Vehicle control method and device, electronic equipment and storage medium

Abstract

The application provides a vehicle control method, device, electronic equipment and storage medium, wherein the vehicle control method comprises: acquiring a motion path of a vehicle, and dividing the motion path of the vehicle into a plurality of discrete path segment nodes based on a discrete space algorithm; determining a target speed trajectory of the vehicle based on a speed trajectory optimization constraint condition and a speed trajectory optimization model, wherein the target speed trajectory comprises a speed, an acceleration and a traction of each discrete path segment node, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint condition, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, and the speed trajectory optimization constraint condition comprises parameters of the discrete path segment nodes; determining a future period state of the vehicle based on current state information of the vehicle and the target speed trajectory; and adjusting a control strategy of the vehicle based on the future period state of the vehicle. The application can at least improve the endurance of the vehicle.

Description

TECHNICAL FIELD

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

[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 come. In the field of intelligent electric vehicles, although traditional intelligent networked electric vehicles have made remarkable progress in recent years, the technical barriers of vehicle cruising have been broken, and the cruising capacity of electric vehicles has been greatly improved, but there is still a defect of low cruising capacity. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a vehicle control method, device, electronic equipment and storage medium, so as to at least improve the cruising capacity of the vehicle.

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

[0005] 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;

[0006] determining a target speed trajectory of the vehicle based on a speed trajectory optimization constraint condition and a speed trajectory optimization model, wherein the target speed trajectory comprises 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 condition, and the speed trajectory under the condition of minimum energy consumption is taken as the target speed trajectory, and the speed trajectory optimization constraint condition comprises parameters of the discrete road segment node;

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

[0008] adjusting the control strategy of the vehicle based on the future period state of the vehicle.

[0009] The method of the present application can obtain a motion path of a vehicle, divide the motion path of the vehicle into a plurality of discrete path nodes based on a discrete space algorithm, determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint condition and a speed trajectory optimization model, wherein the target speed trajectory includes a speed, an acceleration and a traction of each discrete path node, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint condition, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, the speed trajectory optimization constraint condition includes parameters of the discrete path nodes, the future period state of the vehicle can be determined based on the current state information of the vehicle and the target speed trajectory, and the control strategy of the vehicle can be adjusted based on the future period state of the vehicle. Compared with the prior art, the present application can find a speed trajectory with minimum energy consumption in combination with the speed trajectory optimization constraint condition, determine the control strategy of the vehicle based on the speed trajectory with minimum energy consumption, 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 motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the establishment and solution difficulty of the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, the algorithm model can make an optimal solution at each time, and can make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

[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 a length of a target path, a speed limit of the target path, a curve of the target path and a slope of the target path.

[0012] The optional comfort mode can take the length of the target path, the speed limit of the target path, the curve of the target path and the slope of the target path as the speed trajectory optimization constraint condition.

[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 a mass of the vehicle, a running speed of the vehicle, an upper and lower limit of the acceleration of the vehicle, a braking characteristic of the vehicle, a train traction force characteristic of the vehicle, a resistance constraint calculation formula based on the vehicle and an additional resistance constraint calculation formula of the vehicle.

[0015] The optional embodiment can take the mass of the vehicle, the running speed of the vehicle, the acceleration upper and lower limits of the vehicle, the braking characteristics of the vehicle, the train traction force characteristics of the vehicle, the resistance-based constraint calculation formula of the vehicle, and the additional resistance constraint calculation formula of the vehicle as one of the speed trajectory optimization constraints.

[0016] In the optional embodiment, the future period state of the vehicle is determined based on the current state information of the vehicle and the target speed trajectory, including:

[0017] obtaining a dynamics model of the vehicle;

[0018] taking the current state of the vehicle and the target speed trajectory as the output of the dynamics model, so that the dynamics model predicts the state of the future period of the vehicle.

[0019] The optional embodiment can obtain the dynamics model of the vehicle, and then be able to take the current state of the vehicle and the target speed trajectory as the output of the dynamics model, so that the dynamics model predicts the state of the future period of the vehicle.

