Sustainable planning of electric bus systems: Degradation-aware and climate-specific techno-economic analysis
Bibliographic record
Abstract
The transition to battery electric buses (BEBs) offers a promising pathway to decarbonize urban transport. However, planning their deployment requires careful evaluation of both economic and environmental trade-offs. This study presents a novel planning framework that jointly minimizes the total cost of ownership (TCO) and greenhouse gas (GHG) emissions by optimizing onboard battery size, charger power, and number of chargers, while incorporating a detailed battery aging model to capture real-world degradation under varying temperatures and charging conditions. A comparative analysis is conducted across four cities-Singapore, Adelaide, Munich, and Calgary-each representing distinct climate profiles, electricity prices, and grid emission intensities. The study also evaluates three widely used lithium-ion battery chemistries: LFP, NMC, and NCA. Results reveal substantial variation in optimal BEB configurations across regions. Colder cities require larger batteries and higher charging power to maintain reliability, resulting in increased costs and emissions, while milder climates with cleaner grids support more cost-effective and sustainable solutions. Critically, the analysis demonstrates that no single configuration performs best across all locations or chemistries-underscoring that one-size-fits-all approaches are unsuitable for BEB planning. These findings provide actionable insights for policymakers and transit agencies aiming to deploy BEBs effectively under diverse regional conditions. • Proposes a BEB planning framework minimizing both cost and GHG emissions. • Integrates thermal and aging models to capture real-world battery degradation. • Assesses battery chemistries (LFP, NMC, NCA) across four climate-diverse cities. • Reveals that optimal BEB designs are highly location- and climate-dependent. • Provides an open-access tool for sustainable and cost-effective BEB deployment.
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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 | 0.001 | 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".