AARM: An Autonomous Adaptive Routing Model for Electric-Vehicles

Abstract

Electric vehicles started to spread as a green and economic-friendly alternative for those petrol-running ones. Beside the environment and health concerns that started to show up decades ago, petrol price-hikes that we are living nowadays promotes this alternative to be welcomed than ever. Though, for drivers, the experience of electric running vehicles usually comes range anxiety, especially with the lack of sufficient charging infrastructure between distant cities. World-wide, countries are gradually proceeding towards all-electric vehicles running on their streets. For this to be feasible, they need to be ready to handle this acceleration, and provide the adequate infrastructure for such alternative. That could take a decade or so! Consequently, this work presents an Autonomous Adaptive Routing Model that provides for a novel navigation system oriented to electric vehicles. The methodology of this model involves reading the vehicle type, make, and model and according to its battery state of health along with its dynamic charge levels, reroute the vehicle through the shortest route to the final trip destination. Compared to other models in the literature, our model provides a navigations scheme that is autonomous, adaptive, while allowing for the shortest routes and least computing times.