[0020] In the optional embodiment, 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 steering angle of the vehicle.

[0021] The optional embodiment can 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 steering angle of the vehicle.

[0022] In the optional embodiment, the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel steering angle of the vehicle all correspond to a state weight.

[0023] The optional embodiment can distinguish the importance of the output based on the state weight of the lateral displacement of the vehicle, the heading angle of the vehicle, the driving speed of the vehicle, and the front wheel steering angle of the vehicle.

[0024] In the optional embodiment, 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 section are the same.

[0025] The optional embodiment can take the number of the discrete road segment nodes and the length of the discrete road segment nodes as the parameters of the discrete road segment nodes.

[0026] In a second aspect, the present application provides a vehicle control device, which comprises:

[0027] An acquisition module is configured to acquire a motion path of a vehicle and divide the motion path of the vehicle into a plurality of discrete path segment nodes based on a discrete space algorithm;

[0028] A first determination module is 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 comprises a speed, an acceleration and a traction of each discrete path segment node, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, and the speed trajectory optimization constraint comprises parameters of the discrete path segment nodes;

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

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

[0031] The device can find a speed trajectory with minimum energy consumption in combination with a speed trajectory optimization constraint, and then determine a control strategy of the vehicle based on the speed trajectory with minimum energy consumption, so as to reduce the energy consumption of the vehicle and improve the cruising performance of the vehicle and the endurance of the electric vehicle. Meanwhile, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the establishment and solution difficulty of the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, so that the algorithm model can make an optimal solution at each time, and can still make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

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

[0033] a processor;

[0034] a memory configured to store machine-readable instructions, which, when executed by the processor, perform the vehicle control method according to any one of the preceding embodiments.

[0035] The electronic device of the application can find the speed trajectory with the minimum energy consumption in combination with the speed trajectory optimization constraint condition, 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 the endurance of the electric vehicle. Meanwhile, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so as to convert the intelligent electric vehicle speed trajectory optimization problem into a multi-stage decision problem, reduce the difficulty of establishing and solving the intelligent electric vehicle speed trajectory optimization model, and apply the state equation of the intelligent electric vehicle dynamics model constructed again by using the intelligent electric vehicle speed trajectory optimization model to model predictive control, so that the algorithm model can make the optimal solution at each time, and still make a response to make a reasonable plan for the motion of the intelligent electric vehicle again after being disturbed.

[0036] In a fourth aspect, the application provides a storage medium, which stores a computer program, and the computer program is executed by a processor to perform the vehicle control method according to any one of the preceding embodiments.

[0037] The storage medium of the application can find the speed trajectory with the minimum energy consumption in combination with the speed trajectory optimization constraint condition, 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 the endurance of the electric vehicle. Meanwhile, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so as to convert the intelligent electric vehicle speed trajectory optimization problem into a multi-stage decision problem, reduce the difficulty of establishing and solving the intelligent electric vehicle speed trajectory optimization model, and apply the state equation of the intelligent electric vehicle dynamics model constructed again by using the intelligent electric vehicle speed trajectory optimization model to model predictive control, so that the algorithm model can make the optimal solution at each time, and still make a response to make a reasonable plan for the motion of the intelligent electric vehicle again after being disturbed. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0039] Figure 1 is a flowchart of a vehicle control method disclosed by the embodiments of the application;

[0040] Figure 2 is a structural schematic diagram of a vehicle control device disclosed by the embodiments of the application;

[0041] Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the application. DETAILED DESCRIPTION

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

[0043] Embodiment one

[0044] Please refer to Figure 1 , Figure 1 is a flowchart of a vehicle control method disclosed in the embodiments of the present application, as shown in Figure 1 The method of the present application comprises the following steps:

[0045] 101, 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;

[0046] 102, determining a target speed trajectory of the vehicle based on a speed trajectory optimization constraint condition and a speed trajectory optimization model, wherein the target speed trajectory comprises 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 condition, and the speed trajectory under the condition of minimum energy consumption is taken as the target speed trajectory, and the speed trajectory optimization constraint condition comprises the parameters of the discrete road segment node;

[0047] 103, determining the future period state of the vehicle based on the current state information of the vehicle and the target speed trajectory;

[0048] 104, adjusting the control strategy of the vehicle based on the future period state of the vehicle.

