CN116080666A

CN116080666A - Vehicle control method and device, vehicle and storage medium

Info

Publication number: CN116080666A
Application number: CN202310064774.XA
Authority: CN
Inventors: 刘福星; 刘应花; 顾晨光; 罗千
Original assignee: Great Wall Motor Co Ltd
Current assignee: Great Wall Motor Co Ltd
Priority date: 2023-01-16
Filing date: 2023-01-16
Publication date: 2023-05-09

Abstract

The application is applicable to the technical field of vehicles, and provides a vehicle control method, a device, a vehicle and a storage medium, wherein the method comprises the following steps: obtaining a target speed and a target steering wheel corner of a target vehicle, and searching target cornering stiffness corresponding to the target speed and the target steering wheel corner from a pre-stored mapping relation; inputting a target vehicle speed, a target steering wheel corner and target cornering stiffness into a monorail vehicle model to obtain a motion state parameter of a target vehicle; and controlling the operation of the target vehicle based on the motion state parameter. In the application, in the running process of the vehicle, the cornering stiffness corresponding to the current vehicle speed and the steering wheel rotation angle is obtained through searching from the prestored mapping relation, so that the accurate and effective cornering stiffness can be obtained quickly, and when the cornering stiffness is used for a single-rail vehicle model, the single-rail vehicle model can accurately estimate the motion state parameters of the vehicle, and the stable and reliable running of the vehicle is ensured.

Description

Vehicle control method and device, vehicle and storage medium

Technical Field

The application belongs to the technical field of vehicles, and particularly relates to a vehicle control method, a vehicle control device, a vehicle and a storage medium.

Background

Vehicle dynamics models, such as monorail vehicle models, are commonly used to estimate vehicle state of motion parameters. In practice, cornering stiffness (front axle cornering stiffness and rear axle cornering stiffness) of a vehicle is a very critical parameter of a vehicle dynamics model, and the cornering stiffness is usually different under different vehicle speeds and steering wheel angles.

In the related art, since the on-line estimation of the cornering stiffness is large in calculation amount and takes a long time, the cornering stiffness is generally simplified to a fixed value in order to reduce the calculation complexity. However, simplifying the cornering stiffness to a fixed value easily results in insufficient accuracy of the estimated motion state parameters of the vehicle by the vehicle dynamics model.

Disclosure of Invention

The embodiment of the application provides a vehicle control method, a vehicle control device, a vehicle and a storage medium, and aims to solve the problem that in the related art, cornering stiffness is simplified to be a fixed value, and the accuracy of a motion state parameter of a vehicle estimated by a vehicle dynamics model is not high enough easily.

In a first aspect, an embodiment of the present application provides a vehicle control method, including:

obtaining a target speed and a target steering wheel corner of a target vehicle, and searching target cornering stiffness corresponding to the target speed and the target steering wheel corner from a pre-stored mapping relation, wherein the mapping relation is used for indicating the corresponding relation among the speed, the steering wheel corner and the cornering stiffness;

Inputting a target vehicle speed, a target steering wheel corner and target cornering stiffness into a monorail vehicle model to obtain a motion state parameter of a target vehicle;

and controlling the operation of the target vehicle based on the motion state parameter.

In a second aspect, an embodiment of the present application provides a vehicle control apparatus, including:

the information acquisition unit is used for acquiring a target speed and a target steering wheel corner of a target vehicle, and searching target cornering stiffness corresponding to the target speed and the target steering wheel corner from a pre-stored mapping relation, wherein the mapping relation is used for indicating the corresponding relation among the speed, the steering wheel corner and the cornering stiffness;

the parameter determining unit is used for inputting the target vehicle speed, the target steering wheel angle and the target cornering stiffness into the monorail vehicle model to obtain the motion state parameters of the target vehicle;

and the operation control unit is used for controlling the operation of the target vehicle based on the motion state parameters.

In a third aspect, embodiments of the present application provide a vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the vehicle control methods described above when the computer program is executed.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any one of the vehicle control methods described above.

In a fifth aspect, embodiments of the present application provide a computer program product for, when run on a vehicle, causing the vehicle to perform any one of the vehicle control methods described above.

