Optimal Deployment of Overhead Catenary Charging for Electric Bus Transit Systems
Bibliographic record
Abstract
Integrating in-motion charging into battery electric bus (BEB) transit systems offers a promising solution to address challenges associated with stationary charging, including resource limitations, extended charging durations, and higher battery costs. This study develops a generic optimization model integrating overhead catenary charging (OCC) facilities with overnight charging to minimize BEB system costs (capital and operational). Capital costs are reduced by optimizing OCC deployment, BEB battery capacity, and depot charging configurations. Simultaneously, operational costs are minimized by optimizing charging schedules considering electricity time-of-use (ToU) tariffs, greenhouse gas (GHG) emissions intensity (tCo2e), and BEB battery degradation costs. Application of our model to a real-world transit network highlights substantial reductions in on-peak hours electricity demand (56%), GHG emissions (13%), and overall charging costs (27%). Furthermore, sensitivity analysis explains the impact of OCC infrastructure costs on the total system cost. However, increasing the charging power of OCC facilities yields notable cost savings (28%).
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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".