Energy-optimal driving range prediction for electric vehicles

Range anxiety is one of the main obstacles to the wide diffusion of electric vehicles. Better understanding of the vehicle's energy consumption and driving range may help reduce electric vehicles drivers' stress. This work proposes a model-based predictive strategy to estimate the energy driving range of an electric vehicle. The estimation takes into account the specific vehicle parameters, as well as the topology of the road network in which the vehicle operates and the real-time traffic conditions. A macroscopic version of the energy consumption model of the electric vehicle is presented in order to use the aggregated real-time data available on typical maps web-services. The road network is modeled as a weighted directed graph adapted to the proposed energy consumption model. The energy driving range is finally obtained by means of an optimal path search algorithm suitable for online use. The driving range thus obtained offers improved precision and reliability as compared to the average-consumption-based and distance-based strategies.