Compared with the related art, the embodiment of the application has the beneficial effects that: in the running process of the vehicle, the cornering stiffness corresponding to the current vehicle speed and steering wheel rotation angle is obtained through searching from a pre-stored mapping relation, so that the accurate and effective cornering stiffness can be obtained quickly, and when the cornering stiffness is used for a single-rail vehicle model, the single-rail vehicle model can accurately estimate the motion state parameters of the vehicle, and the stable and reliable running of the vehicle is ensured.

It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a vehicle control method according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating another implementation of a vehicle control method according to an embodiment of the present application;

fig. 3 is a schematic structural view of a vehicle control apparatus provided in an embodiment of the present application;

fig. 4 is a schematic structural diagram of a vehicle according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

In order to explain the technical aspects of the present application, the following examples are presented.

Example 1

Referring to fig. 1, an embodiment of the present application provides a vehicle control method, as shown in fig. 1, the vehicle control method may include the following steps 101 to 103.

Step 101, obtaining a target speed and a target steering wheel angle of a target vehicle, and searching target cornering stiffness corresponding to the target speed and the target steering wheel angle from a pre-stored mapping relation.

The mapping relation is used for indicating the corresponding relation among the vehicle speed, the steering wheel rotation angle and the cornering stiffness. In practice, the cornering stiffness generally includes a front axle cornering stiffness and a rear axle cornering stiffness.

The target vehicle may be various vehicles. The target vehicle speed is a vehicle speed of a target vehicle, and the target steering wheel angle is a steering wheel angle of the target vehicle. The target cornering stiffness is a cornering stiffness corresponding to the target vehicle speed and the target steering wheel angle.

In the present embodiment, the execution subject of the above-described vehicle control method is typically a vehicle, and may specifically be the above-described target vehicle. The execution body may acquire a target vehicle speed and a target steering wheel angle of the target vehicle during the running of the target vehicle. And then, the execution main body can search for the cornering stiffness corresponding to the target vehicle speed and the target steering wheel angle from the mapping relation by adopting the target vehicle speed and the target steering wheel angle of the target vehicle, wherein the searched cornering stiffness is the target cornering stiffness.

And 102, inputting the target vehicle speed, the target steering wheel angle and the target cornering stiffness into a monorail vehicle model to obtain the motion state parameters of the target vehicle.

Wherein the movement state parameter is typically a parameter for describing the movement state of the vehicle. The motion state parameters may include yaw rate, centroid slip angle, front wheel rotation angle, rear wheel rotation angle, etc.

The monorail vehicle model is a pre-established two-degree-of-freedom vehicle dynamics model.

Here, the execution body may input the target vehicle speed, the target steering wheel angle, and the target cornering stiffness into the monorail vehicle model, so as to obtain the motion state parameter of the target vehicle output by the monorail vehicle model.

In practice, the basic equations of the monorail vehicle model may include the following equations (1) and (2).

Wherein m is the mass of the whole vehicle, lf is the distance from the mass center to the front axle, lr is the distance from the mass center to the rear axle; kf is the cornering stiffness of the front axle, kr is the cornering stiffness of the rear axle, v _x For longitudinal speed of vehicle v _y Is the lateral vehicle speed;

for vehicle yaw rate, +.>

The yaw acceleration, beta is the centroid slip angle; δf is the front wheel rotation angle, δr is the rear wheel rotation angle; i _z The moment of inertia of the whole vehicle around the Z axis of the vehicle coordinate system is obtained.

Step 103, controlling the operation of the target vehicle based on the motion state parameter.

Here, the execution subject may control the operation of the target vehicle based on the motion state parameter of the target vehicle. As an example, when the yaw rate of the target vehicle is greater than a certain speed threshold, one or more vehicles may be braked, the vehicle speed is reduced, and smooth running of the target vehicle is ensured.

According to the method provided by the embodiment, during the running process of the vehicle, the cornering stiffness corresponding to the current vehicle speed and steering wheel rotation angle is obtained through searching from the pre-stored mapping relation, so that the accurate and effective cornering stiffness can be obtained quickly, and when the cornering stiffness is used for a single-rail vehicle model, the single-rail vehicle model can accurately estimate the motion state parameters of the vehicle, and the stable and reliable running of the vehicle is ensured.

