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Record W2803816889 · doi:10.3390/wevj8010184

Design of a Modular Swift-shift Multi-speed Transmission with Double Dual Clutches for Electric Vehicles

2016· article· en· W2803816889 on OpenAlexaff
Alexei A. Morozov, Ting Zou, Mir Saman Rahimi Mousavi, Jorge Angeles, Benoît Boulet

Bibliographic record

VenueWorld Electric Vehicle Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsModular designClutchTestbedModularity (biology)Automotive engineeringTruckComputer scienceHeavy dutyDual (grammatical number)Transmission (telecommunications)ThroughputSimulationEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper a novel concept of modular swift-shift multi-speed transmission with double dual clutches is proposed, with its architecture, simulation and control strategies discussed in detail. Due to its modularity, the desired number of gear ratios can be readily realized by adding the appropriate number of modules. Further, in this design, both the underdrive and the overdrive modes are combined within one single gear train, which reduces the overall volume and weight significantly. Due to its modularity and compactness, this transmission is suitable for electric vehicles, especially commercial fleets of medium- and heavy-duty electric trucks. A proof-of-concept testbed was developed; its preliminary simulation analysis is reported here.

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)0.000
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: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.227
Teacher spread0.205 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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