Suspension height determination method, storage medium and vehicle

Abstract

The invention discloses a suspension height determination method, a storage medium and a vehicle, and belongs to the technical field of vehicle suspensions. Comprising the following steps: for any wheel in a plurality of wheels of a target vehicle, acquiring a first acceleration and a second acceleration; predicting a first suspension state of the wheel in the ith prediction period based on the first acceleration, the target suspension state in the (i-1) th prediction period and the suspension power parameter; determining a state deviation value of the ith prediction period based on the first suspension state, the second acceleration and the suspension power parameter of the ith prediction period; based on the first suspension state and the state deviation value, the target suspension state of the wheel in the i-th prediction period is determined, and the target suspension state comprises the target suspension height. The prediction value of the current suspension height is calculated based on the suspension height determined last time, then the state deviation is determined, the prediction value is corrected according to the state deviation, and therefore the more accurate suspension height is obtained.

Description

Technical Field

[0001] This application relates to the field of vehicle suspension technology, and in particular to a method for determining suspension height, a storage medium, and a vehicle. Background Technology

[0002] With the development of science and technology, most vehicles are now equipped with suspension control systems based on adjustable shock absorbers, which use electronic signals to adjust the damping force of the vehicle's shock absorbers in real time to achieve active control of the suspension.

[0003] Currently, the suspension control system includes a limit block control function. This function can perform corresponding damping control according to the suspension height to prevent the suspension from directly touching the upper / lower limit blocks set on the suspension when it is under extreme compression or extreme extension, thereby avoiding noise caused by contact and increasing ride comfort.

[0004] However, in some vehicles without height sensors, the suspension height is typically obtained by performing a second integral of the wheel acceleration. This method is prone to zero-drift issues due to integration errors during the suspension height calculation process, leading to inaccurate calculations and consequently preventing the limit block control function from achieving precise control. Summary of the Invention

[0005] This application provides a suspension height determination method, apparatus, vehicle, and storage medium. It can calculate a predicted value for the current suspension height based on a previously determined suspension height, and then correct the predicted value by determining the state deviation, thereby obtaining a more accurate suspension height. The technical solution includes the following:

[0006] Firstly, a method for determining suspension height is provided, the method comprising: For any one of the multiple wheels of the target vehicle, obtain a first acceleration and a second acceleration. The first acceleration refers to the wheel acceleration of the wheel in the (i-1)th prediction period, and the second acceleration refers to the vehicle body acceleration at the position of the wheel in the i-th prediction period. Based on the first acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters, the first suspension state of the wheel in the i-th prediction period is predicted; Based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle, determine the state deviation value of the i-th prediction cycle; Based on the first suspension state and the state deviation value, the target suspension state of the wheel in the i-th prediction period is determined, and the target suspension state includes the target suspension height.

[0007] In this application, when calculating the suspension height of a wheel, the wheel acceleration of that wheel and the vehicle body acceleration at the wheel's position in the (i-1)th prediction period are first obtained. Then, based on this wheel acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters, the first suspension state in the i-th prediction period is predicted. It should be understood that the first suspension state may include a suspension height, i.e., a predicted value for the suspension height is obtained. Next, based on the first suspension state, the vehicle body acceleration, and the suspension dynamic parameters in the i-th prediction period, a state deviation value is determined. Then, based on this state deviation value, the first suspension state is corrected to determine the target suspension state of that wheel in the i-th prediction period, which includes the target suspension height. Since the predicted value generally has a certain error, this solution calculates this state deviation value to represent the prediction deviation. Then, by correcting the first suspension state based on this state deviation value, a more accurate target suspension state in the i-th prediction period can be determined, thereby obtaining a more accurate suspension height. This allows the subsequent limit block control function to achieve precise control based on the suspension height.

[0008] Optionally, obtaining the second acceleration includes: When the wheel is the front wheel, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity in the three-axis acceleration of the target vehicle, the front wheel track of the target vehicle and the first distance, where the first distance is the distance from the center of mass of the target vehicle to the front axle; When the wheel is the rear wheel, the second acceleration is determined based on the Z-axis acceleration, the X-axis angular velocity, the Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance, where the second distance is the distance from the center of mass to the rear axle.

[0009] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; predicting the first suspension state of the wheel in the i-th prediction period based on the first acceleration, the target suspension state in the (i-1)-th prediction period, and the suspension dynamic parameters includes: Based on the target mass, spring stiffness, shock absorber damping, and prediction period in the (i-1)th prediction period, a state transition matrix is ​​determined. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Based on the prediction period, determine the control matrix; The first suspension state is determined based on the state transition matrix, the control matrix, the target suspension state in the (i-1)th prediction period, and the first acceleration.

[0010] In the above method, by determining a state transition matrix and a control matrix, the change pattern of suspension height in two adjacent cycles can be determined. Furthermore, the influence of acceleration on the state change of suspension height can be discretized and quantified. In this way, the subsequent prediction is made by determining the change in suspension height in two adjacent prediction cycles and combining it with the target suspension state of the previous prediction cycle. Based on this, the first suspension state can be determined more accurately.

[0011] Optionally, the target suspension state is represented by a target suspension matrix, the first suspension state is represented by a first suspension matrix, and determining the first suspension state based on the state transition matrix, the control matrix, the target suspension state in the (i-1)th prediction period, and the first acceleration includes: Multiply the control matrix by the first acceleration to obtain the first matrix; Multiply the state transition matrix by the target suspension matrix of the (i-1)th prediction period to obtain the second matrix; The first matrix is ​​added to the second matrix to obtain the first suspension matrix.

[0012] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle, the state deviation value of the i-th prediction cycle is determined, including: Based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period, the observation matrix of the i-th prediction period is determined. The observation matrix is ​​used to describe the relationship between the vehicle body acceleration and suspension state at the wheel position in the i-th prediction period. Based on the observation matrix and the first suspension state, a third acceleration is determined, which is the predicted value of the vehicle body acceleration in the i-th prediction cycle; Based on the third acceleration, the second acceleration, and the first suspension state, the state deviation value of the i-th prediction cycle is determined.

[0013] In the above method, the observation matrix for the i-th prediction period is first determined based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period. That is, the correlation between the suspension state and the vehicle body acceleration is first determined. Then, the third acceleration of the first prediction is determined based on the observation matrix and the first suspension state, so that the subsequent prediction deviation can be achieved based on the same vehicle body acceleration dimension. This allows for more accurate deviation prediction and more accurate correction of the suspension state.

[0014] Optionally, the method further includes: Based on the state transition matrix and the first error covariance of the (i-1)th prediction period, the second error covariance of the ith prediction period is determined. The first error covariance is used to represent the degree of deviation between the target suspension state and the actual suspension state in the (i-1)th prediction period, and the second error covariance is used to represent the degree of deviation between the first suspension state and the actual suspension state. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Based on the second error covariance and the observation matrix, the conversion gain matrix for the i-th prediction period is determined; And, determining the state deviation value for the i-th prediction cycle based on the third acceleration, the second acceleration, and the first suspension state includes: Based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix, the state deviation value of the i-th prediction cycle is determined.

[0015] In the above method, the accuracy of the prior estimation result of the current prediction period is predicted based on the posterior estimation result of the previous prediction period. This is equivalent to predicting the degree of deviation between the predicted value and the actual result of the current prediction period based on empirical estimation, which can achieve an accurate estimate of the degree of deviation.

[0016] Optionally, determining the state deviation value for the i-th prediction period based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix includes: Subtracting the third acceleration from the second acceleration yields the vehicle body acceleration deviation. Multiplying the conversion gain matrix by the vehicle body acceleration deviation yields the state deviation value for the i-th prediction cycle.

