Method, system, device and storage medium for predicting road profile and suspension parameters

Abstract

The application discloses a road surface profile and suspension parameter prediction method, system, device and storage medium. The method comprises the following steps: obtaining vehicle acceleration data; constructing a preset correlation function according to a preset state vector and a road surface profile parameter; constructing a preset suspension model to obtain a preset state space model through the preset suspension model and the preset correlation function; and performing state parameter estimation through 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 preset dynamic state data comprises road surface height data and suspension characteristic data. The embodiment of the application can effectively improve the accuracy and reliability of road surface profile and suspension parameter estimation, reduce the prediction cost, and provide effective support for subsequent suspension control strategies. The application 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 road profile and suspension parameter prediction method, system, device and storage medium. BACKGROUND

[0002] With the continuous development of economy, people's requirements for vehicle ride comfort and handling stability are getting higher and higher. The unevenness of the road 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 driving conditions of the vehicle can be determined, so that different control strategies can be developed for the suspension system according to different working conditions, thereby improving the ride comfort of the vehicle. In addition, the suspension parameter is the main control object of the suspension control system, and the change of the suspension parameter will affect the vibration response of the vehicle, and the change of the vibration response will affect the acquisition of the road profile information. In related technologies, the method for estimating the road profile, such as through a camera or a laser radar, often has low stability, is difficult to ensure estimation accuracy, and has relatively high cost, which is difficult to provide effective support for suspension control.

[0003] To sum up, the technical problems existing in the related art need to be improved. SUMMARY

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

[0005] To achieve the above purpose, one aspect of an embodiment of the present application provides a road profile and suspension parameter prediction method, which comprises the following steps:

[0006] Obtaining 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 to obtain a preset state space model through the preset suspension model and the preset correlation function;

[0009] Estimating state parameters by an extended Kalman filter estimation algorithm containing unknown inputs according to the vehicle acceleration data and the preset state space model to obtain preset dynamic state data; wherein the preset dynamic state data comprises road height data and suspension characteristic data.

[0010] In some embodiments, the acquiring vehicle acceleration data comprises:

[0011] acquiring preset acceleration data by preset acceleration sensors dynamically; wherein, the preset acceleration data comprises sprung acceleration data and unsprung acceleration data;

[0012] inputting the preset acceleration data into a preset low-pass filter for filtering processing to obtain the vehicle acceleration data.

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

[0014] constructing the preset state vector; wherein, the preset state vector comprises sprung displacement, sprung velocity, unsprung displacement, unsprung velocity, damping coefficient, suspension stiffness and tire stiffness;

[0015] constructing a preset correlation function about the preset state vector and the road profile parameter; wherein, the preset correlation function comprises a nonlinear continuous function.

[0016] In some embodiments, the constructing a preset suspension model to construct a preset state space model by the preset suspension model and the preset correlation function comprises:

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

[0018] constructing the preset state space model according to the vehicle suspension motion differential equation model and the preset correlation function; wherein, the preset state space model comprises a preset state space equation and a preset measurement equation.

[0019] In some embodiments, before performing the state parameter estimation by 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 comprises:

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

[0021] discretizing the preset state space equation and the preset measurement equation according to the preset Taylor expansion function to obtain a discrete state space model; wherein, the discrete state space model comprises a discrete state space equation and a discrete measurement equation.

[0022] In some embodiments, the state parameter estimation according to the vehicle acceleration data and the preset state space model is performed by an extended Kalman filter estimation algorithm containing 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, a 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] calculating a Kalman gain matrix according to the prediction error covariance matrix;

[0026] calculating an unknown input error covariance matrix according to the Kalman gain matrix, to construct a road profile prediction model by using the unknown input error covariance matrix;

[0027] predicting the road height data by using the road profile prediction model according to the vehicle acceleration;

[0028] updating the preset state vector according to the road 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 a preset time threshold, determining the preset correction factor matrix as an identity matrix; or when it is determined that the preset parameter matrix has changed within the preset time threshold, solving the preset correction factor matrix according to the prediction parameter matrix by using a constraint optimization algorithm.

