Vehicle control method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a motion path of a vehicle, and dividing the motion path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm; the target speed trajectory of the vehicle is determined based on the speed trajectory optimization constraint condition and a speed trajectory optimization model, the target speed trajectory comprises the speed, the acceleration and the traction force of each discrete road segment node, and the speed trajectory optimization model is used for solving the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint condition. Taking the speed trajectory under the condition of minimum energy consumption as a target speed trajectory, wherein the speed trajectory optimization constraint condition comprises parameters of discrete road section nodes; determining a future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory; a control strategy of the vehicle is adjusted based on the future period state of the vehicle. The cruising ability of the vehicle can be improved at least.

Description

Technical Field

[0001] The present application relates to the field of vehicle energy recovery control, and in particular, to a vehicle control method, device, electronic device and storage medium. Background Art

[0002] The rapid development of the automobile industry has brought great convenience to people's travel. At the same time, a series of problems such as energy crisis, environmental pollution, and rising vehicle use costs have also followed. In the field of smart trams, although traditional smart connected trams have made significant progress in recent years, the technical barriers of vehicle endurance have been continuously overcome, and the endurance of trams has been greatly improved, there is still a defect of low endurance. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a vehicle control method, device, electronic device and storage medium to at least improve the vehicle's endurance.

[0004] In a first aspect, the present invention provides a vehicle control method, the method comprising:

[0005] Acquire a movement path of a vehicle, and divide the movement path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm;

[0006] Determining a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes;

[0007] Determining a future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory;

[0008] The control strategy of the vehicle is adjusted based on the future time period state of the vehicle.

[0009] The method of the present application obtains the motion path of the vehicle, and divides the motion path of the vehicle into several discrete road segment nodes based on the discrete space algorithm, and then determines the target speed trajectory of the vehicle based on the speed trajectory optimization constraint and the speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, and the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment node, and then determines the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory, so as to adjust the control strategy of the vehicle based on the future time period state of the vehicle. Compared with the prior art, the present application can combine the speed trajectory optimization constraint to find the speed trajectory with the minimum energy consumption, and then determine the control strategy of the vehicle based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, and improve the endurance of the electric vehicle. At the same time, the discrete space method is used to discretize the motion control range of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model. The state equation of the intelligent tram dynamics model is reconstructed through the intelligent tram speed trajectory optimization model and applied to model predictive control. The algorithm model can make the optimal solution at every moment, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0010] In an optional embodiment, the speed trajectory optimization constraint condition further includes traffic condition information in the motion path;

[0011] The traffic condition information includes the length of the target road section, the speed limit of the target road section, the curve of the target road section, and the slope of the target road section.

[0012] This optional comfort mode may use the length of the target section, the speed limit of the target section, the curve of the target section and the slope of the target section as speed trajectory optimization constraints.

[0013] In an optional embodiment, the speed trajectory optimization constraint condition further includes characteristic information of the vehicle;

[0014] The characteristic information of the vehicle includes the mass of the vehicle, the running speed of the vehicle, the upper and lower limits of the acceleration of the vehicle, the braking characteristics of the vehicle, the train traction characteristics of the vehicle, the resistance constraint-based calculation formula of the vehicle and the additional resistance constraint calculation formula of the vehicle.

[0015] This optional implementation may use the vehicle's mass, the vehicle's running speed, the vehicle's upper and lower limits of acceleration, the vehicle's braking characteristics, the vehicle's train traction characteristics, the vehicle's resistance constraint calculation formula and the vehicle's additional resistance constraint calculation formula as one of the speed trajectory optimization constraints.

[0016] In an optional implementation manner, determining the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory includes:

[0017] Obtaining a dynamic model of the vehicle;

[0018] The current state of the vehicle and the target speed trajectory are used as outputs of the dynamics model so that the dynamics model predicts the state of the vehicle in a future period.

[0019] This optional implementation can obtain the dynamic model of the vehicle, and then can use the current state of the vehicle and the target speed trajectory as outputs of the dynamic model, so that the dynamic model can predict the state of the vehicle in future time periods.

[0020] In an optional implementation, the current state of the vehicle includes a lateral displacement of the vehicle, a heading angle of the vehicle, a driving speed of the vehicle, and a front wheel turning angle of the vehicle.

[0021] This optional implementation manner may represent the current state of the vehicle based on the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle.

[0022] In an optional implementation, the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle each correspond to a state weight.