[0017] In the above method, the corresponding error minimization calculation of the vehicle acceleration deviation is performed based on the transformation gain matrix, and the vehicle acceleration deviation is converted into a suspension state deviation value. In this process, the importance of the correspondence between the predicted value and the measured value is incorporated, so that the state deviation value of the i-th prediction period can be measured more accurately, that is, the state deviation value with minimized error can be obtained.

[0018] Optionally, based on the first suspension state and the state deviation value, the target suspension state of the wheel in the i-th prediction period is determined, the target suspension state including the target suspension height, comprising: The target suspension state of the wheel in the i-th prediction period is obtained by adding the state deviation value to the first suspension state.

[0019] Secondly, a suspension height determining device is provided, the device comprising: The acquisition module is used to acquire a first acceleration and a second acceleration for any one of the multiple wheels of the target vehicle. The first acceleration refers to the wheel acceleration of the wheel in the (i-1)th prediction period, and the second acceleration refers to the vehicle body acceleration at the position of the wheel in the i-th prediction period. The prediction module is used to predict the first suspension state of the wheel in the i-th prediction period based on the first acceleration, the target suspension state in the (i-1)-th prediction period, and the suspension dynamic parameters. The first determining module is used to determine the state deviation value of the i-th prediction cycle based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle. The second determining module is used to determine the target suspension state of the wheel in the i-th prediction period based on the first suspension state and the state deviation value, wherein the target suspension state includes the target suspension height.

[0020] Optionally, the acquisition module is specifically used for: When the wheel is the front wheel, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity in the three-axis acceleration of the target vehicle, the front wheel track of the target vehicle and the first distance, where the first distance is the distance from the center of mass of the target vehicle to the front axle; When the wheel is the rear wheel, the second acceleration is determined based on the Z-axis acceleration, the X-axis angular velocity, the Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance, where the second distance is the distance from the center of mass to the rear axle.

[0021] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; the prediction module is specifically used for: Based on the target mass, spring stiffness, shock absorber damping, and prediction period in the (i-1)th prediction period, a state transition matrix is ​​determined. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Based on the prediction period, determine the control matrix; The first suspension state is determined based on the state transition matrix, the control matrix, the target suspension state in the (i-1)th prediction period, and the first acceleration.

[0022] Optionally, the target suspension state is represented by a target suspension matrix, the first suspension state is represented by a first suspension matrix, and the prediction module is specifically used for: Multiply the control matrix by the first acceleration to obtain the first matrix; Multiply the state transition matrix by the target suspension matrix of the (i-1)th prediction period to obtain the second matrix; The first matrix is ​​added to the second matrix to obtain the first suspension matrix.

[0023] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; the first determining module is specifically used for: Based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period, the observation matrix of the i-th prediction period is determined. The observation matrix is ​​used to describe the relationship between the vehicle body acceleration and suspension state at the wheel position in the i-th prediction period. Based on the observation matrix and the first suspension state, a third acceleration is determined, which is the predicted value of the vehicle body acceleration in the i-th prediction cycle; Based on the third acceleration, the second acceleration, and the first suspension state, the state deviation value of the i-th prediction cycle is determined.

[0024] Optionally, the device further includes: The third determining module is used to determine the second error covariance of the i-th prediction period based on the state transition matrix and the first error covariance of the i-1th prediction period. The first error covariance is used to represent the degree of deviation between the target suspension state and the actual suspension state in the i-1th prediction period, and the second error covariance is used to represent the degree of deviation between the first suspension state and the actual suspension state. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the i-1th prediction period and the i-th prediction period. The fourth determining module is used to determine the conversion gain matrix for the i-th prediction period based on the second error covariance and the observation matrix. And, the first determining module is specifically used for: Based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix, the state deviation value of the i-th prediction cycle is determined.

[0025] Optionally, the first determining module is specifically used for: Subtracting the third acceleration from the second acceleration yields the vehicle body acceleration deviation. Multiplying the conversion gain matrix by the vehicle body acceleration deviation yields the state deviation value for the i-th prediction cycle.

[0026] Optionally, the second determining module is specifically used for: The target suspension state of the wheel in the i-th prediction period is obtained by adding the state deviation value to the first suspension state.

[0027] Thirdly, a vehicle is provided, the vehicle comprising: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the vehicle to perform the suspension height determination method described above.

[0028] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described suspension height determination method.

[0029] Fifthly, a computer program product containing instructions is provided, which, when run on a computer, causes the computer to perform the steps of the suspension height determination method described above.

[0030] It is understood that the beneficial effects of the second, third, fourth, and fifth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of a scenario for a suspension height determination method provided in an embodiment of this application; Figure 2 This is a flowchart of a suspension height determination method provided in an embodiment of this application; Figure 3 This is a schematic diagram of a vehicle planar coordinate system provided in an embodiment of this application; Figure 4 This is an iterative flowchart of a Kalman filter provided in an embodiment of this application; Figure 5 This is a flowchart of a limit block anti-touch control provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a frame height determining device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0034] It should be understood that "multiple" as mentioned in this application refers to two or more. In the description of this application, unless otherwise stated, " / " indicates "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, to facilitate a clear description of the technical solutions of this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and that "first," "second," etc., do not necessarily imply differences.

[0035] First, the terms used in the embodiments of this application will be explained.

[0036] 1. IMU (Inertial Measurement Unit) An IMU (Integrated Measurement Unit) is used to measure the motion state of a vehicle. It acquires the motion state of an object through a built-in gyroscope and accelerometer. Generally, the IMU can acquire the three-axis angular velocity, that is, the rotational speed of the vehicle when it rotates around the X-axis, Y-axis, and Z-axis respectively, through the gyroscope. It can also acquire the three-axis acceleration, that is, the linear acceleration of the vehicle along the X-axis, Y-axis, and Z-axis, through the accelerometer.

[0037] 2. Kalman Filter Kalman filtering is a linear, unbiased, minimum mean square error recursive filtering algorithm. Its main purpose is to estimate the true state of a dynamic system in real time from data with measurement errors, even in the presence of noise. This process can be achieved recursively by combining the current measurement value with the estimate from the previous time step. The core idea of ​​Kalman filtering is to "first predict based on experience, and then correct the prediction based on actual observations."

[0038] Before describing the suspension height determination method provided in the embodiments of this application, the application scenarios of the embodiments of this application will be explained first.

[0039] For example, Figure 1 This is a schematic diagram of a suspension height determination method provided in an embodiment of this application.

[0040] like Figure 1 As shown, vehicle 101 is a vehicle with adjustable suspension. When vehicle 101 is driving on a bumpy road, it may become unstable. At this time, vehicle 101 actively controls the suspension and adjusts the suspension height to absorb the instability caused by the bumpy road.

[0041] It should be understood that the suspension is an elastic structure connecting the vehicle body and the wheels, which can absorb the bumps caused by uneven road surfaces by extending and contracting. However, its extension and contraction range is limited (that is, there are compression limits and extension limits). At these two extreme positions, there is a mechanical limit block. If the suspension movement reaches these two extreme positions, the suspension will contact the limit block, resulting in noise from the contact between the hardware and increasing the discomfort during the ride.

[0042] In related technologies, the suspension height of vehicle 101 can be monitored in real time. When the suspension height of vehicle 101 indicates that the vehicle's suspension movement is about to reach the upper / lower limit block, the damping of the shock absorber can be adjusted to prevent the suspension from moving to the position of the upper / lower limit block, thereby avoiding noise caused by contact between hardware.

