Indirect Space Vector Modeling of Asynchronous Motor for High-Speed Electric Vehicle Propulsion
Bibliographic record
Abstract
This paper presents a high performance squirrel-cage asynchronous motor (ASM) drive, proposed for high-speed electric vehicle (EV) propulsion. This is an indirect space vector control (IDSVC) scheme in the rotor flux frame of reference, in which the modeling is based on synchronously rotating coordinate system transformation. Space vector pulse width modulation (SVPWM) inverter fetches the controlled stator direct and quadrature axis responses from the speed and current controllers and supply necessary voltages to stator of ASM. The output voltage waveforms of SVPWM inverter are not sinusoidal in nature, which allows undesired harmonics at high frequency of operation. This due to inconsistent switching frequency. A second-order low-pass (SOLP) RLC filter with Butterworth approximation and was designed and connected in series with SVPWM inverter to grab harmonic free speed and torque profile during high speed operations. Similarly, another SOLP with a quality factor equal to 2 is designed and speed profiles of both filters are compared. The system is developed and simulated in MATLAB/SIMULINK to observe the speed and torque tracking capabilities. Simulation results show that the performance of drive under steady and dynamic states were good with Butterworth filter approximation with robust IDSVC scheme adopted over wide range speed-drive curve.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".