Design Methodology of Wireless Power System Based on LLC Compensation Network for Low-Gap Battery Charger Application
Bibliographic record
Abstract
Wireless power transfer (WPT) is becoming increasingly common feature in consumer markets, as an example the series-series (SS) compensated topology has demonstrated a promising solution for both electric vehicle and smartphone chargers. However, SS typically relies on variable DC voltages at both the transmitter and receiver sides to maintain optimal operation, necessitating the use of additional DC/DC converters. This increases system cost, weight, and reduces reliability. This paper investigates the use of an LLC compensation network for low-gap WPT applications. Unlike SS, the LLC topology does not have a compensation network on the secondary side, nor does it require additional DC/DC converters, as it relies solely on frequency modulation to regulate gain and compensate for variations in the coupling factor. Theoretical maximum efficiency and optimum load equations are derived and compared against SS to enable a fair analytical comparison. Furthermore, a figure of merit (FOM) is formulated for LLC WPT to aid in magnetic and coil geometry design. The main focus of this paper is to present a comprehensive design methodology that allows the LLC converter to cover efficiently the V- I plane required by typical battery charging applications. The method accounts for variations in coupling factor and self-inductance due to gap changes and misalignment, enhancing its robustness for real-world implementation. Finally, two WPT prototypes using UU-shaped coils are developed and experimentally tested to validate the proposed methodology.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".