[0032] To achieve the above-mentioned purposes, another aspect of the embodiments of the present application proposes a road profile and suspension parameter prediction system, which comprises:

[0033] a first module configured to acquire vehicle acceleration data;

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

[0035] The third module is configured to construct a preset suspension model, so as to construct a preset state space model by using the preset suspension model and the preset correlation function.

[0036] The fourth module is configured to perform state parameter estimation by using an extended Kalman filter estimation algorithm containing unknown input, so as to obtain preset dynamic state data according to the vehicle acceleration data and the preset state space model, wherein the preset dynamic state data includes road height data and suspension characteristic data.

[0037] To achieve the above object, another aspect of the embodiment of the present application provides an electronic device, which comprises:

[0038] at least one processor;

[0039] at least one memory configured to store 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 object, another aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0042] The embodiment of the present application at least has the following beneficial effects: the present application provides a road profile and suspension parameter prediction method, system, device and storage medium, which firstly acquires vehicle acceleration data. Then, the embodiment of the present application constructs a preset correlation function according to preset state quantities and road profile parameters, and constructs a preset suspension model, so as to construct a preset state space model by using the preset suspension model and the preset correlation function. Finally, the embodiment of the present application performs state parameter estimation by using an extended Kalman filter estimation algorithm containing unknown input, so as to obtain preset dynamic state data according to vehicle acceleration data and the preset state space model, wherein the preset dynamic state data includes road height data and suspension characteristic data, thereby realizing road profile and suspension parameter estimation. It is easy to understand that the embodiment of the present application can effectively reduce the cost of road profile prediction by collecting vehicle acceleration data, and can track suspension parameter changes while performing road profile estimation by using the parameter estimation method of the extended Kalman filter estimation algorithm containing unknown input, 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 DRAWINGS

[0043] Figure 1 is a flowchart of the road profile and suspension parameter prediction method provided by the embodiment of the present application;

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

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

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

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the present application in combination with the drawings and embodiments. 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 relates to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with embodiments of the present application, but are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0048] It can be understood that the terms "first", "second", and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "when" or "in response to determining".

[0049] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, 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 understood by a person skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

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

[0052] Kalman Filter algorithm: It is a tool for estimating the state of a dynamic system by recursively optimizing the state of the system, so that it can effectively track the system variables in a noisy and uncertain environment. Kalman filter is a mathematical model established by state equation and observation equation of the system, and the method of Bayesian inference is used to combine the prediction and observation data of the system to update the state estimation. Kalman filter is mainly divided into linear Kalman filter and extended Kalman filter (EKF). Among them, linear Kalman filter is suitable for linear system and Gaussian noise, and extended Kalman filter is suitable for nonlinear system.

[0053] With the continuous development of economy, people's requirements for vehicle ride comfort and handling stability are getting higher and higher. 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 driving conditions can be determined, so that different control strategies can be developed for the suspension system according to different working conditions, thereby improving the ride comfort of the vehicle. In addition, the suspension parameters are the main control objects of the suspension control system, and the change of the suspension parameters will affect the vibration response of the vehicle, and the change of the vibration response will affect the acquisition of the road profile information. In related technologies, the method for estimating the road profile, such as estimating the road profile by using a camera and a laser radar, often has low stability and is difficult to ensure estimation accuracy, and the cost is relatively high, which is difficult to provide effective support for suspension control. For example, the road profile estimation method based on vision mainly obtains road profile information through a camera, a laser radar and the like, but is easily affected by weather changes and has a relatively high cost. The road profile estimation method based on neural network needs a large amount of training data and is very sensitive to suspension parameters, so it is difficult to ensure estimation accuracy when the parameters change. The road profile estimation method based on traditional Kalman filter regards the road profile as an extended state vector, and makes assumptions about the road profile, usually assuming a random walk model, but the actual road shape does not necessarily meet this assumption.

[0054] Therefore, the embodiment of the present application provides a road profile and suspension parameter prediction method, system, device and storage medium. The method comprises the following steps: obtaining vehicle acceleration data; constructing a preset correlation function according to a preset state quantity and a road profile parameter, and constructing a preset suspension model, so as to construct a preset state space model by using the preset suspension model and the preset correlation function; and performing parameter prediction by using an extended Kalman filter estimation algorithm containing unknown input according to the vehicle acceleration data and the preset state space model, so as to obtain preset dynamic state data. The preset dynamic state data comprises road height data and suspension characteristic data, so that the road profile and suspension parameter estimation is realized, and the accuracy and reliability of the road profile and suspension parameter estimation are improved, thereby providing effective support for a subsequent suspension control strategy.

