Energy Efficient Routing Estimation in Electric Vehicle with Online Rolling Resistance Estimation

Abstract

Against the gas emission and fuel dependency, the number of electrical vehicles (EV) is expected to rise in the future. However, EVs have less autonomy than gas vehicles, causing driver anxiety about EV running out of energy. To reduce driver anxiety of battery electric vehicle (BEV), we aim in this study at finding the near optimal route with minimal energy consumption. First, 10- intersection road network is used to construct the traveling scenario. Road segments vary with respect to length and type of road pavement (Gravel or Asphalt). The proposed routing technique includes a robust estimation of the rolling resistance coefficient. To this end, a Recursive Least Square (RLS)-based algorithm is performed to estimate BEV rolling resistance coefficient on Gravel and Asphalt roads. An experimental work is performed on NEtwork MObility (NEMO) to test and validate the proposed estimation method. Finally, the routing technique is developed in order to give the driver more freedom and convenience to find at each intersection the most efficient road segment in terms of consumed energy. The consumed energy is a function of the segment distance and the estimated rolling resistance coefficient corresponding to Asphalt or Gravel pavement. Our technique allowed finding the near optimal route that allows less power consumption computed at the 5 intersections with the longer distance than the shortest path. This development is a starting point to contribute promoting the use of EVs.