Real-time charging decision with Stochastic battery performance for Commercial Electric Vehicles

Abstract

The use of Electric Vehicles for logistics necessitates routing based on battery capacity constraints and includes trips to the charging station to maintain sufficient level of battery charge. Battery consumption depends on several external parameters and therefore, fixed route solutions may not always remain feasible while execution. This paper presents the idea of decomposing the Electric Vehicle Routing Problem into offline routing over the customers and a online recourse strategy for dynamic charging decisions to suit the practical scenario. To accommodate the stochastic battery consumption, a charging strategy comparable to the inventory control policy is proposed. Two policies based on a.) separate decision parameter for each node and b.) a common parameter over the given network, are tested. The vehicle movement is simulated over fixed routes but with stochastic battery consumption over each arc following charging decisions as specified by the given polices. The three parameters i.e. minimum battery level, number of times vehicle is charged and feasibility of route over each simulation are compared for testing the two policies. It is observed that the network-wide fixed parameter give comparable results to the node dependent parameters, using lesser information.