Cloud control-based centralized vehicle queue predictive cruise control method and device

Abstract

The application relates to a cloud control-based centralized vehicle queue predictive cruise control method and device. The method comprises the following steps: based on a received predictive cruise control request, acquiring real-time position and leading vehicle state information of a vehicle queue, determining an optimal reference speed curve of the vehicle queue according to the real-time position, the leading vehicle state information, a preset fuel consumption model and a preset dynamics model, taking the curve as an input of a centralized queue speed planning algorithm to obtain an optimal driving speed of the vehicle queue through optimization solving, and sending the optimal driving speed to the vehicle queue to realize vehicle queue cruise control. Thus, by combining the advantages of the cloud, the problems of limited prediction range and insufficient information acquisition capability of the existing vehicle queue predictive cruise are solved, the safety, economy and efficiency of the queue predictive cruise control are realized, the problem of gradually increasing energy consumption of the following vehicle caused by grouping is improved, the computing pressure at the vehicle end is relieved, and the energy-saving and system safety and stability boundary capability of the queue is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a predictive cruise control method and device for a centralized vehicle platoon based on cloud control. Background Technology

[0002] Commercial vehicles play a crucial role in logistics transportation on highways. Fuel costs for commercial vehicles account for one-third of the operating costs of heavy-duty vehicles. Therefore, logistics companies have a significant demand for energy-saving technologies for commercial vehicles. Pacifying commercial vehicles into platoons can reduce fuel consumption without altering the underlying vehicle technology. However, static traffic information (i.e., road gradient) has a substantial impact on fuel consumption. For commercial vehicles, due to their large mass and limited power, changes in altitude have a profound effect on movement. Even small inclines can generate significant longitudinal forces on trucks, making it difficult to maintain a constant speed on uphill sections (due to limited engine power) and downhill sections (due to high inertia). Predictive cruise control intelligently combines static and dynamic traffic information ahead to achieve safe and economical driving. Combining predictive cruise control with platooning further integrates the advantages of platooning, achieving predictive cruise control as a whole, thus maximizing the benefits of predictive cruise. Therefore, integrating platooning and road-information-based predictive cruise control is currently an important and significant technology.

[0003] Among related technologies, platooning predictive cruise control methods focus on predictive cruise control combined with road gradient. This method can achieve economical driving of vehicles, and the formation of vehicles can improve road traffic efficiency and throughput.

[0004] However, the following drawbacks exist in the platoon predictive cruise control method: (1) The range of prediction of the vehicle-mounted platoon predictive cruise control is limited, and the ability to acquire and perceive information is limited, which cannot further expand the advantages of platoon predictive cruise control; (2) The existing platoon predictive cruise control only considers the platoon leader vehicle or the average state of the platoon, without considering the overall state of the platoon, and cannot comprehensively plan the optimal state of all vehicles in the platoon; (3) The existing platoon predictive cruise control system still has the problem that the platoon leader vehicle saves energy, and the subsequent vehicles need to accelerate frequently due to the platooning action, which leads to the gradual increase in the energy consumption of the following vehicles, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a cloud-based centralized vehicle platoon predictive cruise control method and device to solve the problems of limited prediction range and insufficient information acquisition capability in existing vehicle platoon predictive cruise, thereby achieving safety, economy and efficiency of platoon predictive cruise control, improving the problem of gradually increasing energy consumption of following vehicles due to platooning, alleviating the computing pressure on the vehicle end, and enhancing the energy saving and system safety and stability boundary capabilities of the platoon.

[0006] To achieve the above objectives, the first aspect of this application proposes a predictive cruise control method for a centralized vehicle platoon based on cloud control, comprising the following steps:

[0007] Receive predictive cruise control requests from the vehicle convoy;

[0008] Based on the predictive cruise control request, the real-time location of the vehicle convoy and the status information of the lead vehicle are obtained; and

[0009] The optimal reference speed curve of the vehicle platoon is determined based on the real-time location, the status information of the navigator vehicle, the preset fuel consumption model, and the preset dynamics model. The optimal reference speed curve is then used as input to a centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon. The optimal driving speed is then sent to the vehicle platoon for cruise control.

[0010] According to one embodiment of this application, determining the optimal reference speed curve of the vehicle convoy based on the real-time location, the navigator vehicle status information, a preset fuel consumption model, and a preset dynamics model includes:

[0011] The road gradient information ahead of the vehicle convoy is determined based on the real-time location.

[0012] Based on the preset dynamic model, the longitudinal force on each vehicle in the vehicle convoy is calculated according to the road slope information ahead of the vehicle convoy and the status information of the lead vehicle.

[0013] The lead vehicle of the vehicle convoy is divided into states within the planning period. Based on the division results and the preset speed planning cost function, the optimal reference speed curve of the vehicle convoy is determined according to the preset fuel consumption model and the longitudinal force on each vehicle in the vehicle convoy.

[0014] According to one embodiment of this application, the step of using the optimal reference speed curve as input to a centralized queue speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle queue includes:

[0015] Based on a preset expansion strategy, the optimal reference speed curve is expanded upwards and downwards to obtain the expanded optimal reference speed curve.

[0016] Based on the preset queue control error model and the preset queue system constraint model, the optimal reference speed curve after expansion is optimized and solved, and the solution is linearly interpolated to obtain the optimal driving speed.

[0017] According to one embodiment of this application, the preset fuel consumption model is:

[0018]

[0019] Where, ξ i,j The parameters T are fitted to the preset fuel consumption model. tq n is the vehicle engine torque, and n is the vehicle engine speed.

[0020] According to one embodiment of this application, the preset dynamic model is:

[0021]

[0022] Where, m i For the quality of the vehicle, For the vehicle's acceleration, F e,i For engine traction, F g,i For slope resistance, F r,i For rolling resistance, F air,i This refers to air resistance.

[0023] According to one embodiment of this application, the preset queue system constraint model is as follows:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Where i represents the vehicle, and j represents time j in the planning period. Let i be the position of vehicle i. The reference speed for velocity planning at time j. Let $\mathbf{j}$ be the upper bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$. Let $\mathbf{j}$ be the lower bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$.

[0030] According to one embodiment of this application, the preset speed planning cost function is:

[0031]

[0032] in, Let J(j,k+1) be the cost function from the current state to the next state, and let J(j,k+1) be the cost from the next state to the final state.

[0033] The predictive cruise control method for centralized vehicle platooning based on cloud control proposed in this application obtains the real-time location of the vehicle platoon and the status information of the lead vehicle based on the received predictive cruise control request. The optimal reference speed curve of the vehicle platoon is determined based on the real-time location, lead vehicle status information, a preset fuel consumption model, and a preset dynamics model. This curve is then used as input to a centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon, which is then sent to the vehicle platoon for cruise control. Therefore, by combining the advantages of the cloud, this method solves the problems of limited prediction range and insufficient information acquisition capabilities in existing vehicle platoon predictive cruise control, achieving safety, economy, and efficiency in platoon predictive cruise control. It also mitigates the problem of gradually increasing energy consumption of following vehicles due to platooning, alleviates the computational pressure on the vehicle end, and enhances the platoon's energy-saving capabilities and the system's safety and stability.

