The Impacts of Battery Capacity Degradation on Optimizing BEBs Transit System Configuration
Bibliographic record
Abstract
Implementing battery electric buses (BEBs) in transit is considered a sustainable mobility means. However, various planning and operational challenges impede the full adoption of BEBs. Currently, BEB system optimization practice assumes that BEBs will deliver the same performance (battery capacity) over the entire service lifespan. However, BEBs' batteries will degrade over time, leading to a limited operating range. As such, the present study addresses the impact of BEBs' battery degradation on the BEB system infrastructure and component sizing optimization. The study develops a multi-stage optimization model to optimize BEB infrastructure and component sizing, considering battery degradation over the transit system service lifespan. The trade-off between adding charging infrastructure and/or battery replacement is assessed. The results show that adding more charging piles is more cost-effective than replacing the batteries for small hup-and-spoke transit networks. The results also show some insights to improve the adoption of BEBs in transit.
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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 | 0.000 |
| 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".