Field orientation based three‐coil decoupled wireless transmitter for electric vehicle charging with large lateral receiver misalignment tolerance
Bibliographic record
Abstract
Abstract A three‐coil decoupled transmitter is developed for the inductive power transfer system of electric vehicle charging. The three‐coil decoupled transmitter consists of three overlapping rectangular coils with magnetic mutual decoupling and is capable of overcoming large receiver lateral misalignment up to 20 cm. A current optimization method is advanced to derive transmitter coil currents for the multi‐phase system that minimizes the coil loss through optimizing the current distribution among transmitter coils. The optimized current distribution reduces the maximum leakage flux by around 40% compared to the best alternative excitation method. The compensation strategy provides unity power factor for high‐power transfer phases and allows a single set of compensation capacitors to be used regardless of receiver alignment. The three‐coil decoupled transmitter is compatible with a typical three‐phase inverter for industrial practical application, which halves the power electronic switches used with previous topologies. The simulation verification is performed at 3.3 kW, achieving 93.76% efficiency at perfect alignment and 92.54% efficiency at 20 cm misalignment. The experimental verification is implemented at 3.3 kW with 200 mm ground clearance, realizing a 92.97% efficiency at perfect alignment and 90.57% efficiency at 20 cm 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 | 0.000 |
| 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".