A Co-Planar Transformer With Ultralow Parasitic Capacitance for EV Chargers
Bibliographic record
Abstract
The rising popularity of Planar Transformers (PTs) in isolated power converters for applications like electrified transportation, and electric vehicle (EV) chargers is driven by their compact design, high power density, and precise manufacturing. However, traditional PTs suffer from elevated inter-winding and intra-winding capacitance due to the extensive surface overlap in their flat winding structure. This high parasitic capacitance can lead to voltage overshoot, ringing, and reduced noise immunity, impacting the converter’s regulation capability. To address these challenges and optimize PT performance for higher frequencies, a new co-planar transformer (CPT) is introduced. The CPT minimizes surface overlap by siting the primary and secondary windings on the same plane, leveraging an ultra-flat conductor thickness. This design reduces both inter- and intra-winding capacitance without compromising winding area or interleaving of layers. This paper explains the proposed CPT structure, and presents design examples to illustrate its effectiveness. Three types of PTs from existing literature are used for benchmarking and comparison with the proposed CPT. Impedance measurements reveal a substantial reduction in winding capacitance through the proposed CPT, reaching approximately 18 times lower stray capacitance. Experimental testing with a 5-kW isolated Dual-Active Bridge (DAB) converter, demonstrates that the proposed CPT outperforms other configurations in terms of power efficiency, ringing noise, and voltage overshoot. Importantly, the enhanced performance is achieved without increasing build material costs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
| 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 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".