Motion posture determination method and device, storage medium, electronic device and product

Abstract

The invention discloses a method and a device for determining a motion posture, a storage medium, an electronic device and a computer program product, and relates to the field of vehicle control, the method for determining the motion posture comprises the following steps: determining motion data of suspensions at at least two different positions of a vehicle according to measurement data of at least one sensor arranged on the vehicle, the degree of freedom of the sensor is at least six degrees of freedom; according to the motion data of the suspensions at at least two different positions of the vehicle and a preset torque model of the vehicle, vertical load data of the vehicle are determined, and the vertical load data are used for indicating the current stress condition of the suspensions of the vehicle; and according to the vertical load data of the vehicle, determining the motion postures of the suspensions at at least two different positions of the vehicle. The method achieves the estimation of the motion posture of the suspension, saves the number of sensors, and saves the cost.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of vehicle control technology, and more specifically, to a method, apparatus, storage medium, electronic device, and computer program product for determining motion posture. Background Technology

[0002] With the rapid development of the automotive industry and the improvement of people's living standards, vehicle comfort and handling stability have become primary considerations when purchasing a car. The most important component affecting a vehicle's comfort and handling stability is its suspension system. Located between the body and wheels, the suspension system's main functions are to support the vehicle body, transmit the forces and torques exerted on the wheels by the road surface to the body, and mitigate continuous impacts from uneven road surfaces and dampen body vibrations. The performance of the suspension system directly determines the vehicle's ride comfort and handling stability.

[0003] In related technologies, semi-active and active suspensions have emerged to improve the performance of suspension systems. These systems can adjust suspension characteristics according to different road conditions, effectively balancing vehicle comfort and handling stability, and are therefore widely used. Accurate estimation of suspension attitude is crucial for achieving intelligent control of the suspension under different road conditions during suspension adjustment.

[0004] However, in related technologies, in order to achieve active adjustment of the suspension, a multi-sensor scheme is usually designed to identify the attitude of the suspension, which makes the sensor arrangement complex and costly. Summary of the Invention

[0005] This application provides a method, apparatus, storage medium, electronic device, and computer program product for determining motion posture.

[0006] According to one aspect of the embodiments of this application, a method for determining motion posture is provided, the method comprising: determining motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom; determining vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the suspension of the vehicle; and determining the motion posture of the suspension at at least two different positions of the vehicle based on the vertical load data of the vehicle.

[0007] In an exemplary embodiment, determining the suspension motion attitude of the vehicle at at least two different positions based on the vehicle's vertical load data includes: establishing a target estimation equation for the vehicle based on a pre-established dynamic equation of the suspension, wherein the dynamic equation is used to indicate the relationship between the vertical motion parameters of the suspension at the target position and the vertical load data of the suspension at the target position, the target position being any one of the at least two different positions; the target estimation equation is used to predict and update the vertical motion parameters of the suspension at the target position at the next moment based on the vertical motion parameters of the suspension at the target position at the previous moment and the vertical load data at the current moment; and determining the suspension motion attitude of the vehicle at the target position based on the target estimation equation.

[0008] In an exemplary embodiment, before establishing the target estimation equation of the vehicle based on the pre-established dynamic equation of the suspension, the method further includes: establishing a first equation and a second equation, wherein the first equation is used to indicate a first dynamic relationship of the suspension, the first dynamic relationship being the relationship between a first resultant force, a second resultant force, and a third resultant force, the first resultant force being determined based on the elastic stiffness of the suspension at the target position, a first displacement of the sprung mass of the suspension at the target position, and a second displacement of the unsprung mass of the suspension at the target position, and the second resultant force being determined based on the damping of the suspension at the target position, a first displacement velocity of the sprung mass of the suspension at the target position, and the unsprung mass of the suspension at the target position. The second displacement velocity is determined, and the third resultant force is determined based on the first displacement acceleration of the equivalent mass of the suspension at the target position and the sprung mass of the suspension at the target position; the second equation is used to indicate the second dynamic relationship of the suspension, which is the relationship between the vertical load data, the first resultant force, the second resultant force, the fourth resultant force, and the fifth resultant force. The fourth resultant force is determined based on the equivalent mass of the suspension at the target position and the equivalent mass of the wheel and axle at the target position, and the fifth resultant force is determined based on the second displacement acceleration of the equivalent mass of the wheel and axle at the target position and the unsprung mass of the suspension at the target position; the dynamic equation includes: the first equation and the second equation.

[0009] In an exemplary embodiment, determining the suspension motion attitude at the target position of the vehicle based on the target estimation equation includes: determining an input excitation based on the vertical load data at the current moment and the mass parameters of the vehicle; and determining the suspension motion attitude at the target position at the next moment based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix.

[0010] In an exemplary embodiment, determining the suspension's motion attitude at the next target position based on the input excitation, the vertical motion parameters of the suspension at the previous target position, and the state transition matrix includes: determining the vertical displacement and vertical displacement velocity of the suspension at the next target position based on the input excitation, the vertical motion parameters of the suspension at the previous target position, and the state transition matrix; and determining the suspension's motion attitude observation matrix at the next target position based on the vertical displacement and vertical displacement velocity of the suspension at the next target position, and the observation transition matrix, wherein the motion attitude observation matrix is ​​used to indicate the suspension's motion attitude, and the motion attitude observation matrix includes vertical displacement velocity and vertical acceleration.

[0011] In one exemplary embodiment, the method for determining motion posture further includes: determining a road surface inequality level based on the motion posture of the suspension at at least two different positions of the vehicle; and determining a road surface excitation level based on the road surface inequality level and the vehicle speed.

[0012] In an exemplary embodiment, determining the road surface inequality level based on the motion posture of the suspension at at least two different positions of the vehicle includes: acquiring vertical displacement data of the suspension at at least two different positions of the vehicle within a preset time period; determining the fuzzy membership value corresponding to the vertical displacement data according to the vertical displacement data and the fuzzy membership function; and determining the fuzzified variable of the road surface inequality level based on the fuzzy membership value to determine the road surface inequality level.

