LLC Converters With Planar Transformers: Issues and Mitigation
Bibliographic record
Abstract
The use of LLC resonant converters has gained popularity in multiple applications that require high conversion efficiency and galvanic isolation. In particular, many applications like portable devices, flat TVs, and electric vehicle battery chargers require demanding slim-profile packaging and enforce the use of planar transformers (PTs) with low-height, low leakage inductance, excellent thermal characteristics, and manufacturing simplicity. The main challenge in successfully designing LLC converters with PT resides in controlling high-parasitic capacitances produced by large overlapping layers in PT windings. When the parasitic capacitances are not controlled, they severely impair the converters' performance and regulation, and limit the application of PTs in high-frequency LLC converters. This paper characterizes the PT capacitance issue in detail and proposes mitigation strategies to improve the performance of LLC converters with PTs. A systematic analysis is performed, and six PT winding layouts are introduced and benchmarked with a traditional design. As a result of the investigation, an optimized structure is obtained, which minimizes both the interwinding capacitance and ac resistance, while improving the regulation performance of LLC converters. Experimental measurements are presented and show a significant reduction of parasitic capacitance by up to 21.2 intra- and 16.6 interwinding capacitances, without compromising resistance. This substantial capacitance reduction has a tangible effect on the regulation performance of LLC resonant converters. Experimental results of the proposed PT structure in a 1.2 kW LLC resonant converter show a reduction in common-mode noise, extended output voltage regulation, and improved overall efficiency of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".