On the Integration of On-Route Fast Chargers for Battery Electric Buses
Bibliographic record
Abstract
The electrification of a bus system using on-route fast chargers will require the installation of high-power chargers at bus stops. This work quantifies the impact of short duration high-power (pulse) charging demands of battery electric buses (BEBs) on power quality in terms of voltage flicker in electric distribution systems; hence, influencing the number of allowed charging events per hour. In addition, the introduced work investigates the minimum required electric distribution systems upgrades to mitigate such impact. The impact of charging is evaluated for both the IEEE-34 distribution test feeder and for a real distribution feeder, for which data was provided by an electric utility in Canada. The results show that the impact of intermittent charging demands of BEBs on power quality is highly affected by the system voltage of the feeder. Moreover, the concurrent charging of buses using multiple chargers connected to the same feeder can increase the voltage dip up to 15%, exceeding the voltage flicker borderline of irritation, which is unacceptable and will require a reduction of the number of allowed charging events per hour. However, the upgrade of distribution systems by adding voltage regulators and/or shunt capacitor banks can reduce the voltage dip to the permissible level, which will allow the hourly number of charging events required for regular operation of the bus system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".