[0023] This optional implementation manner can distinguish the importance of the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle to the output based on the state weight.

[0024] In an optional implementation, the parameters of the discrete road segment nodes include the number of the discrete road segment nodes and the length of the discrete road segment nodes, wherein the lengths of the discrete road segment nodes in the same cross section are the same.

[0025] In this optional implementation, the number of discrete road segment nodes and the length of the discrete road segment nodes can be used as parameters of the discrete road segment nodes.

[0026] In a second aspect, the present invention provides a vehicle control device, the device comprising:

[0027] An acquisition module, used for acquiring a moving path of a vehicle, and dividing the moving path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm;

[0028] a first determination module, configured to determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is configured to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes;

[0029] A second determination module is used to determine the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory;

[0030] A control module is used to adjust the control strategy of the vehicle based on the future time period state of the vehicle.

[0031] The device of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0032] In a third aspect, the present invention provides an electronic device, comprising:

[0033] processor;

[0034] A memory is configured to store machine-readable instructions, which, when executed by the processor, execute the vehicle control method as described in any one of the aforementioned embodiments.

[0035] The electronic device of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then can determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0036] In a fourth aspect, the present invention provides a storage medium storing a computer program, wherein the computer program is executed by a processor as a vehicle control method as described in any one of the aforementioned embodiments.

[0037] The storage medium of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a flow chart of a vehicle control method disclosed in an embodiment of the present application;

[0040] Figure 2 is a structural schematic diagram of a vehicle control device disclosed in an embodiment of the present application;

[0041] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0043] Embodiment 1

[0044] See also Figure 1 , Figure 1 is a flow chart of a vehicle control method disclosed in an embodiment of the present application, such as Figure 1 As shown, the method of the embodiment of the present application includes the following steps:

[0045] 101. Obtain a movement path of the vehicle, and divide the movement path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm;

[0046] 102. Determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes;

[0047] 103. Determine the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory;

[0048] 104. Adjust the vehicle control strategy based on the vehicle's future state.

[0049] The method of the embodiment of the present application obtains the motion path of the vehicle, and divides the motion path of the vehicle into several discrete road section nodes based on the discrete space algorithm, and then can determine the target speed trajectory of the vehicle based on the speed trajectory optimization constraint and the speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road section node, and the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and take the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road section node, and then can determine the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory, so as to adjust the control strategy of the vehicle based on the future time period state of the vehicle. Compared with the prior art, the present application can combine the speed trajectory optimization constraint to find the speed trajectory with the minimum energy consumption, and then can determine the control strategy of the vehicle based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, and improve the endurance of the electric vehicle. At the same time, the discrete space method is used to discretize the motion control range of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model. The state equation of the intelligent tram dynamics model is reconstructed through the intelligent tram speed trajectory optimization model and applied to model predictive control. The algorithm model can make the optimal solution at every moment, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0050] In the embodiment of the present application, the motion path of the vehicle is divided into several discrete road section nodes based on the discrete space algorithm. In this way, the speed trajectory optimization of the motion path is converted into the speed path optimization of discrete road section nodes with smaller granularity, that is, the intelligent tram speed trajectory optimization problem is converted into a multi-stage decision-making problem, which reduces the difficulty of establishing and solving the intelligent tram speed trajectory optimization model. As an example, assuming that the motion path of the vehicle is 100 meters, the prior art directly optimizes the speed trajectory of the 100-meter motion road, which will cause the establishment and solution process of the model to consider a large number of state parameters, and thus the establishment and solution of the speed trajectory optimization model are more difficult. However, the present application divides the motion path into 10 discrete road section nodes of 10 meters, and divides the overall speed path optimization into 10 speed path optimizations, so that each speed path optimization only needs to consider the state variables related to the current discrete road section node, thereby reducing the difficulty of solving, and at the same time, the construction of the speed trajectory optimization model is also easier.

[0051] In the embodiments of the present application, by predicting the state of the vehicle in future time periods, it is possible to adjust the control strategy of the vehicle in advance. For example, assuming that the vehicle is at time t0, the state of the vehicle at time t1 is predicted so that the control strategy of the vehicle can be adjusted in advance before time t1, thereby reducing the energy loss caused by the vehicle deceleration process.

[0052] In the embodiment of the present application, the vehicle may be an electric vehicle. Further, the movement path of the vehicle may refer to a navigation path for the vehicle to move from one location to another, which may be determined by navigation information.

