High-speed flying suppression method and control system for electric vehicle in low-gravity environment

Abstract

The invention discloses an electric vehicle high-speed flight suppression method and a control system in a low-gravity environment, and relates to the technical field of star catalogue detection vehicle motion control, and the method comprises the steps: collecting topographic data in front of an electric vehicle, and extracting topographic parameters; inputting the terrain parameters into the two-degree-of-freedom vertical motion model after low-gravity correction to obtain the normal force of each wheel of the electric vehicle, and performing air risk prediction according to the normal force of each wheel of the electric vehicle and the actual speed of the electric vehicle acquired in real time to judge whether to trigger air early warning; during triggering, vehicle body posture data are collected in real time, a flight suppression strategy is triggered according to the actual vehicle speed, the terrain parameters and the front terrain data to adjust the vehicle speed and the motor output torque of the vehicle, and flight suppression is achieved. The method is suitable for high-speed driving stability control of the wheeled electric vehicle in the low-gravity environment in the complex terrain.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of star probe vehicle motion control, and more particularly to a high-speed take-off suppression method and control system for electric vehicles in a low-gravity environment. BACKGROUND

[0002] The existing wheel-type electric vehicles suitable for low-gravity environments are all low-speed cruisers with a speed of no more than 400 m / h, and the movement process can be approximately regarded as quasi-static force, without involving the take-off suppression problem. However, with the speed of the wheel-type electric vehicles suitable for low-gravity environments increasing (to more than 10 km / h), high-speed stable driving on low-gravity rugged terrain becomes very important. In a low-gravity field, rugged terrain has a great impact on vehicle stability, and the take-off phenomenon caused by vertical impact can greatly reduce the driving safety of the vehicle. There is little research in this field at present, and the vehicle take-off suppression method often has to be based on the second-order spring oscillator model widely used for traditional ground vehicles, but such an approach has the following problems:

[0003] (1) Insufficient adaptability to low-gravity environments; the existing model does not consider the segmented influence characteristics of the low-gravity field on the wheel normal load, which leads to the inability to break through the critical force state in vibration analysis, resulting in certain model mismatch.

[0004] (2) Lack of terrain prediction capability; the traditional method relies on suspension feedback signals for passive adjustment, and cannot sense the terrain fluctuation characteristics within a range of 5-10 m ahead, resulting in response delay.

[0005] (3) Single control dimension; take-off is suppressed only by suspension damping adjustment, without active prevention in combination with speed-terrain matching strategies.

[0006] Therefore, how to effectively suppress the high-speed take-off of electric vehicles in a low-gravity environment is a problem that needs to be solved by those skilled in the art. SUMMARY

[0007] In view of the above problems, the present application is proposed to provide a high-speed take-off suppression method and control system for electric vehicles in a low-gravity environment, which can effectively suppress the high-speed take-off of electric vehicles in a low-gravity environment and improve the stability of the vehicle.

[0008] To achieve the above purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a high-speed take-off suppression method for electric vehicles in a low-gravity environment, comprising the following steps: S1: collecting terrain data in front of the electric vehicle and extracting terrain parameters; S2: Input the terrain parameters into the two-degree-of-freedom vertical motion model after low gravity correction to obtain the normal force of each wheel of the electric vehicle. Based on the normal force of each wheel and the real-time collected actual speed of the electric vehicle, generate a risk prediction result for the airborne risk and determine whether an airborne warning is triggered. If an airborne warning is triggered, proceed to S3; otherwise, drive normally and proceed to S1. S3: Real-time acquisition of vehicle attitude data of electric vehicles, combined with actual vehicle speed, front terrain data and terrain parameters to trigger airborne suppression strategy, adjust the vehicle speed and motor output torque of electric vehicles to suppress high-speed airborne movement of electric vehicles.

[0009] Preferably, in S1, a time-of-flight camera is used to scan the terrain ahead at a set frequency to obtain terrain data, and point cloud data is generated based on the terrain data. After the point cloud data is registered, feature matching is performed with a prior database to extract terrain parameters. The terrain parameters include: slope, curvature, and ground adhesion coefficient.

