Design considerations to obtain a high figure of merit in circular archimedean spiral coils for EV battery charging applications
Bibliographic record
Abstract
It is fairly well-known in circuit theory that the product of magnetic coupling coefficient and system quality factor is the single most important figure of merit (FOM) for optimizing the design of series-series-compensated resonant inductive coupling coils. Coils with high value of FOM can operate efficiently under misalignment conditions, such as poor coupling systems. This would be the case in transit systems with on-road/in-motion charging systems using inductive power transfer (IPT). Archimedean spiral coils are widely used for stationary charging of electric vehicles (EVs). This is due to ease of manufacturing, lower material cost, symmetric coupling profile, ease of experimental characterization, and very well-known magnetic characteristics. In this paper, a comprehensive analysis of Archimedean spiral coil structure has been presented, with the aim of establishing key design parameters, leading to high FOM. Knowing the key parameters in order to adjust the coupling coefficient and system quality factor gives a designer abundant options to design coils that can handle misalignment, and at the same time, minimize losses. For this purpose, 2D analysis of a range of coils with difference geometric parameters have been performed. Finite element analysis (FEA) has been used to establish key coil design parameters, in order to obtain a high value of FOM. Results of this design and analytical work will lead to efficient IPT coil design methodologies, which in turn will lead to considerable cost and energy savings. Due to their scalable and modular nature, this work is also applicable to any lumped system specifically utilizing Archimedean spiral coils.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".