Power factor optimization with semi-definite programming relaxation in three-phase wireless power transfer systems for electric vehicles
Bibliographic record
Abstract
In this paper, an excitation method for a three-phase wireless power transfer system for electric vehicle charging is proposed. The semi-definite programming relaxation current optimization model (SRCOM) is developed to derive transmitter coil currents for the multi-phase system that minimizes the coil loss through optimizing the current distribution among transmitter coils. The power factor constraints for each phase are developed to maintain high power factors in each phase and achieve soft switching for the high power phases. Semi-definite programming relaxation is adopted to convert the non-convex power factor constraint into convex form. Tightening constraints are introduced to compensate for the relaxation and ensure solution feasibility. Simulation has verified SRCOM can improve the power factor under different receiver misalignment scenarios thereby reducing inverter switching loss. This comes at the cost of a small increase in coil conduction loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".