[0013] In an exemplary embodiment, determining the road surface incentive level based on the road surface inequality level and the vehicle speed includes: determining a fuzzy variable for the vehicle speed based on the vehicle's current speed; and determining a fuzzy variable for the road surface incentive level based on the fuzzy variable for the road surface inequality level and the fuzzy variable for the vehicle speed, thereby determining the road surface incentive level.

[0014] In an exemplary embodiment, determining the motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle includes: determining the relative positional relationship between the mounting position of the at least one sensor and the mounting positions of the suspension at at least two different positions of the vehicle; and determining the motion data of the suspension at at least two different positions of the vehicle based on acceleration data, angular velocity data measured by the at least one sensor, and the relative positional relationship.

[0015] In an exemplary embodiment, determining the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions and a preset torque model of the vehicle includes: substituting the motion data of the suspension at at least two different positions into the preset torque model of the vehicle to obtain the first load data of the vehicle; and correcting the first load data using a load compensation value to determine the vertical load data of the vehicle, wherein the load compensation value is determined based on the vertical acceleration of the vehicle, road conditions, vehicle pitch angle, and wheel speed.

[0016] According to another aspect of the embodiments of this application, a motion attitude determination device is also provided. The device includes: a suspension motion determination module, configured to determine motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom; a load determination module, configured to determine vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension; and a suspension attitude determination module, configured to determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vertical load data of the vehicle.

[0017] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described method for determining motion posture when running.

[0018] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for determining motion posture through the computer program.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0020] This application provides a method for determining the vehicle's suspension posture. First, motion data of the suspension at at least two different positions is determined based on measurement data from at least one sensor installed on the vehicle. This eliminates the need for only one sensor, and the motion data of the suspension at at least two different positions can be obtained based on the data measured by that sensor, saving on the number of sensors and reducing costs. Based on the motion data of the suspension at at least two different positions and a preset vehicle torque model, the vehicle's vertical load data is determined. Based on the suspension motion data combined with the vehicle torque model, the vehicle's vertical load data can be determined. Based on the vertical load data, changes in the road surface can be indirectly obtained, thus providing the road surface condition. The motion posture of the suspension at at least two different positions is determined based on the vehicle's vertical load data. Since the vertical load data indicates the force on the vehicle's suspension and characterizes changes in the road surface, and the characteristics and basic parameters of the vehicle's suspension are known, the motion posture of the vehicle's suspension can be determined based on the vehicle's vertical load data, thereby achieving the estimation of the suspension motion posture. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a hardware structure block diagram of the vehicle terminal of the motion posture determination method according to an embodiment of this application;

[0024] Figure 2 This is a flowchart of a method for determining motion posture according to an embodiment of this application;

[0025] Figure 3 This is a second flowchart of a method for determining motion posture according to an embodiment of this application;

[0026] Figure 4 This is the third flowchart of a method for determining motion posture according to an embodiment of this application;

[0027] Figure 5 This is the fourth flowchart of a method for determining motion posture according to an embodiment of this application;

[0028] Figure 6This is a flowchart of a method for determining road surface inequality levels according to an embodiment of this application;

[0029] Figure 7 This is a second flowchart of a method for determining road surface inequality levels according to an embodiment of this application;

[0030] Figure 8 This is a flowchart of a method for determining the road surface incentive level according to an embodiment of this application;

[0031] Figure 9 This is a flowchart of a method for determining suspension motion data according to an embodiment of this application;

[0032] Figure 10 This is a flowchart of a method for determining vehicle load according to an embodiment of this application;

[0033] Figure 11 This is a schematic diagram of a vehicle force model according to an embodiment of this application;

[0034] Figure 12 This is a structural block diagram of an optional motion posture determination device according to an embodiment of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] The methods and embodiments provided in this application can be executed in an in-vehicle terminal or a similar computing device. Taking running on an in-vehicle terminal as an example, Figure 1This is a hardware structure block diagram of the vehicle-mounted terminal for the motion posture determination method according to an embodiment of this application. For example... Figure 1 As shown, the vehicle-mounted terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor (MPU) or a programmable logic device (PLD)) and a memory 104 for storing data are also shown. In one exemplary embodiment, the vehicle-mounted terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned vehicle-mounted terminal. For example, the vehicle-mounted terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 Equivalent functions or ratios shown Figure 1 The functions shown have more different configurations.

[0038] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the motion posture determination method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the vehicle terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0039] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the vehicle terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0040] As described in the background section, the solutions in the related technologies, in order to achieve suspension attitude recognition, involve designing a multi-sensor scheme, which results in complex sensor arrangement, high failure rate, and high cost.

[0041] To address the aforementioned issues, this embodiment provides a method for determining motion posture, applied to an in-vehicle terminal. Figure 2 This is a flowchart of a method for determining a motion posture according to an embodiment of this application. The method includes the following steps S200-S220, wherein:

[0042] Step S200: Determine the motion data of the suspension at at least two different positions of the vehicle based on the measurement data of at least one sensor installed on the vehicle.

[0043] The sensor has at least six degrees of freedom, which include the measurement of velocity and acceleration in three mutually perpendicular directions, as well as the measurement of triaxial angular velocity around the three mutually perpendicular directions.

[0044] Specifically, the motion data of the suspension at at least two different positions of the vehicle can be obtained from the sensor measurement data. For example, the vehicle can be regarded as a rigid body. Based on Euler's theorem, the motion of the rigid body can be divided into translation with respect to the reference coordinate system and rotation about the reference point. By establishing a transformation equation, the sensor measurement data can be converted into suspension motion data. The specific conversion method is described in the following embodiments.

[0045] Step S210: Determine the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and the preset torque model of the vehicle.

[0046] Vertical load data is used to indicate the current stress on the vehicle's suspension. Since changes in road conditions will cause changes in the stress on the suspension, and road conditions cannot be directly measured, vertical load data is used to indicate road conditions, thereby indirectly reflecting the stress on the suspension.