[0055] The road profile and suspension parameter prediction method provided by the embodiment of the present application relates to the technical field of vehicle driving parameter estimation. The road profile and suspension parameter prediction method provided by the embodiment of the present application can be applied to a terminal, can be applied to a server, and can also be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, and the like, but is not limited thereto; the server end can be configured as a stand-alone physical server, can be configured as a server cluster or a distributed system composed of multiple physical servers, can be configured as a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligence platforms, and the server can also be a node server in a blockchain network; and the software can be an application that implements the road profile and suspension parameter prediction method, and the like, 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 devices or portable devices, tablet devices, multi-processor 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, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as a program module. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by 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 flowchart of a road profile and suspension parameter prediction method provided by the embodiments of the present application, Figure 1 The method in the method can include but is not limited to steps S110 to S140.

[0058] Step S110: Obtain vehicle acceleration data.

[0059] Step S120: Construct a preset correlation function according to a preset state vector and a road profile parameter.

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

[0061] Step S140: Estimate a state parameter through an extended Kalman filter estimation algorithm containing unknown input according to the vehicle acceleration data and the preset state space model, so as to obtain preset dynamic state data. The preset dynamic state data includes road height data and suspension characteristic data.

[0062] In the working process of the specific embodiment, the embodiment of the present application first acquires vehicle acceleration data. Specifically, in the process of vehicle driving, the embodiment of the present application acquires the acceleration information of the vehicle in real time through the vehicle-mounted sensor. Among them, most of the schemes in the related art use displacement sensors, such as suspension deflection sensors. Compared with acceleration sensors, such sensors are expensive and are not suitable for mass application. Therefore, the embodiment of the present application only considers that the acceleration signal is measurable under partial observation conditions, predicts the state quantity, and inversely calculates the road input on this basis. Then, the embodiment of the present application constructs a preset correlation function according to a preset state vector and a road profile parameter. Specifically, the preset state vector in the embodiment of the present application refers to a vector containing the current state information of the system, such as displacement, speed, damping coefficient, stiffness and the like. Correspondingly, the road profile parameter refers to the unknown input of the road, that is, the road height. Correspondingly, the embodiment of the present application constructs a preset correlation function associated with the state quantity and the unknown input of the road according to the preset state vector and the road profile parameter. Further, the embodiment of the present application constructs a preset suspension model to construct 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 application refers to a model used to describe the suspension system in the vehicle, which usually includes springs, wheels and vehicle bodies and the like. The embodiment of the present application constructs a preset state space model by constructing a preset suspension model and combining 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 application estimates the state parameters through the extended Kalman filter estimation algorithm containing unknown inputs according to the vehicle acceleration data and the preset state space model, to obtain preset dynamic state data. Specifically, the EKF-UI algorithm in the embodiment of the present application is an extended Kalman filter algorithm, which can handle the case where there is unknown input (or disturbance) in the system and can cope with the uncertainty in the system. The embodiment of the present application combines the collected vehicle acceleration data and the constructed preset state space model, and obtains the preset dynamic state data including the road height data and the suspension characteristic data through the joint estimation method of the extended Kalman filter containing unknown inputs, so as to estimate the road fluctuation degree while tracking the suspension characteristic parameters, and effectively improve the accuracy and reliability of the road profile and suspension parameter estimation, which can provide effective support for the subsequent suspension control strategy.

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

[0064] The preset acceleration data is input into a preset low-pass filter for filtering processing to obtain vehicle acceleration data.

