Cloud-Based Platoon Predictive Cruise Control Considering Fuel-Efficient and Platoon Stability

The rapid development of road transport has brought several serious challenges, such as traffic safety and environmental pollution. Commercial vehicles are the mainstay of road transport and their energy consumption and safety problems are becoming increasingly serious. There is an urgent need for a way to ensure safe driving, reduce energy consumption in freight transport, and improve transport efficiency. An effective way of solving this problem is to organize vehicles into closely spaced vehicle platoons. Various roadside equipment has been built and gradually improved with the advancement of communication technology. The application and rapid progress of vehicle-to-vehicle communication (V2V), vehicle-to-road communication (V2R), and other technologies can provide strong technical support for vehicle platoons. Multivehicle cooperative formation technology in intelligent transportation systems (ITSs) will have significant social and economic value in improving vehicle transportation efficiency, driving safety, energy savings, and emissions reduction.

Due to the practical needs of commercial vehicle platoons and the potential for energy savings, several countries and regions have started research into heavy commercial vehicle platooning technology, and commercial vehicle platoons are now the focus of research. Especially for high-speed scenarios, commercial vehicle platoons can be optimized and controlled using information such as road slope, curvature, and traffic flow, or by reducing platoon spacing to reduce wind resistance.

Studies have shown that reducing overall air resistance using a platoon can reduce overall energy consumption. However, there are differences in the reduction of air resistance between vehicles in different positions in the platoon. Commercial vehicles can save 10%–15% fuel consumption at a platoon spacing of 10 m (Guo and Wang 2019), but too a small distance between vehicles in a high-speed platoon can lead to safety problems, so a more practical research focus is vehicle platoon predictive cruise energy-saving driving control.

Platooning studies are designed to reduce the energy consumption of vehicle platoons. However, the vehicle platoon is a mutually coupled complex system, and changes in the speed of the vehicles in it may lead to instability of the system. The speed fluctuations of the vehicles in front of the platoon will be amplified back along the platoon step by step, causing frequent acceleration and deceleration to ensure string stability. However, frequent acceleration and deceleration will result in higher fuel consumption. A great deal of research has been done on the stability of the platoon, with more focus on the sliding mode controller (SMC) and the model predictive controller (MPC) approaches.

The SMC has the advantage of dealing well with nonlinear dynamical systems, actuator limitations, and diverse communication topologies, and is insensitive to external perturbations. Scholars have focused more on the SMC design in different communication topologies and spacing strategies, and on eliminating jitter as the system plateaus (Yu et al. 2018; Guo et al. 2017; Wu et al. 2020).

Three technical features of MPC—namely predictive model, rolling optimization, and feedback correction—solve the problem of platoon stability. MPC has been applied to platoon stability control by a large number of scholars. Yu et al. (2016) proposed MPC in considering ramp information for hybrid electric vehicle platoons, which can effectively reduce energy consumption. Zheng et al. (2017) proposed a distributed MPC (DMPC) to ensure platoon stability for heterogeneous vehicle platoons with unidirectional topology and a priori heterogeneous vehicle platoons with unknown desired set points. Kazemi et al. (2018) proposed a learning-based driver behavior modeling approach in combination with MPC to improve the overall performance of cooperative adaptive cruise control (CACC).

Research into the control architecture for platoon predictive cruise control (PPCC) has included both hierarchical and centralized approaches. The centralized scheme requires status information of all vehicles in the platoon, has high communication requirements, and is difficult to design. The hierarchical solution has a clear design logic, is less difficult to design, and is easy to implement. It is now the mainstream platoon control architecture. For the upper layer of platoon decision planning, the main problem is the planning of economic vehicle speed. Under the premise of ensuring the safety and stability of the platoon, the lower-layer controller completes the policy following control of the upper-layer speed planning. Guo and Wang (2019) studied the speed planning and tracking control of a commercial vehicle platoon on a hierarchical highway. Yang et al. (2020) developed a new hierarchical ecological-CACC strategy with an energy-saving effect, which can save more than 38.1% of energy consumption compared with traditional speed cruise control. Maged et al. (2020) used the optimal MPC as a speed planner in the upper layer and the planned speed profile was sent to a PID controller in the lower layer to further improve performance. Turri et al. (2017) designed a dynamic planning (DP) layer based on previewing road terrain information in the upper layer and a vehicle control layer based on DMPC in the lower layer for a heavy commercial vehicle platoon.

