LLC converters: Beyond datasheets for MOSFET power loss estimation
Bibliographic record
Abstract
In the past 10 years, LLC resonant converters have become a mainstream topology and multiple design tools have been developed, including LLC controllers, LLC tank regulation techniques, etc. While many design tools are available, methods of estimating power losses in LLC MOSFETs do not exist. In particular, accurate methods of estimating conduction losses, which are dominant in LLC converters, are lacking in the literature. This paper develops a method of accurately estimating MOSFET power losses in LLC converters. This is a fundamental tool for designing LLC converters, and allows the thermal behaviour of the MOSFETs to be predicted before the converter is built. The proposed method overcomes major datasheet limitations by replacing the poor, unrealistic datasheet information with a realistic MOSFET characterization applicable to LLC converters. The method focuses on the detailed characterization of the on-state resistance (RDS(on)), including the effects of the gate-source voltage (VgS), junction temperature (Tj) and drain current (ID). The characterization covers the actual operating points of the LLC converter, and provides an estimating equation to calculate MOSFET losses. As a result, LLC losses can be estimated with improved accuracy, including different operating points and IDpolarities. As verified by simulation and experimental results, the proposed design tool provides an improvement in accuracy of over 100% compared to the results obtained using the simplistic datasheet information when the circuit is operating near resonant frequency.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".