CN115320307A

CN115320307A - Vibration damping method and device for vehicle, electronic equipment and storage medium

Info

Publication number: CN115320307A
Application number: CN202211264130.7A
Authority: CN
Inventors: 王超; 刘洋
Original assignee: Zhejiang Kong Hui Automobile Technology Co ltd
Current assignee: Zhejiang Kong Hui Automobile Technology Co ltd
Priority date: 2022-10-17
Filing date: 2022-10-17
Publication date: 2022-11-11
Anticipated expiration: 2042-10-17
Also published as: CN115320307B

Abstract

The application provides a vibration damping method, a vibration damping device, an electronic device and a storage medium for a vehicle, comprising: acquiring the suspension vertical movement speed of a target vehicle, the vehicle body acceleration and the vehicle body movement speed along the vehicle body direction in the running process of the target vehicle in real time to determine a vehicle response observation value sequence corresponding to a current time window; obtaining a target hidden Markov model which is effective in a current time window according to a vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden Markov model; and determining the bump level of the road where the target vehicle runs in the current time window according to the target hidden Markov model and the vehicle response observation value sequence, selecting a vibration reduction control parameter matched with the bump level of the road, and generating a corresponding vibration reduction control strategy to reduce vibration of the target vehicle. Therefore, a corresponding vibration damping control strategy can be selected according to the bumping grade, the vibration of the vehicle can be accurately reduced, and the vibration damping effect of the vehicle is improved.

Description

Vibration damping method and device for vehicle, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of vehicle vibration reduction technologies, and in particular, to a vibration reduction method, a vibration reduction device, an electronic device, and a storage medium for a vehicle.

Background

With the continuous increase of automobile holding capacity in China, the requirements of drivers and passengers on the driving comfort are higher and higher, and the vibration reduction effect of the automobile has an inseparable relationship with the driving comfort. In the actual driving process of the vehicle, the types of roads are various, and different types of roads have different bump levels. Roads with different bump levels can generate excitation with different frequencies and different amplitudes on the body and wheels of the vehicle, so that the vehicle generates different forms of vibration.

At present, the vehicle vibration damping method in the prior art can only use the same control parameters for roads with different bumping grades to process the excitation generated by the roads with different bumping grades, so that the vibration damping effect of the roads with different bumping grades reaches a certain balance, the overall vibration damping effect of the vehicle is ensured, the vibration damping control on the vehicle is not accurate enough, the vibration damping effect of the vehicle is poor, and the driving comfort of the vehicle is influenced.

Disclosure of Invention

In view of the above, an object of the present application is to provide a method, a device, an electronic device and a storage medium for vehicle vibration damping, which are capable of determining a vehicle response observation value sequence corresponding to a current time window according to a suspension vertical motion speed, a vehicle body acceleration and a vehicle body motion speed of a vehicle; and determining the bumping grade of the road running in the current time window by using the vehicle response observation value sequence and the target hidden Markov model corresponding to the current time window, and further executing a vibration reduction control strategy matched with the bumping grade. Like this, can discern the grade of jolting of current road fast accurately to select corresponding damping control strategy according to the grade of jolting, thereby carry out the vehicle damping more accurately, promote vehicle damping effect and improve the driving travelling comfort of vehicle.

The embodiment of the application provides a vibration reduction method of a vehicle, which comprises the following steps:

the method comprises the steps of obtaining the suspension vertical movement speed of a target vehicle, the vehicle body acceleration and the vehicle body movement speed along the vehicle body direction in each sampling period in the running process of the target vehicle in real time;

for each time window in a plurality of time windows forming the running process, determining a vehicle response observation value of the target vehicle in the time window according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle in each target sampling period in a plurality of target sampling periods corresponding to the time window;

updating a vehicle response observation value sequence of the target vehicle in the driving process according to the vehicle response observation value in the current time window to obtain a vehicle response observation value sequence corresponding to the current time window; wherein the sequence of vehicle response observations consists of vehicle response observations within each of a plurality of time windows of the driving process;

obtaining a target hidden Markov model which is effective in the current time window according to the vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden Markov model;

determining the bumping grade of a road driven by the target vehicle in the current time window according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window;

and selecting a vibration damping control parameter matched with the bump grade of the road on which the target vehicle runs in the current time window based on the bump grade of the road on which the target vehicle runs in the current time window, and generating a corresponding vibration damping control strategy according to the vibration damping control parameter so as to damp the target vehicle.

Further, the step of obtaining the suspension vertical movement speed of the target vehicle and the vehicle body acceleration and the vehicle body movement speed along the vehicle body direction in each sampling period in the running process of the target vehicle in real time comprises:

acquiring a suspension vertical movement stroke of each position in at least one position on a suspension of the target vehicle, a vehicle body acceleration of each position in at least one position on a vehicle body of the target vehicle and a vehicle body movement speed of the target vehicle in each sampling period in the running process of the target vehicle;

calculating an average value of absolute values of the vehicle body acceleration of each position on the vehicle body of the target vehicle, and determining the average value as the vehicle body acceleration of the target vehicle along the vehicle body direction in the sampling period;

for the suspension vertical motion stroke of each position on the suspension of the target vehicle, determining the differential of the suspension vertical motion stroke of the position on the suspension with respect to time as the suspension vertical motion speed of the position on the suspension of the target vehicle;

and calculating the average value of the absolute value of the suspension vertical movement speed of each position on the suspension of the target vehicle, and determining the average value as the suspension vertical movement speed of the target vehicle in the sampling period.

Further, the step of determining, for each of a plurality of time windows constituting the driving process, a vehicle response observed value of the target vehicle within the time window according to the suspension vertical movement velocity, the vehicle body acceleration and the vehicle body movement velocity of the target vehicle within each of a plurality of target sampling periods corresponding to the time window includes:

aiming at each target sampling period of the target vehicle in a plurality of target sampling periods corresponding to the time window, determining a pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs;

carrying out nonlinear transformation on the vehicle body movement speed in the target sampling period by using a transformation rule associated with a pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs to obtain the transformation vehicle body movement speed in the target sampling period;

calculating the average value of the movement speeds of the converted vehicle body in a plurality of target sampling periods corresponding to the time window, and determining the average value of the movement speeds of the converted vehicle body as the average movement speed of the converted vehicle body corresponding to the time window;

calculating the average value of the suspension vertical movement speeds in a plurality of target sampling periods corresponding to the time window, and determining the average value of the suspension vertical movement speeds as the average suspension vertical movement speed corresponding to the time window;

dividing the average suspension vertical motion speed by the average converted vehicle body motion speed to obtain a first vehicle response metric value of the target vehicle in the time window;

calculating the average value of the vehicle body acceleration in a plurality of target sampling periods corresponding to the time window, and determining the average value of the vehicle body acceleration as a second vehicle response measurement value of the target vehicle in the time window;

carrying out non-dimensionalization processing and uniform magnitude processing on the first vehicle response measuring value and the second vehicle response measuring value, and adding the first vehicle response measuring value and the second vehicle response measuring value obtained after the non-dimensionalization processing and the uniform magnitude processing to obtain a vehicle response measuring value of the target vehicle in the time window;

and determining the vehicle response observed value corresponding to the vehicle response measured value in the current time window according to the preset corresponding relation between the value range of the vehicle response measured value and the vehicle response observed value.

