Wireless power transfer technology using full‐bridge current‐fed topology for medium power applications
Bibliographic record
Abstract
This paper studies, explores and analyses a wireless power transfer (WPT) system using current‐fed power electronics topology for electric vehicles and battery charging applications. The main contribution is analysis, design, and implementation of a current‐fed technology for WPT application. The required resonance in both the transmitter and receiver coils is parallel ( L )( C ) and series ( LC ) type, respectively. A detailed mathematical analysis and design have been reported. Stiff DC current at the input of the inverter limits the inverter switch current stress. Also, the inductor in DClink provides natural short‐circuit protection during inverter fault. It is quite important in such application. Resonant converter facilitates soft‐switching at turn‐off of the transmitter side switches. Also, soft‐commutation of rectifier diodes reduces reverse recovery loss. Mathematical analysis is verified by simulation results using PSIM 9.3. A 420 W proof‐of‐concept lab hardware prototype is developed and the experimental results are demonstrated to validate the mathematical analysis and simulation results. The maximum efficiency of DC–DC WPT stage obtained from the proof‐of‐concept lab‐prototype is close to 90% with a coefficient of coupling 18%. It is suitable for solar‐to‐vehicle and single‐phase residential slow charging.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".