[0049] The method of the embodiment of the application can obtain a motion path of a vehicle, divide the motion path of the vehicle into a plurality of discrete path nodes based on a discrete space algorithm, and then determine a target speed trajectory of the vehicle based on a speed trajectory optimization constraint condition and a speed trajectory optimization model, wherein the target speed trajectory includes the speed, acceleration and traction of each discrete path node, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint condition, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, the speed trajectory optimization constraint condition includes parameters of the discrete path nodes, and then the future period state of the vehicle can be determined based on the current state information of the vehicle and the target speed trajectory, so that the control strategy of the vehicle can be adjusted based on the future period state of the vehicle. Compared with the prior art, the application can find a speed trajectory with minimum energy consumption in combination with the speed trajectory optimization constraint condition, and then the control strategy of the vehicle can be determined based on the speed trajectory with minimum energy consumption, so that the energy consumption of the vehicle is reduced and the cruising performance of the vehicle is improved, and the endurance of the electric vehicle is improved. At the same time, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the establishment and solution difficulty of the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, so that the algorithm model can make an optimal solution at each time, and can still make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

[0050] In the embodiment of the application, the motion path of the vehicle is divided into a plurality of discrete path nodes based on the discrete space algorithm, so that the speed trajectory optimization of the motion path is converted into the speed path optimization of the discrete path nodes with smaller granularity, that is, the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, and the establishment and solution difficulty of the intelligent electric vehicle speed trajectory optimization model is reduced. As an example, it is assumed that the motion path of the vehicle is 100 meters, and the prior art directly optimizes the speed trajectory of the 100-meter motion path, which will lead to the need to consider a large number of state parameters in the model establishment and solution process, and the establishment and solution difficulty of the speed trajectory optimization model is higher. However, the application divides the motion path into 10 discrete path nodes of 10 meters, divides the overall speed path optimization into 10 times of speed path optimization, so that only the state variables related to the current discrete path node need to be considered in each speed path optimization, and the solution difficulty is reduced, and the construction of the speed trajectory optimization model is also easier.

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

[0052] In the embodiments of the present application, the vehicle can be an electric vehicle. Further, the motion path of the vehicle can refer to the navigation path of the vehicle moving from one position to another position, which can be determined by navigation information.

[0053] In the embodiments of the present application, the discrete space algorithm refers to a class of algorithms operating in discrete space. The discrete space can be understood as a set composed of separate and discontinuous points or objects.

[0054] In the embodiments of the present application, the speed trajectory refers to the path of the speed, acceleration and traction of the vehicle changing with time during the motion. In the embodiments of the present application, the motion path of the vehicle can be divided into a plurality of discrete path nodes, which can refer to dividing the motion path of the vehicle into 10, 20 or the like discrete path nodes.

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

[0056] The optional comfort mode can take the length of the target path, the speed limit of the target path, the curve of the target path and the slope of the target path as the speed trajectory optimization constraint, and thus the on-board speed of the intelligent electric vehicle can be adjusted based on the actual traffic conditions.

[0057] In the embodiments of the present application, as an optional implementation, the speed trajectory optimization constraint further includes the characteristic information of the vehicle, and further, the characteristic information of the vehicle includes the mass of the vehicle, the running speed of the vehicle, the acceleration upper and lower limit of the vehicle, the braking characteristic of the vehicle, the train traction characteristic of the vehicle, the resistance constraint calculation formula based on the vehicle and the additional resistance constraint calculation formula of the vehicle.

[0058] The optional implementation can take the mass of the vehicle, the running speed of the vehicle, the acceleration upper and lower limit of the vehicle, the braking characteristic of the vehicle, the train traction characteristic of the vehicle, the resistance constraint calculation formula based on the vehicle and the additional resistance constraint calculation formula of the vehicle as one of the speed trajectory optimization constraints.