The PPCC addresses the issues of platoon safety, energy consumption, and stability. Existing research on PCC for platoons mainly assumes that the road is horizontal and that the platoon has a fixed reference speed, but in a realistic environment road topography and traffic conditions are important for the energy performance of the entire platoon operation. This is why the economic driving of commercial vehicle platoons is more concerned with speed optimization and control based on road information. How to combine road information, such as road slopes, with the problem of optimizing speed control is now the focus of research. Existing research mainly involves methods such as MPC and DP, which have been used in many studies to deal with economical driving of complex nonlinear vehicle platoons due to their predictive control characteristics and their ability to handle complex nonlinear multiobjective systems. Prediction of economic speed profiles for platoon driving uses road slope information for economic driving (Zhai et al. 2019b; Bakibillah et al. 2018; Kamal et al. 2011). For heterogeneous vehicle platoons, Guo and Wang (2019) used the platoon’s average state (speed, mass, etc.) as feedback to solve the optimization problem. This saves energy consumption by 6.35%. For the PPCC, the global problem of optimization is discretized into smaller stages to be solved in DP. It applies to multiobjective control of platoon driving (Guo and Wang 2019; Turri et al. 2017; Li et al. 2020a). Cost functions are constructed for optimization objectives such as energy saving and efficiency. Then, the optimal travelling speed is predicted to achieve energy-efficient driving in platoon. Guo and Wang’s (2019) research is based on combined fuel-time integrated cost and is derived using backward DP, which makes the algorithm less computationally demanding than traditional DP algorithms. The algorithm applies to heterogeneous vehicles with different weights and lengths. Turri et al. (2017) combined road slope information and real-time vehicle status to predict a speed profile for optimal platoon energy consumption. This saved 12% of energy consumption by the following vehicle compared with the traditional platoon. There is a dimensional disaster problem in the DP of energy-efficient platoon driving, so large-scale real-time applications are limited. To address this problem, Li et al. (2020a, b) proposed energy-efficient driving control simply based on optimal control theory. Compared with the traditional DP approach, the computation is faster and this intelligent speed planning approach improves the energy-saving potential of truck platoons while avoiding DP dimensional catastrophe.

In summary, platoon cruise control research has focused on stability and driving safety, and has produced a wealth of research results. PPCC focuses on speed planning by combining static road slope information and the current platoon state. Combined with a cloud platform, the problem of platoon predictive speed planning can be solved more efficiently. The cloud can combine static map ramp information, dynamic traffic information ahead, speed limit information, and the like, to plan driving speed further ahead and more intelligently. Research has also begun to focus on PCC based on the cloud platform, combining the cloud and the vehicle side. Li et al. (2020b) proposed an integrated system architecture based on CCS. For energy saving and high efficiency, they proposed the cloud control vehicle energy-saving driving system (Cloud EDS). Ozatay et al. (2014) proposed to place intensive computing in the cloud, where the driver sends destination information. The cloud uses accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route and sends it to the vehicle control, which can save 5%–15% in fuel consumption. Li et al. (2022) proposed cloud-based predictive cruise control (CPCC) based on the CCS, which effectively solves the problem of real-time computing of the CPCC system and its algorithm deployment in the cloud. This method has a fuel-saving rate of 6.17% compared with traditional fixed-speed cruise control.

Cloud-based PPCC computing considering road gradients and traffic conditions remains a challenge. Cloud-based PCC can use real-time and historical data from road traffic, enabling wide-area, long-time sensing, and fast real-time planning calculations. The computational pressure on the vehicle side can be greatly freed up, which means huge potential energy savings and improved safety and stability boundaries of the platoon system.

In this paper, cloud-based platoon predictive cruise control (CPPCC) is proposed,. The objective is a cloud-supported hierarchical control architecture. in which the cloud deploys a predictive cruise algorithm that takes into account the vehicle’s fuel consumption model and operating state. The algorithm solves the optimal speed profile and gear sequence for the platoon’s operation using information about the change in road slope ahead of the platoon’s operation. The lower layer is the vehicle platoon stability controller, which uses vehicle dynamics, external disturbances, and so forth. After accepting the speed and gear sequence sent from the cloud, this controller tracks the speed and gear sequence sent from the cloud under the condition of satisfying platoon tracking accuracy.