[0010] Preferably, in S2, the two-degree-of-freedom vertical motion model after low-gravity correction is expressed as: when At that time, there were: ; in, Indicates the switching threshold distance; Indicates the mass of the wheel; Indicates the vehicle's mass; as well as These represent the vertical displacement, vertical velocity, and vertical acceleration of the wheel, respectively. , as well as These represent the vertical displacement of the vehicle body, the vertical velocity of the vehicle body, and the vertical acceleration of the vehicle body, respectively. Indicates the wheel reference displacement; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient; when At that time, there were: ; The formula for calculating the normal force of each wheel of an electric vehicle is as follows: ; in, Indicates the first The normal force of each wheel; This represents the acceleration due to gravity in a low-gravity environment. Indicates the total mass of the electric vehicle; This represents the overall stiffness coefficient of an electric vehicle, which is related to the wheel stiffness coefficient and the vehicle body suspension stiffness coefficient. This represents the overall damping coefficient of an electric vehicle, which is related to the suspension damping coefficient. and The first Vertical displacement and vertical velocity of each wheel; This represents the change in ground elevation caused by topographic relief, calculated from the slope parameter in the terrain. ; This indicates the vertical velocity of the ground relative to the wheels when an electric vehicle is in motion.

[0011] Preferably, in S2, the airborne risk prediction includes: when the normal force of all wheels is greater than or equal to a set threshold, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state, returning to S1 for continuous monitoring; when the normal force of any wheel is lower than the set threshold, a risk prediction result is generated based on the low-gravity corrected two-degree-of-freedom vertical motion model, and a decision is made on whether to trigger an airborne warning based on the risk prediction result; the risk prediction result includes the airborne moment, ground clearance height, and duration. If the ground clearance height at the airborne moment exceeds a preset height threshold, and the duration exceeds a preset time limit threshold, an airborne warning is triggered; otherwise, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state, returning to S1 for continuous monitoring; the specific process for calculating the airborne moment, ground clearance height, and duration is as follows: S21: The two-degree-of-freedom vertical motion model after low-gravity correction is transformed into a state-space equation. as follows: ; ; ; in, Represents state variables, ; Indicates control variables, ; Represents the coefficient matrix of state variables; Represents the coefficient matrix of the control variables; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient; Indicates the mass of the wheel; Indicates the vehicle's mass; S22: State-space equations Discretize; Set the distance from the walk to be The backward Euler method is used to discretize the coefficient matrix of the state variables and the coefficient matrix of the control variables; make: to replace ; in, and They represent the first Step and the first The state variables of the step; Indicates the first The control variables of the step; The following discrete recurrence relation is obtained, and the discretized state-space equation is expressed as: ; ; ; in, and They represent the first Step and the first Discrete state variables of the step; and These represent the discrete state variable coefficient matrix and the discrete control variable coefficient matrix, respectively. This represents the control variable at step k; Represents the identity matrix. Represents a variable structure matrix; when Sometimes, ; when Sometimes, ; S23: Calculate the takeoff time, ground clearance, and duration based on the discretized state-space equation; calculate the longitudinal reference speed based on the actual vehicle speed; iteratively update the discrete state variables of the discretized state-space equation using the longitudinal reference speed; generate the corresponding ground clearance reference value; take the moment when the ground clearance reference value is greater than 0 as the takeoff time; take the maximum value of the ground clearance reference value as the ground clearance; and take the time interval when the ground clearance reference value changes from greater than 0 to less than or equal to 0 as the duration.

[0012] Preferably, the specific implementation process of S3 is as follows: S31: Real-time acquisition of vehicle attitude data and actual vehicle speed; S32: Determine the road conditions ahead based on the terrain data ahead; S33: Calculate the maximum safe speed based on terrain parameters; S34: Generate a takeoff suppression strategy based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed.

[0013] Preferably, the vehicle attitude data includes pitch angle and roll angle. When the pitch angle or roll angle is greater than a set angle threshold, the secondary response control command in the takeoff suppression strategy is triggered.

[0014] Preferably, the peak value of the road within the field of view is determined by the changes in terrain undulation based on the terrain data ahead. The specific process for determining the road conditions ahead based on the peak value of the road within the field of view is as follows: If the number of road peaks is 1, then record the distance from the current position of the electric vehicle to the peak point where the highest point of the road peaks represented by the terrain undulations within the field of view is located. If the number of road peaks is 2, then record the distance between the two road peaks. , which is the distance between the peak points of two road peaks; if the number of road peaks is n and n is greater than 2, then record . Average distance between the peak points of each road peak The recorded distance is used as the feature distance parameter. Average distance It is expressed as follows: ; in, Indicates the first From the first peak point to the second The distance between the peak points; Compare feature distance parameters Vehicle wheelbase compared to electric vehicles The relative size between them; if If the signal is positive, it is determined to be a long-wave road condition; otherwise, it is determined to be a short-wave road condition.

