Hybrid Renewable Power Integration (Solar+Wind+Thermal+Microwave + Fuel Cell) in eVTOL and Satellites
Bibliographic record
Abstract
This research explores the intelligent integration and optimal scheduling of hybrid renewable energy sources—solar, wind, thermal, microwave, and fuel cell—for electric vertical take-off and landing (eVTOL) aircraft and satellite systems. With growing interest from organizations such as NASA, the Canadian Space Agency (CSA), Bombardier, and Boeing, the demand for weight-efficient, AI-driven energy autonomy has become critical. Leveraging cutting-edge deep learning architectures including deep reinforcement learning, federated learning, and neural combinatorial optimization, this study proposes a unified model to enhance the energy efficiency of solar-powered UAVs, wind-harvesting aerial vehicles, and deep-space exploration platforms. Our methodology is grounded in an in-depth review and synthesis of the most recent and impactful research (2020–2024) across IEEE and related peer-reviewed journals, including ten key papers that span energy optimization, trajectory scheduling, federated UAV learning, and hybrid microgrid control.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".