Joint optimization of bow-type fast charger locations and battery capacity for electric buses
Bibliographic record
Abstract
The transition from fossil fuel-powered buses to battery electric buses (BEBs) is occurring gradually; however, BEBs encounter challenges such as limited driving range and extended charging durations, which highlight the need for the development of optimized charging solutions. The bow-type fast charger, characterized by its high charging power and capability for unmanned operation, presents a viable option. These chargers can be strategically installed at terminals or intermediate stops, enabling BEBs to leverage their dwell time for charging purposes. This study formulates a mixed integer programming model aimed at jointly optimizing the locations of bow-type fast chargers, the battery capacity of the buses, and the bus schedule for a specific bus line. The primary objective is to minimize the combined costs associated with the construction of chargers and the acquisition of vehicles. Empirical data from an operational BEB line in Meihekou City, China, is employed to validate the model. Additionally, the study examines the sensitivity of three critical parameters and the impact of random disturbance factors. The optimization outcomes in scenarios that do not account for charging time limitations at intermediate stops are also evaluated. Findings indicate that the service time utilization rate at intermediate stops equipped with charging bows exceeds 90%. This suggests that BEBs can effectively utilize their dwell time for charging, thereby facilitating the synchronization of BEB charging with the bus schedule.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".