Further, the step of obtaining a target hidden markov model effective in the current time window according to the vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden markov model includes:

according to the vehicle response observation value sequence corresponding to the current time window and the initial hidden Markov model, constructing a log-likelihood function of the initial hidden Markov model;

converting and solving the log-likelihood function by using a vehicle response observation value sequence corresponding to a current time window and a road bump grade sequence corresponding to the current time window to obtain a model parameter which enables the log-likelihood function to have a maximum value; the sequence of the bump levels of the road consists of the bump levels of the road corresponding to each time window in the driving process;

and correspondingly replacing the initial model parameters in the initial hidden Markov model with model parameters which enable the log-likelihood function to have a maximum value, so as to obtain the effective target hidden Markov model in the current time window.

Further, the initial hidden markov model is constructed by:

state set defining a bump level of a road

And defining a set of observations of vehicle response observations

(ii) a Wherein, the first and the second end of the pipe are connected with each other,

and

is a positive integer; state aggregation

Each element in (a) represents a level of jounce of a road; set of observation values

Each element of (a) represents a vehicle response observation;

establishing a state transition probability matrix for a grade of road jolt

Observation probability matrix of occurrence probability of vehicle response observation value under roads of different bump grades

And initial state probability vector of bump level of road

(ii) a Wherein the state transition probability matrix

Each element of

The bumpiness level of the road corresponding to the current time window is represented as

And the bumpy level of the road corresponding to the next time window is converted into the bumpy level

The probability of (d); observation probability matrix

Each element of

Indicating that the vehicle is at a level of jounce of

When driving on the road, the observed value of the vehicle response is

The probability of (d);

，

and

are all positive integers;

according to state transition probability matrix

Observation probability matrix

And initial state probability vector

And constructing an initial hidden Markov model.

Further, the step of defining a set of observation values for the vehicle response observation values includes:

determining a vehicle response metering value sequence generated by a test vehicle in the running process of the road with each bump grade according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the test vehicle in the running process of the road with each bump grade;

determining the value range of the vehicle response metering value by counting the vehicle response metering values appearing in the vehicle response metering value sequence corresponding to the road of each bumping grade;

and dividing the value range of the vehicle response measurement value into a plurality of sections of value intervals, and setting a corresponding vehicle response observation value for each section of value interval of the divided vehicle response measurement value to obtain an observation value set of the vehicle response observation value.

Further, the step of establishing an observation probability matrix of the occurrence probability of the vehicle response observation value under roads of different bump levels includes:

for observation probability matrix

Each element of

According to said test vehicle at the level of jounce

Determining the corresponding relation between each section of value interval of the set vehicle response measurement value and the vehicle response observation value, and determining the test vehicle at the bump grade

The vehicle response observed value sequence generated in the driving process of the road;

according to the bump grade of the test vehicle

The total number of vehicle response observations in a sequence of vehicle response observations generated during travel of the road and the vehicle response observations are

Is determined to be at a vehicle bump level of

The observed value of the vehicle response generated during the travel on the road is

And using the determined probability as an observation probability matrix

Middle element (II)

The value of (c).

Further, the determining a bump level of a road traveled by the target vehicle within the current time window according to the target hidden markov model and the vehicle response observation value sequence corresponding to the current time window includes:

aiming at each bump grade of the road, determining the observation probability of the vehicle response observation value sequence corresponding to the current time window under the bump grade according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window;

and determining the maximum observation probability in the observation probabilities under each bumping grade, and determining the bumping grade corresponding to the maximum observation probability as the bumping grade of the road on which the target vehicle runs in the current time window.

An embodiment of the present application further provides a vibration damping device of a vehicle, the vibration damping device includes:

the acquisition module is used for acquiring the suspension vertical movement speed of the target vehicle and the vehicle body acceleration and the vehicle body movement speed along the vehicle body direction in each sampling period in the running process of the target vehicle in real time;

the first determination module is used for determining a vehicle response observation value of the target vehicle in each time window according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle in each target sampling period in a plurality of target sampling periods corresponding to the time window aiming at each time window in a plurality of time windows forming the driving process;

the updating module is used for updating the vehicle response observation value sequence of the target vehicle in the running process according to the vehicle response observation value in the current time window to obtain a vehicle response observation value sequence corresponding to the current time window; wherein the sequence of vehicle response observations consists of vehicle response observations within each of a plurality of time windows of the driving process;

the generating module is used for obtaining a target hidden Markov model which is effective in the current time window according to the vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden Markov model;

the second determination module is used for determining the bumping grade of the road driven by the target vehicle in the current time window according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window;

and the control module is used for selecting a vibration damping control parameter matched with the bump grade of the road which the target vehicle runs in the current time window based on the bump grade of the road which the target vehicle runs in the current time window, and generating a corresponding vibration damping control strategy according to the vibration damping control parameter so as to damp the target vehicle.

Further, when the obtaining module is used for obtaining the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle in the vehicle body direction in each sampling period in the driving process of the target vehicle in real time, the obtaining module is used for:

Further, when the first determining module is configured to determine, for each of a plurality of time windows constituting the driving process, a vehicle response observed value of the target vehicle within the time window according to the suspension vertical movement velocity, the vehicle body acceleration, and the vehicle body movement velocity of the target vehicle within each of a plurality of target sampling periods corresponding to the time window, the first determining module is configured to:

aiming at each target sampling period in a plurality of target sampling periods corresponding to the time window, determining a pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs;

calculating the average value of the vertical motion speeds of the suspension in a plurality of target sampling periods corresponding to the time window, and determining the average value of the vertical motion speeds of the suspension as the average vertical motion speed of the suspension corresponding to the time window;

Further, when the generating module is configured to obtain a target hidden markov model valid in the current time window according to the vehicle response observation value sequence corresponding to the current time window and the pre-constructed initial hidden markov model, the generating module is configured to:

Further, as shown in fig. 3, the vibration damping device further includes a building block; the building module is configured to build the initial hidden Markov model by:

state set defining a level of bumps of a road

And defining a set of observations of vehicle response observations

(ii) a Wherein the content of the first and second substances,

and

is a positive integer; state collection

Each element in (a) represents a vehicle response observation;

establishing a state transition probability matrix for a grade of road jolt

And initial state probability vector of bump level of road

(ii) a Wherein the state transition probability matrix

Each element of

The bump level of the road corresponding to the current time window is represented as

The probability of (d); observation probability matrix

Each element of

Indicating that the vehicle is at a level of jounce

When driving on the road, the observed value of the vehicle response is

The probability of (d);

，

and

are all positive integers;

according to state transition probability matrix

Observation probability matrix

And initial state probability vector

An initial hidden markov model is constructed.

