Hybrid serial-output converter topology for volume and weight restricted LED lighting applications
Bibliographic record
Abstract
This paper introduces a new high power density step-up LED driver converter architecture based on power processing divided between a switched capacitor (SC) converter and a flyback stage. The step up function is obtained by stacking the output of the flyback on top of the SC converter output. The high power density SC processes the majority of the power of the system and is left unregulated to maximize its efficiency. The small flyback processes only a small portion of the total power and regulates the output voltage. As a result high power efficiency, small converter volume, and tight output voltage regulation are achieved. A digital controller regulates the operation of this hybrid converter topology. In comparison with conventional boost and flyback based solutions, the new architecture drastically reduces the passive component volume and decreases peak voltage stress of switches. The paper also gives design guidelines for this topology and topologies of its kind for a given power processing efficiency target. Experimental results obtained with a 12 V to 55 V, 47 W, 500 kHz prototype show that the hybrid converter has about four times smaller energy storage requirements compared to conventional solutions, while maintaining approximately the same power processing efficiency of 90%.
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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 | 0.000 |
| 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) | 0.005 | 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".