Prediction method, system and equipment for road profile and suspension parameters and storage medium

Abstract

The invention discloses a pavement contour and suspension parameter prediction method, system and device, and a storage medium. The method comprises the following steps: acquiring vehicle acceleration data; constructing a preset correlation function according to the preset state vector and the pavement contour parameters; a preset suspension model is constructed, and a preset state space model is constructed through the preset suspension model and the preset correlation function; according to the vehicle acceleration data and the preset state space model, state parameter estimation is carried out through an extended Kalman filtering estimation algorithm containing unknown input, and preset dynamic state data are obtained; wherein the preset dynamic state data comprises road surface height data and suspension characteristic data. According to the embodiment of the invention, the accuracy and reliability of road profile and suspension parameter estimation can be effectively improved, the prediction cost is reduced, and an effective support is provided for a subsequent suspension control strategy. The method can be widely applied to the technical field of vehicle driving parameter estimation.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle driving parameter estimation, and in particular to a method, system, device and storage medium for predicting road profile and suspension parameters. Background Art

[0002] With the continuous development of the economy, people have higher and higher requirements for vehicle ride comfort and handling stability. The unevenness of the road surface is a key factor affecting the vertical vibration of the vehicle. Therefore, the key to improving comfort lies in obtaining accurate road profile information. With the emergence of semi-active and active suspensions, if the road profile information can be accurately estimated, the vehicle's driving conditions can be judged, so that different control strategies can be formulated for the suspension system according to different working conditions, thereby improving the ride comfort of the car. In addition, suspension parameters are the main control objects of the suspension control system. Changes in suspension parameters will affect the vibration response of the car, and changes in vibration response will affect the acquisition of road profile information. In related technologies, methods for estimating road profiles, such as estimating road profiles through cameras and lidars, often have low stability, are difficult to ensure estimation accuracy, and are relatively costly, making it difficult to provide effective support for suspension control.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method, system, device and storage medium for predicting road profile and suspension parameters, which can effectively improve the accuracy and reliability of road profile and suspension parameter estimation, reduce the prediction cost, and provide effective support for subsequent suspension control strategies.

[0005] To achieve the above object, one aspect of an embodiment of the present application provides a method for predicting road surface profile and suspension parameters, the method comprising the following steps:

[0006] Get vehicle acceleration data;

[0007] Constructing a preset correlation function according to a preset state vector and a road profile parameter;

[0008] Constructing a preset suspension model, so as to construct a preset state space model through the preset suspension model and the preset correlation function;

[0009] According to the vehicle acceleration data and the preset state space model, state parameter estimation is performed through an extended Kalman filter estimation algorithm containing unknown input to obtain preset dynamic state data; wherein the preset dynamic state data includes road height data and suspension characteristic data.

[0010] In some embodiments, obtaining vehicle acceleration data includes:

[0011] Dynamically acquiring preset acceleration data through a preset acceleration sensor; wherein the preset acceleration data includes sprung acceleration data and unsprung acceleration data;

[0012] The preset acceleration data is input into a preset low-pass filter for filtering to obtain the vehicle acceleration data.

[0013] In some embodiments, the step of constructing a preset correlation function according to the preset state vector and the road surface profile parameter includes:

[0014] Constructing the preset state vector; wherein the preset state vector includes sprung displacement, sprung speed, unsprung displacement, unsprung speed, damping coefficient, suspension stiffness and tire stiffness;

[0015] Constructing a preset correlation function between the preset state vector and the road surface profile parameter; wherein the preset correlation function includes a nonlinear continuous function.

[0016] In some embodiments, the step of constructing a preset suspension model to obtain a preset state space model through the preset suspension model and the preset correlation function includes:

[0017] Constructing a vehicle suspension motion differential equation model according to the suspension characteristic parameters and the road profile parameters; wherein the suspension characteristic parameters include sprung parameters, unsprung parameters, the damping coefficient, the suspension stiffness, and the tire stiffness;

[0018] The preset state space model is constructed according to the vehicle suspension motion differential equation model and the preset correlation function; wherein the preset state space model includes a preset state space equation and a preset measurement equation.

[0019] In some embodiments, before performing the state parameter estimation using an extended Kalman filter estimation algorithm with unknown input according to the vehicle acceleration data and the preset state space model to obtain preset dynamic state data, the method further includes:

[0020] Performing a first-order Taylor expansion on the preset correlation function to obtain a preset Taylor expansion function;

[0021] The preset state space equation and the preset measurement equation are discretized according to the preset Taylor expansion function to obtain a discrete state space model; wherein the discrete state space model includes a discrete state space equation and a discrete measurement equation.

