Feasibility Investigation of Hydrogen Refuelling Infrastructure for Heavy-Duty Vehicles in Canada
Bibliographic record
Abstract
A potentially viable solution to the problem of greenhouse gas emissions by vehicles in the transportation sector is the deployment of hydrogen as alternative fuel. A limitation to the diffusion of the hydrogen-fuelled vehicles option is the intricate refuelling stations that vehicles will require. This study examines the practical use of hydrogen fuel within the internal combustion engine (ICE)-powered long-haul, heavy-duty trucking vehicles. Specifically, it appraises the techno-economic feasibility of constructing a network of long-haul truck refuelling stations using hydrogen fuel, across Canada. Hydrogen fuel is chosen as an option for this study due to its low carbon emissions rate compared to diesel. This study also explores various operational methods, including variable technology integration levels and truck traffic flows, truck and pipeline delivery of hydrogen to stations, and the possibility of producing hydrogen onsite. The proposed models created for this work suggest important parameters for economic development, such as capital costs for station construction, the selling price of fuel, and the total investment cost for the infrastructure of a nation-wide refuelling station. Results showed that the selling price of hydrogen gas pipeline delivery option is more economically stable. Specifically, it was found that at 100% technology integration, the range in selling prices was between 8.3 and 25.1 CAD$/kg. Alternatively, at 10% technology integration, the range was from 12.7 to 34.1 CAD$/kg. Moreover, liquid hydrogen, which is delivered by trucks, generally had the highest selling price due to its very prohibitive storage costs. However, truck-delivered hydrogen stations provided the lowest total investment cost; the highest is shown by pipe-delivered hydrogen and onsite hydrogen production processes using high technology integration methods. It is worth mentioning that once hydrogen technology is more developed and deployed, the refuelling infrastructure cost is likely to decrease considerably. It is expected that the techno-economic model developed in this work will be useful to design and optimize new and more efficient hydrogen refuelling stations for any ICE vehicles or fuel cell vehicles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".