A Novel Multilevel Current-Driven DC-DC Converter for Wide Range Applications
Bibliographic record
Abstract
This article presents a novel multi-level current-driven DC-DC converter along with a modulation scheme that is able to maintain a consistently high efficiency across a wide range of input voltages. This proposed circuit is targeted to applications with a wide range of operating conditions, e.g., PV microinverters, energy storage, etc. Industry standard converters such as the flyback and the LLC resonant converter struggle to accommodate the wide range of voltages, and their efficiency suffers when operating points deviate from the nominal. The proposed approach takes advantage of the multi-level structure and flexibility of the modulation scheme to achieve high performance for a wide operating range. In addition to theoretical analysis of the operation of the converter, this paper demonstrates the functionality and performance of a laboratory prototype in direct comparison to resonant converters to show its superior performance across a wide range. The results validate the benefits of the proposed topology and show a significantly flattened efficiency curve over a wide range of input voltages (0.56% variation over an input voltage range of 32 V to 48 V).
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 | 0.000 |
| 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 | 0.001 |
| 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".