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Acceleration Slip Regulation for Electric Vehicles Based on Fuzzy PID Controller

2022· article· en· W4313306893 on OpenAlexaff
An-Toan Nguyen, Binh-Minh Nguyen, João Pedro F. Trovão, Minh C. Ta

Bibliographic record

Venue2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS) · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsControl theory (sociology)Traction control systemSlip ratioSlip (aerodynamics)PID controllerTorquePowertrainElectric vehicleTraction motorAutomotive engineeringTractive forceAccelerationController (irrigation)Computer scienceTraction (geology)Vehicle dynamicsEngineeringControl engineeringPhysicsPower (physics)Mechanical engineeringControl (management)Artificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

This paper proposes an acceleration slip regulation (ASR) by the fuzzy PID (FPID) controller for electric vehicles with four-wheel independent motor drive. The ASR architecture is hierarchically presented by three layers. The upper layer determines the total traction force based on the control of vehicle velocity. The middle layer determines optimum tire longitudinal force distribution depending on the total tire workload. In case of wheel slip, the optimum longitudinal force will be adjusted by the FPID controller. In the lower layer, the traction forces are converted to reference torque for each powertrain. The modeling and control organization of the studied vehicle are presented by using energetic macroscopic representation (EMR). Simulation tests show that ASR with FPID controller can limit the slip ratio of the wheels and improve the vehicle stability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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