Optimization and Management Strategies of Electric Two Wheeled Vehicle Battery Exchange Network Based on Artificial Intelligence

The purpose of this study is to explore artificial intelligence based methods for optimizing and managing the battery swapping network of electric two wheeled vehicles. With the rapid expansion of the electric two wheeled vehicle market, the effective layout and management strategy of battery swapping stations have become the key to improving energy efficiency and user satisfaction. This article proposes a solution based on the GA-CNN (Genetic Algorithm based Convolutional Neural Network) fusion algorithm by analyzing the challenges faced by current battery swapping networks. The algorithm used in this study can not only optimize the layout of the exchange station, but also adjust management strategies in real-time to respond to changes in demand. Four experiments were designed during the experimental phase to evaluate the optimization and management strategies of the electric two wheeled vehicle battery swapping network based on the GA-CNN optimization model. In the first demand prediction evaluation experiment, the mean squared error (MSE) based on the GA-CNN model was 2.8. In the experiment of optimizing the layout of swapping stations, a GA-CNN strategy was used to achieve a coverage rate of $\mathbf{9 5 \%}$, while reducing the average arrival time of users to the nearest swapping station to 5 minutes. In the efficiency experiment of the battery swapping network, the processing capacity of the battery swapping station reached 60 times per hour based on the GA-CNN optimization strategy. The average waiting time for users was only 5 minutes. In the final real-time adjustment strategy response capability experiment, based on the GA-CNN optimization strategy, the service satisfaction rate reached 98% with a response time of 15 minutes, and the change in operating costs was only 5%. From the above experimental conclusions, it can be seen that the GA-CNN optimization model has shown significant advantages in key aspects such as demand prediction, site layout optimization, operational efficiency improvement, and real-time adjustment response ability of electric two wheeled vehicle battery swapping networks, reflecting the potential of the GA-CNN model as an effective battery swapping network management strategy.