Investigating The Suitability of Electrified Powertrain Alternatives for Refuse Trucks with Emphasis in The City of Hamilton
Bibliographic record
Abstract
Refuse trucks, commonly referred to as garbage trucks are a critical component of a municipality’s waste management industry. Their primary purpose is to collect, transport and deposit waste from households or businesses to designated transfer sites or dumps. Historically, refuse trucks have been powered by diesel fuel. The consumption of diesel fuel paired with the frequent accelerations or decelerations between each residential household along a route attribute to high amounts of tailpipe emissions and noise pollution within neighbourhoods. There is significant opportunity to explore avenues of powertrain electrification in refuse trucks to reduce their emissions and improve energy efficiency. To rapidly test promising powertrains, vehicle software models were developed. To accurately model the energy usage and power requirements of refuse trucks, environments for the models to operate were created. The environments were created using on-board diagnostic and positional data collected from refuse trucks in the City of Hamilton in Ontario, Canada. The data collection was done under a research collaboration between the City of Hamilton and the McMaster Automotive Resource Centre. The approaches used to develop the drive and duty cycles for the vehicle models offer some innovative approaches without the need for invasive devices to be installed. The powertrains that were modelled includes an all-electric, ranged extended electric and conventional refuse trucks. A comparative analysis of the pump-to-wheel powertrain efficiencies were completed looking at metrics such as fuel economy, payload capacity and fuel costs. Lastly, a look at truck emissions from a well-to-wheel perspective were completed to investigate the impact of each powertrain on greenhouse gasses and the effect on air quality of their immediate surroundings.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".