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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Hadjoudja, Abdelkader
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Reduce state of charge estimation errors with an extended Kalman filter algorithm El Maliki, Anas; Benlafkih, Abdessamad; Anoune, Kamal; Hadjoudja, Abdelkader
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.pp57-65

Abstract

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.
Advanced particle swarm optimization for efficient and fast global maximum power point tracking under partial shading conditions El Moujahid, Yassine; El Harfaoui, Nadia; Hadjoudja, Abdelkader; Benlafkih, Abdessamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3570-3579

Abstract

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading.
Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm Benlafkih, Abdessamad; El Moujahid, Yassine; Hadjoudja, Abdelkader; El Harfaoui, Nadia; Said, El-Bot; El Idrissi, Mohamed Chafik
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp845-857

Abstract

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.
Estimating the state of charge of lithium-ion batteries using different noise inputs El Maliki, Anas; Anoune, Kamal; Benlafkih, Abdessamad; Hadjoudja, Abdelkader
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp8-18

Abstract

State of charge estimation (SOC) is the most significant functionality of a vehicle's battery management system (BMS). The methods for this estimation are conventionally oriented towards model-based methods. As part of this paper, we introduce a first order equivalent circuit estimation approach known as the Thevenin model, along with an extended Kalman filter (EKF) approach to accurately estimate the SOC. We then deploy and simulate it in MATLAB by using a reference load profile from the new European driving cycle (NEDC). Afterwards, the simulation results are reviewed based on various initial noise values, and the results are compared to those of other EKF algorithms. According to the results, SOC estimation accuracy has significantly increased as a result of the improvements made. Specifically, the root-mean-square error decreased from 0.0068 to 0.0020.
A new approach to solve the problem of partial shading in a photovoltaic system Abdessamad, Benlafkih; El Idrissi Mohamed, Chafik; Hadjoudja, Abdelkader; El Moujahid, Yassine; El Maliki, Anas; Othmane, Echarradi; Mounir, Fahoume
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1298-1308

Abstract

This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.
Efficient high-gain low-noise amplifier topologies using GaAs FET at 3.5 GHz for 5G systems Zarrik, Samia; Bendali, Abdelhak; Fadlaoui, Elmahdi; Benkhadda, Karima; Habibi, Sanae; Kobbi, Mouad El; Sahel, Zahra; Habibi, Mohamed; Hadjoudja, Abdelkader
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3833-3842

Abstract

Achieving a gain greater than 18 dB with a noise figure (NF) below 2 dB at 3.5 GHz remains a formidable challenge for low-noise amplifiers (LNAs) in sub-6 GHz 5G systems. This study explores and evaluates various LNA topologies, including single-stage designs with inductive source degeneration and cascade configurations, to optimize performance. The single-stage topology with inductive source degeneration achieves a gain of 18.141 dB and an NF of 1.448 dB, while the cascade-stage common-source low-noise amplifier with inductive degeneration achieves a gain of 32.714 dB and a noise figure of 1.563 dB. These results underscore the importance of GaAs FET technology in meeting the demanding requirements of 5G systems, specifically in the 3.5 GHz frequency band. The advancements demonstrated in gain, noise figure, and linearity affirm the viability of optimized LNA topologies for high-performance 5G applications, supporting improved signal quality and reliability essential for modern telecommunication infrastructure.
Co-Authors Abdessamad, Benlafkih Anoune, Kamal Bendali, Abdelhak Benkhadda, Karima Benlafkih, Abdessamad El Harfaoui, Nadia El Idrissi Mohamed, Chafik El Idrissi, Mohamed Chafik El Maliki, Anas El Moujahid, Yassine Fadlaoui, Elmahdi Habibi, Sanae Kobbi, Mouad El Mohamed Habibi Mounir, Fahoume Othmane, Echarradi Sahel, Zahra Said, El-Bot Zarrik, Samia