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.

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The autonomous vehicle following problem has been extensively studied for at least two decades with the rapid development of intelligent transport systems. In this context, this paper proposes a robust model predictive control (RMPC) method that aims to find the energy-efficient following velocity of an ego battery electric vehicle and to guarantee a safe rearend distance in the presence of disturbances and modelling errors. The optimisation problem is formulated in the space domain so that the overall problem can be convexified in the form of a semi-definite program, which ensures a rapid solving speed and a unique solution. Simulations are carried out to provide numerical comparisons with a nominal model predictive control (MPC) scheme. It is shown that the RMPC guarantees robust constraint satisfaction for the closed-loop system whereas constraints may be violated when the nominal MPC is in use. Moreover, the impact of the prediction horizon length on optimality is investigated, showing that a finely tuned horizon could produce significant energy savings. v d dt . Further consider m the mass of the controlled vehicle. It is convenient to use