Energy Efficient Electric Vehicle Platooning At Signalized Intersections

Abstract

Growth of
mobility for people and goods transportation has been increasing steadily over the
years. With the perpetual increase in number of road vehicles, comes the
inevitable problems of traffic and pollution. Various intelligent traffic
technologies and strategies have been proposed and implemented over the years
to overcome the road traffic problems.  Platooning is identified as one of the ways to
tackle transportation related problems efficiently. Even though platooning has
been tested and implemented in highways, not much attention has been given to
controlling the platoons at urban roads. When vehicles in the platoon are
connected and automated, it helps them to understand the environment better and
communicate efficiently for better performance. Platoons at the vicinity of
signalized intersections need to accelerate and decelerate in a nonlinear
manner which leads to higher energy consumption and longer travel time. Most of
the previous research approaches focused on optimizing only the energy
consumption or travel time of the platoons. However, studies show that the
recommended driving advice from the controllers is not tracked by the drivers completely.
The current research focuses on optimal trajectory planning of the electric
vehicle platoons at signalized intersections. This is done by designing an
energy efficient longitudinal controller for the platoons using optimal control
method. The goal of this thesis is split between planning and tracking the
trajectory. Thus the optimal speed profile is planned by the high level
controller (i.e the optimal controller) and tracked using a battery electric
vehicle model controlled by a low level controller (i.e a PID controller). The
performance of the controlled platoon was verified using different scenarios
and was found to perform positively under respective control objectives and
constraints. The designed controlled and automated platoon by the optimal
controller was able to achieve energy consumption and cost saving up to 67%
when compared with intelligent driver model (IDM) platoon for a specific
scenario. This research is a collaboration between the Research Institute of
Highways (RIOH) in China and Rijkswaterstaat, Ministry of Infrastructure and
Water Management in The Netherlands, to overcome the common traffic problems on
road. The research was carried out at the ITS-Edulab, a Dutch traffic and
transport laboratory for students. It is a cooperation between Rijkswaterstaat
and the Transport & Planning group of the Delft University of Technology.