Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning

Abstract

Abstract-The operation of groups of heavy-duty vehicles (HDVs) at a small inter-vehicular distance (known as platoon) allows to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of HDVs, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Therefore maintaining a short inter-vehicular distance as required by platooning without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper we propose a two-layer control architecture for HDV platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of standard platoon controllers.

Application of Predictor Feedback to Compensate Time Delays in Connected Cruise Control

“…Note that any other monotonous and sufficiently smooth function F(h) could be used as well. Besides, other range policies also exist, see [23], [34], [44]- [46]. When the leader's velocity exceeds the maximum speed v max allowed by road traffic regulations, we switch off the connected cruise control.…”

“…The weights w i must satisfy m i=1 w i = 1 in order to preserve the equilibrium velocity of the uniform flow for the nonlinear system (1), (44). Parameter m denotes how many previously received data packets are used for computing the predicted leader's velocityhere we keep this fixed, independent of time.…”

“…The dashed-dotted arrows in Fig. 6(d) show how the follower's acceleration is set according to the control law (44).…”

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

“…One example is platooning, where trucks are driven as a group to reduce the air drag and therefore to increase fuel-efficiency. This is possible by using e.g., model predictive control ( mp c; Mayne, 2014) to control the trucks to keep a certain distance, see Turri et al (2015). Here, an accurate model is important as it is used to forecast future outcomes.…”

Safe Adaptive Cruise Control with Road Grade Preview and $\mathbf{V}2\mathbf{V}$ Communication

“…The authors of [11] use a kinematic model to describe the system as a linear timeinvariant (LTI) system and apply backward reachable set analysis to calculate a polytopic control invariant set for ACC. In [12], a control invariant set is presented as the safe set for platooning by considering the lower and upper bounds on the acceleration. The aforementioned studies compute the control invariant set by bounding the unknown parameters such as road slope or the front car's motion.…”