Optimal Routing of Energy-aware Vehicles in Networks with Inhomogeneous Charging Nodes

We study the routing problem for vehicles with limited energy through a network of inhomogeneous charging nodes. This is substantially more complicated than the homogeneous node case studied in [1]. We seek to minimize the total elapsed time for vehicles to reach their destinations considering both traveling and recharging times at nodes when the vehicles do not have adequate energy for the entire journey. We study two versions of the problem. In the single vehicle routing problem, we formulate a mixed-integer nonlinear programming (MINLP) problem and show that it can be reduced to a lower dimensionality problem by exploiting properties of an optimal solution. We also obtain a Linear Programming (LP) formulation allowing us to decompose it into two simpler problems yielding near-optimal solutions. For a multi-vehicle problem, where traffic congestion effects are included, we use a similar approach by grouping vehicles into "subflows". We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy. Numerical results are included to illustrate these approaches.Comment: To appear in proceeding of 22nd Mediterranean Conference on Control and Automation, MED'1

THE PARTIALLY RECHARGEABLE ELECTRIC VEHICLE ROUTING PROBLEM WITH TIME WINDOWS AND CAPACITATED CHARGING STATIONS

Electric vehicles are potentially beneficial for both the environment and an organization\u27s bottom line. These benefits include, but are not limited to, reduced fuel costs, government tax incentives, reduced greenhouse gas emissions, and the ability to promote a company\u27s green image. In order to decide whether or not to convert or purchase electric trucks and install charging facilities, decision makers need to consider many factors including onboard battery capacity, delivery or service assignments, scheduling and routes, as well as weather and traffic conditions in a well-defined modeling framework. We develop a model to solve the partially rechargeable electric vehicle routing problem with time windows and capacitated charging stations. Given destination data and vehicle properties, our model determines the optimal number of vehicles or charging stations needed to meet the network\u27s requirements. Analyzing the model shows the relationships between vehicle range, battery recharge time, and fleet size