Noise-Adaptive Multimode Online Energy Management for PEMFC/Battery Hybrid UAVs
Bibliographic record
Abstract
Hydrogen/battery hybrid unmanned aerial vehicle (UAV) flights present unique challenges to the adaptability of energy management strategy (EMS) due to dynamic operating conditions (altitude, temperature, and humidity) and diverse flight modes (takeoff, cruising, and maneuvering). In this article, a novel multimode EMS is proposed. First, inspired by the variational Bayesian (VB) approach, a noise-adaptive parameter identification method is introduced to monitor the fuel cell (FC) characteristics in-flight. The identification results provide an online reference for energy management. Subsequently, a case recognition logic categorizes the flight mode into cruising and noncruising based on flight power variation. An online rule-based method is deployed for the noncruising case to prioritize system response, and a novel equivalent consumption minimization strategy (ECMS) is used to maximize system endurance during cruising. Extensive ground tests are conducted with a FC in a constant temperature and humidity chamber, and a flight test is carried out on a self-developed 3 kW FC/battery hybrid UAV. Experimental results show that the proposed method outperforms classic EMSs in terms of system efficiency and reduced system stress.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".