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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Mostafa Bouzi
Hassan First University
Control & Systems Engineering Electrical & Electronics Engineering

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Improved MPPT controls for a standalone PV/wind/battery hybrid energy system Otmane Zebraoui; Mostafa Bouzi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (680.474 KB) | DOI: 10.11591/ijpeds.v11.i2.pp988-1001

Abstract

In this paper, we present the modeling, optimization and control of a standalone hybrid energy system combining the photovoltaic and wind renewable energy sources to supply a dc electrical load, with storage batteries as a backup source. With the aim of improving the energy performance of the proposed system, we developed an MPPT controller based on new hybrid and robust approaches to evolve the power quality produced by both PV and Wind subsystems. For the PV subsystem, the proposed approach is based on the methods perturb and observe (P&O), sliding mode control (SMC) and fuzzy logic control (FLC). For the Wind subsystem, the proposed technique is based on the hill climbing search algorithm (HCS) and the fuzzy logic control. Also, to evaluate the efficiency of the developed controls and to analyze the behavior of each system during their maximum power point tracking, a comparison study was carried out with conventional techniques and the simulations are performed under varying weather conditions. The simulations results show the good performance of the proposed MPPT controls compared to other methods with better response time, a higher optimal power point and negligible oscillations.
Online parameter estimation of a lithium-ion battery based on sunflower optimization algorithm Mouncef Elmarghichi; Mostafa bouzi; Naoufl Ettalabi
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.2637

Abstract

For techniques used to estimate battery state of charge (SOC) based on equivalent electric circuit models (ECMs), the battery equivalent model parameters are affected by factors such as SOC, temperature, battery aging, leading to SOC estimation error. Therefore, it is necessary to accurately identify these parameters. Updating battery model parameters constantly also known as online parameter identification can effectively solve this issue. In this paper, we propose a novel strategy based on the sunflower optimization algorithm (SFO) to identify battery model parameters and predict the output voltage in real-time. The identification accuracy has been confirmed using empirical data obtained from CALCE battery group (the center for advanced life cycle engineering) performed on the Samsung (INR 18650 20R) battery cell under one electric vehicle (EV) cycle protocol named dynamic stress test. Comparative analysis of SFO and AFRRLS (adaptive forgetting factor of recursive least squares) is carried out to prove the efficiency of the proposed algorithm. Results show that the calibrated model using SFO has superiority compared with AFFRLS algorithm to simulate the dynamic voltage behavior of a lithium-ion battery in EV application.
Co-Authors Mouncef Elmarghichi Naoufl Ettalabi Otmane Zebraoui