A Reconstructed Circuit Parameters Estimation (RCPE) Strategy of Modular Multiple Dual Active Bridge DC-DC Converters for Power Sharing Control
Bibliographic record
Abstract
Featuring electrical isolation and bidirectional power flow capability, dual-active-bridge (DAB) dc-dc converter can be flexibly connected as modular multiple DAB (MMDAB) dc-dc system, achieving large power rating with low power DAB modules. In MMDAB, it is important to perform power sharing among DAB modules, which requires current sensing and circuitry parameters in traditional power sharing control strategies. The current sensing can result in high cost and the circuit parameters of parallel DAB converter systems, such as inductances and transformer turn ratios, may be known accurately or could vary during operation. In this paper, a reconstructed circuit parameter estimation (RCPE) scheme is proposed to realize power sharing control of MMDAB system without relying on current sensing and the knowledge of circuit parameters. Meanwhile, the excellent dynamic performances can also be obtained under disturbances of input voltage and load. Experimental results are obtained to verify the excellent performance of the proposed methods and the associated analysis in this work.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".