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

[0066] In the embodiment, the preset acceleration sensor is used to dynamically obtain preset acceleration data, and the preset acceleration data is input into a preset low-pass filter for filtering processing to obtain vehicle acceleration data. Specifically, the preset acceleration sensor in the embodiment is an acceleration sensor arranged in the vehicle. For example, the acceleration data obtained by the preset acceleration sensor in real time includes sprung mass acceleration data and unsprung mass acceleration data. The sprung mass acceleration refers to the acceleration of the suspension load mass, such as the acceleration of the vehicle body, and the unsprung mass acceleration refers to the acceleration of the suspension non-load mass, such as the acceleration of the wheel. Accordingly, the preset acceleration sensor in the embodiment is used to collect the sprung mass acceleration data and the unsprung mass acceleration of the vehicle in real time. Then, the preset acceleration data collected is input into a preset low-pass filter for filtering processing to obtain vehicle acceleration data. Specifically, when the preset acceleration data is collected, the preset acceleration data is filtered to improve the data smoothness and reliability, which facilitates subsequent data processing. For example, the preset low-pass filter in the embodiment is set to 0 to 50 Hz, and the obtained acceleration information is processed by the preset low-pass filter to obtain vehicle acceleration data.

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

[0068] The preset state vector is constructed. The preset state vector includes sprung displacement, sprung velocity, unsprung displacement, unsprung velocity, damping coefficient, suspension stiffness, and tire stiffness.

[0069] The preset correlation function about the preset state vector and the road profile parameter is constructed. The preset correlation function includes a nonlinear continuous function.

[0070] In the embodiment, the preset state vector is first constructed. Specifically, in the embodiment, in order to achieve good tracking of the suspension motion state, such as the velocity of the sprung mass, the acceleration of the sprung mass, the velocity of the unsprung mass, and the acceleration of the unsprung mass, the embodiment not only tracks the vehicle motion state, but also tracks the change of the suspension characteristic parameters. Therefore, the embodiment selects corresponding state quantities to construct the preset state vector: x = [x s x u] T, wherein x s is the sprung displacement, x u is the unsprung displacement, and x is the state vector.s spring displacement, spring velocity, x t unsprung displacement, unsprung velocity, k s damping coefficient, k t suspension stiffness, c s tire stiffness. Then, the embodiment of the present application constructs a preset correlation function about the preset state vector and the road profile parameter. Specifically, the road profile parameter in the embodiment of the present application refers to the road unknown input, i.e. the road height data. Accordingly, the preset correlation function in the embodiment of the present application is a nonlinear continuous function. After determining the corresponding preset state vector, the embodiment of the present application constructs a nonlinear continuous function related to the preset state vector x and the unknown input u unknown (the road profile parameter) to obtain the preset correlation function.

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

[0072] A vehicle suspension motion differential equation model is constructed according to the suspension characteristic parameters and the road profile parameters. The suspension characteristic parameters include the spring parameters, the unsprung parameters, the damping coefficient, the suspension stiffness and the tire stiffness.

[0073] A preset state space model is constructed according to the vehicle suspension motion differential equation model and the preset correlation function. 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 application first constructs a vehicle suspension motion differential equation model according to the suspension characteristic parameters and the road profile parameters. Specifically, the suspension characteristic parameters in the embodiment of the present application include the spring parameters, the unsprung parameters, the damping coefficient, the suspension stiffness and the tire stiffness. Among them, the spring parameters in the embodiment of the present application refer to the related parameters of the objects carried by the suspension (such as the vehicle body, the passengers), including the spring mass, the spring displacement, the spring velocity and the spring acceleration, such as the vehicle body mass, the displacement, the velocity, etc. Accordingly, the unsprung parameters refer to the related parameters of the objects not carried by the suspension (such as the wheels), including the unsprung mass, the unsprung displacement, the unsprung velocity and the unsprung acceleration, such as the tire mass, the displacement and the velocity, etc. Exemplarily, as shown in the following formula (1), the vehicle suspension motion differential equation model is constructed according to the suspension characteristic parameters and the road profile parameters. Figure 2 Figure 2 is a quarter suspension model schematic diagram provided by the embodiment of the present application, in which m s is the spring mass, m t is the unsprung mass, c s is the damping coefficient of the damper, k s is the suspension stiffness, k​t is the tire stiffness. Accordingly, the embodiment of the present application constructs a vehicle suspension motion differential equation model according to the determined suspension characteristic number and the road profile parameter, as shown in the following formula (1):

[0075]

[0076] wherein, in the formula, x s is the sprung displacement, is the sprung velocity, is the sprung acceleration, x t is the unsprung displacement, is the unsprung velocity, is the unsprung acceleration, u unknown is the road input to be solved, i.e. the road profile parameter.

