A Novel Hybrid Modelling Approach Towards Comprehensive Drive Cycle Analysis of Si, SiC, and GaN based Electric Motor Drives
Bibliographic record
Abstract
Wide-band gap (WBG) power devices are gaining significant interest over conventional Si insulated gate bipolar transistor (IGBT) power devices for inverters in electric vehicle (EV) propulsion applications. Understanding motor-drive performance on a drive-cycle due to the influence of such emerging inverters is essential to design an optimal electric powertrain system which is superior in terms of cost, weight and efficiency when compared to the state-of-the-art. Specifically, this paper introduces a novel, hybrid approach that includes numerical and analytical simulations and analysis to consider drive-cycle load and switching characteristics of various power switches and harmonics generated by the inverter. A comprehensive list of performance indices including inverter and motor losses, current harmonics and torque ripple are initially selected and used in this approach. The impact of two-level IGBT, GaN, and SiC inverters on a surface permanent magnet machine are determined for a drive-cycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".