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Record W2106923534 · doi:10.1109/pes.2011.6039406

Energy management & scheduling in a fast charging station for PHEV batteries

2011· article· en· W2106923534 on OpenAlexaff
M. de Freige, G. Joós, Maxime R. Dubois

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité LavalMcGill University
Fundersnot available
KeywordsSupercapacitorBattery (electricity)Energy storageTrickle chargingFlywheelAutomotive engineeringState of chargeEnergy managementElectrical engineeringEnvironmental scienceComputer scienceEngineeringPower (physics)Energy (signal processing)CapacitanceElectrodePhysics

Abstract

fetched live from OpenAlex

The current paper focuses on the energy management in a fast charging station for PHEV batteries; that uses in addition to the grid a flywheel energy storage and a supercapacitor; with main objective to minimize the duration of the battery charging process and the time required to recharge the storage devices afterward. The designed station is capable to recharge PHEV batteries with capacities lower or equal to 15 kWh from a minimum of 20% to a maximum of 95% of the battery state-of-charge in a maximum duration of 15 minutes. After a battery has been charged, a waiting period (during which no cars are allowed at the charging station) of a maximum duration of 7.5 minutes is required. During this period, the storage devices are being recharged to their maximum. A scenario displaying the charging process of two different PHEV batteries is presented with the simulation results.

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)0.000
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.269
Teacher spread0.231 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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