Optimal Control of Semi-Dual Active Bridge DC/DC Converter With Wide Voltage Gain in a Fast-Charging Station With Battery Energy Storage
Bibliographic record
Abstract
Ultrawide voltage regulation is required in dc/dc converters interfacing battery energy storage systems (BESSs) and electric vehicle (EV) batteries in dc fast-charging stations with energy storage. Attaining high efficiency of this converter can be challenging due to the wide variation of input and output voltage yet is important due to the high power transfer. This article proposes a novel control strategy for the semi-dual active bridge (DAB) converter to achieve wide voltage gain while increasing the efficiency at operational points with high input voltage and low output voltage, which is a commonly occurring scenario when the BESS is fully charged, and the EV battery is at low charge. Furthermore, this article also provides an algorithm to determine the required phase shift in real time for any operating point, eliminating the need to devise the control trajectory offline. A 550-V, 10-kW experimental prototype is built and tested to validate the proposed control strategy. With a 25-A constant charging current, the prototype shows that the proposed control strategy can improve efficiency by up to 3.5% compared to the well-known dual phase shift control at operating points with high input voltage (450–550 V) and low output voltage (150–275 V), with a peak efficiency of 97.6%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".