Direct Phase-Shift-Angle Optimization Strategy for DAB Converters’ Efficiency Enhancement Based on Fundamental Extended Phase-Shift Modulation
Bibliographic record
Abstract
The dual-active-bridge (DAB) converter is widely employed in dc conversion owing to its symmetrical structure, ease of operation, and high efficiency. However, its efficiency decreases under light-load conditions, especially when the voltages are severely mismatched with the transformer. This article proposes a fundamental extended phase-shift (FEPS) modulation method based on the frequency-domain model to improve light-load efficiency. This strategy can not only improve the converter efficiency but also resolve the difficulty of determining the peak current in the time-domain model inherent to EPS modulation. Moreover, based on the optimization results and exploiting monotonicity, a direct phase-shift-angle optimization (DPO) strategy is further proposed, which can reduce the computation of the DAB system. Experiments are conducted on a small experimental platform and compared with several existing optimization methods. The experimental results of efficiency tests verify the effectiveness of the FEPS modulation and DPO method proposed in this article.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".