GaN on silicon E-HEMT and pure silicon MOSFET in high frequency switching of EV DC/DC converter: A comparative study in a nissan leaf
Bibliographic record
Abstract
This paper presents the merits of using low-losses fast-switching, Gallium Nitride on Silicon Enhanced mode High-Electron-Mobility-Transistor cascode switches; in comparison to pure Silicon MOSFET in the DC/DC converter of the Nissan Leaf electric vehicle (EV). The performance of the car, at steady state conditions, is studied to show the technical benefits of using Gallium Nitride on Silicon Enhanced mode High-Electron-Mobility-Transistor cascode as the switching device. This device belongs to a 600 V power semiconductor devices family of high density wide band gap. It achieves extremely efficient power conversion with fast switching slew rates higher than 150 V/ns compared to 50 V/ns for the pure silicon. Moreover, it has low reverse recovery charge that reduces the switch loss significantly compared to its peers of pure silicon. To assess these merits, an EV powertrain model was simulated in PSIM. This model considers the calculation of both switching and conduction losses for each material. The power losses and efficiencies were observed for different junction temperatures. The results showed lower total switches power losses with the GaN switch. This reduction in losses was recorded at different switching frequencies.
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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 | 0.000 |
| 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".