[0034] To achieve the above objectives, a second aspect of this application provides a predictive cruise control device for a centralized vehicle platoon based on cloud control, comprising:

[0035] The receiving module is used to receive predictive cruise control requests from the vehicle convoy;

[0036] The acquisition module is used to acquire, based on the predictive cruise control request, the real-time location of the vehicle in the vehicle platoon and the status information of the lead vehicle; and

[0037] The control module is used to determine the optimal reference speed curve of the vehicle convoy based on the real-time location, the status information of the navigator vehicle, a preset fuel consumption model, and a preset dynamics model. The optimal reference speed curve is then used as input to a centralized convoy speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle convoy. The optimal driving speed is then sent to the vehicle convoy to perform cruise control on the vehicle convoy based on the optimal driving speed.

[0038] According to one embodiment of this application, the control module is specifically used for:

[0039] The road gradient information ahead of the vehicle convoy is determined based on the real-time location.

[0040] Based on the preset dynamic model, the longitudinal force on each vehicle in the vehicle convoy is calculated according to the road slope information ahead of the vehicle convoy and the status information of the lead vehicle.

[0041] The lead vehicle of the vehicle convoy is divided into states within the planning period. Based on the division results and the preset speed planning cost function, the optimal reference speed curve of the vehicle convoy is determined according to the preset fuel consumption model and the longitudinal force on each vehicle in the vehicle convoy.

[0042] According to one embodiment of this application, the control module is specifically used for:

[0043] Based on a preset expansion strategy, the optimal reference speed curve is expanded upwards and downwards to obtain the expanded optimal reference speed curve.

[0044] Based on the preset queue control error model and the preset queue system constraint model, the optimal reference speed curve after expansion is optimized and solved, and the solution is linearly interpolated to obtain the optimal driving speed.

[0045] According to one embodiment of this application, the preset fuel consumption model is:

[0046]

[0047] Where, ξ i,j The parameters T are fitted to the preset fuel consumption model. tq n is the vehicle engine torque, and n is the vehicle engine speed.

[0048] According to one embodiment of this application, the preset dynamic model is:

[0049]

[0050] Where, m i For the quality of the vehicle, For the vehicle's acceleration, F e,i For engine traction, F g,i For slope resistance, F r,i For rolling resistance, F air,i This refers to air resistance.

[0051] According to one embodiment of this application, the preset queue system constraint model is as follows:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Where i represents the vehicle, and j represents time j in the planning period. Let i be the position of vehicle i. The reference speed for velocity planning at time j. Let $\mathbf{j}$ be the upper bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$. Let $\mathbf{j}$ be the lower bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$.

[0058] According to one embodiment of this application, the preset speed planning cost function is:

[0059]

[0060] in, Let J(j,k+1) be the cost function from the current state to the next state, and let J(j,k+1) be the cost from the next state to the final state.

[0061] The predictive cruise control device for a centralized vehicle platoon based on cloud control proposed in this application can acquire the real-time location of the vehicle platoon and the status information of the lead vehicle based on the received predictive cruise control request. Based on the real-time location, lead vehicle status information, a preset fuel consumption model, and a preset dynamics model, the optimal reference speed curve of the vehicle platoon is determined. This curve is then used as input to a centralized platoon speed planning algorithm for optimization to obtain the optimal driving speed of the vehicle platoon, which is then sent to the vehicle platoon for cruise control. Therefore, by combining the advantages of the cloud, it solves the problems of limited prediction range and insufficient information acquisition capabilities in existing vehicle platoon predictive cruise control, achieving safety, economy, and efficiency in platoon predictive cruise control. It also mitigates the problem of gradually increasing energy consumption of following vehicles due to platooning, alleviates the computational pressure on the vehicle end, and enhances the energy-saving capabilities of the platoon and the system's safety and stability.

[0062] To achieve the above objectives, a third aspect of this application provides a server comprising: one or more processors; a storage device for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the cloud-based centralized vehicle platoon predictive cruise control method as described in the above embodiments.

[0063] To achieve the above objectives, a fourth aspect of this application provides a computer storage medium storing a computer program that is executed by a processor to implement the cloud-based centralized vehicle platoon predictive cruise control method as described in the above embodiments.

[0064] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0065] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 A flowchart of a cloud-based centralized vehicle platoon predictive cruise control method according to an embodiment of this application;

[0067] Figure 2 This is a schematic diagram illustrating the complete system composition and working principle according to an embodiment of this application;

[0068] Figure 3 This is a schematic diagram of a vehicle-cloud centralized architecture for cloud-based centralized vehicle platoon predictive cruise control according to an embodiment of this application.

[0069] Figure 4 A schematic diagram of the dynamic model of a sub-module vehicle according to an embodiment of this application;

[0070] Figure 5 This is a schematic diagram of the state partitioning module in the speed planning algorithm of the cloud-based submodule according to an embodiment of this application;

[0071] Figure 6 This is a schematic diagram of the queue state division of the queue model module in a sub-module centralized queue speed planning algorithm according to an embodiment of this application;

[0072] Figure 7 This is a schematic diagram of the queue control error model of the queue model module in a sub-module centralized queue speed planning algorithm according to an embodiment of this application;

[0073] Figure 8 This is a control flowchart of a cloud-based centralized vehicle platoon predictive cruise control system according to an embodiment of this application;

[0074] Figure 9 This is a block diagram of a cloud-based centralized vehicle platoon predictive cruise control device according to an embodiment of this application;

[0075] Figure 10This is a schematic diagram of the server structure according to an embodiment of this application. Detailed Implementation

[0076] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0077] The predictive cruise control method and apparatus for a centralized vehicle platoon based on cloud control, according to embodiments of this application, will now be described with reference to the accompanying drawings. First, the predictive cruise control method for a centralized vehicle platoon based on cloud control, according to embodiments of this application, will be described with reference to the accompanying drawings.

[0078] Figure 1 This is a flowchart of a cloud-based centralized vehicle platoon predictive cruise control method according to an embodiment of this application.

[0079] Before introducing the predictive cruise control method for centralized vehicle platooning based on cloud control proposed in the embodiments of this application, let's briefly introduce the predictive cruise control system for centralized vehicle platooning based on cloud control involved in this method and its working principle, such as... Figure 2 As shown, the system includes: cloud control platform 1 and vehicle queue 2.

[0080] The cloud control platform 1 includes: a static road slope information module 11, a vehicle fuel consumption model 12, a vehicle dynamics model 13, a cloud-based coarse speed planning algorithm module 14, and a centralized queue speed planning algorithm module 15. Specifically, the location of the vehicle queue is determined on the map within the cloud support platform, thereby obtaining the road slope information from the static road slope information module 11; the vehicle fuel consumption model 12 represents the fuel consumption of the vehicles in the queue; the vehicle dynamics model 13, after acquiring the vehicle's status information uploaded to the cloud, calculates the longitudinal force on the highway segment where the vehicle is currently located, and injects this information into the cloud-based coarse speed planning algorithm module 14, providing the optimal reference speed curve and solution space for the subsequent centralized queue speed planning algorithm module 15; after optimization and solution by the centralized queue speed planning algorithm module 15, the optimal driving speed for the vehicle queue is planned.

