Bidirectional Resonant Frequency Tracking for CLLC Converters Based On Voltage Falling Edges
Bibliographic record
Abstract
Due to high efficiency and bidirectional capability, unregulated CLLC converters have significant potential for the DC transformer and chargers with two stages, where the switching frequency is designed to be fixed at resonance. However, the actual resonant frequency may drift from the designed value because of resonant tank component tolerances, temperature, and aging. This leads to a decrease in efficiency for unregulated CLLC converters with a fixed frequency. To solve these issues, a bidirectional resonant frequency tracking method is proposed for CLLC converters in this paper. Based on the analysis of input and output impedance, a unique feature is discovered that no matter how resonant tank parameters deviate, the switching frequencies harvesting maximum efficiency are identical for bidirectional operations. To track this global optimal switching frequency, the proposed method adopts a voltage edges-based sensing technique, so the bidirectional tracking is realized and costly current sensors of conventional methods are eliminated. It is lower cost and robust against noise. Experimental results show that bidirectional fast-tracking and efficiency improvement are achieved for parameter deviation conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".