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Record W2613713740 · doi:10.1109/icit.2017.7913284

Comparative analysis of DITC and DTFC of switched reluctance motor for EV applications

2017· article· en· W2613713740 on OpenAlexaff
Deepak Ronanki, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSwitched reluctance motorDirect torque controlTorque rippleControl theory (sociology)Reluctance motorTorqueStall torqueControllabilityStatorComputer scienceTorque motorRotor (electric)EngineeringPhysicsControl (management)Induction motorVoltageMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Switched Reluctance Motor (SRM) presents simple construction, high starting torque, wide speed range, high efficiency and high reliability which are appropriate for an Electric Vehicle (EV) drive system. However, SRM has some issues such as high torque ripple, acoustic noise, vibration due to non-linearities, and require a position sensor for control. The purpose of this paper is to compare two different control techniques and to find which method can provide better performance in terms of controllability and efficiency. The first technique is Direct Instantaneous Torque Control (DITC), i.e only torque is controlled with in the hysteresis band. The second technique is Direct Torque and Flux Control (DTFC), i.e both torque as well as stator flux vector is controlled in the hysteresis band. Principles of both these methods are discussed in detail. The performance analysis of these methods for 6/4 SRM are carried out and validated from simulation results. Conclusions are drawn from the results and the advantages and drawbacks of each method are addressed.

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: none
Teacher disagreement score0.840
Threshold uncertainty score0.229

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 head0.023
GPT teacher head0.278
Teacher spread0.255 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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