[0022] In some embodiments, the state parameter estimation is performed according to the vehicle acceleration data and the preset state space model through an extended Kalman filter estimation algorithm with unknown input to obtain preset dynamic state data, including:

[0023] Constructing preset initial data; wherein the preset initial data includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix and a state initial value;

[0024] Constructing a prediction error covariance matrix according to the preset initial data, the preset correction factor matrix and the discrete state space model; wherein the preset correction factor matrix is ​​determined by the damping coefficient, the suspension stiffness and the tire stiffness;

[0025] Calculate the Kalman gain matrix according to the prediction error covariance matrix;

[0026] An unknown input error covariance matrix is ​​calculated according to the Kalman gain matrix, so as to construct a road profile prediction model through the unknown input error covariance matrix;

[0027] The road surface height data is obtained by predicting the road surface profile prediction model according to the vehicle acceleration;

[0028] The preset state vector is updated according to the road surface height data to obtain the suspension characteristic data.

[0029] In some embodiments, the preset correction factor matrix is ​​determined by the following steps:

[0030] Constructing a preset parameter matrix according to the damping coefficient, the suspension stiffness, and the tire stiffness;

[0031] When it is determined that the preset parameter matrix has not changed within the preset time threshold, the preset correction factor matrix is ​​determined to be a unit matrix; or, when it is determined that the preset parameter matrix has changed within the preset time threshold, the preset correction factor matrix is ​​obtained by solving the prediction parameter matrix through a constrained optimization algorithm.

[0032] To achieve the above object, another aspect of the embodiment of the present application provides a road profile and suspension parameter prediction system, the system comprising:

[0033] The first module is used to obtain vehicle acceleration data;

[0034] The second module is used to construct a preset correlation function according to a preset state vector and a road profile parameter;

[0035] A third module is used to construct a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function;

[0036] The fourth module is used to estimate state parameters according to the vehicle acceleration data and the preset state space model through an extended Kalman filter estimation algorithm containing unknown inputs to obtain preset dynamic state data; wherein the preset dynamic state data includes road height data and suspension characteristic data.

[0037] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising:

[0038] at least one processor;

[0039] at least one memory for storing at least one program;

[0040] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0041] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0042] The embodiments of the present application include at least the following beneficial effects: The present application provides a method, system, device and storage medium for predicting road profile and suspension parameters. The scheme first obtains vehicle acceleration data. Then, the embodiment of the present invention constructs a preset correlation function according to the preset state quantity and road profile parameters, and constructs a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function. Finally, the embodiment of the present invention estimates the state parameters according to the vehicle acceleration data and the preset state space model through the extended Kalman filter estimation algorithm with unknown input, and obtains the preset dynamic state data, wherein the preset dynamic state data includes road height data and suspension characteristic data, so as to realize the estimation of road profile and suspension parameters. It is easy to understand that the embodiment of the present invention can effectively reduce the cost of road profile prediction by collecting vehicle acceleration data, and can track the suspension parameter changes while estimating the road profile by combining the extended Kalman filter estimation algorithm with unknown input to estimate parameters, thereby effectively improving the accuracy and reliability of road profile and suspension parameter estimation, and providing effective support for subsequent suspension control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method for predicting road profile and suspension parameters provided in an embodiment of the present application;

[0044] Figure 2 is a schematic diagram of the structure of a quarter suspension model provided in an embodiment of the present application;

[0045] Figure 3 It is a schematic diagram of the structure of the road surface profile and suspension parameter prediction system provided in the embodiment of the present application;

[0046] Figure 4 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0048] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0049] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0052] Kalman filter algorithm: It is a tool for estimating the state of a dynamic system. It can effectively track system variables in a noisy and uncertain environment by recursively optimizing the state of the system. Kalman filtering is to establish a mathematical model for the state equation and observation equation of the system, and use the Bayesian inference method to combine the system's prediction and observation data to continuously update the state estimate. Kalman filtering is mainly divided into linear Kalman filtering and extended Kalman filtering (EKF). Among them, linear Kalman filtering is suitable for linear systems and Gaussian noise, and extended Kalman filtering is suitable for nonlinear systems.

[0053] With the continuous development of the economy, people have higher and higher requirements for vehicle ride comfort and handling stability. The unevenness of the road surface is a key factor affecting the vertical vibration of the vehicle. Therefore, the key to improving comfort lies in obtaining accurate road profile information. With the emergence of semi-active and active suspensions, if the road profile information can be accurately estimated, the vehicle's driving conditions can be judged, so that different control strategies can be formulated for the suspension system according to different working conditions, thereby improving the ride comfort of the car. In addition, the suspension parameters are the main control objects of the suspension control system. The changes in the suspension parameters will affect the vibration response of the vehicle, and the changes in the vibration response will affect the acquisition of road profile information. In the related art, the road profile estimation method, such as the road profile estimation through cameras and laser radars, is often less stable, difficult to ensure the estimation accuracy, and relatively costly, and difficult to provide effective support for suspension control. For example, the road profile estimation method based on vision mainly obtains road profile information through cameras, laser radars, etc., but is easily affected by weather changes and has a relatively high cost. The road profile estimation method based on neural networks requires a large amount of training data, is very sensitive to suspension parameters, and is difficult to ensure estimation accuracy when parameters change. The road profile estimation method based on traditional Kalman filtering regards the road profile as an extended state vector and makes assumptions about the road profile, usually assuming a random walk model, etc. However, the actual road shape does not necessarily meet this assumption.