Further, the build module, when configured to define a set of observations of vehicle response observations, is configured to:

determining a vehicle response metering value sequence generated by a test vehicle in the running process of the road at each bump level according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the test vehicle in the running process of the road at each bump level;

Further, the construction module, when used in an observation probability matrix of the probability of occurrence of vehicle response observations under roads of different bump levels, is configured to:

for observation probability matrix

Each element of

According to said test vehicle at the level of jounce

The vehicle response measuring value sequence generated in the running process of the road and the corresponding relation between each section of value interval of the set vehicle response measuring value and the vehicle response observation value determine the bumping grade of the test vehicle

A vehicle response observation sequence generated during the driving of the road;

according to the bump grade of the test vehicle

Is determined to be at a vehicle bump level of

And using the determined probability as an observation probability matrix

Middle element

The value of (c).

Further, when the second determining module is configured to determine the bump level of the road traveled by the target vehicle within the current time window according to the target hidden markov model and the vehicle response observation value sequence corresponding to the current time window, the second determining module is configured to:

aiming at each bumping grade of the road, determining the observation probability of the vehicle response observation value sequence corresponding to the current time window under the bumping grade according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window;

An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of a method of damping vibration in a vehicle as described above.

Embodiments of the present application also provide a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of a method for damping vibration of a vehicle as described above.

According to the vibration damping method, the vibration damping device, the electronic equipment and the storage medium of the vehicle, a vehicle response observation value sequence corresponding to a current time window can be determined according to the suspension vertical motion speed, the vehicle body acceleration and the vehicle body motion speed of the vehicle; and determining the bumping grade of the road running in the current time window by using the vehicle response observation value sequence and the target hidden Markov model corresponding to the current time window, and further executing a vibration reduction control strategy matched with the bumping grade. Like this, can discern the grade of jolting of current road fast accurately to select corresponding damping control strategy according to the grade of jolting, thereby carry out the vehicle damping more accurately, promote vehicle damping effect and improve the driving travelling comfort of vehicle.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

FIG. 1 is a flow chart illustrating a method for damping vibration in a vehicle according to an embodiment of the present disclosure;

fig. 2 (a) to fig. 2 (f) respectively show a score evaluation comparison diagram of a vibration damping method of a vehicle provided by an embodiment of the present application on different evaluation items;

FIG. 3 is a schematic structural diagram of a vibration damping device of a vehicle according to an embodiment of the present disclosure;

fig. 4 shows a second schematic structural diagram of a vibration damping device of a vehicle according to an embodiment of the present application;

fig. 5 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.

Based on this, the embodiment of the application provides a vibration damping method, a vibration damping device, an electronic device and a storage medium for a vehicle, so as to quickly and accurately identify the bumping grade of the current road, and select a corresponding vibration damping control strategy according to the bumping grade, thereby more accurately performing vehicle vibration damping, improving the vehicle vibration damping effect and improving the driving comfort of the vehicle.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for damping vibration of a vehicle according to an embodiment of the present disclosure. As shown in fig. 1, a vibration damping method provided in an embodiment of the present application includes:

s101, acquiring the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle along the vehicle body direction in each sampling period in the running process of the target vehicle in real time.

It should be noted that the vibration damping method provided by the embodiment of the present application can be applied to a vibration damping control system of a vehicle. Here, the vibration damping control system may acquire the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed in the vehicle body direction through various sensors mounted on the target vehicle, and the sensors may acquire data according to a certain period, so that in this step, the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed in each sampling period during the traveling process may be acquired in real time as the target vehicle travels. The suspension vertical movement speed refers to the speed of the suspension moving up and down along the direction vertical to the ground; the vehicle body acceleration and the vehicle body moving speed in the vehicle body direction refer to the vehicle body acceleration and the vehicle body moving speed in the vehicle advancing direction.

In one possible implementation, step S101 may include:

s1011, for each sampling period in the running process of the target vehicle, acquiring a suspension vertical movement stroke of each position in at least one position on a suspension of the target vehicle, a vehicle body acceleration of each position in at least one position on a vehicle body of the target vehicle and a vehicle body movement speed of the target vehicle in the sampling period.

For example, an acceleration sensor may be installed at any position on the front left portion, the front right portion, and the trunk in the body of the target vehicle, respectively, to obtain the body acceleration and the body movement speed at each of three positions on the body of the target vehicle; the acceleration and the movement speed of the vehicle body can also be obtained by resolving through the side-tipping, pitching and vertical movement characteristics of the vehicle body; meanwhile, a suspension height sensor can be respectively installed at any position of the left front part and the right front part of the suspension of the target vehicle so as to acquire the vertical motion stroke of the suspension at each of the two positions on the suspension of the target vehicle.

And S1012, calculating an average value of the absolute values of the vehicle body acceleration of each position on the vehicle body of the target vehicle, and determining the average value as the vehicle body acceleration of the target vehicle along the vehicle body direction in the sampling period.

Corresponding to the above example, first, the absolute values of the vehicle body accelerations at three positions of the left front portion, the right front portion, and the trunk on the vehicle body of the target vehicle may be calculated, respectively; then, the average value of the absolute values of the vehicle body accelerations at the three positions is calculated again, and the average value is determined as the vehicle body acceleration of the target vehicle in the vehicle body direction in the sampling period.

And S1013, determining the differential of the suspension vertical motion stroke of each position on the suspension of the target vehicle with respect to time as the suspension vertical motion speed of the position on the suspension of the target vehicle according to the suspension vertical motion stroke of each position on the suspension of the target vehicle.

Corresponding to the above-described example, the differentials with respect to time of the suspension vertical movement stroke of the left front portion and the suspension vertical movement stroke of the right front portion on the suspension of the target vehicle may be calculated, respectively, to obtain the suspension vertical movement speed of the left front portion and the suspension vertical movement speed of the right front portion on the suspension of the target vehicle.

And S1014, calculating the average value of the absolute values of the suspension vertical movement speeds of all the positions on the suspension of the target vehicle, and determining the average value as the suspension vertical movement speed of the target vehicle in the sampling period.

Corresponding to the above example, first, the absolute values of the suspension vertical movement speed of the left front portion and the suspension vertical movement speed of the right front portion may be calculated, respectively; then, the average value of the absolute values of the suspension vertical movement speeds of the two positions is calculated, and the average value is determined as the suspension vertical movement speed of the target vehicle in the sampling period.

It should be noted that, in a possible implementation manner, in addition to the above method for obtaining the vertical motion stroke of the suspension by the suspension height sensor and calculating the vertical motion speed of the suspension, the vertical motion speed of the suspension may also be calculated by the vertical motion characteristics of the wheel acceleration sensor and the corresponding position of the vehicle body. In addition, other existing methods can be used to obtain the vertical motion speed of the suspension, and the application is not limited in any way.

S102, aiming at each time window in a plurality of time windows forming the running process, determining a vehicle response observation value of the target vehicle in the time window according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle in each target sampling period in a plurality of target sampling periods corresponding to the time window.

