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Record W2442675860 · doi:10.1109/tvt.2015.2487301

Integrated Electromechanical Double-Rotor Compound Hybrid Transmissions for Hybrid Electric Vehicles

2015· article· en· W2442675860 on OpenAlex
Yinye Yang, N. Schofield, Ali Emadi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsComponent (thermodynamics)Rotor (electric)EngineeringDriving cycleTransmission (telecommunications)Process (computing)Automotive engineeringKey (lock)Electronic engineeringElectric machineControl engineeringElectric vehicleComputer scienceElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Integrated hybrid transmissions serve as the key component in hybrid electric vehicles (HEVs). This paper reviews several technologies of the electromechanical integration of advanced hybrid transmission systems. Detailed configurations, fundamentals of operating principles, and various operating modes were comprehensively explained and analyzed. Combining the merits, an integrated electromechanical double-rotor compound hybrid transmission that is potentially more compact and has better performance is proposed. Detailed operation modes are compared, and functions and cooperation between integrated components are illustrated. As the core component of the integrated transmission, the double-rotor electric machine is studied via drive-cycle analyses, and a scaled-down prototype is built according to the drive-cycle analysis results. Test results successfully validate the design and simulation process.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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