Comparative Evaluation of Energy Efficiency and Performance of Electric and Counterpart Diesel Tractors
Bibliographic record
Abstract
The objective of this trial was to compare the energy efficiency and performance of battery electric and conventional diesel tractors. Controlled road tests replicating normal operations were conducted using two electric and two diesel day-cab tractors. The test protocol was based on the TMC - Type III RP 1103A and SAE J1526 test procedures. The tests were conducted on a 110 km long route that included a 59 km hilly portion with a maximum altitude difference of 307 m. The tractors were divided into test groups of two vehicles. Trailers and drivers were switched throughout the trial between the tractors in a test group. The tests found that the two electric trucks consumed 60% and 63% less energy than their counterpart diesel trucks, respectively. Considering the average emission factor for production of electricity in Canada, the electric trucks emitted on average 82% less GHG emissions than the conventional diesel-powered tractors. The two diesel trucks showed similar fuel consumption and GHG emissions. The difference in electric energy consumption and GHG emissions between the two electric trucks was 7%. Energy and fuel consumption by route section was determined based on data from the vehicle electronic control modules. The trucks that showed the highest energy efficiency on the complete test route, also demonstrated the highest efficiency on specific sections of the test route. The regenerative braking system of the electric trucks charged the batteries with a regeneration ratio up to 38% on the descending section. Observations from drivers and observers were collected during testing. Generally, drivers would not hesitate to use the electric trucks especially if operability and battery range were improved. The driving experience with electric trucks was rated better than with diesel trucks.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".