A quasi‐two‐switch power factor correction converter for on‐board battery chargers
Bibliographic record
Abstract
Summary This paper describes a quasi‐two‐switch buck‐boost power factor correction (PFC) converter for use in on‐board battery chargers to create a variable output voltage that is less than or greater than the peak input voltage. A two‐stage converter links the input grid power to the battery pack both in battery‐operated electric cars (BEVs) and plug‐in hybrid electric vehicles (PHEVs), with battery pack voltages ranging from 100 to 500 V depending on vehicle size and capacity. A universal charger that can manage such a wide range of battery pack voltages is appropriate for all vehicle designs. This requirement is met by supplying a changeable DC link voltage at the input of the DC/DC converter, which is a major obstacle in battery chargers when it comes to achieving universal output voltages. The major contribution of this research is the analysis and design of a dual‐control technique for a cascaded buck‐boost converter suited for a power factor correction (PFC) rectifier. The control loop is designed to allow a seamless transition from buck‐boost operation while putting less stress on the devices. The converter's power loss and small‐signal model are also investigated. From an economic standpoint, this concept allows the automobile industry to manufacture a single power converter, which is flexible and capable of charging numerous vehicle variants. Results have been verified both with a PSIM (11.0) simulation model and an experimental setup for a 1‐kW PFC converter suitable for universal input voltages of 85–265 Vrms and broad output voltages.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".