[0081] Among them, the integrated centralized queue speed planning algorithm deployed in the cloud is a speed planning algorithm for the rolling distance domain.

[0082] In addition, the coarse-grained speed planning algorithm module 14 in the cloud also includes: a state partitioning module 141, a speed planning cost function module 142, and a speed solution module 143. The state partitioning module 141 is used to partition the space for speed solution within the planning period, with the speed interval serving as the upper and lower bounds of the planning and the planning stage as the planning step. The speed planning cost function module 142 is used to set the control objective of the speed planning module to achieve the economy, smoothness, and efficiency of vehicle platoon driving. The speed solution module 143 indexes the value of the cost function solved by the speed planning cost function module 142 and solves for an optimal reference speed curve. The centralized queue speed planning algorithm 15 includes: a queue model 151, an optimization problem solution space module 152 (including the optimal reference speed curve 1521 and the optimization problem constraint space 1522), a queue control error model 153, a queue system constraint module 154, a system objective function solution module 155, a planning algorithm solution module 156, and an optimal speed transmission module 157. First, it is necessary to establish the state of the vehicle queue within the planning period, obtain the optimization problem solution space module 152 obtained from the coarse speed planning algorithm in the cloud, which includes the optimal reference speed curve and the constraint space of the optimization problem, and then determine the constraint module 154 of the queue system. Under the premise of the queue model 151, the queue control error model 153 is determined according to the state of the vehicles in the queue. Combining the queue control error model 153 and the queue system constraint module 154, the system solution objective function module 155 of the centralized queue speed planning algorithm is constructed. After the optimization problem is constructed, the above optimization problem is input into the planning algorithm solution module 156 to solve for the optimal driving speed of the vehicle queue within the planning period. After interpolation and densification of the optimal driving speed, it is sent to the optimal speed sending module 157 to realize the distribution of the optimal driving speed.

[0083] Vehicle queue 2 is the actual vehicle queue, capable of communicating with the cloud and communicating (V2V) between vehicles within the queue. The communication and information processing between vehicle queue 2 and cloud control platform 1 is achieved by the onboard intelligent telematics terminal T-BOX (Telematics-BOX), equipped with GNSS (Global Navigation Satellite System) and RTK (Real Time Kinematic) positioning modules. This allows it to acquire the real-time location of vehicles and upload the information to the cloud to determine their exact location.

[0084] Furthermore, such as Figure 3 As shown, Figure 3This is a schematic diagram of a vehicle-cloud centralized architecture for cloud-based centralized vehicle platoon predictive cruise control according to an embodiment of this application. Its specific working principle is as follows:

[0085] The vehicle convoy requests predictive cruise control services from the cloud. The edge cloud acquires the real-time location of the convoy and the status information of the lead vehicle. It also obtains static traffic information (road gradient information) ahead of the convoy through a positioning module. Taking into account vehicle energy consumption, a centralized convoy speed planning algorithm in the cloud calculates the economical and stable speeds for the convoy, achieving economical, efficient, and stable operation. In the next planning cycle, the cloud again receives service requests from the convoy, and the vehicles re-upload their status, allowing for a renewed planning and control of predictive cruise control, thus forming a rolling closed-loop control system.

[0086] It should be noted that predictive cruise control for vehicle platooning is a real-time application of the cloud control system, requiring deployment on an edge cloud application platform. Through a vehicle-to-cloud gateway, information exchange between the edge cloud and the vehicle platoon is achieved via wireless communication. Uploaded information includes real-time vehicle location information and the lead vehicle's status information; downloaded information is the speed planned in the cloud. The cloud-based infrastructure platform provides road gradient information, and the cloud application platform deploys vehicle fuel consumption models, vehicle dynamics models, platoon control error models, and cloud-based coarse-grained speed planning algorithm modules and centralized platoon speed planning algorithm modules.

[0087] Specifically, such as Figure 1 As shown, this cloud-based, centralized vehicle platoon predictive cruise control method includes the following steps:

[0088] In step S101, a predictive cruise control request from the vehicle queue is received.

[0089] Understandably, a vehicle queue can request predictive cruise control services from the cloud, and the cloud can execute subsequent control operations after receiving the predictive cruise control request from the vehicle queue.

[0090] In step S102, based on the predictive cruise control request, the real-time location of the vehicle convoy and the status information of the lead vehicle are obtained.

[0091] Specifically, based on the predictive cruise control requests issued by the vehicle convoy, the edge cloud can obtain the real-time location of the vehicle convoy and the status information of the lead vehicle, including its position, speed, and acceleration.

[0092] In step S103, the optimal reference speed curve of the vehicle platoon is determined based on the real-time location, the status information of the navigator vehicle, the preset fuel consumption model, and the preset dynamics model. The optimal reference speed curve is then used as the input of the centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon. The optimal driving speed is then sent to the vehicle platoon to perform cruise control on the vehicle platoon based on the optimal driving speed.

[0093] It is understood that, in the embodiments of this application, based on a preset fuel consumption model (i.e. Figure 2 The vehicle fuel consumption model 12) can comprehensively consider the vehicle's energy consumption, and combine the real-time position of the vehicle convoy, the status information of the lead vehicle, and the preset dynamics model (i.e., Figure 2 The vehicle dynamics model 13) is solved by the coarse speed planning algorithm in the cloud. The optimal reference speed curve of the vehicle queue can be determined. The optimal reference speed curve is used as the input of the centralized queue speed planning algorithm for optimization. The optimal driving speed of the vehicle queue can be obtained and sent to the vehicle queue so as to perform cruise control of the vehicle queue according to the optimal driving speed.

[0094] Furthermore, in some embodiments, determining the optimal reference speed curve of the vehicle convoy based on real-time location, navigator vehicle status information, a preset fuel consumption model, and a preset dynamics model includes: determining the road slope information ahead of the vehicle convoy based on the real-time location; calculating the longitudinal force on each vehicle in the convoy based on the road slope information ahead of the convoy and the navigator vehicle status information, according to the preset dynamics model; dividing the navigator vehicle of the convoy into states within the planning period, and determining the optimal reference speed curve of the vehicle convoy based on the division results and a preset speed planning cost function, according to the preset fuel consumption model and the longitudinal force on each vehicle in the convoy.

[0095] Specifically, based on the real-time location of the vehicle convoy, the cloud-based system uses a map positioning module to determine the static road information ahead of the convoy. The road gradient information and the state information of the lead vehicle (i.e., all other vehicles in the convoy) are used as inputs to the cloud-based speed planning module. Combined with a pre-defined dynamics model, the module calculates the longitudinal force acting on each vehicle in the convoy. The state partitioning module partitions the state of the lead vehicle within the planning period and, based on the partitioning results and a pre-defined speed planning cost function (i.e., ... Figure 2 The speed planning cost function module 142 in the system determines the optimal reference speed curve of the vehicle queue based on the preset fuel consumption model and the longitudinal force on each vehicle in the vehicle queue.

