Modelling and Performance Evaluation of 18w PEM Fuel Cell Considering H2 Pressure Variations
Bibliographic record
Abstract
The chemical energy of a hydrogen-oxygen reaction is converted directly into dc electrical energy by fuel cells (FC). PEMFCs (Proton Exchange Membrane Fuel Cells) are a feasible alternative for electrical transportation and stationary applications. This paper presented a PEMFC modelling approach using Artificial Intelligence. The main objective of this research is to build a model of an 18w Polymer Electrolyte Membrane (PEM) fuel cell and test its performance under different hydrogen pressure conditions. The physical model of the 18W hydrogen fueled PEM fuel cell is designed and tested at BHEL R&D. Additionally, a method for predicting a PEMFC's operating temperature using the voltage and current measures is suggested and successfully tested. However, the proposed technique is validated using experimental data from an 18W fuel cell. The analytical data and testing procedures required for determining the parameter values used in the proposed model are specified.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".