An Asymmetrical DAB Converter Modulation and Control Systems to Extend the ZVS Range and Improve Efficiency
Bibliographic record
Abstract
This article presents a new optimized hybrid modulation and control systems based on symmetrical and asymmetrical operations of a dual-active-bridge converter to minimize the transformer root-mean-square (RMS) current and extend the zero-voltage-switching (ZVS) range. Various modulation modes are analyzed, and their corresponding power, RMS current equations, and soft-switching conditions are derived. The RMS equations are minimized using the multivariable optimization method, and the corresponding parametric equations are obtained. A hybrid control system has been proposed to regulate the battery current, to ensure smooth inductor current transients, and eliminate the need for a blocking capacitor on the low-voltage side. Using asymmetrical-extended-phase-shift and conventional symmetrical modes, a wider ZVS range is achieved compared to advanced modulations, such as triple phase shift. Moreover, in the low-power region, an optimized RMS current is achieved by applying an optimum dc voltage on the blocking capacitor at the HV side. Hybrid modulation that extended ZVS range and improved RMS current makes the proposed approach a suitable modulation for high-frequency applications and also high-voltage or high-current applications with high$C_{\text{oss}}$losses. The efficiency, ZVS operation, and control system performance are validated by a 5 kW converter.
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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) | 0.003 | 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".