[0096] Furthermore, in some embodiments, the optimal reference speed curve is used as input to a centralized queue speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle queue. This includes: expanding the optimal reference speed curve upwards and downwards based on a preset expansion strategy to obtain an expanded optimal reference speed curve; optimizing and solving the expanded optimal reference speed curve based on a preset queue control error model and a preset queue system constraint model, and performing linear interpolation on the solution results to obtain the optimal driving speed.

[0097] Specifically, embodiments of this application can expand the optimal reference speed curve upwards and downwards based on a preset expansion strategy. The optimal driving speed planning algorithm also needs to comprehensively consider a preset dynamics model, a preset fuel consumption model, and a preset queue control error model (i.e., Figure 2 The queue control error module 153 and the preset queue system constraint model (i.e. Figure 2 The queue system constraint module 154 in the system optimizes the optimal reference speed curve after expansion based on a preset queue control error model and a preset queue system constraint model. The cloud-based coarse speed planning algorithm provides the solution space for the optimization problem and the optimal reference speed curve for the centralized queue speed planning algorithm. Together, they constitute the cloud-based speed planning algorithm. The cloud-based speed planning algorithm performs linear interpolation on the planned speed of the vehicle queue within the planning period to obtain the optimal driving speed, and then sends the optimal driving speed to the vehicle queue to realize cloud-supported predictive cruise control.

[0098] It should be noted that, in order to offset the uncertainty of the vehicle convoy's movement, the vehicles in the convoy only need to execute the optimal driving speed planned in the first stage of the planning cycle, and abandon the optimal driving speed planned in subsequent stages of the planning cycle.

[0099] In some embodiments, the preset fuel consumption model is:

[0100]

[0101] Where, ξ i,j T is the parameter fitted to the preset fuel consumption model. tq n is the vehicle engine torque, and n is the vehicle engine speed.

[0102] In some embodiments, the preset dynamic model is as follows:

[0103]

[0104] Where, m i For the quality of the vehicle, For the vehicle's acceleration, F e,i For engine traction, Fg,i For slope resistance, F r,i For rolling resistance, F air,i This refers to air resistance.

[0105] Furthermore, in some embodiments, the preset queue system constraint model is as follows:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Where i represents the vehicle, and j represents time j in the planning period. Let i be the position of vehicle i. The reference speed for velocity planning at time j. Let $\mathbf{j}$ be the upper bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$. Let $\mathbf{j}$ be the lower bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$.

[0112] Furthermore, in some embodiments, the preset speed planning cost function is:

[0113]

[0114] in, Let J(j,k+1) be the cost function from the current state to the next state, and let J(j,k+1) be the cost from the next state to the final state.

[0115] To facilitate those skilled in the art to further understand the predictive cruise control method for centralized vehicle platooning based on cloud control proposed in the embodiments of this application, the following detailed description of the specific implementation method is provided.

[0116] First, it is necessary to determine the vehicle-to-cloud communication between the vehicle control platform and the vehicle queue, as well as the communication within the vehicle queue. After combining with the cloud platform, the communication topology of the queue needs to take into account the actual needs and applications of vehicle queue and cloud communication. This application adds cloud nodes to the traditional vehicle queue to change the traditional communication topology. For existing queue communication topologies, such as PF, PLF, etc., cloud nodes are added.

[0117] Furthermore, the cloud control platform includes a cloud control infrastructure platform and a cloud control application platform. The cloud control infrastructure platform provides basic hardware and software platforms, standard components for perception fusion and decision-making, and can provide basic hardware, software and information support for the cloud control application platform.

[0118] The static road slope information module can locate the position of the vehicle convoy after obtaining the position information of the lead vehicle. The cloud data support platform provides the slope information of the road ahead of the vehicle's position and sends it to the cloud coarse speed planning algorithm module of the cloud control application platform as static traffic information for the predictive cruise algorithm.

[0119] The vehicle's fuel consumption model is based on the general characteristic data of a certain commercial vehicle model. The relationship between engine speed, torque and fuel consumption rate is fitted according to the actual engine fuel consumption model, as shown in equation (1).

[0120] Wherein, the fitting parameter ξ i,j Subsequently, the polynomial fuel consumption model can be updated and modified based on the different engine fuel consumption model data of different vehicle models, and then updated and deployed on the application platform of the cloud control system.

[0121] The vehicle's dynamics model can comprehensively analyze the longitudinal forces acting on the vehicle based on the characteristics of the engine model. This requires considering vehicle power (i.e., engine traction), air resistance (i.e., wind resistance), rolling resistance, and gradient resistance. Due to the mass and power characteristics of commercial vehicles, the impact of road gradient on longitudinal dynamics also needs to be considered, thus incorporating speed optimization into the optimization process.

[0122] Vehicle dynamics model analysis, such as Figure 4 As shown, the longitudinal force analysis of the vehicle is performed, and the dynamic model of vehicle i is shown in equation (2).

[0123] Based on the above, the vehicle's net torque T can be obtained. e The relationship with the desired acceleration is as follows:

[0124]

[0125] Where vehicle i travels on a road with an inclination angle of θ, g is the acceleration due to gravity, and c r ρ is the rolling resistance coefficient. a C is the density of air. d A is the air drag coefficient. F V is the frontal windward area of ​​a vehicle. wind For wind speed, I e I is the rotational inertia of the engine. w R is the moment of inertia of the tire. P For the gear ratio, It Let r be the rotational inertia of the turbine. eff The effective radius of the tire. For the desired acceleration, V x This represents the vehicle's longitudinal speed.

[0126] The above describes the process of establishing a vehicle dynamics model. After obtaining the state feedback of the vehicle convoy, the dynamics model can calculate the longitudinal forces acting on the vehicles in the cloud, and then consider the road gradient, i.e., the impact of gradient resistance on vehicle operation.

[0127] The cloud-based coarse speed planning algorithm module can comprehensively solve the economic speed of vehicle operation by integrating road gradient information, vehicle fuel consumption model, and vehicle dynamics model. This algorithm module is a dynamic programming algorithm in the rolling distance domain. This part of the algorithm can solve the problem of queuing economic driving in cloud-based queuing predictive cruise control system, and provide a reference speed and optimization problem solution space for subsequent centralized queuing speed planning algorithms.

[0128] The state partitioning module can partition the state of the lead vehicle in the vehicle platoon within the planning cycle. The specific partitioning is as follows: Figure 5 As shown, the acquired road gradient information of X km is divided into sections, where the planning period is divided into N sections. P The process involves several stages, including velocity planning within the planning period. This includes defining the state points within the planning period using the dynamic programming algorithm, and optimizing the prediction region of the planning period. The prediction region is divided into N parts based on the same spacing. P The optimization problem is decomposed into several sub-problems for solution. Spatially, the velocity optimization problem is divided into stages for solution, with the interval of each planning point being ΔS. RDP At each planning point, the algorithm plans at its maximum speed. and minimum speed For the interval, the speed interval is set to... The dynamic programming process is divided into states based on the speed range, with the speed range serving as the upper and lower bounds of the planning process. The planning stages are used as the steps in the planning process to divide the state space. The entire planning path is also divided. The planning algorithm is designed as a rolling distance domain dynamic programming algorithm, which executes only the first step of the planning process at a time and then restarts the planning of the speed for the next stage. Figure 5 The stages shown are stage1, stage2...stageN. This completes the state space partitioning of the navigator vehicle within the planning cycle using the dynamic programming algorithm.

