A Co-Planar Transformer With Ultralow Parasitic Capacitance for EV Chargers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it