCEV (250km) and future releases of hydrogen FCT (up to 1,500km). Results highlight the capabilities of the model in identifying the number and location of the HRS based on operation distances, in addition to determining the optimal hydrogen production technology for each HRS. The findings also confirm the viability of green hydrogen production through solar energy, which could play a critical role in a low-carbon transportation future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".