[0077] Next, the embodiment of the present application constructs a preset state space model according to the vehicle suspension motion differential equation model and a preset correlation function. Specifically, the preset state space model in the embodiment of the present application includes a preset state space equation and a preset measurement equation. Accordingly, after the preset state vector is determined to be x= , the observation is selected to be The corresponding state space equation (the preset state space equation) is shown in the following formula (2):

[0078]

[0079] wherein, in the formula, x represents the derivative of the state, i.e. the rate of change with respect to time, u unknown is the road unknown input, i.e. the road height, g(x, u unknown ) is a nonlinear continuous function (the preset correlation function) about the state x, the unknown input u unknown , and w(t) represents the modeling error.

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

[0081]

[0082] wherein, in the formula, h(x, u unknown ) is a nonlinear continuous function (the preset correlation function) about the state x, the unknown input u unknown , and v(t) represents the measurement error.

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

[0084] The preset correlation function is first-order Taylor expanded 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. The discrete state space model includes a discrete state space equation and a discrete measurement equation.

[0086] In the specific embodiments, before parameter prediction, the preset state space model constructed is first discretized. Specifically, the preset correlation function is first-order Taylor expanded to obtain a preset Taylor expansion function. In the embodiments of the present application, the preset correlation function includes h(x,u unknown ) and g(x,u unknown ), and the two nonlinear functions are first-order Taylor expanded to obtain the preset Taylor expansion function, as shown in the following formula (4):

[0087]

[0088] The corresponding Jacobian partial derivative matrix is shown in the following formula (5):

[0089]

[0090] Further, the preset Taylor expansion function obtained by expansion and the corresponding preset state space model are discretized to obtain the discrete state space equation and the measurement equation, as shown in the following formula (6):

[0091]

[0092] In the formula, 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 an identity matrix, u unknown,k+1 is the true value of the unknown input at time k+1, y k+1 is the observation 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 the unknown input at time k, respectively, is the predicted value of the state at time k+1, and Gk|k H k+1|k B k|k D k+1|k Let be the corresponding Jacobian partial derivative matrix.

[0093] In some embodiments of the present invention, state parameters are estimated 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, including but not limited to the following steps:

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

[0095] The prediction error covariance matrix is ​​constructed based on preset initial data, a preset correction factor matrix, and a discrete state-space model. The preset correction factor matrix is ​​determined using damping coefficients, suspension stiffness, and tire stiffness.

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

[0097] The unknown input error covariance matrix is ​​calculated based on the Kalman gain matrix, and a road profile prediction model is constructed using the unknown input error covariance matrix.

[0098] Road height data is predicted using a road profile prediction model based on vehicle acceleration.

[0099] The preset state vector is updated based on the road surface height data to obtain suspension feature data.

[0100] In this specific embodiment, the present invention first constructs preset initial data. Specifically, the preset initial data in this embodiment includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix, and initial state values. For example, this embodiment sets the initial state error covariance matrix P... k|k Process noise matrix Q k Measurement noise matrix R k Initial state value In the embodiments of the present invention, the initial values ​​of the parameters are as follows: The setting can deviate from the true value to a certain extent. Next, the embodiment of the present invention constructs the prediction error covariance matrix based on the preset initial data, the preset correction factor matrix, and the discrete state space model framework. Specifically, the embodiment of the present invention first calculates the one-step prediction value of the state, as shown in the following equation (7):

[0101]

[0102] Correspondingly, the step prediction error covariance matrix in the embodiment of the present application is shown in the following formula (8) :

[0103]

[0104] wherein E represents taking mean value.

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

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

[0107] Correspondingly, since the suspension parameters change during operation, the x j,k+1 mutates. Wherein x j,k+1 is the jth component of the state quantity at k+1 time. In order to track the change, the embodiment of the present application substitutes instead of wherein λ j is a correction factor, and j generally refers to an element in the state quantity, which has 7 in total, that is, the value of j is from 1 to 7. Correspondingly, when λ j =1, it is considered that the parameter does not change, and when λ j >1, it is considered that the parameter changes. Therefore, the step prediction error covariance matrix can be converted into the following formula (10) :

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

[0109] wherein ψ is a 7x7 diagonal matrix, that is, a 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. P k|k is the state error covariance matrix at k time, and P k+1|k is the step prediction error covariance matrix of k time to k+1 time.

