Exploring the Contributing Factors of Fuel Economy of Hybrid-Electric Versus Conventional-Gasoline Vehicles in Real-World Conditions: A Case Study in Cold Cities in Urban Quebec
Bibliographic record
Abstract
Hybrid electric vehicles (HEVs) are considered as a promising technological solution for decreasing dependency on fossil fuels and reducing transport-generated greenhouse gas emissions. Currently, the number of HEVs in the market remains limited, but this picture will change in the coming years as HEVs are likely to pave the way for cleaner technologies in transport. This paper investigates the fuel efficiency of commercial HEVs and compares their performance with respect to standard gasoline vehicles. The effect of different factors on fuel efficiency is also studied including road driving conditions (city vs. highway), temperature, speed and cold-starts. For this study, fuel consumption data in real-world driving conditions from a sample of 74 instrumented vehicles is used, 24 of which are HEVs. Among other results, the beneficial fuel efficiency merits of hybrid vehicles are demonstrated in particular in low speeds in urban (city) driving conditions. Despite the significant energy benefits of HEVs, the particularly low temperature associated with the winter season is identified as one of the critical factors negatively affecting their performance. This is an important aspect to take into account in cold North American cities with very long winters and many days of low temperatures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".