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Record W4366961276 · doi:10.12700/aph.15.7.2018.7.7

Fuzzy Logic-based Maximum Power Point Tracking for a Solar Electric Vehicle

2018· article· en· W4366961276 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

VenueActa Polytechnica Hungarica · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité du Québec à Rimouski
FundersUniversité du Québec à Chicoutimi
KeywordsFuzzy logicElectric vehicleTracking (education)Maximum power point trackingMaximum power principleComputer sciencePoint (geometry)Solar powerPower (physics)MathematicsPhotovoltaic systemEngineeringArtificial intelligenceElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A maximum power point tracking (MPPT) system, for a very high-efficiency photovoltaic array applied to a solar-powered vehicle, was studied in this work.Photovoltaic energy is a promising alternative energy; however, its high initial cost, it is essential to improve the energy conversion efficiency.Regarding a particular incident solar insolation and temperature, there is a specific voltage at which maximum power may be harvested (Maximum power point, MPP).The Maximum Power Point is therefore achieved at a specific voltage that depends on insolation and temperature.A proper maximum power point tracking system is particularly important for solar-powered vehicles relating to the rapid change of insolation due to the dynamic motion of the vehicle.In this paper, the emphasis is on the potential of energy conversion improvement of a PV system, associated with a moving vehicle via the use of a fuzzy based maximum power point tracking algorithm.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.273
Teacher spread0.253 · 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