[0129] The velocity planning cost function module can set the transition cost between two state points, as shown in equation (10) for position point P. (j,h)To location point P (j,h+1) The formula for the transition of vehicle speed state between state points is:

[0130]

[0131] Where ΔS represents the interval between two state points. Indicates in P (j，h) speed, Indicates in P (j，h+1) The speed at which the state transition function is determined.

[0132] Based on the state space divided by dynamic programming, the speed planning cost function is set as shown in Equation (8). This ensures that the state point to the final state is also optimal, that is, it guarantees the global optimality of the state transition.

[0133] The cost function from one state point to the next state point is β, where the cost function is shown in equation (11):

[0134]

[0135]

[0136] Among them, W cost_fuel W is a penalty factor for the fuel consumption optimization term during operation, which ensures fuel economy during vehicle operation. cost_ref W is a penalty factor for the optimization term that accounts for the deviation between the planned speed and the reference speed, limiting the planned speed from deviating excessively from the set reference speed. cost_Δv W is a penalty factor for velocity changes between states, used to avoid large velocity fluctuations. cost_Δa This is a penalty factor for acceleration changes, used to avoid excessive acceleration fluctuations.

[0137] The velocity solution module can index the value of the cost function of the last state based on the solution of the state transition cost, determine whether it is the minimum global cost, solve for a minimum velocity sequence (i.e. the optimal reference velocity curve), and send the velocity curve to the optimization problem solution space module.

[0138] When performing centralized queuing predictive cruise control based on cloud control, a queuing model needs to be established for the centralized queuing speed planning algorithm. The queuing model is the establishment of the queuing state model within the planning period. Establishing the queuing state model requires first completing the system description of a single vehicle within the planning period. For example, the connection curve used for the state transition of a vehicle from state k to k+1 is a piecewise acceleration curve. It is assumed that the acceleration of the vehicle's motion between the two state points, i.e., the impact degree of the vehicle, is constant. The variables are defined as follows:

[0139] Vehicles in planning period N P Discrete time points are: Where N P ∈Z + t0 represents the initial state time of the planning period, t i This indicates the time at step i within the planning period;

[0140] The time increment for vehicle state transition is: dt i =t i -t i-1 i∈[0,N p -1];

[0141] Vehicle at t i State at any given moment: Position s i ∈R, velocity acceleration i∈[0,N p ];

[0142] The segmented acceleration of the vehicle during state transition is: i∈[0,N p -1];

[0143] During the segmented acceleration motion of the vehicle, the coefficient matrix and control matrix are derived based on the vehicle state transition matrix described above. The state relationship of vehicle i from stage k to stage k+1 is described as follows:

[0144]

[0145]

[0146]

[0147] At this point, the coefficient matrix of the system control can be obtained as follows:

[0148]

[0149] The control matrix of the system state transition equation is:

[0150]

[0151] The state division within the planning period: the vehicle's state (including position, velocity, and acceleration) is considered as the state variables of the vehicle system. Within one planning period, this application can define a planning period as N. p In this stage, we will model the vehicles and vehicle queues. The state of the vehicle queues during the planning period is as follows: Figure 6 As shown, the vehicle status and queue status are marked at the beginning of the planning cycle and at stage n.

[0152] The time within the planning period is discretized as follows:

[0153] This indicates that the i-th (i = 1, 2, ..., n) car in the queue is in the j-th (j = 1, 2, ..., k, ..., N) car. p The state during the planning cycle,

[0154] This represents the state of the i-th (i = 1, 2, ..., n) vehicle in the queue at the initial time.

[0155] This indicates that the i-th (i = 1, 2, ..., n) car in the queue is in the j-th (j = 1, 2, ..., k, ..., N) car. p The coefficient matrix during the planning period;

[0156] This indicates that the i-th (i = 1, 2, ..., n) car in the queue is in the j-th (j = 1, 2, ..., k, ..., N) car. p Control matrix during the planning cycle;

[0157] This indicates that the i-th (i = 1, 2, ..., n) car in the queue is in the j-th (j = 1, 2, ..., k, ..., N) car. p Control quantities during the planning cycle.

[0158] At this point, the state transition equation for vehicle i during the planning period from k to k+1 can be expressed as:

[0159]

[0160] in, Let be the coefficient matrix of vehicle i at time k. Let be the system control matrix for vehicle i at time k.

[0161] After the vehicle system modeling is completed, this application can define the state of the vehicle queue. Within a cycle, the queue system model is built, and the state of the vehicle queue is defined as follows:

[0162] This indicates that the queue is in position j (j = 1, 2, ..., k, ..., N). p The state during the planning cycle;

[0163] This indicates the initial state of the queue;

[0164] This indicates that the queue is in position j (j = 1, 2, ..., k, ..., N). p The coefficient matrix during the planning period;

[0165] This indicates that the queue is in position j (j = 1, 2, ..., k, ..., N). p Control matrix during the planning cycle;

[0166] This indicates that the queue is in position j (j = 1, 2, ..., k, ..., N). p Control quantities during the planning cycle.

[0167] At this point, the state transition equation for the vehicle queue at planning period k+1 can be expressed as:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174] Equation (18-a) describes the vehicle queue state at time k+1, which includes the state of all vehicles at time k+1; Equation (18-b) describes the vehicle queue state at time k, which includes the state of all vehicles at time k; Equation (18-c) describes the control input of the vehicle queue at time k, which includes the control input of all vehicles at time k; Equation (18-d) describes the coefficient matrix of the vehicle queue system transition at time k; and Equation (18-e) describes the control matrix of the queue system transition at time k. These formulas can be used as the transition equations for the vehicle queue from stage k to stage k+1. Similarly, the transition equations for the vehicle queue during the planning period N can be used. p Description of all states within.

[0175] The optimization problem-solving space module serves as the solution space opened up by the cloud-based coarse-speed planning algorithm module for the centralized queue speed planning algorithm module. Its purpose is to (1) reduce the solution space for the subsequent queue speed planning algorithm through the dynamic planning algorithm of the cloud-based coarse-speed planning algorithm module, thereby reducing the amount of computation of the algorithm; (2) solve the problem of non-global optimal solution caused by the coarseness of state division in the dynamic planning of the cloud-based coarse-speed planning algorithm module through the re-solution of the centralized queue speed planning algorithm module, thereby reducing the amount of computation of the centralized queue speed planning algorithm while ensuring the optimality of the algorithm.

[0176] The optimal reference speed curve is the optimal speed sequence obtained by the cloud-based coarse speed planning algorithm module. This speed sequence serves as the optimal reference speed curve for the subsequent centralized queue speed planning algorithm.

