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Record W2884604220 · doi:10.1109/tte.2018.2859409

Shaft Design for Electric Traction Motors

2018· article· en· W2884604220 on OpenAlexafffund
Jianbin Liang, James Weisheng Jiang, Berker Bilgin, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsTraction motorTraction (geology)Automotive engineeringElectric motorElectric tractionEngineeringSwitched reluctance motorMagnetBrushed DC electric motorElectric vehicleMechanical engineeringComputer scienceAC motorRotor (electric)Physics

Abstract

fetched live from OpenAlex

This paper presents shaft design techniques specifically for applications in electric traction motors for hybrid electric vehicles (HEVs) and electric vehicles (EVs). The typical characteristics of several shafts used in traction motors for commercial HEVs/EVs are studied. Then, the shaft design for a 24/16 switched reluctance machine (SRM) is presented. The 24/16 SRM is designed to replace an interior permanent magnet synchronous traction motor used in an HEV application. The requirements of shaft design, design procedures, circumferential positioning, bearing positioning, and design of output end and sensor end are presented and discussed in this paper.

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 score0.968
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
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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