MétaCan
Menu
Back to cohort

Analytical Approach to Nonlinear Behavior Study of an Electric Vehicle

2019· article· en· W3011705744 on OpenAlexaff
Cyrus Mehdipour, Fazel Mohammadi, Iman Mehdipour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicFractional Differential Equations Solutions
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNonlinear systemTorqueControl theory (sociology)InertiaExcitationElectric motorElectric vehiclePerturbation (astronomy)Computer sciencePower (physics)PhysicsEngineeringMechanical engineeringClassical mechanicsElectrical engineering

Abstract

fetched live from OpenAlex

Permanent Magnet Direct Current (PMDC) motors have great application potentials in Electric Vehicles (EVs) industry, due to their advantages, such as no need for the field excitation arrangement, no input power consumption for the excitation, and having lower costs for low power rating applications. To analyze the nonlinear behavior of the EV equipped with the PMDC motor, the electromechanical equations related to a typical model of the PMDC motor are derived, and the electrical equivalent circuit related to the mechanical equations is presented in this paper. Homotopy Perturbation Method (HPM) and Variational Iteration Method (VIM) as the two analytical techniques are applied to solve the evolution equations. The performance of the PMDC motor that is concerned with the nonlinear parameters (viscous friction, torque, and inertia) is evaluated. In addition, the accuracy of the two analytical techniques is compared with the numerical solution. The results confirm the applicability of the two analytical techniques to analyze the nonlinear behavior of the system.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.322

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 head0.080
GPT teacher head0.366
Teacher spread0.286 · 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 designTheoretical or conceptual
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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicFractional Differential Equations SolutionsFrench-language works237,207