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

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Journal : International Journal of Electrical and Computer Engineering

 1 
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.
Rapid and efficient maximum power point tracking in photovoltaic systems with modified fuzzy logic approach Said, El-bot; Moujahid, Yassine El; Mohamed, Chafik El Idrissi; Benlafkih, Abdessamad
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.pp3621-3631

Abstract

Photovoltaic systems (PVs) often face difficulties in maximizing their output power and maintaining a stable DC-DC connection voltage, especially under variable weather conditions (VWC). The power produced by photovoltaic panels is very sensitive to changes in sunlight and temperature, which vary throughout the day. This paper presents the design of an intelligent controller approach based on modified fuzzy logic (MFLC), adapted to enable the most effective maximum power point tracking (MPPT) of a photovoltaic solar module. The technique reduces delays in MPPT and sustains efficiency despite changing environmental conditions. A DC-DC boost converter is connected to the photovoltaic solar module, which in turn is linked to a load, and computer simulations using MATLAB/Simulink were used to validate the method's effectiveness. Results reveal that the MFLC controller significantly enhances the efficiency of the PVs, achieving improvements of up to 97.05%, with a rapid settling time of less than 10 milliseconds across all test scenarios.
Co-Authors Anoune, Kamal El Harfaoui, Nadia El Idrissi, Mohamed Chafik El Maliki, Anas El Moujahid, Yassine Hadjoudja, Abdelkader Mohamed, Chafik El Idrissi Moujahid, Yassine El Said, El-Bot