Smarter Energy Management for Multistack Fuel Cells: Artificial intelligence coordination for heavy-duty transport.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imagine a freight truck crossing a continent not with one giant hydrogen fuel cell (FC), but with a team of smaller stacks, each quietly playing its part. Some work harder on uphill stretches, others rest when temperatures rise, and a few step in when a teammate begins to weaken. This is the vision behind multistack FC hybrid electric vehicles (MFCHEVs): modular systems in which several low-power FC stacks operate together with a battery to deliver traction power. Such architectures promise higher efficiency, improved durability, and enhanced fault tolerance. However, these benefits can only be realized through artificial intelligent (AI) energy management and effective coordination; otherwise, stacks may degrade unevenly, waste hydrogen, or fail prematurely.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it