Vehicle suspension parameter determination method, device, electronic equipment, vehicle and medium

Abstract

The present application discloses a method, device, electronic device, vehicle and medium for determining vehicle suspension parameters, and relates to the field of vehicle technology. The vehicle suspension parameter determination method includes constructing a road-suspension coupling model; establishing a state space model of the vehicle suspension based on the road-suspension coupling model, and iteratively processing the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters; performing maximum likelihood processing on a pre-constructed noise function using a maximum likelihood estimation algorithm according to the state space model and the state variable sequence and observation variable sequence generated during the iterative processing to obtain a noise value corresponding to the priori prediction value; and determining the final value of the vehicle suspension parameter by superposition based on the priori prediction value and the noise value. The vehicle suspension parameter determination method of the present application can improve the accuracy of suspension parameter estimation and avoid low precision of results caused by unknown noise parts.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, electronic equipment, vehicle and medium for determining vehicle suspension parameters. Background Art

[0002] In modern society, cars have become an indispensable means of transportation for people, bringing great convenience to daily travel. As an important component responsible for support and shock absorption in the car, the performance of the suspension system will directly affect the vehicle's handling stability, driving smoothness and road adaptability.

[0003] However, due to the complex and changeable vehicle driving conditions, there is currently no method in the relevant technology that can effectively estimate the suspension parameters, and naturally there is no way to guarantee that the vehicle is in the optimal operating state under any conditions. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the related art, it is desirable to provide a method, device, electronic device, vehicle and medium for determining vehicle suspension parameters, which can improve the accuracy of suspension parameter estimation.

[0005] In a first aspect, the present application provides a method for determining vehicle suspension parameters, the method comprising:

[0006] Construct road-suspension coupling model;

[0007] A state space model of the vehicle suspension is established based on the road-suspension coupling model, and the state space model is iteratively processed using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters, wherein the components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass;

[0008] According to the state space model and the state variable sequence and the observation variable sequence generated in the iterative processing process, a pre-constructed noise function is subjected to maximum likelihood processing using a maximum likelihood estimation algorithm to obtain a noise value corresponding to the prior prediction value;

[0009] The final value of the vehicle suspension parameter is determined by superimposing the priori prediction value and the noise value.

[0010] Optionally, in some embodiments of the present application, the road-suspension coupling model includes:

[0011] ;

[0012] ;

[0013] Where M represents the sprung mass, m represents the unsprung mass, C represents the damping coefficient of the shock absorber, K represents the spring stiffness, and K t represents the tire cornering stiffness, q represents the ground excitation; x1 represents the vertical displacement of the unsprung mass, x2 represents the vertical displacement of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical velocity of the sprung mass, is the acceleration of the unsprung mass, It represents the acceleration of the sprung mass and its value can be directly measured by the sensor.

[0014] Optionally, in some embodiments of the present application, the state space model includes:

[0015] ;

[0016] in, represents the sampling time, t represents the generalized continuous time, express The state variable of the vehicle suspension system at the sampling time is , represents the derivative of the state variable, express The input variable of the vehicle suspension system at the sampling time is , express The observed variable of the vehicle suspension system at the sampling time is , T represents transpose; express The nonlinear state function corresponding to the sampling time, express The nonlinear measurement function corresponding to the sampling time; and Represents uncorrelated Gaussian white noise and obeys normal distribution. The initial state and , Also unrelated.

[0017] Optionally, in some embodiments of the present application, the iterative processing of the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters includes:

[0018] Initialize the state space model, construct 2n+1 first Sigma points, and set weight values ​​of the first Sigma points respectively, where n represents the dimension of the state x;

[0019] Calculating a first prediction point set of the first Sigma point, and obtaining a one-step prediction value of a state variable and a prediction error covariance according to a weight value of the first Sigma point;

[0020] Based on the one-step predicted value of the state variable and the prediction error covariance, construct 2n+1 second Sigma points;

[0021] A second prediction point set of the second Sigma point is calculated, and according to the weight value of the first Sigma point, a one-step prediction value of the observed variable and an observation error covariance are obtained.

