Inductor design for multiphase bidirectional DC-DC boost converter for an EV/HEV application
Bibliographic record
Abstract
Magnetics design is a major factor within the design of any high-power converter due to its large volume, weight, and cost. Typically inductor design is a time-consuming iterative process. Therefore, any technique which speeds this process up will help reducing the time spent for the design. As such this paper discusses typical inductor design, and proposes a population based optimization technique for a high power inductor for an EV/HEV application. The Genetic Algorithm (GA) optimization technique will be used to design inductors implementing powdered iron core (FeSi) with rectangular wire and ferrite core with litz wire at different operating frequencies and for different number of phases for a 40kW nominal, 60kW peak bidirectional DC-DC boost converter. The comparison will show what phase number at what operating frequency provides the lowest inductor volume. This paper will also discuss a lumped parameter thermal network for the core temperature estimation which will be used within the Genetic Algorithm.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".