In some optional implementations of the present embodiment, the vehicle control method may further include the following steps: first, a simulated data set is generated, the simulated data in the simulated data set including a simulated vehicle speed and a simulated steering wheel angle. And then, according to the preset basic cornering stiffness, determining the cornering stiffness corresponding to each simulation data. And finally, generating a mapping relation according to the cornering stiffness corresponding to each simulation data.

The simulated vehicle speed is a vehicle speed generated by simulation, for example, 20 km/h, 30 km/h, 40 km/h, etc. The simulated steering wheel angle is a simulated generated steering wheel angle, such as 10 degrees, 15 degrees, 20 degrees, etc.

The base cornering stiffness is generally a predetermined cornering stiffness. In some application scenarios, the cornering stiffness may be reduced to the base cornering stiffness, and the vehicle dynamics model may estimate a motion state parameter of the vehicle based on the reduced cornering stiffness.

Here, the execution subject may employ the base cornering stiffness for each of the simulation data, generating the cornering stiffness for the simulation data. As an example, if the base cornering stiffness is kf0 and kr0, where kf0 is the front axle cornering stiffness, and kr0 is the rear axle cornering stiffness, the cornering stiffness of each of the simulation data can be generated by: kf (i) =kf0+randn×0.01×kf0, kr (i) =kr0+randn×0.01×kr0. Wherein randn is a random function, kf (i) is the front axle cornering stiffness corresponding to the ith analog data, and kr (i) is the rear axle cornering stiffness corresponding to the ith analog data.

Then, the execution subject can adopt the simulation data and the cornering stiffness corresponding to the simulation data to generate the mapping relation. The generated map may then be stored, for example, in an ECU of the vehicle.

According to the embodiment, the mapping relation can be generated in advance for the vehicle under the condition that the vehicle does not run, so that the cornering stiffness corresponding to the current speed and steering wheel rotation angle can be quickly searched based on the mapping relation in the running process of the vehicle, the motion state parameters of the vehicle can be accurately estimated by the monorail vehicle model, and the stable and reliable running of the vehicle is ensured.

In some optional implementations of this embodiment, determining the cornering stiffness corresponding to each analog data according to the preset base cornering stiffness may include the following steps one to four.

Generating iterative cornering stiffness corresponding to each simulation data respectively according to the basic cornering stiffness.

The iterative cornering stiffness is the cornering stiffness used in the iterative process.

Here, the execution body may employ the base cornering stiffness for each of the simulation data, generating the iterative cornering stiffness for the simulation data.

In practice, if the base cornering stiffness is kf0 and kr0, where kf0 is the front axle cornering stiffness, and kr0 is the rear axle cornering stiffness, the iterative cornering stiffness of each simulation data may be generated by the following formula (3) and formula (4).

kf(i)＝kf0+randn×0.02×kf0 (3)

kr(i)＝kr0+randn×0.02×kr0 (4)

Wherein randn is a random function, kf (i) is the front axle cornering stiffness corresponding to the ith analog data, and kr (i) is the rear axle cornering stiffness corresponding to the ith analog data.

In practical application, the execution body can also generate the rigidity change speed of each simulation data through the following formula (5) and formula (6). Wherein the rate of stiffness change is used to describe the rate of change of yaw stiffness possible.

vf(i) ＝ randn*0.1* kf0 (5)

vr(i) ＝ randn*0.1* kr0 (6)

Wherein vf (i) is the rigidity change speed of the front axle cornering rigidity corresponding to the ith analog data, and vr (i) is the rigidity change speed of the rear axle cornering rigidity corresponding to the ith analog data.

Step two, inputting corresponding simulation data into a complex vehicle model aiming at each simulation data to obtain a first yaw rate and a first centroid side deflection angle, and inputting corresponding simulation data and corresponding iterative side deflection stiffness into a monorail vehicle model to obtain a second yaw rate and a second centroid side deflection angle.

The complex vehicle model is usually a high-precision vehicle model with multiple degrees of freedom. The monorail vehicle model is typically a two-degree-of-freedom vehicle model.

