Hybrid powertrain control with dynamic traffic prediction based on real-world V2X information
IEEE, IEEE, 2021
Author(s): | Deng J., Del Re L., Adelberger D. |
Year: | 2021 |
Abstract: | 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and an optimal strategy based on outdated information may not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. |