A Multilevel, Multimodule DC-DC Converter Architecture For Universal Electric Vehicle (EV) Battery Charging Applications
Bibliographic record
Abstract
The paper presents a multi-level, multi-module DC-DC converter architecture intended for universal EV battery charging applications. The converter architecture is centered around a multi-level, multi-primary, single common secondary linear step-up transformer. The multiple primaries are individually fed with high frequency resonant inverters which are stepped up in the secondary side and subsequently rectified by an active bridge to generate DC voltage at the battery load. The proposed converter is capable of generating a wide output voltage of 150-950V at a peak power of 22kW at 97% peak efficiency, while maintaining soft-switching transition for all switches, for the full operating voltage range. A mathematical model aiding in design and selection of converter temporal characteristics for different loading conditions is presented. A scaled down experimental prototype (voltage range of 28V-140V and power range of 0.18kW – 0.6kW) with peak efficiency of 96% is built as a proof of concept.
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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".