Energy-efficient routing for electric vehicles using metaheuristic optimization frameworks

Abstract

Electric vehicles are gaining an increased market share. People are becoming more acceptable of this new technology as it continues to gain momentum especially in the North American and European markets. The main reasons behind this trend are the growing concerns about the environment, energy dependency, and the unstable fuel prices. Traditional source-to-destination routing problems are designed for conventional fossil-fuel vehicles. These routing methods are based on Dijkstra or Dijkstra-like algorithms and they either optimize the traveled time or the traveled distance. These optimizers will most likely not yield an energy efficient route selection for an electric vehicle. Electric vehicles might regenerate energy causing negative edge costs that deem Dijkstra or Dijkstra-like algorithms not useful for this application (at least without some modifications). In this paper, we present examples of why traditional routing algorithms would not work for electric vehicles. A metaheuristic study of the energy-efficient routing problem is presented. Ant Colony Optimization and Particle Swarm Optimization are then used to solve the energy efficient routing problem for electric vehicles. The 2 metaheuristic methods are analyzed and studied; the results and performance of each are then compared and contrasted.