Single-Phase Bridgeless Converter for On-Board EV Charger With Flexible Charging Capabilities
Bibliographic record
Abstract
This article puts forward the analysis and design of single-phase bridgeless Cuk-derived dc–dc converter for a novel vehicle-to-vehicle (V2V) charge transfer technique. V2V charging adds flexibility to electric vehicle (EV) charging process and aids in mitigating range anxiety. The proposed V2V charge transfer technique has only one power conversion stage and only one onboard charger is used to charge the battery. The charger is capable of both grid-to-vehicle and V2V charging. The converter design is in discontinuous conduction mode which reduces the control complexity. The converter has a lower component count rendering the charger lightweight, power dense, and cost effective. The overall converter efficiency is high owing to low switching losses. This work has been compared to several state-of-the-art V2V topologies and configurations. The complete steady-state analysis and converter design operating in dc charging mode have been presented. Moreover, to validate the analysis, design, and applicability of the converter for dc V2V charge transfer, PSIM simulation, and laboratory-based hardware prototype have been built and the experimental results are provided.
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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".