MOSFET Power Loss Estimation in <i>LLC</i> Resonant Converters: Time Interval Analysis
Bibliographic record
Abstract
In the past ten years, LLC resonant converters have become a mainstream topology for dc/dc power conversion, and multiple design tools have been developed for this topology, including controllers, regulators, soft-switching techniques, etc. While many tools are available for designing this converter, techniques for accurately determining power losses in the inverter MOSFETs of the topology based on time-domain analysis have not been fully explored yet. Precise power loss estimation is fundamental to determine the thermal behavior of the switches before the converter is built, which accelerates and optimizes the thermal management design process. In addition, accurate methods of estimating conduction losses, which are dominant in this topology, switching losses, and body diode losses are lacking in the literature. This paper proposes a method for enhancing power loss estimation in LLC inverter MOSFETs based on time-domain analysis of the converter. Moreover, a detailed characterization of MOSFET's conduction losses (Pcond), switching losses (Psw), and body diode losses (Pdiode), including the effects of different parameters such as gate-source voltage (VGS), junction temperature (Tj), drain current (ID), and drain-source voltage (VDS), is presented, which can further improve power loss assessment in this topology. The developed method based on time interval analysis replaces the simplistic first-harmonic approximation (FHA), which allows for improved power loss calculations. Further improvement is obtained with the detailed characterization of the switching device. As verified by simulation and experimental results, the proposed estimation tool provides a significant boost in accuracy for power loss determination when compared to the existing method for power loss estimation using FHA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".