An Improved Mutual Inductance Electromagnetic Model for Inductive Power Transfer Systems Under Misalignment Conditions
Bibliographic record
Abstract
One of the most challenging components of an inductive power transfer system to be designed is the coil set (transmitter and receiver coils). Due to the great number of variables, finite element software is frequently used in the simulation of the electromagnetic quantities of coil sets. These simulations take a long time to produce results. To shorten the time it takes to design a coil set, different electromagnetic models have been published. However, the published models are still dependant on extensive equations that require several numerical loops to be solved, or are not completely validated. In this paper, a new compact electromagnetic model is proposed. The proposed model uses quadrupole superposition to predict the magnetic field, induced voltage, and inductances of a coil set through a cubic polynomial function, whose coefficients are based on the parameters of the coil set. With the proposed model, it is possible to compute the mutual inductance of multiple coil sets for different horizontal misalignment conditions, after simulating (or measuring) the open-circuit voltage of only one coil set at zero misalignment. Thus, the proposed model helps greatly for coil topology analysis and design. The proposed model is validated through seven different experimental set ups featuring different coil sets, including receivers and transmitters formed by multiple quadrupoles, a transmitter formed by one main quadrupole and a resonant booster, and coil sets working at 5 kHz and 85 kHz. The experimental results show an excellent agreement with the proposed model.
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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 | 0.001 |
| 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 | 0.001 |
| 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".