Design Optimization of a Wide Voltage Range LLC Resonant Converter with Topology Morphing
Bibliographic record
Abstract
An LLC converter design process includes the determination of resonant tank values, transformer turns ratio, resonant frequency and range of switching frequencies. Maximum converter efficiency is achieved by optimizing these converter parameters. LLC resonant converters with frequency control for wide voltage range applications may result in a wide range of switching frequencies. The efficiency of the resonant converter reduces as it moves away from the resonant frequency (fs). Morphing the primary bridge between a full bridge (FB) and a half bridge (HB) helps in reducing the range of effective voltage gain, thus reducing the range of fs. The work in this paper is based on evaluating the optimum design parameters for an LLC converter with topology morphing and synchronous rectification for maximizing the overall converter efficiency. The optimization is based on a time domain mathematical model with improved accuracy compared to a first harmonic approximation (FHA) based model. A loss model is developed based on the semiconductor and magnetic losses to estimate the converter efficiency. A design example for an electric vehicle (EV) on-board battery charger (OBC) application with specifications of 300 V - 600 V input voltage, 250 V-450 V output voltage, and 3.3 kW power is discussed to outline the proposed optimization method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".