Experimental evaluation of a hybrid electric propulsion system for small UAVs
Bibliographic record
Abstract
Purpose This paper aims to experimentally evaluate the performance of a parallel hybrid propulsion system for use in small unmanned aerial vehicles (UAVs). Design/methodology/approach The objective is to combine all the individual components of the hybrid electric propulsion system (HEPS) into a modular test bench to characterize the performance of a parallel hybrid propulsion system, and to evaluate a rule-based controller based on the ideal operating line concept for the control of the power plant. Electric motor (EM) designed to supplement the power of the internal combustion engine (ICE) to reduce the overall fuel consumption, with the supervisory controller optimizing ICE torque. Findings The EM was able to supplement the power of the ICE to reduce fuel consumption, and proved the capability of acting as a generator to recharge the batteries drawing from ICE power. Furthermore, the controller showed that it is possible to reduce the fuel consumption with a HEPS when compared to its gasoline counterpart by running simulated representative UAV missions. The findings also highlighted the challenges to build and integrate the HEPS in small UAVs. Originality/value The modularity of the test bench allows each component to be changed to assess its impact on the performance of the system. This allows for further exploration and improvements of the HEPS in a controlled environment.
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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 | 0.001 |
| 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) | 0.003 | 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".