Nonlinear decoupling control for highly dynamic fuel cell inlet gas conditioning
Bibliographic record
Abstract
Fuel cells in transient automotive applications face highly dynamic conditions. Since the inlet gas states are strongly coupled, safe and precise fuel cell operation necessitates advanced gas control. This paper employs the nonlinear control methodology of exact input–output linearization and a parametrized model to decouple cathode inlet pressure and mass flow. The feedforward control obtains the input trajectories for the fuel cell’s peripheral components to guide pressure and mass flow along independent reference paths. The feedforward control is robustly realized in a two-degree-of-freedom architecture on a commercial Greenlight Innovation G60 testbed, allowing for imposing highly dynamic test cycles. It is validated in several experiments on a single cell, underscoring significant improvements in dynamic gas conditioning compared to conventional control approaches. These achieved testing capabilities are particularly valuable for diagnostics, rapid stress and end-of-line testing, and dynamic scenario emulation, advancing fuel cell development by enhancing experimental investigations during transient operation. • Nonlinear gas decoupling control based on exact input–output linearization. • Precise imposition of decoupled cathode pressure and flow trajectories. • Experimental validation on a commercially available fuel cell testbed. • Highly dynamic validation test cycles performed with a working single cell. • Improved repeatability of transient tests and cell diagnostic experiments.
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) | 0.000 | 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) | 0.001 | 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".