Vehicle suspension open-loop control method, system and equipment based on road preview

Abstract

The invention provides a vehicle suspension open-loop control method, system and equipment based on road preview, which are applied to the technical field of vehicle suspension control, and the method comprises the following steps: obtaining road elevation data in a vehicle longitudinal preview range, and determining a target road type and target road related information according to the road elevation data; the target road type comprises a continuous random road and / or a mixed road, the continuous random road and the mixed road have different control requirements on the suspension, and the target road related information is used for representing the road grade and / or the road amplitude corresponding to the road in the longitudinal preview range; determining a matched target optimal control current in a suspension control law according to the target road related information, and transmitting the matched target optimal control current to a suspension system module, so that the suspension system module performs open-loop control on a suspension based on the target optimal control current; the suspension control law comprises various kinds of different road related information and the optimal control current corresponding to each kind of road related information. By adopting the technical scheme, the calculation load can be reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle suspension control technology, and in particular to a vehicle suspension open-loop control method, system and device based on road preview. Background Technology

[0002] As a key mechanical component connecting the vehicle body and wheels, the vehicle suspension system's dynamic performance directly determines the transmission characteristics of road excitation to the vehicle body. Traditional passive suspensions, when encountering road unevenness disturbances, can only passively dissipate vibration energy after the excitation occurs, making it difficult to balance ride comfort and handling stability. With the evolution of intelligent sensing and real-time control technologies, road-predictive intelligent suspension control technology has become a cutting-edge method for improving overall vehicle performance. Utilizing high-precision depth sensors such as LiDAR or stereo vision, the system can acquire longitudinal profile elevation data of the road ahead before the wheels arrive, converting this feedforward information into a vertical disturbance input. Subsequently, the suspension controller solves for optimal damping or applies dynamics online, achieving intelligent adjustment before the excitation arrives, thereby significantly suppressing vehicle acceleration and optimizing the overall vehicle ride quality.

[0003] In related technologies, model predictive control (MPC) is often used to control vehicle suspension. Specifically, for MPC-based anti-suspension controllers, a high-order state-space model is typically used as the solution framework. Within each sampling period, the anticipated road surface elevation value and all system state variables are integrated to optimize the desired driving force in real time.

[0004] However, at the real-time computing level, the control hardware in the above technologies needs to simultaneously handle three tasks: road excitation prediction, vibration state estimation, and optimal active force solution. The computational load increases exponentially, placing extremely high demands on the computing power of the embedded hardware modules. Summary of the Invention

[0005] This invention provides a vehicle suspension open-loop control method, system, and device based on road preview, to address the shortcomings of existing technologies where control hardware must simultaneously handle three tasks: road excitation prediction, vibration state estimation, and optimal active force solution. This results in an exponentially increasing computational load and extremely high computational requirements for embedded hardware modules. The invention achieves this by acquiring road elevation data in advance online and classifying roads into continuous and mixed types. Based on relevant road information, the optimal control current is determined in the offline-defined suspension control law to control the suspension. This hierarchical control architecture of "offline global optimization - online parameter identification - real-time current tracking" effectively reduces the real-time computational load and computational power requirements of the embedded platform.

[0006] This invention provides a vehicle suspension open-loop control method based on road preview, comprising: Obtain road elevation data corresponding to the road within the longitudinal preview range of the vehicle; Based on road elevation data, the target road type and related information of the target road within the longitudinal pre-aiming range are determined. The target road type includes continuous random roads and / or mixed roads. Mixed roads are a combination of continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The related information of the target road is used to characterize the road grade and / or road amplitude of the road within the longitudinal pre-aiming range. Based on the target road information, a target optimal control current matching the target road information is determined in the suspension control law, and the target optimal control current is transmitted to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the above suspension control law includes a variety of different road information and the optimal control current corresponding to each type of road information.

[0007] According to the present invention, a vehicle suspension open-loop control method based on road preview is provided, wherein the road elevation data includes multiple road elevation values, and the method for determining the target road type corresponding to the road within the longitudinal preview range based on the road elevation data includes: Root mean square calculation is performed on the elevation values ​​of each road to obtain the root mean square elevation value corresponding to each road elevation value. The system iterates through the road elevation values ​​using a sliding window of a set length, determines the multiple road elevation values ​​that fall within each sliding window, and calculates the standard deviation of the multiple road elevation values ​​within each sliding window to obtain the standard deviation corresponding to the multiple road elevation values ​​within each sliding window. Based on the standard deviation and root mean square of the elevation values ​​of multiple roads within each sliding window, the target road type corresponding to a segment of road within each sliding window is determined, and the target road type corresponding to a segment of road within each sliding window is determined as the target road type corresponding to the road within the longitudinal pre-aiming range.

[0008] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the method for determining the target road type corresponding to a segment of road within each sliding window based on the standard deviation and root mean square of multiple road elevation values ​​within each sliding window includes: For each sliding window, if the standard deviation of multiple road elevation values ​​within the sliding window is less than or equal to a set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be a continuous random road. If the standard deviation of multiple road elevation values ​​within the sliding window is greater than a set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be a mixed road.

[0009] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the determination of target road-related information corresponding to the target road type based on road elevation data includes: For each sliding window, if the target road type corresponding to a segment of road within the sliding window is determined to be a continuous random road, the standard deviation of multiple road elevation values ​​within the sliding window is used as the target road information corresponding to a segment of road within the sliding window; the standard deviation of multiple road elevation values ​​within the sliding window corresponds to the road level corresponding to a segment of road within the sliding window. If the target road type corresponding to a segment of road within the sliding window is determined to be a mixed road, the road amplitude corresponding to the segment of road within the sliding window is determined based on multiple road elevation values ​​within the sliding window, and the road amplitude corresponding to the sliding window and / or the standard deviation corresponding to multiple road elevation values ​​within the sliding window are used as the target road related information corresponding to the segment of road within the sliding window.

[0010] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the determination method of the above-mentioned suspension control law includes: Obtain the speed characteristic curves of the shock absorber in the suspension under different current excitations; the above speed characteristic curves are the variation curves of the speed and displacement of the shock absorber with the damping force under the corresponding current excitations; For each road excitation condition, the vibration of the suspension under the road excitation condition is simulated according to the speed characteristic curves under different current excitations, and the vibration response of the suspension under each current excitation and road excitation condition is determined. The vibration response includes multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under the corresponding current excitation and road excitation conditions. Different road excitation conditions correspond to different road grades and / or different road amplitudes. Based on multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under each current excitation and road excitation condition, determine the optimal control current for controlling the suspension under road excitation condition. Obtain road-related information under different road excitation conditions, and establish a suspension control law based on the road-related information under different road excitation conditions and the optimal control current for controlling the suspension under different road excitation conditions.

[0011] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the method determines the optimal control current for controlling the suspension under road excitation conditions based on multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition, including: For each road excitation condition, the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation are calculated based on the multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition. Based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition, the optimal control current for controlling the suspension under the road excitation condition is determined among each current excitation.

[0012] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the optimal control current for controlling the suspension under the road excitation condition is determined based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition, including: If the road excitation condition is a continuous random road type, then the control requirements for the suspension under the road excitation condition are determined according to the road excitation condition, and the selection object is determined from the root mean square of vehicle acceleration and the root mean square of tire dynamic deformation according to the control requirements for the suspension under the road excitation condition. Based on the selected object determined under the road excitation condition, determine the current excitation corresponding to the minimum selected object among the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation condition and the road excitation condition. The current excitation corresponding to the minimum value of the selected object is taken as the optimal control current for controlling the suspension under road excitation conditions.

[0013] According to the open-loop control method for vehicle suspension based on road preview provided by the present invention, the optimal control current for controlling the suspension under the road excitation condition is determined based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition, including: If the road excitation condition is a mixed road type including discrete impact roads, then the peak value of the vehicle body acceleration under each current excitation and road excitation condition is determined based on the multiple vehicle body accelerations of the suspension under each current excitation and road excitation condition. Based on the peak value and root mean square value of the vehicle body acceleration under each current excitation and road excitation condition, determine the vehicle body acceleration increment under each current excitation and road excitation condition. The current excitation corresponding to the minimum increase in vehicle acceleration is taken as the optimal control current for controlling the suspension under road excitation conditions.

[0014] The present invention also provides a vehicle suspension open-loop control system based on road preview, including... The road depth information perception and calculation module is used to obtain road elevation data corresponding to the road within the longitudinal preview range of the vehicle; The road type classification module is used to determine the target road type and related information of the target road within the longitudinal pre-aiming range based on road elevation data. The target road type includes continuous random roads and / or mixed roads. Mixed roads are a combination of continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The related information of the target road is used to characterize the road grade and / or road amplitude of the road within the longitudinal pre-aiming range. The suspension controller module is used to determine the target optimal control current that matches the target road information in the suspension control law based on the target road information, and transmit the target optimal control current to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the suspension control law includes a variety of different road information and the optimal control current corresponding to each type of road information.

