Sequential Mutual-Inductance Identification Method for Wireless Power Transfer Systems of Electric Vehicles
Bibliographic record
Abstract
In a multi-transmitter static wireless power transfer (WPT) system for electric vehicles (EV), the transmitter-to-receiver mutual-inductance value is required to assign current to each transmitter. This article proposes a sequential mutual-inductance identification method (SMIM) that identifies the transmitter-receiver mutual inductance transmitter by transmitter in sequence. The proposed method simplifies the multi-transmitter system into a single-transmitter system through the current blocking principle. The identification only requires measuring the magnitude of DC bus voltage and transmitter rms currents where no receiver-side sensor is needed. The mutual-inductance can be obtained through simple calculations with low computation requirements. It is demonstrated that SMIM robustness against transmitter self-inductance deviation is greatly enhanced by introducing a two-point identification technique. High SMIM accuracy is achieved by optimizing test conditions over the entire viable charging zone of WPT system. The experimental results demonstrate a maximum 3.11% error in identified mutual-inductance, provided coupling coefficient exceeds 0.02.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".