Comprehensive Transition Plan for Battery-Electric Bus Fleets Considering Utility Constraints
Bibliographic record
Abstract
Public transportation electrification is gaining significant traction due to its substantial positive environmental impact. However, the introduction of new electric fleets imposes a considerable additional load on the electrical system, creating a unique challenge that incorporates the interests of both transit agencies (TAs) and electric utilities. To address this issue, this paper proposes a novel dynamic approach for battery electric bus (BEB) fleet transitioning that integrates the viewpoints of both stakeholders. The proposed approach consists of several stages; the input stage determines the optimal depot location, the optimal fleet, and charging sizing while determining the charging schedules and route assignment. Subsequently, the detailed transition stage aims to minimize the net present value for purchase decisions, resulting in an optimal transition plan. To illustrate the effectiveness of the proposed model, a transit system comprising four short-distance routes is examined, considering two charging modes: overnight and opportunity charging. Additionally, to display the flexibility, scalability, and adaptability of this model four long-distance routes are also examined. The results highlight the efficacy of the proposed approach in achieving electrification targets while considering the limitations of the distribution system. Furthermore, the approach ensures the continuity of service for the TA. In conclusion, this paper presents a comprehensive and integrated fleet transition plan, encompassing the perspectives of TAs and electric utilities. By applying this approach, transit systems can make optimal purchasing decisions over a long-term planning horizon, facilitate electrification and environmental targets, and maintain seamless transit operation, while respecting the constraints of the distribution system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".