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Record W1997495853 · doi:10.1049/cp.2014.0460

A brushless exciter design for a hybrid permanent magnet generator applied to series hybrid electric vehicles

2014· article· en· W1997495853 on OpenAlexaff
Omid Beik, N. Schofield

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExciterRotor (electric)StatorRectifier (neural networks)MagnetShunt generatorGenerator (circuit theory)Electric generatorPermanent magnet synchronous generatorComputer scienceControl theory (sociology)Electrical engineeringEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

This paper discusses the design of a brushless exciter for a hybrid permanent magnet (HPM) generator for application in series hybrid electric vehicles (SHEV). The brushless exciter has a 36-pole stator winding and multi-phase rotor configuration that supplies the HPM wound field (WF) rotor via a rotating rectifier. The brushless exciter rotor components are all assembled on the same shaft as the HPM generator rotor. The HPM machine is engine mounted and has to accommodate a through shaft. This forms one of the constraints on the HPM generator and brushless exciter design. Moreover, to minimize component count the brushless exciter design is constrained to use the same laminations as those used in the HPM generator. A multiphase design approach is used for the brushless exciter rotor that, together with an uncontrolled passive rectifier, delivers a high quality DC output to the HPM WF. This eliminates the necessity for passive smoothing of the rectified DC output and hence improves the reliability of the system. Dynamic behaviour of the brushless exciter is analysed to examine the response and effectiveness of the proposed design.

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 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 score0.802
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
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.191
Teacher spread0.181 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations11
Published2014
Admission routes1
Has abstractyes

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