Unlocking Free-Position Electric Vehicle Charging: A Neural Network-Driven Approach for Optimisation of Multi-coil Wireless Power Transfer Systems
Bibliographic record
Abstract
Wireless power transfer offers a convenient solution for public electric vehicle charging by eliminating physical connectors, enabling seamless operation. However, traditional single-coil inductive power transfer systems suffer from sensitivity to misalignment, reducing power transfer efficiency (PTE) and limiting real-world viability. This study proposes a multi-coil transmitter design to enable free-position parking, improving adaptability to vehicle misalignment and receiver variations. A blackbox optimisation framework is implemented, leveraging a neural network-based surrogate model trained on simulation data, achieving a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$9000 \times$</tex> speedup while maintaining prediction errors below 2.5 %. A scenario-based stochastic optimisation formulation solved via the meshadaptive direct search algorithm ensures adaptability across real-world receiver conditions. The optimised four-coil design achieves an average PTE of 86.93 % while minimising material costs, balancing efficiency and affordability. These findings confirm the feasibility of a scalable, user-friendly universal wireless charging system for commercial parking environments.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".