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Habib Kraiem received his Ph.D. in electrical engineering from the National Engineering School of Gabès, Tunisia, in October 2010. Following this, he joined the Department of Electrical, Higher Institute of Industrial Systems Gabès, Tunisia. He is currently with the Department of Electrical Engineering, Faculty of Engineering at Northern Border University in Saudi Arabia. His current research interests include power electronics, machine drives, automatic control, and renewable energy.
This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.
Habib Kraiem; Flah Aymen; Lobna Yahya; Alicia Triviño; Mosleh Alharthi; Sherif S. M. Ghoneim. A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System. Applied Sciences 2021, 11, 7732 .
AMA StyleHabib Kraiem, Flah Aymen, Lobna Yahya, Alicia Triviño, Mosleh Alharthi, Sherif S. M. Ghoneim. A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System. Applied Sciences. 2021; 11 (16):7732.
Chicago/Turabian StyleHabib Kraiem; Flah Aymen; Lobna Yahya; Alicia Triviño; Mosleh Alharthi; Sherif S. M. Ghoneim. 2021. "A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System." Applied Sciences 11, no. 16: 7732.
A photovoltaic-powered electric vehicle is a complex system that necessitates the use of a high-performance control algorithm. This paper aims to boost the performance of a photovoltaic system by employing a suitable algorithm to control the power interface. The main goal is to find an effective and optimal control law that will enable the photovoltaic generator (GPV) to generate the maximum amount of power possible. The main facts dealt with in this article are the mathematical simulation of the photovoltaic system, its function, and its characteristics, considering the synthesis of the step-up converter and the analysis of the maximum power point tracking algorithm. This study examines and compares two control techniques for extracting full power from the solar energy system. These two techniques are the classical "perturbation and observation" (P&O) method and the intelligent solution "particle swarm optimization (PSO) method." The PSO solution is tested for two versions: the online PSO version and the table PSO version. The Simulink/MATLAB tool is used for simulation and comparative experiments based on the performance metrics provided. The study revealed that smart technology delivers improved efficiency than the classic edition.
Habib Kraiem; Aymen Flah; Naoui Mohamed; Majed Alowaidi; Mohit Bajaj; Shailendra Mishra; Naveen Kumar Sharma; Sunil Kumar Sharma. Increasing Electric Vehicle Autonomy Using a Photovoltaic System Controlled by Particle Swarm Optimization. IEEE Access 2021, 9, 72040 -72054.
AMA StyleHabib Kraiem, Aymen Flah, Naoui Mohamed, Majed Alowaidi, Mohit Bajaj, Shailendra Mishra, Naveen Kumar Sharma, Sunil Kumar Sharma. Increasing Electric Vehicle Autonomy Using a Photovoltaic System Controlled by Particle Swarm Optimization. IEEE Access. 2021; 9 (99):72040-72054.
Chicago/Turabian StyleHabib Kraiem; Aymen Flah; Naoui Mohamed; Majed Alowaidi; Mohit Bajaj; Shailendra Mishra; Naveen Kumar Sharma; Sunil Kumar Sharma. 2021. "Increasing Electric Vehicle Autonomy Using a Photovoltaic System Controlled by Particle Swarm Optimization." IEEE Access 9, no. 99: 72040-72054.
Electrical vehicle fed by photovoltaic energy represents a complex system, which needs a high-performance control algorithm. Regarding the real situations, mostly the electric vehicle will be moving inside the city. If this system is covered by photovoltaic cells, the efficiency of this renewable energy source will depend on various factors. The shade areas or sunlight zones which exist in the city make the solar system unstable. Resolving this problem can increase the battery autonomy and allow addition of some running kilometers to the vehicle. Based on this objective, this study deals with the problem of solar variation and its influence on vehicle efficiency within the city. The problem is how to extract the maximum energy in this case. In order to maximize the global energy performance and increase vehicle autonomy, the optimal control method will be applied to this photovoltaic system taking into account some performance indicators such as the obtained power, the tracking speed, and the chattering level. Therefore, this study explores two control techniques in order to extract the maximum power from the solar energy system, which are the incremental method and the particle swarm optimization method. Simulink/MATLAB tool is used for simulation and comparison study based on the offered performance indicators. The obtained results show that the particle swarm optimization method has high global performance and an energy gain is obtained.
Habib Kariem; Ezzedine Touti; Tamer Fetouh. The efficiency of PSO-based MPPT technique of an electric vehicle within the city. Measurement and Control 2020, 53, 461 -473.
AMA StyleHabib Kariem, Ezzedine Touti, Tamer Fetouh. The efficiency of PSO-based MPPT technique of an electric vehicle within the city. Measurement and Control. 2020; 53 (3-4):461-473.
Chicago/Turabian StyleHabib Kariem; Ezzedine Touti; Tamer Fetouh. 2020. "The efficiency of PSO-based MPPT technique of an electric vehicle within the city." Measurement and Control 53, no. 3-4: 461-473.
Autonomy is considered an important criterion that characterizes the performance of electric vehicles. It is represented by the distance that could be traveled by a fully electric vehicle which mainly depends on several parameters such as the vehicle model, type of battery, type of motor, etc. In this context, to improve the autonomy of electric vehicles, this paper represents an optimization study for the electric motor based on two contributions. The first devise an energy optimization algorithm to reduce the motor losses by calculation of the stator flux reference according to the electromagnetic torque and the rotation speed. The second is concerned with controller parameters adjustment using the Particle Swarm Optimization (PSO) technique to improve the efficacy and robustness of the drive. The performance of this strategy is evaluated in terms of torque, flux ripples, and transient response to step variations of the torque control. A comparative study of the designed PI controllers based on PSO with four other control algorithms and tuning methods is established in order to prove the efficiency of PI_PSO. The analysis, modeling, and simulation results are presented to verify the validity of the proposed overall optimization study.
Habib Kraiem; Shaaban M Shaaban. Energy optimization of an electric car using losses minimization and intelligent predictive torque control. Journal of Algorithms & Computational Technology 2020, 14, 1 .
AMA StyleHabib Kraiem, Shaaban M Shaaban. Energy optimization of an electric car using losses minimization and intelligent predictive torque control. Journal of Algorithms & Computational Technology. 2020; 14 ():1.
Chicago/Turabian StyleHabib Kraiem; Shaaban M Shaaban. 2020. "Energy optimization of an electric car using losses minimization and intelligent predictive torque control." Journal of Algorithms & Computational Technology 14, no. : 1.