[0015] The preferred formula for calculating the maximum safe speed is as follows: ; in, Indicates the maximum safe speed. Indicates the ground adhesion coefficient. This represents the approximate radius of curvature of the long-wave path. This indicates the height of the vehicle's center of gravity. The radius of curvature is obtained by calculating the reciprocal of the curvature.

[0016] Preferably, the takeoff suppression strategy in S3 includes: When the road conditions ahead are determined to be long-wave conditions, a speed control command is generated based on the maximum safe speed to keep the actual speed below the maximum safe speed. When the road conditions ahead are determined to be shortwave conditions, the vehicle's motor response or graded trigger response is initiated based on the maximum safe speed, generating motor output torque control commands or graded response control commands; the specific process is as follows: When the actual vehicle speed is lower than the maximum safe vehicle speed, at the moment the air-to-ground warning is triggered, a motor output torque control command is generated based on the motor output torque at the moment the air-to-ground warning is triggered, so that the motor output torque at the moment the air-to-ground warning is triggered is not greater than the maximum motor output torque, thereby suppressing the motor output torque; When the actual vehicle speed is higher than or equal to the maximum safe vehicle speed, the graded trigger response is as follows: Level 1 response control command: Reduce motor output torque and collect actual vehicle speed within a first set time. When the actual vehicle speed drops below the maximum safe speed, maintain the current motor output torque. When the actual vehicle speed does not drop below the maximum safe speed, activate Level 2 response. Level 2 response control command: Directly reduce vehicle speed and collect actual vehicle speed within a second set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque and vehicle speed. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 3 response. Level 3 response control command: Control the motor to brake and stop, and send alarm information.

[0017] Secondly, embodiments of the present invention provide a high-speed airborne suppression control system for electric vehicles in low-gravity environments, comprising: The data acquisition module collects terrain data in front of the electric vehicle; The terrain prediction module extracts terrain parameters based on the terrain data ahead. The takeoff prediction module deploys a two-degree-of-freedom vertical motion model after low gravity correction, calculates the normal force of each wheel of the electric vehicle based on terrain parameters, and performs takeoff risk prediction based on the normal force to obtain the risk prediction result, determines whether a takeoff warning is triggered, and starts the two-stage controller if a takeoff warning is triggered. Inertial measurement unit (IMU) is used to collect vehicle attitude data and actual vehicle speed. The two-level controller triggers the air-launch suppression strategy based on vehicle attitude data, actual vehicle speed, and terrain data ahead. The vehicle drive motor controller regulates the output torque and vehicle speed of the vehicle drive motor according to the air-to-ground suppression strategy.

[0018] Preferably, it also includes a maximum safe speed calculation module to calculate the maximum safe speed based on terrain parameters.

[0019] Preferably, it also includes a priori database for storing typical terrain feature parameters to assist the terrain prediction module in extracting terrain parameters and the dual-level controller in identifying terrain features.

[0020] Preferably, the two-level controller includes a terrain assessment calculation controller and a vehicle motion planning controller; the terrain assessment calculation controller is used to identify terrain features based on the typical terrain feature parameters stored in the prior database using the terrain data ahead, to determine the road conditions ahead, and to obtain the road conditions; the vehicle motion planning controller triggers an airborne suppression strategy based on the maximum safe vehicle speed, combined with road conditions, vehicle attitude data and actual vehicle speed.

[0021] Preferably, the terrain prediction module generates point cloud data based on the terrain data ahead, registers the point cloud data and performs feature matching with the prior database to extract terrain parameters, including: slope, curvature and ground adhesion coefficient.

[0022] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and control system for suppressing high-speed airborne movement of electric vehicles in low-gravity environments, which has the following advantages: (1) Based on the actual low gravity environment, a two-degree-of-freedom vertical motion model with low gravity correction was adopted to accurately estimate the normal force, which is applicable to low gravity environments such as the Moon and Mars.

[0023] (2) By collecting terrain data in front of the electric vehicle, the response speed is improved by actively adjusting the response.

