Energy Efficiency Evaluation of a Hybrid Electric Tractor-Semi-Trailer Prototype
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The objective of the project was to evaluate the energy efficiency of a hybrid electric tractor-semi-trailer combination prototype. The prototype was developed for log hauling application by integrating an existing tractor with an electric semi-trailer to improve fuel consumption and reduce greenhouse gas emissions. One of the conventional axles of the quad axle semi-trailer was replaced with a drive axle powered by an electric motor. Tests were conducted on a 105 km test route with a maximum difference in elevation of 355 m, including a hilly section with a length of 89 km. The results indicated fuel savings ranging from 10.5% to 14% per test run, with an average fuel savings of 12% when the electric drive axle was engaged. The hybrid electric tractor semi-trailer consumed 17.5% less fuel up-hill and 9.4% less down-hill. Throughout each test run, the battery’s state of charge fluctuated, averaging between 88% at the start and 52% at the end. Prior to each run, the batteries were recharged using the regenerative system’s acceleration-deceleration cycles, lasting approximately one hour. The tests showed an average GHG emission reduction of 23.3 kg/100 km when the electric drive axle was activated on the hybrid electric tractor semi-trailer. Considering an average annual mileage of 100 000 km, the prototype hybrid electric tractor semi-trailer shows a potential to reduce annual GHG emissions of 23 tonnes per vehicle. These reductions will provide the cost-benefits that are of strong interest to the forest sector as they continue the move towards reducing carbon footprint. In addition, operational benefits of having a powered rear-axle on the trailer were confirmed to provide traction benefits, and safety improvements for forestry trailers operating in challenging terrain.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".