Flexible and Secure EV Charging System Using Blockchain Technology with Namib Beetle Optimization and Continual Spatio-Temporal Graph Convolutional Networks

The advancement in electric vehicles (EVs) has heightened the need to have better, safer, and smart charging solutions. There may be a problem with the implementing blockchain technology which may be a problem of scalability that can result in narrow space in providing services for multiple similar activities at different EV places at anyone time thus slowing down the rate of charging. This paper proposes a hybrid method for a flexible and secure smart charging system that combines blockchain technology with advanced techniques. The proposed method is the combined execution of together Namib beetle optimization algorithm (NBOA) and Continual Spatio- Temporal Graph Convolutional Networks (CSTGCN). Henceforth, it is called as NBOA-CSTGCN method. The objective of the proposed technique is to improve the efficiency and stability of EV charging systems. The NBOA is utilized to optimize EV charging schedules, ensuring a balanced load on the power grid. The CSTGCN is employed to predict and adapt to dynamic energy demand, facilitating efficient energy distribution across charging stations. By then, the proposed method is realized in MATLAB platform and associated to various existing approaches for example Particle Swarm Optimization (PSO), Adaptive Non-dominated Sorting Genetic Algorithm II (ANSGA-II), Krill Herd Algorithm (KHA). The proposed NBOA-CSTGCN method achieves the highest efficiency of 92.1 %, outperforming PSO (70.5%), ANSGA-II (78.2%), and KHA (86.7%). It also shows superior utility, with the Aggregator increasing from 2 in PSO to 7 in NBOA-CSTGCN, and the EV utility rising from 3.1 in PSO to 5.9 in NBOA-CSTGCN. These results demonstrate that NBOA-CSTGCN offers the best performance and effectiveness compared to existing methods.