SiC devices performance overview in EV DC/DC converter: A case study in a Nissan Leaf
Bibliographic record
Abstract
This paper presents the technical and economical merits of using low losses fast switching Silicon Carbide (SiC) accumulated gate field effect transistor (ACCUFET) switches in comparison with a Hybrid module of Silicon (Si) IGBT with anti-parallel schottky barrier diode (SBD) in DC/DC converter applications of the Nissan Leaf electric vehicle (EV) motor drive. The performance of the car, at steady state conditions, is studied at different temperatures. The power losses, current, voltage, torque and speed results are given. The results showed lower total switching power losses with the SiC ACCUFET. This reduction in losses was recorded at different switching frequencies in comparison to the hybrid switch. The fast switching of SiC Trench ACCUFET reduces current-voltage cross-over losses and enables high frequency operation thus achieving high record efficiency. The high frequency operation will result in a smaller foot print of the converter together with a weight reduction; thus making the car lighter. It is expected that a lighter car will result in a longer range Nissan Leaf with the same battery. Increasing the car mileage of the battery and lowering the cost of magnetic components in the EV will surpass the higher cost of the SiC ACCUFET from an economical point of view.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".