A multi-variable control technique for ZVS phase-shift full-bridge DC/DC converter
Bibliographic record
Abstract
In this paper a multivariable control system is proposed for an efficient ZVS full-bridge dc-dc converter used in a Plug-in Hybrid Electric Vehicle (PHEV). This converter processes the power between the high voltage traction battery and low voltage (12V) battery. Generally, Phase-shift between the two legs of the full-bridge converter is the main control parameter to regulate the output power. However, the zero voltage switching cannot be guaranteed by merely controlling the phase-shift particularly for light load conditions. In order to extend the soft switching operation of the converter for light loads, asymmetrical passive auxiliary circuits are used to provide reactive current. However, the auxiliary circuits increase extra current burden on the power MOSFETs, leading to lower efficiency. In this paper, the duty cycle of bridge legs (as another control parameter) is also controlled to minimize the conduction losses of the converter. Basically, the multivariable controller has to adjust the control parameters in such a way that the circulating currents are kept at their minimum level for soft switching while the output power is regulated. The system operating principle, soft switching and mathematical model are discussed. Experimental results are also presented that validate the effectiveness of the control method for a 2KW prototype.
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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) | 0.002 | 0.000 |
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".