Implementation of a Novel Digital Active EMI Technique in a DSP-Based DC–DC Digital Controller Used in Electric Vehicle (EV)
Bibliographic record
Abstract
With ever increasing green-house gas emissions from fossil fuel-driven automobiles leading to acute environmental pollution, and ever depleting reserves of fossil fuel, today need for the development of pure electric vehicle (EV) is of utmost importance. Presently, there is an immense impetus to develop plug-in EVs. High switching frequency and high-power ac-dc PFC converter with an isolated output and a dc-dc isolated converter are essential systems for transferring from utility mains to the different battery packs which store energy for propelling the EVs. Electromagnetic compatibility (EMC) with strict regulatory standards is an essential requirement which any switch mode power converter must comply with not only for its own operation but also for safe and secure operation of surrounding electrical equipment. EVs possess many sophisticated electronic circuits in the vicinity of the battery charging power converters, so strict EMC standards of the on-board power converters should be met. For a cost-effective design approach, EMC should be considered at the primitive stages of the power converter design. The most commonly used passive electromagnetic interference (EMI) filters used for EMI mitigation in power converters come at the expense of cost, size and weight, power losses, and printed circuit board (PCB) real estate. In this paper, a novel embedded digital active EMI filter (DAEF) integrated into the DSP-based digital controller of a dc-dc converter applicable for charging the low-voltage battery bank of an EV is proposed and analyzed. Experimental results and comparison of the performance of the proposed embedded DAEF with a conventional EMI filter are presented in this paper so as to validate the feasibility of the proposed EMI filter and its advantages over the conventional one.
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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 | 0.000 | 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".