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Processes
Volume 13
Issue 5
10.3390/pr13051453
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Deep Learning-Based Performance Modeling of Hydrogen Fuel Cells Using Artificial Neural Networks: A Comparative Study of Optimizers

by
Hafsa Abbade
*,
Hassan El Fadil
,
Abdellah Lassiou
*,
Abdessamad Intidam
,
Ahmed Hamed
,
Yassine El Asri
,
Abdelouahad Fhail
and
Anwar Hasni
ISA Laboratory ENSA, Ibn Tofail University, Kénitra 14000, Morocco
*
Authors to whom correspondence should be addressed.
Processes 2025, 13(5), 1453; https://doi.org/10.3390/pr13051453
Submission received: 2 April 2025 / Revised: 1 May 2025 / Accepted: 7 May 2025 / Published: 9 May 2025
(This article belongs to the Special Issue Sustainable Hydrogen Technologies and Their Value Chains)
Versions Notes

Abstract

Today, hydrogen fuel cells occupy a crucial position in sustainable energy systems. However, a precise model of their performance is needed to improve their efficiency and integrate them into hydrogen electric vehicles. This paper presents a hydrogen fuel cell model based on artificial neural networks (ANNs) to predict its performance characteristics. Using experimental data from a PEMFC NEXA 1200 hydrogen fuel cell in the ISA laboratory, an ANN model optimized by deep learning was developed, integrating advanced training techniques. The model’s performance was evaluated on independent test sets, revealing predictive precision with a low mean squared error (MSE) of 0.0429, a low Mean Absolute Percentage Error (MAPE) of 1.05%, a low Root-Mean-Square Error (RMSE) of 0.2071, and a high coefficient of determination (R2) of 0.9071. The model’s development and evaluation will be reviewed here in order to visualize the training progress and the results of the simulation. The main advantages of the proposed ANN model lie in both its flexible architecture, which can capture complex relationships without the need for explicit physical models, and its predictive and optimization capability.
Keywords: hydrogen fuel cell; artificial neural network model; deep learning; PEMFC NEXA 1200; ADAM optimizer; comparative study hydrogen fuel cell; artificial neural network model; deep learning; PEMFC NEXA 1200; ADAM optimizer; comparative study

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MDPI and ACS Style

Abbade, H.; El Fadil, H.; Lassiou, A.; Intidam, A.; Hamed, A.; El Asri, Y.; Fhail, A.; Hasni, A. Deep Learning-Based Performance Modeling of Hydrogen Fuel Cells Using Artificial Neural Networks: A Comparative Study of Optimizers. Processes 2025, 13, 1453. https://doi.org/10.3390/pr13051453

AMA Style

Abbade H, El Fadil H, Lassiou A, Intidam A, Hamed A, El Asri Y, Fhail A, Hasni A. Deep Learning-Based Performance Modeling of Hydrogen Fuel Cells Using Artificial Neural Networks: A Comparative Study of Optimizers. Processes. 2025; 13(5):1453. https://doi.org/10.3390/pr13051453

Chicago/Turabian Style

Abbade, Hafsa, Hassan El Fadil, Abdellah Lassiou, Abdessamad Intidam, Ahmed Hamed, Yassine El Asri, Abdelouahad Fhail, and Anwar Hasni. 2025. "Deep Learning-Based Performance Modeling of Hydrogen Fuel Cells Using Artificial Neural Networks: A Comparative Study of Optimizers" Processes 13, no. 5: 1453. https://doi.org/10.3390/pr13051453

APA Style

Abbade, H., El Fadil, H., Lassiou, A., Intidam, A., Hamed, A., El Asri, Y., Fhail, A., & Hasni, A. (2025). Deep Learning-Based Performance Modeling of Hydrogen Fuel Cells Using Artificial Neural Networks: A Comparative Study of Optimizers. Processes, 13(5), 1453. https://doi.org/10.3390/pr13051453

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