[0015] The present invention also provides a vehicle suspension open-loop control device based on road preview, including... The road depth information perception and calculation module is used to obtain road elevation data corresponding to the road within the longitudinal preview range of the vehicle; The road type classification module is used to determine the target road type and related information of the target road within the longitudinal pre-aiming range based on road elevation data. The target road type includes continuous random roads and / or mixed roads. Mixed roads are a combination of continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The related information of the target road is used to characterize the road grade and / or road amplitude of the road within the longitudinal pre-aiming range. The suspension controller module is used to determine the target optimal control current that matches the target road information in the suspension control law based on the target road information, and transmit the target optimal control current to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the suspension control law includes a variety of different road information and the optimal control current corresponding to each type of road information.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the open-loop control method for vehicle suspension based on road preview as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the open-loop control method for vehicle suspension based on road preview as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the open-loop control method for vehicle suspension based on road preview as described above.

[0019] The present invention provides a vehicle suspension open-loop control method, system, and device based on road preview. This method acquires road elevation data corresponding to roads within the vehicle's longitudinal preview range. Based on the road elevation data, it determines the target road type and related target road information within the longitudinal preview range. Then, based on the target road information, it determines the optimal target control current matching the target road information in the suspension control law and transmits this optimal target control current to the suspension system module. This enables the suspension system module to perform open-loop control of the suspension based on the optimal target control current. The suspension control law includes various types of road information and the optimal control current corresponding to each type. The target road type includes continuous random roads or mixed roads including continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The target road information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal preview range. This method significantly enhances the suspension system's ability to perceive and classify complex road surface excitations by acquiring road elevation data in advance and classifying road excitations into continuous and mixed types for each type. Simultaneously, by acquiring road elevation data online and classifying roads into continuous and mixed types, and determining the optimal control current based on the relevant road information in the offline-determined suspension control law, this hierarchical control architecture of "offline global optimization - online parameter identification - real-time current tracking" effectively reduces the real-time computational load and computing power requirements of the embedded platform. Furthermore, by using the optimal control current as a direct output, it naturally aligns with the unidirectional energy dissipation characteristics of semi-active suspensions, eliminating the need for additional power conversion, thereby improving ride comfort and other driving characteristics under real-world conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the open-loop vehicle suspension control system based on road preview provided by the present invention; Figure 2 This is one of the flowcharts of the open-loop control method for vehicle suspension based on road preview provided by the present invention; Figure 3 This is the second flowchart of the vehicle suspension open-loop control method based on road preview provided by the present invention; Figure 4 This is a schematic diagram of the semi-active vehicle suspension model structure provided by the present invention; Figure 5 These are the speed characteristic curves of the shock absorber provided by this invention under different current excitations; Figure 6 This is a schematic diagram of the multi-objective optimization results under ISO Class A road excitation provided by the present invention; Figure 7 This is a schematic diagram of the suspension control law corresponding to continuous random road types provided by the present invention; Figure 8 This is a schematic diagram of the suspension control law corresponding to the mixed road type provided by the present invention; Figure 9 This is a schematic diagram of the open-loop control device for vehicle suspension based on road preview provided by the present invention; Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Currently, magnetorheological, electrorheological, and solenoid valve-based controllable damper technologies have extensive mass production experience. Compared to fully active suspensions, semi-active suspensions based on these dampers adjust the damping coefficient using only low-power solenoid valves or magnetorheological dampers, eliminating the need for continuous high-power output. This results in a simpler structure, higher reliability, and compliance with passive stability constraints. Furthermore, in the event of sensor failure or controller saturation, it can still revert to a traditional passive suspension, avoiding the risks of negative damping instability or actuator overtravel. Leveraging these advantages, semi-active suspensions have become a core component for shock absorption in mid-to-high-end passenger cars, commercial vehicles, and special-purpose vehicle platforms, playing a crucial role in improving ride comfort and overall driving quality.

[0024] In related technologies, Model Predictive Control (MPC) is mostly used to control vehicle suspension. In practical implementation, for MPC-based anticipatory suspension controllers, a high-order state-space model is typically used as the solution framework. Within each sampling period, the anticipated road surface elevation value and all system state variables are fused to optimize the desired active force in real time. This approach places stringent demands on the sensor-observation link: on the one hand, high-precision depth sensors are required to acquire information on road surface unevenness in front of the vehicle; on the other hand, high-precision accelerometers and full-dimensional state observers are needed to calculate all state variables of the suspension system, resulting in complex hardware architecture and significantly increased costs. At the real-time computing level, the control hardware needs to simultaneously handle three tasks: road excitation prediction, vibration state estimation, and optimal active force calculation. The computational load increases exponentially, placing extremely high demands on the computing power of embedded hardware modules. If this framework is transferred to a semi-active suspension, the expected active force cannot be directly realized due to its unidirectional energy dissipation characteristics. Control commands must be executed after saturation mapping or constraint projection, which leads to a significant degradation of the optimal solution and a significant deterioration in both real-time performance and theoretical effect, thus hindering its engineering implementation on a real vehicle platform.

[0025] Based on this, embodiments of the present invention provide a vehicle suspension open-loop control method, system, and device based on road preview, which can solve the above-mentioned technical problems.

[0026] It should be noted that the execution subject of this invention embodiment can be a road-predictive-based vehicle suspension open-loop control system, a road-predictive-based vehicle suspension open-loop control device, an electronic device, or other systems or devices. The following embodiments will use a road-predictive-based vehicle suspension open-loop control system as an example for illustration. See also... Figure 1The diagram shown illustrates the structure of a road-predictive-based open-loop vehicle suspension control system. This system can include a road depth information perception and calculation module, a road type classification module, a continuous road information calculation module, a discrete road information calculation module, a suspension controller module, and a suspension system module. These six modules work together to achieve open-loop control of the vehicle suspension. The actual control flow of this system includes: 1. The road depth information perception and calculation module calculates the road surface elevation value through the depth pre-aiming sensor and sends the road surface elevation value information to the road type classification module; 2. The road type classification module receives the elevation value information through the road classifier, classifies it into continuous road elevation value information and discrete road elevation value information, sends the continuous road elevation value information to the continuous road information calculation module, and sends the discrete road elevation value information to the discrete road information calculation module; 3. The continuous road information calculation module calculates the standard deviation of the continuous road elevation value information through the road standard deviation calculator and sends it to the suspension controller module; 4. The discrete road information calculation module calculates the amplitude of the discrete road using the road amplitude calculator and sends it to the suspension controller module; 5. The suspension controller module receives the standard deviation information of the continuous road and the amplitude information of the discrete road through the suspension controller and calculates the optimal control current of the suspension damper, and sends the control current to the suspension system module; 6. The suspension system module switches the damper mode under the action of the control current to complete the control.

[0027] Figure 2 This is one of the flowcharts illustrating the open-loop control method for vehicle suspension based on road preview provided by this invention, such as... Figure 2 As shown, the method includes the following steps: Step 202: Obtain the road elevation data corresponding to the road within the longitudinal preview range of the vehicle.

[0028] The road depth information perception and calculation module can include a depth prediction sensor for non-contact measurement to collect road depth information in advance. This depth prediction sensor can be a lidar, millimeter-wave radar, or similar technology. Taking lidar as an example, during vehicle suspension control, lidar can collect point cloud data of the road within the vehicle's longitudinal prediction range based on prediction technology. Specifically, it can collect road point cloud data within the longitudinal prediction range in front of the vehicle. For example, a multi-line lidar can acquire the road point cloud data within the longitudinal prediction range in front of the vehicle with a single data acquisition. After obtaining the road point cloud data within the longitudinal prediction range in front of the vehicle, the depth prediction sensor, which includes multiple sampling points, can obtain the depth value and attitude information of each sampling point. Combining these two data points, the vertical coordinates (e.g., Z-axis coordinates) and longitudinal coordinates (e.g., Y-axis coordinates) of each sampling point can be calculated. Then, combined with rut estimation, the prediction result of road surface unevenness can be obtained. The size of the aforementioned longitudinal prediction range can be set according to actual conditions, such as 10 meters.

[0029] In addition, after obtaining the vertical and longitudinal coordinates of each sampling point in the road point cloud data, the depth preview sensor can use the vertical coordinates of each sampling point as the road elevation data corresponding to the road within the longitudinal preview range. Then, the depth preview sensor can transmit the road elevation data corresponding to the road within this longitudinal preview range to the road type classification module for road type classification processing.

[0030] Step 204: Based on the road elevation data, determine the target road type corresponding to the road within the longitudinal pre-aiming range and the target road related information corresponding to the target road type; the target road type includes continuous random roads and / or mixed roads, and the mixed road is a mixed road including continuous random roads and discrete impact roads. The continuous random roads and mixed roads have different control requirements for the suspension. The target road related information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal pre-aiming range.

