A load/line adaptive zero voltage switching DC/DC converter used in electric vehicles
Bibliographic record
Abstract
This paper presents a load/line adaptive Zero Voltage Switching (ZVS) full-bridge converter, which is able to optimize the amount of reactive current required to guarantee ZVS of the power MOSFETs. The proposed DC/DC converter is used as a battery charger for an electric vehicle. Since this application demands a wide range of load/line variations, the converter should be able to sustain ZVS under different conditions. The converter employs coupled inductors to provide the reactive current for the full-bridge semiconductor switches, which ensures ZVS at turn-on times. The coupled inductor along with the specific control system is able to generate the optimum value of the reactive current injected by the auxiliary circuit in order to minimize extra conduction losses in the power MOSFETs, as well as the losses in the coupled inductors. In the proposed approach, the peak value of the reactive current is controlled by the phase-shift between the leading leg and lagging leg of the full-bridge converter to optimize the load impact. Also, the reactive current is controlled by the switching frequency in order to compensate for the input voltage variations. Experimental results for a 2kW DC/DC converter are presented. The results show an improvement in efficiency and better performance of the converter particularly for heavy loads.
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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 | 0.001 |
| 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".