[0054] In view of this, a method, system, device and storage medium for predicting road profile and suspension parameters are provided in an embodiment of the present application. The scheme first obtains vehicle acceleration data. Then, the embodiment of the present invention constructs a preset correlation function based on preset state quantities and road profile parameters, and constructs a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function. Finally, the embodiment of the present invention performs parameter prediction based on the vehicle acceleration data and the preset state space model through an extended Kalman filter estimation algorithm containing unknown inputs to obtain preset dynamic state data. Among them, the preset dynamic state data in the embodiment of the present invention includes road height data and suspension characteristic data, so as to realize the estimation of road profile and suspension parameters, effectively improve the accuracy and reliability of road profile and suspension parameter estimation, and provide effective support for subsequent suspension control strategies.

[0055] The prediction method of road profile and suspension parameters provided in the embodiment of the present application relates to the technical field of vehicle driving parameter estimation. The prediction method of road profile and suspension parameters provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application of a method for predicting road profile and suspension parameters, etc., but is not limited to the above forms.

[0056] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0057] Figure 1 is an optional flow chart of the method for predicting road profile and suspension parameters provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S140.

[0058] Step S110: Acquire vehicle acceleration data.

[0059] Step S120: constructing a preset correlation function according to the preset state vector and the road profile parameters.

[0060] Step S130: constructing a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function.

[0061] Step S140: Estimating state parameters by using an extended Kalman filter estimation algorithm with unknown input according to the vehicle acceleration data and the preset state space model to obtain preset dynamic state data, wherein the preset dynamic state data includes road height data and suspension characteristic data.

[0062] During the working process of this specific embodiment, the embodiment of the present invention first obtains the vehicle acceleration data. Specifically, during the driving process of the vehicle, the embodiment of the present invention obtains the acceleration information of the vehicle in real time through the vehicle-mounted sensor. Among them, most solutions in the related art use displacement sensors, such as suspension dynamic deflection sensors. Such sensors are expensive compared to acceleration sensors and are not suitable for mass application. Therefore, under partial observation conditions, the embodiment of the present invention only considers that the acceleration signal can be measured, predicts the state quantity, and inverts the road surface input on this basis. Then, the embodiment of the present invention constructs a preset correlation function based on the preset state vector and the road surface profile parameter. Specifically, the preset state vector in the embodiment of the present invention refers to a vector containing the current state information of the system, such as displacement, velocity, damping coefficient, stiffness and other information. Accordingly, the road surface profile parameter refers to the unknown input of the road surface, that is, the road surface height. Accordingly, the embodiment of the present invention constructs a preset correlation function associated with the state quantity and the unknown input of the road surface according to the preset state vector and the road surface profile parameter. Further, the embodiment of the present invention constructs a preset suspension model to obtain a preset state space model through the preset suspension model and the preset correlation function. Specifically, the preset suspension model in the embodiment of the present invention refers to a model used to describe the suspension system in the vehicle, which usually includes springs, wheels, and a body, etc. The embodiment of the present invention constructs a preset suspension model, and constructs a preset state space model in combination with a preset correlation function, so as to express the input, output, and state of the vehicle suspension system in the form of a vector, which can effectively describe the dynamic behavior of the suspension system. Finally, the embodiment of the present invention estimates the state parameters according to the vehicle acceleration data and the preset state space model through an extended Kalman filter estimation algorithm with unknown inputs to obtain preset dynamic state data. Specifically, the extended Kalman filter with unknown inputs (Extended Kalman Filter with unknown inputs, EKF-UI) algorithm in the embodiment of the present invention is an extended Kalman filter algorithm that can handle situations where unknown inputs (or interference) exist in the system and can cope with uncertainties in the system. The embodiment of the present invention combines the collected vehicle acceleration data with the constructed preset state space model, and obtains preset dynamic state data including road height data and suspension characteristic data through an iterative update joint estimation method of an extended Kalman filter containing unknown inputs, thereby achieving tracking of suspension characteristic parameters while estimating the degree of road undulation, and effectively improving the accuracy and reliability of road profile and suspension parameter estimation, which can provide effective support for subsequent suspension control strategies.

