Battery Electric Bus Transit Planning: Operations Research, Applied Mathematical Modelling, and Advanced Optimization Techniques
Bibliographic record
Abstract
Battery electric buses (BEBs) represent a promising transit solution for curbing transportation-related greenhouse gas (GHG) emissions. However, strategic planning for BEB transit systems is imperative due to the intricate interdependency of various parameters and the necessity to reconcile numerous conflicting objectives. This dissertation introduces cutting-edge mathematical models for the sizing/location of charging infrastructure, fleet configuration, and charging schedule, utilizing advanced optimization techniques. A surrogate model-based-space mapping algorithm is developed to embed trip-level BEB energy consumption estimation while ensuring model simplicity and linearity. The impact of optimization approaches on BEB system configuration is investigated, underscoring the importance of integrating infrastructure costs, operational expenses, and GHG emissions for a holistic design. Consequently, a generic model is devised to optimize BEB system infrastructure and operation, minimizing capital costs, utility impact, and GHG emissions. Furthermore, this research expands the charging system to encompass on-site Photovoltaic (PV) panels and stationary energy storage systems (ESSs), assessing their economic, operational, and environmental benefits. A robust optimization model addresses BEB energy consumption and PV power output uncertainties with seasonal variations, while an energy management system (EMS) orchestrates power flow. Moreover, a resilient two-stage robust optimization model mitigates vulnerabilities of the BEB system against charging station disruptions, ensuring a resilient BEB system design. These models and techniques, deployed across diverse real-world transit networks, demonstrate effectiveness and generality. The dissertation advocates integrating trip-level energy consumption into the BEB system design to optimize resource allocation. Additionally, it confirms the benefits of PV panels, ESS, and EMS in reducing costs, utility impact, and GHG emissions. The proposed two-stage robust model safeguards uninterrupted BEB system operation at a minor added cost. This ensures continuous mobility provision, enabling social interaction and economic productivity. Overall, this research contributes significantly to the body of knowledge on public transit electrification planning, serving practitioners, policymakers, and academia alike.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".