Electric automobile route scheduling with time setting using machine learning methods

Electric automobiles transform urban mobility by offering a clean and eco-friendly alternative to traditional internal combustion vehicles. Planning and optimizing electric automobile (EA) travel routes involve considering energy consumption, battery range, and the availability of charging stations. By optimizing routes that account for delivery deadlines and charging outlet availability, the Electric Automobile Path Planning with Time Frame (EAPPTF) aims to reduce travel time and energy consumption. While decision tree models forecast and optimize energy consumption and charging stops, route planning uses Dijkstra’s algorithm to identify the shortest path. Using real-time data from Indian cities, this article enhances electric automobile routing by providing insights into traffic flow and charging station availability, thereby enabling efficient travel and energy management. Data from Bengaluru and Hyderabad demonstrate that the proposed methods effectively address the routing challenges for electric automobiles, resulting in shorter trips and lower energy consumption, thereby promoting sustainable urban mobility. By improving autonomous driving technologies and charging infrastructure, these algorithms can also boost urban mobility and sustainability. This strategy supports optimal vehicle operation while addressing pressing issues in emerging nations.