[0043] Generally, some vehicles can be equipped with height sensors in their suspension systems. These sensors can collect data on the vehicle's suspension height, enabling real-time monitoring of the suspension level.

[0044] However, some vehicles may not have height sensors installed in their suspension. Normally, when the suspension height changes, the wheels move up and down accordingly (for example, when the suspension rises, the wheels move downwards away from the vehicle body; when the suspension lowers, the wheels move upwards towards the vehicle body). This upward and downward movement of the wheels generates acceleration, which can be used to infer the suspension height.

[0045] In this case, wheel acceleration sensors can be installed at the wheels of vehicle 101. The wheel acceleration can be collected by the wheel acceleration sensors, and the suspension height can be obtained by performing a second integration on the wheel acceleration.

[0046] However, the process of calculating the suspension height can cause zero drift due to the accumulation of integral errors, resulting in inaccurate calculated suspension height and consequently preventing the limit block control function from achieving precise control.

[0047] Therefore, this application provides a method for determining suspension height, which can be applied to scenarios where the suspension height of a vehicle needs to be detected, such as when the vehicle is driving on bumpy roads, slopes, or other scenarios where the suspension height needs to be monitored.

[0048] Specifically, based on the wheel acceleration detected in the previous prediction cycle, as well as the optimal prediction result and suspension dynamic parameters output in the previous prediction cycle, the first suspension state for the current prediction cycle can be predicted, which is to obtain the predicted value for the current prediction cycle. Then, based on the vehicle acceleration, suspension dynamic parameters, and predicted value for the current prediction cycle, the state deviation value for the current prediction cycle is determined. Finally, based on the first suspension state and this state deviation value, the target suspension state for the current prediction cycle is determined, which is to determine the target suspension height for the current prediction cycle.

[0049] In this case, since the predicted value will generally have a certain error, the above method calculates the state deviation value of the current prediction period to represent the prediction deviation of the predicted value. Then, by correcting the first suspension state based on this state deviation value, a more accurate target suspension state in the current prediction period can be determined, thereby obtaining a more accurate suspension height. This allows the subsequent limit block control function to achieve precise control based on the suspension height.

[0050] The suspension height determination method provided in the embodiments of this application will be explained in detail below.

[0051] Figure 2This is a flowchart illustrating a suspension height determination method provided in an embodiment of this application. This method can be applied to vehicle controllers, such as EDC (Electronic Damper Control) controllers or CDC (Continuous Damping Control) controllers. See also... Figure 2 The method includes the following steps.

[0052] Step 201: For any one of the multiple wheels of the target vehicle, obtain the first acceleration and the second acceleration. The first acceleration refers to the wheel acceleration of this wheel in the (i-1)th prediction period, and the second acceleration refers to the vehicle body acceleration at the position of this wheel in the i-th prediction period.

[0053] Where i is an integer greater than or equal to 1.

[0054] It should be noted that when determining the suspension height of each wheel of the target vehicle, the suspension height determination method provided in this application embodiment can be executed for each wheel to obtain the suspension height of each wheel. This application embodiment takes the determination of the suspension height of one wheel as an example to illustrate the specific implementation process of determining the suspension height of each wheel.

[0055] The prediction period is used to represent the time interval for predicting the suspension height. In this embodiment, the prediction period can be preset by a technician according to actual needs.

[0056] The first acceleration can be obtained by wheel acceleration sensors mounted on the wheels of the target vehicle. The second acceleration can be calculated based on the inertial motion parameters collected by the IMU and the vehicle parameters.

[0057] In this application embodiment, the target vehicle can be a vehicle equipped with an IMU and two wheel acceleration sensors, or a vehicle equipped with an IMU and four wheel acceleration sensors.

[0058] First, let's explain the steps for obtaining the first acceleration.

[0059] When the target vehicle is a vehicle equipped with an IMU and four wheel acceleration sensors, the four wheel acceleration sensors can be arranged on the corresponding wheels respectively. Then, the wheel acceleration of each wheel can be collected by the corresponding wheel acceleration sensor. Thus, the first acceleration can be the wheel acceleration collected by the wheel acceleration sensor arranged on this wheel in the previous prediction cycle.

[0060] When the target vehicle is equipped with an IMU and two wheel acceleration sensors, the two wheel acceleration sensors can be placed on the two front wheels of the target vehicle.

[0061] In this case, when the wheel (the wheel whose suspension height needs to be calculated) is the front wheel, the wheel acceleration sensor on this wheel can collect the wheel acceleration in each prediction cycle, and then obtain the wheel acceleration collected in the (i-1)th prediction cycle (the previous prediction cycle) as the first acceleration.

[0062] When this wheel is the rear wheel, a target time can be determined based on the vehicle speed and the wheelbase between the front and rear axles. The wheel acceleration collected by the wheel acceleration sensor of the front wheel on the same side at the target time before the (i-1)th prediction period is determined as the first acceleration of this wheel.

[0063] In the above method, since there are no wheel acceleration sensors installed at the rear wheels of the target vehicle, the wheel acceleration of the rear wheels cannot be directly collected by the wheel acceleration sensors. However, the wheel acceleration of the front wheels can be transmitted to the rear wheels through a time delay. That is, after a time delay, the wheel acceleration of the rear wheel will be equal to the wheel acceleration of the front wheel on the same side before the time delay. This time delay can be calculated using the vehicle speed and the wheelbase between the front and rear axles. The principle is: at a certain vehicle speed, the distance that the wheel speed on the front axle travels to the rear axle should be equal to the wheelbase between the front and rear axles. Therefore, by dividing this wheelbase by the vehicle speed, the target time can be obtained, meaning that after the target time, the wheel acceleration of the front axle can be transmitted to the rear wheels.

[0064] Under the above principle, when it is necessary to obtain the wheel acceleration of the rear wheel in the (i-1)th prediction period, that is, the wheel acceleration of the rear wheel in the (i-1)th prediction period is transmitted from the wheel acceleration of the front wheel before the target time. Therefore, the wheel acceleration collected by the wheel acceleration sensor of the front wheel on the same side collected at the target time before the (i-1)th prediction period can be determined as the first acceleration of this wheel.

[0065] The method for obtaining the second acceleration will be explained below.

[0066] Specifically, the step of obtaining the second acceleration can be achieved in the following two possible scenarios.

[0067] In the first case, when the wheel is the front wheel, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity, and Y-axis angular velocity of the target vehicle's three-axis acceleration, the track width of the target vehicle's front wheels, and the first distance.

[0068] The first distance is the distance from the center of gravity of the target vehicle to the front axle.

[0069] It should be noted that the Z-axis acceleration in the above three-axis acceleration and the X-axis angular velocity and Y-axis angular velocity in the three-axis angular velocity were collected in the i-th prediction period (the current prediction period).

[0070] It should be understood that the target vehicle's IMU can acquire the target vehicle's three-axis acceleration and three-axis angular velocity. The three-axis acceleration includes X-axis acceleration, Y-axis acceleration, and Z-axis acceleration. X-axis acceleration represents the target vehicle's linear acceleration along the X-axis, Y-axis acceleration represents the target vehicle's linear acceleration along the Y-axis, and Z-axis acceleration represents the target vehicle's linear acceleration along the Z-axis. The three-axis angular velocity includes X-axis angular velocity, Y-axis angular velocity, and Z-axis angular velocity. X-axis angular velocity represents the target vehicle's angular velocity about the X-axis, Y-axis angular velocity represents the target vehicle's linear angular velocity about the Y-axis, and Z-axis angular velocity represents the target vehicle's linear angular velocity about the Z-axis.

