Development of a Dual-Fuel Power Generation System for an Extended Range Plug-in Hybrid Electric Vehicle
Bibliographic record
Abstract
In recent decades, there has been a growing global concern with regard to vehicle-generated greenhouse gas emissions and the resulting air pollution. In response, automotive original equipment manufacturers focus their efforts on developing ¿greener¿ propulsion solutions in order to meet the societal demand and ecological need for clean transportation. Hydrogen is an ideal vehicle fuel for use not only in fuel cells (FCs) but also in a spark-ignition internal combustion engines (ICEs). The combustion of hydrogen ( H2) fuel offers vastly superior tail-pipe emissions when compared with gasoline and can offer improved performance. H2is ideally suited for use in an extended range plug-in hybrid electric vehicle architecture where engine efficiency can be optimized for a single engine speed. H2ICEs are significantly more cost effective then an equivalent-sized H2FC making them a better near-term solution. Before hydrogen can replace gasoline and diesel as the main source of automotive fuel, a number of hurdles must first be overcome. One such hurdle includes developing a suitable hydrogen infrastructure, which could take decades. As such, dual-fuel capabilities will help to create a transition between gasoline- and hydrogen-powered vehicles in the near term, while a full-service hydrogen infrastructure is developed.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".