LLC resonant converter with shared power switches and dual coupled resonant tanks to achieve automatic current sharing
Bibliographic record
Abstract
In this paper, a novel LLC resonant converter with parallel input and parallel output circuit topology is proposed to achieve the good performance of reduced switch count and medium power compared with conventional parallel half-bridge resonant converter. The proposed LLC resonant converter has two resonant circuits, two resonant circuits use the same power switches to transmit power so that the switch counts are reduced compared with the conventional two-phase LLC resonant converter. The resonant inductors of each resonant circuit are shared to achieve automatic resonant current sharing performance and then to balance the current stress of passive elements, such as transformer, secondary-side rectifier even sough there are components tolerance of each resonant circuit. Mathematical model based on Fundamental Harmonic analysis (FHA) is built. The FHA analysis shows that there is same ZVS and ZCS performance with conventional LLC Converter. Two-phase conventional LLC converter and proposed LLC converter can be analyzed. A 600W experiment prototype is built to verify the feasibility and excellent current sharing performance has been demonstrated. The experimental results are shown that the current sharing error of two tanks is smaller 5% at worst case. The resonant current error of each tank is only 2.5% at total rated load power with the proposed LLC converter.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".