Research and analysis of electromagnetic interference of a motor drive control system based on PMSM with SiC MOSFET for new energy electric vehicles
Bibliographic record
Abstract
Sustainable development in the 21st century faces significant challenges due to finite reserves of fossil fuels and environmental pollution. In the context of new energy electric vehicles (NEEVs), the wide-bandgap semiconductor known as the silicon carbide–metal oxide–semiconductor field-effect transistor (SiC MOSFET) and the permanent magnet synchronous motor (PMSM) have emerged as advantageous sources. However, the use of these components gives rise to electromagnetic interference (EMI) issues, which impede the achievement of electromagnetic compatibility (EMC) standards in the motor drive control system. This paper aims to elucidate the generation mechanism, propagation path, and test infrastructure of EMI. Furthermore, it proposes a system-level conducted EMI equivalent circuit model for the motor drive control system, encompassing the power battery pack, busbar cable, LISN, three-phase inverter, and PMSM. Building upon this foundation, the principles for suppressing and optimizing EMI noise are discussed. The paper concludes with the validation of simulations and experimental results, which demonstrate the effectiveness of the proposed approach. It is anticipated that professionals with an interest in the field of EMI/EMC will find this paper to be of both theoretical and practical importance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".