Current-Sharing Worst-Case Analysis of Three-Phase <i>CLLC</i> Resonant Converters
Bibliographic record
Abstract
Three-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> resonant converters provide higher power conversion capability as compared to half-bridge and full-bridge structures. In addition to the unique features of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> converters for bidirectional applications, the three-phase structure provides significantly reduced output current ripple (smaller output capacitor), parallel power processing (reduced components size and current peak stress), and better thermal distribution (smaller heatsinks). However, with practical, i.e., nonzero, resonant component tolerances, these benefits are normally less, and sometimes significantly less than expected in the ideal case. In this article, the unbalanced behavior of the converter with 15 unknown resonant components is identified and analyzed. A new analysis methodology is proposed to investigate the worst-cases of current-sharing among above 32 000 possible scenarios in three-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> resonant converters. In addition, this article shows that phase-shifting techniques can be effective to mitigate the unbalanced behavior of the converter. The proposed analysis in this article helps to determine the highest admissible tolerance in the components to keep the converter working within a certain range of unbalanced behavior without requiring any balancing techniques. The proposed analytical framework is verified with experimental and simulation results of a 3-kW bidirectional three-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> experimental prototype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".