Determination of Conducted EMI in SiC Based Dual Active Bridge Converter
Bibliographic record
Abstract
Power converters are required to work faster and with higher power density with the developing technology. Therefore, the converter is expected to work in more than one direction. Usage of Dual Active Bridge DC-DC Converter is an example. To increase the power density of the converters, it is necessary to increase the switching frequency. In conventional Si MOSFET based converters, power losses are very high and cause high electromagnetic interferences at high frequencies. These disadvantages lead developers to the use of wide-band gap semiconductor based converters such as SiC However, SiC MOSFETs will also emit electromagnetic interference (EMI) above a certain frequency. In this study, the EMI, emitted at certain frequencies by the Dual Active Bridge (DAB) DC-DC Converter, is simulated by the LTspice. It was observed that the Si-based inverter parts of the DAB converter generate 10 V EMI on the linear base, that means 140 dBµV EMI on the logarithmic base, at 20 kHz. The SiC-based converter does not emit any noise at the same frequency. However, when the frequency was increased to 250 kHz, it was determined that the SiC based converter emitted 2.3 V noise on the linear base, thus 123 dBµV noise on the logarithmic base. This study shows that not only Si MOSFET’s but only SiC MOSFET’s emit EMI over a certain frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".