It should be noted that the driving process of the target vehicle may be divided into a plurality of time windows, and each time window corresponds to a plurality of sampling periods. In this step, for each of a plurality of time windows constituting the driving process, a vehicle response observation value of the target vehicle in the time window may be determined according to a suspension vertical movement velocity, a vehicle body acceleration, and a vehicle body movement velocity in each of a plurality of target sampling periods corresponding to the time window.

In one possible implementation, step S102 may include:

and S1021, aiming at each target sampling period of the target vehicle in a plurality of target sampling periods corresponding to the time window, determining a pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs.

In the step, the value range of the vehicle body movement speed can be divided into a plurality of speed intervals in advance; for example, the vehicle body movement speed may include: 0 to 20km/h,20 to 40km/h,40 to 60km/h,60 to 120km/h and more than 120 km/h; after the speed intervals are divided, determining the speed interval to which the vehicle body movement speed belongs according to the vehicle body movement speed in each target sampling period.

And S1022, carrying out nonlinear transformation on the vehicle body movement speed in the target sampling period by using a transformation rule associated with a pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs, so as to obtain the transformed vehicle body movement speed in the target sampling period.

In the step, each speed interval has a pre-configured associated conversion rule, and the vehicle body movement speed in the target sampling period can be subjected to nonlinear conversion by using the conversion rule associated with the pre-divided speed interval to which the vehicle body movement speed in the target sampling period belongs, so that the vehicle body movement speed is converted into the corresponding empirical vehicle speed, and the converted vehicle body movement speed is obtained. The form of the transformation rule can be a transformation function obtained by fitting the vehicle body movement speed and the corresponding empirical vehicle speed, and the vehicle body movement speed in the target sampling period is brought into the transformation function to obtain the transformation vehicle body movement speed; the form of the transformation rule can also be a table between the vehicle body movement speed and the corresponding transformation rule or between the vehicle body movement speed and the corresponding transformation vehicle body movement speed, the transformation rule corresponding to each speed interval or the transformation vehicle body movement speed can be preset in the table, and the transformation rule corresponding to the speed interval to which the vehicle body movement speed in the target sampling period belongs can be determined through a query table; then, obtaining the movement speed of the converted vehicle body by using a conversion rule; or, the table may preset the converted vehicle body movement speed corresponding to each speed interval, and the converted vehicle body movement speed corresponding to the speed interval to which the vehicle body movement speed belongs in the target sampling period is directly determined through the lookup table.

And S1023, calculating the average value of the converted vehicle body movement speed in a plurality of target sampling periods corresponding to the time window, and determining the average value of the converted vehicle body movement speed as the average converted vehicle body movement speed corresponding to the time window.

And S1024, calculating the average value of the vertical motion speeds of the suspension in a plurality of target sampling periods corresponding to the time window, and determining the average value of the vertical motion speeds of the suspension as the average vertical motion speed of the suspension corresponding to the time window.

And S1025, dividing the average suspension vertical motion speed by the average converted vehicle body motion speed to obtain a first vehicle response measurement value of the target vehicle in the time window.

And S1026, calculating the average value of the vehicle body acceleration in a plurality of target sampling periods corresponding to the time window, and determining the average value of the vehicle body acceleration as a second vehicle response measurement value of the target vehicle in the time window.

In the practical application process of the embodiment of the application, the influence of the motion speed of the vehicle body on the measurement result corresponding to the vertical motion speed of the suspension is large, and the influence on the measurement result corresponding to the acceleration of the vehicle body is weak. Therefore, the average suspension vertical movement speed is divided by the average conversion vehicle body movement speed to obtain a first vehicle response measuring value of the target vehicle in the time window, and the influence of different vehicle body movement speeds on the first vehicle response measuring value can be eliminated.

S1027, carrying out non-dimensionalization processing and uniform magnitude processing on the first vehicle response metric value and the second vehicle response metric value, and adding the first vehicle response metric value and the second vehicle response metric value obtained after the non-dimensionalization processing and the uniform magnitude processing to obtain the vehicle response metric value of the target vehicle in the time window.

In this step, since the first vehicle response measurement value and the second vehicle response measurement value have different measurement units and magnitude, it is necessary to perform non-dimensionalization processing and uniform magnitude processing on the first vehicle response measurement value and the second vehicle response measurement value, and then add the first vehicle response measurement value and the second vehicle response measurement value obtained after the non-dimensionalization processing and the uniform magnitude processing to obtain the vehicle response measurement value of the target vehicle within the time window.

S1028, determining a vehicle response observed value corresponding to the vehicle response measured value in the current time window according to a preset corresponding relation between a value range of the vehicle response measured value and the vehicle response observed value.

It should be noted that the vehicle response measurement value may have an infinite variety of values, and in order to facilitate obtaining a subsequent target hidden markov model, a value range of the vehicle response measurement value may be divided into a plurality of value intervals in advance, and a corresponding vehicle response observation value is set for each value interval of the vehicle response measurement value; exemplarily, setting a corresponding vehicle response observed value to be 5 according to the vehicle response measurement value falling into the value range of 0-10; and if the vehicle response measurement value in the current time window is 8, determining that the vehicle response observation value in the current time window is 5. In this way, vehicle response observations can be defined to be a finite plurality in order to facilitate subsequent target hidden Markov models.

S103, updating the vehicle response observation value sequence of the target vehicle in the driving process according to the vehicle response observation value in the current time window to obtain a vehicle response observation value sequence corresponding to the current time window.

Wherein the sequence of vehicle response observations consists of vehicle response observations within each of a plurality of time windows of the driving process. Specifically, the vehicle response observations within each of a plurality of time windows of the driving process may be arranged in a front-to-back order of the time windows to form a vehicle response observation sequence. Here, as the vehicle travels, the time is continuously advanced, the time window during the travel process is continuously increased, the vehicle response observation value sequence is also continuously updated, and the vehicle response observation value in the current time window may be added to the vehicle response observation value sequence corresponding to the time window before the current time window, so as to obtain the vehicle response observation value sequence corresponding to the current time window.

And S104, obtaining a target hidden Markov model which is effective in the current time window according to the vehicle response observation value sequence corresponding to the current time window and the pre-constructed initial hidden Markov model.

In one possible implementation, the initial hidden markov model may be constructed by:

the first step is as follows: state set defining a bump level of a road

And defining a set of observations of vehicle response observations

. Wherein, the first and the second end of the pipe are connected with each other,

and

is a positive integer; state collection

Each element in (a) represents a vehicle response observation.

Wherein the state set of the bump level of the road

Each element in (a) represents a predefined level of pitch of a road,

representing the number of defined bump levels of the road; set of observations of vehicle response observations

Each element of (a) represents a preset type of vehicle response observation that may occur during travel,

representing the number of defined vehicle response observations.

In one possible embodiment, the step of defining a set of observations of vehicle response observations comprises:

step 1, aiming at each bumpy road, determining a vehicle response metering value sequence generated by a test vehicle in the driving process of the bumpy road according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the test vehicle in the driving process of the bumpy road.

