Multi-Loop State-Plane Control of DAB Converters
Bibliographic record
Abstract
DAB converters are widely used in bidirectional DC-DC power conversion due to their galvanic isolation and high power density. Traditional linear control strategies often struggle with slow transient responses and increased transformer stress. This paper introduces a novel Multi-Loop State-Plane control (MLSPC) approach that leverages a large-signal state-plane model to achieve fast output current regulation and while simultaneously mitigating DC bias on the transformer side. The proposed control method dynamically calculates the optimal output trajectory to control the current and adjusts inner phase-shifts once the power changes, ensuring rapid transient output response and minimizing RMS transformer current. The algorithm maintains the inductor current balance within two switching cycles, preventing transformer core saturation and efficiency losses. The effectiveness of the proposed control is validated through simulation and experimental results, using a scaled-down DAB converter. The controller features fast dynamic response, minimization of DC bias current in the transformer, and an implementation compatible with low-cost microcontrollers, offering a promising solution for efficient and reliable power conversion.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".