A Comparative Study of Power Supply Architectures in Wireless EV Charging Systems
Bibliographic record
Abstract
This paper examines two of the primary power supply architectures being predominantly used for wireless electric vehicle (EV) charging, namely the series LC (SLC) resonant and the hybrid series-parallel (LCL) resonant full-bridge inverter topologies. The study of both of these topologies is presented in the context of designing a 3-kW primary-side controlled stationary wireless EV charger with nominal operating parameters of 30-kHz center frequency, a range of coupling in the neighborhood of 0.18-0.26, and a parallel secondary pick-up with partial series coil compensation. A comparison of both architectures is made in terms of their design methodology, physical size, cost, complexity, and efficiency. It is found that the SLC architecture is 2.45% less costly than the LCL topology. On the other hand, it is observed that the LCL architecture achieves almost 10% higher peak efficiency at rated load and minimum coupling. The study also showed that the SLC topology suffers from poor light load efficiency, while the LCL topology maintains very high efficiency over its full range of coupling and loading. The study also revealed that the capacitor voltage stress is significantly higher in the SLC topology. Finally, it is also shown that the control complexity of the SLC architecture is higher than that of the LCL architecture because of its sensitivity to changes in the reflected secondary impedance, which result in loss of constant current source and ZVS operation unless a suitable combination of parameters are modulated by the closed-loop controller.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".