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Comparative Analysis of Integrated Onboard Battery Chargers for Zero Charging Torque Using PMSM with and without Neutral Point Access

2024· article· en· W4407316283 on OpenAlexaff
Kamal M. Vaghasiya, Amrutha K. Haridas, Wesam Taha, Yicheng Wang, Aniket Anand, Sreejith Chakkalakkal, Ali Emadi, Phil Kollmeyer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTorqueBattery (electricity)Point (geometry)Automotive engineeringDirect torque controlZero (linguistics)Computer scienceElectrical engineeringControl theory (sociology)EngineeringVoltagePhysicsInduction motorMathematicsPower (physics)Control (management)

Abstract

fetched live from OpenAlex

This study compares the performance of integrated onboard battery chargers (IOBCs) in electric vehicles (EVs) when using motor windings as filter inductors. Both conventional three-wire three-phase permanent magnet synchronous motor (PMSM) and neutral-point-accessible PMSM (NP-PMSM) are studied in a split single-phase charger. A charging torque minimization strategy is analysed to address the torque oscillations that are inherently generated in conventional PMSMs. Simulation studies are conducted to analyze the charging torque oscillations in IOBCs based on conventional PMSMs and NP-PMSMs. The associated power losses in the power converter are meticulously examined to gain valuable insights for selecting an appropriate IOBC configuration. The study demonstrates that NP-PMSM-based IOBCs offer significant advantages, exhibiting no charging torque and lower power loss compared to conventional PMSM-based IOBCs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.052
GPT teacher head0.350
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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