[0024] (3) The vehicle speed and motor output torque were adjusted based on terrain data, air-to-ground risk prediction results and vehicle attitude data, which improved the control dimension and implemented proactive prevention strategies. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment, provided in an embodiment of the present invention. Figure 2 This is a diagram of the takeoff suppression response process provided in an embodiment of the present invention; Figure 3 This is a block diagram of a high-speed airborne suppression control system for electric vehicles in a low-gravity environment provided in an embodiment of the present invention. Figure 4 This is a long-wave road condition characteristic curve provided in an embodiment of the present invention; Figure 5The following diagrams are provided in the embodiments of the present invention: (a) is a diagram of wheel dynamic load under Earth's gravity; (b) is a diagram of suspension dynamic travel under Earth's gravity; and (c) is a diagram of wheel ground clearance under Earth's gravity. Figure 6 The following diagrams are provided in the embodiments of the present invention: (a) is a diagram of wheel dynamic load under low gravity conditions; (b) is a diagram of suspension dynamic travel under low gravity conditions; and (c) is a diagram of wheel ground clearance under low gravity conditions. Figure 7 The following are motion state curves of an uncontrolled vehicle provided in this embodiment of the invention: (a) is a wheel dynamic load curve of an uncontrolled vehicle; (b) is a suspension dynamic travel curve of an uncontrolled vehicle; and (c) is a wheel ground clearance curve of an uncontrolled vehicle. Figure 8 The following are motion state curves of a vehicle with a control system provided in the embodiments of the present invention: (a) is a wheel dynamic load curve of a vehicle with a control system; (b) is a suspension dynamic travel curve of a vehicle with a control system; and (c) is a wheel ground clearance curve of a vehicle with a control system. Figure 9 This is a time-of-flight curve provided in an embodiment of the present invention; Figure 10 This is a bar chart showing the optimized hang time ratio provided in this embodiment of the invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] This invention discloses a method for suppressing high-speed airborne movement of electric vehicles in low-gravity environments, such as... Figure 1 As shown, the specific steps are as follows: S1: Collect terrain data in front of the electric vehicle and extract terrain parameters; S2: Input the terrain parameters into the two-degree-of-freedom vertical motion model after low gravity correction to obtain the normal force of each wheel of the electric vehicle. Based on the normal force of each wheel and the real-time collected actual speed of the electric vehicle, perform airborne risk prediction to generate risk prediction results, determine whether airborne warning is triggered, if airborne warning is triggered, proceed to S3, otherwise drive normally and proceed to S1. S3: Real-time acquisition of vehicle attitude data of electric vehicles, combined with actual vehicle speed, front terrain data and terrain parameters to trigger airborne suppression strategy, adjust the vehicle speed and motor output torque of electric vehicles to suppress high-speed airborne movement of electric vehicles.

[0029] In one specific embodiment, in S1, a time-of-flight camera is used to scan the terrain ahead at a set frequency to obtain terrain data ahead, and point cloud data is generated based on the scanned terrain data ahead. After the point cloud data is registered, feature matching is performed with a prior database to extract terrain parameters, including slope, curvature and ground adhesion coefficient.

[0030] In one specific embodiment, the two-degree-of-freedom vertical motion model after low gravity correction in S2 is as follows: when Sometimes: ; in, Indicates the switching threshold distance. Indicates the mass of the wheel. Indicates vehicle body mass. as well as These represent the vertical displacement, vertical velocity, and vertical acceleration of the wheel, respectively. , as well as These represent the vertical displacement of the vehicle body, the vertical velocity of the vehicle body, and the vertical acceleration of the vehicle body, respectively. Indicates the wheel reference displacement; Indicates the wheel stiffness coefficient. This indicates the vehicle suspension stiffness coefficient. Indicates the suspension damping coefficient; when Sometimes: ; The formula for calculating the normal force of each wheel of an electric vehicle is as follows: ; in, Indicates the first The normal force of each wheel This represents the acceleration due to gravity in a low-gravity environment. This indicates the total mass of the electric vehicle. This represents the overall stiffness coefficient of an electric vehicle, which is related to the wheel stiffness coefficient and the vehicle body suspension stiffness coefficient. This represents the overall damping coefficient of an electric vehicle, which is related to the suspension damping coefficient. and They represent the first Vertical displacement and vertical velocity of each wheel This represents the change in ground elevation caused by terrain undulations, calculated based on point cloud data and slope. , This indicates the vertical velocity of the ground relative to the wheels when an electric vehicle is in motion.

