A Passive-Impedance-Matching Technology to Achieve Automatic Current Sharing for a Multiphase Resonant Converter
Bibliographic record
Abstract
A passive-impedance-matching (PIM) technology is proposed to achieve automatic current sharing for multiphase resonant converters through matching the input impedance of each phase. The series inductors (or series capacitors) of each phase are connected in parallel to achieve a couple of virtual resistors including positive and negative resistors and variably series inductors (or capacitors). A virtual positive (or negative) resistor increases (or decreases) the input impedance of the respective phase, and the variably series inductors can also compensate the component tolerance such that the impedance of each phase is matched. The current-sharing performance of the common-inductor two-phase LLC resonant converter (as one example) is evaluated under the first-harmonic-approximation assumption. The virtual positive and negative resistors and variably virtual inductors are calculated. The proposed method can share the primary resonant current and the load current for all phases without any additional circuit and control strategy. The PIM technology is extended to other resonant converter topologies, including common-inductor or common-capacitor series-resonant converter, LCC, CLL resonant converter, etc. A 600-W 12-V common-inductor two-phase LLC resonant converter prototype is built to verify the feasibility and demonstrate advantages of PIM technology.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".