An adaptive suspension control system for a vehicle

Abstract

The application discloses a kind of adaptive suspension control systems for vehicle, and the application relates to the technical field of suspension control, solve the problem that the front road surface is not analyzed in advance to adjust the hardness of suspension, the present application can ensure that the dynamic change of road surface can be captured in time by real-time monitoring of the image of the road surface in front of the vehicle, and the image is accurately screened and analyzed according to the set definition, and the low-definition area is eliminated, which greatly reduces the subsequent invalid data processing amount and improves the system operation efficiency;The road surface measurement processing end uses image pixel value and gradient feature algorithm to construct the calibration model surface in detail, fully considers the details of each point of the image, not only can accurately restore the actual condition of the road surface, but also lays a solid foundation for subsequent accurate control, so that the vehicle seems to know every fluctuation of the road surface in advance, realizes the digital presentation of the front road condition in near real time and accurately, real-time monitoring of the instantaneous bounce speed of the wheel, dynamically integrated into the operation process, and real-time fine tuning execution.

Description

Technical Field

[0001] This invention relates to the field of suspension control technology, specifically to an adaptive suspension control system for vehicles. Background Technology

[0002] The vehicle suspension is one of the most important chassis components of a car, playing a crucial role in connecting the body and wheels, buffering road impacts, and ensuring vehicle handling stability and ride comfort. Structurally, a common suspension system mainly consists of elastic elements, shock absorbers, and a guiding mechanism. Elastic elements, such as coil springs, leaf springs, and air springs, are responsible for absorbing and buffering vertical impacts from the road surface. For example, coil springs utilize their elastic deformation capacity to absorb energy when the vehicle drives over potholes, preventing severe vibrations to the body. Shock absorbers work in conjunction with the elastic elements, often employing hydraulic or pneumatic damping. They quickly dissipate the energy generated during spring compression and rebound, preventing continuous bumps and oscillations and ensuring the wheels quickly return to normal contact with the ground. The guiding mechanism, generally composed of various links and control arms, precisely controls the wheel's trajectory, ensuring that the wheels maintain the correct angle and position according to design requirements during bouncing, steering, and braking, thus maintaining vehicle stability.

[0003] Application CN104709025B discloses a road-adaptive hydraulic active suspension system, including a variable stiffness and variable damping active suspension, a hydraulic system, and a control system. The active suspension includes a variable damping shock absorber, a variable stiffness spring, and a hydraulic actuator. The control system includes an electronic control unit and several sensors for testing vehicle status. This suspension system can simultaneously and automatically control suspension stiffness and damping to achieve good ride comfort and handling stability. The control system has a fast response speed. The vehicle height can be adaptively or manually adjusted to improve the vehicle's passability. The structure and control strategy are simple, the operation is stable, and the service life is long. Moreover, when the active suspension components fail, the vehicle's performance remains the same as that of a traditional passive suspension system, allowing it to continue operating normally.

[0004] During the control process of the vehicle's suspension, the stiffness of the suspension needs to be adjusted in real time based on the corresponding wheel vibration to ensure the comfort of the driver and passengers. However, in the actual process, the road surface ahead is not analyzed. Instead, the damping force or air pressure is adjusted based on the wheel vibration. This will cause vibration to still exist in the initial stage of adjustment. If the road surface is analyzed in advance and the optimal suspension stiffness is determined in advance, the driving experience of the corresponding driver can be guaranteed and their comfort can be improved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an adaptive suspension control system for vehicles, which solves the problem of not performing road surface analysis and adjusting suspension stiffness in advance.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive suspension control system for vehicles, comprising:

[0007] The visual monitoring terminal monitors the road surface image associated with the vehicle in real time and transmits the real-time monitored road surface image to the road surface measurement and processing terminal.