[0022] Optionally, in some embodiments of the present application, the noise value includes a process noise value and a measurement noise value, and the determining the final value of the vehicle suspension parameter by superposition based on the priori prediction value and the noise value includes:

[0023] adding the process noise value to the prediction error covariance, and adding the measurement noise value to the observation error covariance;

[0024] Performing cross-covariance calculation on the prediction error covariance added with the noise value and the observation error covariance, and obtaining an unscented Kalman filter gain according to the cross-covariance;

[0025] Based on the unscented Kalman filter gain, the one-step prediction value of the state variable and the prediction error covariance are updated respectively.

[0026] Optionally, in some embodiments of the present application, the process noise value for

[0027] ;

[0028] The measurement noise value for

[0029] ;

[0030] in, represents the sampling time, diag represents the diagonal matrix, represents the state variable of the vehicle suspension system at sampling time j, represents the input variable of the vehicle suspension system at sampling time j, represents the observed variable of the vehicle suspension system at sampling time j, f represents the nonlinear state function, h represents the nonlinear measurement function, and T represents the transpose.

[0031] In a second aspect, the present application provides a vehicle suspension parameter determination device, the vehicle suspension parameter determination device comprising:

[0032] A construction module configured to construct a road-suspension coupling model;

[0033] an iterative processing module, configured to establish a state space model of the vehicle suspension based on the road-suspension coupling model, and iteratively process the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters, wherein the components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass;

[0034] A maximum likelihood processing module is configured to perform maximum likelihood processing on a pre-constructed noise function using a maximum likelihood estimation algorithm according to the state space model and the state variable sequence and the observation variable sequence generated during the iterative processing to obtain a noise value corresponding to the priori prediction value;

[0035] A determination module is configured to determine a final value of the vehicle suspension parameter by superimposing the priori prediction value and the noise value.

[0036] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the vehicle suspension parameter determination method described in any one of the first aspects.

[0037] In a fourth aspect, the present application provides a vehicle, comprising the electronic device described in the third aspect.

[0038] In a fifth aspect, the present application provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the vehicle suspension parameter determination method described in any one of the first aspects.

[0039] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0040] The embodiments of the present application provide a method, device, electronic device, vehicle and medium for determining vehicle suspension parameters. The method constructs a road suspension coupling model and a state space model that conform to actual conditions, and then iterates the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters. The method also uses a maximum likelihood estimation algorithm to perform maximum likelihood processing on a pre-constructed noise function based on the state space model, the state variable sequence and the observation variable sequence generated during the iterative processing to obtain a noise value corresponding to the prior prediction value. The noise value is then added to the prior prediction value, and the resulting suspension parameters are more accurate, avoiding the low precision of the result caused by the unknown noise part. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic flow chart of a method for determining vehicle suspension parameters provided in an embodiment of the present application;

[0043] Figure 2 A schematic structural diagram of a road suspension coupling system provided in an embodiment of the present application;

[0044] Figure 3 A structural block diagram of a vehicle suspension parameter determination device provided in an embodiment of the present application;

[0045] Figure 4 A structural block diagram of another vehicle suspension parameter determination device provided in an embodiment of the present application;

[0046] Figure 5 A structural block diagram of another vehicle suspension parameter determination device provided in an embodiment of the present application;

[0047] Figure 6 A structural block diagram of an electronic device provided in an embodiment of the present application;

[0048] Figure 7 A structural block diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0051] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. Figures 1 to 7 The vehicle suspension parameter determination method, device, electronic equipment, vehicle and medium provided in the embodiments of the present application are described in detail.

[0052] Please refer to Figure 1 , which is a flow chart of a method for determining vehicle suspension parameters provided in an embodiment of the present application, the method specifically comprises the following steps:

[0053] S101, construct a road-suspension coupling model.

