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Record W2894982489 · doi:10.1504/ijpt.2018.10016584

Investigation of challenges in interior and surface permanent magnet synchronous machines during integrated charging operation in electric vehicles

2018· article· en· W2894982489 on OpenAlexaff
Narayan C. Kar, Kaushik Mukherjee, Himavarsha Dhulipati, Shruthi Mukundan, Chunyan Lai, K. Lakshmi Varaha Iyer

Bibliographic record

VenueInternational Journal of Powertrains · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMagnetAutomotive engineeringPermanent magnet synchronous motorPermanent magnet synchronous generatorSynchronous motorEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The beneficial nature of employing power electronics and drive components in conventional electric vehicle (EV) towards level 3 charging capability has propelled research activities towards integrated charging (IC) technology. However, alternating magnetic fields produced in the air-gap by AC charging currents and the stationary nature of the rotor leads to unusual loss and magnet operating characteristics. Since the same permanent magnet synchronous motor (PMSM) will be used for both traction and IC, it is important to understand the machine's behaviour during IC to optimally design the machine for both applications. Hence, this paper exclusively investigates: 1) permanent magnet operation; 2) electrical and magnet losses; 3) temperature rise; and 4) effect of winding inductances on voltages and currents, in both surface and interior traction PMSMs employed for IC in EVs. Results obtained from investigations conducted on laboratory interior and surface PMSMs using finite element analysis and experimentation are analysed and discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.230
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes1
Has abstractyes

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