DCM-Based Bridgeless PFC Converter for EV Charging Application
Bibliographic record
Abstract
This article proposes a single-phase switched-mode bridgeless ac-dc buck-boost derived converter that can serve as a front-end converter for the on-board electric vehicle (EV) charging application. The bridgeless scheme rules out the orthodox bridge rectifier and the affiliated losses. The proposed converter operates in discontinuous current conduction mode (DCM), thus achieving natural power factor correction for variable ac input. In addition to this, sensing of input voltage and input current is fended off because of DCM operation making the converter reliable, cost-effective, and robust compared with conventional continuous current conduction mode converters. Furthermore, the control becomes simple with the employment of a single sensor and the elimination of the phase-locked loop. The proposed front-end converter is well suited for low-voltage battery chargers ranging between 1.0 and 3.3 kW installed in golf-carts and E-rickshaws. A comprehensive steady-state analysis for one switching cycle and the design equations of the proposed converter are presented. The small-signal model of the proposed converter is presented for the implementation of the closed-loop control. Experimental results from a 1.0-kW concept-proof hardware prototype have been demonstrated, which upholds the converter analysis.
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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) | 0.007 | 0.002 |
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".