Vehicle safety, passenger comfort, formation control, and fuel economy are the major objectives in the cooperative adaptive cruise control of intelligent vehicles. However, almost none of the current literature takes all of the above objectives into comprehensive consideration. In this paper, a switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints is proposed, in which the above four major objectives are all taken into account. To achieve the four major objectives, the distributed model predictive control with multiple objectives (DMPCMO) is proposed. To solve the problem that the DMPCMO controller cannot be achieved because of the inappropriate initial states of the vehicle platoon or the large perturbations in front of the platoon, the safety controller acting as the emergency brake to guarantee vehicle safety is also designed. The switch between the DMPCMO controller and the safety controller forms the switched control strategy of heterogeneous vehicle platoon for multiple objectives (SCSHPM). Since the DMPCMO controller has to be obtained by solving the nonlinear DMPCMO problem, in order to reduce the computational burden as much as possible, the DMPCMO problem is transformed into non-negative and non-positive 0-1 mixed integer linear programming problems after the fuel consumption table is constructed and calculated off-line. Finally, extensive experiments of heterogeneous platoon under different communication topologies are performed in simulation to demonstrate the effectiveness of the proposed SCSHPM.

Higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made due to public concerns about energy crisis and environmental issues. By organizing a group of automated vehicles into a platoon at a short inter-vehicular distance, the overall fuel consumption and greenhouse gas emissions of vehicle platoon can be decreased due to reduced aerodynamic drag, which is called the platooning technology. In addition, the eco-driving technology can help further increase the fuel efficiency of vehicle platoon by optimizing speed trajectories of vehicles. However, little research has been done into the combination of the eco-driving and platooning technologies. Based on distributed model predictive control (DMPC), this paper proposes an ecological cooperative look-ahead control strategy for a platoon of automated vehicles travelling on a freeway with varying slopes, where both the eco-driving and platooning technologies are used. To maximize the fuel efficiency of vehicle platoon, an ecological cooperative look-ahead control problem (Eco-CLC) is first formulated based on DMPC, where rotational inertia coefficient related to reduction ratio of gear box, aerodynamic drag related to spacing and model constraints are considered. Since the Eco-CLC problem is a nonconvex and nonlinear optimization problem with hard state constraints, it is very difficult to quickly obtain its optimal solution. To enhance real-time control performance, after the hard state constraints of the Eco-CLC problem are transformed into parts of the multi-objective function using the band-stop function, the improved ecological cooperative look-ahead control (iEco-CLC) based on DMPC is given. A particle swarm optimization algorithm with multiple dynamic populations is further presented to quickly solve the iEco-CLC problem online. Simulation results demonstrate, compared with benchmarks, the proposed strategy can save more than 21% of fuel for vehicle platoon.

To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this paper proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon.

Electric vehicles (EVs) are considered to relieve energy crisis and environmental problems due to their high efficiency and low emissions, and energy management strategies (EMSs) have been extensively studied to improve the performance of hybrid energy storage systems (HESSs) for EVs. To effectively reduce HESS energy loss and extend battery life, this paper proposes a predictive EMS (PEMS) for the battery/supercapacitor HESSs. First, the pattern sequence-based velocity predictor is presented to accurately predict the future short-term velocity profile. Second, the PEMS is proposed by formulating an HESS power split optimization problem, where the HESS energy loss and the battery capacity loss are considered. Third, an improved chaotic particle swarm optimization algorithm is presented to solve the formulated optimization problem. Simulation results demonstrate that, compared with the benchmark, the proposed PEMS can effectively reduce the HESS energy loss and extend the battery lifetime at the same time.

Public concerns about energy crisis and environmental issues lead to higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles, and ecological cooperative adaptive cruise control (eco-CACC) is considered to be effective in reducing fuel consumption and greenhouse gas emissions of vehicle platoons. In this paper, an eco-CACC strategy is presented for heterogeneous platoons with time delays and input saturations to achieve platoon stability, fuel economy, riding comfort and driving efficiency. The proposed eco-CACC strategy includes distributed linear feedback control (DLFC) for following vehicles and model predictive control (MPC) for the leading one. To obtain platoon stability, the DLFC protocol is designed using state errors between an ego vehicle and its preceding one, and the frequency domain method is employed to derive the sufficient conditions of platoon stability for the DLFC protocol. Then, to achieve fuel economy, passenger comfort and driving efficiency without violating the different input saturations of vehicles, the constrained fuel consumption optimization problem of the leading vehicle (CFCO-LV) is formulated based on delay MPC, where the weighted sum of the overall fuel consumption and velocity band-stop functions of vehicle platoon is minimized. To quickly obtain the MPC controller, an improved particle swarm optimization (PSO) algorithm is used to solve the CFCO-LV problem. Simulations are implemented to validate the effectiveness of the proposed eco-CACC strategy, and the simulation results demonstrate that, compared with benchmark, the proposed strategy can save 2.19% $\sim$ 8.24% of fuel for the heterogeneous platoon under different velocity ranges.