[0177] The constraint space of the optimization problem is the optimal reference speed curve obtained by the cloud-based coarse speed planning algorithm module. This curve is then expanded upwards and downwards to serve as the solution space for subsequent optimization problems. This constraint space can greatly reduce the algorithmic computation workload of the centralized queue speed planning algorithm module.

[0178] The queue control error model is used to solve for the speed error and spacing error of vehicles in the queue. The positional relationship of vehicles in the queue, that is, the relationship between vehicle i-1 and vehicle i in the queue, is as follows: Figure 7 As shown, where, and Let i and j represent the positions of vehicle i-1 and vehicle i respectively during stage j of the planning period. and These represent the speeds of vehicle i-1 and vehicle i respectively during stage j of the planning period. This represents the expected distance between vehicles at the current stage, while This indicates the actual positional relationship between the vehicles.

[0179] The desired queue spacing adopts a constant time headway (CTH) strategy, which is a commonly used spacing strategy in existing queue studies and can improve the stability of queue movement. Based on this spacing strategy, the desired queue spacing strategy is as follows:

[0180]

[0181] Where h represents the time interval coefficient and r represents the stationary distance of the vehicle. Since the queue is a homogeneous queue, this parameter applies to all vehicles in the queue.

[0182] The actual vehicle spacing is:

[0183]

[0184] At this point, the distance error between the vehicle queues It can be represented as:

[0185]

[0186] In the control of this application embodiment, the spacing error between vehicles in the queue tends to be 0. When designing the queue system, the stationary distance r of the vehicles can be set to 0m, and the speed error of the queue in stage j, i.e., the deviation between the speed transmitted from the cloud and the actual speed of vehicle i in the queue, can be set as follows:

[0187]

[0188] in, The queue velocity at time j is dynamically planned for the rolling distance domain.

[0189] The queue system constraint module is for considering the constraints of the system in actual operation. The system constraints mainly include equality constraints of system state transition and inequality constraints of system state. The system state transition constraints of queue vehicle i in the speed planning process are shown in equations (3) to (7).

[0190] Equation (3) represents the positional relationship constraint of the vehicles; Equation (4) represents the optimization solution space opened up by the cloud coarse speed planning algorithm; Equation (5) represents the queue speed constraint, which ensures that the optimal queue speed obtained by the combined queue optimal speed and queue characteristic algorithm is within the set range; Equation (6) represents the queue acceleration constraint, which is to ensure that the acceleration of the queue vehicles is consistent with the actual acceleration of the vehicles; and the impact constraint is set to ensure the smoothness of the queue driving process. The smoothness of commercial vehicle driving is a very important control indicator, and Equation (7) represents the impact constraint.

[0191] The objective function of the system's cloud-based centralized platoon predictive cruise control module aims to achieve safety, energy efficiency, and high efficiency in platoon driving, as well as smoothness and stability of platoon driving. Stability includes consideration of speed and spacing errors in the platoon driving. Based on these platoon cruise control objectives, the cost function of the cloud-based centralized platoon predictive cruise control is defined as shown in equation (23), the spacing error of the platoon vehicles is shown in equation (24), and the speed error of the platoon vehicles is shown in equation (25).

[0192]

[0193]

[0194]

[0195] In equation (23), the first term ω of the cost function cost_ref As a penalty factor for the deviation from the reference speed calculated by the dynamic programming algorithm, this term uses the reference optimal speed solved by dynamic programming as the expected speed of the vehicles in the queue, which is the solved economic speed; the second term ω cost_a This is a penalty factor for the acceleration cost of vehicles in a platoon. This term is set to limit excessive vehicle acceleration, as excessive acceleration will lead to increased energy consumption and uneven control; the third term ω cost_jerk The first term is the penalty factor for vehicle ride comfort, as ride comfort is a crucial indicator for commercial vehicles during operation. This term is used to ensure the ride comfort of vehicles in a platoon. The fourth term is the platoon spacing error cost term in the platoon stability index, ω. cost_err_s As the penalty factor for the queue spacing error term, E is set according to the CTH spacing strategy model mentioned above. s (i) represents the vehicle spacing error cost term in the queue; the fifth term represents the queue speed error cost term in the queue stability index, ω. cost_err_v This is a penalty factor for the queuing speed error term, which represents the speed error between the vehicles in front and behind in the queuing. E is set to... v (i) represents the speed error cost term of the queue.

[0196] The planning algorithm solution module is based on the cost function formulated by equations (23) to (25) of the system objective function solution module and the system constraints of equations (3) to (7) of the queue system constraint module. It completes the solution of the cloud-based integrated queue speed and queue characteristic algorithm by transforming the optimization problem into a quadratic programming (QP) solution.

[0197] The quadratic programming problem satisfies the following equation:

[0198]

[0199]

[0200] Where f(x) is the objective function of the system solution, satisfying the following equality and inequality constraints, and the minimum value of the function needs to be found under these constraints. Where A is the constraint matrix of the inequalities, and equations (3) to (7) need to be transformed into matrix A; A eq Let b be the equality constraint matrix. eqEquations (17) and (18) need to be converted into system equation constraints, while ub and lb are the upper and lower limit coefficient matrix constraints of the system. Their range is the optimization solution space of dynamic programming constraints. By converting the cost function of the cloud-based centralized queue predictive cruise control into a quadratic programming function of f(x), the optimization problem of the centralized queue speed planning algorithm can be constructed and solved.

[0201] The optimal speed curve obtained by the optimal speed transmission module and the joint planning algorithm solution module is the discrete speed information of the road points corresponding to the vehicle convoy ahead. The discrete road points depend on the road points divided within the planning period. Considering the computational load of the cloud algorithm and actual needs, this application uses discrete road points with a road point interval of ΔS. RDP To ensure real-time control, linear interpolation is required for the planned speeds of adjacent road points. This interpolation process is used to calculate the speeds corresponding to road points between planned road points, resulting in a more densely packed speed planning curve. This allows the vehicle queue to find the optimal driving speed corresponding to the interpolated point based on its current location at each planned road point.

[0202] The vehicle queue can receive speed control commands from the cloud control platform to achieve economic and stability control of the queue. At the start of the planning cycle, it feeds back its own status to the cloud control platform to re-plan the vehicle queue speed. In addition, the vehicle queue also includes the specific actuator systems of the vehicles.

[0203] In summary, the control flowchart of a cloud-based hierarchical vehicle platooning predictive cruise control system according to one embodiment of this application is as follows: Figure 8 As shown, it includes the following steps:

[0204] Step S801, determine Is the condition met? If it is met, proceed to step S802; otherwise, proceed to step S815.

[0205] Step S802: Obtain vehicle location from the cloud.

[0206] Step S803: Obtain the slope information Xkm ahead of the vehicle queue.

[0207] Step S804: Determine if X < S is true. If true, proceed to step S805; otherwise, end the process.

[0208] Step S805: Divide the vehicle queue into states within the planning period.

[0209] Step S806: Generate the DP state space.

[0210] Step S807: Solve for the state transition cost function.

[0211] Step S808: Solve for the optimal velocity sequence in DP.

[0212] Step S809: Generate the optimal reference velocity curve.

