Optimizing Charging Infrastructures of Electric Bus Routes to Minimize Total Ownership Cost
Bibliographic record
Abstract
Electrification of public transport systems has become a strategic priority due to the highly versatile potential of electric vehicles (EVs) as distributed energy resources (DERs) Despite the logistic ease of transitioning conventional public transport systems to fully-electric ones, the latter must be properly designed to adhere to technical constraints and minimize their total ownership cost (TOC). Most published work addresses this issue with a narrow scope, such as modeling a very specific case study, leading to non-generic models which are difficult to apply for a universal case. In this study, a mixed integer linear programming (MILP) model is formulated to model a generic public transport network, comprising the routes, electric bus models, and charging infrastructures. The objective function minimizes the TOC of a generic network, which can be applied to any universal case study. The constructed model is tested by simulating a system with different routes, bus models, and types of chargers to obtain the optimal configuration of the charging infrastructure.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".