Optimized Electric Vehicle Wireless Chargers With Reduced Output Voltage Sensitivity to Misalignment
Bibliographic record
Abstract
In this article, an optimized design of wireless charger for electric vehicle (EV) applications is presented to reduce the misalignment effect on the output voltage and efficiency of the wireless charger system. The existing methods to regulate the output voltage require either the communication link between the EV and charging station to control the charging station converter or a dc-dc converter on the EV. This article provides a solution to optimize compensation networks to reduce output voltage sensitivity with respect to misalignment and improve the efficiency of the overall system. Four topologies are studied in details, and the optimized compensation network is developed for each topology. The compensation networks are also designed to satisfy zero voltage switching (ZVS) for a wide range of misalignments. The performance of the optimized circuits is compared in detail in terms of efficiency, output voltage performance, size of the resonant network, and power loss distribution. This article also shows that LCC-LCC and LCC-series are the best candidates for operation in a wide range of misalignments. A 500-W/85-kHz prototype charger is built for each topology, and the performance of the optimized resonant networks is evaluated experimentally.
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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) | 0.001 | 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".