And 2, determining the value range of the vehicle response measurement value by counting the vehicle response measurement values appearing in the vehicle response measurement value sequence corresponding to the road of each bumping grade.

And 3, dividing the value range of the vehicle response measurement value into a plurality of sections of value intervals, and setting a corresponding vehicle response observation value for each section of value interval of the divided vehicle response measurement value to obtain an observation value set of the vehicle response observation values.

In specific implementation, for each predefined bumping grade in the steps 1 to 3, the test vehicle is driven on the road with the bumping grade under laboratory conditions, the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the test vehicle during the driving process of the road with the bumping grade are obtained, and the vehicle response metric value sequence generated during the driving process of the test vehicle on the road with the bumping grade is determined. It should be understood that unlike actual roads in real life where the level of jounce varies constantly, roads under laboratory conditions are uniform, and roads at each level of jounce maintain the same level of jounce throughout.

Here, the manner of obtaining the suspension vertical movement speed, the vehicle body acceleration, and the vehicle body movement speed of the test vehicle during the driving process, and determining the vehicle response measurement value sequence generated by the test vehicle during the driving process of the road at the bumpy level according to the suspension vertical movement speed, the vehicle body acceleration, and the vehicle body movement speed may refer to the description in the foregoing step S101 to step S103, and may achieve the same technical effect, which is not described herein again.

After the vehicle response metering value sequence generated by the test vehicle in the running process of the road with each bump grade is determined, the value range of the vehicle response metering value is determined by counting the vehicle response metering values appearing in the vehicle response metering value sequence corresponding to the road with each bump grade.

Further, the value range of the vehicle response measurement value is divided into a plurality of sections of value intervals, each section of value interval of the vehicle response measurement value obtained through division and the corresponding vehicle response observation value are set, and an observation value set of the vehicle response observation value is obtained. Here, the vehicle respondsWhen the value range of the metering value is divided into a plurality of sections of value intervals, the value range can be uniformly divided according to a certain value interval, and illustratively, the value range of the vehicle response metering value is divided into 10 value intervals

And waiting for a plurality of value intervals.

Further, corresponding vehicle response observation values are set for each section of value range of the vehicle response measurement values obtained through division, and an observation value set of the vehicle response observation values is obtained. In the example, a value interval [0,10) of the vehicle response measurement value corresponds to the vehicle response observation value 5, a value interval [10,20) corresponds to the vehicle response observation value 15, a value interval [20,30) corresponds to the vehicle response observation value 25, and a value interval [30,40) corresponds to the vehicle response observation value 35; from this, a set of observations of vehicle response observations is obtained

Wherein, in the step (A),

。

second, establishing a state transition probability matrix of the bump grade of the road

And initial state probability vector of bump level of road

。

Wherein the state transition probability matrix

Each element of

The probability of (d); observation probability matrix

Each element of

Indicating that the vehicle is at a level of jounce of

When driving on the road, the observed value of the vehicle response is

The probability of (d);

，

and

are all positive integers.

In particular implementations, the state transition probability matrix may be represented by the following formula

Observation probability matrix

And initial state probability vector

：

Wherein the state transition probability matrix

Each element of

The probability of (c). When present, is

In the state of the bump level, the states exist independently and do not influence each other, so the probability of switching each state can be regarded as equal probability event, therefore, the state transition probability matrix

Each element of

Is equal to

。

Wherein the probability matrix is observed

Each element in

Indicating that the vehicle is at a level of jounce of

When driving on the road, the observed value of the vehicle response is

The probability of (c).

In particular, the sampling can be in the form of random sampling

Extracting any one of the states of the bump grade from the bump grade road as an initial state, so that the probability vector of the initial state

=

。

In one possible embodiment, the step of establishing an observation probability matrix of the probability of occurrence of the vehicle response observation under roads of different levels of jounce includes:

step 1, aiming at observation probability matrix

Each element of

According to said test vehicleAt the level of jounce

A vehicle response observation sequence generated during travel of the road.

Step 2, according to the bumping grade of the test vehicle

The total number of vehicle response observed values in a vehicle response observed value sequence generated in the driving process of the road and the vehicle response observed value are

Is determined to be at a vehicle bump level of

And using the determined probability as an observation probability matrix

Middle element

The value of (c).

In specific implementation, for the above step 1 to step 2, for the predefined

The bump grade is set so that the test vehicle is in factThe actual vehicle running is carried out on the road of each bumping grade under the laboratory condition, and the actual vehicle running is obtained

Individual vehicles respond to the observation sequence. And determining an observation probability matrix according to the total number of vehicle response observation values in a vehicle response observation value sequence generated in the driving process of the test vehicle on the road of each bump grade and the distribution condition of M vehicle response observation values in the vehicle response observation value sequence

。

For example, if the test vehicle is at a bump level

The total number of vehicle response observations in the sequence of vehicle response observations generated during travel of the road is

And vehicle response observed value in vehicle response observed value sequence

Is in an amount of

The vehicle is at a bump level of

Has a probability of

Thus observing the probability matrix

Middle element

。

Thirdly, according to the state transition probability matrix

Observation probability matrix

And initial state probability vector

An initial hidden markov model is constructed.

In particular implementations, the probability matrix can be transitioned according to the state

Observation probability matrix

And initial state probability vector

Constructing an initial hidden Markov model

。

In one possible implementation, step S104 includes:

s1041, according to the vehicle response observation value sequence corresponding to the current time window and the initial hidden Markov model, building a log-likelihood function of the initial hidden Markov model.

The vehicle response observation value sequence corresponding to the current time window can be represented as:

in the formula (I), the compound is shown in the specification,

；

is shown as

Vehicle response observations for each time window.

In the step, according to a vehicle response observation value sequence O corresponding to a current time window and an initial hidden Markov model

A log-likelihood function of the initial hidden Markov model may be constructed

。

S1042, converting and solving the log-likelihood function by using a vehicle response observation value sequence corresponding to the current time window and a bump grade sequence of a road corresponding to the current time window to obtain a model parameter enabling the log-likelihood function to have a maximum value.

The sequence of the grade of the road is composed of the grade of the road corresponding to each time window in the driving process, and the sequence of the grade of the road corresponding to the current time window can be expressed as:

in the formula (I), the compound is shown in the specification,

；

denotes the first

The time window corresponds to the level of the road bump.

The following description will specifically describe a vehicle response observation sequence corresponding to a current time window

The sequence of the bump level of the road corresponding to the current time window is aligned with the log-likelihood function

The conversion and solution processes are as follows:

sequence of grade of bump due to unknown road

Vehicle response observed value sequence as hidden variable

There is a direct relationship, and according to the addition rule and the multiplication rule of probability calculation, there is a relationship shown by the following formula:

according to the Qinyang inequality, the log-likelihood function can be obtained

To the maximization problem of

Maximization of the lower limit function. Let L (α (i)) be the result of a certain iteration of the likelihood function, then it is associated with

The difference between them can be tabulatedShown as follows:

order:

then a function

Is that

Is a function of the lower limit value of (c).