[0047] Specifically, suspension motion data can include suspension displacement, displacement velocity, etc. The motion of the suspension changes due to changes in road conditions. Therefore, based on the suspension motion data and the vehicle's torque model, the vehicle's vertical load data can be obtained. The vertical load data can include the vertical loads at at least two different suspension positions to facilitate the subsequent determination of the suspension's motion attitude.

[0048] Step S220: Determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vehicle's vertical load data.

[0049] Specifically, the vehicle's vertical load data can characterize the current stress state of the vehicle's suspension. Therefore, using the vertical load data as an external excitation input, the suspension motion attitude at at least two different positions of the vehicle can be estimated. This estimation can be achieved, for example, using a Kalman estimator.

[0050] In this embodiment, the motion data of the suspension at at least two different positions of the vehicle is first determined based on the measurement data from at least one sensor installed on the vehicle. This requires only one sensor, and the motion data of the suspension at at least two different positions can be obtained based on the data measured by that sensor, saving on the number of sensors and reducing costs. Based on the motion data of the suspension at at least two different positions and a preset vehicle torque model, the vertical load data of the vehicle is determined. Based on the suspension motion data combined with the vehicle torque model, the vertical load data of the vehicle can be determined. Based on the vertical load data, changes in the road surface can be indirectly obtained, thus providing the road surface condition. The motion attitude of the suspension at at least two different positions of the vehicle is determined based on the vehicle's vertical load data. Since the vertical load data indicates the stress on the vehicle's suspension and characterizes changes in the road surface, and the characteristics and basic parameters of the vehicle's suspension are known, the motion attitude of the vehicle's suspension can be determined based on the vertical load data, thereby achieving the estimation of the suspension motion attitude.

[0051] In one embodiment, such as Figure 3 As shown, step S220 determines the suspension motion attitude at at least two different positions of the vehicle based on the vehicle's vertical load data. This includes steps S300-S310. Wherein:

[0052] Step S300: Based on the pre-established dynamic equations of the suspension, establish the target estimation equations for the vehicle.

[0053] The dynamic equations are used to indicate the relationship between the vertical motion parameters of the suspension at the target position and the vertical load data of the suspension at the target position. The target position can be any one of at least two different positions. The target estimation equations are used to predict and update the vertical motion parameters of the suspension at the target position at the next time step based on the vertical motion parameters of the suspension at the target position at the previous time step and the vertical load data at the current time step.

[0054] Specifically, based on the dynamic equations of the suspension, the relationship between the forces acting on the suspension and its motion can be obtained, which describes the motion state of the suspension. Therefore, based on the dynamic equations of the suspension, a state update model, such as a Kalman filter, can be applied to establish a target estimation equation, which can then be used to predict and update the motion state of the suspension.

[0055] Step S310: Based on the target estimation equation, determine the motion attitude of the suspension at the target position of the vehicle.

[0056] Among them, by using the target estimation equation, the motion state of the suspension at the target position of the vehicle can be predicted, thereby estimating the motion attitude of the suspension at the target position.

[0057] In this embodiment, the target estimation equation of the vehicle can be established based on the dynamic equation of the suspension. Then, based on the target estimation equation of the vehicle, the motion attitude of the suspension is estimated and the motion attitude of the suspension is determined. Thus, the motion attitude of the suspension can be determined by calculation without the need for multiple sensors for measurement, saving costs.

[0058] In one embodiment, in step S300, a target estimation equation for the vehicle is established based on a pre-established dynamic equation for the suspension. Previously, methods for determining motion attitude also included:

[0059] Establish the first equation and the second equation.

[0060] The first equation indicates the first dynamic relationship of the suspension, which is the relationship between the first resultant force, the second resultant force, and the third resultant force. The first dynamic relationship indicates the internal force relationship of the suspension when it is not subjected to external forces.

[0061] The first resultant force is determined based on the elastic stiffness of the suspension at the target position, the first displacement of the sprung mass of the suspension at the target position, and the second displacement of the unsprung mass of the suspension at the target position.

[0062] Since experiments show that the elastic stiffness and damping force of the wheel can be ignored, the first resultant force can be expressed by the following formula:

[0063] F1=k s (z u -z s )

[0064] Where F1 is the first resultant force, k s For the elastic stiffness of the suspension, z u This is the displacement of the unsprung mass relative to its equilibrium position (the vertical upward direction is defined as positive).

[0065] The second resultant force is determined based on the damping of the suspension at the target position, the first displacement velocity of the sprung mass of the suspension at the target position, and the second displacement velocity of the unsprung mass of the suspension at the target position.

[0066] The second resultant force can be expressed by the following formula:

[0067]

[0068] Where F2 is the second resultant force, b s For suspension damping, Let be the displacement acceleration of the unsprung mass relative to its equilibrium position (defined as vertically upward as the positive direction). It is the displacement velocity of the sprung mass (sprung mass) relative to the equilibrium position (the vertical upward direction is defined as positive).

[0069] The third resultant force is determined based on the first displacement acceleration of the equivalent mass of the suspension at the target position and the sprung mass of the suspension at the target position.

[0070] The third resultant force can be expressed by the following formula:

[0071]

[0072] Among them, F3 is the third resultant force, m s The equivalent mass of the suspension at the target location (e.g., approximately one-quarter of the vehicle's weight if the vehicle is a four-wheeled vehicle). It is the displacement acceleration of the sprung mass (sprung mass) relative to the equilibrium position (the vertical upward direction is defined as positive).

[0073] The first equation is shown in the following formula:

[0074] F3 = F1 + F2

[0075] That is,

[0076] The meaning of each parameter can be found in the description above, and will not be repeated here.

[0077] The second equation is used to indicate the second dynamic relationship of the suspension, which is the relationship between the vertical load data, the first resultant force, the second resultant force, the fourth resultant force, and the fifth resultant force.

[0078] The fourth resultant force is determined based on the equivalent mass of the suspension at the target location, and the equivalent mass of the wheels and axles at the target location.

[0079] The fourth resultant force can be expressed by the following formula:

[0080] F4=(m s +m u )·g

[0081] Among them, F4 is the fourth resultant force, m s Let m be the equivalent mass of the suspension at the target location. u Let g be the equivalent mass of the wheel and axle at the target position, and g be the acceleration due to gravity.

