Bidirectional Parallel Low-Voltage Series High-Voltage DAB-based Converter Analysis and Design
Bibliographic record
Abstract
The system requirements of galvanic isolation with bidirectional energy flow capability create new opportunities for research and development in power conversion with applications in renewable and energy storage systems. A dual active bridge converter derivation topology with transformer parallel low-voltage (LV) and series high-voltage (HV) windings connected to three full-bridges is investigated in this research work. The common-mode current circulation, which leads to increased electromagnetic emissions, has been reduced within the DAB-based converter through the proposed design approach. The design methodology aims for magnetics comparison and modulator design for soft-switching power conversion operation. The parallel-LV and series-HV DAB-based converter has been evaluated with two different transformers. The typical designs for high-current transformer windings are with copper foil. The drawbacks of high interwinding capacitance have reduced by five times through a specific transformer construction. The experimental results show improved results with respect to electromagnetic emissions. Furthermore, the DAB-based converter has been compared in hard-switching and soft-switching operation with respect to electromagnetic emissions. The experimental results have been performed using a 5kW rated power DAB-based converter with silicon carbide (SiC) power semiconductors.
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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 | 0.000 | 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) | 0.005 | 0.002 |
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".