Overview and Comparison of Modulation and Control Strategies for a Nonresonant Single-Phase Dual-Active-Bridge DC–DC Converter
Bibliographic record
Abstract
The nonresonant single-phase dual-active-bridge (NSDAB) dc-dc converter has been increasingly adopted for isolated dc-dc power conversion systems. Over the past few years, significant research has been carried out to address the technical challenges associated with modulations and controls of the NSDAB dc-dc converter. The aim of this paper is to review and compare these recent state-of-the-art modulation and control strategies. First, the modulation strategies for the NSDAB dc-dc converter are analyzed. All possible phase-shift patterns are demonstrated, and the correlation analysis of the typical phases-shift modulation methods for the NSDAB dc-dc converter is presented. Then, an overview of steady-state efficiency-optimization strategies is discussed for the NSDAB dc-dc converter. Moreover, a review of optimized techniques for dynamic responses is also provided. For both the efficiency and dynamic optimizations, thorough comparisons and recommendations are provided in this paper. Finally, to improve both steady-state and transient performances, a combination approach to optimize both the efficiency and dynamics for an NSDAB dc-dc converter based on the reviewed methods is presented in this paper.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".