Design and Performance Analysis of Pads for Dynamic Wireless Charging of EVs using the Finite Element Method
Bibliographic record
Abstract
Increasing problems of air pollution caused by petrol-fueled vehicles had a positive impact on the expanded use and acceptance of the electric vehicles (EVs). Currently, both academic and institutional researchers are conducting studies to explore alternative methods of charging vehicles in a fast, reliable, and safe way that would compensate for the drawbacks of the otherwise beneficial and sustainable EVs. The wireless power transfer (WPT) systems are now offered as a possible option. Another option is the dynamic wireless charging (DWC) system, which is considered the best application of a WPT system by many practitioners and researchers because it enables vehicles to increase their driving ranges and decrease their battery sizes, which are the main problems of the EVs. A DWC system is composed of many sub-systems that require different approaches for their design and optimization. The aim of this work is to find the most functional and optimal configuration of magnetic couplers for a DWC system. This was done by performing an investigation of the main magnetic couplers adopted by the system using Ansys® Maxwell as a finite element method software. The results were analyzed in detail to identify the best option. The values of the coupling coefficients have been obtained for every configuration examined. The results disclosed that the best trade-off between performance and economic feasibility is the DD–DDQ pad, which is characterized by the best values of coupling coefficient and misalignment tolerance, without the need for two power converters for each side, as in the DDQ–DDQ configuration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".