[0031] In this step, after the road type classification module obtains the road elevation data within the longitudinal preview range, the road elevation data can include the vertical coordinates of each sampling point in the point cloud data of the road within the longitudinal preview range. The vertical coordinates of each sampling point can reflect the elevation information or unevenness information of the road surface.

[0032] The road type classification module compares the vertical coordinates of each sampling point with a set elevation threshold to determine the target road type corresponding to the road within the longitudinal preview range. For example, if the vertical coordinates of most sampling points are not greater than the set elevation threshold, the target road type corresponding to the road within the longitudinal preview range is determined to be a continuous random road; otherwise, the target road type corresponding to the road within the longitudinal preview range is determined to be a mixed road. Alternatively, if the vertical coordinates of some continuous sampling points are greater than the set elevation threshold, and the vertical coordinates of other continuous sampling points are not greater than the set elevation threshold, the target road type corresponding to the road within the longitudinal preview range is determined to include both continuous random roads and mixed roads.

[0033] Alternatively, the road type classification module can further process the vertical coordinates of each sampling point and compare the processed values ​​with a set elevation threshold to determine the target road type corresponding to the road within the longitudinal pre-aiming range. For example, if most of the processed vertical coordinates of each sampling point are not greater than the set elevation threshold, the target road type corresponding to the road within the longitudinal pre-aiming range is determined to be a continuous random road; otherwise, the target road type corresponding to the road within the longitudinal pre-aiming range is determined to be a mixed road. Or, if the processed vertical coordinates of some continuous sampling points are greater than the set elevation threshold, while the processed vertical coordinates of other continuous sampling points are not greater than the set elevation threshold, the target road type corresponding to the road within the longitudinal pre-aiming range is determined to include both continuous random roads and mixed roads.

[0034] Alternatively, the road type classification module can group the sampling points, compare the vertical coordinates or the result of processing the vertical coordinates of multiple sampling points in each group with a set elevation threshold, determine the target road type of the road corresponding to each group of sampling points within the longitudinal preview range, and then comprehensively determine the target road type corresponding to the road within the longitudinal preview range.

[0035] Alternatively, the road type classification module can use other methods for classification. The elevation threshold can be set in advance based on experience, or it can be determined using the vertical coordinates of each sampling point.

[0036] The target road types mentioned above can include continuous random roads and / or mixed roads. Mixed roads are a combination of continuous random roads and discrete impact roads. Continuous random roads differ from discrete impact roads. Continuous random roads include, for example, asphalt roads and concrete roads, while discrete impact roads are random impact roads independent of continuous random roads, such as speed bumps and potholes. The two types of roads have different requirements for suspension control. For example, continuous random roads generally require suspension control for handling stability, while discrete impact roads generally require suspension control for comfort.

[0037] In addition, the road type classification module may include a road classifier, which can classify roads within the longitudinal preview range as described above.

[0038] After the road type classification module / road classifier classifies roads within the longitudinal preview range based on road elevation data, it can determine the road grade for continuous random roads and the road grade and road amplitude for mixed roads based on the vertical coordinates of each sampling point during the classification process. These can be used as relevant information for the target roads within the longitudinal preview range. For example, the standard deviation of the vertical coordinates of some or all sampling points can be calculated to determine the road grade. Alternatively, the road amplitude can be calculated by averaging the larger vertical coordinates among some or all sampling points, or the largest vertical coordinate (or its absolute value) among the vertical coordinates of each sampling point can be used as the road amplitude.

[0039] The process of calculating the target road information described above can be performed by the road type classification module, or the road type classification module can send the vertical coordinates (i.e., elevation data) of the sampling points related to continuous random roads to the continuous road information calculation module to calculate the road grade, and / or send the vertical coordinates (i.e., elevation data) of the sampling points related to mixed roads to the discrete road information calculation module to calculate the road amplitude and / or road grade, and finally obtain the target road information.

[0040] After the road type classification module, continuous road information calculation module and / or discrete road information calculation module determine the target road information, they can send the target road information to the suspension controller module for subsequent selection of the optimal control current.

[0041] Step 206: Based on the target road information, determine the target optimal control current that matches the target road information in the suspension control law, and transmit the target optimal control current to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the suspension control law includes a variety of different road information and the optimal control current corresponding to each type of road information.

[0042] Before the suspension controller module selects the optimal control current, a suspension control law can be established. This law includes various road-related information parameters and their corresponding optimal control currents. The suspension control law can be presented as a graph or a table. Taking road-related information parameters such as road grade (e.g., expressed using standard deviation) and road amplitude as an example, the suspension control law can include optimal control currents under different road grades and amplitudes. The establishment of the suspension control law can be achieved by pre-establishing road excitation models and suspension models for different road grades and / or amplitudes, and conducting suspension control experiments on these models to collect optimal control currents under different road grades and amplitudes, establishing the corresponding relationships, and thus obtaining the suspension control law.

[0043] After establishing the suspension control law, the suspension controller module can input target road information into the control law to find the optimal control current that matches both the road grade and / or road amplitude in the target road information. This optimal control current is then transmitted to the suspension system module, which also transmits road excitation. The suspension system module, which includes the suspension itself, is directly controlled by the current. Under the influence of the optimal control current, the suspension system module can switch damper modes to complete the control. In other words, the suspension can be controlled under road excitation by the optimal control current, controlling the suspension to output corresponding action force, thus achieving open-loop control of the suspension.

[0044] The open-loop control in this embodiment refers to control without feedback. Specifically, this embodiment uses road preview information (i.e., road elevation data within the vehicle's longitudinal preview range) as feedforward information and switches the parameters of the suspension control system through this road preview information. Compared with the current MPC-type control algorithm, the suspension control system in this embodiment does not need to calculate the state parameters of the suspension system in real time during the control process, that is, there is no output feedback, which belongs to open-loop control.

[0045] The aforementioned suspension can be either a semi-active or a fully active suspension, with a semi-active suspension being preferred due to its simple structure and minimal power consumption during operation. Furthermore, the suspension system module can be a dynamic model of the suspension during system simulation.

[0046] In this embodiment, by acquiring road elevation data corresponding to the road within the longitudinal preview range of the vehicle, the target road type and related target road information corresponding to the target road type are determined based on the road elevation data. Based on the target road information, the optimal target control current matching the target road information is determined in the suspension control law, and the optimal target control current is transmitted to the suspension system module so that the suspension system module performs open-loop control of the suspension based on the optimal target control current. The suspension control law includes various different road information and the optimal control current corresponding to each type of road information. The target road type includes continuous random roads or mixed roads including continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The target road information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal preview range. This method significantly enhances the suspension system's ability to perceive and classify complex road surface excitations by acquiring road elevation data in advance and classifying road excitations into continuous and mixed types for each type. Simultaneously, by acquiring road elevation data online and classifying roads into continuous and mixed types, and determining the optimal control current based on the relevant road information in the offline-determined suspension control law, this hierarchical control architecture of "offline global optimization - online parameter identification - real-time current tracking" effectively reduces the real-time computational load and computing power requirements of the embedded platform. Furthermore, by using the optimal control current as a direct output, it naturally aligns with the unidirectional energy dissipation characteristics of semi-active suspensions, eliminating the need for additional power conversion, thereby improving ride comfort and other driving characteristics under real-world conditions.

[0047] The above embodiments propose a process for classifying roads based on road elevation data using various methods. The following embodiments will explain the implementation of classification by grouping.

[0048] In one embodiment, step 204 above, which involves determining the target road type corresponding to the road within the longitudinal pre-aiming range based on road elevation data, may include: Step A1: Calculate the root mean square of the elevation values ​​of each road to obtain the root mean square of the elevation value corresponding to each road elevation value.

[0049] The aforementioned road elevation data includes multiple road elevation values, each of which corresponds to the vertical coordinate of each sampling point. After obtaining the vertical coordinates of multiple sampling points within the longitudinal pre-aiming range, the root mean square formula can be used to calculate the root mean square of the vertical coordinates of all sampling points, thus obtaining the root mean square of all sampling points, i.e., the root mean square of each road elevation value, denoted as the root mean square of the elevation value. .

[0050] Step A2: Traverse the road elevation values ​​according to the set window length of the sliding window, determine the multiple road elevation values ​​that fall within each sliding window, and calculate the standard deviation of the multiple road elevation values ​​within each sliding window to obtain the standard deviation corresponding to the multiple road elevation values ​​within each sliding window.

[0051] In this step, a window function can be used to slide through all sampling points. Specifically, a sliding window of a set length can be used to slide through each sampling point from its starting point, with no overlap between windows. This allows for the selection of sampling points that fall within each sliding window. Then, the standard deviation of the vertical coordinates of the sampling points falling within each sliding window can be calculated. For each sliding window, the mean of the vertical coordinates of the multiple sampling points falling within it can be calculated to obtain the mean value for that sliding window. Then, using the standard deviation calculation formula, the standard deviation of the multiple sampling points within the sliding window can be calculated using the vertical coordinates and the mean value of the sliding window. This gives the standard deviation of the multiple road elevation values ​​within each sliding window. The specific size of the set window length can be determined according to the actual situation, generally smaller than the length corresponding to the longitudinal preview range.

