Accurate Real-Time Simulation of CLLLC Converters on FPGA: A Study of Sampling Resolution and Beating Effect
Bibliographic record
Abstract
The CLLLC resonant converter, used for high-efficiency applications such as battery chargers, presents major challenges for real-time simulation, notably by its use at high frequencies and by the involvement of natural switching. Furthermore, the resonant nature and high-frequency AC transformer waveforms characterizing such converters make the simulation highly sensitive to sampling errors introduced by the discrete nature of the simulation. This work proposes an approach based on a reconfigurable switched matrix solver associated with explicit switch handling and implemented on a low-cost FPGA target to meet these requirements. The hardware architecture achieves a computational step size as low as 25 ns, even on an affordable FPGA platform. The algorithms, validated by comparison with SIMBA software, guarantee accuracy with relative errors of less than 2%. These results demonstrate the feasibility of using low-cost FPGAs for demanding applications, offering an effective solution for real-time simulation and control of high-frequency resonant converters. The model’s limitations are also explored, and avenues for improvement are proposed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".