Analytical Dual-Phase-Shift Optimization for Reactive Power Minimization in Dual Active Bridge Converters under Light-Load Conditions
Bibliographic record
Abstract
textit-The ability of Dual Active Bridge (DAB) converters to provide isolated, bidirectional power conversion offers distinct advantages over traditional converters, making them ideal for Vehicle-to-Grid (V2G) applications. Effective power regulation in such applications requires advanced modulation techniques. This paper proposes an innovative dual-phase shift strategy for DAB converters to enhance active power transfer and efficiency. Through analytical modeling and simulations, this approach demonstrates improved performance in managing bidirectional power flow. The results confirm its alignment with prior research, underscoring its potential for scalable and efficient energy systems. Specifically, the proposed Analytical Dual-Phase-Shift (ADPS) method derives closed-form expressions for optimal phase shift angles, minimizing reactive power and ensuring Zero-Voltage Switching (ZVS) across all bridge legs under light-load conditions. Compared to conventional single-phase and dual-phase shift methods, ADPS achieves a reduction of up to 12% in reactive power and an improved power factor without increasing control complexity. Simulation results validate the effectiveness of the approach, making it a promising solution for high-efficiency DAB control in modern DC-DC power conversion applications.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".