Wireless Electric Vehicle Charger With Electromagnetic Coil-Based Position Correction Using Impedance and Resonant Frequency Detection
Bibliographic record
Abstract
Wireless power transfer (WPT) is an enabling technology for electric vehicles (EV), as it eliminates driver intervention during charging. Major challenges to the adoption of WPT systems include coil misalignment and large air-gap WPT, both of which degrade the transfer efficiency. To address these issues, this article presents a compact WPT charger for the rear of an EV, capable of correcting lateral misalignment Δx using two integrated electromagnetic coils. The air-gap Δy is reduced due to the rear placement of the charging pads. The variation in Δy, however, increases due to vehicle parking accuracy, which impacts the operation of the electromagnetic coils. This article proposes a closed-loop three-stage position-correcting control algorithm capable of detecting the impedance and resonant frequency of the system, which, in conjunction with the electromagnetic coils, results in improved vehicle alignment. The experimental prototype achieves a peak dc-dc efficiency of 90.1% at 5 kW WPT. With Δy = 50 mm, the electromagnetic coils laterally align the pads within 1.75 s and correct Δx as large as 240 mm. The position-correcting control was successfully demonstrated in four parking scenarios with the charging pads being corrected to within a 10-mm radius of perfect alignment.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".