Multi-Variable Hybrid Switching Frequency- Duty Cycle Based Phase-Shift Control for DC-DC Resonant Converters
Bibliographic record
Abstract
This paper presents a multi-variable hybrid control strategy that can simultaneously adjust both operating switching frequency, fs, and duty cycle of a dc-dc resonant converter in response to variation of either input voltage or load conditions. Conventional resonant converter control strategies are mono--variable i.e., either fsor duty cycle is changed at a time in response to varying operating conditions. This strategy might eliminate MOSFET turn-ON losses due to zero voltage switching (ZVS). However, such control strategy may inadvertently cause large circulating reactive current flow which can nullify ZVS power savings. Hence, it is necessary to operate the converter such that ZVS operation with minimum primary circulating current is guaranteed while ensuring output power regulation for a wide range of operating conditions. A multi-variable ZVS control strategy that achieves simultaneous variation of fsand duty cycle to changing operating conditions is implemented using a F-D control law relationship. This control law has been developed for a hybrid Frequency modulated (FM)-Phase-shift modulated (PSM) LLC resonant converter based on ZVS boundary conditions established using FHA. Efficiency improvements of at most 1.5% are observed when compared with mono-variable FM-PSM control.
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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 | 0.000 |
| 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".