Optimized GaN-Based Dual Active Bridge (DAB) Converter for More Electric Aircraft (MEA)
Bibliographic record
Abstract
Sustainable transportation and electrified transportation have gained traction in recent years. Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) enable higher efficiency, higher power density, and smaller passive components resulting in lighter, smaller and more efficient electrical systems as opposed to conventional Silicon (Si) based devices. This thesis investigates the detailed benefits of using GaN devices in transportation electrification applications. The material properties of GaN including the applications of GaN HEMTs at different switch ratings are presented. The challenges currently facing the transportation industry are introduced, and possible solutions are presented. A detailed review of the use of GaN in the Electric Vehicle (EV) powertrain is discussed. The implementation of GaN devices in aircraft, ships, rail vehicles, and heavy-duty vehicles is briefly covered. Future trends of GaN devices in terms of cost, voltage level, gate driver design, thermal management and packaging are investigated. 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. The detailed design of isolated bidirectional DC/DC converters for more electric aircraft (MEA) is investigated. Use of wide bandgap (WBG) devices to enhance system efficiency is considered. 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). An optimized modulation scheme for a DC/DC converter operating at various voltage and power levels is proposed. The proposed control strategy maximizes power transmission efficiency between the high voltage DC (HVDC) link and the low voltage (LV) bus, on the aircraft. The optimization algorithm is developed for a Gallium Nitride (GaN)-Silicon (Si) based DAB converter. GaN is considered for minimizing the switching losses on the HVDC bus, and maximizing efficiency. The dual phase shift (DPS) and extended phase shift (EPS) modulation techniques are optimized using Genetic Algorithm (GA). The proposed algorithm generates optimal phase shift angles at minimum backflow power (BFP) and peak current. A 3 kW GaN-Si DAB converter prototype is designed, and the analysis is experimentally validated.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".