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Record W2005938781 · doi:10.1109/ever.2013.6521636

Advances in Ecological Modern Electric and Hybrid Electric Vehicles

2013· article· en· W2005938781 on OpenAlexaff
Md. Azizur Rahman

Bibliographic record

Venue2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER) · 2013
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAutomotive industryElectric vehicleAutomotive engineeringTraction motorElectric motorReliability (semiconductor)MagnetComputer scienceElectric tractionTraction (geology)EngineeringElectrical engineeringMechanical engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

The past twenty years have been an exciting period with tremendous advances in the development of interior permanent magnet (IPM) electrical machines. Over this period, the interior permanent magnet synchronous machines (IPMSM) have expanded their presence in the automotive marketplace of high-efficiency electric traction drives for the latest generation of electric vehicles (EV) and hybrid-electric vehicles (HEV). Closer examination reveals that several different knowledge-based technological advancements and market forces have combined to accelerate the development of the impressive IPMSM drives technology. The purpose of this paper is to provide a brief statement on impacts of the various factors that lead to the current state-of-the-art IPM motor technology, and to illustrate its application success in the automotive industry. Particularly, the impact of IPM machines on cost and reliability for EV and HEV applications is highlighted in the 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.783

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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2013
Admission routes1
Has abstractyes

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