Energy Savings and Range Extension from Aerodynamic Improvements of Emerging Zero-Emission Heavy Vehicle Concepts
Bibliographic record
Abstract
An energy-use analysis is presented to examine the potential energy-savings and range-extension benefits of aerodynamic improvements to tractors and trailers used in commercial transportation. The impetus for the study was the observation of aerodynamically-redesigned/optimized tractor shapes of emerging zero-emission commercial vehicles that have the potential for significant drag reduction over conventional aerodynamic tractors. Using wind-tunnel test results, a series of aerodynamic performance models were developed representing a range of tractor and trailer combinations. From modern day-cab and sleeper-cab tractors to aerodynamically-optimized zero-emission cab concepts, paired with standard dry-van trailers or low-drag trailer concepts, the study examines the energy use, and potential savings thereof, from implementing various fleet configurations for different operational duty cycles. An energy-use analysis was implemented to estimate the energy-rate contributions associated with inertial accelerations, grade forces, rolling resistances, and aerodynamic-drag forces for three types of duty cycles: Long Haul, Regional Haul, and Urban Delivery. A duty-cycle-simulation approach was implemented using speed-dependent wind-averaged-drag models, adapted for local wind-speed magnitudes representative of each duty-cycle environment. This method was validated for the long-haul cycle against a constant-speed wind-climate-simulation approach applied to a fleet-transportation network. Results demonstrate that Urban Delivery operations expend a smaller magnitude, and smaller relative proportion, of energy use to overcome aerodynamic drag, but that significant savings are nonetheless possible for these operations with aerodynamic improvements to the trucks. Over the range of tractor- and trailer-aerodynamic improvements examined, the analyses reveal the potential for 4-27% energy-rate savings and 5-37% range extension for the Long Haul cycle, 3-16% energy-rate savings and 3-18% range extension for the Regional Haul cycle, and with 2-9% energy-rate savings and 2-10% range extension estimated for the Urban Delivery Cycle. Although results show significant reductions in energy use associated with emerging zero-emission-tractor shapes, trailer-aerodynamic improvements are shown to have about twice the potential for energy savings and range reduction than do tractor-aerodynamic improvements.
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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) | 0.000 | 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".