Optimal Motion Control for Connected and Automated Electric Vehicles at Signal-Free Intersections

Abstract

Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated.

Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections

“…In this paper, the autonomous intersection crossing problem is modeled in the space domain for two reasons: (a) the free end-time optimization problem in the time domain is avoided and (b) the problem can be formulated in convex form [11]. Let s denote the variable of traveled distance and v i (s) denote the velocity of the ith vehicle.…”

“…where t i (0) is the arrival time of CAV i at the CZ. The electric energy cost of each CAV, J b,i , is evaluated based on the static efficiency map of the tank-to-wheel energy path [11], given in Fig. 2 I.…”

“…The trade-off between travel time and energy consumption minimization is investigated in [9], where the problem is addressed by a hierarchical and convex optimization method. More recently, in [10], [11], the problem is recast by considering electric CAVs and various friction losses, and a convex reformulation approach is proposed to alleviate the overall computational complexity. The development of the more realistic modeling framework by the inclusion of explicit powertrain and vehicle dynamics models is shown to lead to a more accurate energy consumption evaluation, as compared to existing work that employs conventional simplistic lossless vehicles models.…”

“…As such, online implementation is allowed with superior optimality as compared to other non-optimization methods. The modeling framework with electric CAVs proposed in prior work [11] of the authors is utilized in the present work. The optimization problem is formulated in a convex form under the space domain rather than the commonly used time domain, which enables a convenient deployment of DMPC, as the boundary conditions (in terms of the travel distance) are fixed.…”

“…The optimization problem is formulated in a convex form under the space domain rather than the commonly used time domain, which enables a convenient deployment of DMPC, as the boundary conditions (in terms of the travel distance) are fixed. The optimality of the DMPC solution is validated by numerical investigation into the impact of the prediction horizon length and by a comparison with the optimal solutions obtained by a centralized method [11] under the same modeling framework. The computational efficiency of the proposed decentralized control scheme is examined by evaluating the running time for a single MPC at each distance sample, and the results provide insight into the setting of the sampling distance interval and also show the practicality of the proposed methodology.…”