[0052] For example, taking a longitudinal preview range of 10 meters as an example, let the length of the window function be 0.5 meters, that is, set the window length to 0.5 meters. A total of 20 sliding windows are calculated within the 10-meter longitudinal preview range. The standard deviation of the sampling points in each sliding window can be calculated separately, and a total of 20 standard deviations are obtained.

[0053] Step A3: Based on the standard deviation and root mean square of the elevation values ​​of multiple roads within each sliding window, determine the target road type corresponding to a segment of road within each sliding window, and then determine the target road type corresponding to a segment of road within each sliding window as the target road type corresponding to the road within the longitudinal pre-aiming range.

[0054] In this step, the sampling point in each sliding window corresponds to a road segment within the longitudinal preview range. After obtaining the standard deviation of the sampling point in each sliding window and the root mean square of the elevation values ​​of all sampling points within the longitudinal preview range, the standard deviation of each sliding window can be directly compared with the root mean square of the elevation values ​​to determine the target road type corresponding to a road segment within each sliding window.

[0055] Alternatively, the standard deviation of each sliding window can be directly compared with a set multiple of the root mean square of the elevation values. Optionally, for each sliding window, if the standard deviations of multiple road elevation values ​​within the sliding window are less than or equal to the set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be a continuous random road; if the standard deviations of multiple road elevation values ​​within the sliding window are greater than the set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be a mixed road. The set multiple is a significance threshold preset based on statistical principles, used to identify abnormal situations where the elevation value of a sampling point is significantly higher than the elevation values ​​of other sampling points. Optionally, considering that the road elevation value signal exhibits Gaussian white noise characteristics, taking the 3sigma rule for road type classification as an example, the set multiple here can be 3. If the standard deviation of a sliding window is less than or equal to 3 times... If the standard deviation of a sliding window is greater than 3 times the standard deviation of the road segment, then the road segment within the sliding window does not contain discrete impact roads, and its corresponding target road type is a continuous random road; If so, it is determined that a segment of road within the sliding window contains discrete impact roads, and the corresponding target road type is a mixed road.

[0056] After determining the target road type corresponding to a segment of road within each sliding window, the vertical coordinates of each sliding window can be determined based on the vertical coordinates of the sampling points within each sliding window. For example, the vertical coordinates of the first or last sampling point within the sliding window, or the mean / median of the vertical coordinates of all sampling points within the sliding window, can be used as the vertical coordinates of the corresponding sliding window. Then, the target road types within all sliding windows and their respective vertical coordinates can be combined to obtain the target road type corresponding to the road within the vertical preview range, and the vertical coordinates of each sliding window can be recorded.

[0057] Furthermore, based on the standard deviation calculation for each sliding window, the relevant information of the target road corresponding to the target road type can be further determined based on the standard deviation of each sliding window, the vertical coordinates of the sampling points within each sliding window, and the target road type of the road within each sliding window.

[0058] Optionally, step 204 above, which involves determining the target road-related information corresponding to the target road type based on road elevation data, may include: For each sliding window, if the target road type corresponding to a segment of road within the sliding window is determined to be a continuous random road, the standard deviation of multiple road elevation values ​​within the sliding window is used as the target road information corresponding to a segment of road within the sliding window; the standard deviation of multiple road elevation values ​​within the sliding window corresponds to the road level corresponding to a segment of road within the sliding window. If the target road type corresponding to a segment of road within the sliding window is determined to be a mixed road, the road amplitude corresponding to the segment of road within the sliding window is determined based on multiple road elevation values ​​within the sliding window, and the road amplitude corresponding to the sliding window and / or the standard deviation corresponding to multiple road elevation values ​​within the sliding window are used as the relevant information of the target road corresponding to the segment of road within the sliding window.

[0059] The above method allows us to obtain the target road type corresponding to a road segment within each sliding window. If the target road type corresponding to a road segment within a certain sliding window is a continuous random road, then the standard deviation of that sliding window can be used as the relevant information for the target road within that sliding window. Here, there is a correlation between the standard deviation of the sampling points within the sliding window and the road grade corresponding to a road segment within the sliding window. This allows us to characterize the road grade of a road segment within the sliding window using the calculated standard deviation. The specific reason is that the variance of road surface roughness can generally be obtained by integrating the road's power spectral density with frequency, as shown in the following formula: ; in, The power spectral density represents the power of roads of different road grades, and the unit is m. 3 ; n represents the spatial frequency of the road, with the unit being m. -1 The pre-defined specifications stipulate that the spatial frequency range of roads is 0.011 to 2.83; W Represents the frequency index, generally W The range is 1.6 to 2.4, optionally, W =2; n0 represents the reference space frequency; n0=0.1m -1 , As a reference power spectral density for road classification, roads can be divided into multiple levels, such as eight levels from A to H. The higher the road level, the higher the power spectral density. The smaller the value.

[0060] The variance of road surface roughness is represented by the standard deviation, which is the square root of the variance. Therefore, there is a correlation between the standard deviation of road surface roughness and road grade. It can be used to solve for the reference power spectral density and serve as a standard for classifying road grades based on road surface roughness. Since the standard deviation of road surface elevation values ​​is only related to the vertical coordinates of the sampled point cloud and does not require frequency domain analysis, it is simpler to characterize road grades by calculating the standard deviation than to determine the road grade index by analyzing the power spectral density of the road surface roughness function from the frequency domain.

[0061] If a road segment within a sliding window corresponds to a mixed road type, the road amplitude within that segment is determined by the vertical coordinates of the sampling points within the sliding window. For example, the maximum value H in the vertical coordinates of the sampling points within the sliding window can be used. max The absolute value of the amplitude is taken as the road amplitude corresponding to a segment of road within the sliding window. Then, the road amplitude and / or the standard deviation corresponding to the sliding window can be used as the target road information corresponding to a segment of road within the sliding window.

[0062] The above method can be used to obtain the target road information corresponding to a segment of road within each sliding window in the longitudinal preview range. Then, the target road information corresponding to all sliding windows can be combined as the target road information corresponding to the road in the longitudinal preview range.

[0063] Furthermore, when performing real-time control of the vehicle suspension, if the road within the target sliding window is a continuous random road, the optimal control current is searched in the suspension control law based on the standard deviation of that road. If the road within the target sliding window is a mixed road including discrete impact roads, the following two cases apply: 1. If the target sliding window includes one discrete impact road, the optimal control current is searched in the suspension control law based on the standard deviation of the road amplitude of the discrete impact road and the continuous random road. 2. If the target sliding window includes multiple discrete impact roads, such as if there are still discrete impact roads after a certain discrete impact road, the optimal control current is searched among the results in the suspension control law where the standard deviation is 0, i.e., the optimal control current under the corresponding road amplitude is searched.

[0064] In this embodiment, the road type within each sliding window is determined by calculating the root mean square (RMS) of each elevation value within the pre-aiming range and the standard deviation of each sliding window. This allows for rapid and accurate classification of roads within the longitudinal pre-aiming range. Furthermore, determining the road type within each sliding window using the RMS of all elevation values ​​within the longitudinal pre-aiming range and the standard deviation of the elevation values ​​within each sliding window further improves the efficiency and accuracy of road classification. Moreover, by using the standard deviation of the sliding window as relevant information for finding the current in the suspension control law in the case of continuous roads, and using the amplitude and / or standard deviation of the sliding window as relevant information for finding the current in the suspension control law in the case of mixed roads, it is easier to quickly and accurately find the optimal control current corresponding to the target road information in the suspension control law.

[0065] The above embodiments briefly illustrate the process of establishing the suspension control law. The following embodiments will explain the specific implementation process of establishing the suspension control law.

[0066] Figure 3 This is the second flowchart of the open-loop control method for vehicle suspension based on road preview provided by the present invention, as shown below. Figure 3 As shown, the methods for determining the above suspension control law include: Step 302: Obtain the speed characteristic curves of the shock absorber in the suspension under different current excitations; the speed characteristic curves are the curves showing the changes in the speed and displacement of the shock absorber and the damping force under the corresponding current excitations.

[0067] In this step, before establishing the suspension control law, a two-degree-of-freedom suspension model can be established first. Based on vehicle dynamics theory, the dynamic equations and state-space equations of this model are derived. Specifically, taking a semi-active suspension as an example (which can also be called a semi-active shock absorber), and using a real quarter-vehicle semi-active suspension model as an example, this model is the smallest driving unit of the vehicle, including the suspension and tires. By theoretically abstracting and simplifying this model, its corresponding two-degree-of-freedom suspension model can be obtained. See [link / reference]. Figure 4 The schematic diagram of the semi-active vehicle suspension model shown is a two-degree-of-freedom suspension model that includes sprung mass elements, unsprung mass elements, suspension elastic elements, adjustable damping elements, and tire elastic elements. The mass of the sprung mass element is denoted as [missing information]. m b The mass of the unsprung mass element is denoted as m t The stiffness coefficient of the suspension elastic element is denoted as k s The stiffness coefficient of the tire's elastic element is denoted as... k t The displacement of the spring-loaded mass is denoted asz b The unsprung mass displacement is denoted as z t Road vibration amplitude is recorded as z r .

