Optimal Design of Battery Swapping-Based Electrified Public Bus Transit Systems
Bibliographic record
Abstract
This article proposes a novel model to optimize the design of fully electrified public bus transit (PBT) systems that are operating using the concept of battery swapping. The proposed optimization model is formulated as a multiobjective mixed-integer nonlinear programming model that aims at minimizing the overall capital and operation expenditures of the electrified bus transit system. The formulated model determines the optimal configuration design parameters of the electrified bus transit system, including capacity of the onboard electric bus batteries, rated power of chargers, and the number of installed chargers and battery modules at the battery swapping station (BSS). Also, the model yields the optimal schedules of batteries swapping and charging scheme of batteries at the BSS. The model takes into consideration several physical and operation constraints, such as the limits of batteries state of charge, dynamic changes in the electricity prices, impacts of traffic conditions and heat, ventilation, and air conditioning (HVAC) operation on the battery electric bus (BEB) energy consumption, stiff and flexible schedule of bus assignments, and impacts of bus transit rush hour periods on batteries swapping. Several case studies are carried out on a real PBT system in the province of Ontario, Canada, to validate the effectiveness of the proposed model. The proposed model could be utilized as a decision-making tool to investigate the applicability of using the battery swapping concept to electrify bus transits based on the operation requirements and preferences of their operators.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".