Comparison of Emissions and Fuel Consumption between Gasoline and E85 in a Simulated Hybrid Electric Vehicle
Bibliographic record
Abstract
This paper presents the effects of using E85 (15% gasoline and 85% Ethanol) on the emissions and fuel consumption of a simulated hybrid electric vehicle (HEV) as compared to the usage of gasoline fuel. The benefits of successive engine modifications to obtain a optimized E85 engine (increased compression ratio) in an HEV are quantified. The results demonstrate that the largest reduction in pollutant emissions, of between 20% and 40%, depending on the specific pollutant, is obtained when the fuel is changed to E85. Increasing the compression ratio to take advantage of the high octane rating of E85 provides a slight improvement in fuel economy and emissions. Finally, the modification of the hybrid control strategy parameters only brings about a slight improvement in fuel economy and emissions. However, the parameter values are different for the FTP and US06 cycles. This latter finding demonstrates that the hybrid strategy must be adapted to match driving conditions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".