A Review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells
Bibliographic record
Abstract
The real-time model-based control of polymer electrolyte membrane (PEM) fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions, involving the pressure, temperature, humidity, and stoichiometry ratio. In this article, recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed. The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described. The reduced-dimensional physics-based models (pseudo-two-dimensional, one-dimensional numerical and zero dimensional analytical models) and the non-physics-based models (zero-dimensional empirical and data-driven models) have been systematically examined, and the comparison of these models has been performed. It is found that the current trends for the real-time control models are (i) to couple the single cell model with balance of plants to investigate the system performance, (ii) to incorporate aging effects to enable long-term performance prediction, (iii) to increase the computational speed (especially for one-dimensional numerical models), and (iv) to develop data-driven models with artificial intelligence/machine learning algorithms. This review will be beneficial for the development of physics or non-physics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".