[0063] In some embodiments of the present invention, obtaining vehicle acceleration data includes but is not limited to the following steps:

[0064] The preset acceleration data is dynamically acquired through the preset acceleration sensor, wherein the preset acceleration data includes sprung acceleration data and unsprung acceleration data.

[0065] The preset acceleration data is input into a preset low-pass filter for filtering to obtain vehicle acceleration data.

[0066] In this specific embodiment, the embodiment of the present invention first dynamically obtains preset acceleration data through a preset acceleration sensor, and then inputs the preset acceleration data into a preset low-pass filter for filtering processing to obtain vehicle acceleration data. Specifically, the preset acceleration sensor in the embodiment of the present invention refers to an acceleration sensor set in the vehicle. For example, the acceleration data obtained in real time by the preset acceleration sensor in the embodiment of the present invention includes sprung acceleration data and unsprung acceleration data. Among them, sprung acceleration refers to the acceleration of the mass carried by the suspension, such as the vehicle body acceleration, and unsprung acceleration refers to the acceleration of the mass not carried by the suspension, such as the wheel acceleration. Accordingly, the embodiment of the present invention collects the sprung acceleration data and unsprung acceleration of the vehicle in real time through the preset acceleration. Then, the embodiment of the present invention inputs the collected preset acceleration data into a preset low-pass filter for filtering processing to obtain vehicle acceleration data. Specifically, when the corresponding preset acceleration data is collected, the embodiment of the present invention performs filtering processing on the preset acceleration data to improve the data smoothness and reliability, which is convenient for subsequent data processing. For example, in the embodiment of the present invention, the preset low-pass filter is set to 0 to 50 Hz, and the acquired acceleration information is processed through the preset low-pass filter to obtain vehicle acceleration data.

[0067] In some embodiments of the present invention, a preset correlation function is constructed according to a preset state vector and a road surface profile parameter, including but not limited to the following steps:

[0068] Construct a preset state vector, wherein the preset state vector includes sprung displacement, sprung velocity, unsprung displacement, unsprung velocity, damping coefficient, suspension stiffness, and tire stiffness.

[0069] A preset correlation function between a preset state vector and a road profile parameter is constructed, wherein the preset correlation function includes a nonlinear continuous function.

[0070] In this specific embodiment, the embodiment of the present invention first constructs a preset state vector. Specifically, in order to achieve good tracking of the suspension motion state, such as the speed of the sprung mass, the acceleration of the sprung mass, the speed of the unsprung mass, and the acceleration of the unsprung mass, the embodiment of the present invention not only tracks the vehicle motion state, but also tracks the changes in the suspension characteristic parameters. Therefore, the embodiment of the present invention selects the corresponding state quantity to construct the preset state vector: Among them, xs represents the sprung displacement, is the sprung speed, x t represents the unsprung displacement, is the unsprung speed, k s represents the damping coefficient, k t represents the suspension stiffness, c s Represents tire stiffness. Next, the embodiment of the present invention constructs a preset correlation function between the preset state vector and the road profile parameter. Specifically, the road profile parameter in the embodiment of the present invention refers to the unknown road input, that is, the road height data. Accordingly, the preset correlation function in the embodiment of the present invention is a nonlinear continuous function. After determining the corresponding preset state vector, the embodiment of the present invention constructs a correlation function between the preset state vector x and the unknown input u unknown (pavement profile parameters) related nonlinear continuous function to obtain a preset correlation function.

[0071] In some embodiments of the present invention, a preset suspension model is constructed to obtain a preset state space model through the preset suspension model and the preset correlation function, including but not limited to the following steps:

[0072] The vehicle suspension motion differential equation model is constructed based on the suspension characteristic parameters and road profile parameters, wherein the suspension characteristic parameters include sprung parameters, unsprung parameters, damping coefficient, suspension stiffness and tire stiffness.

[0073] The preset state space model is constructed according to the vehicle suspension motion differential equation model and the preset correlation function, wherein the preset state space model includes a preset state space equation and a preset measurement equation.