[0068] Since semi-active suspension has certain passive damping characteristics, it is modeled as a passive suspension. Based on the principles of vehicle vertical dynamics, the dynamic equations of the above semi-active suspension model are as follows: Where Cs represents the damping of the damping element, i.e., the damping of the suspension; a dot above the letter indicates the first derivative operation, such as the first derivative of the sprung mass displacement, and two dots above the letter indicate the second derivative operation, such as the second derivative of the sprung mass displacement.

[0069] Generalizing to a state-space equation, it can be expressed as: in, x It is a state vector. yes x The first derivative, w This is a disturbance quantity, corresponding to a quantity related to the excitation of road surface unevenness. y It is the output vector, representing the expected output of the system model during the design process. It is generally set to represent three basic parameters representing the suspension vibration: vehicle body acceleration / sprung acceleration (or discomfort parameter, BA), suspension dynamic deflection (or suspension dynamic travel, SWS), and tire dynamic deformation (or tire dynamic displacement, DTD). A 0、 B 0、 C These are the control coefficients. The variables in the above state-space equations can be expressed as follows: ; ; ; ; ; ; The ' denotes the transpose of the matrix. Let... k To control the time step, the discretized form of the state-space equation can be expressed as: ; in, x ( k )express k The state vector at time t, x ( k +1 indicates k The state vector at time +1 y ( k )expressk The output vector at time t, w ( k )express k The amount of disturbance at any given moment. A and B These are the control coefficients after discretization.

[0070] Based on the semi-active suspension model constructed above, taking a semi-active damper as an example, the mechanical response of the semi-active damper differs under different current excitations. Taking a magnetorheological damper as an example, when the electromagnetic coil inside the piston is energized, a radial magnetic field is generated. Micron-sized soft magnetic particles suspended in the fluid instantaneously form a chain-like structure along the magnetic field lines, causing the fluid to change from a Newtonian state to a near-solid state, thereby altering the shear yield stress and achieving adjustable damping force. Before establishing the suspension control law, a semi-active damper model can be established first. This requires using a dynamometer to measure the mechanical response of the damper under different current excitations. The mechanical response of the damper under each current excitation includes the damping force of the damper at different speeds and displacements under that current excitation. By using the damping forces of the damper at multiple different speeds and displacements under the same current excitation, the variation curves between the speed and displacement of the damper and the damping force under that current excitation can be constructed. By combining the variation curves between the speed and displacement of the damper and the damping force under all current excitations, the speed characteristic curves of the damper under different current excitations can be obtained. Taking a certain magnetorheological damper as an example, the velocity characteristic curves of this magnetorheological damper under different current excitations can be found in [reference needed]. Figure 5 The speed characteristic curves of the shock absorber under different current excitations shown can be seen that the greater the current excitation of the control suspension, the greater the range of damping force of the shock absorber.

[0071] Step 304: For each road excitation condition, the vibration of the suspension under the road excitation condition is simulated according to the speed characteristic curve under different current excitations to determine the vibration response of the suspension under each current excitation and road excitation condition. The vibration response includes multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under the corresponding current excitation and road excitation conditions. Different road excitation conditions correspond to different road grades and / or different road amplitudes.

[0072] In this step, we can first establish a road excitation model for the suspension system model. First, we determine the range of road grades commonly used by the vehicle, obtaining multiple road grades, and then establish stochastic road surface excitation models for these multiple road grades as the road excitation for the suspension system model. The road excitation models established for different road types can be different. For continuous stochastic roads, the following formula can be used to establish a continuous stochastic road excitation model as the road excitation for the subsequent suspension state equations: ; in,q The vertical height of the road. l The longitudinal length of the road. for q about l The differential or derivative, n i The road is divided into multiple sections, numbered as follows: i The center frequency of the interval, For the number i The interval is random variables within, for n i The power spectral density, I This represents the total number of intervals.

[0073] To facilitate the representation of road classification, let... d This is a road classification index, which is determined by the reference power spectral density of different road classifications. The solution obtained can be expressed as: ; For ISO Class A roads d =4; For ISO Class D roads d =10; and so on.

[0074] This embodiment takes urban passenger vehicles as an example. Assuming the commonly used road grades for this type of vehicle are A to D as defined in the specifications, and combining the formula of the aforementioned continuous random road excitation model, multiple (e.g., 11) continuous random road excitation models can be established using interpolation, constructing multiple road excitation conditions under continuous random roads. Of course, the actual road grades are not limited to A to D. This embodiment only uses road grades A to D for urban passenger vehicles as an example for illustration; suspension open-loop control under other road grades can also be performed using the method of this embodiment.

[0075] For discrete impact roads, pavement roughness models can also be established for each road grade, and their longitudinal profile formulas are shown below: ; in, q The vertical height of the road. l Let H be the longitudinal length of the road, and H be the amplitude of the discrete impact road. Assuming the longitudinal length of the discrete impact road is 0.5 meters (500 mm), and the road amplitude is set to 0 to 0.2 meters, then the range of H is [0, 200] mm. l The variation range is [0, 500] mm. In this way, multiple discrete impact road excitation conditions can be established for each road grade.

[0076] It is understandable that the road level is different for each road excitation condition under the multiple continuous random roads established above, and the road amplitude is different for each road excitation condition under the multiple discrete impact roads under each road level.

[0077] After constructing various road excitation conditions, the semi-active suspension system was optimized using NSGA-II (Non-Dominated Sorting Genetic Algorithms II). During optimization, the road excitation was fixed, with all road models averaging 1000 meters in length. The vibration response of the suspension under these road excitations was solved through simulation. The independent variable was set as the control current, and the dependent variables were parameters related to vehicle acceleration and tire dynamic deformation. The optimization objective was to minimize the dependent variable. Specifically, based on the speed characteristic curves constructed above under different current excitations, multiple damping forces under each current excitation were applied to the suspension under the same road excitation condition, and NSGA-II was used for simulation to obtain multiple vibration responses of the suspension under the same road excitation condition and the same current excitation. Each vibration response included the vehicle acceleration and / or tire dynamic deformation of the suspension under a specific damping force under that road excitation condition and current excitation. These responses, when combined, yielded multiple vehicle accelerations and / or multiple tire dynamic deformations under the same road excitation condition and the same current excitation.

[0078] For example, taking ISO Class A road incentives as an example, the multi-objective optimization results under ISO Class A road incentives can be found in [reference needed]. Figure 6 The diagram shown illustrates the results of the multi-objective optimization, where different points correspond to different control currents.

[0079] Step 306: Determine the optimal control current for controlling the suspension under road excitation conditions based on multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition.

[0080] In this step, the simulation results obtained above can yield multiple vehicle body accelerations and / or multiple tire dynamic deformations under the same road excitation condition and the same current excitation. These multiple vehicle body accelerations and / or multiple tire dynamic deformations under the same road excitation condition and the same current excitation can then be processed to determine the corresponding processing data under the same road excitation condition and the same current excitation. Based on the processing data of different current excitations under the same road excitation condition, a target processing data is selected, such as the minimum processing data among multiple processing data. The current excitation corresponding to this target processing data is then used as the optimal control current for the suspension under that road excitation condition.

[0081] Step 308: Obtain road-related information for different road excitation conditions, and establish a suspension control law based on the road-related information for different road excitation conditions and the optimal control current for controlling the suspension under different road excitation conditions.

[0082] In this step, by constructing different road excitation conditions, the road grade and / or road amplitude corresponding to each road excitation condition can be obtained. Then, the road grade and / or road amplitude corresponding to each road excitation condition can be used as road-related information for that road excitation condition. This road-related information is then bound to the optimal control current determined above for that road excitation condition to establish the suspension control law. It should be noted that the road grade in the road excitation conditions can also be represented using the standard deviation.

[0083] In this embodiment, the vibration response of the suspension under various road excitations is simulated by the variation curves of the shock absorber's speed and displacement with the damping force. Based on the simulation results, the optimal control current under each road excitation condition is selected, and the suspension control law is constructed by combining the road-related information of each road excitation condition. This allows for the construction of a more accurate suspension control law.

[0084] The above embodiments briefly illustrate the process of determining the optimal control current for controlling the suspension under each road excitation condition. The following embodiments will describe a specific implementation of determining the optimal control current for controlling the suspension under each road excitation condition.