[0082] The fifth resultant force is determined based on the second displacement acceleration of the equivalent mass of the wheel and axle at the target position and the unsprung mass of the suspension at the target position.

[0083] The fifth resultant force can be expressed by the following formula:

[0084]

[0085] Among them, F5 is the fifth resultant force, m s The equivalent mass of the suspension at the target location. It is the displacement acceleration of the sprung mass (sprung mass) relative to its equilibrium position.

[0086] The second equation is shown in the following formula:

[0087] F r -F1-F2-F4=F5

[0088]

[0089] Among them, F r The vertical load data indicates the vertical reaction force of the ground on the tires. The vertical load data characterizes the external excitation generated by the vehicle due to external factors such as road surface changes. The second dynamic relationship indicates the force relationship of the suspension under external forces.

[0090] The dynamic equations include: the first equation and the second equation.

[0091] In this embodiment, a dynamic equation for a suspension is provided, which can indicate the internal force relationship of the suspension when it is not subjected to external forces, and the force relationship of the suspension when it is subjected to external forces, thereby describing the state of the suspension and facilitating the subsequent establishment of the target estimation equation for the suspension, thereby realizing the estimation of the motion state of the suspension.

[0092] In one embodiment, such as Figure 4 As shown, step S310 determines the suspension motion attitude at the target position of the vehicle based on the target estimation equation. This includes steps S400-S410, where:

[0093] Step S400: Determine the input excitation based on the current vertical load data and the vehicle's mass parameters.

[0094] The vehicle's mass parameters can include the equivalent mass of the suspension at the target location, and the equivalent mass of the wheels and axles at the target location. These parameters can be obtained by pre-tuning the overall vehicle parameters; the total vehicle mass can be pre-determined by looking up a table. Other parameters include the unsprung mass corresponding to the suspension, axle, and tire masses; the suspension spring rate; the actual damping coefficient of the currently adjustable suspension; and the relative position of the suspension mounting position to the sensor mounting position. Since the suspension and vehicle body motion characteristics are highly sensitive to sprung mass, estimating the total vehicle mass is necessary for more accurate parameters. Common methods for estimating total vehicle mass include recursive least squares.

[0095] The input stimulus can be determined using the following formula:

[0096] [F r -(ms+mu)·g]

[0097] Among them, F r For vertical load data, m s Let m be the equivalent mass of the suspension at the target location. u Let g be the equivalent mass of the wheel and axle at the target position, and g be the acceleration due to gravity.

[0098] Step S410: Determine the motion attitude of the suspension at the target position at the next moment based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix.

[0099] The input excitation is the external excitation generated by external factors such as changes in the road surface.

[0100] The state transition matrix can be used to describe the dynamic changes of the suspension state over time and predict the changes in the suspension. It can be obtained through a Kalman filter.

[0101] The Kalman filter state update equation is expressed by the following formula:

[0102] X(k+1|k)=F·X(k|k)+B·u(k)

[0103] Where X(k+1|k) is the motion state of the suspension at the next moment, F is the state transition matrix, X(k|k) is the motion state of the suspension at the previous moment, B is the control input matrix, which can map the input quantity (input excitation) to the state and can describe the influence of the input quantity on the motion state of the suspension, and u(k) is the input excitation.

[0104] Furthermore, the specific form of the Kalman filter state update equation is expressed by the following formula:

[0105]

[0106] in, This represents the suspension's motion state at the next moment, including the displacement z of the sprung mass of the suspension relative to its equilibrium position. s (k+1|k), the displacement z of the unsprung mass of the suspension relative to the equilibrium position at the next moment. u (k+1|k), the displacement velocity of the sprung mass of the suspension relative to the equilibrium position at the next moment. The displacement velocity of the unsprung mass of the suspension relative to its equilibrium position at the next moment Let b be the state transition matrix. s For the damping of the suspension, m s Let m be the equivalent mass of the suspension at the target location. u k represents the equivalent mass of the wheel and axle at the target location. s Let be the elastic stiffness of the suspension, and Δt be the time interval. This represents the suspension's motion state at the previous moment. To control the input matrix, [F r -(ms+mu)·g] represents the input stimulus.

[0107] In this embodiment, a target estimation equation for a suspension is provided. Based on the Kalman filter principle, it can estimate the motion attitude of the suspension at the target position at the next moment according to the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix, thereby determining the motion attitude of the suspension at the target position at the next moment.

[0108] In one embodiment, such as Figure 5 As shown, step S410 determines the suspension's motion attitude at the next target position based on the input excitation, the vertical motion parameters of the suspension at the target position in the previous moment, and the state transition matrix. This includes steps S500-S510.

[0109] Step S500: Based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix, determine the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment.

[0110] Based on the aforementioned Kalman filter state update equation, by substituting the input excitation and the vertical motion parameters of the suspension at the target position in the previous moment, the vertical displacement and vertical velocity of the suspension at the target position in the next moment can be determined. The specific process is detailed in the above formula and will not be elaborated further.

[0111] Step S510: Determine the motion attitude observation matrix of the suspension at the target position at the next moment based on the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment and the observation transformation matrix.

[0112] The motion attitude observation matrix is ​​used to indicate the motion attitude of the suspension, and includes vertical displacement velocity and vertical acceleration.

[0113] The motion attitude observation matrix is ​​determined using the following formula:

[0114] Y(k+1)=C·X(k+1|k)

[0115] Where Y(k+1) represents the suspension motion state at the target position in the next time step, C is the observation transformation matrix, and X(k+1|k) represents the suspension motion state at the target position in the previous time step.

[0116] Furthermore, the specific contents of the motion attitude observation matrix are determined by the following formula:

[0117]

[0118] in, The motion state of the suspension at the target position in the next moment. The vertical displacement velocity of the suspension at the target position. Let be the vertical displacement acceleration of the suspension at the target position. For the observation transformation matrix, b s For the damping of the suspension, m s k is the equivalent mass of the suspension at the target location. s This refers to the elastic stiffness of the suspension. The motion state of the suspension at the target position at the previous moment.

