A Soft Switched Boost Cascaded-by-Buck Power Factor Correction Converter for On-board Battery Charger Application
Bibliographic record
Abstract
On-board battery chargers resolve the anxiety of frequent charging of the battery operated electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) with an available household input supply. Conventionally, a two-stage converter connects the input grid supply to the battery pack whose voltage typically differs for various range of vehicle architectures. This paper mainly focuses on analysis and operation of a zero-current-switched (ZCS) boost cascaded by- buck converter employed in a power factor correction (PFC) application. A combination of resonant inductor and capacitor are used to create ZCS during turn-off of switch. In addition, the designed control loop structure provides the variable DC link output voltages at PFC with universal input voltages. It also provides smooth input current and maintains a power factor of 0.99. Finally, the simulation and experimental results of the proposed converter are performed and efficiency of 96.4% is achieved.
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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 | 0.000 |
| 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".