[0054] For example, Figure 2 As shown, it is a schematic diagram of the structure of a road suspension coupling system provided in an embodiment of the present application. Further, the road suspension coupling model in the embodiment of the present application may include

[0055] (1)

[0056] (2)

[0057] Where M represents the sprung mass, m represents the unsprung mass, C represents the damping coefficient of the shock absorber, K represents the spring stiffness, and K t represents the tire cornering stiffness, q represents the ground excitation; x1 represents the vertical displacement of the unsprung mass, x2 represents the vertical displacement of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical velocity of the sprung mass, is the acceleration of the unsprung mass, It represents the acceleration of the sprung mass and its value can be directly measured by the sensor.

[0058] S102, a state space model of the vehicle suspension is established based on the road-suspension coupling model, and the state space model is iteratively processed using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters. The components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass.

[0059] Exemplarily, the state space model in the embodiment of the present application may include

[0060] (3)

[0061] in, represents the sampling time, t represents the generalized continuous time, express The state variable of the vehicle suspension system at the sampling time is , represents the derivative of the state variable, express The input variable of the vehicle suspension system at the sampling time is , express The observed variable of the vehicle suspension system at the sampling time is , T represents transpose; express The nonlinear state function corresponding to the sampling time, express The nonlinear measurement function corresponding to the sampling time; and Represents uncorrelated Gaussian white noise and obeys normal distribution. The initial state and , Also unrelated.

[0062] It should be noted that, in the embodiment of the present application, the unsprung mass acceleration and the sprung mass acceleration with some unknown measurement noise data, as well as the unsprung mass vertical velocity and vertical displacement, and the sprung mass vertical velocity and vertical displacement with known measurement noise data can be obtained through suspension sensors installed on the vehicle.

[0063] Furthermore, in the iterative processing, the embodiment of the present application ① initializes the state space model, that is,

[0064] (4)

[0065] (5)

[0066] Where E represents the mathematical expectation, Indicates the initial state The covariance matrix of .

[0067] Then, construct 2n+1 first Sigma points ( ) and set the weight value of each first Sigma point separately ( , ), n represents the dimension of state x, n ≥ 1, that is,

[0068] (6)

[0069] as well as,

[0070] (7)

[0071] in, , and is a constant, and its value can be selected according to the actual working conditions.

[0072] ② Calculate the first prediction point set of the first Sigma point ( ),Right now

[0073] (8)

[0074] Then, according to the weight value of the first Sigma point, the one-step prediction value of the state variable is obtained ( ) and the forecast error covariance ( ),Right now

[0075] (9)

[0076] (10)

[0077] ③ Based on the one-step prediction value of the state variable and the prediction error covariance, construct 2n+1 second Sigma points ( ),Right now

[0078] (11)

[0079] ④ Calculate the second prediction point set of the second Sigma point ( ),Right now

[0080] (12)

[0081] Then, according to the weight value of the first Sigma point, the one-step predicted value of the observed variable is obtained ( ) and the observation error covariance ( ),Right now

[0082] (13)

[0083] (14)

[0084] S103, according to the state space model and the state variable sequence and the observation variable sequence generated in the iterative processing process, the pre-constructed noise function is subjected to maximum likelihood processing using a maximum likelihood estimation algorithm to obtain a noise value corresponding to the prior prediction value.

[0085] Exemplarily, the noise function pre-constructed in the embodiment of the present application can be , Q represents process noise, R represents measurement noise, then the maximum likelihood estimate Can be

[0086] (15)

[0087] in, represents the likelihood function.

[0088] Furthermore, using the Markov characteristic, the likelihood function right Decompose, that is

[0089] (16)

[0090] in, , , represents the probability density function.

[0091] The initial state variables The conditional probability expression is

[0092] (17)

[0093] in, Indicates the mean , the variance is Gaussian distribution, Represents the determinant of a matrix.

[0094] Furthermore, the one-step forecast of the conditional expectation is

[0095] (18)

[0096] The one-step prediction of the fused observation parameters is

[0097] (19)

[0098] Where d represents the dimension of the observed variable, d≥1.

[0099] Furthermore, combining equations (17) to (19), we can obtain

[0100] (20)

[0101] in, and Independent of each other, and is a constant.

