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Energy efficient electric vehicle platooning at signalized intersections

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.

This report 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.

Publication date: December 16, 2020
Download report: MSc_Thesis_Raj_Kumar_Muniyandi

Research topic:
Traffic management

Research question:
Environmental effects of traffic
Cooperative and automated driving
New traffic management measures

Control strategy for truck platoons at signalised intersections

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