Frequency Domain Modelling & Design of an LCC Resonant Converter with Capacitive Output Filter
Bibliographic record
Abstract
Resonant DC-DC converters have been widely discussed with one of the most popular being the LCC topology. It’s application towards low to high voltage converters warrants the use of a capacitive output filter to limit the voltage stress on the rectifier. Designs with a high quality factor (Q) suffer from large resonant component sizes and stresses leading to power losses in the magnetic components. It is then desirable to design a low Q converter to minimize these stresses for compactness and efficiency. Previous time domain analysis shows the converter predominantly operates in one mode so a frequency domain analysis was possible. Due to the voltage charging and clamping action of the parallel resonant capacitor as a result of the capacitive output filter, enhanced fundamental harmonic approximation (FHA) models were used to analyze this topology. These were accurate for high Q designs with approximately sinusoidal waveforms but degraded for light load, low Q conditions. In this thesis, a frequency domain model considering higher order harmonics is presented for the LCC resonant converter with a capacitive output filter. This general model applies to the study of the converter under variable frequency and phase shift modulation control techniques. Converter characteristics can be studied using the provided generalized curves of voltage gain (Vo / Vi), phase shift (ϕ), and non-conduction angle (θ). Using the nth harmonic equivalent circuit, steady state performance is easily obtained. The model is verified against a 380V, 250W experimental prototype with 22-44V input and the commercial simulation software PSIM. A simple design procedure is detailed focusing on low Q designs helping minimize the stress and size of the resonant components. An optimal parallel to series capacitance ratio (k) and Q selection helps reduce conduction and magnetic losses of the converter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.002 |
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".