[0102] Perform mathematical expectation calculation on formula (20) and obtain

[0103] (twenty one)

[0104] And, taking partial derivatives of Q and R and equal to 0, we get

[0105] Process noise value for

[0106] (twenty two)

[0107] Measurement noise value for

[0108] (twenty three)

[0109] in, represents the sampling time, diag represents the diagonal matrix, represents the state variable of the vehicle suspension system at sampling time j, represents the input variable of the vehicle suspension system at sampling time j, represents the observed variable of the vehicle suspension system at sampling time j, f represents the nonlinear state function, h represents the nonlinear measurement function, and T represents the transpose.

[0110] Furthermore, the calculation result of formula (22) is substituted into formula (10), and the calculation result of formula (23) is substituted into formula (14), thereby realizing the online update of noise and improving the adaptability of vehicle suspension parameter estimation to different working conditions.

[0111] S104, determining the final value of the vehicle suspension parameter by superimposing the priori predicted value and the noise value.

[0112] For example, the embodiment of the present application may be in the prediction error covariance ( ) adds the process noise value ( ), as well as the observed error covariance ( ) adds the measured noise value ( Then, the cross-covariance of the prediction error covariance and the observation error covariance after the noise value is added is calculated, that is,

[0113] (twenty four)

[0114] And the unscented Kalman filter gain is obtained according to the cross-covariance, that is,

[0115] (25)

[0116] Then, based on the unscented Kalman filter gain, the one-step prediction value of the state variable and the prediction error covariance are updated respectively, that is,

[0117] (26)

[0118] as well as,

[0119] (27)

[0120] The vehicle suspension parameter determination method provided in the embodiment of the present application constructs a road suspension coupling model and a state space model that conform to the actual situation, and then uses a nonlinear function probability distribution approximate filtering algorithm to iteratively process the state space model to obtain a priori prediction values ​​of the vehicle suspension parameters. Based on the state space model, the state variable sequence and the observation variable sequence generated during the iterative processing, the pre-constructed noise function is subjected to maximum likelihood processing using a maximum likelihood estimation algorithm to obtain the noise value corresponding to the prior prediction value, and then the noise value is added on the basis of the prior prediction value. The resulting suspension parameters are more accurate, avoiding the low precision of the result caused by the unknown noise part.

[0121] Based on the above embodiments, the present application provides a vehicle suspension parameter determination device. The vehicle suspension parameter determination device 100 can be applied to Figure 1~Figure 2 In the vehicle suspension parameter determination method of the corresponding embodiment. Please refer to Figure 3 , the vehicle suspension parameter determination device 100 comprises:

[0122] A construction module 101 is configured to construct a road-suspension coupling model;

[0123] an iterative processing module 102, configured to establish a state space model of the vehicle suspension based on the road-suspension coupling model, and iteratively process the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters, wherein the components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass;

[0124] The maximum likelihood processing module 103 is configured to perform maximum likelihood processing on the pre-constructed noise function using a maximum likelihood estimation algorithm according to the state space model and the state variable sequence and the observation variable sequence generated during the iterative processing process to obtain a noise value corresponding to the prior prediction value;

[0125] The determination module 104 is configured to determine the final value of the vehicle suspension parameter by superimposing the prior prediction value and the noise value.

[0126] Optionally, in some embodiments of the present application, the road-suspension coupling model includes:

[0127] ;

[0128] ;

[0129] Where M represents the sprung mass, m represents the unsprung mass, C represents the damping coefficient of the shock absorber, K represents the spring stiffness, and K t represents the tire cornering stiffness, q represents the ground excitation; x1 represents the vertical displacement of the unsprung mass, x2 represents the vertical displacement of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical velocity of the sprung mass, is the acceleration of the unsprung mass, It represents the acceleration of the sprung mass and its value can be directly measured by the sensor.