Since the finding is in the process of repeated iteration, the finding can find the function

Of maximum value of

According to the idea, the calculation results of all the i moments are the results of iterative generation, and the known values can be not considered, so that the function can be processed

Further transformation, as shown in the following formula:

order:

then will be

Hidden Markov model for maximization

The result is the result.

According to the probabilistic algorithm and the model definition of the initial hidden Markov model

The following transformations are performed:

in the above formula, the initial state probability vector, the state transition probability matrix element and the observation probability matrix element are independent by the three addition terms, and the values of the corresponding elements enabling the three addition terms to obtain the maximum value are calculated respectively, so that the target hidden Markov model can be constructed.

Specifically, the problem of maximizing the three addition terms can be described by the formula:

(1) The first maximization problem:

(2) The second maximization problem:

(3) The third maximization problem:

constructing Lagrange functions separately and separately for

、

And

and calculating the partial derivatives, and making the partial derivatives result to be 0, so as to calculate the model parameters which enable the log likelihood function to have the maximum value. Specifically, the model parameter that maximizes the log-likelihood function may be expressed as:

s1043, replacing the initial model parameter in the initial hidden Markov model with the model parameter which enables the maximum value of the log likelihood function to obtain the effective target hidden Markov model in the current time window. Wherein the target hidden Markov model is a sequence of observations that enable vehicle response

The model with the highest probability of occurrence.

Thus, as the target vehicle travels, the vehicle response observation sequence can be continuously updated and the target hidden Markov model valid in the current time window can be iteratively updated in real time to determine the bump level of the road on which the target vehicle travels in the current time window. When the driving process of the target vehicle advances to the next time window of the current time window, the effective target hidden Markov model in the next time window can be determined according to the vehicle response observation value sequence corresponding to the next time window, and then the bump level of the road corresponding to the next time window is determined.

And S105, determining the bumping grade of the road driven by the target vehicle in the current time window according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window.

This step is carried outAfter the target hidden Markov model is determined, a bump level sequence of the road with the maximum occurrence probability of the current vehicle response observation value sequence can be obtained in each time window

. Furthermore, a dynamic calculation mode can be adopted, calculation results corresponding to all time windows before the current time window are taken as known quantities, and only the probability of the current time window is considered, so that the calculation load is effectively reduced.

In one possible implementation, step S105 includes:

s1051, aiming at each bumping grade of the road, determining the observation probability of the vehicle response observation value sequence corresponding to the current time window under the bumping grade according to the target hidden Markov model and the vehicle response observation value sequence corresponding to the current time window.

And S1052, determining the maximum observation probability in the observation probabilities under each bumping grade, and determining the bumping grade corresponding to the maximum observation probability as the bumping grade of the road driven by the target vehicle in the current time window.

S106, based on the bumping grade of the road where the target vehicle runs in the current time window, selecting a vibration damping control parameter matched with the bumping grade of the road where the target vehicle runs in the current time window, and generating a corresponding vibration damping control strategy according to the vibration damping control parameter to damp the target vehicle.

It should be noted that the vibration damping method provided by the embodiment of the present application can be applied to a vibration damping control system of a vehicle, for example, a semi-active electronically controlled vibration damper system. In order to achieve a better vibration damping control effect, the vibration damping control system can respectively adopt a ceiling control method to restrain the vibration of the vehicle body, a floor control method to restrain the vibration of the wheels, and a control method to restrain the side inclination of the vehicle body during steering. According to the vibration damping method provided by the embodiment of the application, the vibration damping control parameters matched with the bump grade of the road can be selected according to the bump grade of the road where the target vehicle runs in the current time window, and then the corresponding vibration damping control strategies are generated according to the vibration damping control parameters to control the components of the target vehicle so as to damp the target vehicle.

For example, for each control method including a skyhook control method for suppressing vibration of a vehicle body, a ground-ceiling control method for suppressing vibration of wheels, a control method for suppressing roll of a vehicle body at the time of steering, and the like, the vibration damping control system configures vibration damping control parameters corresponding to different levels of pitching for each control method in advance. When the target hidden Markov model identifies that the bumping grade of the road where the target vehicle runs in the current time window is a second-level bumping road, the vibration damping control system determines the vibration damping control parameters corresponding to each control method when the bumping grade is the second-level bumping road, further generates the corresponding actual vibration damping control strategy of each control method according to the vibration damping control parameters, and performs vibration damping control on a plurality of components of the target vehicle according to the actual vibration damping control strategy, so that the vehicle vibration damping is performed more accurately, and a good vibration damping control effect is achieved.

In a specific experiment, the embodiment of the application selects various standard bump grades of bump road surfaces set by relevant standards as experimental road surfaces. In the experiment, aiming at the experimental road surface of each standard bump grade, the test vehicle is respectively controlled to simulate driving on the experimental road surface by different preset vibration damping control parameters, and the best matched vibration damping control parameter is selected for each bump grade by combining the evaluation score of a professional vehicle subjective evaluation engineer on the vehicle performance and the objective performance of experimental data, so that the adjustment and calibration of the vibration damping control parameter of the electric control vibration damper of the test vehicle are realized. For example, the damping control parameters may include current parameters for damping control; intensity parameters of vertical motion, pitching motion and side-tipping motion of the vehicle body; wheel hop control parameters, etc.

It should be noted that, in the method for adjusting and calibrating vibration damping of a vehicle in the prior art, only one set of control parameters is calibrated for the vehicle, that is, no matter what road surface the vehicle is running on, the vehicle uses the set of control parameters to perform vibration damping control, so that the vibration damping control effects of different road surfaces are balanced as much as possible, which obviously makes the precision of the vibration damping control of the vehicle on different road surfaces insufficient.

Therefore, after the adjustment and calibration of the vibration damping control parameters of the test vehicle are completed, the embodiment of the application selects the random road surface with different bump grades and randomly appearing on the road surface as the comparison road surface. The test vehicle and the comparison vehicle using the existing chassis vibration damping adjustment mode can be respectively controlled to carry out the comparison experiment of simulating driving on the comparison road surface. Recording vehicle experimental data in an experiment, such as basic current of a vehicle electronic control shock absorber on different road surfaces, damping force of vehicle shock absorption and the like; meanwhile, a subjective evaluation engineer will score a plurality of indexes of the vehicle in a vehicle scoring table according to a subjective evaluation system commonly used in the field, for example: the pitch degree of the vehicle body, the roll degree of the vehicle body, the driving smoothness of the vehicle and the like; and further giving out comprehensive evaluation scores, evaluation conclusions and the like of the test vehicle in a weighting calculation mode and other modes. The experimental results show that: compared with the method of only adopting a set of damping control parameters in the prior art, the method has better performance no matter whether the tested vehicle is objective experimental data or comprehensive evaluation scores on a comparison road surface, and can improve the damping effect of the vehicle and improve the driving comfort of the vehicle.

