Intelligent Algorithm Based Interactive Optimization Charging Strategy for Vehicle Network

Electric vehicles are increasingly entering households, and their popularity has made the optimization of charging strategies through vehicle network interaction a hot research topic. This paper analyzes user charging behavior patterns and simulates the real-time operation status of the power grid by establishing an electric vehicle charging demand model and a smart grid operation model. This study adopted genetic algorithm as the core of intelligent algorithm and constructed a multi-objective optimization model aimed at minimizing charging costs and peak valley load differences in the power grid, while improving the utilization rate of charging facilities. Through multiple rounds of iterative testing, the convergence speed and stability of the algorithm have been verified. The experimental results show that the optimized charging strategy significantly improves the charging efficiency, reaching up to 98.4%, while the utilization rate of charging facilities also increases from 56.3% to over 85%. Despite certain limitations and potential risks, the optimization strategy proposed in this article provides an efficient, flexible, and reliable solution for charging management in vehicle network interaction.