A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Photovoltaic System Optimization Techniques · Advanced Battery Technologies Research · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
Processes, Energy, Environment and Electrical Systems, National Engineering School of Gabès, University of Gabès, Gabes 6072, Tunisia
Department of Electrical Engineering, University of Málaga, 29071 Málaga, Spain
Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Funding
Taif University Researchers Supporting Project Number (TURSP-2020/122)
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