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Keywords = PEMFC NEXA 1200

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17 pages, 8612 KB  
Article
Intelligent Extremum Seeking Control of PEM Fuel Cells for Optimal Hydrogen Utilization in Hydrogen Electric Vehicles
by Hafsa Abbade, Hassan El Fadil, Abdessamad Intidam, Abdellah Lassioui, Tasnime Bouanou and Ahmed Hamed
World Electr. Veh. J. 2026, 17(1), 15; https://doi.org/10.3390/wevj17010015 - 25 Dec 2025
Viewed by 996
Abstract
In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC [...] Read more.
In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article
(This article belongs to the Special Issue Vehicle System Dynamics and Intelligent Control for Electric Vehicles)
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14 pages, 3007 KB  
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 Lassioui, Abdessamad Intidam, Ahmed Hamed, Yassine El Asri, Abdelouahad Fhail and Anwar Hasni
Processes 2025, 13(5), 1453; https://doi.org/10.3390/pr13051453 - 9 May 2025
Cited by 10 | Viewed by 2411
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Sustainable Hydrogen Technologies and Their Value Chains)
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15 pages, 3890 KB  
Article
New Equivalent Electrical Model of a Fuel Cell and Comparative Study of Several Existing Models with Experimental Data from the PEMFC Nexa 1200 W
by Fatima Zahra Belhaj, Hassan El Fadil, Zakariae El Idrissi, Abdessamad Intidam, Mohamed Koundi and Fouad Giri
Micromachines 2021, 12(9), 1047; https://doi.org/10.3390/mi12091047 - 30 Aug 2021
Cited by 50 | Viewed by 9550
Abstract
The present work investigates different models of polymer electrolyte membrane fuel cell. More specifically, three models are studied: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent RC electrical circuit. A new equivalent electrical RL model is proposed, [...] Read more.
The present work investigates different models of polymer electrolyte membrane fuel cell. More specifically, three models are studied: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent RC electrical circuit. A new equivalent electrical RL model is proposed, and the methodology for determining its parameters is also given. An experimental test bench, based on a 1200-W commercial PEMFC, is built to compare the static and dynamic behaviour of the existing models and the proposed RL model with the experimental data. The comparative analysis highlights the advantages and drawbacks of each of these models. The major advantages of the proposed RL model lie in both its simplicity and its ability to provide a similar transitory behaviour compared to the commercially manufactured PEMFC employed in this research. Full article
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