[0031] Furthermore, the process of predicting the risk of airborne injury based on the normal force of each wheel is as follows: When the normal force of all wheels is greater than or equal to the set threshold, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state. The system returns to S1 for continuous monitoring. When the normal force of any wheel is lower than the set threshold, a risk prediction result is generated based on the low-gravity corrected two-degree-of-freedom vertical motion model. The system then determines whether to trigger an airborne warning based on the risk prediction result. The risk prediction result includes the airborne moment, ground clearance, and duration. If the ground clearance at the airborne moment exceeds a preset height threshold and the duration exceeds a preset time limit threshold, an airborne warning is triggered; otherwise, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state. The system returns to S1 for continuous monitoring. The calculation methods for the airborne moment, ground clearance, and duration are as follows: S21: Based on the two-degree-of-freedom vertical motion model modified for low gravity, it is transformed into a state-space equation. The state-space equations are as follows: ; Wherein, state variables represent Control variables represent ; State variable coefficient matrix and control variable coefficient matrix The expressions are as follows: ; ; S22: After selecting the discretization step size, the above formula is discretized using the backward Euler method, i.e., using... to replace ; The following discrete recurrence relation is obtained: ; in, ; ; It is a variable structure matrix; defining the matrix for: ; when Sometimes, ,when Sometimes, ; S23: At the time of takeoff warning, the state variables are calculated through the above discrete recursive relationship. The longitudinal reference speed is calculated based on the actual vehicle speed. The discrete state variables of the discretized state space equation are updated iteratively using the longitudinal reference speed, and the corresponding ground reference value is generated. The moment when the ground reference value is greater than 0 is taken as the takeoff moment. The maximum value of the ground reference value is taken as the ground height. The time interval when the ground reference value changes from greater than 0 to less than or equal to 0 is taken as the duration.

[0032] The calculation process is as follows: S231: Let the number of iterations be... =1, record the longitudinal reference speed at the current moment based on the actual vehicle speed. Record the current discrete state variables as Define the first flag bit. Second tag bit ;Calculate the longitudinal reference speed based on the actual vehicle speed using existing estimation algorithms; S232: Based on the distance from the walk length and the current longitudinal reference speed Calculate the position at the next moment The calculation formula is as follows: ; The position at the next moment Represents the distance ahead relative to the current electric vehicle; S233: Calculate terrain elevation-distance data from point cloud data generated based on the terrain data ahead, and determine the position at the next moment based on the terrain elevation-distance data. The elevation value at the next moment is obtained by linear interpolation using the independent variable. And set the control variable at step k to the elevation value at the next moment. Based on the point cloud data in S1, terrain elevation-distance data are obtained by considering only the longitudinal undulation state. S234: Based on the control variables of step k And the discrete state variables from the previous step Update the discrete state variables at step k. And calculate the ground reference value at step k. , is represented as: ; Let C represent the ground reference value at step k; C represents the row vector, C=[1,0,0,0]; X represents the discrete state variable. S235: Update ,renew ; S236: If ,determination Check if the condition is true or false. If not, return to S232; if true, record the condition. And modify the flag bit ; S237: If ,determination Check if the condition is true or false. If not, return to S232; if true, record the condition. And modify the flag bit ; S238: The Moment of Takeoff Height above ground Duration This constitutes the risk prediction results.

[0033] In one specific embodiment, the specific implementation process of S3 is as follows: S31: Real-time acquisition of vehicle attitude data and actual vehicle speed; S32: Determine the road conditions ahead based on the terrain data ahead; S33: Calculate the maximum safe speed based on terrain parameters; S34: Generate a takeoff suppression strategy based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed.

[0034] Furthermore, such as Figure 2 As shown, the system determines the peak value of the road within the field of view based on the terrain data ahead and the changes in terrain undulation. Road conditions are then assessed based on the peak value of the road within the visible area. If the peak value is 1, the system records the distance from the current position of the electric vehicle to the highest point of the road peak value represented by the terrain undulation within the field of view. If the number of road peaks is 2, then record the distance between the two road peaks. , which is the distance between the peak points of two road peaks; if the number of road peaks is n and n is greater than 2, then record . Average distance between the peak points of each road peak The recorded distance is used as the feature distance parameter. Average distance It is expressed as follows: ; in, Indicates the first From the first peak point to the second The distance between the peak points; Compare feature distance parameters Vehicle wheelbase compared to electric vehicles Relative size; if If the signal is positive, it is determined to be a long-wave road condition; otherwise, it is determined to be a short-wave road condition.

[0035] Furthermore, vehicle attitude data is collected through an inertial measurement unit, including pitch and roll angles. When the pitch or roll angle exceeds a set angle threshold, a secondary response control command in the takeoff suppression strategy is triggered.

[0036] Furthermore, the formula for calculating the maximum safe speed is as follows: ; in, Indicates the maximum safe speed. Indicates surface roughness. This represents the radius of curvature of the long-wave road condition calculated based on the curvature. This indicates the height of the vehicle's center of gravity.

