A push-pull Class E converter with improved PDM control
Bibliographic record
Abstract
In this paper, an improved Pulsed Density Modulation (PDM) method for high frequency converters such as push/pull Class E DC-DC or quasi resonant boost converters is proposed. Conventional PDM control provides high efficiency at any load when modulation frequency is low. However, the efficiency drops significantly at higher modulation frequencies due to hard-switching during transients. The proposed method not only reduces the transient power loss of the converter, but also decreases the voltage stress of the switches. Startup behavior of a class E push-pull converter is analyzed. Soft switching conditions and the switches' peak voltages are studied as a function of the first switching cycle. In each power pulse, the first cycle timing of the converter is calculated to minimize the voltage overshoot and switching loss of the converter. A 1 kW, 3 MHz push-pull Class-E inverter with Class-DE rectifier is implemented to verify the analysis and simulation results. A maximum efficiency of 93 percent is obtained. Further, the efficiency drops only 2 percent at one tenth of the load. In addition, about 1/5 reduction in the switches' peak voltage is obtained as compared to the conventional PDM method.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".