Developing a resilient framework for electric vehicle charging stations harnessing solar energy, standby batteries and grid integration with advanced control mechanisms

Abstract

Abstract A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions.

Keywords

• dragon fly optimization
• electric vehicles
• neural network
• solar PV system
• standby battery