A Cutting-Edge Deer Hunting Optimized Converter Control (DHOCC) Based Dynamic Wireless IPT System for EV Charging Applications
Bibliographic record
Abstract
Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".