[0071] It should be understood that the X-axis, Y-axis, and Z-axis refer to the three coordinate axes of the vehicle coordinate system. The X-axis is positive along the direction of the target vehicle's movement, the Y-axis is perpendicular to the X-axis and positive along the left side of the target vehicle, and the Z-axis is perpendicular to the ground and positive upwards.

[0072] For example, Figure 3 This is a schematic diagram of a vehicle planar coordinate system provided in an embodiment of this application, such as... Figure 3 As shown, the vehicle's planar coordinate system includes an X-axis and a Y-axis, where the X-axis is positive along the target vehicle's direction of travel, and the Y-axis is perpendicular to the X-axis. The intersection point O of the X-axis and Y-axis is the target vehicle's center of mass. A corresponds to the left front wheel, B to the right front wheel, C to the left rear wheel, and D to the right rear wheel. AB is the target vehicle's front axle, and CD is the target vehicle's rear axle. As shown in the figure, W... f W is half the track width of the front wheels. r It is half the rear wheel track. In addition, the distance from the center of gravity O to the front axle AB (the first distance) is L. f The distance (second distance) from the center of mass O to the rear axle CD is L. r .

[0073] In this case, the operation of determining the second acceleration based on the Z-axis acceleration of the target vehicle's three-axis acceleration, the X-axis angular velocity and Y-axis angular velocity of the three-axis angular velocities, the front wheel track of the target vehicle, and the first distance can be achieved in the following two cases.

[0074] In case 1, when the wheel is the left front wheel, the second acceleration of the wheel is calculated based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity, the first distance and half of the front wheel track, using the following formula (1).

[0075] (1) in, The acceleration of the vehicle body at the position of the left front wheel (the second acceleration of the left front wheel). Z-axis acceleration The angular velocity along the X-axis. This represents the angular velocity along the Y-axis.

[0076] In the second case, when the wheel is the right front wheel, the second acceleration of the wheel is calculated based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity, the first distance and half of the front wheel track, using the following formula (2).

[0077] (2) in, This refers to the vehicle acceleration at the location of the right front wheel (the second acceleration of the right front wheel).

[0078] In the second scenario, when the wheel is the rear wheel, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity, Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance.

[0079] The second distance is the distance from the target vehicle's center of gravity to the rear axle.

[0080] Similarly, based on the Z-axis acceleration, X-axis angular velocity, Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance, the specific operation to determine the second acceleration can be implemented in the following two possible scenarios.

[0081] In case 1, when the wheel is the left rear wheel, the second acceleration of the wheel is calculated using the following formula (3) based on the Z-axis acceleration, X-axis angular velocity, Y-axis angular velocity, second distance, and half of the rear wheel track.

[0082] (3) in, The acceleration of the vehicle body at the position of the left rear wheel (the second acceleration of the left rear wheel).

[0083] In the second case, when the wheel is the right rear wheel, the second acceleration of the wheel is calculated using the following formula (4) based on the Z-axis acceleration, X-axis angular velocity, Y-axis angular velocity, second distance, and half of the rear wheel track.

[0084] (4) in, The acceleration of the vehicle body at the position of the right rear wheel (the second acceleration of the right rear wheel).

[0085] It should be understood that the acceleration of the vehicle body at any position is the superposition of the translational acceleration of the center of mass and the centripetal acceleration generated by the rotation of the vehicle body at that position, that is, the superposition of the translational term and the rotational term. Since the distance and direction from the axis of rotation in the X and Y directions are different at different positions, the sign of the rotational term will change from positive to negative. Therefore, the acceleration of the vehicle body at the four wheel positions can be calculated by the above formulas (1)-(4).

[0086] Step 202: Based on the first acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters, predict the first suspension state of this wheel in the i-th prediction period.

[0087] The target suspension state in the (i-1)th prediction period refers to the posterior estimation result of the previous prediction period, that is, the suspension state of this wheel determined in the previous prediction period. The target suspension state in the (i-1)th prediction period includes the target suspension height and target motion speed. The target suspension height is the suspension height of this wheel finally determined in the (i-1)th prediction period, and the target motion speed is used to represent the motion speed of this wheel's suspension in the (i-1)th prediction period.

[0088] The first suspension state is the suspension state of the current prediction period predicted based on the posterior estimation result of the previous prediction period; that is, the first suspension state is a predicted value. In the embodiments of this application, the first suspension state may include a first suspension height and a first motion speed. The first suspension height represents the predicted value of the suspension height in the current prediction period, and the first motion speed represents the predicted value of the suspension's motion speed in the current prediction period. The first motion speed can be calculated based on the first suspension height.

[0089] Suspension dynamic parameters refer to the dynamic parameters related to the suspension. In the embodiments of this application, suspension dynamic parameters may include spring stiffness, shock absorber damping, target mass, etc., wherein the target mass refers to half of the sprung mass of the axle corresponding to this wheel, which is used to represent the vehicle weight borne by this suspension.

[0090] In the above method, the first suspension state of the wheel in the i-th prediction period is predicted based on the first acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters. This is equivalent to combining the posterior estimation result output by the previous prediction period to estimate the prediction result of the current prediction period, which is based on historical experience values. This can improve the accuracy of the prediction result as much as possible, that is, a more accurate first suspension height can be obtained.

[0091] One possible approach is that step 202 can be performed as follows: based on the target mass, spring stiffness, shock absorber damping, and prediction period of the (i-1)th prediction period, determine the state transition matrix; based on the prediction period, determine the control matrix; based on the state transition matrix, the control matrix, the target suspension state of the (i-1)th prediction period, and the first acceleration, determine the first suspension state.

[0092] This state transition matrix describes the change in suspension height of this wheel between the (i-1)th prediction period and the ith prediction period. It should be understood that there is a certain correlation between the vehicle's suspension dynamic parameters and the suspension's motion characteristics. For example, how do the suspension dynamic parameters change during suspension movement? Conversely, the change in suspension height can be inferred from the changes in these parameters. Therefore, the above method can determine the change in suspension height between two adjacent prediction periods based on the target mass, spring stiffness, shock absorber damping, and the prediction period in the (i-1)th prediction period.

[0093] The control matrix is ​​used to describe how the first acceleration affects the change in suspension height within a prediction period. Since the effect of acceleration on suspension height is generally continuous, and this continuous effect can be converted into a state increment within a discrete time step, the effect can be quantified by the prediction period. Therefore, a control matrix can be determined based on the prediction period so that the effect of the first acceleration on suspension height can be discretized, making it easy to determine the discretized state changes.

[0094] In the above method, by determining a state transition matrix and a control matrix, the change pattern of suspension height in two adjacent cycles can be determined. Furthermore, the influence of acceleration on the state change of suspension height can be discretized and quantified. In this way, the subsequent prediction is made by determining the change in suspension height in two adjacent prediction cycles and combining it with the target suspension state of the previous prediction cycle. Based on this, the first suspension state can be determined more accurately.

[0095] The operation of determining the state transition matrix based on the target mass, spring stiffness, damper damping and prediction period of the (i-1)th prediction period can be as follows: the state transition matrix is ​​determined by the following formula (5) based on the target mass, spring stiffness, damper damping and prediction period of the (i-1)th prediction period.

[0096] (5) Where F is the state transition matrix and K is the spring stiffness. For the prediction cycle, The target mass is half of the spring mass of the axle corresponding to the j-th wheel, where j is an integer greater than or equal to 1 and less than or equal to 4. When j=1, This represents half the mass of the spring on the left front wheel corresponding to the axle. When j=2, This indicates that half of the mass on the spring of the right front wheel corresponding to the axle, when j=3. This represents half the mass of the left rear wheel on the axle spring, when j=4. This indicates that half of the mass on the spring of the right rear wheel corresponding to the axle. Let be the damper damping for the (i-1)th prediction cycle.

