Optimal Current Balancing in Interleaved CLLC Converters Using Dual and Triple Phase Shift Modulation Techniques
Bibliographic record
Abstract
Interleaved CLLC converters offer high power density and improved thermal performance, but achieving balanced currents between cells remains challenging in the presence of component mismatches. This paper introduces the first application of Dual-Phase Shift Modulation (DPSM) and Triple-Phase Shift Modulation (TPSM) to dual-phase interleaved CLLC converters. The goal is to achieve effective current balancing, high efficiency, and Zero-Voltage Switching (ZVS) across a wide load range. Analytical expressions for resonant currents are derived using harmonic approximation techniques, considering the influence of phase-shift parameters and passive component mismatches. A condition is introduced to prevent reverse power flow in either power cell, and ZVS boundaries are identified for all switches. An optimization framework is proposed for selecting phase-shift values that satisfy ZVS, positive power transfer, and minimum RMS current simultaneously. Experimental results from a 2 kW interleaved CLLC prototype validate the theoretical findings, demonstrating the superior performance of TPSM over DPSM in terms of current balancing, circulating current reduction, and efficiency, particularly under mismatch conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".