Interleaved LCLC Resonant Converter With Precise Current Balancing Over a Wide Input Voltage Range
Bibliographic record
Abstract
An interleaved LCLC resonant converter with accurate current balancing performance over a wide input voltage range is proposed. Resonant tank components have slight tolerances that can unbalance load sharing between paralleled phases. Because of the steep voltage gain curve of the LCLC resonant tank, most conventional current sharing approaches might not be effective. In the proposed converter, the impedances of the resonant tanks are matched by a switch-controlled capacitor (SCC) that is in series with the resonant capacitor resulting in precise load current balancing between phases. The impact of various unbalanced situations on the interleaved LCLC resonant converter is investigated, a minimum operating angle for the SCC circuit is identified, the basic accuracy of SCC current balancing via digital control is investigated, and a control strategy is proposed to perform the current sharing. Computer simulation results and experimental results from a GaN-based prototype validate the performance of the proposed interleaved LCLC converter with precise current sharing over a wide input voltage range from 250 to 400 V. Using enhancement mode GaN and benefiting from the interleaving feature, a high conversion efficiency is achieved on a two-phase 1 kW LCLC converter with peak efficiency of 96.7%.
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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 | 0.001 |
| 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) | 0.001 | 0.000 |
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".