Efficient MMC Devices with Reduced Radiated and Conducted Interferences for Electric Vehicles Application
Bibliographic record
Abstract
This paper presents a new cell module for modular multilevel converter (MMC) used in electric vehicles application. Traditionally, MMC has been used in high-power high-voltage application. The large number of power electronic devices required is rather used for reducing their voltage stress than the harmonic contents. Using the proposed cell topology, higher numbers of levels are achieved while the number of component remains low. This characteristic makes it an ideal configuration for a low switching high-efficiency AC/DC power converter, required for the soon to be common for plug-in vehicles application. Such converter can play two roles, not only can it be used as an active bidirectional charger, but also as active filter increasing network reliability. A predictive control algorithm is also presented in this paper, which will demonstrate the possibility to impose a charging curve on the DC side, even when the converter is used as active filter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".