Fleet-wide transit bus energy consumption modelling and techno-economic analysis of stationary energy storage systems for high-power electric bus charging
Bibliographic record
Abstract
Electric buses offer a range of benefits, including a drastic reduction of greenhouse gas emissions compared to personal transit, or to conventional diesel buses. Unfortunately, electric buses also require additional planning to ensure affordable and reliable operation. This thesis proposes two contributions that help to model and plan electric bus deployments, and generally examines how system-focused thinking is required for this application. First, a novel data driven method for estimating the energy consumption of a bus is presented and validated against 1 Hz driving data. Rather than requiring ad hoc data collection, or entire theoretical drivecycle patterns, this new method leverages existing low fidelity driving data from public transit feeds. This data driven method can be used to quickly and accurately model the driving patterns and energy consumption of a whole fleet of buses, as is demonstrated for a case study in Victoria, BC, Canada. Second, using the energy estimating methods previously mentioned, the electricity demand profile for a high-power electric bus charging hub is modelled for various locations and charging systems. Using this modelled demand profile, the potential for using a stationary energy storage system to reduce the peak power demand is investigated. The advantages of three different energy storage technologies (lithium ion, redox flow, and flywheel energy storage systems) are explored. Energy storage was found to be optimal for most charging scenarios modelled, with lithium ion providing the most economical solution for 65% of cases considered. Both the data drive energy estimation modelling, and the energy storage feasibility study constitute novel contributions to the literature. These contributions help to advance the knowledge surrounding electric bus planning and modelling, and help to underpin the systems level thinking required for electric bus deployments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".