Here, for each piece of simulation data, the simulation data may be input into a complex vehicle model, resulting in a yaw rate and a centroid slip angle output by the complex vehicle model. Here, for convenience of distinguishing description, the yaw rate of the complex vehicle model output is referred to as the first yaw rate, and the centroid slip angle of the complex vehicle model output is referred to as the first centroid slip angle. In addition, the simulation data and the iterative cornering stiffness corresponding to the simulation data can be input into the monorail vehicle model, and the yaw rate and the centroid cornering angle output by the monorail vehicle model can be obtained. Here, for convenience of distinguishing description, the yaw rate of the monorail vehicle model output is referred to as the second yaw rate, and the centroid slip angle of the monorail vehicle model output is referred to as the second centroid slip angle.

Generating a particle swarm, wherein particles in the particle swarm correspond to the simulation data, and the particles comprise a first yaw rate, a second yaw rate, a first centroid side deflection angle, a second centroid side deflection angle, iterative side deflection rigidity and fitness value of the corresponding simulation data, wherein the fitness value is used for describing yaw rate deviation and centroid side deflection angle deviation.

Wherein the yaw rate deviation is typically a deviation between the first yaw rate and the second yaw rate, and the centroid slip angle deviation is typically a deviation between the first centroid slip angle and the second centroid slip angle.

Here, one particle may be generated for each simulation data, thereby obtaining a particle group.

In some alternative implementations, the calculation formula of the fitness value of the particles in the particle swarm includes the following formula (7).

Wherein Fitness (p _i ) For the fitness value of the ith particle, beta (j) is the first centroid slip angle corresponding to the jth particle, beta _2dof (j) For the second centroid slip angle corresponding to the jth particle, γ (j) is the first yaw rate corresponding to the jth particle, γ _2dof (j) For a second yaw rate corresponding to the jth particle,

is the square of the maximum in the second centroid slip angle for i particles, +. >

The square of the maximum value in the second yaw rate corresponding to i particles.

Selecting target particles with corresponding fitness values meeting preset selection conditions from the particle swarm, and updating the iteration cornering stiffness and fitness values of other particles in the particle swarm according to the iteration cornering stiffness of the target particles; when the preset stopping condition is met currently, determining the current iterative cornering stiffness of each particle as the cornering stiffness of the simulation data corresponding to the corresponding particle.

The preset selection condition is usually a preset selection condition. As an example, the preset selection condition may be: and selecting particles with the smallest corresponding fitness value from the particle group as target particles. It should be noted that, the smaller the fitness value corresponding to the particle, the more accurate the deviation between the second yaw rate and the second centroid slip angle obtained by the monorail car model through prediction and the first yaw rate and the first centroid slip angle obtained by the complex vehicle model through prediction is, that is, the more accurate the monorail car model is. The preset stopping condition is generally a preset condition for stopping iteration, and as an example, the preset stopping condition may include at least one of the following: the fitness value of each particle is all smaller than a preset value, for example, smaller than 5, when the preset iteration number is reached.

Here, the execution body may select, as the target particle, a particle whose corresponding fitness value satisfies a preset selection condition from the particle group. And then, updating the iterative cornering stiffness and the fitness value of other particles by adopting the iterative cornering stiffness of the target particles.

After updating the iterative cornering stiffness and fitness value of each particle, the execution main body can judge whether the preset stopping condition is met currently, and if so, for each particle, the iterative cornering stiffness obtained by the current iteration of the particle is determined as the cornering stiffness of the simulation data corresponding to the particle.

Optionally, after updating the iterative cornering stiffness and fitness values of each other particle in the particle swarm, further comprising: if the preset stopping condition is not met, continuing to select target particles with the corresponding fitness value meeting the preset selecting condition from the particle swarm, and updating the iteration cornering stiffness and the fitness value of each other particle in the particle swarm according to the iteration cornering stiffness of the target particles.

According to the embodiment, the accurate cornering stiffness of the simulation data can be obtained through iteration in an iteration mode, and the accuracy and the stability are high.

Optionally, the preset selection condition may include at least one of the following: and selecting particles with the smallest corresponding fitness value from the particle group. And selecting particles with the maximum corresponding occurrence probability from the particle group as target particles. And selecting particles with the corresponding occurrence probability larger than a preset probability threshold from the particle swarm as target particles.

The probability of occurrence of each particle is usually calculated based on a predetermined probability calculation formula. In practice, the probability calculation formula may be the following formula (8).

