Performance Investigation of Two Novel HSFSI Demodulation Algorithms for Encoderless FOC of PMSMs Intended for EV Propulsion
Bibliographic record
Abstract
This paper investigates the performance of two novel half-switching frequency signal injection (HSFSI) demodulation algorithms for encoderless field-oriented control (FOC) of permanent magnet synchronous machines (PMSMs) intended for electric vehicle (EV) propulsion. The proposed rotating and pulsating HSFSI demodulation algorithms do not require voltage measurements or approximations for estimating the rotor position angle. The proposed HSFSI algorithms have been quantitatively and qualitatively compared by MATLAB-SimPowerSystems simulations, as well as experimentally against the two equivalent classical high-frequency signal injection (HFSI) approaches. A 2.5 kW PMSM with radially inset rotor magnets has been used for experimentally evaluating and validating the performance analysis and the comparison of the four algorithms fully implemented to work in real-time on a TI C2000 digital signal processor (DSP). The results obtained with an 80 kW interior (I)-PMSM intended for EV propulsion also show that the proposed algorithms allow a better machine control performance in terms of a smaller torque ripple generation and a faster current control loop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".