Fuzzy Logic Control for Wireless Power Transfer in Light Electric Vehicle Charging Applications
Bibliographic record
Abstract
This paper presents the design and implementation of a fuzzy logic controller (FLC) for a Wireless Power Transfer (WPT) system used to charge Light Electric Vehicles (LEVs). The system employs a phase-shifted full-bridge converter with a series-series compensation network for efficient power transfer. The FLC dynamically adjusts the phase shift to regulate output current and voltage in constant current (CC) and constant voltage (CV) modes, ensuring optimal performance. Simulation results in MATLAB/Simulink validate the FLC’s effectiveness, demonstrating smooth transitions between CC and CV modes while maintaining current and voltage ripples within acceptable limits. The power converter achieves a peak efficiency of $93.2 \%$ at 108 W output power, with switching and conduction losses calculated for various power levels. This research contributes to the development of a robust control in charging solutions for the growing LEV market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".