High-Efficiency Variable Turns-Ratio Semi-Dual Active Bridge Converter for a DC Fast Charging Station With Energy Storage
Bibliographic record
Abstract
In electric vehicle fast charging applications, the isolated dc/dc converter charging a battery electric vehicle from a battery energy storage system should provide high efficiency over a wide voltage gain. The semi-Dual Active Bridge (semi-DAB) converter is an excellent choice for this unidirectional application. However, achieving the high efficiency of these converters is challenging when the input and output voltages vary over a wide range. Thus, a novel two-winding semi-DAB converter is proposed in this article to improve the overall efficiency with a variable turns-ratio, which minimizes the converter conversion effort, so that the required effective voltage gain is within a reasonable range even as input and output voltages vary widely. Furthermore, a control law is also proposed to determine the operational structure of the converter, i.e., the turns-ratio and the bridge configuration, which reduces the converter peak and rms currents. A 550-V, 10-kW semi-DAB converter is built on a four-layer PCB to test the proposed converter and control. The proposed converter with control provides efficiency improvement of up to 3.5% in comparison to the standard structure and control, and has the most improvement during the common fast charging scenario with high input and low output voltages. The peak efficiency achieved is 98.5%.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".