Hydrogen Gas Refueling Infrastructure for Heavy-Duty Trucks: A Feasibility Analysis
Bibliographic record
Abstract
Abstract In view of serious environmental problems occurring around the world and in particular climate change caused significantly by dangerous CO2 emissions into the biosphere in the developmental process, it has become imperative to identify alternative and cleaner sources of energy. Compressed hydrogen is being considered as a potential fuel for heavy-duty applications because it will substantially reduce toxic greenhouse gas emissions and other pollutant emissions. The cost of hydrogen will be the main element in the acceptance of compressed hydrogen internal combustion engine (ICE) vehicles in the marketplace because of its effect on the levelized cost of driving. This paper investigates the feasibility of developing a nationwide network of hydrogen refueling infrastructure with the aim to assist in a conversion of long-haul, heavy-duty (LHHD) truck fleet from diesel fuel to hydrogen. This initiative is taken in order to reduce vehicle emissions and support commitments to the climate plans reinforcing active transportation infrastructure together with new transit infrastructure and zero-emission vehicles. Two methods based on constant and variable traffics, using data about hydrogen infrastructure and ICE vehicles, were created to estimate fueling conditions for LHHD truck fleet. Furthermore, a thorough economic study was carried out on several test cases to evaluate how diverse variables affect the final selling price of hydrogen. This gave an understanding of what elements go into the pricing of hydrogen and if it can compete with diesel in the trucking market. Results revealed that the cost to purchase green hydrogen is the utmost part in the pump price of hydrogen. Due to the variety in hydrogen production, there is no defined cost, which renders estimates difficult. Moreover, it was found that the pump price of green hydrogen is on average 239% more expensive than diesel fuel. The methodology proposed and models created in this feasibility study may serve as a valuable tool for future techno-economics of hydrogen refueling stations for other types of ICE fleets 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".