Energy-Efficient Electric Vehicle Charging Using Metaheuristic Control Architectures

Abstract

The rapid adoption of electric vehicles (EVs) has significantly increased the demand for efficient charging infrastructure and intelligent energy management solutions. Although EVs contribute to sustainable transportation and reduced carbon emissions, large-scale charging activities introduce challenges related to energy consumption, charging costs, battery degradation, grid instability, and peak load management. Conventional charging strategies often rely on fixed charging schedules that fail to adapt to dynamic electricity pricing, battery health conditions, user requirements, and grid constraints. Consequently, metaheuristic optimization techniques have emerged as promising approaches for developing intelligent charging control systems capable of optimizing charging operations while minimizing energy losses and operational costs. This research proposes an Energy-Efficient Electric Vehicle Charging Framework using Metaheuristic Control Architectures (EEEVC-MCA) to optimize EV charging processes, improve charging efficiency, reduce charging costs, and enhance battery lifespan. The framework integrates battery condition monitoring, charging demand forecasting, metaheuristic optimization, adaptive charging control, and intelligent energy scheduling into a unified architecture. Metaheuristic algorithms are employed to identify optimal charging strategies by considering battery state-of-charge, electricity pricing, charging station availability, and grid operating conditions.