[0130] Optionally, in some embodiments of the present application, the state space model includes:

[0131] ;

[0132] in, represents the sampling time, t represents the generalized continuous time, express The state variable of the vehicle suspension system at the sampling time is , represents the derivative of the state variable, express The input variable of the vehicle suspension system at the sampling time is , express The observed variable of the vehicle suspension system at the sampling time is , T represents transpose; express The nonlinear state function corresponding to the sampling time, express The nonlinear measurement function corresponding to the sampling time; and Represents uncorrelated Gaussian white noise and obeys normal distribution. The initial state and , Also unrelated.

[0133] Alternatively, if Figure 4 As shown, in some embodiments of the present application, the iterative processing module 102 includes:

[0134] A first construction unit 1021 is configured to initialize the state space model, construct 2n+1 first Sigma points, and respectively set the weight value of each first Sigma point, where n represents the dimension of the state x;

[0135] A first calculation unit 1022 is configured to calculate a first prediction point set of a first Sigma point, and obtain a one-step prediction value of a state variable and a prediction error covariance according to a weight value of the first Sigma point;

[0136] A second construction unit 1023 is configured to construct 2n+1 second Sigma points based on the one-step prediction value of the state variable and the prediction error covariance;

[0137] The second calculation unit 1024 is configured to calculate a second prediction point set of a second Sigma point, and obtain a one-step prediction value of the observed variable and an observation error covariance according to the weight value of the first Sigma point.

[0138] Optionally, the noise value includes a process noise value and a measurement noise value, such as Figure 5 As shown, in some embodiments of the present application, the determination module 104 includes:

[0139] An adding unit 1041 is configured to add a process noise value to the prediction error covariance and to add a measurement noise value to the observation error covariance;

[0140] The third calculation unit 1042 is configured to perform cross-covariance calculation on the prediction error covariance and the observation error covariance after the noise value is added, and obtain the unscented Kalman filter gain according to the cross-covariance;

[0141] The updating unit 1043 is configured to update the one-step prediction value of the state variable and the prediction error covariance based on the unscented Kalman filter gain.

[0142] Optionally, in some embodiments of the present application, the process noise value for

[0143] ;

[0144] Measurement noise value for

[0145] ;

[0146] in, represents the sampling time, diag represents the diagonal matrix, represents the state variable of the vehicle suspension system at sampling time j, represents the input variable of the vehicle suspension system at sampling time j, represents the observed variable of the vehicle suspension system at sampling time j, f represents the nonlinear state function, h represents the nonlinear measurement function, and T represents the transpose.

[0147] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0148] The vehicle suspension parameter determination device provided in the embodiment of the present application constructs a road-suspension coupling model that conforms to the actual situation through a construction module. Secondly, the iterative processing module establishes a state space model of the vehicle suspension based on the road-suspension coupling model, and uses a nonlinear function probability distribution approximate filtering algorithm to iteratively process the state space model to obtain a priori prediction values ​​of the vehicle suspension parameters. Then, the maximum likelihood processing module uses a maximum likelihood estimation algorithm to perform maximum likelihood processing on a pre-constructed noise function based on the state space model, the state variable sequence and the observation variable sequence generated during the iterative processing to obtain the noise value corresponding to the prior prediction value. Then, the determination module adds the noise value on the basis of the prior prediction value, and the obtained suspension parameters are more accurate, avoiding the low precision of the result caused by the unknown noise part.

[0149] Based on the above embodiments, the present application provides an electronic device. Figure 6 The electronic device 200 may include a processor 201 and a memory 202. The memory 202 stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by the processor 201 to implement Figure 1~Figure 2 The steps of the vehicle suspension parameter determination method of the corresponding embodiment.

[0150] As another aspect, an embodiment of the present application provides a vehicle. Figure 7 , the vehicle 300 may include Figure 6 An electronic device 200 is shown.

[0151] As another aspect, the present application provides a computer-readable storage medium for storing program code, the program code for executing the aforementioned Figure 1~Figure 2 Any implementation of the vehicle suspension parameter determination method of the corresponding embodiment.

