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Record W3206446222 · doi:10.5592/co/cetra.2020.1299

Charging power optimisation for electric buses at terminals

2021· article· en· W3206446222 on OpenAlexaff
Bálint Csonka

Bibliographic record

VenueRoad and rail infrastructure · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsTransport Canada
Fundersnot available
KeywordsPower (physics)Electric powerLinear programmingInteger programmingAutomotive engineeringTotal costCharging stationPublic transportOperating costPower system simulationElectric vehicleService (business)Unit (ring theory)Computer scienceElectrical engineeringElectric power systemEngineeringTransport engineeringBusiness

Abstract

fetched live from OpenAlex

Charging infrastructure has a key role in the operation of electric buses in public transportation. In this paper, mixed-integer linear programming was used to model the bus service and capture the relationship among the network characteristics, vehicles, and charging unit attributes. The model supports the charging power optimisation at terminals to reduce the total operating costs of electric buses and charging units. The model was applied for the bus network of Kőbánya, Budapest. It was found that despite using more expensive high-power chargers, the total cost is lower because of the lower number of electric buses. It was also found that higher charging power does not affect the total cost significantly if it is higher than 350kW.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.204
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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