[0008] The road surface measurement processing unit selects an analysis image from the monitored road surface images. Based on the different pixel values ​​associated with different points within the analysis image, it confirms the gradient features associated with the corresponding points. Then, it selects a standard region from the analysis image. Based on the different spatial location features associated with different pixels within the standard region, it sequentially confirms the spatial location features of other pixels, generating the calibration model surface associated with the corresponding analysis image. The specific method is as follows:

[0009] Based on the monitored road surface images, the areas with a clarity ≥ Y1 in the road surface images are identified, and the identified areas are labeled as analysis images, where Y1 is a preset value, and its specific value is determined by the operator based on experience;

[0010] From the determined analysis image, identify the different pixel values ​​associated with different points within the image, and label the different pixel values ​​associated with different points as X. i Where i represents different points, a set of points is randomly selected as the center point, and other points adjacent to this center point are marked as neighboring points. The pixel values ​​associated with the neighboring points are recorded, and the pixel values ​​of the center point and the neighboring points are sorted as follows: H1-H8 represent the pixel values ​​associated with the neighboring points around the center point;

[0011] Using ZX i = (-1)×H1+0×H2+1×H3+(-2)×H4+0×X i +2×H5+(-1)×H6+0×H7+1×H8 confirms the vertical gradient ZX associated with this center point. i ;

[0012] Using ZY i = (-1)×H1+(-2)×H2+(-1)×H3+0×H4+0×X i +0×H5+1×H6+2×H7+1×H8 confirms the horizontal gradient ZY associated with this center point. i ;

[0013] use Determine the gradient feature G associated with this center point. i ;

[0014] In the confirmed analysis image, a set of circles with a radius of 2m are generated with the center point of the vehicle as the center, and the specific area belonging to this circle is identified from the analysis image and marked as the standard area.

[0015] Based on the different gradient features associated with different points within the standard area, the spatial location features associated with the corresponding points are identified, and the differences in gradient features and the height differences in spatial location features between adjacent points are identified. The associated height difference value is denoted as J. i The associated gradient feature difference value is denoted as Gz. i J i ÷Gz i =TZ i Confirm its associated characteristics;

[0016] Based on the confirmed correlation features, starting from the specific area, the points are confirmed outwards. Based on the difference in gradient features of adjacent points, the difference in spatial location features is locked, thereby confirming the spatial location features of the corresponding adjacent points. The spatial location features of the adjacent points outside the specific area are confirmed in detail, and so on, to complete the construction of the entire analysis image calibration model surface.

[0017] The road surface feature determination end, based on a preset reference plane, confirms the height of points on the determined calibration model surface, and determines the road surface feature value from several confirmed height values. The determined road surface feature value is then transmitted to the stiffness parameter selection end. Specifically:

[0018] Based on the determined calibration model surface and the preset reference plane, the reference plane is a preset plane that is parallel to the vehicle chassis;

[0019] Confirm the vertical height between different points on the calibration model surface and the reference plane, and average the vertical heights confirmed by several different points to confirm the characteristic average value. Use the following formula: characteristic average value - X1 = road surface characteristic value to confirm the road surface characteristic value belonging to this calibration model surface, where X1 is a preset value, and transfer the determined road surface characteristic value to the stiffness parameter selection end.

[0020] At the stiffness parameter selection end, based on the determined road surface characteristic value, the damping force or air pressure associated with this road surface characteristic value is locked from the preset characteristic table, and the locked damping force or air pressure is calibrated as the stiffness parameter.

[0021] On the real-time control side, based on the instantaneous rebound speed of the corresponding wheel and the selected stiffness parameters, the corresponding suspension execution parameters are determined and real-time control is performed. Specifically:

[0022] The instantaneous bounce speed of the corresponding wheel is calibrated as V. k , where k represents different wheels;

[0023] Using (V) k -Bz)×C1+Stiffness parameter = Execution parameter, where C1 is a preset fixed coefficient factor that corresponds one-to-one with the selected stiffness parameter. Different stiffness parameters correspond to different standard bounce velocities Bz, and the standard velocity Bz is extracted from the preset feature table.

