Space-Vector Based Excitation of a Bipolar Transmitter for Wireless Power Transfer Applications
Bibliographic record
Abstract
A space-vector (SV) based excitation method for wireless power transfer systems with a bipolar transmitter is introduced in this article. Unlike traditional bipolar excitation techniques that rely on fixed transmitter current distributions, this SV approach provides an elegant analytical method of redistributing a greater magnitude of current to the transmitter coil, which is closer to a misaligned receiver. This method is shown to require a smaller transmitter current vector magnitude for a required output power compared to all other bipolar coil excitation schemes over a broad range of receiver misalignments, thereby reducing conduction losses within the system. Additionally, the gradual redistribution of transmitter current as a function of receiver misalignment achieved by the SV method causes it to be less influenced by errors in receiver position sensing compared to conventional methods. Experimental results shows that the proposed method in conjunction with single coil operation at extreme misalignments was able to achieve a coil efficiency profile with minimal variation due to misalignment (93.2% at perfect alignment to 92.7% at a misalignment of 20 cm, for 2 kW power transfer into a 200 V battery load.)
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".