Enhanced Small-Signal Modeling for Charge-Controlled Resonant Converters
Bibliographic record
Abstract
Charge control for resonant converters introduces an inner feedback loop that improves the system dynamic characteristics. However, small-signal modeling is not straightforward with charge-controlled resonant converters due to the nature of resonant behavior. Conventional small-signal models for charge-controlled resonant converters are developed based on the converter input and output energy balance. This simplified approach overlooks the dynamics of the magnetizing inductor current and generates errors in small-signal frequency response. To improve the analytical model and enable high-bandwidth design, this paper proposes a new methodology for modeling charge-controlled resonant converters. The energy stored in the resonant tank is analyzed using the theory of Extended Describing Function (EDF), which accounts for the effect of the magnetizing current. This methodology applies to a wide range of charge control variants, such as Bang-Bang Charge Control and Hybrid-Hysteretic Control. To demonstrate the modeling procedure, this paper considers a high-order, five resonant-component half-bridge CLLC resonant converter as a case study. The proposed analytical model is applied to a 1-kW, 400-V power supply prototype for simulation and experimental validation. The proposed model successfully predicts the frequency response of the resonant converter across frequency and load conditions, providing rapid frequency-domain evaluation in the design process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".