RMS Current Minimization in a SiC-Based Dual Active Bridge Converter Using Triple-Phase-Shift Modulation
Bibliographic record
Abstract
In this article, we propose an optimization approach targeting rms current minimization in a dual active bridge (DAB) converter controlled by triple-phase-shift modulation. The proposed technique overcomes the drawbacks in the existing optimization solutions, namely, the high complexity of the time-domain optimization and the low accuracy of the fundamental harmonic minimization. A finite-component harmonic model is employed in the optimization approach to calculate the current harmonic values in the converter. The total rms current is approximated as the summation of dominant harmonics using the harmonic model. A numerical assessment technique is also proposed in this research that guarantees the accuracy of the adopted harmonic model. The proposed method ensures that the harmonic approximation error is less than a certain level over the operating range. The converter's optimal parameters are calculated through a standard nonlinear optimization procedure. The results are verified in the PLECS simulation environment and experimentally validated on a $5{\rm{\ kW}}$ DAB converter. The prototype input and output voltage ranges are $600{\rm{\ V}} - 800{\rm{\ V}}$ and $200{\rm{\ V}} - 450{\rm{\ V}}$ , respectively.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".