[0097] It should be understood that during the suspension movement, the sprung mass is subjected to the spring force, resulting in a certain displacement increment. Furthermore, the sprung mass is affected by the damping force of the shock absorber, causing a certain decrease in its movement speed. In the above formula (5), by defining the effects of the spring force and the damper damping on the sprung mass respectively, the change law of the suspension height can be reflected based on the effects of the spring force and the damper damping on the sprung mass, thereby obtaining a more accurate state change matrix.

[0098] Among them, the operation of determining the control matrix based on the prediction period can be: the control matrix is ​​determined based on the prediction period by the following formula (6).

[0099] (6) in, This is the control matrix.

[0100] In the embodiments of this application, the target suspension state can be represented by the target suspension matrix, and the first suspension state can also be represented by the first suspension matrix.

[0101] The operation of determining the first suspension state based on the state transition matrix, control matrix, target suspension state in the (i-1)th prediction period, and first acceleration can be as follows: multiply the control matrix by the first acceleration to obtain the first matrix; multiply the state transition matrix by the target suspension matrix in the (i-1)th prediction period to obtain the second matrix; add the first matrix and the second matrix to obtain the first suspension matrix.

[0102] The above specific operation is to determine the state of the first suspension through the following formula (7).

[0103] (7) in, Indicates the first suspension state. This represents the target suspension state for the (i-1)th cycle. As the input signal of the previous cycle, in this embodiment of the application, , For the wheel acceleration of the j-th wheel in the (i-1)-th prediction cycle (the first acceleration of the j-th wheel), at j=1, The first acceleration of the left front wheel, at j=2, The first acceleration of the right front wheel is given at j=3. The first acceleration of the left rear wheel is given at j=4. This is the first acceleration of the right rear wheel.

[0104] In this embodiment of the application, the prediction target (target suspension state) for the current prediction period can be defined as: , This represents the suspension height for the final determined i-th prediction period (the current prediction period). This represents the speed of the suspension during the i-th prediction period.

[0105] Then the target suspension state in the (i-1)th prediction period can be represented by the following formula (8).

[0106] (8) in, This represents the target suspension height finally determined in the (i-1)th prediction period. Based on The calculated value represents the suspension's speed during the (i-1)th prediction period.

[0107] It is worth noting that when i=1, that is, when the first suspension state is determined in the first prediction period, there is no target suspension state in the previous prediction period. In this case, the first suspension state of this wheel in the first prediction period can be predicted based on the first acceleration, the initial suspension state and the initial suspension dynamic parameters.

[0108] The initial suspension state can include the initial suspension height and the initial speed of motion. The initial suspension height can be the default value of the target vehicle's suspension height, and the initial speed of motion can be set to 0.

[0109] Initial suspension dynamic parameters may include spring stiffness, target mass, and initial shock absorber damping. It should be understood that the initial shock absorber damping may be the default damping value of the target vehicle's shock absorbers.

[0110] Furthermore, the specific operation of predicting the first suspension state of this wheel in the first prediction cycle based on the first acceleration, the initial suspension state, and the initial suspension dynamic parameters is similar to the operation of predicting the first suspension state of the wheel in the i-th prediction cycle based on the first acceleration, the target suspension state in the (i-1)-th prediction cycle, and the suspension dynamic parameters, and will not be repeated here.

[0111] Step 203: Based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle, determine the state deviation value of the i-th prediction cycle.

[0112] The state deviation value of the i-th prediction period is used to represent the prediction deviation of the first suspension state, that is, the deviation between the predicted value and the measured value in the i-th prediction period.

[0113] One possible approach is that step 203 can be performed as follows: based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction cycle, determine the observation matrix for the i-th prediction cycle; based on the observation matrix and the first suspension state, determine the third acceleration; based on the third acceleration, the second acceleration, and the first suspension state, determine the state deviation value for the i-th prediction cycle.

[0114] This observation matrix describes the relationship between the vehicle body acceleration at the wheel position and the suspension state during the i-th prediction period. It should be understood that if the suspension's acceleration is closely related to the vehicle body acceleration, then the suspension height and suspension speed are also closely related to the vehicle body acceleration. This relationship should be related to the suspension dynamic parameters, meaning it can be reflected through these parameters.

[0115] The third acceleration is the predicted value of the vehicle body acceleration at the position of this wheel in the i-th prediction cycle.

[0116] Since the target vehicle does not have a height sensor, the suspension height cannot be measured using a height sensor, meaning that the measurement value of the suspension state cannot be obtained. However, since there is a correlation between the vehicle body acceleration and the suspension state, a third acceleration can be determined by predicting the first suspension state, thus obtaining a predicted value of the vehicle body acceleration. Therefore, the prediction deviation of the suspension state can be determined by using the measured value and the predicted value of the vehicle body acceleration.

[0117] In the above method, the observation matrix for the i-th prediction period is first determined based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period. That is, the correlation between the suspension state and the vehicle body acceleration is first determined. Then, the third acceleration of the first prediction is determined based on the observation matrix and the first suspension state, so that the subsequent prediction deviation can be achieved based on the same vehicle body acceleration dimension. This allows for more accurate deviation prediction and more accurate correction of the suspension state.

[0118] The operation of determining the observation matrix of the i-th prediction period based on the target mass, spring stiffness, and damper damping of the i-th prediction period can be as follows: the observation matrix is ​​determined by the following formula (9) based on the target mass, spring stiffness, and damper damping of the i-th prediction period.

[0119] (9) Where H is the observation matrix, Let be the damper damping for the i-th prediction cycle.

[0120] The operation to determine the third acceleration based on the observation matrix and the first suspension state can be as follows: multiply the observation matrix with the first suspension state to obtain the third acceleration.

[0121] Since the observation matrix represents the relationship between vehicle acceleration and suspension state, multiplying the suspension state by the observation matrix will yield the corresponding vehicle acceleration, thus providing the third acceleration.

[0122] It is worth noting that sensors also experience noise during data measurement, which may lead to errors in the acquired second acceleration. In such cases, when both the predicted and measured values ​​have errors, it is necessary to determine whether the predicted or measured value is more accurate—that is, whether to trust the predicted or measured value more—in order to obtain a more accurate target suspension state. In this embodiment, a transformation gain matrix can be set to balance the contributions of the predicted and measured values ​​to the determination of the suspension state, thereby allowing for a more accurate determination of the correction amount when correcting the first suspension state.

[0123] Specifically, after predicting the first suspension state in the i-th prediction period, the first suspension state can be evaluated to assess the accuracy of the prediction, that is, to evaluate the error of the prediction value, and then the conversion gain matrix can be determined accordingly.

[0124] One possible approach is to determine the second error covariance of the i-th prediction period based on the state transition matrix and the first error covariance of the (i-1)-th prediction period; and to determine the transformation gain matrix of the i-th prediction period based on the second error covariance and the observation matrix.

[0125] The first error covariance represents the degree of deviation between the target suspension state and the actual suspension state in the (i-1)th prediction period, equivalent to the degree of deviation between the posterior estimate and the actual result in the (i-1)th prediction period. The second error covariance represents the degree of deviation between the first suspension state and the actual suspension state, equivalent to the degree of deviation between the prior estimate and the actual result in the (i-1)th prediction period.

