Optimized <italic>LCC</italic>-Series Compensated Resonant Network for Stationary Wireless EV Chargers
Bibliographic record
Abstract
In this paper, an optimal design procedure for LCC-series compensation network is proposed for a stationary wireless electric vehicle charger. The main focus of this paper is to optimize the resonant network suitable for a wide range of operation from no-load to full-power operation. The conventional methods only consider the full-load condition to design the resonant network; in contrast, the proposed method employs a time-weighted average efficiency for different coupling conditions to achieve high efficiency over a wide load range including light-load and no-load operation. The resonant network is tuned to realize zero voltage switching for the primary side inverter. Moreover, a finite-element analysis is performed to calculate self- and mutual inductances as well as core losses for magnetic couplers. In order to validate the feasibility of the proposed design, a 1 kW/85 kHz prototype with circular magnetic couplers is implemented. According to simulations and experiments, flat profiles for both efficiency and output voltage against output power variations are achieved. Experimental results demonstrate a 94.8% peak efficiency for the full-load operation.
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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 | 0.001 | 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".