Hybrid SC-Inductive Based Topologies for Low-volume High-efficiency Switched-mode Power Supplies
Bibliographic record
Abstract
Switched-mode power supplies (SMPS) are widely used as the voltage or current regulators for electronic devices. The most commonly used SMPS are inductive converters based on conventional buck and boost converter topologies due to their simplicity and relatively high power density and efficiency. However, in applications with large voltage difference between their inputs and outputs, these converters suffer from excessive losses and increased volume due to more demanding filtering and also cooling requirements. Therefore strong and ever increasing demands for developing smaller and more efficient SMPS with high conversion ratios exist.In this work two new converter topologies suitable for applications with large input-to-output voltage difference are introduced for both input voltage step-down and step-up in two different types of applications, i.e. for dc-dc converters and rectifiers with power factor correction. These converters are developed based on the concept of merging Switched-capacitor (SC) and inductive based converter stages by means of newly emerged digital controllers, improving efficiency and reducing the overall volume of the converter. Hence, the benefits of high power density SC-based converters are exploited and at the same time the input-to-output voltage ratio for the inductive stage is reduced improving its power density and efficiency. Compared to two-stage solutions, by merging SC and inductive based stages, switches between two stages are shared, reducing conduction losses, intermediate/flying capacitors are eliminated and controllers are unified.The buck-based, voltage step-down solution combines a capacitive divider and an interleaved buck to reduce the volume of multi-phase step-down converters. Experimental results obtained with a 7V-to-1V, 10A, 1 MHz prototype demonstrate that the merged capacitor converter has 15% smaller inductor, 13% smaller output capacitor, up to 35% lower power losses and 15% shorter settling time after transients.In the boost-based voltage step-up solution, the improvements are achieved by replacing the output capacitor of the boost converter with a non-symmetric active capacitive divider, with a 2:1 division ratio, effectively providing four-level converter behavior. Experimental results obtained with a 350 W, 200 kHz, universal input voltage (85Vrms - 265Vrms) PFC prototype demonstrate 66% reduction of boost converter inductor and up to 10% improvement of efficiency.
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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 | 0.000 |
| 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) | 0.003 | 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".