[0024] Based on the determined execution parameters, the spring components of the suspension associated with the current vehicle are identified, and the working parameters of these spring components are adjusted to the execution parameters to complete the overall control of the suspension associated with the corresponding wheel.

[0025] Preferably, the visual monitoring terminal acquires road surface images every 2 seconds.

[0026] Preferably, the preset feature table stores different damping forces or air pressures corresponding to different road surface feature values, which can be directly selected by the stiffness parameter selection end, and different damping forces or air pressures correspond to different standard bounce speeds.

[0027] Preferably, it also includes: a real-time monitoring terminal, which monitors the instantaneous bounce speed of the wheels of the vehicle and transmits the real-time monitored instantaneous bounce speed to the real-time control terminal.

[0028] This invention provides an adaptive suspension control system for vehicles. Compared with the prior art, it has the following advantages:

[0029] Real-time monitoring of road surface images ahead of the vehicle ensures timely capture of dynamic changes in the road surface. Based on a set resolution, images are precisely filtered and analyzed to eliminate low-resolution areas of no value, greatly reducing the amount of subsequent invalid data processing and improving the system's computing efficiency. The road surface measurement processing end uses image pixel values ​​and gradient feature algorithms to meticulously construct a calibration model surface, fully considering the details of each point in the image. This not only accurately restores the actual road surface conditions but also lays a solid foundation for subsequent precise control, allowing the vehicle to seem to know every undulation of the road surface in advance, achieving a near real-time and accurate digital presentation of the road conditions ahead.

[0030] It monitors the instantaneous rebound speed of the wheels in real time, dynamically integrates it into the calculation process, and fine-tunes the execution parameters in real time. No matter how the road surface changes, it can respond quickly and accurately adjust the working parameters of the spring components to keep the vehicle in a stable and comfortable driving posture. This not only avoids violent body swaying caused by road bumps and ensures ride comfort, but also optimizes the suspension stiffness to ensure that the tires are in close contact with the road surface under conditions such as high-speed driving and turning, thereby enhancing handling stability and comprehensively improving the vehicle's driving quality. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0032] Figure 2 This is a schematic diagram illustrating the determination of the execution parameters of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] First Embodiment

[0035] Please see Figure 1 This application provides an adaptive suspension control system for vehicles, including: a visual monitoring end, a road surface measurement processing end, a road surface feature determination end, a stiffness parameter selection end, a real-time control end, and a real-time monitoring end. The visual monitoring end is electrically connected to the input node of the road surface measurement processing end, and the road surface measurement processing end is electrically connected to the input node of the road surface feature determination end. The road surface feature determination end, the stiffness parameter selection end, and the real-time control end are all electrically connected from the output node to the input node, and the real-time monitoring end is electrically connected to the input node of the real-time control end.

[0036] The visual monitoring terminal monitors the road surface image associated with the vehicle in real time and transmits the real-time monitored road surface image to the road surface measurement and processing terminal. The road surface image is monitored and acquired every 2 seconds. The 2 seconds is not a fixed value. The specific value can be determined in advance by the relevant operators. The maximum value shall not exceed 5 seconds. The length of the road surface monitored by the corresponding probe is limited. Based on the set resolution, the image that meets the specified resolution is used as the monitored and analyzed image.

[0037] In the road surface measurement processing unit, an analysis image is selected from the monitored road surface images. Based on the different pixel values ​​associated with different points in the analysis image, the gradient features associated with the corresponding points are confirmed. Then, a standard area is selected from the analysis image. Based on the different spatial location features associated with different pixels in the standard area, the spatial location features of other pixels are confirmed in turn to generate the calibration model surface associated with the corresponding analysis image. Specifically, when the camera acquires images, it will acquire some areas with low clarity. Such areas have no analytical value during image analysis, so such images need to be removed.

