Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models
Bibliographic record
Abstract
In favor low emissions and high efficiency of fuel cell, Fuel cell is regarded as next generation power devices in smart cities and sustainable mobility.Fuel cells convert the chemical energy stored in fuels to electricity in an electrochemically way.A suitable diagnostic is required to identify the different faults that may occur in fuel cell systems.This paper aims at illustrating a novel technique to increase the service life and understand the aging mechanisms in fuel cell systems by modifying air flow rate (qwin) and humidifying gases to guarantee the proper operation of the PEMFC.In this paper, the artificial intelligence technology (i.e.neural network ANN) is used for determining the overall performance and resistance losses of PEMFC at numerous operating conditions.The proposed model in this study deals with the parameters of the electrochemical impedance spectroscopy and polarization curves, to estimate and diagnose the state of health of the fuel cell in both case flooding and drying out of the FC.This model identifies a set of three parameters of Randles model in different state of humidification, at either low or high relative humidity RH conditions.Simulation experiments show that the proposed technique enables to monitoring the water management in a simple way that helps to define the state of health (SOH) of the PEMFC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".