[0110] Next, in this embodiment of the invention, the Kalman gain matrix is ​​calculated based on the prediction error covariance matrix. Specifically, in this embodiment of the invention, the Kalman gain is used to weight the one-step predicted state value calculated from the model with the measured value obtained from the measurement, thereby updating the state value. In this embodiment of the invention, the Kalman gain is calculated based on the corresponding prediction error covariance matrix P. k+1|k The Kalman gain matrix is ​​calculated as shown in equation (11):

[0111]

[0112] Next, in this embodiment of the invention, the position input error matrix covariance matrix is ​​calculated based on the Kalman gain matrix. Then, a road surface profile prediction model is constructed using the unknown input error covariance matrix. Based on the vehicle acceleration, the road surface height data is predicted using the road surface profile prediction model. The preset state vector is then updated based on the road surface height data to obtain suspension feature data. Specifically, this embodiment of the invention first uses the calculated Kalman gain matrix K... k+1 The unknown input error covariance matrix is ​​calculated as shown in equation (12):

[0113]

[0114] Accordingly, the road surface profile prediction model constructed in this embodiment of the invention is shown in equation (13) below:

[0115]

[0116] Where, in the formula This is the predicted value of the road surface height at time k+1. Accordingly, in this embodiment of the invention, the road surface height data is calculated using the constructed road surface profile prediction model based on the acquired vehicle acceleration, such as sprung acceleration and unsprung acceleration. Then, this embodiment of the invention updates the state value using the following formula (14):

[0117]

[0118] It is easy to understand that the state value setting includes velocity, displacement, and suspension parameters; therefore, updating the state value is a true estimate of the suspension parameters. Accordingly, this embodiment of the invention calculates the state error covariance matrix at time k+1 using the following equation (15):

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

[0120] In the formula, I is the identity matrix, and k and k+1 both represent time points.

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

[0122] The preset parameter matrix is constructed according to the damping coefficient, the suspension stiffness and the tire stiffness.

[0123] When it is determined that the preset parameter matrix does not change within the preset time threshold, the preset correction factor matrix is determined as the unit matrix. Alternatively, when it is determined that the preset parameter matrix changes within the preset time threshold, the preset correction factor matrix is solved according to the prediction parameter matrix by a constraint optimization algorithm.

[0124] In the specific embodiments, the preset parameter matrix is first constructed according to the damping coefficient, the suspension stiffness and the tire stiffness. Specifically, the part of the preset state vector related to the parameters is defined as the 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] Then, the present application 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 does not change within the preset time threshold, the preset correction factor matrix is determined as the unit matrix I. Correspondingly, when it is determined that the prediction parameter matrix changes within the preset time threshold, the preset correction factor matrix is solved by a constraint optimization algorithm in the present application. Exemplarily, the objective function in the present application is defined as shown in the following formula (17):

[0127]

[0128] wherein in the formula is the i-th parameter prediction value at the k+1 moment, is the i-th parameter prediction value at the k moment, and the objective function in the formula (10) can guarantee that the parameter change rate is minimum and converges to the true value of the parameter as soon as possible.

[0129] Correspondingly, the constraint condition constructed in the present application is shown in the following formula (18) and (19):

[0130]

[0131]

[0132] wherein in the formula V k+1 is the output error covariance matrix at the k+1 moment, and R k+1is a measurement noise matrix at k+1 moment, δ is a very small normal number, and ‖·‖ is a Frobenius norm of a matrix.

[0133] wherein in the above optimization solving process, the initial value of ψ is selected by the following way: assuming that the diagonal elements of the matrix are equal, that is, I is a unit matrix.

