Parameter extraction of fuel cells using hybrid interior search algorithm
Bibliographic record
Abstract
Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current-voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".