An Improved Layout Strategy for Common-Mode EMI Suppression Applicable to High-Frequency Planar Transformers in High-Power DC/DC Converters Used for Electric Vehicles
Bibliographic record
Abstract
Presently, there is an immense impetus in the automotive industry to develop plug-in electric vehicles (PIEVs) to reverse the ever increasing green house gas emissions from fossil fuels and depleting fossil fuel resources. High-frequency ac-dc converters with an isolated output are one of the essential building blocks for transferring power from utility mains to the traction battery packs which store energy for propelling the EV. Generally, the ac/dc converters used in EVs include a PFC stage at the input side and an isolated dc/dc converter at the battery side. Due to the switching nature of the converter, electromagnetic compatibility (EMC) of these converters is an essential requirement, to ensure not only its own operation but also the safe and secure operation of surrounding electrical equipment. EVs possess a lot of sophisticated electronic circuits in the vicinity of the battery charging power converters. Thus, strict EMC standards of the on-board power converters must be met according to the CISPR 12 or SAEJ551/5 relevant EMC standards. Conventional passive filters used for EMI mitigation in power converters, comes at the expense of cost, size and weight, power losses, and printed circuit board real estate. In this paper, an electromagnetic interference (EMI) filter embedded into the main high-frequency planar transformer used in the dc/dc converter is proposed as a very cost-effective and efficient solution for EVs. The proposed structure is able to significantly suppress the common-mode (CM) EMI noise generated in the dc/dc converter. Experimental results have been obtained from a 3-kW prototype in order to prove the feasibility and performance of the proposed EMI filter. The results show that the proposed embedded EMI filter can effectively suppress the CM noise particularly for high switching frequency power converters. The proposed structure can be a very simple and cost-effective EMI filtering solution for future PIEVs.
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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) | 0.001 | 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) | 0.002 | 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".