Investigation of Electric Bus Fleet Charging Infrastructure: Modeling Ultra-Fast Charger Deployment and on-Board Solar Generation
Bibliographic record
Abstract
In the transition to a sustainable transportation system, electric buses have garnered much attention due to the high pollution levels produced by conventional buses and their regular routes and charging opportunities that mesh well with electrification. This is the first study to investigate the use of onroute fast-chargers and on-board solar generation for battery electric buses (BEBs). A MATLAB/Simulink model is created and validated, then an economic analysis is performed using the simulation results to estimate the capital and electricity costs required for a small and large fleet. The simulation results show that for the drive cycle considered, without on-route fast charging, 2 BEBs would be required to complete the full day drive cycle, compared to only one BEB for the on-route fast-charging scenario. The economic analysis shows that on-route fast-charging is more beneficial for larger fleets where the installed chargers get more utilization. For the large fleet studied, the fast-charging BEB saved 19.42 % of costs over 10 years and the fast-charging solar-charged BEB saved 20.40 % over 10 years, compared to the baseline BEB. Also, the on-board solar generation showed a payback period of approximately 1.7 years and provides further benefits such as extended range and reduced battery depth-of-discharge.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".