[0213] Step S810: Generate the system constraint space.

[0214] Step S811: Solve the QP optimization problem.

[0215] Step S812: Generate the optimal control sequence (i.e., optimal driving speed) for the vehicle queue.

[0216] Step S813: Send to vehicle queue control.

[0217] Step S814, proceed to the next rolling planning cycle (return to step S801).

[0218] Step S815, determine Is the condition met? If it is met, proceed to step S816; otherwise, proceed to step S817.

[0219] Step S816,

[0220] Step S817,

[0221] Therefore, the predictive cruise control method for a centralized vehicle platoon based on cloud control proposed in this application embodiment (1) proposes a centralized platoon predictive cruise control system based on the cloud control system. The cloud control platform's beyond-line-of-sight perception integrates the full range of static maps and the dynamic traffic flow information perceived by numerous roadside devices, providing the vehicle platoon predictive cruise control system with beyond-line-of-sight information, namely static map information. Combined with the real-time and historical data of road traffic available in the cloud, it can not only achieve wide-area / long-term perception, but also perform rapid real-time planning and decision calculations, which can greatly alleviate the computing pressure on the vehicle end and further improve the energy saving and system safety and stability boundary capabilities of the vehicle platoon; (2) vehicle-cloud communication method and specific implementation algorithm. Compared with the traditional platoon predictive cruise control, it reduces the design of the vehicle-cloud communication structure combined with the cloud, and designs a planning control algorithm under the rolling distance domain to offset the uncertainty of future vehicle platoon operation. Under this control architecture, the cloud can comprehensively plan the optimal driving speed of the vehicle platoon based on the current state of the vehicle platoon. By unifying the planning of all vehicle states in the vehicle queue, the advantages of the vehicle queue can be further released; (3) The vehicle queue uploads its own state information to the cloud. The cloud determines the static road information in front of the queue through the map positioning module based on the vehicle state information, target speed information and location information fed back by the navigator. The road slope information, vehicle state information fed back by the navigator, vehicle dynamics model, vehicle fuel consumption model and system constraint model in the static road information are used as inputs for cloud speed planning. After the dynamic planning algorithm of the rolling distance domain is used to solve, the optimal reference speed curve for the vehicle queue is obtained. This speed curve is used as the reference speed curve for the centralized queue speed planning algorithm. The solution of the waypoints and the corresponding optimal driving speed within the planning period is used as the reference for subsequent algorithm solutions. The upper and lower boundaries are expanded to open up the optimization solution space for subsequent algorithms. The cloud-based coarse-grained speed planning algorithm uses the optimal reference speed curve as input to the centralized queue speed planning algorithm. This algorithm also needs to consider the state of all vehicles in the queue during the current planning period, integrate the position, speed, and acceleration information of the vehicles in the queue, and, under the constraint of vehicle dynamics, transform the problem into a quadratic programming problem to solve, thereby finding the optimal control sequence of the queue within the planning period. The optimal control sequence includes the optimal control sequence of the vehicles at each time point within the discrete planning period. The optimal waypoint sequence and speed sequence are then distributed to control all vehicles in the queue, realizing the speed planning and control of the queue within a planning period.

[0222] The predictive cruise control method for centralized vehicle platooning based on cloud control proposed in this application obtains the real-time location of the vehicle platoon and the status information of the lead vehicle based on the received predictive cruise control request. The optimal reference speed curve of the vehicle platoon is determined based on the real-time location, lead vehicle status information, a preset fuel consumption model, and a preset dynamics model. This curve is then used as input to a centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon, which is then sent to the vehicle platoon for cruise control. Therefore, by combining the advantages of the cloud, this method solves the problems of limited prediction range and insufficient information acquisition capabilities in existing vehicle platoon predictive cruise control, achieving safety, economy, and efficiency in platoon predictive cruise control. It also mitigates the problem of gradually increasing energy consumption of following vehicles due to platooning, alleviates the computational pressure on the vehicle end, and enhances the platoon's energy-saving capabilities and the system's safety and stability.

[0223] Next, referring to the accompanying drawings, a predictive cruise control device based on cloud control and a centralized vehicle platoon, according to an embodiment of this application, is described.

[0224] Figure 9 This is a block diagram of a cloud-based centralized vehicle platoon predictive cruise control device according to an embodiment of this application.

[0225] like Figure 9 As shown, the cloud-based centralized vehicle platoon predictive cruise control device 10 includes: a receiving module 100, an acquisition module 200, and a control module 300.

[0226] The receiving module 100 is used to receive predictive cruise control requests from the vehicle convoy.

[0227] The acquisition module 200 is used to acquire real-time location information of the vehicle platoon and the status information of the lead vehicle based on predictive cruise control requests; and

[0228] The control module 300 is used to determine the optimal reference speed curve of the vehicle platoon based on the real-time location, the status information of the navigator vehicle, the preset fuel consumption model and the preset dynamic model, and to use the optimal reference speed curve as the input of the centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon, and to send the optimal driving speed to the vehicle platoon so as to perform cruise control of the vehicle platoon based on the optimal driving speed.

[0229] Furthermore, in some embodiments, the control module 300 is specifically used for:

[0230] Determine the road gradient information ahead of the vehicle convoy based on real-time location;

[0231] Based on the preset dynamic model, the longitudinal force on each vehicle in the vehicle platoon is calculated according to the road slope information ahead of the vehicle platoon and the status information of the lead vehicle.

[0232] The lead vehicle in the vehicle platoon is divided into states within the planning period. Based on the division results and the preset speed planning cost function, the optimal reference speed curve of the vehicle platoon is determined according to the preset fuel consumption model and the longitudinal force on each vehicle in the platoon.

[0233] Furthermore, in some embodiments, the control module 300 is specifically used for:

[0234] Based on the preset expansion strategy, the optimal reference speed curve is expanded upwards and downwards to obtain the expanded optimal reference speed curve.

[0235] Based on the preset queue control error model and the preset queue system constraint model, the optimal reference speed curve after expansion is optimized and solved, and the solution is linearly interpolated to obtain the optimal driving speed.

[0236] Furthermore, in some embodiments, the preset fuel consumption model is:

[0237]

[0238] Where, ξ i,j T is the parameter fitted to the preset fuel consumption model. tq n is the vehicle engine torque, and n is the vehicle engine speed.

[0239] Furthermore, in some embodiments, the preset dynamic model is:

[0240]

[0241] Where, m i For the quality of the vehicle, For the vehicle's acceleration, F e,i For engine traction, F g,i For slope resistance, F r,i For rolling resistance, F air,i This refers to air resistance.

[0242] Furthermore, in some embodiments, the preset queue system constraint model is as follows:

[0243]

[0244]

[0245]

[0246]

[0247]

[0248] Where i represents the vehicle, and j represents time j in the planning period. Let i be the position of vehicle i. The reference speed for velocity planning at time j. Let $\mathbf{j}$ be the upper bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$. Let $\mathbf{j}$ be the lower bounds of the constraints on the position, velocity, acceleration, and jerk of vehicle $i$ at time $j$.

