Energy Efficiency Evaluation of a Hybrid Electric Tractor-Semi-Trailer Prototype
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.001 |
| 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) | 0.004 | 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".