[0119] In this embodiment, based on the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment, as well as the observation transformation matrix, the motion attitude observation matrix of the suspension at the target position can be determined, thereby facilitating the determination of the motion attitude of the suspension at the target position at the next moment.

[0120] In one embodiment, such as Figure 6 As shown, the method also includes steps S600-S610.

[0121] Step S600: Determine the road surface inequality level based on the suspension motion posture at at least two different positions of the vehicle.

[0122] By measuring the suspension's motion posture at at least two different locations on the vehicle, information such as the vertical motion and frequency of the suspension can be determined. This information can then be used to determine the suspension's motion posture and its corresponding level according to a pre-defined classification method, thus identifying the road surface inequality level.

[0123] Step S610: Determine the road surface incentive level based on the road surface inequality level and the vehicle speed.

[0124] Among them, the road surface excitation level indicates the degree of impact on the vehicle. It is an assessment of the impact excitation of the road surface on the wheel in a single incident, and is therefore related to both the road surface inequality level and the vehicle speed.

[0125] In this embodiment, the road surface unevenness level can be determined based on the motion posture of the suspension at at least two different positions, thereby realizing a quantitative analysis of the road surface smoothness. The road surface excitation level can also be determined based on the road surface unevenness level and the vehicle speed, thereby realizing a quantitative analysis of the intensity of the impact on the vehicle.

[0126] In one embodiment, such as Figure 7 As shown, step S600 determines the road surface inequality level based on the suspension motion attitude at at least two different positions of the vehicle. This includes steps S700-S720. Wherein:

[0127] Step S700: Obtain vertical displacement data of the suspension at at least two different positions of the vehicle within a preset time period.

[0128] The preset time period can be a historical period, which means acquiring the vertical displacement data of the vehicle's suspension over a historical period, and then using the vertical displacement data of the suspension over a historical period to assess the level of road surface inequality.

[0129] The vertical displacement data of the suspension includes the dynamic displacement and dynamic velocity of the suspension.

[0130] Step S710: Determine the fuzzy membership value corresponding to the vertical displacement data based on the vertical displacement data and the fuzzy membership function.

[0131] Appropriate fuzzy membership functions, such as bell-shaped, trapezoidal, and trigonometric functions, can be selected to describe the degree of membership of vertical displacement data to a fuzzy set. The suspension dynamic displacement *s* can be set to 7 levels of fuzziness, with corresponding fuzzification variables [s1, s2, s3, s4, s5, s6, s7]. *s1* represents extremely small displacement, and *s7* represents extremely large displacement. The suspension dynamic velocity *v* can be set to 7 levels of fuzziness, with fuzzification variables [v1, v2, v3, v4, v5, v6, v7]. *v1* represents very slow velocity, and *v7* represents very fast suspension movement. The road surface unevenness level can be set to 5 levels of fuzziness, with fuzzification variables [LV0, LV1, LV2, LV3, LV4]. LV0 represents a smooth road surface, LV1 a slightly rough road surface, LV2 a rough road surface, LV3 a slightly off-road surface, and LV4 an off-road surface.

[0132] For example, trigonometric functions can be used as membership functions. The membership value of the intersection of two fuzzy subsets is selected as a composite number as needed. If the value is too large, although it improves the control stability and smooths the changes, it will also worsen the system's sensitivity. Conversely, if the value is too small, although the system's sensitivity improves, its stability will worsen, thus reducing the fuzziness of the fuzzy controller. Generally, a membership value of 0.2-0.7 is suitable for the intersection, for example, 0.33. The membership elements of the output quantity k must satisfy clear boundaries and element dissimilarity; therefore, the membership degree of the immediately adjacent fuzzy subset is set to zero.

[0133] Step S720: Based on the fuzzy membership values, determine the fuzzy variables of the road surface inequality level to determine the road surface inequality level.

[0134] Among them, based on the fuzzy membership value, it is possible to determine which fuzzy variable of the road inequality level the current vertical displacement data belongs to, and thus the road inequality level can be determined.

[0135] In this embodiment, fuzzy control is used to determine the road surface unevenness level based on the vertical displacement data of the suspension, so as to quantify the road surface unevenness level and thus adjust the stiffness of the vehicle's suspension according to the road surface unevenness level, thereby improving the vehicle's comfort.

[0136] In one embodiment, such as Figure 8 As shown, step S610 determines the road surface incentive level based on the road surface unevenness level and the vehicle speed. This includes steps S800-S810. Wherein:

[0137] Step S800: Determine the fuzzy variable of vehicle speed based on the vehicle's current speed.

[0138] The vehicle speed can be divided into 5 levels, with fuzzy variables [VS, S, M, F, VF]. VS represents a very low vehicle speed, and VF represents a very high vehicle speed.

[0139] Step S810: Based on the fuzzy variables of road surface inequality level and vehicle speed, determine the fuzzy variables of road surface excitation level to determine the road surface excitation level.

[0140] The road surface incentive level *k* can be set to 13 fuzzy levels, with corresponding fuzzy variables [k0, k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12], where *k0* represents extremely low road surface incentive and *k12* represents the strongest road surface incentive. By comprehensively considering the fuzzy variables of vehicle speed and road surface undulation level, the fuzzy variables for the road surface incentive level can be determined. This is because the road surface incentive level is specifically related to vehicle speed and road surface undulation. Based on road surface undulation and vehicle speed, the road surface incentive level can be estimated; that is, the faster the vehicle speed, the higher the road surface incentive level, and the higher the road surface undulation level, the higher the road surface incentive level. Therefore, based on these two elements—vehicle speed and road surface undulation level—the road surface incentive level can be comprehensively determined. The specific correspondence can be obtained by referring to a table based on the actual situation.

[0141] When a vehicle travels on surfaces with high excitation levels, such as off-road terrain, the motion of the sprung mass exhibits significant hysteresis and amplitude decay compared to wheel motion under random amplitude conditions. In such situations, the accuracy and timeliness of using a Kalman estimator to determine the suspension's motion attitude decrease significantly. Therefore, at road excitation levels exceeding a certain threshold, the suspension can be directly softened to improve passenger comfort.

