A Control Strategy for Wireless EV Charging System to Improve Weak Coupling Output Based on Variable Inductor and Capacitor
Bibliographic record
Abstract
The double-sided inductor capacitor capacitor topology has been proposed in some national standards for wireless charging systems. However, misalignment between the transmitting and receiving coils usually leads to detuning and weak coupling, resulting in low output power and inefficiency. To improve power output under weak coupling, this article proposes a control method based on the variable inductor and capacitor (VLC). First, it is deduced that the zero phase angle input and full output power can be achieved by VLC regulation under certain misalignment. Second, the variable inductor using an EE-shaped core and pulsewidth modulation controlled capacitor are applied to achieve VLC regulation, and the principles and implementation of VLC are analyzed. Third, as the coupling coefficientkcan be identified through the primary electrical parameters, a convenient and accurate coupling coefficient identification method is utilized to help VLC regulation. Finally, a prototype with a 3.3 kW output power and an 87.5% maximum system efficiency (dc–dc) is built to validate the proposed method. The experimental results are consistent with the theoretical analysis, proving that the output power is almost constant whenkvaries from 0.08 to 0.18.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".