Modulation Improvements for High-Phase-Count Series-Capacitor Buck Converters
Bibliographic record
Abstract
This article presents three modulation improvements for the series-capacitor buck (SCB) converter and its topological derivatives. The first consists of various phase activation sequences (PHACTSs) which raise the maximum input-to-output voltage conversion ratio of anN-inductor,N-phase SCB converter beyond the traditional limit of 1/N2, without incurring any additional voltage stress to the switches. Phase counts up to 16 are analyzed with conversion ratios increasing by a factor of up to 7. Due to the inherent link between the converter's maximum attainable output voltage and maximum output current slew rate, these PHACTSs offer a significant improvement to the load-voltage transient response. Utilizing the flying capacitors that link adjacent inductors, a second modulation technique is introduced that effectively increases the digital pulse-width-modulator's (DPWM) output-voltage resolution, by a factor ofN, by employing a novel method of minimum duty increments (MDIs). Despite the commonly-held assumption of automatic steady-state inductor-current-balancing present in anN-inductor SCB, large-signal modelling reveals that slight current imbalances inevitably arise, even in lossless configurations, with three or more output inductors. After elucidating its origin, this article introduces a third modulation technique that can reduce these inductor current imbalances through a particular implementation of MDI. A discrete prototype of an 11-inductor, 48 V-to-1.0 V, 275 A-load, SCB converter was fabricated to experimentally demonstrate and validate the simulated results of the increase in both the output voltage ceiling and DPWM resolution, as well as to evaluate the MDI-DPWM output-voltage linearity. Finally, the maintenance of both inductor current balancing and low switch-voltage-stress is experimentally substantiated when using MDI.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".