Wherein TF (p) _i ) Is the probability of occurrence of the ith particle, f (p _i ) Is the fitness value of the ith particle, f (p _g ) For the particle with the smallest fitness value, T _t For the simulated annealing temperature at time T, T _t+1 ＝λT _t ，T ₀ ＝f(p _g0 )/ln5，f(p _g0 ) In order to obtain a fitness value of particles with minimum fitness at the initial time, λ is an annealing constant, and in practice, the value of λ may be 0.8.

In practical applications, the particle with the smallest corresponding fitness value is usually the individual optimal particle. The particles with the highest probability of occurrence are typically population-optimal particles.

In an alternative implementation of some embodiments, updating the iterative cornering stiffness and fitness value of each other particle in the particle swarm according to the iterative cornering stiffness of the target particle, comprises:

First, for each other particle, according to the iterative cornering stiffness of the corresponding other particle, the iterative cornering stiffness of the target particle and a preset updating formula, the updated cornering stiffness of the corresponding other particle is calculated, and the iterative cornering stiffness of the corresponding particle is switched to the updated cornering stiffness.

The update formula is generally a preset formula for updating the iterative cornering stiffness. In practice, the update formula may be implemented as the following formula (9) and formula (10). For each other particle, the iterative cornering stiffness of the particle can be updated using equations (9) and (10) below.

Wherein v is _i (t+1) is the rate of change of the stiffness of the ith particle at time t+1, v _i (t) the rate of change of the stiffness of the ith particle at time t,

c ₁ 、c ₂ for learning factor, c=c ₁ +c ₂ ，r ₁ 、r ₂ Is a random number, p _g For iterative cornering stiffness of particles with minimum fitness value, p' _g For iterative cornering stiffness, k, corresponding to particles with the highest probability of occurrence _i (t+1) is the iterative cornering stiffness, k of the ith particle at time t+1 _i And (t) is the iterative cornering stiffness of the ith particle at time t.

And then, according to the iterative yaw stiffness of each particle and the monorail vehicle model, calculating to obtain updated second yaw rate and second centroid yaw angle of each particle, and switching the second yaw rate and the second centroid yaw angle of each particle into the updated second yaw rate and the updated second centroid yaw angle respectively.

Here, for each particle, the updated iterative yaw stiffness of the particle and the simulation data corresponding to the particle may be used to input a model of the monorail vehicle, so as to obtain an updated second yaw rate and a second centroid yaw angle output by the model of the monorail vehicle, after which the second yaw rate of the particle may be switched to the updated second yaw rate, and the second centroid yaw angle of the particle may be switched to the updated second centroid yaw angle.

And finally, determining the fitness value corresponding to each particle according to the first yaw rate, the second yaw rate, the first centroid side deflection angle and the second centroid side deflection angle of each particle.

Here, for each particle, a new fitness value of the particle may be calculated by using the aforementioned formula (7), and the fitness value of the particle may be switched to the new fitness value, thereby realizing updating of the fitness value of each particle.

The embodiment can update the cornering stiffness and the fitness value of each particle.

Example two

With continued reference to fig. 2, fig. 2 is a process diagram of implementation of the vehicle control method according to the embodiment of the present application. As shown in fig. 2, the vehicle control method may include the following steps 201 to 204.

Step 201, inputting the simulation data into the monorail vehicle model and the complex vehicle model respectively to obtain a first yaw rate gamma and a first centroid slip angle beta output by the complex vehicle model, and a second yaw rate gamma output by the monorail vehicle model _2dof And a second centroid slip angle beta _2dof 。

The simulation data comprise a simulation vehicle speed V and a simulation steering wheel angle delta.

Here, the analog data set generally has a large number of analog data, for example, 40 analog data may be present. For each simulation data in the simulation data set, a first yaw rate, a first centroid side slip angle, a second yaw rate and a second centroid side slip angle corresponding to the simulation data can be obtained.

Step 202, inputting a first yaw rate, a first centroid sideslip angle, a second yaw rate and a second centroid sideslip angle corresponding to each simulation data into a particle swarm optimization algorithm (Simulated Annealing-Particle Swarm Optimization, SAPSO) for simulated annealing, so as to obtain sideslip stiffness corresponding to each simulation data, wherein the sideslip stiffness comprises front axle sideslip stiffness and rear axle sideslip stiffness.