[0038] The specific method for generating the calibration model surface is as follows:

[0039] Based on the monitored road surface images, the areas with a clarity ≥ Y1 in the road surface images are identified, and the identified areas are labeled as analysis images, where Y1 is a preset value, and its specific value is determined by the operator based on experience;

[0040] From the determined analysis image, identify the different pixel values ​​associated with different points within the image, and label the different pixel values ​​associated with different points as X. i Where i represents different points, a set of points is randomly selected as the center point, and other points adjacent to this center point are marked as neighboring points. The pixel values ​​associated with the neighboring points are recorded, and the pixel values ​​of the center point and the neighboring points are sorted as follows: H1-H8 represent the pixel values ​​associated with the neighboring points around the center point;

[0041] Using ZX i = (-1)×H1+0×H2+1×H3+(-2)×H4+0×X i +2×H5+(-1)×H6+0×H7+1×H8 confirms the vertical gradient ZX associated with this center point. i ;

[0042] Using ZY i = (-1)×H1+(-2)×H2+(-1)×H3+0×H4+0×X i +0×H5+1×H6+2×H7+1×H8 confirms the horizontal gradient ZY associated with this center point. i ;

[0043] use Determine the gradient feature G associated with this center point. i ;

[0044] In the confirmed analysis image, a set of circles with a radius of 2m are generated with the center point of the vehicle as the center, and the specific area belonging to this circle is identified from the analysis image and marked as the standard area.

[0045] Based on the different gradient features associated with different points within the standard area, the spatial location features associated with the corresponding points are identified, and the differences in gradient features and the height differences in spatial location features between adjacent points are identified. The associated height difference value is denoted as J. i The associated gradient feature difference value is denoted as Gz. i J i ÷Gz i =TZ i Confirm its associated characteristics;

[0046] Based on the confirmed association features, starting from the specific area, the points are confirmed outwards. Based on the difference in gradient features between adjacent points, the difference in spatial location features is locked, thereby confirming the spatial location features of the corresponding adjacent points. The spatial location features of the adjacent points outside the specific area are confirmed in turn, and so on, to complete the construction of the entire analysis image calibration model surface. The height features of close-up images can be directly confirmed. When determining the spatial location height, the preset standard horizontal plane in the vehicle system is used as the reference. The acquired analysis image is placed above the preset standard horizontal plane, and the spatial location height associated with the corresponding point can be locked.

[0047] Specifically, the road surface area close to the vehicle body can be directly numerically confirmed, and its corresponding point and associated height features can be directly confirmed. Thus, the corresponding standard features can be confirmed. Subsequently, based on the different gradient features associated with different points, the feature parameters associated with each different pixel can be locked. Based on the spatial location of the corresponding point and the associated height features, the corresponding virtual plane can be constructed, thereby determining the corresponding calibration model surface.

[0048] Second Embodiment

[0049] In the specific implementation process, compared with the above embodiments, this embodiment mainly focuses on the specific control of vehicle suspension. Based on the corresponding calibration model surface confirmed in Embodiment 1, the numerical parameters are specifically determined. Its specific execution end includes the subsequent road surface feature determination end, stiffness parameter selection end, real-time control end, and real-time monitoring end.

[0050] The road surface feature determination end, based on a preset reference plane, confirms the height of points on the determined calibration model surface, and determines the road surface feature value from several confirmed height values. The determined road surface feature value is then transmitted to the stiffness parameter selection end. The specific method for determining the road surface feature value is as follows:

[0051] Based on the determined calibration model surface and the preset reference plane, the reference plane is a preset plane that is parallel to the vehicle chassis;

[0052] Confirm the vertical height between different points on the calibration model surface and the reference plane, and average the vertical heights confirmed by several different points to confirm the characteristic average value. The characteristic average value is calculated as: characteristic average value - X1 = road surface characteristic value. Confirm the road surface characteristic value belonging to this calibration model surface, where X1 is a preset value, generally the height of the vehicle chassis from the horizontal ground, which is determined in advance by relevant operators. Then, transmit the determined road surface characteristic value to the stiffness parameter selection end.

