An Intuitive and Noniterative Design Methodology for CLLC Chargers Employing Simplified Operation Modes Model
Bibliographic record
Abstract
This article mainly focuses on the simplified operation modes (SOM) model and resonant parameter design for the CLLC charger. Based on the mathematical and detailed operation waveform assumptions, the voltage gain model expressions and the operation mode boundaries are calculated directly, providing the high efficiency and high reliability of the CLLC converter. The proposed SOM model is more accurate in depicting the voltage gain compared with the conventional fundamental harmonic approximation model. Moreover, the SOM model is more intuitive and has less computational complexity than the complicated and unsolvable time-domain model. As for the parameter design process, the inductance ratio$k$and characteristic impedance$Z_{0}$are selected instead of specific inductances and capacitances. Relying on the SOM model, a step-by-step parameter design methodology is studied, which avoids repetitive iterations and streamlines the procedure. The voltage gain range, efficiency, soft-switching operation, mode boundaries, and system stability are considered comprehensively and realized in this process. The simulations and experiments validate that the proposed SOM model is accurate, and the design methodology is straightforward through a 1-kW CLLC charger prototype with 97% peak efficiency.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".