[0126] The conversion gain matrix is ​​used to weigh the contributions of predicted and measured values ​​to the determination of the suspension state. In this embodiment, the conversion gain matrix is ​​used to measure the contributions of the third acceleration and the second acceleration to the determination of the suspension state. Furthermore, the conversion gain matrix is ​​also a dimensionless matrix that enables the conversion between vehicle acceleration and suspension state.

[0127] In the above method, the accuracy of the prior estimation result of the current prediction period is predicted based on the posterior estimation result of the previous prediction period. This is equivalent to predicting the degree of deviation between the predicted value and the actual result of the current prediction period based on empirical estimation, which can achieve an accurate estimate of the degree of deviation.

[0128] The operation of determining the second error covariance of the i-th prediction period based on the state transition matrix and the first error covariance of the i-1 prediction period can be as follows: Based on the state transition matrix and the first error covariance of the i-1 prediction period, the second error covariance of the i-th prediction period is determined by the following formula (10).

[0129] (10) in, Let be the second error covariance for the i-th prediction period. Let the first error covariance be the (i-1)th prediction period. Let Q be the transpose of the state transition matrix, and let Q be the process noise covariance. In this embodiment, Q can be preset, for example, Q can be set to 0.01.

[0130] It should be understood that when i=1, that is, when determining the second error covariance of the first prediction period, there is no first error covariance of the (i-1)th prediction period. In this case, the second error covariance of the first prediction period can be calculated based on the initial variance value, which can be set in advance.

[0131] It is worth noting that before determining the second error covariance of the i-th prediction period, the first error covariance of the i-1 prediction period can be determined based on the second error covariance of the i-1 prediction period, the transformation gain matrix of the i-1 prediction period, and the observation matrix.

[0132] Specifically, the first error covariance of the (i-1)th prediction period can be determined based on the second error covariance of the (i-1)th prediction period, the transformation gain matrix of the (i-1)th prediction period, and the observation matrix, using the following formula (11).

[0133] (11) Where I is the identity matrix, The conversion gain matrix determined for the (i-1)th prediction period. Let be the second error covariance for the (i-1)th prediction period.

[0134] After obtaining the second error covariance for the i-th prediction period, the conversion gain matrix for the i-th prediction period can be determined based on the second error covariance and the observation matrix.

[0135] Specifically, the conversion gain matrix for the i-th prediction period can be determined based on the second error covariance of the i-th prediction period and the observation matrix, using the following formula (12).

[0136] (12) in, This represents the conversion gain matrix for the i-th prediction period. Let R be the transpose of the observation matrix, and let R be the measurement noise covariance, used to measure the degree of deviation of the measurement noise. In this embodiment, R can be preset, for example, R can be set to 0.01. And as can be seen from the above formula (12), the transformation gain matrix... The unit can be s 2 (square seconds), which can convert acceleration into corresponding distance parameters, in the embodiments of this application, It can be used as a state correction quantity in the process of converting the deviation value describing the vehicle body acceleration into the deviation amount of the suspension state (including the deviation amount of suspension height and suspension motion speed). That is, on the one hand, it can realize the balance of errors between predicted values ​​and measured values, and on the other hand, it can realize the conversion between vehicle body acceleration and suspension state.

[0137] In the embodiments of this application, the conversion gain matrix can be a two-row, one-column matrix.

[0138] After determining the conversion gain matrix for the i-th prediction period, the state deviation value for the i-th prediction period can be determined by combining the conversion gain matrix for the i-th prediction period.

[0139] In this case, the operation of determining the state deviation value of the i-th prediction period based on the third acceleration, the second acceleration, and the first suspension state can be: determining the state deviation value of the i-th prediction period based on the third acceleration, the second acceleration, the first suspension state, and the state gain matrix.

[0140] In the above method, since the conversion gain matrix of the i-th prediction period can balance the error between the predicted value and the measured value, and can also realize the conversion between the vehicle acceleration and the suspension state, the prediction deviation of the first suspension state can be accurately determined when the measured value and the predicted value of the vehicle acceleration are known.

[0141] Specifically, the operation of determining the state deviation value of the i-th prediction period based on the third acceleration, the second acceleration, the first suspension state, and the state gain matrix can be as follows: subtract the third acceleration from the second acceleration to obtain the vehicle body acceleration deviation; multiply the conversion gain matrix by the vehicle body acceleration deviation to obtain the state deviation value of the i-th prediction period.

[0142] It should be understood that the difference between the measured and predicted values ​​of the vehicle body acceleration in the i-th prediction period, which is the second acceleration minus the third acceleration, is the vehicle body acceleration deviation. Since there is a certain correlation between vehicle body acceleration and suspension state, this vehicle body acceleration deviation can indicate the deviation value of the suspension state. Therefore, the deviation value of the suspension state can be calculated subsequently through the transformation gain matrix, which means the state deviation value of the i-th prediction period can be obtained.

[0143] In the above method, the corresponding error minimization calculation of the vehicle acceleration deviation is performed based on the transformation gain matrix, and the vehicle acceleration deviation is converted into a suspension state deviation value. In this process, the importance of the correspondence between the predicted value and the measured value is incorporated, so that the state deviation value of the i-th prediction period can be measured more accurately, that is, the state deviation value with minimized error can be obtained.

[0144] It is worth noting that steps 202 and 203 above can determine a predicted value and a prediction deviation value for the suspension state in the i-th prediction cycle. In this case, the two can be fused to obtain a more accurate suspension state.

[0145] Step 204: Based on the first suspension state and the state deviation value of the i-th prediction period, determine the target suspension state of this wheel in the i-th prediction period. The target suspension state includes the target suspension height.

[0146] In the above method, by fusing the two, the prediction error of the first suspension state can be minimized, thereby determining a more accurate suspension state, that is, a more accurate suspension height can be determined. This can solve the problem of inaccurate suspension height calculated by acceleration because the vehicle is not equipped with a height sensor.

[0147] One possible approach is to add the state deviation value of the i-th prediction cycle to the first suspension state to obtain the target suspension state of this wheel in the i-th prediction cycle.

[0148] The above method can also be achieved through the following formula (13).

[0149] (13) in, Let i be the target suspension state of this wheel in the i-th prediction cycle. Let be the state deviation value for the i-th prediction period. For the second acceleration, This is the third acceleration.

[0150] It should be understood that the target suspension state of this wheel in the i-th prediction period may include the target suspension height and target speed of this wheel in the i-th prediction period. It should also be understood that the target suspension state of this wheel in the i-th prediction period is the final, optimally estimated suspension state obtained by correcting the predicted values ​​of the suspension state.

[0151] It is important to note that after determining the suspension height for the i-th prediction period, the first error covariance for the i-th prediction period needs to be determined based on the second error covariance, the transformation gain matrix, and the observation matrix of the i-th prediction period. This will enable the subsequent calculation of the suspension height for the (i+1)-th prediction period.

[0152] Specifically, the first error covariance of the i-th prediction period can be determined by the following formula (14) based on the second error covariance of the i-th prediction period, the transformation gain matrix of the i-th prediction period, and the observation matrix.

[0153] (14) in, Let I be the first error covariance for the i-th prediction period, and let I be the identity matrix. Let be the second error covariance for the i-th prediction period.

[0154] It is worth noting that the suspension height determination method provided in this application embodiment performs a priori estimation of the suspension state in the current prediction period by combining the posterior estimation result of the previous prediction period. Then, a state deviation value is determined based on the priori estimation result and the measured value of the sensor. Subsequently, the priori estimation is corrected based on the state deviation value. The entire process described above uses the iterative approach of Kalman filtering to accurately determine the suspension state, thereby achieving accurate determination of the suspension height.

