Unified Bidirectional Resonant Frequency Tracking for <i>CLLC</i> Converters
Bibliographic record
Abstract
UnregulatedCLLCconverters have significant potential for dc transformers and two-stage battery chargers due to their high efficiency and bidirectional capability. In these applications, unregulatedCLLCconverters are usually designed to have their switching frequency fixed at resonance for maximum efficiency. However, the resonant frequency drifts from the designed value due to resonant tank parameter deviations, which leads to a decrease in efficiency for unregulated converters. To address this issue, this article proposes the bidirectional resonant frequency and a unified tracking algorithm. By analyzing the generalized resonant frequency equations in depth, an interesting feature is discovered forCLLCconverters: forward mode and backward mode achieve maximum efficiency at an identical (but not constant) frequency. Thus, this unique frequency is defined as bidirectional resonant frequency (BRF). As the BRF varies with parameter deviations, a unified bidirectional tracking algorithm is proposed, which unifies the tracking of bidirectional maximum efficiency points into the tracking of a BRF. The introduction of the BRF combined with the unified tracking algorithm provides a simple, accurate, and low-cost solution for bidirectional resonance tracking of unregulatedCLLCconverters. Finally, experimental results show that unified bidirectional tracking and efficiency improvement are achieved under parameter deviations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".