Health-aware Energy Management Strategy for Fuel Cell Hybrid Self-Guided Vehicle with Conflict-aware Navigation Approach
Bibliographic record
Abstract
Industry 4.0 has significantly advanced automation and robotics, making Self-Guided Vehicles (SGVs) essential in modern manufacturing for improving efficiency and productivity. Hybrid SGVs, combining batteries and Fuel Cells (FCs), are proposed as a robust solution to meet energy concerns. However, the dynamic and unpredictable nature of manufacturing environments requires an advanced Energy Management System (EMS) for such SGVs. This EMS must adapt to varying conditions and frequent start-stop cycles, which is crucial for maintaining the health and efficiency of the FC. To make the EMS more suitable for these environments, adaptive navigation should be designed to optimize the process by learning from previous missions, improving motion execution, and generating efficient power profiles. Therefore, this paper proposes a FC health-aware EMS integrated with adaptive navigation to optimize power distribution between the FC and battery. Our approach addresses the challenges of dynamic environments, enhancing energy efficiency and extending the operational lifespan of SGVs. The results show that combining the two strategies improves power distribution of the energy sources of Hybrid SGVs in dynamic environments.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".