Analytical Approach to Nonlinear Behavior Study of an Electric Vehicle
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".