Comparison of Isolated Bidirectional DC/DC Converters Using WBG Devices for More Electric Aircraft
Bibliographic record
Abstract
With the aim towards lighter and more efficient electrical systems in future aircraft, design of DC/DC converters with high efficiency, power density and improved thermal management becomes necessary. This paper investigates the detailed design of isolated bidirectional DC/DC converters for more electric aircraft (MEA). Use of wide bandgap (WBG) devices to enhance system efficiency is considered. A transformer optimization technique based on switching frequency for different converter topologies is investigated. The control strategy of the discussed configurations are verified in the PLECS simulation environment. Dual active bridge (DAB), input-series output-parallel (ISOP), neutral point clamped (NPC) and active neutral point clamped (ANPC) converters are considered to exploit benefits offered by WBG devices for MEA. A comparison is performed in terms of efficiency, thermal management, power density and electromagnetic interference (EMI). The results indicate that the ANPC converter is better for efficiency, thermal management and power density. The DAB converter showed similar results to the ISOP with the DAB yielding higher efficiency. The NPC and ANPC configurations resulted in the best EMI performance. The NPC converter proved to be the least effective solution with regard to efficiency, thermal management and power density.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".