Systematic Review of the Design Process for LLC Resonant Converters: Modelling, Voltage Gain Range, and Efficiency Optimization
Bibliographic record
Abstract
ABSTRACT The proliferation of power conversion requirements in renewable energy systems and rail transit systems demands LLC resonant converters with enhanced voltage adaptability and efficiency. This review systematically studies three elements in the design progress for LLC resonant converters: (1) modelling methodologies; (2) extended voltage gain range solutions; and (3) efficiency optimization strategies. Existing modelling approaches, including time‐domain analysis (TDA), fundamental harmonic approximation (FHA), and hybrid analytical methods, are systematically compared, revealing inherent limitations in predicting parasitic parameter effects and mode transition boundaries under high voltage operation. For voltage gain extension, methods are categorized into topology modifications (e.g., dual bridge architectures, resonant tank morphing) and control‐based solutions (e.g., hybrid frequency‐pulse width modulation), with detailed analyses of their impacts on component stress and dynamic stability. Efficiency optimization is analysed through three key approaches: light‐load efficiency improvement, resonant frequency tracking techniques for loss minimization, and synchronous rectification implementations. By comparing the key parameters with the multi‐objective optimization framework, the application boundaries of the existing design methods are summarized, and the current challenges and future development directions in this field are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".