[0155] To make it easier to understand, let's first combine... Figure 4 The iterative process of Kalman filtering provided in the embodiments of this application will be described by way of example, for example, Figure 4 This is an iterative flowchart of a Kalman filter provided in an embodiment of this application.

[0156] like Figure 4 As shown, in the i-th prediction period, the first suspension state and the second error covariance of the i-th prediction period are predicted first by formula (7) and formula (10) above, that is, the predicted value is obtained. Then the predicted value is corrected. In the correction process, the transformation gain matrix of the i-th prediction period can be determined first based on formula (12) above, and then the first suspension state is corrected based on formula (13) to obtain the suspension state (suspension height) predicted in the i-th prediction period. After the correction of the first suspension state is completed, the first error covariance of the i-th prediction period also needs to be calculated. Then the suspension state (optimal state estimate) and error covariance (first error covariance) predicted in the i-th prediction period are output and used as the corresponding value of the next prediction period, so that the suspension height of the next prediction period can be determined.

[0157] It is worth noting that the target suspension height of this wheel in the i-th prediction cycle can be calculated through steps 201-204 described above. However, since the above prediction method relies on empirical estimation, the initial prediction result has a large covariance error, and therefore cannot be used in the limit block control function. In this embodiment, the predicted target suspension height can be set after the target duration and used in the limit block control function to achieve precise control of the shock absorber damping based on the suspension height of the target vehicle, thereby preventing the suspension from touching the upper / lower limit blocks.

[0158] Specifically, when the target vehicle is powered on for the target duration, it is determined whether the current speed of the target vehicle is greater than or equal to a preset speed threshold; if the current speed is greater than or equal to the preset speed threshold, the limit block function is disabled; if the current speed is less than the preset speed threshold, the limit block control function is enabled.

[0159] The preset vehicle speed threshold can be set in advance, and the preset vehicle speed threshold can be set to a relatively large value.

[0160] Because suddenly adjusting the shock absorber damping while the vehicle is traveling at high speed can cause a significant impact on the vehicle, affecting driving experience and safety. Therefore, at high speeds, the limit block control function should be prohibited, meaning that limit block control based on the target suspension height is disabled. At lower speeds, the limit block control function can be activated, allowing limit block control based on the target suspension height.

[0161] For example, Figure 5 This is a flowchart of a limit block anti-touch control provided in an embodiment of this application.

[0162] like Figure 5 As shown, after the target vehicle is powered on, the system time is monitored. It is determined whether the system time has elapsed for a target duration. If the system time has not elapsed for the target duration, monitoring of the system time continues. After the system time has elapsed for the target duration, it is determined whether the target vehicle's current speed is greater than or equal to a preset speed threshold. If the current speed is greater than or equal to the preset speed threshold, the limit block control function is disabled. If the current speed is less than the preset speed threshold, the limit block control function is enabled.

[0163] In this embodiment, when calculating the suspension height of a wheel, the EDC / CDC controller first obtains the wheel acceleration of that wheel during the (i-1)th prediction period and the vehicle body acceleration at the wheel's position during the ith prediction period. Then, based on this wheel acceleration, the target suspension state during the (i-1)th prediction period, and the suspension dynamic parameters, it predicts the first suspension state for the ith prediction period. It should be understood that the first suspension state may include a suspension height, i.e., a predicted suspension height value is obtained. Next, based on the first suspension state, the vehicle body acceleration, and the suspension dynamic parameters for the ith prediction period, a state deviation value is determined. Then, based on this state deviation value, the first suspension state is corrected to determine the target suspension state of that wheel during the ith prediction period. The target suspension state includes the target suspension height. Since the predicted value generally has a certain error, this solution calculates this state deviation value to represent the prediction deviation. Then, by correcting the first suspension state based on this state deviation value, a more accurate target suspension state in the i-th prediction period can be determined, thereby obtaining a more accurate suspension height. This allows the subsequent limit block control function to achieve precise control based on the suspension height.

[0164] Figure 6 This is a schematic diagram of a suspension height determining device provided in an embodiment of this application. The suspension height determining device can be implemented as part or all of a vehicle by software, hardware, or a combination of both. The vehicle can be described below. Figure 7 The vehicle shown. See also Figure 6 The device includes: an acquisition module 601, a prediction module 602, a first determination module 603, and a second determination module 604.

[0165] The acquisition module 601 is used to acquire a first acceleration and a second acceleration for any one of the multiple wheels of the target vehicle. The first acceleration refers to the wheel acceleration of the wheel in the (i-1)th prediction period, and the second acceleration refers to the vehicle body acceleration at the position of the wheel in the i-th prediction period. Prediction module 602 is used to predict the first suspension state of the wheel in the i-th prediction period based on the first acceleration, the target suspension state in the i-1th prediction period, and the suspension dynamic parameters. The first determining module 603 is used to determine the state deviation value of the i-th prediction cycle based on the first suspension state, the second acceleration and the suspension dynamic parameters of the i-th prediction cycle. The second determining module 604 is used to determine the target suspension state of the wheel in the i-th prediction period based on the first suspension state and the state deviation value. The target suspension state includes the target suspension height.

[0166] Optionally, the acquisition module 601 is specifically used for: When the wheels are the front wheels, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity in the three-axis acceleration of the target vehicle, the front wheel track of the target vehicle, and the first distance. The first distance is the distance from the center of mass of the target vehicle to the front axle. When the wheels are rear wheels, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity, Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance, where the second distance is the distance from the center of mass to the rear axle.

[0167] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, where the target mass is half the sprung mass of the axle corresponding to the wheel; the prediction module 602 is specifically used for: Based on the target mass, spring stiffness, shock absorber damping, and prediction period in the (i-1)th prediction period, the state transition matrix is ​​determined. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Determine the control matrix based on the prediction period; The first suspension state is determined based on the state transition matrix, control matrix, target suspension state in the (i-1)th prediction period, and first acceleration.

[0168] Optionally, the target suspension state is represented by a target suspension matrix, the first suspension state is represented by a first suspension matrix, and the prediction module 602 is specifically used for: Multiply the control matrix by the first acceleration to obtain the first matrix; Multiply the state transition matrix by the target suspension matrix of the (i-1)th prediction period to obtain the second matrix; Add the first matrix to the second matrix to obtain the first suspension matrix.

[0169] Optionally, the suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, where the target mass is half the sprung mass of the axle corresponding to the wheel; the first determining module 603 is specifically used for: Based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period, the observation matrix of the i-th prediction period is determined. The observation matrix is ​​used to describe the relationship between the vehicle body acceleration and suspension state at the wheel position in the i-th prediction period. Based on the observation matrix and the first suspension state, the third acceleration is determined. The third acceleration is the predicted value of the vehicle body acceleration in the i-th prediction cycle. Based on the third acceleration, the second acceleration, and the first suspension state, the state deviation value for the i-th prediction cycle is determined.

[0170] Optionally, the device further includes: The third determining module is used to determine the second error covariance of the i-th prediction period based on the state transition matrix and the first error covariance of the i-1th prediction period. The first error covariance is used to represent the degree of deviation between the target suspension state and the actual suspension state in the i-1th prediction period, and the second error covariance is used to represent the degree of deviation between the first suspension state and the actual suspension state. The fourth determination module is used to determine the conversion gain matrix for the i-th prediction period based on the second error covariance and the observation matrix. And, the first determining module 603 is specifically used for: Based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix, the state deviation value for the i-th prediction period is determined.

