A New Bifurcation-Free Inductive Power Transfer System With Interoperable Power Level Capability for Wireless Charging of Electric Vehicles
Bibliographic record
Abstract
The bifurcation phenomenon in inductive power transfer (IPT) based wireless charging prevents the use of the same transmitter for charging an electric vehicle (EV) at multiple power levels. Thus, it requires a separate IPT charger for every power level. Consequently, it lacks interoperability across multiple power levels, which restricts system compatibility and increases infrastructure costs. This manuscript presents a new bifurcation-free interoperable IPT system capable of stable multi-power-level operation using a single coupler architecture. The proposed configuration includes an active clamped inverter (ACI) and a tapping-based transmitter coil with a reconfigurable compensation. The operating principles of ACI and the systematic bifurcation-free design of the tapped-based coil are discussed in detail. The proposed coupler is designed analytically and verified through finite element analysis (FEA). The efficacy of the proposed IPT system is validated through MATLAB simulations and tested through a field-programmable gate array (FPGA)-controlled scaled-down silicon-carbide (SiC) based laboratory prototype. Experimental results demonstrate that the designed multi-power-level IPT system effectively transfers power without the bifurcation phenomenon under aligned and misaligned conditions meeting the SAE J2954 and ICNIRP standards.
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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".