Transition of heavy‐duty trucks from diesel to hydrogen fuel cells: Opportunities, challenges, and recommendations
Bibliographic record
Abstract
Fuel cell and hydrogen energy technologies have been investigated by academia, industry, and government to reduce air pollution and mitigate global warming. The transition of heavy-duty trucks (HDTs) from diesel to hydrogen fuel cells (HFCs) could maintain the contribution of HDTs to freight transport at nearly zero carbon dioxide emission. This paper analyzes and discusses the opportunities and challenges of this transition and presents recommendations. The opportunities include the global need for HDTs with low emissions and a long driving range, the unique advantages of HFCs for future energy systems, the anticipated cost reduction of HFCs owing to economies of scale, and the excellent market potential of zero-emission trucks. The challenges include the high total usage cost, research and development barriers, and market share competition from other alternatives. Some recommendations are proposed: synergistic utilization of a battery and HFCs in the HDTs power system, an incremental transition pathway, and the construction of a hydrogen highway based on renewable electricity. This study attempts to provide techno-economic insights and direction for the transition of HDTs from diesel to HFCs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".