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Record W4285816000 · doi:10.1109/td43745.2022.9817010

Centralized Control Design for Bidirectional DC Charging Stations to Enable V2G

2022· article· en· W4285816000 on OpenAlexaff
Asal Zabetian‐Hosseini, G. Joós, Benoît Boulet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCharging stationState of chargeController (irrigation)Energy storageGridComputer scienceElectrical engineeringVehicle-to-gridElectric vehicleCharge controllerEngineeringAutomotive engineeringPower (physics)Battery (electricity)

Abstract

fetched live from OpenAlex

In this study, a centralized primary control design is proposed for the DC charging station (DC-CS) to enable vehicle-to-grid (V2G) for electric vehicles (EVs). The proposed control design aggregates EVs with the optional energy storage system (ESS) in the charging station while balancing their state of charges (SoCs). Unlike the conventional methods, the proposed controller deploys EVs data including their energy capacities, SoC limits, and charging and discharging power limits to ensure the safe operation of EV batteries in the V2G mode. Additionally, it balances SoC significantly faster compared to conventional distributed controllers deploying a proposed centralized method to protect EV batteries from over-charge/over-discharge and extend the available total energy capacity in the charging station. The performance of the proposed controller is studied in a case study in the DC-CS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.034
GPT teacher head0.286
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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