[0142] For example, in addition to obtaining the road surface incentive level, the number of times the road surface incentive level is reached over a period of time can also be recorded. If a certain level is not reached for a period of time, the number of times will be gradually forgotten. Using the number of times the corresponding road surface incentive level is reached as the evaluation criterion, input into the fuzzy controller, and combined with the vehicle speed, a comprehensive assessment of the road surface incentive level can be obtained. For example, a road surface incentive level of K6 only means that the vehicle receives an incentive of K6 at a certain moment, but if the road surface incentive level reaches K6 10 times over a period of time, then based on this number, the overall road surface incentive level can be judged to be K6. However, if the number of times K6 is reached is less than 10, it can be judged that the overall road surface incentive level has not yet reached K6, and only a small number of points have reached the peak value of K6.

[0143] In this embodiment, the road surface excitation level is comprehensively judged based on vehicle speed and road surface inequality, so that when the road surface excitation level is high, the vehicle suspension can be softened in a timely manner to improve the comfort of the occupants.

[0144] In one embodiment, such as Figure 9 As shown, step S200 involves determining the motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle. This includes steps S900-S910.

[0145] Step S900: Determine the relative positional relationship between the mounting position of at least one sensor and the mounting positions of the suspension at at least two different locations on the vehicle.

[0146] Specifically, the distance between the installation location of at least one sensor and the installation locations of the suspension at at least two different locations on the vehicle can be determined in three mutually perpendicular directions.

[0147] Step S910: Based on the acceleration data, angular velocity data, and relative positional relationship obtained by at least one sensor, determine the motion data of the suspension at at least two different positions of the vehicle.

[0148] In this approach, the vehicle is considered a rigid body. Based on Euler's theorem, the motion of a rigid body can be divided into translation with respect to a reference coordinate system and rotation about a base point. By establishing a transformation equation, the sensor measurement data can be converted into suspension motion data. For example, the vehicle's suspension motion data can be determined using the following formula:

[0149]

[0150] Among them, a x a y a z Let a be the lateral, longitudinal, and vertical accelerations at the suspension point. x_snsr a y_snsr a z_snsr , where p, q, and r are the sensor's lateral, longitudinal, and vertical accelerations, respectively; and p, q, and r are the sensor's measured roll, pitch, and yaw angular velocities, respectively. snsr2CG Y snsr2CG Z snsr2CG Let denot be the vector length of the sensor's mounting position relative to the suspension's mounting position in the longitudinal, lateral, and vertical directions.

[0151] In this embodiment, by establishing a transformation equation, the sensor measurement data can be converted into suspension motion data, thereby enabling the determination of suspension motion data at at least two different positions of the vehicle using measurement data from at least one sensor, saving the number of sensors and reducing costs.

[0152] In one embodiment, such as Figure 10As shown, step S210 determines the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions and a preset torque model of the vehicle. This includes steps S1000-1010.

[0153] Step S1000: Substitute the motion data of the suspension at at least two different positions into the preset vehicle torque model to obtain the first load data of the vehicle.

[0154] The vehicle's torque model can be referenced. Figure 11 As shown, G is the vehicle's center of mass, hg is the height of the center of mass, Fj is the acceleration experienced by the vehicle, Fz1 is the vertical reaction force from the road surface to the front wheels, Fxb1 is the front wheel resistance, Fz2 is the vertical reaction force from the road surface to the rear wheels, Fxb2 is the rear wheel resistance, L is the wheelbase, a is the y-direction distance between the front wheels and the center of mass, and b is the y-direction distance between the rear wheels and the center of mass. This model can be used to describe the force situation of the vehicle.

[0155] The formula for the torque model can be shown below:

[0156]

[0157] in, This refers to the vertical load on the front wheels of the vehicle. For the vertical load on the rear wheels of the vehicle, I y Let be the moment of inertia of the vehicle about the y-axis. Let be the vehicle's acceleration about the y-axis, mg be the vehicle's weight, θ be the vehicle's tilt angle, and l be the acceleration about the y-axis. r a is the horizontal distance from the rear axle to the center of gravity of the vehicle. z h represents the vertical acceleration component of the vehicle. cog Let a be the distance from the vehicle's center of mass to the ground. x F represents the lateral acceleration component of the vehicle. offset_front For the front wheel load compensation value, l f The horizontal distance from the front axle to the center of gravity of the vehicle. F is the vertical acceleration of the vehicle's center of mass. offset_rear This is the rear wheel load compensation value, where l is the vehicle's wheelbase.

[0158] Step S1010: Correct the first load data using the load compensation value to determine the vehicle's vertical load data.

[0159] The above torque model can be used to obtain the vehicle's first load data. However, due to the influence of the vertical acceleration of the vehicle's unsprung mass, which is generally greater than the overall vertical acceleration of the vehicle, a load compensation value needs to be added to correct the result. This is F in the above formula. offset_front and F offset_rearThe load compensation value is determined based on the vehicle's vertical acceleration, road conditions, vehicle pitch angle, and wheel speed. The specific value is calibrated according to the actual situation.

[0160] In this embodiment, a method for determining the vertical load data of a vehicle is provided. Based on the lever principle of the vehicle as a whole, the torque model of the vehicle can be obtained, and then the vertical load of the vehicle can be calculated. This facilitates the subsequent calculation of the displacement of the vehicle's suspension and provides key data for evaluating the motion attitude of the suspension.

[0161] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0162] This embodiment also provides a motion posture determination device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0163] Figure 12 This is a structural block diagram of an optional motion posture determination device according to an embodiment of this application.

[0164] like Figure 12 As shown, it includes:

[0165] The suspension motion determination module 1201 is used to determine the motion data of the suspension at at least two different positions of the vehicle based on the measurement data of at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom.

[0166] The load determination module 1202 is used to determine the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension.

[0167] The suspension attitude determination module 1203 is used to determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vehicle's vertical load data.