And 203, generating front and rear axle cornering rigidities MAP by adopting the cornering rigidities respectively corresponding to the simulation data.

Here, the front and rear axle yaw rigidities MAP are the aforementioned MAP.

Step 204, obtaining the current vehicle speed V1 and the current steering wheel rotation angle delta 1 in the running process of the vehicle, searching for the cornering stiffness corresponding to the current vehicle speed and the current steering wheel rotation angle from the front and rear axle cornering stiffness MAP generated in step 203, and inputting the cornering stiffness obtained by searching for the cornering stiffness into a monorail vehicle model, so that the monorail vehicle model can estimate the running state of the vehicle based on the real-time accurate cornering stiffness.

In step 202, the SAPSO algorithm is implemented as follows.

In a first step, each particle is initialized.

Here, one particle may be generated for each simulation data, thereby obtaining a particle group. The particles in the particle swarm include corresponding simulated data of a first yaw rate, a second yaw rate, a first centroid slip angle, a second centroid slip angle, an iterative slip stiffness, and a fitness value.

The iterative cornering stiffness of each particle can be calculated by the foregoing formula (3) and formula (4) at the initial moment. The stiffness change rate corresponding to each particle can be calculated by the above-described formula (5) and formula (6).

And secondly, calculating the fitness value of each particle, and determining the particle with the smallest corresponding fitness value as the individual optimal particle.

Here, the fitness value of each particle can be calculated by the aforementioned formula (7).

And thirdly, initializing an annealing temperature. T (T) ₀ ＝f(p _g 0)/ln5，f(p _g 0) The fitness value of the particle with the smallest fitness corresponding to the initial moment.

Thirdly, adopting a roulette strategy to find out the optimal particles of the population from the particles.

Here, the probability of occurrence of each particle may be calculated one by one using the aforementioned formula (8) until the probability of occurrence of a certain particle is greater than a preset probability threshold value, and the particle greater than the preset probability threshold value is determined as the population optimal particle. The preset probability threshold is usually a preset probability value, for example, may be 0.8.

And fourthly, updating the iterative cornering stiffness of each other particle by adopting the iterative cornering stiffness of the population optimal particle, the formula (9) and the formula (10).

And fifthly, calculating new fitness values of the particles, and determining new individual optimal particles and population optimal particles according to the new fitness values of the particles.

And sixthly, performing temperature-reducing operation.

Here, the temperature-lowering mode is T _t+1 ＝λT _t Where λ is an annealing constant, and in practice, the value of λ may be 0.8.

And seventhly, judging whether to terminate the optimization iteration according to the set termination condition. If the termination condition is met, the loop is exited, the final iteration cornering stiffness of each particle is output, and if not, the fourth step is continuously executed.

Here, the termination condition is the same concept as the previously described preset stop condition.

Example III

Corresponding to the vehicle control method of the above embodiment, fig. 3 shows a block diagram of the vehicle control apparatus 300 provided in the embodiment of the present application, and only the portions relevant to the embodiment of the present application are shown for convenience of explanation. Referring to fig. 3, the apparatus includes an information acquisition unit 301, a parameter determination unit 302, and an operation control unit 303.

An information obtaining unit 301, configured to obtain a target vehicle speed and a target steering wheel angle of a target vehicle, and find a target cornering stiffness corresponding to the target vehicle speed and the target steering wheel angle from a pre-stored mapping relationship, where the mapping relationship is used to indicate a correspondence relationship among the vehicle speed, the steering wheel angle, and the cornering stiffness;

the parameter determining unit 302 is configured to input the target vehicle speed, the target steering wheel angle and the target cornering stiffness into a monorail vehicle model to obtain a motion state parameter of the target vehicle;

An operation control unit 303 for controlling the operation of the target vehicle based on the motion state parameter.

In some embodiments, the apparatus further comprises a data generation unit, a data determination unit, and a relationship generation unit.

The data generation unit is used for generating a simulation data set, wherein the simulation data in the simulation data set comprises a simulation vehicle speed and a simulation steering wheel corner;

the data determining unit is used for determining the cornering stiffness corresponding to each piece of simulation data according to the preset basic cornering stiffness;

and the relation generating unit is used for generating a mapping relation according to the cornering stiffness corresponding to each piece of simulation data.