[0074] In this specific embodiment, the embodiment of the present invention first constructs a vehicle suspension motion differential equation model based on the suspension characteristic parameters and the road profile parameters. Specifically, the suspension characteristic parameters in the embodiment of the present invention include sprung parameters, unsprung parameters, damping coefficients, suspension stiffness, and tire stiffness. Among them, the sprung parameters in the embodiment of the present invention refer to the relevant parameters of the objects carried by the suspension (such as the vehicle body, passengers), including sprung mass, sprung displacement, sprung velocity, and sprung acceleration, such as vehicle body mass, displacement, velocity, etc. Correspondingly, unsprung parameters refer to the relevant parameters of the objects not carried by the suspension (such as wheels), including unsprung mass, unsprung displacement, unsprung velocity, and unsprung acceleration, such as tire mass, displacement, velocity, etc. Exemplarily, as Figure 2 As shown, Figure 2 A schematic diagram of a quarter suspension model provided by an embodiment of the present invention, in which m s is the sprung mass, m t is the unsprung mass, c s is the damping coefficient of the damper, k s is the suspension stiffness, kt is the tire stiffness. Accordingly, the embodiment of the present invention constructs a vehicle suspension motion differential equation model according to the determined suspension feature adoption number and road profile parameters, as shown in the following equation (1):

[0075]

[0076] Among them, x s is the sprung displacement, is the sprung speed, is the sprung acceleration, x t is the unsprung displacement, is the unsprung speed, is the unsprung acceleration, u unknown The road surface input that needs to be inverted is the road surface profile parameters.

[0077] Next, the embodiment of the present invention constructs a preset state space model based on the vehicle suspension motion differential equation model and the preset correlation function. Specifically, the preset state space model in the embodiment of the present invention includes a preset state space equation and a preset measurement equation. Accordingly, when determining that the selected preset state vector is x= Then, the observation quantity is selected as The corresponding state space equation (preset state space equation) is shown in equation (2):

[0078]

[0079] Among them, represents the derivative of the state, that is, the rate of change with respect to time, u unknown is the unknown input of the road surface, i.e., the road surface height, g(x,u unknown ) is the state quantity x, unknown input u unknown is a nonlinear continuous function (preset correlation function), w(t) represents the modeling error.

[0080] Accordingly, the preset measurement equation in the embodiment of the present invention is shown in the following formula (3):

[0081]

[0082] Among them, h(x,u unknown ) is the state quantity x, unknown input u unknown is a nonlinear continuous function (preset correlation function), v(t) represents the measurement error.

[0083] In some embodiments of the present invention, before performing state parameter estimation using an extended Kalman filter estimation algorithm with unknown inputs based on vehicle acceleration data and a preset state space model to obtain preset dynamic state data, the method for predicting road profile and suspension parameters provided by an embodiment of the present invention further includes but is not limited to the following steps:

[0084] Perform a first-order Taylor expansion on the preset correlation function to obtain a preset Taylor expansion function.

[0085] The preset state space equation and the preset measurement equation are discretized according to the preset Taylor expansion function to obtain a discrete state space model, wherein the discrete state space model includes the discrete state space equation and the discrete measurement equation.

[0086] In this specific embodiment, before performing parameter prediction, the embodiment of the present invention first discretizes the preset state space model. Specifically, the embodiment of the present invention first performs a first-order Taylor expansion on the preset correlation function to obtain a preset Taylor expansion function. The preset correlation function in the embodiment of the present invention includes h(x,u unknown ) and g(x,u unknown ), respectively perform first-order Taylor expansion on these two nonlinear functions to obtain the preset Taylor expansion function, as shown in the following formula (4):

[0087]

[0088] Among them, the corresponding Jacobian partial derivative matrix is ​​shown in the following formula (5):

[0089]

[0090] Furthermore, the embodiment of the present invention discretizes the preset Taylor expansion function obtained by expansion and the corresponding preset state space model to obtain the discretized state space equation and measurement equation, as shown in the following equation (6):

[0091]

[0092] Among them, x k is the state quantity at time k, x k+1 is the true value of the state quantity at time k+1, I is the unit matrix, u unknown,k+1 is the true value of the unknown input at time k+1, y k+1 is the observed value at time k+1, w k is the system noise at time k, v k is the measurement noise at time k; Δt is the sampling time, are the predicted values ​​of the state quantity and unknown input at time k, is the state prediction value at time k for time k+1, Gk|k ,H k+1|k ,B k|k ,D k+1|k is the corresponding Jacobian partial derivative matrix.

[0093] In some embodiments of the present invention, state parameter estimation is performed based on vehicle acceleration data and a preset state space model by using an extended Kalman filter estimation algorithm with unknown input to obtain preset dynamic state data, including but not limited to the following steps:

[0094] Construct preset initial data, wherein the preset initial data includes the initial state error covariance matrix, the process noise matrix, the measurement noise matrix and the state initial value.

[0095] The prediction error covariance matrix is ​​constructed according to the preset initial data, the preset correction factor matrix and the discrete state space model, wherein the preset correction factor matrix is ​​determined by the damping coefficient, the suspension stiffness and the tire stiffness.

[0096] Calculate the Kalman gain matrix from the prediction error covariance matrix.

[0097] The unknown input error covariance matrix is ​​calculated according to the Kalman gain matrix, so as to construct a road profile prediction model through the unknown input error covariance matrix.