This paper proposes a communication topology for the cloud-supported vehicle platoon based on a hierarchical control architecture to implement information transfer between the cloud and the vehicle side. Its main contributions are (1) a hierarchical control architecture and communication topology for the CPPCC, and (2) coupled control of vehicle platoon predictive cruise speed planning and control of platoon stability based on road slope in the cloud.

The following will introduce the CCS and the CPPCC system architecture, the cloud-based economic predictive cruise control algorithm based on road slope, and the platoon stability controller at the vehicle end. The effectiveness of the CPPCC is then discussed.

Rolling optimization-based platoon speed planning for cloud deployment has the objective of reducing fuel consumption and total running time. The optimization problem is solved in a discrete space using a vehicle longitudinal dynamics model, a fuel consumption model, and receding dynamic programming (RDP).

After an optimized speed sequence is sent from the cloud, the speed-tracking control layer’s distributed controller ensures the stability of the entire platoon while tracking the speed from the cloud. The vehicle side carries a DMPC to track the speed sent from the cloud and to ensure the previous vehicle’s following distance. Platoon stability control framework is shown in Fig. 7, we treat the platoon as composed of $n$ homogeneous vehicles. The variables ${\mathrm{x}}_i$, ${\mathrm{v}}_i$, and $a_i$ denote the position, velocity, and acceleration of vehicle $i$ ($i=1,2,\dots ,n$, when the $i\mathrm{th}=1$ vehicle is the leader vehicle). The spacing of the vehicles is set as constant time headway (CTH).

This section is concerned with the DMPC in the vehicle control layer. Vehicle $i$ receives the optimal speed sent down from the cloud and obtains the state information of the vehicle in front of it. By tracking optimal speed and spacing, optimal control sequences are generated by tracking the optimal speed profile and vehicle spacing requirements while satisfying safety constraints. Before performing the DMPC derivation, we assume that (1) the body is rigid and symmetrical from side to side, (2) the vehicle is driven on a flat, dry asphalt surface where the longitudinal slip of the wheels is neglected, and (3) the drive and brake torques are integrated into a single control input.

The derivation of DMPC, which includes the vehicle model, the vehicle system constraint limits, safety constraints, and the cost function of the system, is presented next.

To verify the effectiveness of CPPCC, we built simulation experiments based on Trucksim and Matlab/Simulink. The simulation experiments were carried out on the Yi yuan-Zhu ge section of the G22 highway in Shandong Province as shown in Fig. 8. The real vehicle was selected from a real vehicle factory, and the vehicle parameters included the basic parameters of the vehicle and the engine model.

This paper proposed a hierarchical CPPCC approach based on the CCS. The hierarchical control architecture and communication topology of CPPCC was proposed to implement the deployment of a PCC algorithm based on road slope information in the cloud and DMPC on the vehicle side. Coupling control of PPCC and platoon stability control based on road slope in the cloud was developed. The cloud-based PCC algorithm addressed energy efficiency, enabling economical and efficient driving in the entire platoon. DMPC on the vehicle side calculated the recommended speed command issued by the cloud and guaranteed the stability of the platoon while ensuring tracking accuracy. A comprehensive solution to PPCC safety, energy saving, and efficiency was achieved. Simulation experiments with real road and vehicle data models compared the method proposed in this paper with traditional PLF-CC and PF-CC. The speed error remained within [$-0.25$, 0.30] ($\mathrm{m}/\mathrm{s}$) and the spacing error was guaranteed to be within [$-0.13$, 0.66] (m). The fuel saving was more than 5.13%, and overall operating efficiency was improved by 5.71%, which proves the effectiveness of the method. CPPCC is decoupled from hierarchical in this paper. It tackles the problem of platoon stability, while the cloud focuses on traffic efficiency and energy consumption.

To highlight the effectiveness of CPPCC, the effect of operating in more ideal conditions without interfering with vehicles in front was assumed. In the future, with the advantages of roadside sensing, in-vehicle sensing, and cloud-based information fusion, the results of forward over-the-horizon obstacle sensing and dynamic traffic flow information will be taken into account. Using these results, the issues of energy efficiency and stable tracking of an integrated platoon in the cloud can be achieved. As for commercial vehicle platoons, the impact of actuator hysteresis on overall control is huge. For this reason, the problem of vehicle actuator hysteresis needs to be considered in CPPCC.

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

This work was supported by the National Key Research and Development Program (2021YFB2501000), and the R&D Project on Key Technologies for Intelligent connected Vehicles based on Modular computing base platform (20202000342).