[0053] In the embodiments of the present application, discrete space algorithms refer to a type of algorithms that operate in discrete spaces. Discrete spaces can be understood as a set of separate, discontinuous points or objects.

[0054] In the embodiment of the present application, the speed trajectory refers to the path of the vehicle's speed, acceleration, and traction changing over time during the vehicle's motion. In the embodiment of the present application, dividing the vehicle's motion path into a number of discrete road segment nodes may refer to dividing the vehicle's motion path into 10, 20, or other discrete road segment nodes.

[0055] In an embodiment of the present application, as an optional implementation, the speed trajectory optimization constraint also includes traffic condition information in the motion path. Further, the traffic condition information includes the length of the target section, the speed limit of the target section, the curve of the target section, and the slope of the target section.

[0056] This optional comfort mode can use the length of the target section, the speed limit of the target section, the curve of the target section and the slope of the target section as speed trajectory optimization constraints, and thus can adjust the vehicle speed of the smart tram based on actual traffic conditions.

[0057] In an embodiment of the present application, as an optional implementation, the speed trajectory optimization constraint condition also includes the characteristic information of the vehicle. Further, the characteristic information of the vehicle includes the mass of the vehicle, the running speed of the vehicle, the upper and lower limits of the acceleration of the vehicle, the braking characteristics of the vehicle, the train traction characteristics of the vehicle, the vehicle's resistance constraint calculation formula and the vehicle's additional resistance constraint calculation formula.

[0058] This optional implementation may use the vehicle's mass, the vehicle's running speed, the vehicle's upper and lower limits of acceleration, the vehicle's braking characteristics, the vehicle's train traction characteristics, the vehicle's resistance constraint calculation formula and the vehicle's additional resistance constraint calculation formula as one of the speed trajectory optimization constraints.

[0059] In the embodiment of the present application, as an optional implementation, step: determining the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory includes the following steps:

[0060] Obtaining a dynamic model of the vehicle;

[0061] The current state and target speed trajectory of the vehicle are taken as the output of the dynamics model so that the dynamics model can predict the state of the vehicle in the future period.

[0062] This optional implementation can obtain a dynamic model of the vehicle, and then can use the current state and target speed trajectory of the vehicle as outputs of the dynamic model, so that the dynamic model can predict the state of the vehicle in future periods.

[0063] In the embodiment of the present application, as an optional implementation manner, the current state of the vehicle includes the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle.

[0064] This optional implementation manner may represent the current state of the vehicle based on the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle.

[0065] In the embodiment of the present application, as an optional implementation manner, the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle each correspond to a state weight.

[0066] This optional implementation can distinguish the importance of the vehicle's lateral displacement, the vehicle's heading angle, the vehicle's driving speed, and the vehicle's front wheel turning angle to the output based on the state weights.

[0067] In an embodiment of the present application, as an optional implementation, the parameters of the discrete road segment nodes include the number of discrete road segment nodes and the length of the discrete road segment nodes, wherein the lengths of the discrete road segment nodes of the same section are the same.

[0068] In this optional implementation manner, the number of discrete road segment nodes and the length of the discrete road segment nodes can be used as parameters of the discrete road segment nodes.

[0069] In the embodiments of this application, as an example:

[0070] The tram movement route consists of a series of nodes. Let N be the set of all nodes, that is, N = {i, j, p, q, ...}. The entire intelligent tram movement section is divided into multiple sections. Let U be the set of all divided sections, that is, U = {(i, j), (j, p), (p, q), ...}, i, j, p,, q ∈ N, and then each section is discretized into n i,j ,n j,p ,n p,q ,...small segments, and the discrete segments on the same cross section have the same length.

[0071] Furthermore, the input parameters of the energy-saving optimization problem of the intelligent tram speed trajectory considering the actual traffic conditions include:

[0072] The length, speed limit, curve, slope, fixed speed limit value and the number of discrete sections of each section of the traffic lights on the smart tram line are known;

[0073] Vehicle mass, red light stop time, operating speed, upper and lower limits of acceleration, train traction and braking force characteristics, as well as calculation formulas for basic resistance constraints and additional resistance constraints are given.

