A Comprehensive Comparison of Two Fast-Dynamic Control Structures for the DAB DC–DC Converter
Bibliographic record
Abstract
Benefitting from some significant advantages, the dual active bridge (DAB) dc–dc converter has become one of the most promising candidates for dc–dc power conversion. In recent years, some strategies have been proposed to boost the dynamic performance of DAB dc–dc converter under the disturbance of input voltage and load condition. According to the relationship between the compensation part and the model-based part, these existing schemes can be divided into two structures including the parallel structure and series structure. In the parallel control structure, the compensation part is added to the model-based part. In contrast, the compensation part is multiplied with the model-based part in the series control structure. By adopting proper feedback control, both control structures can provide excellent dynamic performance for DAB dc–dc converter easily. Hence, the modified parallel-structure fast-dynamic control scheme and the modified series-structure fast-dynamic control scheme are both proposed in this article. Then, using these two proposed schemes as examples, the merits and the demerits of both structures are analyzed, and the corresponding compensation methods are also presented. Moreover, a general PI design principle of the model-based control scheme for the DAB dc–dc converter is provided, which is different from the traditional concept for designing the PI parameters. In addition, the control delay of these two proposed schemes is analyzed, and a compensation method is also proposed. Finally, simulation results and 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".