Evaluation of Class 7 Diesel-Electric Hybrid Trucks
Bibliographic record
Abstract
The objectives of this project were to evaluate the reduction in fuel consumption and greenhouse gas (GHG) emissions made possible by hybrid technology, and to identify good driving habits with this type of vehicle. Two diesel-electric hybrid pick-up and delivery trucks and one diesel-electric hybrid utility vehicle equipped with an electric driven PTO (power take-off) system were included in the project. The first phase was the evaluation in actual operating conditions. Onboard computers were installed in the vehicles to record parameters that make it possible to determine driving habits. Based on operational data, specific duty cycles were built and track tests were conducted to measure the fuel consumption on these duty cycles. It was therefore possible to compare the hybrid trucks with other diesel trucks featuring similar characteristics. The delivery hybrid trucks showed up to 34% fuel savings during the track tests. Achieving major savings will require driving mainly on urban hauling routes, in a not too aggressive manner, to allow the recharge of batteries and electric-assisted power. An appropriate training of drivers is recommended and a best driving practices manual was prepared to that end. For the utility truck, the savings are greater when the PTO time is longer. Compared with the conventional control diesel vehicle, track tests showed 11% fuel improvement for a duty cycle composed of 67% driving time and 33% PTO time, and 19% fuel improvement for the duty cycle with 50% driving time and 50% PTO time. The project confirmed the potential of this technology to reduce fuel consumption and GHG emissions, even though it was generally slightly below the 30% savings reported by hybrid truck manufacturers. The GHG reduction for these trucks was evaluated at between 2 to 5.4 tonnes, based on an annual distance in kilometres of 17,620 km, which corresponds to the average annual distance travelled by a medium-duty truck in the Province of Quebec (Canada).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".