[0059] In the embodiments of the present application, as an optional implementation, the step of determining the future 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 velocity trajectory of the vehicle are used as the output of the dynamics model so that the dynamics model can predict the state of the vehicle in the future.

[0062] This optional implementation can obtain a dynamic model of the vehicle, and then 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 time periods.

[0063] In an 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 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 an 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 weight.

[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, 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 embodiment of this application, as an example:

[0070] The tram 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 route 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] Further, the input parameters of the intelligent electric vehicle speed trajectory energy-saving optimization problem considering the actual traffic road condition influence include:

[0072] The length, speed limit, curve, slope, fixed speed limit value and discrete section number of each section of the road where the traffic lights exist on the intelligent electric vehicle line are known;

[0073] The vehicle mass, red light parking time, running speed, acceleration upper and lower limits, train traction and braking force characteristics, and the calculation formula of the basic resistance constraint and the additional resistance constraint are given.

[0074] Further, the intelligent electric vehicle speed trajectory optimization problem considering the traffic road condition influence is determined, which needs to determine the speed, acceleration and traction of each discrete road section node. The problem is to solve the optimal energy-saving speed trajectory of the intelligent electric vehicle running on the entire section. The objective of the speed trajectory optimization model based on this is to minimize the energy consumption of the intelligent electric vehicle driving, that is:

[0075]

[0076] Where E is the actual energy consumption of the intelligent electric vehicle driving, f i,j,k is the traction of the intelligent electric vehicle driving at a certain time k, on a certain section j and at a certain position i, |L i,,j is the total displacement length of the intelligent electric vehicle on the section (i,j), ΔL i，j is the discrete section length of the section (i,j).

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

[0078]

[0079] Where m(1+r)a i,,j,k represents the acceleration of the intelligent electric vehicle at a certain time k, on a certain section j and at a certain position i, which is determined by the weight of the vehicle, traction or braking force, basic resistance constraint and additional resistance constraint, and r is the turning coefficient of the intelligent electric vehicle; represents the basic resistance constraint of the intelligent electric vehicle running to the (i,j,k) position, where (i,j,k) represents a certain time k, a certain section j and a certain position i, v i,j,k represents the vehicle speed at a certain time k, on a certain section j and at a certain 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 electric vehicle, and g represents the acceleration of gravity. represents the additional resistance constraint when the smart trolley is running. Wherein, the resistance generated when the smart trolley passes through a curve and a slope is independent of the speed, and is respectively represented as and represents the ground friction coefficient of a certain road section j and a certain position i, and the additional resistance received when passing through a curve or a slope, which is independent of the speed and is mainly determined by the ground characteristics. represents the air density coefficient of a certain road section j and a certain position i.

[0080] Further, the physical kinematic relationship between the running distance, running time, speed and acceleration of the discrete section smart trolley can be represented 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 based on the physical kinematic relationship between the running distance, running time, speed and acceleration, the motion equation of the smart trolley, the speed and acceleration can be calculated in sequence under the condition that the traction force under the condition of minimum energy consumption is known, 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 a time interval, represents the time interval between time k and time k+1 of a certain road section j and a certain position i at a certain time k.

[0084] Further, the global speed trajectory tracking and adaptive control are realized based on model predictive control (MPC), that is, the global speed trajectory tracking and adaptive control of the smart trolley are realized based on the above algorithm, and model predictive control (MPC) needs to be introduced. The dynamics model of the smart trolley is defined again to control the process under the condition of meeting the constraints. This algorithm is based on the solution of real-time optimization problem, and the process model is used to predict the future behavior to achieve better control effect. Considering the single-axle vehicle model, a simplified Bicycle Model can be used. The state variables of this model include the lateral displacement (x, y) of the vehicle, the heading angle (ψ), the running speed (v i,j,k ) of the trolley at this position, and the front wheel steering angle (δ) of the vehicle.