Specifically, fig. 2 (a) to fig. 2 (f) are respectively score evaluation comparison diagrams of a vehicle vibration damping method provided in the embodiment of the present application on different evaluation items. More specifically, fig. 2 (a) to 2 (f) are respectively comparison graphs of evaluation index scores of respective dimensions in (1) comfort in straight road, (2) comfort in bumpy road, (3) comfort in straight road, (5) stability in straight driving, (4) stability in straight driving, and (6) driving controllability of a vehicle (i.e., a state under road recognition), a vehicle (low damping state) using only an existing low damping chassis vibration damping adjustment method without recognizing the degree of road bumping, and a vehicle (high damping state) using only an existing high damping chassis vibration damping adjustment method without recognizing the degree of road bumping, respectively) using the vibration damping method based on recognizing the degree of road bumping provided by the embodiment of the present application.

And (2) carrying out comparison experiments on the comfort evaluation project of the flat and straight road surface, wherein the experimental road surface conditions comprise a smooth asphalt road surface, a rough asphalt road surface and a flat and straight cement road surface, and the vehicle speed condition is 0-100kph. As shown in fig. 2 (a), the experimental results show that the road surface recognition functions as: when the vehicle runs on a straight road surface in a straight line, the vehicle does not have much movement and vibration generated by ground excitation. A better choice at this point is low damping; the small damping provides proper body control and better vibration comfort.

And (2) a comparison experiment of a bumpy road comfort evaluation project, wherein the experimental road conditions comprise an asphalt joint road surface, an asphalt patch road surface, a damaged cement road surface and a Belgian road surface, and the vehicle speed condition is 0-100kph. As shown in fig. 2 (b), the experimental results indicate that the road surface recognition functions as: when a vehicle runs on a bumpy road in a straight line, the road surface structure and the vehicle body generated by road surface excitation move and vibrate more, and the frequency components are complex. At the moment, the vehicle needs more damping to restrain the motion of the vehicle body, the vibration generated by the excitation of the road surface is absorbed, the large damping provides enough vehicle body control, and the good vibration convergence is ensured.

And (3) carrying out comparison experiments on independent event comfort evaluation projects such as the deceleration strip and the like, wherein the experimental road conditions comprise bridge deck jump, parking lot deceleration strips, brazilian deceleration strips, continuous deceleration strips, a certain test field long wave road-No. 9 road, a certain test field long wave road-No. 11 road and the vehicle speed condition of 0-120kph. As shown in fig. 2 (c), the experimental results indicate that the road surface recognition functions as: road surface identification can provide softer impact feel (small damping) and better aftershock absorption capacity (large damping); in typical structural roadways such as long wave roads, high jump, arched bridges, etc., a significantly increased damping capacity is required to provide adequate vehicle body motion control.

And (4) carrying out comparison experiments on the linear driving stability evaluation project, wherein the experimental road conditions comprise that the speed is passed through a deceleration strip on one side, is passed through a well cover pit on one side, is bumpy and damaged, is flat, is belgium, is bumpy, is a deceleration strip and is in a speed condition of 0-80kph. As shown in fig. 2 (d), the experimental results show that the road surface recognition functions as: when the vehicle passes through a damaged road surface and a road surface with a single structure, the damping capacity is high, the straight-line driving capacity of the vehicle can be improved, and the influence of jumping steering on the straight-line driving stability is reduced; on a flat road, greater damping is desired to achieve greater anti-pitch control; on bumpy roads, however, less damping is desired to ensure comfort against pitch control.

And (5) carrying out comparison experiments on the curve stability evaluation project, wherein the experimental road conditions comprise a straight road, a bumpy road and a vehicle speed condition of 0-100kph. As shown in fig. 2 (e), the experimental results show that the road surface recognition functions as: on flat road surfaces, greater damping is desired to obtain greater anti-roll control; on bumpy roads, proper damping is desired to ensure some comfort of anti-roll control.

In the comparative experiment of the driving drivability evaluation item (6), the experimental road conditions were a straight road surface, a bumpy road surface, and a vehicle speed condition of 0 to 100kph. As shown in fig. 2 (f), the experimental results show that the road surface recognition functions as: for maneuverability, greater damping necessarily results in better maneuverability; but also to take into account the acceptable comfort margin under different road surfaces. That is, under a bumpy road, the controllability is improved by increasing the damping while the comfort margin is allowed.

According to the vibration reduction method for the vehicle, a vehicle response observation value sequence corresponding to a current time window can be determined according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the vehicle; and determining the bumping grade of the road running in the current time window by using the vehicle response observation value sequence and the target hidden Markov model corresponding to the current time window, and further executing a vibration reduction control strategy matched with the bumping grade. Therefore, the bumping grade of the current road can be rapidly and accurately identified, and the corresponding vibration damping control strategy is selected according to the bumping grade, so that the vibration damping of the vehicle is more accurately carried out, the vibration damping effect of the vehicle is improved, and the driving comfort of the vehicle is improved.

Referring to fig. 3 and 4, fig. 3 is a first schematic structural diagram of a vehicle vibration damping device according to an embodiment of the present disclosure, and fig. 4 is a second schematic structural diagram of a vehicle vibration damping device according to an embodiment of the present disclosure. As shown in fig. 3, the vibration damping device 200 includes:

the acquisition module 210 is used for acquiring the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of a target vehicle in each sampling period in the running process of the target vehicle in real time;

the first determining module 220 is configured to determine, for each of a plurality of time windows constituting the driving process, a vehicle response observed value of the target vehicle within the time window according to a suspension vertical movement speed, a vehicle body acceleration and a vehicle body movement speed of the target vehicle within each of a plurality of target sampling periods corresponding to the time window;

the updating module 230 is configured to update the vehicle response observation value sequence of the target vehicle in the driving process according to the vehicle response observation value in the current time window, so as to obtain a vehicle response observation value sequence corresponding to the current time window; wherein the sequence of vehicle response observations consists of vehicle response observations within each of a plurality of time windows of the driving process;

a generating module 240, configured to obtain a target hidden markov model that is valid in a current time window according to a vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden markov model;

a second determining module 250, configured to determine, according to the target hidden markov model and a vehicle response observation sequence corresponding to a current time window, a bumping level of a road on which the target vehicle travels within the current time window;

and the control module 260 is configured to select a vibration damping control parameter matched with the bump level of the road on which the target vehicle runs in the current time window based on the bump level of the road on which the target vehicle runs in the current time window, and generate a corresponding vibration damping control strategy according to the vibration damping control parameter so as to damp the target vehicle.