[0168] Using the aforementioned device, the motion data of the suspension at at least two different positions of the vehicle are first determined based on the measurement data from at least one sensor installed on the vehicle. This requires only one sensor, and the motion data of the suspension at at least two different positions can be obtained based on the data measured by that sensor, saving on the number of sensors and reducing costs. Based on the motion data of the suspension at at least two different positions and a preset vehicle torque model, the vertical load data of the vehicle is determined. Based on the suspension motion data combined with the vehicle torque model, the vertical load data of the vehicle can be determined. Based on the vertical load data, changes in the road surface can be indirectly obtained, thus providing the road surface condition. The motion attitude of the suspension at at least two different positions of the vehicle is determined based on the vehicle's vertical load data. Since the vertical load data indicates the stress on the vehicle's suspension and also characterizes changes in the road surface, and the characteristics and basic parameters of the vehicle's own suspension are known, the motion attitude of the vehicle's suspension can be determined based on the vehicle's vertical load data, thus achieving the estimation of the suspension motion attitude.

[0169] In one exemplary embodiment, the suspension attitude determination module 1203 includes:

[0170] The target estimation equation establishment unit is used to establish the target estimation equation of the vehicle based on the pre-established dynamic equation of the suspension. The dynamic equation is used to indicate the relationship between the vertical motion parameters of the suspension at the target position and the vertical load data of the suspension at the target position. The target position is any one of at least two different positions. The target estimation equation is used to predict and update the vertical motion parameters of the suspension at the target position at the next moment based on the vertical motion parameters of the suspension at the target position at the previous moment and the vertical load data at the current moment.

[0171] The motion attitude determination unit is used to determine the motion attitude of the suspension based on the target estimation equation.

[0172] In an exemplary embodiment, the above-described apparatus further includes: a kinematic equation establishing module, used to establish a first equation and a second equation, wherein the first equation is used to indicate a first dynamic relationship of the suspension, the first dynamic relationship being the relationship between a first resultant force, a second resultant force, and a third resultant force, the first resultant force being determined based on the elastic stiffness of the suspension at the target position, the first displacement of the sprung mass of the suspension at the target position, and the second displacement of the unsprung mass of the suspension at the target position, the second resultant force being determined based on the damping of the suspension at the target position, the first displacement velocity of the sprung mass of the suspension at the target position, and the second displacement velocity of the unsprung mass of the suspension at the target position, and the third resultant force being determined based on the equivalent mass of the suspension at the target position and the first displacement acceleration of the sprung mass of the suspension at the target position. The second equation indicates the second dynamic relationship of the suspension, which is the relationship between vertical load data, the first resultant force, the second resultant force, the fourth resultant force, and the fifth resultant force. The fourth resultant force is determined based on the equivalent mass of the suspension at the target position, the equivalent mass of the wheel and axle at the target position, and the second displacement acceleration based on the equivalent mass of the wheel and axle at the target position and the unsprung mass of the suspension at the target position. The dynamic equations include: the first equation and the second equation.

[0173] In one exemplary embodiment, the motion posture determination unit includes:

[0174] The input excitation unit is used to determine the input excitation based on the current vertical load data and the vehicle's mass parameters.

[0175] The motion attitude acquisition unit is used to determine the motion attitude of the suspension at the target position at the next moment based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix.

[0176] In one exemplary embodiment, the motion posture obtaining unit includes:

[0177] The vertical displacement determination unit is used to determine the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix.

[0178] The observation matrix determination unit is used to determine the motion attitude observation matrix of the suspension at the target position at the next moment based on the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment and the observation transformation matrix. The motion attitude observation matrix is ​​used to indicate the motion attitude of the suspension and includes vertical displacement velocity and vertical acceleration.

[0179] In one exemplary embodiment, the above-described apparatus further includes:

[0180] The road surface inequality level determination unit is used to determine the road surface inequality level based on the motion posture of the suspension at at least two different positions of the vehicle.

[0181] The road surface incentive level determination unit is used to determine the road surface incentive level based on the road surface inequality level and the vehicle speed.

[0182] In one exemplary embodiment, the road surface inequality level determination unit includes:

[0183] The vertical displacement data determination unit is used to acquire vertical displacement data of the suspension at at least two different positions of the vehicle within a preset time period.

[0184] The fuzzy membership value determination unit is used to determine the fuzzy membership value corresponding to the vertical displacement data based on the vertical displacement data and the fuzzy membership function.

[0185] The road surface inequality level quantization unit is used to determine the fuzzy variables of the road surface inequality level based on the fuzzy membership value, so as to determine the road surface inequality level.

[0186] In an exemplary embodiment, the road surface excitation level determination unit is further configured to determine a fuzzy variable of vehicle speed based on the vehicle's current speed. The fuzzy variable of the road surface inequality level and the fuzzy variable of vehicle speed are used to determine the fuzzy variable of the road surface excitation level, thereby determining the road surface excitation level.

[0187] In one exemplary embodiment, the suspension motion determination module 1201 includes:

[0188] A relative position determination unit is used to determine the relative positional relationship between the mounting position of at least one sensor and the mounting positions of the suspension at at least two different locations on the vehicle.

[0189] The suspension motion data determination unit is used to determine the suspension motion data at at least two different positions of the vehicle based on acceleration data, angular velocity data, and relative positional relationships measured by at least one sensor.

[0190] In one exemplary embodiment, the load determination module 1202 includes:

[0191] The first load data determination unit is used to substitute the motion data of the suspension at at least two different positions into the preset torque model of the vehicle to obtain the first load data of the vehicle.

[0192] The first load data correction unit is used to correct the first load data using load compensation values ​​to determine the vehicle's vertical load data. The load compensation values ​​are determined based on the vehicle's vertical acceleration, road conditions, vehicle pitch angle, and wheel speed.

[0193] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0194] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0195] S1, determine motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom.

[0196] S2, based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, determine the vertical load data of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension.

[0197] S3, determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vehicle's vertical load data.

[0198] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0199] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0200] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0201] S1, determine motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom.

[0202] S2, based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, determine the vertical load data of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension.

[0203] S3, determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vehicle's vertical load data.

