Intrinsic and Robust Voltage Balancing of FCML Converters with Coupled Inductors
Bibliographic record
Abstract
Flying capacitor voltage balancing is critical for the performance of flying capacitor multilevel (FCML) converters. This paper investigates the fundamentals of intrinsic capacitor voltage balancing of multiphase FCML converters with coupled inductors. It is shown that the coupled inductor provides a new, intrinsic mechanism of flying capacitor voltage balancing that is robust to periodic disturbances and independent of the inductor quality factor. This balancing mechanism is typically stronger than natural balancing. The imbalances with uncoupled inductors are analytically predicted and shown to be dependent on the quality factor of the inductor, thus leading to much larger imbalances compared to the coupled inductor balancing mechanism. A dynamic model for voltage balancing of the flying capacitors with coupled inductors is derived and used to estimate the time required to settle from an initial imbalance. The theoretical analysis is verified with simulations and experimental results. Adding coupled inductors to FCML converters can greatly enhance the robustness of FCML converters against physical nonidealities and periodic disturbances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".