High Efficiency Wide Input Voltage Range LCLC Resonant Converter Using Nonlinear Frequency Controller
Bibliographic record
Abstract
In server and telecommunication applications, LLC resonant converter featuring high efficiency and high power density becomes an excellent candidate for the frontend DC/DC stage. But, for conventional LLC converter, the performance will be severely deteriorated once the converter is designed for accommodating regulation over wide input voltage range. A modified LLC topology with changeable magnetizing inductance, LCLC resonant converter, has been successfully proved to extend the input voltage range without sacrificing the normal efficiency. Moreover, due to the strong nonlinear feature of the LCLC resonant converter, conventional linear control strategies such as PI controller are very hard to optimize. This paper proposes a nonlinear frequency control method for LCLC converter to achieve better output voltage regulation over wide input voltage range. In the controller, the switching frequency is calculated from a quadratic function, which expresses the opposite variation of the voltage gain. Finally, a 250-400V input 12V/500W output LCLC prototype is built and tested. Experimental results demonstrate the effectiveness of the proposed nonlinear frequency control strategy.
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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) | 0.002 | 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".