Quantum firefly algorithm for optimizing electric vehicle charging: a synergistic approach with adaptive neural control and deep reinforcement learning

This paper proposes a hierarchical framework for optimizing electric vehicle (EV) charging by integrating the Quantum Firefly Algorithm (QFA), Adaptive Neural Control (ANC), and Deep Reinforcement Learning (DRL). The QFA mimics firefly attraction and employs quantum computing to explore beyond local optima. ANC adjusts charging parameters in real-time based on demand and grid conditions, while DRL continuously updates the system by learning from historical data and feedback. This integrated approach enhances charging efficiency, scalability, adaptability, and grid stability, with tests showing a 25% reduction in charging time compared to conventional optimization methods. By managing the load during peak periods and minimizing energy wastage, the system prevents strain on the grid and reduces charging costs. Additionally, the framework promotes the use of renewable energy and reduces emissions, making EV charging more environmentally friendly. The reflexive capabilities of ANC combined with DRL’s learning abilities allow the system to adapt to varying conditions, meeting diverse user demands. Therefore, the QFA-based approach provides an effective solution for optimizing large-scale EV charging networks, enhancing overall energy efficiency and environmental sustainability.