[0098] The road surface height data is predicted based on the vehicle acceleration using the road surface profile prediction model.

[0099] The preset state vector is updated according to the road surface height data to obtain the suspension characteristic data.

[0100] In this specific embodiment, the embodiment of the present invention first constructs preset initial data. Specifically, the preset initial data in the embodiment of the present invention includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix, and a state initial value. For example, the embodiment of the present invention sets the initial state error covariance matrix P k|k , process noise matrix Q k , measurement noise matrix R k , initial value of state Among them, the initial value of the parameter in the embodiment of the present invention is The setting may have a certain deviation from the true value. Next, the embodiment of the present invention constructs a prediction error covariance matrix based on the preset initial data, the preset correction factor matrix and the discrete state space model. Specifically, the embodiment of the present invention first calculates the state one-step prediction value, as shown in the following formula (7):

[0101]

[0102] Accordingly, the one-step prediction error covariance matrix in the embodiment of the present invention is shown in the following formula (8):

[0103]

[0104] Among them, E represents the mean value.

[0105] Next, the embodiment of the present invention substitutes the discrete state space model, such as formula (6), and the state one-step prediction value, such as formula (7), into the one-step prediction error covariance matrix, such as formula (8), to obtain the following formula (9):

[0106] P k+1|k =(I+ΔtG k|k ) k|k (I+ΔtG k|k ) T +Q k (9)

[0107] Accordingly, since the suspension parameters will change during operation, x j,k+1 A mutation occurs. Among them, x j,k+1 is the jth component of the state quantity at time k+1. In order to track this change, the embodiment of the present invention uses replace where λ j is called a correction factor, j refers to an element in the state quantity, there are 7 state quantities here, that is, the value of j is from 1 to 7. Correspondingly, when λ j =1, the parameter is considered unchanged. j >1, it is considered that the parameter has changed. Therefore, the one-step prediction error covariance matrix can be transformed into the following formula (10):

[0108] P k+1|k =ψ[(I+ΔtG k|k ) k|k (I+ΔtG k|k ) T ]ψ T +Q k (10)

[0109] Where ψ is a 7×7 diagonal matrix, i.e., the preset correction factor matrix. The first four elements of ψ correspond to the displacement and velocity in the state quantity, which can be set to 1, and the last three elements correspond to the suspension parameters k. s ,k t ,c s , whose values ​​are λ1, λ2, and λ3 respectively. k|k is the state error covariance matrix at time k, P k+1|k is the one-step prediction error covariance matrix from time k to time k+1.

[0110] Next, the embodiment of the present invention calculates the Kalman gain matrix according to the prediction error covariance matrix. Specifically, the Kalman gain in the embodiment of the present invention is used to weight the state one-step prediction value obtained by the model calculation and the measured value obtained by the measurement, so as to update the state value. k+1|k Calculate the Kalman gain matrix as shown in equation (11):

[0111]

[0112] Next, the embodiment of the present invention calculates the position input error matrix covariance matrix according to the Kalman gain matrix, and then constructs a road profile prediction model through the unknown input error covariance matrix, so as to predict the road height data through the road profile prediction model according to the vehicle acceleration, and then update the preset state vector according to the road height data to obtain the suspension characteristic data. Specifically, the embodiment of the present invention first calculates the Kalman gain matrix K k+1 Calculate the unknown input error covariance matrix as shown in equation (12):

[0113]

[0114] Accordingly, the road surface profile prediction model constructed in the embodiment of the present invention is shown in the following formula (13):

[0115]

[0116] Among them, is the predicted value of the road height at time k+1. Accordingly, the embodiment of the present invention calculates the road height data based on the acquired vehicle acceleration, such as sprung acceleration and unsprung acceleration, by constructing a road profile prediction model. Next, the embodiment of the present invention updates the state value by the following formula (14):

[0117]

[0118] It is easy to understand that the speed, displacement and suspension parameters are included when the state value is set, so updating the state value is the true estimation of the suspension parameters. Accordingly, the embodiment of the present invention calculates the state error covariance matrix at time k+1 by the following formula (15):

[0119] P k+1|k+1 =(IL k+1 H k+1|k ) k+1|k (15)

[0120] Wherein, I is the unit matrix, and k and k+1 represent time.

[0121] In some embodiments of the present invention, the preset correction factor matrix is ​​determined by the following steps:

[0122] A preset parameter matrix is ​​constructed based on the damping coefficient, suspension stiffness, and tire stiffness.

[0123] When it is determined that the preset parameter matrix has not changed within the preset time threshold, the preset correction factor matrix is ​​determined to be the unit matrix. Alternatively, when it is determined that the preset parameter matrix has changed within the preset time threshold, the preset correction factor matrix is ​​obtained by solving the prediction parameter matrix through the constraint optimization algorithm.

