With the development of automatic driving technology and the internet of vehicles, platooning based on control of connected autonomous vehicles has become one of the most promising approaches to improve traffic efficiency. This paper studies the control problem of mixed vehicle platoons consisting of human-driven vehicles and connected autonomous vehicles. Firstly, we propose a data-driven method to model mixed vehicle platoons based on Koopman operator theory. This method gives a way to represent the mixed vehicle platoon by a linear model in a high-dimensional space, the approximation of which is obtained by a neural network framework. Secondly, we employ model predictive control (MPC) to address the platoon control problem of mixed vehicle platoons, where both centralized MPC and distributed MPC algorithms are designed. Finally, the effectiveness of the data-driven modeling method and the centralized/distributed MPC algorithms is verified by numerical simulations. It is revealed that the proposed data-driven DMPC algorithm exhibits comparable control performance with less computation cost compared with the centralized MPC algorithm, and it shows faster convergence speed than the nonlinear model based DMPC algorithm.

Plug-in hybrid electric vehicles (PHEVs) with fuel and electricity have demonstrated the capability to reduce fuel consumption and emissions by adopting appropriate energy management strategies. In the existing energy management strategies, the dynamic programming (DP) -based energy management strategy (EMS) can realize the global optimization of the fuel consumption if the global vehicle-speed trajectory is known in advance. The global vehicle-speed trajectory can be obtained by applying GPS data of vehicles when the trip path is determined. However, for a trip path without GPS data, the global vehicle-speed trajectory is difficult to be gained. In this case, the DP-based EMS cannot be utilized to achieve the globally optimal fuel consumption, which is the issue discussed in this paper. This paper makes the following two contributions to solve this issue. First of all, the cell transmission model (CTM) of the road traffic flow and the vehicle kinematics are introduced to obtain the traffic speeds of road segments and the accelerations of the PHEV. On this basis, a hybrid trip model is presented to obtain the vehicle-speed trajectory for the trip path without GPS data. Next, a DP-based EMS with prediction horizon is proposed, and moreover, in order to improve its real-time implementation, a search range optimization algorithm of the state of charge (SOC) is designed to reduce the computational load of DP. In summary, we propose a computation-optimized DP-based EMS through applying the hybrid trip model. Finally, a simulation study is conducted for applying the proposed EMS to a practical trip path in Beijing road network. The results show that the hybrid trip model can effectively construct the vehicle-speed trajectory online and the average accuracy of the vehicle-speed trajectory is more than 78%. In addition, compared with the existing optimization algorithm for DP calculation, the SOC search range optimization algorithm can further reduce the calculation load of DP. More importantly, compared to the globally optimal DP-based EMS, although the proposed EMS make the fuel consumption grow less than 5.36%, it can be implemented in real-time. Moreover, compared with the existing real-time strategies, it can further reduce the fuel consumption and emissions. Thus, the proposed EMS can offer an effective solution for the PHEV applying it online in the trip path without GPS data.

For the on-line energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming-based on-line energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the heuristic dynamic programming to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an on-line energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. Firstly, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Secondly, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing on-line energy management strategies, although its fuel consumption is more than that of the off-line global optimization energy management strategy by 4% approximately.

This paper investigates the consensus problem of general linear discrete-time multi-agent systems by using distributed model predictive control (DMPC) with self-triggered mechanism. First, a novel DMPC based consensus algorithm is proposed, where each agent only needs to obtain its neighbors' predicted state sequences once at each time step. We prove that the resultant DMPC optimization problem is feasible, and the proposed algorithm guarantees the dynamic consensus of agents. Then, to further reduce the communication cost and the energy consumption of control updates, a self-triggered DMPC based consensus algorithm is proposed with the control input and the triggering interval jointly optimized. Numerical examples including the benchmark problem with platooning vehicles are provided to verify the effectiveness and advantages of the proposed algorithms.

In this article, the consensus of constrained linear heterogeneous multiagent systems under prediction and optimization is investigated. By optimizing the consensus problems constrained to state equations and general linear constraints, two types of distributed analytical model predictive controllers are proposed. Furthermore, stability conditions for the two types of controllers are derived, in which a relationship between the communication topology and dynamics of heterogeneous agents is clarified. Simulation examples of networked heterogeneous agents illustrate the convergence and validity of the proposed controllers.