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0154] In addition, each functional module in each embodiment of the present application may be integrated into a processing unit, or each module may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0155] Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the vehicle suspension parameter determination method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0156] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for determining vehicle suspension parameters, characterized in that: The vehicle suspension parameter determination method comprises: Construct road-suspension coupling model; A state space model of the vehicle suspension is established based on the road-suspension coupling model, and the state space model is iteratively processed using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters, wherein the components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass; According to the state space model and the state variable sequence and the observation variable sequence generated in the iterative processing process, a pre-constructed noise function is subjected to maximum likelihood processing using a maximum likelihood estimation algorithm to obtain a noise value corresponding to the prior prediction value; Determine the final value of the vehicle suspension parameter by superimposing the priori prediction value and the noise value; Wherein, the state space model includes: ； In the above formula, represents the sampling time, t represents generalized continuous time, express The state variable of the vehicle suspension system at the sampling time is , represents the derivative of the state variable, express The input variable of the vehicle suspension system at the sampling time is , express The observed variable of the vehicle suspension system at the sampling time is , T represents transpose; express The nonlinear state function corresponding to the sampling time, express The nonlinear measurement function corresponding to the sampling time; and represents uncorrelated Gaussian white noise and obeys normal distribution. The initial state variable and , They are also unrelated; The method of iteratively processing the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters includes: initializing the state space model and constructing 2 n +1 first Sigma point and set the weight value of each first Sigma point respectively, n represents the dimension of the state variables, n ≥1; calculate the first prediction point set of the first Sigma point, and obtain the one-step prediction value of the state variable and the prediction error covariance according to the weight value of the first Sigma point; and construct 2 based on the one-step prediction value of the state variable and the prediction error covariance. n +1 second Sigma point; calculate the second prediction point set of the second Sigma point, and obtain the one-step prediction value of the observed variable and the observation error covariance according to the weight value of the first Sigma point; The method of performing maximum likelihood processing on a pre-constructed noise function according to the state space model and the state variable sequence and the observation variable sequence generated in the iterative processing process using a maximum likelihood estimation algorithm to obtain a noise value corresponding to the prior prediction value includes: The pre-constructed noise function is , Q represents the process noise, R represents the measurement noise, then the maximum likelihood estimate for , represents the likelihood function; Using the Markov characteristic, the likelihood function right Decompose, that is ； In the above formula, , , represents the conditional probability density function; The initial state variables The conditional probability density function expression is: ； In the above formula, It means the mean , the variance is Gaussian distribution, , E represents the mathematical expectation, Represents the initial state variable The covariance matrix of , represents the determinant of a matrix; The one-step forecast of the conditional expectation is ； The one-step prediction of the fused observation parameters is ； In the above formula, d represents the dimension of the observed variable, d ≥1; Combining the above conditional probability density functions, we get ； In the above formula, and Independent of each other, and is a constant; Represents Sigma point; Perform mathematical expectation calculation on the above formula and we get ； And, yes Q and R Find the partial derivative and equal it to 0, and we get The value of the process noise for ； The value of the measurement noise for ； In the above formula, diag represents a diagonal matrix, express j The state variables of the vehicle suspension system at the sampling time, express j The input variables of the vehicle suspension system at the sampling time, express j The observed variables of the vehicle suspension system at the sampling time, f represents the nonlinear state function, h represents the nonlinear measurement function, T Indicates transpose.

2. The method for determining vehicle suspension parameters according to claim 1, characterized in that: The road-suspension coupling model includes: ； ； in, M is the sprung mass, m is the unsprung mass, C represents the damping coefficient of the shock absorber, K represents the spring stiffness, K t Indicates the tire cornering stiffness. q represents ground excitation; x 1 represents the vertical displacement of the unsprung mass, x 2 represents the vertical displacement of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical velocity of the sprung mass, is the acceleration of the unsprung mass, It represents the acceleration of the sprung mass and its value can be directly measured by the sensor.

3. The method for determining vehicle suspension parameters according to claim 1, characterized in that: The step of determining the final value of the vehicle suspension parameter by superimposing the priori prediction value and the noise value comprises: adding the value of the process noise to the prediction error covariance, and adding the value of the measurement noise to the observation error covariance; Performing cross-covariance calculation on the prediction error covariance added with the noise value and the observation error covariance, and obtaining an unscented Kalman filter gain according to the cross-covariance; Based on the unscented Kalman filter gain, the one-step prediction value of the state variable and the prediction error covariance are updated respectively.

