A Novel Transformable Multi-Mode Three-Phase AC/DC LLC Converter for Fast DC Charging Applications
Bibliographic record
Abstract
This paper proposed a novel transformable multi-mode three-phase AC/DC LLC converter for electrical vehicle (EV) fast DC charging applications. The proposed converter operates in four distinct modes. First of all, the input-side power factor correction (PFC) stage can operate in continuous current mode (CCM) with two-stage conversion for high-power conditions or operate in a single-stage conversion in discontinuous current mode (DCM) for low-power conditions. Secondly, the output rectifier of the proposed converter can operate either as a full-bridge rectifier or a voltage doubler, enabling a wide output voltage range. With this approach, a wide range of very high efficiency can be maintained for a wide load condition for different output battery levels. The output voltage is regulated by the LLC resonant converter with frequency modulation control. The operations of each mode of the proposed converter are introduced in this paper. The simulation of the proposed converter in a 10kW-20kW,$\mathbf{480}\mathbf{V}_{\mathbf{LLrms}}$input,$\mathbf{400}\mathbf{V}_{\mathbf{dc}^{-}} \mathbf{800}\mathbf{V}_{\mathbf{dc}}$output system in PSIM software and a 1kW,$\mathbf{120}\mathbf{V}_{\mathbf{LLrms}^{-}}$input,$\mathbf{200}\mathbf{V}_{\mathbf{dc}}-\mathbf{360}\mathbf{V}_{\mathbf{dc}}$output proof-of-concept prototype is presented to validate the functionality of the proposed converter.
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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 | 0.001 |
| 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".