[0204] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0205] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods in various embodiments of this application.

[0206] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by a processor:

[0207] S1, determine motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom.

[0208] S2, based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, determine the vertical load data of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension.

[0209] S3, determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vehicle's vertical load data.

[0210] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0211] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0212] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining a motion posture, characterized in that, The method includes: Motion data of the suspension at at least two different positions of the vehicle are determined based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom. Based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, the vertical load data of the vehicle is determined, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension. The motion attitude of the suspension at at least two different positions of the vehicle is determined based on the vertical load data of the vehicle.

2. The method for determining motion posture according to claim 1, characterized in that, Determining the suspension motion attitude of the vehicle at at least two different positions based on the vehicle's vertical load data includes: Based on the pre-established dynamic equations of the suspension, a target estimation equation for the vehicle is established, wherein the dynamic equations are used to indicate the relationship between the vertical motion parameters of the suspension at the target position and the vertical load data of the suspension at the target position, wherein the target position is any one of the at least two different positions, and the target estimation equations are used to predict and update the vertical motion parameters of the suspension at the target position at the next moment based on the vertical motion parameters of the suspension at the target position at the previous moment and the vertical load data at the current moment; Based on the target estimation equation, the suspension motion attitude of the vehicle at the target position is determined.

3. The method for determining motion posture according to claim 2, characterized in that, Before establishing the target estimation equation for the vehicle based on the pre-established dynamic equations of the suspension, the method further includes: A first equation and a second equation are established, wherein the first equation is used to indicate a first dynamic relationship of the suspension, which is the relationship between a first resultant force, a second resultant force, and a third resultant force. The first resultant force is determined based on the elastic stiffness of the suspension at the target position, the first displacement of the sprung mass of the suspension at the target position, and the second displacement of the unsprung mass of the suspension at the target position. The second resultant force is determined based on the damping of the suspension at the target position, the first displacement velocity of the sprung mass of the suspension at the target position, and the second displacement velocity of the unsprung mass of the suspension at the target position. The third resultant force is determined based on the equivalent mass of the suspension at the target position and the first displacement acceleration of the sprung mass of the suspension at the target position. The second equation is used to indicate the second dynamic relationship of the suspension, which is the relationship between the vertical load data, the first resultant force, the second resultant force, the fourth resultant force, and the fifth resultant force. The fourth resultant force is determined based on the equivalent mass of the suspension at the target position and the equivalent mass of the wheel and axle at the target position. The fifth resultant force is determined based on the second displacement acceleration of the equivalent mass of the wheel and axle at the target position and the unsprung mass of the suspension at the target position. The kinetic equations include: the first equation and the second equation.

4. The method for determining motion posture according to claim 2, characterized in that, The process of determining the suspension motion attitude of the vehicle at the target position based on the target estimation equation includes: The input excitation is determined based on the current vertical load data and the vehicle's mass parameters; Based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix, the motion attitude of the suspension at the target position at the next moment is determined.

5. The method for determining motion posture according to claim 4, characterized in that, The step of determining the suspension's motion attitude at the next target position based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix includes: Based on the input excitation, the vertical motion parameters of the suspension at the target position at the previous moment, and the state transition matrix, determine the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment. Based on the vertical displacement and vertical displacement velocity of the suspension at the target position at the next moment, and the observation transformation matrix, the motion attitude observation matrix of the suspension at the target position at the next moment is determined. The motion attitude observation matrix is ​​used to indicate the motion attitude of the suspension and includes vertical displacement velocity and vertical acceleration.

6. The method for determining motion posture according to any one of claims 1-5, characterized in that, The method further includes: The road surface inequality level is determined based on the motion posture of the suspension at at least two different positions of the vehicle; The road surface incentive level is determined based on the road surface inequality level and the vehicle speed.

7. The method for determining motion posture according to claim 6, characterized in that, Determining the road surface inequality level based on the suspension motion posture at at least two different positions of the vehicle includes: Obtain vertical displacement data of the suspension at at least two different positions of the vehicle within a preset time period; The fuzzy membership value corresponding to the vertical displacement data is determined based on the vertical displacement data and the fuzzy membership function; Based on the fuzzy membership values, fuzzy variables are determined to identify the road surface inequality level.

8. The method for determining motion posture according to claim 6, characterized in that, The step of determining the road surface incentive level based on the road surface inequality level and the vehicle speed includes: Based on the vehicle's current speed, determine the fuzzy variable of the vehicle speed; Based on the fuzzy variables of the road surface inequality level and the vehicle speed, the fuzzy variables of the road surface excitation level are determined to determine the road surface excitation level.

9. The method for determining motion posture according to claim 1, characterized in that, Determining the suspension motion data at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle includes: Determine the relative positional relationship between the mounting position of the at least one sensor and the mounting positions of the suspension at at least two different locations on the vehicle; Based on the acceleration data, angular velocity data, and relative positional relationship obtained from the at least one sensor, the motion data of the suspension at at least two different positions of the vehicle are determined.

10. The method for determining motion posture according to claim 1, characterized in that, The step of determining the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle includes: The motion data of the suspension at at least two different positions are substituted into the preset torque model of the vehicle to obtain the first load data of the vehicle. The first load data is corrected using a load compensation value to determine the vertical load data of the vehicle. The load compensation value is determined based on the vehicle's vertical acceleration, road conditions, vehicle pitch angle, and wheel speed.

11. A device for determining motion posture, characterized in that, The device includes: A suspension motion determination module is used to determine the motion data of the suspension at at least two different positions of the vehicle based on measurement data from at least one sensor installed on the vehicle, wherein the sensor has at least six degrees of freedom. The load determination module is used to determine the vertical load data of the vehicle based on the motion data of the suspension at at least two different positions of the vehicle and a preset torque model of the vehicle, wherein the vertical load data is used to indicate the current stress condition of the vehicle's suspension. The suspension attitude determination module is used to determine the motion attitude of the suspension at at least two different positions of the vehicle based on the vertical load data of the vehicle.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1-10.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1-10.

14. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-10.

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