Real-world steep drive cycles and gradeability performance analysis of hybrid electric and conventional class 8 regional-haul truck
Bibliographic record
Abstract
Gradeability is a key determinant for powertrain component sizing of class 8 heavy-duty (HD) trucks; thus, knowing real-world steep roads along with their grade profiles are essential for design and performance analysis of commercial HD trucks. This paper refines the traditional gradeability criterion established by the SAE J2807 standard for conventional HD trucks, tailoring it to address the testing requirements of hybridized HD trucks with downsized internal combustion engines (ICE). To achieve this, grade–distance profiles of 14 steep highways worldwide from the U.S. , Canada, Europe, and China used by HD trucks are introduced. These profiles serve as benchmarks for assessing the gradeability performance of both conventional and pre-transmission parallel hybrid class 8 truck models. The optimal component sizing of the conventional and hybrid models is refined using each newly identified steep highway as the gradeability criterion. Results show that the equivalent fuel consumption of the parallel hybrid truck models, with the refined component sizing, is 3.4% to 8.9% lower than the conventional counterpart, over a representative highway drive cycle. This superiority is attributed to the hybrid’s ability to maximize engine efficiency , reduce engine-on time, and utilize regenerative braking .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".