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Record W2067408035 · doi:10.4271/2014-01-0323

Motor Control in Auxiliary Drive Systems How to Choose the Best Fitting Electronic Solution

2014· article· en· W2067408035 on OpenAlexaff
Thomas Liebetrau, Philip Brockerhoff

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2014
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsControl (management)Control engineeringComputer scienceStepper motorMachine controlControl theory (sociology)Control systemMotor driveAutomotive engineeringEngineeringElectrical engineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">In modern vehicles, the number of small electrical drive systems is still increasing continuously for blowers, fans and pumps as well as for window lifts, sunroofs and doors. Requirements and operating conditions for such systems varies, hence there are many different solutions available for controlling such motors. In most applications, simple, low-cost DC motors are used. For higher requirements regarding operating time and in stop-start capable systems, the focus turns to highly efficient and durable brushless DC motors with electronic commutation.</div><div class="htmlview paragraph">This paper compares various electronic control concepts from a semiconductor vendor point of view. These concepts include discrete control using relays or MOSFETs. Furthermore integrated motor drivers are discussed, including system-on-chip solutions for specific applications, e.g. specific ICs for window lift motors with LIN interface.</div><div class="htmlview paragraph">In most cases, system suppliers have the choice between several electronic partitioning concepts, based on specific technical and economic conditions up to given specific preferences of the supplier.</div></div>

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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