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All Journal International Journal of Electrical and Computer Engineering
Younes Boujoudar
Sidi Mohamed Ben Abdellah University
Computer Science & IT Electrical & Electronics Engineering

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Intelligent control of battery energy storage for microgrid energy management using ANN Younes Boujoudar; Mohamed Azeroual; Hassan Elmoussaoui; Tijani Lamhamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2760-2767

Abstract

In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.
Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network Younes Boujoudar; Hassan Elmoussaoui; Tijani Lamhamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (634.375 KB) | DOI: 10.11591/ijece.v9i5.pp3415-3422

Abstract

In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of theSoC.
Co-Authors Hassan Elmoussaoui Mohamed Azeroual Tijani Lamhamdi