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Record W2062073028 · doi:10.1109/cca.2014.6981411

Dynamic modeling and controller design for a seamless two-speed transmission for electric vehicles

2014· article· en· W2062073028 on OpenAlexaff
Mir Saman Rahimi Mousavi, Ali Pakniyat, Benoît Boulet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPowertrainGear trainControl theory (sociology)Controller (irrigation)TorqueComputer scienceTransmission (telecommunications)LinearizationKinematicsAutomatic transmissionControl engineeringMATLABEngineeringAutomotive engineeringNonlinear systemClutchControl (management)

Abstract

fetched live from OpenAlex

Transmission is one of the crucial elements of the driveline that affects vehicle fuel economy and comfort. It can transfer power in different combinations of torque and speed. This paper focuses on the modeling, simulation and control of a two-speed transmission for electric vehicles which has seamless gear shifting specification. The transmission incorporates two-stage planetary gear sets and two braking mechanisms to control the gear shifting. The dynamic model is developed by using the kinematic equations of the planetary gear trains and the Euler-Lagrange equations to derive the equations of motion. The mathematical model is validated by using the SimDriveLine library of MATLAB/Simulink®. The controller design employs optimal control methods to provide seamless shifting with minimum transition time. Then, by relaxing ideal constraints, a feasible controller is designed based on input-output and input-state feedback linearization. Simulation results demonstrate the ability of the proposed transmission to have smooth shifting without excessive oscillations in the output torque and speed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.528

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations24
Published2014
Admission routes1
Has abstractyes

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