Hybrid powertrain control with dynamic traffic prediction based on real-world V2X information

Abstract—A priori information about the future traffic conditions along the planned route can be essential for optimal hybrid powertrain energy management. However, due to the limited sensor range of a single vehicle, it cannot be acquired locally. In recent time, V2X (decentralized wireless vehicle to everything) has been receiving much attention as a way to obtain and share information from different distributed sources. V2X data can provide updated information on traffic at different locations. Still, this information will be obsolete when the corresponding positions are reached due to changing traffic, and an optimal strategy based on outdated information may not bring the full benefit. Against this background, we propose a method based on a velocity prediction approach which utilizes V2X data currently available in the market in combination with historical data, to obtain a prediction of the expected traffic conditions at in the close future. Actual measurements on a city highway in Linz, Austria, are used to estimate the potential of the approach. Even for rather mild changes in traffic conditions, a reduction of up to 4% in terms fuel consumption over this track was found, confirming the potential benefit of this method.