An Approach for Selecting Compensation Capacitances in Resonance-Based EV Wireless Power Transfer Systems With Switched Capacitors
Bibliographic record
Abstract
Wireless chargers for electric vehicles (EVs) can achieve high power-transfer efficiency by utilizing magnetic resonance. However, the efficiency depends on the position of the receiver coil on board the EV relative to the charging pad, which may present a challenge in some coil topologies. In multi-coil topologies, the additional auxiliary coils increase the magnetic coupling between the primary transmitter and receiver coil, helping to improve the misalignment tolerance of a wireless power transfer (WPT) system. In this paper, an approach is presented for selecting compensation capacitances in resonance based multi-coil WPT systems that utilize switched capacitor compensation. The approach for selecting the compensation capacitors is based on maintaining operation within the split resonant frequency region while misaligned. In this paper, the compensation capacitor design approach is applied to a four-coil WPT system with overlapping auxiliary coils with switched capacitors on each auxiliary coil. Experimental results are presented which show that with the selected compensation capacitances the power factor is maintained above 0.9 from 0- to 20-cm misalignment compared to a power factor that decreases to 0.34 at 20 cm when the compensation capacitors are not retuned based on misalignment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".