[0074] Furthermore, the speed trajectory optimization problem of the smart tram considering the influence of traffic conditions is determined. It is necessary to determine the speed, acceleration, and traction of each discrete road section node. This problem is to solve the optimal energy-saving speed trajectory of the smart tram running on the entire road section. The goal of this speed trajectory optimization model is to minimize the energy consumption of the smart tram, that is:

[0075]

[0076] Among them, E is the actual energy consumption of the smart tram, f i,j,k is the traction force of the smart tram at a specific time k, a certain road section j and a certain position i, |L i,,j | is the total displacement length of the smart tram on the road section (i, j), ΔL i，j is the discrete segment length of the road segment (i, j).

[0077] Furthermore, the smart tram is regarded as a mass point and its running space is discretized. According to Newton's second law, the motion equation of the smart tram is:

[0078]

[0079] Among them, m(1+r)a i,,j,k It represents the acceleration of the smart tram at a certain time k, a certain road section j and a certain position i, which is determined by the vehicle's own weight, traction or braking force, basic resistance constraint and additional resistance constraint. r is the turning coefficient of the smart tram; represents the basic resistance constraint when the smart tram runs to the position (i, j, k), where (i, j, k) represents a certain section j and a certain position i at a specific time k, v i,j,k It represents the vehicle speed at a specific time k, a road section j and a position i, μ1 represents the static friction coefficient, μ2 represents the rolling resistance coefficient, μ3 represents the air resistance coefficient, m represents the mass of the tram, and g represents the acceleration of gravity. represents the additional resistance constraint when the smart tram is running. Among them, the resistance generated when the smart tram passes through a curve and a slope is independent of the vehicle speed and is expressed as and It represents the ground friction coefficient at a certain road section j and a certain position i. It refers to the additional resistance encountered when driving on a slope or a curve. This part of the resistance is independent of the vehicle speed and is mainly determined by the ground characteristics. Represents the air density coefficient at a certain road section j and a certain position i.

[0080] Furthermore, the physical kinematic relationship between the running distance, running time, speed and acceleration of the discrete-segment smart tram can be expressed as:

[0081]

[0082] ∑ΔL i,j =L i,j ;

[0083] In summary, based on the above process, the traction force under the condition of minimum energy consumption can be calculated first, and then, under the condition that the traction force under the condition of minimum energy consumption is known, the speed and acceleration can be calculated in turn based on the physical kinematic relationship between the running distance, running time, speed and acceleration, and the motion equation of the intelligent tram, so as to determine the target speed trajectory based on the speed, acceleration and traction force of each discrete road section node. It should be noted that t represents the time interval, It represents the time interval between time k and time k+1 at a certain time k, a certain road section j and a certain position i.

[0084] Furthermore, global speed trajectory tracking and adaptive control are realized based on model predictive control (MPC). That is, in order to realize global speed trajectory tracking and adaptive control of the smart tram based on the above algorithm, it is necessary to introduce model predictive control (MPC, Model Predictive Control, and redefine the dynamic model of the smart tram to control the process while satisfying the constraints. The algorithm is based on the solution of the real-time optimization problem and uses the process model to predict future behavior to achieve better control effect. Considering the single-axis vehicle model, a simplified Bicycle Model can be used. The state variables of the model include the lateral displacement (x, y) of the vehicle, the heading angle (ψ), the speed of the tram at this position (v i,j,k ), and the vehicle front wheel steering angle (δ).

[0085] The state equation of the dynamic model of the smart tram can be expressed as:

[0086]

[0087]

[0088] Where L is the vehicle wheelbase and a is the longitudinal acceleration of the vehicle.

[0089] The goal of MPC is to minimize a cost function that incorporates the weights of the vehicle state and control inputs. For global tracking and adaptive control, the objective function can be defined as:

[0090]

[0091] Among them, (x k ,y k ,ψ k ,v k ,δ k ) is the vehicle state (x) at step k ref,k ,y ref,k ,ψ ref,k ,v ref,k ,δ ref,k ) is the pre-computed target velocity trajectory at step k.

[0092] Weight term ω x ,ω y ,ω ψ ,ω v ,ω δ Used to adjust the importance of each state and control input.

[0093] At each moment in the MPC, an optimization problem is solved to determine the best control input. When a sudden disturbance occurs, the MPC can re-plan the vehicle speed while taking safety into account, adapting to the new situation by adjusting weights and constraints.

[0094] Embodiment 2

[0095] See also Figure 2 , Figure 2 is a schematic diagram of the structure of a vehicle control device disclosed in an embodiment of the present application, such as Figure 2 As shown, the device of the embodiment of the present application includes the following functional modules:

[0096] An acquisition module 201 is used to acquire a moving path of a vehicle and divide the moving path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm;

[0097] A first determination module 202 is used to determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes;

[0098] A second determination module 203, for determining the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory;

[0099] The control module 204 is used to adjust the control strategy of the vehicle based on the state of the vehicle in the future period.

