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$9000 \times$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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".