4. A vehicle suspension parameter determination device, characterized in that: The vehicle suspension parameter determination device comprises: A construction module configured to construct a road-suspension coupling model; an iterative processing module, configured to establish a state space model of the vehicle suspension based on the road-suspension coupling model, and iteratively process the state space model using a nonlinear function probability distribution approximate filtering algorithm to obtain a priori prediction values ​​of the vehicle suspension parameters, wherein the components of the input variable sequence in the state space model include the vertical displacement of the unsprung mass, the vertical velocity of the unsprung mass, the vertical displacement of the sprung mass, and the vertical velocity of the sprung mass; A maximum likelihood processing module is configured to perform maximum likelihood processing on a pre-constructed noise function using a maximum likelihood estimation algorithm according to the state space model and the state variable sequence and the observation variable sequence generated during the iterative processing to obtain a noise value corresponding to the priori prediction value; A determination module configured to determine a final value of the vehicle suspension parameter by superimposing the priori prediction value and the noise value; Wherein, the state space model includes: ； In the above formula, represents the sampling time, t represents generalized continuous time, express The state variable of the vehicle suspension system at the sampling time is , represents the derivative of the state variable, express The input variable of the vehicle suspension system at the sampling time is , express The observed variable of the vehicle suspension system at the sampling time is , T represents transpose; express The nonlinear state function corresponding to the sampling time, express The nonlinear measurement function corresponding to the sampling time; and represents uncorrelated Gaussian white noise and obeys normal distribution. The initial state variable and , They are also unrelated; The iterative processing module includes a first construction unit, a first calculation unit, a second construction unit and a second calculation unit; the first construction unit is configured to initialize the state space model and construct 2 n +1 first Sigma point and set the weight value of each first Sigma point respectively, n represents the dimension of the state variables, n ≥1; the first calculation unit is configured to calculate the first prediction point set of the first Sigma point, and obtain the one-step prediction value of the state variable and the prediction error covariance according to the weight value of the first Sigma point; and the second construction unit is configured to construct 2 based on the one-step prediction value of the state variable and the prediction error covariance. n +1 second Sigma point; the second calculation unit is configured to calculate a second prediction point set of the second Sigma point, and obtain a one-step prediction value of the observed variable and an observation error covariance according to the weight value of the first Sigma point; The pre-constructed noise function is , Q represents the process noise, R represents the measurement noise, then the maximum likelihood estimate for , represents the likelihood function, and the maximum likelihood processing module is specifically used to utilize the Markov characteristic to convert the likelihood function right Decompose, that is ； In the above formula, , , represents the conditional probability density function; The initial state variables The conditional probability density function expression is: ； In the above formula, It means the mean , the variance is Gaussian distribution, , E represents the mathematical expectation, Represents the initial state variable The covariance matrix of , represents the determinant of a matrix; The one-step forecast of the conditional expectation is ； The one-step prediction of the fused observation parameters is ； In the above formula, d represents the dimension of the observed variable, d ≥1; Combining the above conditional probability density functions, we get ； In the above formula, and Independent of each other, and is a constant; Represents Sigma point; Perform mathematical expectation calculation on the above formula and we get ； And, yes Q and R Find the partial derivative and equal it to 0, and we get The value of the process noise for ； The value of the measurement noise for ； In the above formula, diag represents a diagonal matrix, express j The state variables of the vehicle suspension system at the sampling time, express j The input variables of the vehicle suspension system at the sampling time, express j The observed variables of the vehicle suspension system at the sampling time, f represents the nonlinear state function, h represents the nonlinear measurement function, T Indicates transpose.

5. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the vehicle suspension parameter determination method described in any one of claims 1 to 3.

6. A vehicle, characterized in that: The vehicle includes the electronic device according to claim 5.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the vehicle suspension parameter determination method according to any one of claims 1 to 3.

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