[0171] Optionally, the first determining module 603 is specifically used for: Subtracting the third acceleration from the second acceleration yields the vehicle body acceleration deviation. Multiplying the conversion gain matrix by the vehicle body acceleration deviation yields the state deviation value for the i-th prediction cycle.

[0172] Optionally, the second determining module 604 is specifically used for: The target suspension state of the wheel in the i-th prediction cycle is obtained by adding the state deviation value to the first suspension state.

[0173] In this embodiment, when calculating the suspension height of a wheel, the wheel acceleration of the wheel and the vehicle body acceleration at the wheel's position in the (i-1)th prediction period are first obtained. Then, based on this wheel acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters, the first suspension state in the i-th prediction period is predicted. It should be understood that the first suspension state may include a suspension height, i.e., a predicted value for the suspension height is obtained. Next, based on the first suspension state, the vehicle body acceleration, and the suspension dynamic parameters in the i-th prediction period, a state deviation value is determined. Then, based on this state deviation value, the first suspension state is corrected to determine the target suspension state of the wheel in the i-th prediction period, which includes the target suspension height. Since the predicted value generally has a certain error, this solution calculates this state deviation value to represent the prediction deviation. Then, by correcting the first suspension state based on this state deviation value, a more accurate target suspension state in the i-th prediction period can be determined, thereby obtaining a more accurate suspension height. This allows the subsequent limit block control function to achieve precise control based on the suspension height.

[0174] It should be noted that the suspension height determining device provided in the above embodiments is only illustrated by the division of the above functional modules when determining the suspension height. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0175] The functional units and modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0176] The suspension height determination device and the suspension height determination method provided in the above embodiments belong to the same concept. The specific working process and technical effects of the units and modules in the above embodiments can be found in the method embodiments section, and will not be repeated here.

[0177] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0178] For example, such as Figure 7As shown, the vehicle 700 includes a memory 71 and a processor 70, wherein the memory 71 stores executable program code 72, and the processor 70 is used to call and execute the executable program code 72 to perform the above-mentioned suspension height determination method.

[0179] This embodiment can divide the vehicle into functional modules according to the above method example. For example, each function can be assigned to a separate module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0180] When each functional module is divided according to its corresponding function, the vehicle may include: an acquisition module, a prediction module, a first determination module, and a second determination module. It should be noted that all relevant content regarding the steps involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0181] The vehicle provided in this embodiment is used to execute the suspension height determination method described above, and therefore can achieve the same effect as the above implementation method.

[0182] When using integrated units, the vehicle may include a processing module and a storage module. The processing module is used to control and manage the vehicle's actions. The storage module is used to support the vehicle in executing corresponding program code and data.

[0183] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0184] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the suspension height determination method described in the above embodiment.

[0185] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the suspension height determination method described in the above embodiment.

[0186] In this embodiment, the vehicle, computer-readable storage medium, computer program product, or chip are all used to execute the method described above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the method described above, and will not be repeated here.

[0187] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0188] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are illustrative; for instance, the division of modules or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0189] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining suspension height, characterized in that, The method includes: For any one of the multiple wheels of the target vehicle, obtain a first acceleration and a second acceleration. The first acceleration refers to the wheel acceleration of the wheel in the (i-1)th prediction period, and the second acceleration refers to the vehicle body acceleration at the position of the wheel in the i-th prediction period. Based on the first acceleration, the target suspension state in the (i-1)th prediction period, and the suspension dynamic parameters, the first suspension state of the wheel in the i-th prediction period is predicted; Based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle, determine the state deviation value of the i-th prediction cycle; Based on the first suspension state and the state deviation value, the target suspension state of the wheel in the i-th prediction period is determined, and the target suspension state includes the target suspension height.

2. The method as described in claim 1, characterized in that, Obtaining the second acceleration includes: When the wheel is the front wheel, the second acceleration is determined based on the Z-axis acceleration, X-axis angular velocity and Y-axis angular velocity in the three-axis acceleration of the target vehicle, the front wheel track of the target vehicle and the first distance, where the first distance is the distance from the center of mass of the target vehicle to the front axle; When the wheel is the rear wheel, the second acceleration is determined based on the Z-axis acceleration, the X-axis angular velocity, the Y-axis angular velocity, the rear wheel track of the target vehicle, and the second distance, where the second distance is the distance from the center of mass to the rear axle.

3. The method as described in claim 1, characterized in that, The suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; the prediction of the first suspension state of the wheel in the i-th prediction period based on the first acceleration, the target suspension state in the (i-1)-th prediction period, and the suspension dynamic parameters includes: Based on the target mass, spring stiffness, shock absorber damping, and prediction period in the (i-1)th prediction period, a state transition matrix is ​​determined. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Based on the prediction period, determine the control matrix; The first suspension state is determined based on the state transition matrix, the control matrix, the target suspension state in the (i-1)th prediction period, and the first acceleration.

4. The method as described in claim 3, characterized in that, The target suspension state is represented by a target suspension matrix, and the first suspension state is represented by a first suspension matrix. Determining the first suspension state based on the state transition matrix, the control matrix, the target suspension state in the (i-1)th prediction period, and the first acceleration includes: Multiply the control matrix by the first acceleration to obtain the first matrix; Multiply the state transition matrix by the target suspension matrix of the (i-1)th prediction period to obtain the second matrix; The first matrix is ​​added to the second matrix to obtain the first suspension matrix.

5. The method as described in claim 1, characterized in that, The suspension dynamic parameters include target mass, spring stiffness, and shock absorber damping, wherein the target mass is half of the sprung mass of the axle corresponding to the wheel; based on the first suspension state, the second acceleration, and the suspension dynamic parameters of the i-th prediction cycle, the state deviation value of the i-th prediction cycle is determined, including: Based on the target mass, spring stiffness, and shock absorber damping of the i-th prediction period, the observation matrix of the i-th prediction period is determined. The observation matrix is ​​used to describe the relationship between the vehicle body acceleration and suspension state at the wheel position in the i-th prediction period. Based on the observation matrix and the first suspension state, a third acceleration is determined, which is the predicted value of the vehicle body acceleration in the i-th prediction cycle; Based on the third acceleration, the second acceleration, and the first suspension state, the state deviation value of the i-th prediction cycle is determined.

6. The method as described in claim 5, characterized in that, The method further includes: Based on the state transition matrix and the first error covariance of the (i-1)th prediction period, the second error covariance of the ith prediction period is determined. The first error covariance is used to represent the degree of deviation between the target suspension state and the actual suspension state in the (i-1)th prediction period, and the second error covariance is used to represent the degree of deviation between the first suspension state and the actual suspension state. The state transition matrix is ​​used to describe the change law of the suspension height of the wheel between the (i-1)th prediction period and the ith prediction period. Based on the second error covariance and the observation matrix, the conversion gain matrix for the i-th prediction period is determined; And, determining the state deviation value for the i-th prediction cycle based on the third acceleration, the second acceleration, and the first suspension state includes: Based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix, the state deviation value of the i-th prediction cycle is determined.

7. The method as described in claim 6, characterized in that, The determination of the state deviation value for the i-th prediction period based on the third acceleration, the second acceleration, the first suspension state, and the conversion gain matrix includes: Subtracting the third acceleration from the second acceleration yields the vehicle body acceleration deviation. Multiplying the conversion gain matrix by the vehicle body acceleration deviation yields the state deviation value for the i-th prediction cycle.

8. The method as described in claim 1, characterized in that, Determining the target suspension state of the wheel within the i-th prediction period based on the first suspension state and the state deviation value includes: The target suspension state of the wheel in the i-th prediction period is obtained by adding the state deviation value to the first suspension state.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 8.

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