A Two-Stage Four-Switch Buck-Boost Integrated Dual-Active-Bridge Converter with Wide Range Soft-Switching and Minimized Backflow Power
Bibliographic record
Abstract
Dual-active-bridges (DABs) inherently lack the ability to ensure zero-voltage switching (ZVS) during light load operation. To achieve soft-switching across the entire load range and enhance system efficiency, DABs are recommended to operate in the DC transformer (DCX) mode, which enables unity output voltage gain. Therefore, to widen the voltage gain range, this paper proposes a two-stage four-switch Buck-Boost (FSBB) integrated DAB (FI-DAB) with high control flexibility. The hybrid structure allows the sharing of a bridge arm between FSBB and DAB, significantly reducing the number of utilized switches and system conduction loss. To further reduce the system loss caused by the backflow power of DAB, an optimized dual-phase-shift with bidirectional inner phase shifts (ODPS-BIPS) modulation method is applied to the DAB. Additionally, based on the DCX concept, to match the system output voltage, the output voltage of FSBB is regulated by adjusting its duty cycle with a PI compensator plus input voltage feedforward. A fast-dynamic response control method is simultaneously introduced to alter the phase shift of DAB, aiming to improve the system’s transient response performance during load variations. Furthermore, the FI-DAB employs an additional phase shift, providing extra control freedom to further boost the system’s overall efficiency. Consequently, implementing the proposed structure enables full-range ZVS and ultra-fast output transient response, and simulations and experiments are conducted to validate the effectiveness of the proposed configuration and control method.
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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) | 0.001 | 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 | 0.001 |
| 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".