[0249] Furthermore, in some embodiments, the preset speed planning cost function is:

[0250]

[0251] in, Let J(j,k+1) be the cost function from the current state to the next state, and let J(j,k+1) be the cost from the next state to the final state.

[0252] It should be noted that the foregoing explanation of the predictive cruise control method embodiment for cloud-based centralized vehicle platoons also applies to the predictive cruise control device for cloud-based centralized vehicle platoons in this embodiment, and will not be repeated here.

[0253] According to the cloud-based centralized vehicle platoon predictive cruise control device proposed in this application, the real-time position of the vehicle platoon and the status information of the lead vehicle can be obtained based on the received predictive cruise control request. Based on the real-time position, lead vehicle status information, preset fuel consumption model, and preset dynamics, the optimal reference speed curve of the vehicle platoon is determined. This curve is then used as input to a centralized platoon speed planning algorithm for optimization to obtain the optimal driving speed of the vehicle platoon, which is then sent to the vehicle platoon for cruise control. Thus, by combining the advantages of the cloud, the device solves the problems of limited prediction range and insufficient information acquisition capabilities in existing vehicle platoon predictive cruise control, achieving safety, economy, and efficiency in platoon predictive cruise control. It also mitigates the problem of gradually increasing energy consumption of following vehicles due to platooning, alleviates the computational pressure on the vehicle end, and enhances the platoon's energy-saving capabilities and the system's safety and stability.

[0254] Figure 10 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0255] The server includes a processor 1001, a storage device 1002, and a communication device 1003; the number of processors 1001 in the server can be one or more. Figure 10Taking a processor 1001 as an example; the processor 1001, storage device 1002, and communication device 1003 in the server can be connected via a bus or other means. Figure 10 Taking the bus connection method between China and Israel as an example.

[0256] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described predictive cruise control method for a centralized vehicle platoon based on cloud control.

[0257] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0258] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0259] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A predictive cruise control method for a centralized vehicle platoon based on cloud control, characterized in that, The method is applied to a predictive cruise control system for a centralized vehicle platoon based on cloud control. The system includes a cloud control platform, which comprises a preset fuel consumption model, a preset dynamics model, a cloud-based coarse speed planning algorithm module, and a centralized platoon speed planning algorithm module. The centralized platoon speed planning algorithm module includes a platoon model, an optimization problem solution space module, a platoon control error model, a platoon system constraint module, a system objective function solution module, a planning algorithm solution module, and an optimal speed transmission module. The optimization problem solution space module includes an optimal reference speed curve and an optimization problem constraint space. The method includes the following steps: Receive predictive cruise control requests from the vehicle convoy; Based on the predictive cruise control request, the real-time location of the vehicle convoy and the status information of the lead vehicle are obtained; and The optimal reference speed curve of the vehicle platoon is determined based on the real-time location, the status information of the navigator vehicle, the preset fuel consumption model, and the preset dynamics model. The optimal reference speed curve is then used as input to a centralized platoon speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle platoon. The optimal driving speed is then sent to the vehicle platoon to perform cruise control on the vehicle platoon based on the optimal driving speed. The centralized queue speed planning algorithm is integrated into the centralized queue speed planning algorithm module. The step of using the optimal reference speed curve as input to the centralized queue speed planning algorithm for optimization to obtain the optimal driving speed of the vehicle queue includes: establishing the state of the vehicle queue within the planning period; obtaining the optimization problem solution space obtained by the coarse speed planning algorithm in the cloud to determine the queue system constraints; determining the queue control error model based on the queue model and the state of the vehicles in the queue; constructing the system solution objective function by combining the queue control error model and the queue system constraints; inputting the system solution objective function into the planning algorithm solution module to obtain the optimal driving speed of the vehicle queue within the planning period; and sending the optimal driving speed after interpolation and densification to the optimal speed sending module to realize the distribution of the optimal driving speed.

2. The method according to claim 1, characterized in that, The step of determining the optimal reference speed curve for the vehicle convoy based on the real-time location, the lead vehicle status information, a preset fuel consumption model, and a preset dynamics model includes: The road gradient information ahead of the vehicle convoy is determined based on the real-time location. Based on the preset dynamic model, the longitudinal force on each vehicle in the vehicle convoy is calculated according to the road slope information ahead of the vehicle convoy and the status information of the lead vehicle. The lead vehicle of the vehicle convoy is divided into states within the planning period. Based on the division results and the preset speed planning cost function, the optimal reference speed curve of the vehicle convoy is determined according to the preset fuel consumption model and the longitudinal force on each vehicle in the vehicle convoy.

3. The method according to claim 1 or 2, characterized in that, The step of using the optimal reference speed curve as input to a centralized queue speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle queue includes: Based on a preset expansion strategy, the optimal reference speed curve is expanded upwards and downwards to obtain the expanded optimal reference speed curve. Based on the preset queue control error model and the preset queue system constraint model, the optimal reference speed curve after expansion is optimized and solved, and the solution is linearly interpolated to obtain the optimal driving speed.

4. The method according to claim 2, characterized in that, The preset fuel consumption model is as follows: ； in, The parameters are fitted to the preset fuel consumption model. This refers to the vehicle's engine torque. This refers to the vehicle's engine speed.

5. The method according to claim 2, characterized in that, The preset dynamic model is as follows: ； in, m i For the quality of the vehicle, For the vehicle's acceleration, F e,i For engine traction, F g,i For slope resistance, F r,i For rolling resistance, F air,i This refers to air resistance.

6. The method according to claim 3, characterized in that, The preset queue system constraint model is as follows: ； ； ； ； ； in, i For vehicles, j For planning cycle j time, For vehicles i Location, for j Reference speed for time-of-flight speed planning They are respectively j Time vehicle i The upper bounds of the constraints on position, velocity, acceleration, and jerk. They are respectively j Time vehicle i Lower bounds on position, velocity, acceleration, and jerk.

7. The method according to claim 3, characterized in that, The preset speed planning cost function is: ； in, Let be the cost function from the current state to the next state. The cost of moving from the next state to the final state.

8. A predictive cruise control device for a centralized vehicle platoon based on cloud control, characterized in that, The device is applied to the predictive cruise control method for a centralized vehicle platoon based on cloud control as described in any one of claims 1-7, the device comprising: The receiving module is used to receive predictive cruise control requests from the vehicle convoy; The acquisition module is used to acquire, based on the predictive cruise control request, the real-time location of the vehicle in the vehicle platoon and the status information of the lead vehicle; and The control module is used to determine the optimal reference speed curve of the vehicle convoy based on the real-time location, the status information of the navigator vehicle, a preset fuel consumption model, and a preset dynamics model. The optimal reference speed curve is then used as input to a centralized convoy speed planning algorithm to optimize and solve for the optimal driving speed of the vehicle convoy. The optimal driving speed is then sent to the vehicle convoy to perform cruise control on the vehicle convoy based on the optimal driving speed.

9. A server, characterized in that, include: One or more processors; Storage device; an application that stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the predictive cruise control method for a centralized vehicle platoon based on cloud control as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the predictive cruise control method for a centralized vehicle platoon based on cloud control as described in any one of claims 1-7.

Images