Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles

Abstract

Electric vehicles (EVs) present several benefits over conventional internal combustion engine vehicles. They emit zero tailpipe emissions, thereby aiding in the reduction of air pollution and the mitigation of climate change. In addition, EVs tend to have lower operating expenses due to cheaper electricity compared to gasoline or diesel. They also provide a smoother and quieter driving experience. Furthermore, EVs help promote energy independence by decreasing dependence on fossil fuels. Overall, they represent a cleaner, more sustainable transportation option for the future. However, EVs encounter some important constraints such as inefficiency of energy consumption management, charging time, and battery range problems. To address these challenges, hybrid energy storage systems (HESSs) offer a solution by combining different energy storage technologies. These systems can improve energy efficiency, reduce charging times, and extend the driving range of EVs, making them more practical and appealing to consumers. In this study, a new controller design is realized using the grey wolf optimization (GWO) algorithm, and the energy consumption demands of EV HESS are optimized with the designed system. The performance results of the proposed system are compared with other energy management systems in the literature, and it is concluded from this study that the proposed system is much superior to previous methods and typically reduces energy consumption by 12.88%.

Keywords

• Electric vehicles
• hybrid energy storage systems
• metaheuristic algorithm
• grey wolf optimization

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