In some embodiments, the data determining unit comprises an information generating module, a data processing module, a particle generating module and a data updating module.

The information generation module is used for generating iterative cornering stiffness corresponding to each simulation data respectively according to the basic cornering stiffness;

the data processing module is used for inputting corresponding simulation data into the complex vehicle model aiming at each simulation data to obtain a first yaw rate and a first centroid slip angle, and inputting corresponding simulation data and corresponding iterative slip stiffness into the single-rail vehicle model to obtain a second yaw rate and a second centroid slip angle;

The particle generation module is used for generating a particle swarm, wherein particles in the particle swarm correspond to the simulation data, the particles comprise a first yaw rate, a second yaw rate, a first centroid side deflection angle, a second centroid side deflection angle, iterative side deflection rigidity and fitness values of the corresponding simulation data, and the fitness values are used for describing yaw rate deviation and centroid side deflection angle deviation;

the data updating module is used for selecting target particles with corresponding fitness values meeting preset selection conditions from the particle swarm, and updating the iteration cornering stiffness and the fitness values of other particles in the particle swarm according to the iteration cornering stiffness of the target particles; when the preset stopping condition is met currently, determining the current iterative cornering stiffness of each particle as the cornering stiffness of the simulation data corresponding to the corresponding particle.

In some embodiments, the data updating module is further configured to, if the preset stopping condition is not met, continue to execute selecting a target particle from the particle swarm, where the corresponding fitness value meets the preset selecting condition, and update the iteration cornering stiffness and the fitness value of each other particle in the particle swarm according to the iteration cornering stiffness of the target particle.

In some embodiments, the calculation formula of the fitness value of the particles in the particle swarm includes:

is the square of the maximum in the second centroid slip angle for i particles, +.>

In some embodiments, the preset selection condition includes at least one of:

selecting particles with the smallest corresponding fitness value from the particle group as target particles;

and selecting particles with the maximum corresponding occurrence probability from the particle group as target particles.

In some embodiments, in the data updating module, updating the iterative cornering stiffness and fitness value of each other particle in the particle swarm according to the iterative cornering stiffness of the target particle, including:

aiming at each other particle, calculating to obtain updated cornering stiffness of the corresponding other particle according to the iterative cornering stiffness of the corresponding other particle, the iterative cornering stiffness of the target particle and a preset updating formula, and switching the iterative cornering stiffness of the corresponding particle to the updated cornering stiffness;

According to the iterative yaw stiffness of each particle and the monorail vehicle model, calculating to obtain updated second yaw rate and second centroid yaw angle of each particle, and respectively switching the second yaw rate and the second centroid yaw angle of each particle into the updated second yaw rate and the updated second centroid yaw angle;

and determining the fitness value corresponding to each particle according to the first yaw rate, the second yaw rate, the first centroid side deflection angle and the second centroid side deflection angle of each particle.

According to the device provided by the embodiment, during the running process of the vehicle, the cornering stiffness corresponding to the current vehicle speed and steering wheel rotation angle is obtained through searching from the pre-stored mapping relation, so that the accurate and effective cornering stiffness can be obtained quickly, and when the cornering stiffness is used for a single-rail vehicle model, the single-rail vehicle model can accurately estimate the motion state parameters of the vehicle, and the stable and reliable running of the vehicle is ensured.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

Example IV

Fig. 4 is a schematic structural diagram of a vehicle 400 according to an embodiment of the present application. As shown in fig. 4, the vehicle 400 of this embodiment includes: at least one processor 401 (only one processor is shown in fig. 4), a memory 402, and a computer program 403, such as a vehicle control program, stored in the memory 402 and executable on the at least one processor 401. The steps of any of the various method embodiments described above are implemented by processor 401 when executing computer program 403. The processor 401, when executing the computer program 403, implements the steps of the embodiments of the respective vehicle control methods described above. The processor 401, when executing the computer program 403, implements the functions of the modules/units in the above-described embodiments of the apparatus, such as the functions of the information acquisition unit 301, the parameter determination unit 302, and the operation control unit 303 shown in fig. 3.

By way of example, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to complete the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function, the instruction segments describing the execution of the computer program 403 in the vehicle 400. For example, the computer program 403 may be divided into an information acquisition unit, a parameter determination unit, and an operation control unit, and specific functions of each unit are described in the above embodiments, which are not described herein.

