Optimal configuration of hydrogen- and battery-based electric bus transit systems
Bibliographic record
Abstract
Electric bus transit is crucial in reducing greenhouse gas (GHG) emissions, decreasing fossil fuel reliance, and combating climate change . However, the transition to electric-powered buses demands a comprehensive plan for optimal resource allocation, technology choice, infrastructure deployment, and component sizing. This study develops system configuration optimization models for battery electric buses (BEBs) and hydrogen fuel cell buses (HFCBs), minimizing all related costs (i.e., capital and operational costs). These models optimize component sizing of the charging/refueling stations, fleet configuration, and energy/fuel management system in three operational schemes: BEBs opportunity charging, BEBs overnight charging, and electrolysis-powered HFCBs overnight refueling. The results indicate that the BEB opportunity system is the most economically viable choice. Meanwhile, HFCB requires a higher cost (134.5%) and produces more emissions (215.7%) than the BEB overnight charging system. A sensitivity analysis indicates that a significant reduction in the HFCB unit and electricity costs is required to compete economically with BEB systems.
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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 | 0.000 | 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 | 0.001 | 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".