[0085] In one embodiment, step 306 above, which determines the optimal control current for controlling the suspension under road excitation conditions based on multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition, includes: For each road excitation condition, the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation are calculated based on the multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition. Based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition, the optimal control current for controlling the suspension under the road excitation condition is determined among each current excitation.

[0086] The simulation results described above allow for the acquisition of multiple vehicle body accelerations and / or multiple tire dynamic deformations under the same road excitation and current excitation conditions. Then, root mean square (RMS) calculations can be performed on these multiple vehicle body accelerations and / or multiple tire dynamic deformations under the same road excitation and current excitation conditions to obtain the root mean square (RMS) of both vehicle body acceleration and / or tire dynamic deformation under the same road excitation and current excitation conditions. Subsequently, based on the RMS of vehicle body acceleration and / or tire dynamic deformation under different current excitations under the same road excitation condition, the optimal control current for each road excitation condition can be determined; alternatively, based on the RMS of vehicle body acceleration and / or tire dynamic deformation under different current excitations under the same road excitation condition, and considering the control requirements for the suspension under different road excitation conditions, the optimal control current for each road excitation condition can be determined.

[0087] The following explains the process of determining the optimal control current using the root mean square of vehicle body acceleration and / or the root mean square of tire dynamic deformation for two different road excitation conditions: continuous random road and mixed road including discrete impact road.

[0088] For road excitation conditions that are continuous random roads: Optionally, the above-mentioned determination of the optimal control current for controlling the suspension under road excitation conditions based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition includes: If the road excitation condition is a continuous random road type, then the control requirements for the suspension under the road excitation condition are determined according to the road excitation condition, and the selection object is determined from the root mean square of vehicle acceleration and the root mean square of tire dynamic deformation according to the control requirements for the suspension under the road excitation condition. Based on the selected object determined under the road excitation condition, determine the current excitation corresponding to the minimum selected object among the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation condition and the road excitation condition. The current excitation corresponding to the minimum value of the selected object is taken as the optimal control current for controlling the suspension under road excitation conditions.

[0089] The control requirements can be the expected performance indicators that the suspension control system needs to achieve, including but not limited to handling stability and comfort. Preferably, for urban passenger vehicle applications, adaptive switching is performed based on road surface grade and driving conditions: on smooth roads such as Class A or Class B as specified in ISO 8608, the control requirement is configured as / can be handling stability; on bumpy roads such as Class C or Class D as specified in ISO 8608, the control requirement is configured as / can be smoothness or comfort.

[0090] If the road excitation condition is a continuous random road, then this road excitation condition corresponds to a road grade. Different road grades result in different suspension vibration responses, and thus different control requirements for the suspension and the driving requirements for the vehicle. High-grade roads, due to their lower elevation and higher construction quality, generally have higher vehicle speeds, requiring higher demands for handling stability and safety. Conversely, low-grade roads are more bumpy, resulting in lower vehicle speeds and more noticeable vehicle vibration, necessitating reduced vehicle acceleration and improved ride comfort. Therefore, in this embodiment, corresponding suspension control requirements can be pre-set for different road grades. For high-grade road excitations, such as Class A roads, the suspension control requirements focus on handling stability; for low-grade road excitations, such as Class D roads, the suspension control requirements focus on ride comfort.

[0091] After obtaining each road excitation condition, the control requirements for that road excitation condition can be determined based on the corresponding road grade. These control requirements then determine which type of data, either the root mean square of vehicle acceleration or the root mean square of tire dynamic deformation, should be selected as the target data for that road excitation condition. After determining the target data, the minimum value can be identified from the root mean squares corresponding to multiple target data under different current excitations for that road excitation condition. The current excitation corresponding to this minimum value is then used as the optimal control current for controlling the suspension under that road excitation condition.

[0092] For example, for ISO Class A roads, where the control requirements of the suspension focus on handling stability, the tire dynamic deformation is more critical in determining the optimal control current. The root mean square of the tire dynamic deformation can be used as the selection target for the ISO Class A road. Then, the minimum root mean square of the tire dynamic deformation can be determined from multiple root mean squares of the tire dynamic deformation under multiple different current excitations on the ISO Class A road. The current excitation corresponding to the minimum root mean square of the tire dynamic deformation is then used as the optimal control current for controlling the suspension under the excitation conditions of that road.

[0093] The above method allows us to obtain the optimal control current for suspension control under various continuous random road excitation conditions. Then, using the road grade corresponding to each continuous random road excitation condition as the x-axis and the optimal control current as the y-axis, we establish the suspension control law corresponding to each continuous random road type. For details, please refer to [link to relevant documentation]. Figure 7The diagram shows the suspension control law corresponding to the continuous random road type. In it, I is the optimal control current in A, d is the road grade, ranging from 0 to 10, and H is the amplitude of the discrete impact road in m. For the continuous random road excitation condition, the road amplitude is generally taken as 0, that is, when H=0 it is a continuous random road (i.e., it does not contain discrete impact roads), and when H is not 0 it is a mixed road including discrete impact roads.

[0094] For mixed roads with road excitation conditions including discrete impact roads: Optionally, the above-mentioned determination of the optimal control current for controlling the suspension under road excitation conditions based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and road excitation condition includes: If the road excitation condition is a mixed road type including discrete impact roads, then the peak value of the vehicle body acceleration under each current excitation and road excitation condition is determined based on the multiple vehicle body accelerations of the suspension under each current excitation and road excitation condition. Based on the peak value and root mean square value of the vehicle body acceleration under each current excitation and road excitation condition, determine the vehicle body acceleration increment under each current excitation and road excitation condition. The current excitation corresponding to the minimum increase in vehicle acceleration is taken as the optimal control current for controlling the suspension under road excitation conditions.

[0095] If the road excitation condition is a mixed road including discrete impact road, the discrete impact road is a random impact independent of the continuous random road, such as speed bumps and potholes. The discrete impact road applies high-intensity vertical pulse excitation to the wheels in a very short time, inducing subjective discomfort of the occupants and organ-seat resonance, directly reducing the ride comfort of the vehicle. Therefore, when controlling the suspension under discrete impact road excitation, the goal should be to improve ride comfort. The vibration behavior of vehicles when passing through discrete impact roads is quite complex. When the damping is small, the peak value of sprung mass acceleration can be reduced, but the residual oscillation decays slowly after the impact, and the vehicle body produces a continuous "aftershock", resulting in a poor subjective evaluation. Conversely, when the damping is large, the high-frequency impact will be directly transmitted to the vehicle body, and the peak value of sprung mass acceleration and impact intensity will increase significantly, but the residual oscillation will disappear quickly. Therefore, the comfort evaluation index under discrete impact roads includes the peak value of vehicle body acceleration (i.e., peak value of sprung mass acceleration) and the root mean square value of vehicle body acceleration. Since discrete impact roads often coexist with continuous random roads in reality, the optimization strategy for hybrid roads including discrete impact roads in this embodiment is as follows: combine discrete impact roads with continuous random roads, and use the minimum product of the peak value of vehicle body acceleration and the root mean square value of vehicle body acceleration under this road segment as the index to calculate the optimal control current under the hybrid road.

[0096] Specifically, in the case of a mixed road excitation condition, this excitation condition includes discrete impact roads and continuous random roads. Discrete impact roads have corresponding road amplitudes, and continuous random roads have corresponding road grades. Therefore, this road excitation condition has corresponding road grades and road amplitudes. As explained above, in mixed road conditions, vehicle ride comfort is generally the control objective, meaning vehicle body acceleration is selected as a key consideration. Here, among the multiple vehicle body accelerations under each current excitation in this road excitation condition, the peak value of the vehicle body acceleration under that current excitation is selected first. Then, the peak value of the vehicle body acceleration under that current excitation is multiplied by the root mean square of the vehicle body acceleration. The resulting product is the vehicle body acceleration increment under that current excitation for this road excitation condition. This method allows obtaining the vehicle body acceleration increments under various current excitations for this road excitation condition. Then, the minimum vehicle body acceleration increment can be determined among these increments, and the current excitation corresponding to the minimum vehicle body acceleration increment is taken as the optimal control current for this road excitation condition, i.e., the optimal control current for that combination of road grade and road amplitude.

[0097] Following the above method, the optimal control current corresponding to various road grades and road amplitude combinations under various mixed road excitation conditions can be obtained. Then, based on the power spectral density function of the road excitation model for various continuous random road excitation conditions, the standard deviation corresponding to the road grade is calculated. The road grade in each combination of road grade and road amplitude is replaced with the calculated standard deviation of the response road grade. This yields the optimal control current corresponding to each standard deviation and road amplitude combination. Then, using the standard deviation as the x-axis and the optimal control current as the y-axis, a suspension control law corresponding to the mixed road type is established. For details, please refer to [link to relevant documentation]. Figure 8 The diagram shows the suspension control law for mixed road types, where I represents the optimal control current in amperes (A). The standard deviation, corresponding to road grade, ranges from 0 to 30 (in mm), and H represents the amplitude of discrete impact roads (in meters). H=0 indicates a continuous random road, while H≠0 indicates a mixed road including discrete impact roads. It is understandable that here... Figure 8 The suspension control law in the continuous random road is the suspension control law, where the curve with H=0 is the suspension control law under the continuous road excitation condition.