The vehicle 400 may include: but are not limited to, a processor 401, a memory 402. It will be appreciated by those skilled in the art that fig. 4 is merely an example of a vehicle 400 and is not intended to limit the vehicle 400, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the vehicle may further include input and output devices, network access devices, buses, etc.

The processor 401 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 402 may be an internal storage unit of the vehicle 400, such as a hard disk or a memory of the vehicle 400. The memory 402 may also be an external storage device of the vehicle 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the vehicle 400. Further, the memory 402 may also include both internal storage units and external storage devices of the vehicle 400. The memory 402 is used to store computer programs and other programs and data required by the vehicle. The memory 402 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/vehicle and method may be implemented in other ways. For example, the apparatus/vehicle embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Wherein the computer readable storage medium may be nonvolatile or volatile. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable storage medium may be appropriately scaled according to the requirements of jurisdictions in which such computer readable storage medium does not include electrical carrier signals and telecommunication signals, for example, according to jurisdictions and patent practices.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A vehicle control method, characterized in that the method comprises:

inputting the target vehicle speed, the target steering wheel angle and the target cornering stiffness into a monorail vehicle model to obtain the motion state parameters of the target vehicle;

and controlling the target vehicle to run based on the motion state parameter.

2. The vehicle control method according to claim 1, characterized in that the method further comprises:

generating a simulation data set, wherein the simulation data in the simulation data set comprises a simulation vehicle speed and a simulation steering wheel angle;

determining the cornering stiffness corresponding to each analog data according to the preset basic cornering stiffness;

and generating the mapping relation according to the cornering stiffness corresponding to each simulation data.

3. The vehicle control method according to claim 2, wherein the determining the cornering stiffness to which each of the simulation data corresponds, based on the preset base cornering stiffness, includes:

generating iterative cornering stiffness corresponding to each simulation data respectively according to the basic cornering stiffness;

inputting corresponding simulation data into a complex vehicle model aiming at each simulation data to obtain a first yaw rate and a first centroid slip angle, and inputting corresponding simulation data and corresponding iterative slip stiffness into the single-rail vehicle model to obtain a second yaw rate and a second centroid slip angle;

generating a particle swarm, wherein particles in the particle swarm correspond to the simulation data, and the particles comprise a first yaw rate, a second yaw rate, a first centroid side slip angle, a second centroid side slip angle, iterative side slip stiffness and a fitness value of the corresponding simulation data, wherein the fitness value is used for describing yaw rate deviation and centroid side slip angle deviation;

4. The vehicle control method according to claim 3, characterized by further comprising, after the updating of the iterative cornering stiffness and fitness values of each other particle in the particle swarm:

if the preset stopping condition is not met currently, continuing to execute the selection of the target particles with the corresponding fitness value meeting the preset selecting condition from the particle swarm, and updating the iteration cornering stiffness and the fitness value of each other particle in the particle swarm according to the iteration cornering stiffness of the target particles.

5. The vehicle control method according to claim 3, characterized in that the calculation formula of the fitness value of the particles in the particle swarm includes:

6. The vehicle control method according to claim 3, characterized in that the preset selection condition includes at least one of:

selecting particles with the smallest corresponding fitness value from the particle group as the target particles;

and selecting particles with maximum corresponding occurrence probability from the particle group as the target particles.

7. The vehicle control method according to any one of claims 3 to 6, characterized in that the updating of the iterative cornering stiffness and fitness value of each other particle in the particle group according to the iterative cornering stiffness of the target particle comprises:

for each other particle, calculating to obtain updated cornering stiffness of the corresponding other particle according to the iterative cornering stiffness of the corresponding other particle, the iterative cornering stiffness of the target particle and a preset updating formula, and switching the iterative cornering stiffness of the corresponding particle to the updated cornering stiffness;

8. A vehicle control apparatus, characterized in that the apparatus comprises:

the parameter determining unit is used for inputting the target vehicle speed, the target steering wheel angle and the target cornering stiffness into a monorail vehicle model to obtain the motion state parameters of the target vehicle;

And the operation control unit is used for controlling the operation of the target vehicle based on the motion state parameter.

9. A vehicle comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the vehicle control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the vehicle control method according to any one of claims 1 to 7.