[0124] In this specific embodiment, the embodiment of the present invention first constructs a preset parameter matrix according to the damping coefficient, the suspension stiffness and the tire stiffness. Specifically, the embodiment of the present invention first defines the relevant parameter part in the preset state vector as a preset parameter matrix θ, wherein the preset parameter matrix θ includes the damping coefficient, the suspension stiffness and the tire stiffness, as shown in the following formula (16):

[0125]

[0126] Next, the embodiment of the present invention determines whether the preset parameter matrix changes within the preset time threshold to determine the corresponding preset correction factor matrix. Specifically, when it is determined that the prediction parameter matrix has not changed within the preset time threshold, the preset correction factor matrix is ​​determined to be the unit matrix I. Accordingly, when it is determined that the prediction parameter matrix has a sudden change within the preset time threshold, the embodiment of the present invention solves it through a constrained optimization algorithm to obtain the preset correction factor matrix. Exemplarily, the objective function is defined in the embodiment of the present invention as shown in the following formula (17):

[0127]

[0128] Among them, is the predicted value of the i-th parameter at time k+1, is the predicted value of the ith parameter at time k, and the objective function in formula (10) It can ensure that the parameter change rate is minimized and converges to the true value of the parameter as quickly as possible.

[0129] Accordingly, the constraints constructed in the embodiment of the present invention are shown in the following equations (18) and (19):

[0130]

[0131]

[0132] Among them, V k+1 is the output error covariance matrix at time k+1, R k+1is the measurement noise matrix at time k+1, δ is a very small positive constant, and ‖·‖ is the Frobenius norm of the matrix.

[0133] In the above optimization solution process, the initial value of ψ is selected in the following way: Assuming that the diagonal elements of the matrix are equal, that is, but I is the identity matrix.

[0134] Accordingly, in the embodiment of the present invention, the above formula (10) can be converted into the following formula (20):

[0135]

[0136] Therefore, the constructed objective function and constraint function, i.e., equations (17), (18) and (19), can be used to obtain the following equation (21):

[0137]

[0138] Wherein, T1 and T2 are as shown in the following formula (22):

[0139]

[0140] Accordingly, the embodiment of the present invention can obtain the initial value of ψ by taking traces on both sides of the above equation (20), that is, As shown in the following formula (23):

[0141]

[0142] Among them, T a 、T b and T c As shown in the following formula (24):

[0143]

[0144] It is easy to understand that the embodiment of the present invention completes the optimization solution of ψ through the above steps, and then calculates the one-step prediction matrix P k+1|k , and then through the subsequent calculation of the Kalman gain matrix and the unknown input error covariance matrix, the road profile, that is, the road height data, is estimated, so as to update the state value and determine the corresponding suspension characteristic data.

[0145] It is easy to understand that the embodiment of the present invention uses an iteratively updated joint estimation method using an extended Kalman filter with unknown inputs, and only uses acceleration sensors for sprung and unsprung masses, without the need for other sensors, which effectively reduces the estimation cost of road roughness. At the same time, while estimating the degree of road undulation, the embodiment of the present invention can also track changes in suspension parameters, providing an important reference for the formulation of subsequent control strategies. Among them, the embodiment of the present invention can use only partial observations, that is, only acceleration sensors, without the need to obtain physical quantities such as displacement and velocity through integration, and can invert the road input at the same time, realizing full observation of the state, physical parameters, and road input.

[0146] See also Figure 3 The embodiment of the present application also provides a road surface profile and suspension parameter prediction system, which can implement the above-mentioned road surface profile and suspension parameter prediction method, and the system includes:

[0147] The first module 210 is used to obtain vehicle acceleration data.

[0148] The second module 220 is used to construct a preset correlation function according to a preset state vector and a road profile parameter.

[0149] The third module 230 is used to construct a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function.

[0150] The fourth module 240 is used to estimate the state parameters according to the vehicle acceleration data and the preset state space model through the extended Kalman filter estimation algorithm with unknown input to obtain the preset dynamic state data, wherein the preset dynamic state data includes the road height data and the suspension characteristic data.

[0151] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0152] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned road profile and suspension parameter prediction method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0153] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0154] See also Figure 4 , Figure 4 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0155] The processor 310 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0156] The memory 320 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 320 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 320, and the processor 310 calls and executes the method for predicting the road profile and suspension parameters of the embodiment of this application;

[0157] Input / output interface 330, used to implement information input and output;

[0158] Communication interface 340, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0159] bus 350 , which transmits information between the various components of the device (e.g., processor 310 , memory 320 , input / output interface 330 , and communication interface 340 );

[0160] The processor 310 , the memory 320 , the input / output interface 330 and the communication interface 340 are connected to each other in communication within the device via the bus 350 .

