A design for switched capacitor and single‐switch DC–DC boost converter by a small signal‐based PI controller
Bibliographic record
Abstract
Abstract In this study, a switched capacitor (SC)‐based single‐switch DC–DC boost converter structure operating under the high voltage gain and the low duty ratio is proposed using the PI control technique. High current and voltage stresses across the power switches and power diodes can be reduced by using the projected SC block. In addition, the proposed converter can achieve high voltage gain through shorter duty cycles, which directly reduces the voltage stress and dynamic losses in the power semiconductors. On the other hand, because the proposed converter includes a single power switch under different output powers and different loads, the control process is simpler than multiswitch structures. With the proposed converter, an output voltage of 10 times greater rather than the input voltage is obtained at 0.57 of the duty cycle. In this study, the fundamental functions of the proposed converter and the controller design steps are analyzed mathematically and tested in MATLAB/SIMULINK environment. As a result of the analysis, it was determined that the proposed topology works with a high performance at high frequency and variable load ranges. To validate the proposed converter and theoretical calculations, a 200‐W prototype was established under a continuous conduction mode (CCM) working state, with 48‐VDC input voltage and 400‐VDC output voltage. Finally, the simulation results were tested and verified through the experimental results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".