[0100] The device of the embodiment of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then can determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0101] It should be noted that for other detailed descriptions of the device in the embodiment of the present application, please refer to the relevant description of the embodiment of the present application, and the embodiment of the present application will not go into details.

[0102] Embodiment 3

[0103] See also Figure 3 , Figure 3 is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application, such as Figure 3 As shown, the electronic device of the embodiment of the present application includes:

[0104] Processor 301;

[0105] The memory 302 is configured to store machine-readable instructions, which, when executed by the processor 301, execute the vehicle control method of any one of the aforementioned embodiments.

[0106] The electronic device of the embodiment of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then can determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0107] Embodiment 4

[0108] An embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor as a vehicle control method such as any one of the aforementioned implementations.

[0109] The storage medium of the embodiment of the present application can combine the speed trajectory optimization constraint conditions to find the speed trajectory with the minimum energy consumption, and then determine the vehicle control strategy based on the speed trajectory with the minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle, thereby improving the endurance of the tram. At the same time, the discrete space method is used to discretize the motion control interval of the intelligent tram, thereby converting the intelligent tram speed trajectory optimization problem into a multi-stage decision-making problem, reducing the difficulty of establishing and solving the intelligent tram speed trajectory optimization model, and re-constructing the state equation of the intelligent tram dynamics model through the intelligent tram speed trajectory optimization model for application in model predictive control. At each moment, the algorithm model can make the optimal solution, and can still respond after being disturbed and re-rationally plan the movement of the intelligent tram.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0111] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0113] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0114] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0115] The above are only embodiments of the present application and are not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Acquire a movement path of a vehicle, and divide the movement path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm; Determining a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is used to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes; Determining a future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory; The control strategy of the vehicle is adjusted based on the future time period state of the vehicle.

2. The method according to claim 1, characterized in that The speed trajectory optimization constraint condition also includes traffic condition information in the motion path; The traffic condition information includes the length of the target road section, the speed limit of the target road section, the curve of the target road section, and the slope of the target road section.

3. The method according to claim 2, characterized in that The speed trajectory optimization constraint condition also includes characteristic information of the vehicle; The characteristic information of the vehicle includes the mass of the vehicle, the running speed of the vehicle, the upper and lower limits of the acceleration of the vehicle, the braking characteristics of the vehicle, the train traction characteristics of the vehicle, the resistance constraint-based calculation formula of the vehicle and the additional resistance constraint calculation formula of the vehicle.

4. The method according to claim 1, characterized in that The determining of the future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory includes: Obtaining a dynamic model of the vehicle; The current state of the vehicle and the target speed trajectory are used as outputs of the dynamics model so that the dynamics model predicts the state of the vehicle in a future period.

5. The method according to claim 4, characterized in that The current state of the vehicle includes the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle.

6. The method according to claim 4, characterized in that The lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel turning angle of the vehicle each correspond to a state weight.

7. The method according to claim 1, characterized in that The parameter package of the discrete road segment nodes includes the number of the discrete road segment nodes and the length of the discrete road segment nodes, wherein the lengths of the discrete road segment nodes in the same section are the same.

8. A vehicle control device, characterized in that: The device comprises: An acquisition module, used for acquiring a moving path of a vehicle, and dividing the moving path of the vehicle into a plurality of discrete road segment nodes based on a discrete space algorithm; a first determination module, configured to determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration, and traction of each discrete road segment node, the speed trajectory optimization model is configured to solve the speed trajectory under the condition of minimum energy consumption based on the speed trajectory optimization constraint, and use the speed trajectory under the condition of minimum energy consumption as the target speed trajectory, and the speed trajectory optimization constraint includes the parameters of the discrete road segment nodes; A second determination module, configured to determine a future time period state of the vehicle based on the current state information of the vehicle and the target speed trajectory; A control module is used to adjust the control strategy of the vehicle based on the future time period state of the vehicle.

9. An electronic device, characterized in that: include: processor; A memory configured to store machine-readable instructions, which, when executed by the processor, execute the vehicle control method as described in any one of claims 1-7.

10. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is executed by a processor to implement the vehicle control method according to any one of claims 1 to 7.

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