[0161] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for predicting road surface profile and suspension parameters.

[0162] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0163] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0164] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0165] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0166] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0168] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0169] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0170] In the several embodiments provided in the present application, it should be understood that the disclosed 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 above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0173] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0174] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for predicting road profile and suspension parameters, characterized in that: The method comprises the following steps: Get vehicle acceleration data; Constructing a preset correlation function according to a preset state vector and a road profile parameter; Constructing a preset suspension model, so as to construct a preset state space model through the preset suspension model and the preset correlation function; According to the vehicle acceleration data and the preset state space model, state parameter estimation is performed through an extended Kalman filter estimation algorithm containing unknown input to obtain preset dynamic state data; wherein the preset dynamic state data includes road height data and suspension characteristic data.

2. The method according to claim 1, characterized in that The obtaining of vehicle acceleration data comprises: Dynamically acquiring preset acceleration data through a preset acceleration sensor; wherein the preset acceleration data includes sprung acceleration data and unsprung acceleration data; The preset acceleration data is input into a preset low-pass filter for filtering to obtain the vehicle acceleration data.

3. The method according to claim 1, characterized in that The step of constructing a preset correlation function according to the preset state vector and the road surface profile parameter includes: Constructing the preset state vector; wherein the preset state vector includes sprung displacement, sprung speed, unsprung displacement, unsprung speed, damping coefficient, suspension stiffness and tire stiffness; Constructing a preset correlation function between the preset state vector and the road surface profile parameter; wherein the preset correlation function includes a nonlinear continuous function.

4. The method according to claim 3, characterized in that The step of constructing a preset suspension model to obtain a preset state space model through the preset suspension model and the preset correlation function includes: Constructing a vehicle suspension motion differential equation model according to the suspension characteristic parameters and the road profile parameters; wherein the suspension characteristic parameters include sprung parameters, unsprung parameters, the damping coefficient, the suspension stiffness, and the tire stiffness; The preset state space model is constructed according to the vehicle suspension motion differential equation model and the preset correlation function; wherein the preset state space model includes a preset state space equation and a preset measurement equation.

5. The method according to claim 4, characterized in that Before performing the state parameter estimation by using an extended Kalman filter estimation algorithm containing unknown input according to the vehicle acceleration data and the preset state space model to obtain preset dynamic state data, the method further includes: Performing a first-order Taylor expansion on the preset correlation function to obtain a preset Taylor expansion function; The preset state space equation and the preset measurement equation are discretized according to the preset Taylor expansion function to obtain a discrete state space model; wherein the discrete state space model includes a discrete state space equation and a discrete measurement equation.

6. The method according to claim 5, characterized in that The method of estimating state parameters by using an extended Kalman filter estimation algorithm containing unknown input according to the vehicle acceleration data and the preset state space model to obtain preset dynamic state data includes: Constructing preset initial data; wherein the preset initial data includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix and a state initial value; Constructing a prediction error covariance matrix according to the preset initial data, the preset correction factor matrix and the discrete state space model; wherein the preset correction factor matrix is ​​determined by the damping coefficient, the suspension stiffness and the tire stiffness; Calculate the Kalman gain matrix according to the prediction error covariance matrix; An unknown input error covariance matrix is ​​calculated according to the Kalman gain matrix, so as to construct a road profile prediction model through the unknown input error covariance matrix; The road surface height data is obtained by predicting the road surface profile prediction model according to the vehicle acceleration; The preset state vector is updated according to the road surface height data to obtain the suspension characteristic data.

7. The method according to claim 6, characterized in that The preset correction factor matrix is ​​determined by the following steps: Constructing a preset parameter matrix according to the damping coefficient, the suspension stiffness, and the tire stiffness; When it is determined that the preset parameter matrix has not changed within a preset time threshold, determining that the preset correction factor matrix is ​​a unit matrix; Alternatively, when it is determined that the preset parameter matrix changes within the preset time threshold, the preset correction factor matrix is ​​obtained by solving the prediction parameter matrix through a constraint optimization algorithm.

8. A road profile and suspension parameter prediction system, characterized in that: The system comprises: The first module is used to obtain vehicle acceleration data; The second module is used to construct a preset correlation function according to a preset state vector and a road profile parameter; A third module is used to construct a preset suspension model, so as to obtain a preset state space model through the preset suspension model and the preset correlation function; The fourth module is used to estimate state parameters according to the vehicle acceleration data and the preset state space model through an extended Kalman filter estimation algorithm containing unknown inputs to obtain preset dynamic state data; wherein the preset dynamic state data includes road height data and suspension characteristic data.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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