Fuel Consumption Simulation Model for Transit Buses Based on Real Operating Condition to Assist Bus Electrification
Bibliographic record
Abstract
Public transit bus fuel consumption is a function of the duty cycle, power demand when driving and idling, and the efficiency of the bus. Accurate evaluation of fuel consumption is best assessed by comparative testing over relevant drive cycles. Generally, in transit service, a bus serves along predetermined route and bus stops. To increase the accuracy of fuel consumption estimation to simulate buses electrification scenarios, a fuel consumption simulation model for transit buses is carried out based on the analysis of a large number of transit bus journeys composed of 82 buses tested along 124 testing routes. The bus fuel consumption simulation is executed using Matlab. The estimation fuel consumptions for representative service routes with different passenger loads and bus model year are obtained. The simulation results on transit bus fuel consumption are compared with values available from testing reports and fuel consumption estimated by Winnipeg Transit. The comparison shows that the proposed fuel consumption model is more accurate and can predict fuel consumption more reliability for transit buses. The large database gathered and fuel consumption simulation data can then be used next to investigate various electric bus powertrains and charging scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".