[0085] The state equation of the dynamics model of the smart trolley can be represented 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 contains weights for the vehicle states and control inputs. For global tracking and adaptive control, the objective function can be defined as:

[0090]

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

[0092] The weight terms ω x ,ω y ,ω ψ ,ω v ,ω δ are used to adjust the importance of each state and control input.

[0093] At each time instant of the MPC, an optimization problem is solved to determine the optimal control input. When a sudden disturbance occurs, the MPC can consider safety while re-planning the vehicle speed by adjusting the weights and constraints to adapt to the new situation.

[0094] Embodiment Two

[0095] Please refer to Figure 2 , Figure 2 is a schematic structural diagram of a vehicle control device disclosed in an embodiment of the present application, as shown in the figure, the device of the embodiment of the present application comprises the following functional modules: Figure 2

[0096] The acquisition module 201 is configured to acquire a motion path of the vehicle, and divide the motion path of the vehicle into a plurality of discrete path nodes based on a discrete space algorithm.

[0097] ​The first determining module 202 is configured to determine a target speed trajectory of the vehicle based on speed trajectory optimization constraint conditions and a speed trajectory optimization model, wherein the target speed trajectory comprises a speed, an acceleration and a traction of each discrete road segment node, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint conditions, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, and the speed trajectory optimization constraint conditions comprise parameters of the discrete road segment nodes.

[0098] The second determining module 203 is configured to determine a future period state of the vehicle based on current state information of the vehicle and the target speed trajectory.

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

[0100] The device of the embodiment of the application can find a speed trajectory with minimum energy consumption in combination with speed trajectory optimization constraint conditions, and then determine a control strategy of the vehicle based on the speed trajectory with minimum energy consumption, so as to reduce energy consumption of the vehicle and improve cruising performance of the vehicle and improve electric vehicle endurance. Meanwhile, the intelligent electric vehicle motion control interval is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the establishment and solution difficulty of the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, the algorithm model can make an optimal solution at each time, and can still make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

[0101] It should be noted that other detailed descriptions of the device of the embodiment of the application can be referred to the related descriptions of the first embodiment of the application, and the embodiment of the application will not be described herein.

[0102] Embodiment three

[0103] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiment of the application, as Figure 3 shown, the electronic device of the embodiment of the application comprises:

[0104] a processor 301;

[0105] a memory 302 configured to store machine readable instructions, the instructions being executed by the processor 301 to perform the vehicle control method of any one of the preceding embodiments.

[0106] The electronic device provided in the embodiments of the present application can find a speed trajectory with minimum energy consumption in combination with a speed trajectory optimization constraint condition, and then determine a control strategy of the vehicle based on the speed trajectory with minimum energy consumption, so that the energy consumption of the vehicle is reduced and the cruising performance of the vehicle is improved, and the endurance of the electric vehicle is improved. Meanwhile, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the difficulty of establishing and solving the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, so that the algorithm model can make an optimal solution at each time, and can still make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

[0107] Embodiment four

[0108] The storage medium provided in the embodiments of the present application stores a computer program, and the computer program is executed by a processor to perform the vehicle control method according to any one of the preceding embodiments.

[0109] The storage medium provided in the embodiments of the present application can find a speed trajectory with minimum energy consumption in combination with a speed trajectory optimization constraint condition, and then determine a control strategy of the vehicle based on the speed trajectory with minimum energy consumption, so that the energy consumption of the vehicle is reduced and the cruising performance of the vehicle is improved, and the endurance of the electric vehicle is improved. Meanwhile, the motion control interval of the intelligent electric vehicle is discretized by using the discrete space method, so that the intelligent electric vehicle speed trajectory optimization problem is converted into a multi-stage decision problem, the difficulty of establishing and solving the intelligent electric vehicle speed trajectory optimization model is reduced, and the state equation of the intelligent electric vehicle dynamics model is constructed again by using the intelligent electric vehicle speed trajectory optimization model and applied to model predictive control, so that the algorithm model can make an optimal solution at each time, and can still make a response and reasonably plan the motion of the intelligent electric vehicle again after being disturbed.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The above-described device embodiments are only schematic. For example, the division of the units is only a logical function division. There can be another division manner for the 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. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through other devices.