[0053] Specifically, the road surface feature value is the unevenness of the points inside the calibration model surface. When the specific parameters of the unevenness differ too much, the determined road surface feature value will be large. Similarly, when the height difference of the corresponding uneven plane is too large, the corresponding road surface feature value will also be large. The greater the difference, the more uneven the corresponding model surface is.

[0054] In the stiffness parameter selection end, based on the determined road surface characteristic value, the damping force or air pressure associated with this road surface characteristic value is locked from the preset characteristic table (where the damping force corresponds to the normal shock absorber, and the adjustment of the damping force can be achieved by changing the size of the oil flow channel inside the shock absorber or by electromagnetic, hydraulic or other means, and the air pressure corresponds to the air spring, and the overall strength of the corresponding shock absorber is determined by changing the air pressure intensity inside the air spring). The locked damping force or air pressure is calibrated as the stiffness parameter, and the calibrated stiffness parameter is transmitted to the real-time control end.

[0055] The preset feature table is prepared in advance by relevant operators. It stores different damping forces or air pressures corresponding to different road surface feature values. It can be directly selected from the stiffness parameter selection end, and different damping forces or air pressures correspond to different standard bounce speeds.

[0056] The real-time monitoring terminal monitors the instantaneous bounce speed of the vehicle's wheels and transmits the monitored instantaneous bounce speed to the real-time control terminal.

[0057] Among them, combined Figure 2 On the real-time control end, based on the instantaneous rebound speed of the corresponding wheel and the selected stiffness parameters, the execution parameters of the corresponding suspension are determined and real-time control is performed. The specific method for determining the execution parameters of the corresponding suspension is as follows:

[0058] The instantaneous bounce speed of the corresponding wheel is calibrated as V. k , where k represents different wheels;

[0059] Using (V) k -Bz)×C1+Stiffness parameter = Execution parameter, where C1 is a preset fixed coefficient factor, determined by relevant operators based on experience, and Bz is a preset standard bounce speed, which corresponds one-to-one with the selected stiffness parameter. Different stiffness parameters correspond to different standard bounce speeds Bz, and the standard speed Bz is extracted from a preset feature table. Specifically, because there are different uneven areas in the corresponding region, the determined stiffness parameter is only an intermediate parameter. In order to make the control process more precise and achieve better control effect, the instantaneous bounce speed is monitored in real time and adjusted in real time, and the stiffness parameter is adjusted in real time to achieve a more precise buffer control effect.

[0060] Based on the determined execution parameters, the spring components of the suspension associated with the current vehicle are identified, and the working parameters of these spring components are adjusted to the execution parameters to complete the overall control of the suspension associated with the corresponding wheel.

[0061] Third Embodiment

[0062] In its specific implementation, this embodiment includes the entire implementation process of the two sets of embodiments described above.

[0063] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0064] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An adaptive suspension control system for vehicles, characterized in that, The application relates to a vehicle suspension system, which comprises the following parts: a visual monitoring end which monitors the road surface image in front of the vehicle in real time and transmits the real-time monitored road surface image to a road surface measurement processing end; the road surface measurement processing end selects an analysis image from the monitored road surface image and confirms the gradient feature of a corresponding point based on different pixel values associated with different points in the analysis image, then selects a standard region from the analysis image, confirms the spatial position feature of other pixel points in sequence based on different spatial position features of different pixel points in the standard region, and generates a calibration model surface associated with the corresponding analysis image; the road surface feature determination end confirms the height of the point of the determined calibration model surface based on a preset reference plane, determines a road surface feature value from the confirmed height values, and transmits the determined road surface feature value to a stiffness parameter selection end; the stiffness parameter selection end locks the damping force or air pressure associated with the road surface feature value from the preset feature table based on the determined road surface feature value, and calibrates the locked damping force or air pressure as a stiffness parameter; the real-time control end determines the execution parameter of the corresponding suspension based on the real-time monitored instantaneous rebound speed of the corresponding wheel and the selected stiffness parameter, and performs real-time control; the preset feature table internally stores different damping forces or air pressures corresponding to different road surface feature values, which can be directly selected by the stiffness parameter selection end, and different damping forces or air pressures correspond to different standard rebound speeds; the real-time monitoring end monitors the instantaneous rebound speed of the wheel of the driving vehicle and transmits the real-time monitored instantaneous rebound speed to the real-time control end; the specific way of determining the execution parameter of the corresponding suspension is as follows: The instantaneous bounce speed of the corresponding wheel is denoted as V k where k represents different wheels. Adopt (V k Bz) x C1 + stiffness parameter = execution parameter, wherein C1 is a preset fixed coefficient factor corresponding to the selected stiffness parameter, different stiffness parameters correspond to different standard bounce speeds Bz, and the standard speed Bz is extracted from a preset feature table; based on the determined execution parameter, the spring part of the suspension associated with the current vehicle is confirmed, the working parameter of the spring part is adjusted to the execution parameter, and the overall control of the suspension associated with the corresponding wheel is completed.

