Optimized Minimum-Loss Hybrid Multiple Phase Shift Modulation Technique for Dual Active Bridge Converters for MEA Applications
Bibliographic record
Abstract
This paper reviews zero-voltage switching (ZVS) possibilities with multiple phase shift modulations for the more electric aircraft (MEA). Three additional triple-phase shift modes are proposed and analyzed in detail based on ZVS conditions beyond the traditional six modes. A minimum peak current stress closed-loop hybrid multiple phase shift (HMPS) modulation scheme based on the proposed modes of operation is presented. Multiple phase shift angles are optimized based on accurate pre-calculation of output current, providing the minimum peak current and decreasing the switching loss, enabling the design of power-dense converters. The proposed HMPS modulation shows higher efficiency at light load and boundary operating points than the existing SPS and unified TPS (UTPS) methods. Finally, a 10kW SiC DAB converter hardware setup for the MEA application is implemented. The efficiency of the setup is up to 96.5%. At boundary operating points, the converter obtains a peak efficiency of 94.7% with the proposed HMPS method. The converter shows peak efficiency improvements of 1%-2% compared to traditional modulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".