Online Diagnostics of HTPEM Fuel Cells Using Small Amplitude Transient Analysis for CO Poisoning
Bibliographic record
Abstract
Recent developments in materials have allowed PEM fuel cells to operate at higher temperatures and alleviate some of the problems that occur during operation. High-temperature PEM fuel cells are still under development, and very little has been done to study transient conditions, specifically the application of small amplitude load transients for diagnostic purposes. This paper presents the evolution of the fuel cell voltage transient for small current pulses over a range of operating conditions. A fault mechanism in the form of CO poisoning is introduced to further study and evaluate the transients for diagnostic purposes. A new two-stage diagnostic method is proposed based on the voltage transient. The first stage makes use of the discrete S-transform for fault marker identification and provides fast estimations on the fuel cell state of health. The second stage makes use of a population-based incremental learning (PBIL) algorithm for equivalent circuit parameter extraction, required for detailed diagnostics. The method is evaluated for both the healthy and the faulted CO poisoning condition in order to verify performance.
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 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".