Integrated capacitor for common-mode EMI mitigation applicable to high frequency planar transformers used in electric vehicles DC/DC converters
Bibliographic record
Abstract
Currently, there is immense incentives-driven research, in the automotive industry to develop Plug-In Electric Vehicles (PIEVs) to reduce the green-house gas emissions and to comply with the Kyoto accord. Isolated High frequency AC-DC converters are a major component to transfer power from utility mains to the traction battery packs which store energy for the EV motors propulsion. The front-end AC/DC converters used in EVs are essentially made of two stages, the PFC stage at the input side and an isolated DC/DC converter at the battery side. Due to the switching frequency operation of these converters, electromagnetic compatibility (EMC) is required, to ensure a safe and secure operation of the electrical sub-system components in the vicinity. Thus, strict EMC standards of the on-board power converters must be met according to the CISPR 12 or SAEJ551/5 Conventional passive EMI filters comes at the expense of cost, size and weight, power losses and PCB real estate. In this paper, an EMI filter embedded into the main high frequency (HF) planar transformer used in the DC/DC converter is proposed as a 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 by using different dielectric material to achieve the required capacitance and to reduce the numbers of the planar transformer PCB layers. Experimental results have been obtained from a 3KW prototype in order to prove the feasibility and performance of the proposed EMI solution. The results show that the proposed embedded EMI filter can effectively suppress the CM noise particularly for high switching frequency based power converters using a minimum number of PCB layers for the main HF transformer. The proposed structure is a simple and cost-effective EMI filtering solution for future PIEVs.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".