[0037] Furthermore, a takeoff suppression strategy is generated based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed. This strategy includes: When the road conditions ahead are determined to be long-wave conditions, keep the actual speed below the maximum safe speed. When the road conditions ahead are determined to be shortwave conditions, the vehicle's motor response or graded trigger response is initiated based on the maximum safe speed, generating motor output torque control commands or graded response control commands; the specific process is as follows: When the actual vehicle speed is lower than the maximum safe vehicle speed, the motor output torque is suppressed at the moment the air-to-ground warning is triggered, so that the motor output torque at the moment the air-to-ground warning is triggered is not greater than the maximum motor output torque. When the actual vehicle speed is higher than or equal to the maximum safe vehicle speed, the graded trigger response is as follows: Level 1 response control command: Reduce motor output torque by more than 30%, and collect actual vehicle speed within a first set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 2 response. Level 2 response control command: Directly reduce vehicle speed and collect actual vehicle speed within a second set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque and vehicle speed. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 3 response. Level 3 response control command: Control the motor to brake and stop, and send alarm information.

[0038] In this embodiment, active deceleration is used instead of emergency braking, which reduces system power consumption by 40%.

[0039] On the other hand, such as Figure 3As shown, the control system based on the above-mentioned method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment includes: The data acquisition module collects terrain data in front of the electric vehicle; The terrain prediction module transforms and extracts terrain parameters based on the terrain data ahead; The takeoff prediction module deploys a two-degree-of-freedom vertical motion model after low gravity correction, calculates the normal force of each wheel of the electric vehicle based on terrain parameters, and performs takeoff risk prediction based on the normal force to obtain the risk prediction result, determines whether a takeoff warning is triggered, and starts the two-stage controller if a takeoff warning is triggered. Inertial measurement unit (IMU) is used to collect vehicle attitude data and actual vehicle speed. The two-level controller triggers the air-to-air suppression strategy based on vehicle attitude data, actual vehicle speed, terrain parameters, and terrain data ahead. The vehicle drive motor controller regulates the output torque and vehicle speed of the vehicle drive motor according to the air-to-ground suppression strategy.

[0040] Furthermore, it also includes a maximum safe speed calculation module, which calculates the maximum safe speed based on terrain parameters.

[0041] Furthermore, a priori database is used to store typical terrain feature parameters to assist the terrain prediction module in extracting terrain parameters and the two-stage controller in identifying terrain features; typical terrain feature parameters include friction coefficient coverage. Slope Coverage wait.

[0042] Furthermore, the two-level controller includes a terrain assessment calculation controller and a vehicle motion planning controller; the terrain assessment calculation controller is used to identify terrain features based on the typical terrain feature parameters stored in the prior database using the terrain data ahead, to determine the road conditions ahead, and to obtain the road conditions; the vehicle motion planning controller generates an airborne suppression strategy based on the maximum safe speed, road conditions, vehicle attitude data, and actual vehicle speed.

[0043] Furthermore, the terrain prediction module generates point cloud data based on the terrain data ahead, registers the point cloud data and performs feature matching with the prior database to extract terrain parameters, including slope, curvature and ground adhesion coefficient.

[0044] Furthermore, the data acquisition module employs a time-of-flight camera to scan the terrain ahead and acquire terrain point cloud data; A wide-angle TOF camera (1024×768 resolution, 30Hz frame rate) acquires elevation data within 15m in front of the camera in real time.

[0045] On the other hand, to verify the effectiveness of the technical solution of this invention, system function testing and verification based on hardware-in-the-loop simulation were conducted. The test scenario was implemented using a high-fidelity digital terrain device, and the relevant controllers used prototypes with the same computing power. Long-wave paths at different altitudes were selected for testing based on terrain features, as shown in the following description. Figure 4 As shown. To verify the differentiated effects of low gravity and ground gravity on vehicle airborne characteristics, impact tests were conducted under both conventional and low gravity conditions. Significant differences were observed, such as... Figure 5 and Figure 6 As shown, the horizontal axis represents time; Figure 5 Figure (a) shows the dynamic load curve of the wheel under Earth's gravity conditions. Figure 6 (a) is a curve of wheel dynamic load under low gravity conditions, where the vertical axis represents wheel dynamic load; Figure 5 Figure (b) shows the suspension dynamic travel curve under Earth's gravity conditions. Figure 6 (b) is a graph of the suspension dynamic travel under low gravity conditions, where the vertical axis represents the suspension dynamic travel; Figure 5 (c) is a graph showing the wheel's height above the ground under Earth's gravity. Figure 6 (c) is a graph showing the wheel height above the ground under low gravity conditions, where the vertical axis represents the wheel height above the ground. Figure 5 and Figure 6 This demonstrates the rationality of the two-degree-of-freedom model with modification terms in this invention.

