Combined Rolling Resistance and Road Grade Estimation Based on EV Powertrain Data
Camiel Beckers, Igo Besselink, Henk Nijmeijer
Abstract
Energy consumption prediction is increasingly important for eco-driving, energy management, and charging scheduling of electric vehicles. Detailed knowledge of the rolling resistance and road grade, combined here in a road-resistance profile, improves the accuracy of these predictions. This paper presents a recursive method to identify the position-dependent road-resistance coefficient using GPS position, powertrain power, and vehicle speed. The calculations make explicit assumptions regarding the spatial continuity of both road gradient and rolling resistance by defining road segments. A recursive least-squares method with Gaussian basis functions allows the estimates to be updated whenever a route segment is traversed anew. The method is tested on data gathered by a 12 m battery electric bus. The resulting road-resistance profile shows a strong resemblance to the road slope and captures changes in rolling resistance well, including a dependency on ambient temperature, which is in accordance with literature on tire rolling resistance. Including the resistance profile in a vehicle model reduces the error of the predicted powertrain power by 1.7 percent point compared to a conventional method, without the limitation of requiring a high-resolution digital elevation model.