2. The adaptive suspension control system for a vehicle according to claim 1, wherein The visual monitoring end monitors and obtains the road surface image once every 2 seconds.

3. The adaptive suspension control system for a vehicle according to claim 1, wherein The specific way of confirming the gradient feature of the corresponding point by the road surface measurement processing end is as follows: based on the monitored road surface image, the area with a clarity greater than Y1 in the road surface image is confirmed, and the confirmed area image is calibrated as an analysis image, wherein Y1 is a preset value, and the specific value is determined by an operator according to experience; Confirming different pixel values associated with different point positions in the image from the determined analysis image, and marking different pixel values associated with different point positions as X i where i represents different point positions, randomly selecting a group of point positions as center points, marking other point positions adjacent to the center points as adjacent points, and recording pixel values associated with the adjacent points, and sorting the pixel values of the center points and the adjacent points as: where H1-H8 represent pixel values associated with adjacent points around the center points; ZX i = (-1) x H1 + 0 x H2 + 1 x H3 + (-2) x H4 + 0 x X i + 2 x H5 + (-1) x H6 + 0 x H7 + 1 x H8 i ; ZY i = (-1) x H1+ (-2) x H2+ (-1) x H3+ 0 x H4+ 0 x X i + 0 x H5+ 1 x H6+ 2 x H7+ 1 x H8 i ; Adopting determining a gradient feature G associated with this center point i .

4. The adaptive suspension control system for a vehicle according to claim 3, wherein The specific way of generating the calibration model surface by the road surface measurement processing end is as follows: in the confirmed analysis image, a group of circles with a radius of 2 m are generated with the center point of the vehicle as the center, and the specific area belonging to the circles is identified from the analysis image, and the specific area is calibrated as a standard region; Based on different gradient features associated with different point positions in a standard area, confirm the spatial position features associated with the corresponding point positions, identify the gradient feature difference between adjacent point positions and the height difference of the spatial position features, and mark the associated height difference value as J i Mark the associated gradient feature difference value as Gz i , adopt J i ÷Gz i =TZ i Confirm its associated features; based on the confirmed associated feature, the point confirmation is started from the determined specific area, the difference of the spatial position feature is locked based on the difference of the gradient features of adjacent points, so that the spatial position feature of the corresponding adjacent points is confirmed, the specific confirmation of the spatial position feature of the points adjacent to the outside of the specific area is performed in sequence, and the construction of the calibration model surface of the entire analysis image is completed in this way.

5. The adaptive suspension control system for a vehicle according to claim 4, wherein The specific way of determining the road surface feature value by the road surface feature determination end is as follows: Based on the determined calibration model surface and the preset reference plane, the reference plane is parallel to the vehicle chassis; Confirm the vertical height between different point positions in the calibration model surface and the reference plane, and perform mean value processing on the several groups of vertical heights confirmed by the several different point positions to confirm the feature mean value, adopt: feature mean value-X1=road surface feature value, confirm the road surface feature value belonging to the calibration model surface, wherein X1 is a preset value, and transmit the determined road surface feature value to the stiffness parameter selection end.

Images