A Magnetic Switch Sensor Based Inductive Power Transfer System With Power Control and Efficiency Maximization for Vehicular Applications
Bibliographic record
Abstract
In order to establish an efficient inductive power transfer (IPT) mechanism for electric vehicles (EVs) it is necessary that a system with effective power control and efficiency maximization is established. As the equivalent resistance of the on-board battery charger continuously fluctuates during operation, a battery charging algorithm based on an improvised continuous current (CC)–constant voltage (CV) is proposed. This article introduces the design of an integrated stationary IPT system to inductively transfer power from a transmitter pad positioned on the ground and the receiver pad embedded under the chassis of an EV. An innovative feature of the design is the implementation of a magnetic switch sensor that is incorporated into both the transmitting and receiving wireless charging circuitry to ensure optimum alignment for IPT. The power electronics design focuses on the implementation of an H-bridge converter incorporating series–series (SS) compensation topology to use an innovative control algorithm to prioritize battery charging operations. The system is validated through a simulation model in PSIM and a hardware-in-the-loop (HIL) simulation in Typhoon before hardware implementation and testing of the developed prototype. At a test resonant frequency of 23.74 kHz and a nominal air gap separation of 120 mm, the developed IPT system had an overall efficiency of 93.41%.
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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 | 0.000 |
| 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 | 0.001 | 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".