Boost-Cascaded-by-Buck Power Factor Correction Converter for Universal On-Board Battery Charger in Electric Transportation
Bibliographic record
Abstract
A two-stage battery charger in battery operated electric vehicles (BEVs) and plug-in-hybrid electric vehicles (PHEVs) with wide output voltage range of 100-500 V is most suitable for all vehicle architectures. Existing battery chargers have a different limited range of output voltages of 36-48 V, 72-150 V and 200-450 V is achieved by varying the output voltage of DC/DC converters keeping a fixed voltage at DC link. A universal charger, which can address this wide range of battery pack voltages is suitable for all vehicle architectures. The most feasible way to meet this requirement of wide output voltage range is to vary the voltage at DC link with a fixed conversion voltage ratio at DC/DC converter. In this paper, we propose the use of cascaded converter in power factor correction (PFC) converters to achieve the wide DC link voltages for battery chargers. The primary focus of the paper is on the analysis and operation of boost-cascaded by buck (BoCBB) converter. The control implementation presented in the paper achieves a high input power quality, wide DC link voltages with universal input voltage ranges of 85-265 V. It also provides the degree of control freedom to operate even if the VNm (output voltage to the peak of Input) <; 0.5. Simulations of the proposed converter with 1 kW power rating are carried out in PSIM 11.0 software and the results with wide DC link voltage of 150-400 V are presented in the paper.
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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) | 0.003 | 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".