[0111] In addition, the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

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

[0113] It should be noted that if the function is realized 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 solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the various embodiments of the method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and various program codes that can be stored in the medium.

[0114] In this paper, such as first and second relationship terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations.

[0115] The above is only an embodiment of the present application and does not limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle control method characterized by, The method comprises: acquiring a motion path of a vehicle and dividing the motion path of the vehicle into a plurality of discrete path 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 comprises a speed, an acceleration and a traction of each of the discrete path nodes, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, and the speed trajectory optimization constraint comprises parameters of the discrete path nodes; determining a future period state of the vehicle based on current state information of the vehicle and the target speed trajectory; adjusting a control strategy of the vehicle based on the future period state of the vehicle; the speed trajectory optimization constraint further comprises traffic condition information in the motion path; the traffic condition information comprises a length of a target path, a speed limit of the target path, a curve of the target path and a slope of the target path; the speed trajectory optimization constraint further comprises characteristic information of the vehicle; the characteristic information of the vehicle comprises a mass of the vehicle, a running speed of the vehicle, an upper limit and a lower limit of the acceleration of the vehicle, a braking characteristic of the vehicle, a train traction characteristic of the vehicle, a resistance constraint calculation formula based on the vehicle and an additional resistance constraint calculation formula of the vehicle.

2. The method of claim 1, wherein, The determination of the future period state of the vehicle based on the current state information of the vehicle and the target speed trajectory comprises: acquiring a dynamics model of the vehicle; taking the current state of the vehicle and the target speed trajectory as an output of the dynamics model, so that the dynamics model predicts a future period state of the vehicle.

3. The method of claim 2, wherein, The current state of the vehicle comprises a lateral displacement of the vehicle, a heading angle of the vehicle, a running speed of the vehicle and a front wheel steering angle of the vehicle.

4. The method of claim 3, wherein, The lateral displacement of the vehicle, the heading angle of the vehicle, the running speed of the vehicle and the front wheel steering angle of the vehicle each correspond to a state weight.

5. The method of claim 1, wherein, The parameters of the discrete path nodes comprise a number of the discrete path nodes and lengths of the discrete path nodes, wherein the lengths of the discrete path nodes in a same section are the same.

6. A vehicle control device characterized by comprising: The device comprises: an acquisition module configured to acquire a motion path of a vehicle and divide the motion path of the vehicle into a plurality of discrete path 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 comprises a speed, an acceleration and a traction of each of the discrete path nodes, the speed trajectory optimization model is used to solve a speed trajectory under a minimum energy consumption condition based on the speed trajectory optimization constraint, and the speed trajectory under the minimum energy consumption condition is taken as the target speed trajectory, and the speed trajectory optimization constraint comprises parameters of the discrete path nodes; a second determining module, configured to determine a future period state of the vehicle based on current state information of the vehicle and the target speed trajectory; a control module, configured to adjust a control strategy of the vehicle based on the future period state of the vehicle; and the speed trajectory optimization constraint condition further comprises traffic condition information in the motion path; the traffic condition information comprises a length of a target road section, a speed limit of the target road section, a curve of the target road section, and a slope of the target road section; and the speed trajectory optimization constraint condition further comprises characteristic information of the vehicle; the characteristic information of the vehicle comprises a mass of the vehicle, an operating speed of the vehicle, an acceleration upper and lower limit of the vehicle, a braking characteristic of the vehicle, a train traction force characteristic of the vehicle, a resistance-based constraint calculation formula of the vehicle, and an additional resistance constraint calculation formula of the vehicle.

7. An electronic device, comprising: comprise: a processor; a memory configured to store machine readable instructions, which, when executed by the processor, perform the vehicle control method according to any one of claims 1-5.

8. A storage medium, characterized by The storage medium stores a computer program, and the computer program is executed by the processor to perform the vehicle control method according to any one of claims 1-5.

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