An Effective Charger for Plug-In Hybrid Electric Vehicles (PHEV) with an Enhanced PFC Rectifier and ZVS-ZCS DC/DC High-Frequency Converter
Bibliographic record
Abstract
A plug-in hybrid electric vehicles (PHEV) charger adapter consists of an AC/DC power factor correction (PFC) circuit accompanied by a full-bridge isolated DC/DC converter. This paper introduces an efficient two-stage charger topology with an improved PFC rectifier as front-end and a high-frequency zero voltage switching (ZVS). Current switching (ZCS) DC/DC converter is the second part. The front-end converter is chosen as bridgeless interleaved (BLIL) boost converter, as it provides the advantages like lessened input current ripple, capacitor voltage ripple, and electromagnetic interference. Resettable integrator (RI) control technique is employed for PFC and DC voltage regulation. The controller achieves nonlinear switching converter control and makes it more resilient with the faster transient response and input noise rejection. The second stage incorporates a resonant circuit, which helps in achieving ZVS/ZCS for inverter switches and rectifier diodes. PI controller with phase shift modulator is used for second-stage converter. It improves the overall efficacy of the charger by lowering the switching losses, lowering the voltage stress on the power semiconductor devices, and reversing recovery losses of the diodes. The simulations and experimental results infer that the overall charging efficiency increases to 96.5%, which is 3% higher than the conventional two-stage approach using the interleaved 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".