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

[0135]

[0136] Therefore, the following formula (21) can be obtained from the constructed objective function and constraint function, that is, the above formula (17), (18) and (19):

[0137]

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

[0139]

[0140] Correspondingly, the initial value of ψ can be obtained by taking the trace of both sides of the above formula (20) in the embodiment of the application, that is, as shown in the following formula (23):

[0141]

[0142] wherein T a , T b and T c are shown in the following formula (24):

[0143]

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

[0145] ​It is easy to understand that the joint estimation method of iterative updating by the extended Kalman filter with unknown input in the embodiment of the application, and only using the on-spring and off-spring mass acceleration sensors, without other sensors, effectively reduces the estimation cost of the road roughness. At the same time, the embodiment of the application can track the suspension parameter changes while estimating the road roughness, which provides an important reference for the subsequent control strategy. Among them, the embodiment of the application can use only part of the observation, that is, only use the acceleration sensor, without obtaining displacement, velocity and other physical quantities by integration, and at the same time, the road input can be obtained by inversion, which realizes the full observation of the state, physical parameters and road input.

[0146] Please refer to Figure 3 The embodiment of the application also provides a road profile and suspension parameter prediction system, which can realize the road profile and suspension parameter prediction method described above, and the system comprises:

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

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

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

[0150] The fourth module 240 is configured to estimate a state parameter by using an extended Kalman filter estimation algorithm with unknown input according to the vehicle acceleration data and the preset state space model, so as to obtain preset dynamic state data. The preset dynamic state data comprises road height data and suspension characteristic data.

[0151] It can be understood that the contents in the above method embodiment are applicable to the system embodiment, the system embodiment specifically realizes the same functions as the above method embodiment, and achieves the same beneficial effects as the above method embodiment.

[0152] The embodiment of the application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor realizes the road profile and suspension parameter prediction method described above when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer, a vehicle-mounted computer and the like.

[0153] It can be understood that the contents in the above method embodiment are applicable to the device embodiment, the device embodiment specifically realizes the same functions as the above method embodiment, and achieves the same beneficial effects as the above method embodiment.

[0154] Please refer toFigure 4 , Figure 4 Fig. 1 illustrates a hardware structure of an electronic device according to another embodiment of the present application, and the electronic device includes:

[0155] The processor 310 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0156] The memory 320 can be implemented by a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 320 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 320 and are called and executed by the processor 310 to implement the road surface profile and suspension parameter prediction method according to the embodiments of the present application.

[0157] The input / output interface 330 is configured to implement information input and output.

[0158] The communication interface 340 is configured to implement communication interaction between the device and other devices. The communication can be implemented by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0159] The bus 350 is configured to transmit information between various components (for example, the processor 310, the memory 320, the input / output interface 330, and the communication interface 340) of the device.

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

[0161] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the road surface profile and suspension parameter prediction method.

[0162] It can be understood that the contents in the above method embodiments are applicable to the present storage medium embodiments. The present storage medium embodiments specifically implement the same functions as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0163] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory that is remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above 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 used 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 by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

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

[0166] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0167] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0168] The terms "first", "second", "third", "fourth" and the like used in the specification of the present application and the above-described drawings (if any) are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0169] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B, and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, and c can be single or multiple.

[0170] In several embodiments provided in the 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 illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0172] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0173] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0174] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for predicting road surface profile and suspension parameters, characterized in that, The method includes the following steps: Acquire vehicle acceleration data; A preset association function is constructed based on the preset state vector and road surface contour parameters; A preset suspension model is constructed, and a preset state space model is obtained by constructing the preset suspension model and the preset correlation function; Based on the vehicle acceleration data and the preset state space model, the state parameters are estimated using an extended Kalman filter estimation algorithm with unknown inputs to obtain preset dynamic state data; wherein, the preset dynamic state data includes road surface height data and suspension feature data; The step of constructing a preset association function based on a preset state vector and road surface contour parameters includes: Construct the preset state vector; wherein, the preset state vector includes sprung displacement, sprung velocity, unsprung displacement, unsprung velocity, damping coefficient, suspension stiffness, and tire stiffness; Construct a preset correlation function between the preset state vector and the road surface contour parameters; wherein, the preset correlation function includes a nonlinear continuous function; 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: A differential equation model of vehicle suspension motion is constructed based on the suspension characteristic parameters and the road surface 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 based on the vehicle suspension motion differential equation model and the preset correlation function; wherein, the preset state space model includes preset state space equations and preset measurement equations; The method further includes, before performing the state parameter estimation algorithm using an extended Kalman filter with unknown input based on the vehicle acceleration data and the preset state space model to obtain preset dynamic state data: Perform a first-order Taylor expansion on the preset correlation function to obtain the preset Taylor expansion function; Discretize the preset state-space equation and the preset measurement equation according to the preset Taylor expansion function to obtain a discrete state-space model; wherein, the discrete state-space model includes discrete state-space equation and discrete measurement equation; The step of estimating state parameters using an extended Kalman filter estimation algorithm with unknown inputs based on the vehicle acceleration data and the preset state space model to obtain preset dynamic state data includes: Construct preset initial data; wherein, the preset initial data includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix, and initial state values; A prediction error covariance matrix is ​​constructed based on 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 based on the prediction error covariance matrix; The unknown input error covariance matrix is ​​calculated based on the Kalman gain matrix, and a road surface profile prediction model is constructed using the unknown input error covariance matrix. The road height data is predicted by the road profile prediction model based on the vehicle acceleration. The preset state vector is updated based on the road surface height data to obtain the suspension feature data; The preset correction factor matrix is ​​determined through the following steps: A preset parameter matrix is ​​constructed based on the damping coefficient, the suspension stiffness, and the tire stiffness; If it is determined that the preset parameter matrix has not changed within a preset time threshold, the preset correction factor matrix is ​​determined to be an identity matrix; or, if 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 preset parameter matrix using a constraint optimization algorithm.

