Modeling and Minimization of Switching Loss in Dual Active Bridge Converters
Bibliographic record
Abstract
This paper presents a closed-form formulation to model the switching loss in single-phase dual active bridge converters (DAB). The proposed model is general and covers all modulation techniques with one, two, or three control parameters. Moreover, in this paper, the switching loss is minimized in a DAB converter controlled by the extended phase shift modulation (EPS) utilizing the model. In other words, the proposed loss model is used in a standard nonlinear optimization approach targeting the switching loss. By this optimization, the efficiency of the DAB converter is improved notably. Particularly, in IGBT-based converters with high switching loss, the optimization approach reveals significant improvement. In the designed 10kW DAB converter controlled by EPS modulation with optimal parameters, the switching loss is reduced by 22% compared to the traditional single-phase shift modulation (SPS). The total efficiency is increased by 0.6%, employing the proposed model and the optimization process.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".