[0098] In this embodiment, by calculating the root mean square (RMS) of vehicle acceleration and / or the RMS of tire dynamic deformation for each road condition under each current excitation, the optimal control current for that road condition is selected from each current excitation based on these two parameters. This allows for the rapid construction of the suspension control law. Furthermore, under continuous road conditions, the optimal control current is selected based on the control requirements of the road excitation condition, specifically using the RMS of vehicle acceleration and / or the RMS of tire deformation. Then, the current excitation corresponding to the minimum RMS of vehicle acceleration and / or tire deformation is selected as the optimal control current. This allows for the rapid determination of the optimal control current under various continuous random road conditions, facilitating the rapid establishment of the suspension control law corresponding to continuous random road conditions. Further, under mixed road conditions, by calculating the RMS of vehicle acceleration and the peak value of vehicle acceleration for each road condition under each current excitation, the optimal control current for that road condition is selected from each current excitation based on their product. This allows for the rapid determination of the optimal control current under various mixed road conditions, facilitating the rapid establishment of the suspension control law corresponding to mixed road conditions.

[0099] Based on a 10-meter pre-aiming distance, the following comparisons are made using the same hardware and platform on continuous and mixed roads at ISO Class A and ISO Class B, comparing the current passive suspension control results, the semi-active suspension control results using MPC, and the semi-active suspension control results of this embodiment of the invention. See the table below for details:

[0100] It can be seen that the control / operation results (i.e., the root mean square value of vehicle acceleration under continuous roads and the peak value of vehicle acceleration under mixed roads) of the suspension open-loop control method of this embodiment are not significantly different from those of the semi-active suspension control using MPC, but are significantly better than the control results of the passive suspension, proving the feasibility of the semi-active suspension control method of this embodiment. Meanwhile, the average operating time / computation time (0.03s) of the suspension open-loop control method of this embodiment at a 10-meter preview distance is much smaller than the average operating time / computation time (0.72s) of the semi-active suspension control using MPC, proving that the semi-active suspension control method of this embodiment can effectively reduce the computational load and achieve better results.

[0101] The open-loop control device for vehicle suspension based on road preview provided by the present invention will be described below. The open-loop control device for vehicle suspension based on road preview described below can be referred to in correspondence with the open-loop control method for vehicle suspension based on road preview described above.

[0102] Figure 9 This is a schematic diagram of the open-loop control device for vehicle suspension based on road preview provided by the present invention. See also: Figure 9As shown, the device may include: The road depth information perception and calculation module 910 is used to acquire road elevation data corresponding to the road within the longitudinal preview range of the vehicle; The road type classification module 920 is used to determine the target road type and related target road information corresponding to the road within the longitudinal pre-aiming range based on road elevation data. The target road type includes continuous random roads and / or mixed roads. Mixed roads are a combination of continuous random roads and discrete impact roads. Continuous random roads and mixed roads have different control requirements for the suspension. The related target road information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal pre-aiming range. The suspension controller module 930 is used to determine the target optimal control current that matches the target road information in the suspension control law based on the target road information, and transmit the target optimal control current to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the suspension control law includes a variety of different road information and the optimal control current corresponding to each type of road information.

[0103] In one embodiment, the road elevation data includes multiple road elevation values. The road type classification module 920 is specifically used to perform root mean square calculation on each road elevation value to obtain the root mean square of the elevation value corresponding to each road elevation value; to traverse each road elevation value according to a sliding window of a set window length to determine multiple road elevation values ​​falling within each sliding window, and to calculate the standard deviation of the multiple road elevation values ​​within each sliding window to obtain the standard deviation corresponding to the multiple road elevation values ​​within each sliding window; based on the standard deviation and root mean square of the elevation values ​​corresponding to the multiple road elevation values ​​within each sliding window, to determine the target road type corresponding to a segment of road within each sliding window, and to determine the target road type corresponding to a segment of road within each sliding window as the target road type corresponding to the road within the longitudinal pre-aiming range.

[0104] Optionally, the road type classification module 920 is specifically used for each sliding window to determine the target road type of a segment of road in the sliding window as a continuous random road if the standard deviation of multiple road elevation values ​​in the sliding window is less than or equal to a set multiple of the root mean square of the elevation values; and to determine the target road type of a segment of road in the sliding window as a mixed road if the standard deviation of multiple road elevation values ​​in the sliding window is greater than a set multiple of the root mean square of the elevation values.

[0105] Optionally, the road type classification module 920 is specifically used for each sliding window. If the target road type corresponding to a segment of road within the sliding window is determined to be a continuous random road, the standard deviation of multiple road elevation values ​​within the sliding window is used as the target road information corresponding to that segment of road within the sliding window. The standard deviation of multiple road elevation values ​​within the sliding window corresponds to the road grade corresponding to a segment of road within the sliding window. If the target road type corresponding to a segment of road within the sliding window is determined to be a mixed road, the road amplitude corresponding to that segment of road within the sliding window is determined based on the multiple road elevation values ​​within the sliding window, and the road amplitude and / or the standard deviation of multiple road elevation values ​​within the sliding window are used as the target road information corresponding to that segment of road within the sliding window.

[0106] In one embodiment, the above-mentioned apparatus further includes: The speed characteristic curve acquisition module is used to acquire the speed characteristic curves of the shock absorber in the suspension under different current excitations; the speed characteristic curves are the curves showing the changes in the speed and displacement of the shock absorber and the damping force under the corresponding current excitations. The simulation module is used to simulate the vibration of the suspension under different road excitation conditions based on the speed characteristic curves under different current excitations for each road excitation condition, and to determine the vibration response of the suspension under each current excitation and road excitation condition. The vibration response includes multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under the corresponding current excitation and road excitation conditions. Different road excitation conditions correspond to different road grades and / or different road amplitudes. The optimal control current determination module is used to determine the optimal control current for controlling the suspension under road excitation conditions based on multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and road excitation condition. The suspension control law establishment module is used to acquire road-related information under different road excitation conditions, and establish the suspension control law based on the road-related information under different road excitation conditions and the optimal control current for controlling the suspension under different road excitation conditions.

[0107] Optionally, the aforementioned optimal control current determination module is specifically used to, for each road excitation condition, perform root mean square calculations based on multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under each current excitation and road excitation condition to determine the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation of the suspension under each current excitation and road excitation condition; and based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation of the suspension under each current excitation and road excitation condition, determine the optimal control current for controlling the suspension under the road excitation condition in each current excitation.

[0108] Optionally, the aforementioned optimal control current determination module is specifically used to determine the control requirements for the suspension under the road excitation conditions if the road excitation conditions are continuous random road types. Based on these road excitation conditions, it determines the selection target from the root mean square of vehicle acceleration and the root mean square of tire dynamic deformation. Based on the selection target determined under the road excitation conditions, it determines the current excitation corresponding to the minimum value of the selection target among the root mean square of vehicle acceleration and / or the root mean square of tire dynamic deformation under each current excitation condition and the road excitation conditions. The current excitation corresponding to the minimum value of the selection target is then used as the optimal control current for controlling the suspension under the road excitation conditions.

[0109] Optionally, the aforementioned optimal control current determination module is specifically used to determine the peak value of the vehicle body acceleration under each current excitation and road excitation condition if the road excitation condition is a mixed road type including discrete impact roads; determine the increment of the vehicle body acceleration under each current excitation and road excitation condition based on the peak value and root mean square of the vehicle body acceleration under each current excitation and road excitation condition; and take the current excitation corresponding to the minimum increment of vehicle body acceleration as the optimal control current for controlling the suspension under the road excitation condition.

[0110] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0111] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 10As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communications bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other through the communications bus 1040. The processor 1010 can call logic instructions in the memory 1030 to execute a vehicle suspension open-loop control method based on road preview. This method includes: acquiring road elevation data corresponding to roads within the vehicle's longitudinal preview range; determining, based on the road elevation data, the target road type corresponding to the road within the longitudinal preview range and target road-related information corresponding to the target road type; the target road type includes continuous random roads and / or mixed roads, where a mixed road includes both continuous random roads and discrete impact roads, and the continuous random roads and mixed roads have different control requirements for the suspension; the target road-related information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal preview range; based on the target road-related information, determining a target optimal control current matching the target road-related information in the suspension control law, and transmitting the target optimal control current to the suspension system module so that the suspension system module performs open-loop control of the suspension based on the target optimal control current; the suspension control law includes multiple different road-related information and the optimal control current corresponding to each type of road-related information.

