Effects of Rise Time and Overshoot of WBG-Based Power Converters on Hairpin Winding With Corona-Resistant and Non-Corona-Resistant Wires in EVs
Bibliographic record
Abstract
This paper investigates the impact of integrating hairpin windings and wide-bandgap (WBG) power electronics in electric vehicles (EVs), focusing on the effects of WBG-based drives on turn-to-turn insulation in hairpin windings. A novel back-to-back sample, constructed using flat enameled wires commonly employed in EVs, is utilized to evaluate the turn-to-turn enamel coating performance under fast transients generated by a high-voltage SiC-MOSFET pulse generator, in hairpin windings. Two types of flat wires—corona-resistant and non-corona-resistant—are tested for insulation performance. The wires are aged under partial discharge (PD) conditions with varying rise times and overshoots. Key parameters measured include partial discharge inception voltage (PDIV), percentage dissipation factor (%DF), sample surface temperature, and imaging, before and after aging. The findings reveal superior insulation performance in corona-resistant wires compared to their non-corona-resistant counterparts under PD activity. Overshoot predominantly drives the increase in %DF for both wire types. However, the factors affecting PDIV vary, in which, the rise time significantly influences corona-resistant wires, while overshoot is the primary factor for non-corona-resistant wires.
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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".