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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Robotics and Control Systems
Ennaji, Mohamed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus Haidoury, Mohamed; Rachidi, Mohammed; El Hadraoui, Hicham; Laayatii, Oussama; Kourab, Zakaria; Tayane, Souad; Ennaji, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp110-128

Abstract

The increasing adoption of electric vehicles (EVs) presents a promising solution for achieving sustainable transportation and reducing carbon emissions. To keep pace with technological advancements in the vehicular industry, this paper proposes the development of a hybrid energy storage system (HESS) and an energy management strategy (EMS) for EVs, implemented using Proteus Spice Ver 8. The HESS consists of a proton exchange membrane fuel cell (PEMFC) as the primary source and a supercapacitor (SC) as the secondary source. The EMS, integrated into an electronic board based on the STM32, utilizes a low-pass filter algorithm to distribute energy between the sources. The accuracy of the proposed PEMFC and SC models is validated by comparing Proteus simulation results with experimental tests conducted on the Bahia didactic bench and Maxwell SC bench, respectively. To optimize energy efficiency, simulations of the HESS system involve adjusting the hybridization rate through changes in the cutoff frequency. The analysis compares the state-of-charge (SOC) of the SC and the voltage efficiency of the fuel cell (FC), across different frequencies to optimize overall system performance. The results highlight that the chosen strategy satisfies the energy demand while preserving the FC’s dynamic performance and optimizing its utilization to the maximum.
Analyzing the Flow of Injection Molding for Water Filter Handle: Filling, Packing, and Warpage Achor, Zineb; Zahraoui, Yassine; Tayane, Souad; Ennaji, Mohamed; Gaber, Jaafar
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1561

Abstract

Injection molding is a crucial manufacturing technology for producing complex, high-quality parts at scale, making it essential in various industries, including consumer electronics and automotive sectors. However, a lack of understanding about how injection parameters impact common defects like sink marks, short shots, and warpage often limits the widespread adoption of injection molding. This research aims to bridge this gap by providing a comprehensive digital simulation of the injection molding process within a complex mold cavity. Utilizing Moldex3D and the Finite Volume Method (FVM), this study characterizes essential material properties–viscosity, specific heat, density, and thermal conductivity–and examines the effects of gate location and part design on minimizing weld lines and warpage. The FVM involves dividing the computational domain into a finite number of small control volumes. This method is particularly well-suited for handling complex geometries and flow conditions, facilitating detailed and accurate simulations. This study employs Moldex3D, a leading simulation software in the field of injection molding, to demonstrate the use of CAE for design verification and process innovation. Moldex3D’s advanced capabilities make it an ideal tool for simulating injection molding processes, helping improve the quality of parts and contributing to the overall advancement of molding skills in the industry. The simulations revealed optimal gate locations that significantly improved filling patterns, reduced warpage by 50%, and minimized weld lines, thereby enhancing overall part quality. Key contributions of this research include the identification of critical flow characteristics, the reduction of defect-prone regions, and the enhancement of plastic component rigidity. This study provides valuable insights into optimizing injection molding processes, offering a pathway to improved efficiency and part quality in advanced manufacturing.
Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification Taleb, Ismail Ait; Kourab, Zakaria; Tayane, Souad; Ennaji, Mohamed; Gaber, Jaafar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2804-2818

Abstract

This paper presents a novel equivalent electrical circuit model for proton exchange membrane fuel cells (PEMFCs) and introduces a physics-informed neural network (PINN) algorithm for parameter identification. The proposed model provides a more accurate representation of the fuel cell’s dynamic behavior while maintaining computational efficiency. Unlike conventional methods, the PINN framework integrates physical constraints with data-driven learning, ensuring physically consistent parameter estimation. To validate its effectiveness, the proposed model is compared with the widely used RC equivalent circuit and a generic PEMFC model. Experimental data from a 1.2 kW PEMFC test bench serve as a benchmark for evaluating the transient and steady-state performance of each modeling approach. Results demonstrate that the proposed circuit, combined with PINN-based identification, yields enhanced accuracy in predicting voltage response under various operating conditions. Additionally, the model exhibits improved adaptability to transient phenomena compared to conventional equivalent circuits. These findings highlight the potential of physics-informed machine learning for advancing fuel cell modeling and control strategies.
Co-Authors Achor, Zineb El Hadraoui, Hicham Gaber, Jaafar Haidoury, Mohamed Kourab, Zakaria Laayatii, Oussama Rachidi, Mohammed Taleb, Ismail Ait Tayane, Souad Zahraoui, Yassine