A reliable energy consumption path finding algorithm for electric vehicles considering the correlated link travel speeds and waiting times at signalized intersections

Abstract

Electric vehicles are an essential part of the urban green transportation network, with their popularization and application being of great significance to the sustainable development of cities. In recent years, electric vehicles have continued to increase their merit in the vehicle fleet, yet they are restricted by battery capacity and the availability of charging stations. Proper path planning for electric vehicles is conducive to improving the utilization rate of road resources and easing traffic congestion, and is of great significance to the vehicles’ energy utilization rate and reducing greenhouse gas emissions. Hence, this paper deals with a reliable energy consumption path finding (RECPF) algorithm for signalized traffic networks with electric vehicles in the presence of uncertainty. The RECPF problem is formulated as the probability of completing a trip without exhausting a given battery energy budget. Moreover, we extend RECPF models accounting for the link travel speed correlations and delays at signalized intersections. Addressing the non-additivity and nonlinearity of the suggested optimization model, we propose a modified heuristic algorithm based on the Dijkstra and k shortest path algorithms that solve the RECPF problems. The established algorithm’s efficiency and usefulness are demonstrated on the grid-based road network of Hong Kong, revealing that incorporating link travel speed correlations and delays at signalized intersections enhances the electric vehicle’s energy consumption prediction accuracy and produces a more efficient, optimal, and reliable path recommendations.