The Comprehensive Circuit-Parameter Estimating Strategies for Output-Parallel Dual-Active-Bridge DC–DC Converters With Tunable Power Sharing Control
Bibliographic record
Abstract
The centralized output-parallel dual-active-bridge (OP-DAB) dc-dc system is a promising candidate for achieving isolated dc-dc energy conversion with large current and power rating. To implement the flexible power sharing performance of the OP-DAB dc-dc converter, a simple tunable power sharing (TPS) strategy is proposed with the single-phase-shift method in this article. Based on the TPS strategy, the excellent dynamic performance under disturbances of input voltage for each module and load resistor can be provided. However, inaccurate circuit-parameter information always damages the power sharing performance among different DAB converters. Therefore, the comprehensive circuit-parameter estimating schemes proposed for different conditions of the OP-DAB dc-dc system including the start-up process, the working process and plugging-in a new DAB dc-dc converter, respectively. Moreover, the hot swap (plug-in and plug-out) control methods of the DAB converter without large influence on output voltage is also discussed in detail. Experimental results are obtained to verify the analysis in this article and the excellent performance of the proposed methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".