Optimising Electric Vehicle Wireless Charging Systems Using Neural Networks to Enable Free-Position Parking
Bibliographic record
Abstract
This paper explores wireless power transfer (WPT) systems for public electric vehicle charging, focusing on optimising the transmitter design to enhance interoperability across various receiver coil geometries and alignment conditions. Due to the complex non-linear relationships inherent to WPT systems, traditional optimisation methods are computationally expensive. Therefore, this study proposes an approach using artificial neural networks (ANNs) trained on finite element method (FEM) data to develop a surrogate model of the WPT system. This model is integrated into a blackbox optimisation solver, enabling faster identification of improved transmitter designs. The proposed method achieves computational speeds 6,000 times faster than traditional FEM simulations, with post-validation on the final solutions verifying prediction errors below 0.6%. The results demonstrate a significant acceleration in the optimisation process, establishing this method as an effective framework for developing practical WPT systems for public charging applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".