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Record W2043101526 · doi:10.1109/ccece.2006.277736

A Survey and Comparison of Characteristics of Motor Drives Used in Electric Vehicles

2006· article· en· W2043101526 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsElectric motorAutomotive engineeringMiles per gallon gasoline equivalentTraction motorElectric vehicleAtmosphere (unit)Battery electric vehicleTransport engineeringEnvironmental economicsEnvironmental scienceBusinessComputer scienceEngineeringGreen vehicleElectrical engineeringFuel efficiencyEconomicsMeteorology

Abstract

fetched live from OpenAlex

In recent times, the worldwide price of fuel is showing an upward surge. One of the major factors leading to this can be attributed to the exponential increase in demand. In a country like Canada, where a majority of the people own vehicles, and more being added to the roads, this demand for fuel is surely going to increase in the future and will also be severely damaging to the environment as transportation sector alone is responsible for a larger share of pollutants emitted into the atmosphere. Electric vehicles offer one way to reduce the level of emissions. Electric motor drives are an integral component of an electric vehicle and consist of one or more electric motors. In this paper an effort has been made to compare different characteristics of motor drives used in electric vehicles and also given is a comprehensive list of references papers published in the field of electric vehicles

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.285

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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

Quick stats

Citations80
Published2006
Admission routes2
Has abstractyes

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