MétaCan
Menu
Back to cohort
Record W1502203166 · doi:10.1109/tte.2015.2426508

Design and Development of a Smart Control Strategy for Plug-In Hybrid Vehicles Including Vehicle-to-Home Functionality

2015· article· en· W1502203166 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsOntario Tech UniversityConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomotive engineeringState of chargeController (irrigation)EngineeringInternal combustion engineElectrificationEnergy consumptionFuzzy logicPlug-inControl engineeringComputer scienceElectricityElectrical engineeringPower (physics)Battery (electricity)Operating system

Abstract

fetched live from OpenAlex

Plug-in hybrid electric vehicles (PHEVs) are seen to be a step forward in transportation electrification, to replace internal combustion engine (ICE)-based conventional vehicles. However, to consider the vehicle-to-home (V2H) and home-tovehicle (H2V) capabilities, new energy control strategy has to be developed to avoid new peaks consumption. This paper presents a novel controller based on fuzzy logic, which integrates an objective state-of-charge (SoC) for V2H application. The V2H capability is used when the PHEV is connected to the home to help the grid to meet the household loads during peak period. The SoC objective is the minimum SoC that the PHEV has to have when the driver connects the PHEV to the home. The proposed controller is applied on fourth different scenario.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.769

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 head0.039
GPT teacher head0.247
Teacher spread0.209 · 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