2. The method according to claim 1, characterized in that, The acquisition of vehicle acceleration data includes: Preset acceleration data is dynamically acquired 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. A prediction system for road surface profile and suspension parameters, characterized in that, The system includes: The first module is used to acquire vehicle acceleration data; The second module is used to construct a preset association function based on the preset state vector and road surface contour parameters; The third module is used to construct a preset suspension model, so as to construct a preset state space model through the preset suspension model and the preset correlation function; The fourth module is used to estimate state parameters based on the vehicle acceleration data and the preset state space model using an extended Kalman filter estimation algorithm with unknown inputs, to obtain preset dynamic state data; wherein, the preset dynamic state data includes road surface height data and suspension feature data; The step of constructing a preset association function based on a preset state vector and road surface contour parameters includes: Construct the preset state vector; wherein, the preset state vector includes sprung displacement, sprung velocity, unsprung displacement, unsprung velocity, damping coefficient, suspension stiffness, and tire stiffness; Construct a preset correlation function between the preset state vector and the road surface contour parameters; wherein, the preset correlation function includes a nonlinear continuous function; 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: A differential equation model of vehicle suspension motion is constructed based on the suspension characteristic parameters and the road surface 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 based on the vehicle suspension motion differential equation model and the preset correlation function; wherein, the preset state space model includes preset state space equations and preset measurement equations; The method further includes, before performing the state parameter estimation algorithm using an extended Kalman filter with unknown input based on the vehicle acceleration data and the preset state space model to obtain the preset dynamic state data: Perform a first-order Taylor expansion on the preset correlation function to obtain the preset Taylor expansion function; Discretize the preset state-space equation and the preset measurement equation according to the preset Taylor expansion function to obtain a discrete state-space model; wherein, the discrete state-space model includes discrete state-space equation and discrete measurement equation; The step of estimating state parameters using an extended Kalman filter estimation algorithm with unknown inputs based on the vehicle acceleration data and the preset state space model to obtain preset dynamic state data includes: Construct preset initial data; wherein, the preset initial data includes an initial state error covariance matrix, a process noise matrix, a measurement noise matrix, and initial state values; A prediction error covariance matrix is ​​constructed based on 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 based on the prediction error covariance matrix; The unknown input error covariance matrix is ​​calculated based on the Kalman gain matrix, and a road surface profile prediction model is constructed using the unknown input error covariance matrix. The road height data is predicted by the road profile prediction model based on the vehicle acceleration. The preset state vector is updated based on the road surface height data to obtain the suspension feature data; The preset correction factor matrix is ​​determined through the following steps: A preset parameter matrix is ​​constructed based on the damping coefficient, the suspension stiffness, and the tire stiffness; If it is determined that the preset parameter matrix has not changed within a preset time threshold, the preset correction factor matrix is ​​determined to be an identity matrix; or, if 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 preset parameter matrix using a constraint optimization algorithm.

4. 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 as described in any one of claims 1-2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 2.

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