Improved Triple-Phase-Shift Modulation for Bidirectional <i>CLLC</i> Converters
Bibliographic record
Abstract
CLLCresonant converters are an attractive option for battery charging applications due to their bidirectional power flow and high efficiency. Pulse frequency modulation (PFM) is the simple method to driveCLLCconverters. However, PFM cannot regulate the output voltage under the light-load condition properly, which leads to high switching and core losses. Phase-shift modulation techniques, such as dual-phase-shift (DPS) modulation, can overcome this challenge. However, more improvements are demanded due to high circulating currents in phase-shift modulations. In this article, an improved modulation technique based on triple phase shift (TPS) has been applied inCLLCconverters to improve the light-load efficiency. TPS modulation like any other phase-shift modulation keeps the switching frequency ofCLLCconverters fixed at the resonant frequency under the light-load condition and regulates the output voltage by its three phase-shift parameters. The improved TPS modulation has lower circulating current at the same average output current compared to DPS modulation. Therefore, higher efficiency, smaller peak, and RMS current values can be achieved under the light-load condition. In addition, the proposed TPS modulation has a better flexibility in regulating the output voltage under the light-load condition due to an extra phase-shift parameter compared to DPS modulation. The proposed TPS modulation has been analyzed thoroughly in this article, and a proper selection of phase-shift values has been explained to improve the efficiency of the converter. Finally, advantages of the improved TPS modulation have been validated by simulation and a 1-kW experimental setup.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".