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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
El Maliki, Anas
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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.
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.
Co-Authors Abdessamad, Benlafkih Anoune, Kamal Benlafkih, Abdessamad El Idrissi Mohamed, Chafik El Moujahid, Yassine Hadjoudja, Abdelkader Mounir, Fahoume Othmane, Echarradi