Wide Bandgap Devices in Electric Vehicle Converters: A Performance Survey
Bibliographic record
Abstract
This paper introduces a unique quantified study about using low-losses fast-switching wide bandgap (WBG) devices, i.e., gallium nitride (GaN) and silicon carbide (SiC), over traditional Silicon (Si) devices in the switching of dc/dc converters, focusing on electric vehicles' (EVs) machine drive and battery charger. A detailed model of the power train of a Nissan Leaf was developed in PSIM software, with WBG semiconductors' capability. The model was simulated one time using GaN semiconductors and another time using SiC devices. Simulation results are quantified and a comparison between different semiconductors in terms of total losses and efficiency is presented. The developed PSIM model can also be extended to other EVs like Chevy Volt. A proof of concept prototype for a Nissan Leaf dc/dc converter was built in the laboratory and results were collected. Componentwise experimental results are presented and their correlation with simulation findings is demonstrated. In addition, experimental results of the overall power train test bench are found to be matched with the simulation results on a system level as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".