An Improved Topology Graph and Ant Colony Optimization Approach for Optimizing Electric Vehicle Travel Path Considering Time and Charging Cost

Abstract

The integration of high-tech in electric vehicles enhances the driving experience, which is more environmentally friendly. However, the uneven distribution of charging stations and limited battery capacity require a balance between travel time and charging cost during long-distance travel. Different charging strategies and path planning can lead to different charging cost and travel time. Introducing artificial intelligence into path planning aims to enhance the long-distance travel experience for drivers and passengers. Therefore, the study adopts topology graphs to isomerize the roads during travel, and the algorithm topology graph is improved to remove redundant paths. Then, it is combined with ant algorithm to construct a path optimization model. The experiment used the electricity price in C1 city and the charging charging parameters in the parking lot at Ya as experimental data to simulate the real environment. The simulation results showed that the research algorithm achieved convergence after the 23rd iteration, with a comprehensive total cost of 38. The computational efficiency and results are superior to other algorithms. The average total cost of the travel path optimization model based on improved topology and ant algorithm was 7% -26% lower than other models. The results indicate that the research model has a better balance effect when considering travel time and charging cost comprehensively, which can plan the optimal travel strategy. The research results can make a positive contribution to autonomous driving.