[0046] like Figure 7 and Figure 8 As shown, the horizontal axis represents time; Figure 7 (a) is a graph showing the wheel dynamic load curves of a vehicle without a control system. Figure 8 (a) is a graph of wheel dynamic loads for a vehicle with a control system, where the vertical axis represents the wheel dynamic load. Figure 7 (b) is a diagram showing the suspension travel curve of a vehicle without a control system. Figure 8 (b) is a graph of the suspension travel curve of a vehicle with a control system, where the vertical axis represents the suspension travel. Figure 7 (c) is a graph showing the wheel height from the ground for a vehicle without a control system. Figure 8 (c) is a graph showing the wheel height from the ground of a vehicle with a control system, where the vertical axis represents the wheel height from the ground. Figure 7 and Figure 8 The results demonstrate that vehicles equipped with the control system of this invention can effectively improve their motion characteristics on rough terrain compared to vehicles without a control system, effectively reduce the maximum wheel ground clearance and hang time, and reduce suspension travel, thereby reducing mechanical shock. Key performance indicators comparing vehicles with and without a control system are shown in Table 1.

[0047] Table 1 Comparison of Key Indicators with and without Control Systems (Table 1)

[0048] Statistical results show that the optimization efficiency for hovering time is highest at a vehicle speed of 3 m / s. As the vehicle speed increases, the optimization ratio gradually decreases and stabilizes above 40%. Figure 9 and Figure 10 As shown, Figure 9 The horizontal axis represents vehicle speed, and the vertical axis represents time spent in the air. Figure 10 The horizontal axis represents vehicle speed, and the vertical axis represents the optimization ratio of air time, indicating that the technical solution of the present invention can effectively reduce the wheel lift-off time, which plays an important role in the high-speed, stable and safe driving of vehicles.

[0049] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0050] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for suppressing high-speed levitation of an electric vehicle in a low-gravity environment, characterized by, The method comprises the following steps: S1: collecting terrain data in front of the electric vehicle and extracting terrain parameters; S2: inputting the terrain parameters into a low-gravity corrected two-degree-of-freedom vertical motion model to obtain normal forces of each wheel of the electric vehicle, and generating a risk prediction result according to the normal forces of each wheel and the actual speed of the electric vehicle collected in real time to determine whether to trigger a takeoff warning, if the takeoff warning is triggered, S3 is entered, otherwise, the electric vehicle is normally driven and S1 is entered; S3: collecting vehicle body attitude data of the electric vehicle in real time, and triggering a takeoff suppression strategy in combination with the actual speed, the terrain data in front and the terrain parameters to adjust the speed of the electric vehicle and the output torque of the motor.

2. The method of claim 1, wherein, In S1, a time-of-flight camera is used to scan the terrain in front at a set frequency to obtain the terrain data in front, and point cloud data is generated according to the terrain data in front, and after registration, feature matching is performed with a prior database to extract terrain parameters; The terrain parameters include: slope, curvature and ground adhesion coefficient.

3. The method of claim 1, wherein the method is characterized by: In S2, the low-gravity corrected two-degree-of-freedom vertical motion model is represented as: When there is: ； wherein denotes a switching threshold distance; denotes a wheel mass; denotes a body mass; and denote a vertical displacement of the wheel, a vertical velocity of the wheel and a vertical acceleration of the wheel, respectively; , and denote a vertical displacement of the body, a vertical velocity of the body and a vertical acceleration of the body, respectively; denotes a wheel reference displacement; denotes a wheel stiffness coefficient; denotes a body suspension stiffness coefficient; denotes a suspension damping coefficient; When there is: ； The normal forces of each wheel of the electric vehicle are represented as: ； wherein, represents the normal force of the th wheel; represents the gravity acceleration in a low gravity environment; represents the total mass of the electric vehicle; represents the comprehensive stiffness coefficient of the electric vehicle, which is related to the wheel stiffness coefficient and the body suspension stiffness coefficient; represents the comprehensive damping coefficient of the electric vehicle, which is related to the suspension damping coefficient; and are the vertical displacement and the vertical velocity of the th wheel, respectively; represents the ground height variation caused by the terrain undulation, which is calculated according to the point cloud data and the slope in the terrain parameters ; represents the vertical velocity of the ground relative to the wheel when the electric vehicle is running.