[0112] Furthermore, the logical instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the open-loop control method for vehicle suspension based on road preview provided by the above methods. The method includes: acquiring road elevation data corresponding to roads within the longitudinal preview range of the vehicle; determining, based on the road elevation data, the target road type corresponding to the road within the longitudinal preview range and target road-related information corresponding to the target road type; the target road type includes continuous random roads and / or mixed roads, wherein the mixed road is a mixed road including continuous random roads and discrete impact roads, and the continuous random roads and mixed roads have different control requirements for the suspension; the target road-related information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal preview range; determining, based on the target road-related information, a target optimal control current matching the target road-related information in the suspension control law, and transmitting the target optimal control current to the suspension system module so that the suspension system module performs open-loop control of the suspension based on the target optimal control current; the suspension control law includes multiple different road-related information and the optimal control current corresponding to each type of road-related information.

[0114] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the open-loop control method for vehicle suspension based on road preview provided by the methods described above. This method includes: acquiring road elevation data corresponding to roads within the longitudinal preview range of the vehicle; determining, based on the road elevation data, a target road type corresponding to the road within the longitudinal preview range and target road-related information corresponding to the target road type; the target road type includes continuous random roads and / or mixed roads, wherein a mixed road is a hybrid road including continuous random roads and discrete impact roads, and the continuous random roads and mixed roads have different control requirements for the suspension; the target road-related information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal preview range; determining, based on the target road-related information, a target optimal control current matching the target road-related information in the suspension control law, and transmitting the target optimal control current to the suspension system module, so that the suspension system module performs open-loop control of the suspension based on the target optimal control current; the suspension control law includes multiple different road-related information and an optimal control current corresponding to each type of road-related information.

[0115] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle suspension open-loop control method based on road preview, characterized in that, include: Obtain road elevation data corresponding to the road within the longitudinal preview range of the vehicle; Based on the road elevation data, the target road type corresponding to the road within the longitudinal pre-aiming range and the target road related information corresponding to the target road type are determined; the target road type includes continuous random roads and / or mixed roads, the mixed road is a mixed road including continuous random roads and discrete impact roads, the continuous random roads and the mixed roads have different control requirements for the suspension; the target road related information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal pre-aiming range. Based on the target road information, a target optimal control current matching the target road information is determined in the suspension control law, and the target optimal control current is transmitted to the suspension system module so that the suspension system module performs open-loop control of the suspension based on the target optimal control current; the suspension control law includes multiple different road information and the optimal control current corresponding to each road information.

2. The vehicle suspension open-loop control method based on road preview as described in claim 1, characterized in that, The road elevation data includes multiple road elevation values. Determining the target road type corresponding to the road within the longitudinal pre-aiming range based on the road elevation data includes: Root mean square calculation is performed on each of the road elevation values ​​to obtain the root mean square elevation value corresponding to each of the road elevation values. The system iterates through the road elevation values ​​according to a sliding window of a set window length, determines the multiple road elevation values ​​that fall within each sliding window, and calculates the standard deviation of the multiple road elevation values ​​within each sliding window to obtain the standard deviation corresponding to the multiple road elevation values ​​within each sliding window. Based on the standard deviation and root mean square of the multiple road elevation values ​​within each sliding window, the target road type corresponding to a segment of road within each sliding window is determined, and the target road type corresponding to a segment of road within each sliding window is determined as the target road type corresponding to the road within the longitudinal pre-aiming range.

3. The vehicle suspension open-loop control method based on road preview as described in claim 2, characterized in that, The step of determining the target road type corresponding to a segment of road within each sliding window based on the standard deviation of multiple road elevation values ​​within each sliding window and the root mean square of the elevation values ​​includes: For each sliding window, if the standard deviation of multiple road elevation values ​​within the sliding window is less than or equal to a set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be the continuous random road. If the standard deviation of multiple road elevation values ​​within the sliding window is greater than a set multiple of the root mean square of the elevation values, then the target road type corresponding to a segment of road within the sliding window is determined to be the mixed road.

4. The open-loop control method for vehicle suspension based on road preview as described in claim 2 or 3, characterized in that, The step of determining the target road-related information corresponding to the target road type based on the road elevation data includes: For each sliding window, if the target road type corresponding to a segment of road within the sliding window is determined to be the continuous random road, the standard deviation of multiple road elevation values ​​within the sliding window is used as the target road related information corresponding to a segment of road within the sliding window; the standard deviation of multiple road elevation values ​​within the sliding window corresponds to the road grade corresponding to a segment of road within the sliding window. If the target road type corresponding to a segment of road within the sliding window is determined to be the mixed road, the road amplitude corresponding to the segment of road within the sliding window is determined based on multiple road elevation values ​​within the sliding window, and the road amplitude corresponding to the sliding window and / or the standard deviation corresponding to multiple road elevation values ​​within the sliding window are used as the target road related information corresponding to the segment of road within the sliding window.

5. The vehicle suspension open-loop control method based on road preview according to any one of claims 1 to 3, characterized in that, The methods for determining the suspension control law include: Obtain the speed characteristic curves of the shock absorber in the suspension under different current excitations; the speed characteristic curves are the curves showing the changes in the speed and displacement of the shock absorber with the damping force under the corresponding current excitations; For each road excitation condition, the vibration of the suspension under the road excitation condition is simulated according to the speed characteristic curve under different current excitations, and the vibration response of the suspension under each current excitation and the road excitation condition is determined. The vibration response includes multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under the corresponding current excitation and road excitation conditions. Different road excitation conditions correspond to different road grades and / or different road amplitudes. Based on multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under each current excitation and the road excitation condition, determine the optimal control current for controlling the suspension under the road excitation condition. Obtain road-related information under different road excitation conditions, and establish the suspension control law based on the road-related information under different road excitation conditions and the optimal control current for controlling the suspension under different road excitation conditions.

6. The vehicle suspension open-loop control method based on road preview as described in claim 5, characterized in that, The step of determining the optimal control current for controlling the suspension under the road excitation condition based on multiple vehicle body accelerations and / or multiple tire dynamic deformations under each current excitation and the road excitation condition includes: For each road excitation condition, the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation are calculated based on the multiple vehicle body accelerations and / or multiple tire dynamic deformations of the suspension under each current excitation and the road excitation condition. Based on the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation of the suspension under each current excitation and the road excitation condition, the optimal control current for controlling the suspension under the road excitation condition is determined among the current excitations.

7. The vehicle suspension open-loop control method based on road preview as described in claim 6, characterized in that, The step of determining the optimal control current for controlling the suspension under the road excitation condition based on the root mean square of the vehicle acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and the road excitation condition includes: If the road excitation condition is a continuous random road type, then the control requirements for the suspension under the road excitation condition are determined according to the road excitation condition, and the selection object is determined from the root mean square of vehicle acceleration and the root mean square of tire dynamic deformation according to the control requirements for the suspension under the road excitation condition. Based on the selected object determined under the road excitation condition, determine the current excitation corresponding to the minimum of the selected object among the root mean square of the vehicle body acceleration and / or the root mean square of the tire dynamic deformation of the suspension under each current excitation condition and the road excitation condition. The current excitation corresponding to the minimum selected object is taken as the optimal control current for controlling the suspension under the road excitation condition.

8. The open-loop control method for vehicle suspension based on road preview as described in claim 6, characterized in that, The step of determining the optimal control current for controlling the suspension under the road excitation condition based on the root mean square of the vehicle acceleration and / or the root mean square of the tire dynamic deformation under each current excitation and the road excitation condition includes: If the road excitation condition is a mixed road type including discrete impact roads, then the peak value of the vehicle body acceleration under each current excitation and the road excitation condition is determined based on the multiple vehicle body accelerations of the suspension under each current excitation and the road excitation condition. Based on the peak value and root mean square value of the vehicle body acceleration under each current excitation and road excitation condition, the vehicle body acceleration increment under each current excitation and road excitation condition is determined. The current excitation corresponding to the minimum vehicle body acceleration increment is taken as the optimal control current for controlling the suspension under the road excitation condition.

9. A vehicle suspension open-loop control system based on road preview, characterized in that, include: The road depth information perception and calculation module is used to obtain road elevation data corresponding to the road within the longitudinal preview range of the vehicle; The road type classification module is used to determine the target road type and related target road information corresponding to the road within the longitudinal pre-aiming range based on the road elevation data. The target road type includes continuous random roads and / or mixed roads. The mixed road is a hybrid road that includes both continuous random roads and discrete impact roads. The continuous random roads and the mixed roads have different control requirements for the suspension. The related target road information is used to characterize the road grade and / or road amplitude corresponding to the road within the longitudinal pre-aiming range. The suspension controller module is used to determine the target optimal control current that matches the target road-related information in the suspension control law based on the target road-related information, and to transmit the target optimal control current to the suspension system module so that the suspension system module can perform open-loop control of the suspension based on the target optimal control current; the suspension control law includes a variety of different road-related information and the optimal control current corresponding to each type of road-related information.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the open-loop control method for vehicle suspension based on road preview as described in any one of claims 1 to 8.

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