Further, when the obtaining module 210 is configured to obtain, in real time, the suspension vertical movement speed, the body acceleration and the body movement speed of the target vehicle in the body direction in each sampling period during the running of the target vehicle, the obtaining module 210 is configured to:

for each sampling period in the running process of the target vehicle, acquiring a suspension vertical movement stroke of each position in at least one position on a suspension of the target vehicle, a vehicle body acceleration of each position in at least one position on a vehicle body of the target vehicle and a vehicle body movement speed of the target vehicle in the sampling period;

Further, when the first determining module 220 is configured to determine, for each of a plurality of time windows constituting the driving process, a vehicle response observed value of the target vehicle within the time window according to the suspension vertical movement velocity, the vehicle body acceleration and the vehicle body movement velocity of the target vehicle within each of a plurality of target sampling periods corresponding to the time window, the first determining module 220 is configured to:

Further, when the generating module 240 is configured to obtain a target hidden markov model valid in the current time window according to the vehicle response observation value sequence corresponding to the current time window and the pre-constructed initial hidden markov model, the generating module 240 is configured to:

Further, as shown in fig. 4, the vibration damping device further includes a building block 270; the building module 270 is configured to build the initial hidden markov model by:

state set defining a level of bumps of a road

And defining a set of observations of vehicle response observations

(ii) a Wherein the content of the first and second substances,

and

is a positive integer; state collection

Each element in (a) represents a vehicle response observation;

establishing a state transition probability matrix for a bump level of a road

And initial state probability vector of bump level of road

(ii) a Wherein the state transition probability matrix

Each element in

The probability of (d); observation probability matrix

Each element of

Indicating that the vehicle is at a level of jounce

When driving on the road, the observed value of the vehicle response is

The probability of (d);

，

and

are all positive integers;

according to state transition probability matrix

Observation probability matrix

And initial state probability vector

An initial hidden markov model is constructed.

Further, the build module 270, when configured to define a set of observations of vehicle response observations, the build module 270 is configured to:

Further, the building module 270, when the observation probability matrix for the occurrence probability of the vehicle response observation value under the roads with different bump levels is used, the building module 270 is configured to:

for observation probability matrix

Each element of

According to said test vehicle at the level of jounce

according to the bump grade of the test vehicle

Is determined to be at a vehicle bump level of

And using the determined probability as an observation probability matrix

Middle element

The value of (c).

Further, when the second determining module 250 is configured to determine the level of jolt of the road traveled by the target vehicle within the current time window according to the target hidden markov model and the vehicle response observation sequence corresponding to the current time window, the second determining module 250 is configured to:

According to the vibration damping device for the vehicle, a vehicle response observation value sequence corresponding to a current time window can be determined according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the vehicle; and determining the bumping grade of the road running in the current time window by using the vehicle response observation value sequence and the target hidden Markov model corresponding to the current time window, and further executing a vibration reduction control strategy matched with the bumping grade. Therefore, the bumping grade of the current road can be rapidly and accurately identified, and the corresponding vibration damping control strategy is selected according to the bumping grade, so that the vibration damping of the vehicle is more accurately carried out, the vibration damping effect of the vehicle is improved, and the driving comfort of the vehicle is improved.

Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

The memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 runs, the processor 410 and the memory 420 communicate with each other through the bus 430, and when the machine-readable instructions are executed by the processor 410, the steps of the method for damping vibration of a vehicle in the method embodiments shown in fig. 1 and fig. 2 (a) to 2 (f) may be executed.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of a method for damping vibration of a vehicle in the method embodiments shown in fig. 1 and fig. 2 (a) to 2 (f) may be executed.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A vibration damping method of a vehicle, characterized by comprising:

the method comprises the steps of obtaining the suspension vertical movement speed of a target vehicle and the vehicle body acceleration and the vehicle body movement speed along the vehicle body direction in each sampling period in the running process of the target vehicle in real time;

for each time window in a plurality of time windows forming the driving process, determining a vehicle response observation value of the target vehicle in the time window according to the suspension vertical movement speed, the vehicle body acceleration and the vehicle body movement speed of the target vehicle in each target sampling period in a plurality of target sampling periods corresponding to the time window;

updating the vehicle response observation value sequence of the target vehicle in the driving process according to the vehicle response observation value in the current time window to obtain a vehicle response observation value sequence corresponding to the current time window; wherein the sequence of vehicle response observations consists of vehicle response observations within each of a plurality of time windows of the driving process;

obtaining a target hidden Markov model which is effective in a current time window according to a vehicle response observation value sequence corresponding to the current time window and a pre-constructed initial hidden Markov model;

2. The vibration damping method according to claim 1, wherein the step of acquiring in real time the suspension vertical movement speed, the body acceleration in the body direction and the body movement speed of the target vehicle in each sampling period during the running of the target vehicle comprises:

3. The vibration damping method according to claim 1, wherein the step of determining, for each of a plurality of time windows constituting the driving process, a vehicle response observed value of the target vehicle within the time window according to the suspension vertical movement velocity, the vehicle body acceleration and the vehicle body movement velocity of the target vehicle within each of a plurality of target sampling periods corresponding to the time window comprises:

4. The vibration reduction method according to claim 1, wherein the step of obtaining a target hidden markov model valid in the current time window according to the vehicle response observation sequence corresponding to the current time window and a pre-constructed initial hidden markov model comprises:

according to a vehicle response observation value sequence corresponding to a current time window and the initial hidden Markov model, constructing a log-likelihood function of the initial hidden Markov model;

5. Vibration damping method according to claim 1, characterized in that the initial hidden markov model is constructed by:

state set defining a level of bumps of a road

And defining a set of observations of vehicle response observations

(ii) a Wherein the content of the first and second substances,

and

is a positive integer; state collection

Each element in (a) represents a vehicle response observation;

establishing a state transition probability matrix for a grade of road jolt

And an observation probability matrix of the occurrence probability of the vehicle response observation value under roads of different bump levels

And initial state probability vector of bump level of road

(ii) a Wherein the state transition probability matrix

Each element of

The probability of (d); observation probability matrix

Each element of

Indicating that the vehicle is at a level of jounce of

When driving on the road, the observed value of the vehicle response is

The probability of (d);

，

and

are all positive integers;

according to state transition probability matrix

Observation probability matrix

And initial state probability vector

An initial hidden markov model is constructed.

6. The method of damping according to claim 5, wherein the step of defining a set of observations of vehicle response observations comprises:

7. The vibration damping method according to claim 6, wherein the step of establishing an observation probability matrix of the occurrence probability of the vehicle response observation values under roads of different levels of jounce includes:

for observation probability matrix

Each element of

According to said test vehicle at the level of jounce

according to the bump grade of the test vehicle

Of a vehicle, determining the vehicleAt a jounce level of

And using the determined probability as an observation probability matrix

Middle element

The value of (c).

8. The vibration reduction method according to claim 1, wherein the determining a bump level of a road traveled by the target vehicle within a current time window according to the target hidden markov model and a vehicle response observation sequence corresponding to the current time window comprises:

9. A vibration damping device for a vehicle, characterized by comprising:

and the control module is used for selecting a vibration damping control parameter matched with the bump grade of the road driven by the target vehicle in the current time window based on the bump grade of the road driven by the target vehicle in the current time window, and generating a corresponding vibration damping control strategy according to the vibration damping control parameter so as to damp the target vehicle.

10. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of a method of damping vibration in a vehicle according to any of claims 1 to 8.

11. A computer-readable storage medium, characterized in that a computer program is stored thereon which, when being executed by a processor, carries out the steps of a method of damping of a vehicle according to any one of claims 1 to 8.