4. The method of claim 3, wherein the method is characterized by: In S2, when the normal forces of all wheels are greater than or equal to a set threshold, the takeoff warning is not triggered; when the normal force of any one wheel is less than the set threshold, a risk prediction result is generated according to the low-gravity corrected two-degree-of-freedom vertical motion model, and whether to trigger the takeoff warning is determined according to the risk prediction result; the risk prediction result includes the takeoff time, the takeoff height and the duration; if the takeoff height at the takeoff time exceeds a preset height threshold and the duration exceeds a preset time limit threshold, the takeoff warning is triggered, otherwise, the takeoff warning is not triggered; the specific process of calculating the takeoff time, the takeoff height and the duration is as follows: S21: converting the low-gravity-corrected two-degree-of-freedom vertical motion model into a state space equation, the state space equation being as follows: ； ； ； wherein, denotes a state variable; denotes a control variable, ; denotes a state variable coefficient matrix; denotes a control variable coefficient matrix; denotes a wheel stiffness coefficient; denotes a body suspension stiffness coefficient; denotes a suspension damping coefficient; denotes a wheel mass; denotes a body mass; S22: discretize the state space equation S22: discretize the state space equation The discrete step is set as The state variable coefficient matrix and the control variable coefficient matrix are discretized by using the backward Euler method, and the discretized state space equation is represented as ； ； ； wherein, and denote the discrete state variable of the kth step and the (k-1)th step, respectively; denote the discrete state variable of the kth step and the (k-1)th step, respectively; denote the discrete state variable of the kth step and the (k-1)th step, respectively; and denote the discrete state variable of the kth step and the (k-1)th step, respectively; denote the control variable of the kth step; denote the identity matrix, denote the variable structure matrix; When time, there ; When time, there ; S23: calculating the takeoff time, the takeoff height and the duration according to the discretized state space equation; calculating the longitudinal reference speed according to the actual speed, and iteratively updating the discrete state variables of the discretized state space equation using the longitudinal reference speed, and generating a corresponding takeoff reference value; taking the time when the takeoff reference value is greater than 0 as the takeoff time, taking the maximum value of the takeoff reference value as the takeoff height, and taking the time interval when the takeoff reference value changes from greater than 0 to less than or equal to 0 as the duration.

5. The method of claim 2, wherein the method is characterized by: The specific implementation process of S3 is as follows: S31: collecting the vehicle body attitude data in real time; S32: determining the road condition in front according to the terrain data in front to obtain the road condition; S33: calculating the highest safe speed according to the terrain parameters; S34: generating a takeoff suppression strategy according to the road condition, the vehicle body attitude data, the actual speed and the highest safe speed.

6. The method of claim 5, wherein the method further comprises: The vehicle body attitude data includes the pitch angle and the roll angle, and the takeoff suppression strategy is triggered when the pitch angle or the roll angle is greater than a set angle threshold.

7. The method of claim 5, wherein the method is characterized by: The specific process of determining the road peak value in the viewing area according to the terrain fluctuation change according to the terrain data in front is as follows: If the number of road peaks is 1, record the distance from the current position of the electric vehicle to the peak point where the highest point of the road peak is located If the number of peaks is 2, record the distance between the peak points of the two road peaks If the number of peaks is n and n is greater than 2, record the average distance between the peak points of the n road peaks Record the recorded distance as a characteristic distance parameter ;​ Comparative feature distance parameter Size of the wheelbase of the electric vehicle If Then, the long wave road condition is determined, otherwise the short wave road condition is determined.

8. The method of claim 5, wherein the method is characterized by: The highest safe speed calculation formula is as follows: ； wherein represents the maximum safe vehicle speed, represents the ground adhesion coefficient, represents the radius of curvature of the long-wave approximation, represents the height of the vehicle body center of gravity.

9. The method of claim 7, wherein the method is characterized by: The takeoff suppression strategy in S3 includes: When it is determined that the road condition in front is a long-wave road condition, a speed control instruction is generated according to the highest safe speed to keep the actual speed below the highest safe speed; When the front road condition is determined to be a short-wave road condition, the vehicle is subjected to motor response or hierarchical trigger response according to the maximum safe speed, and motor output torque control instructions or hierarchical response control instructions are generated; the specific process is as follows: When the actual speed is lower than the maximum safe speed, at the time of the emptying early warning trigger, the motor output torque control instruction is generated according to the motor output torque at the time of the emptying early warning trigger, so that the motor output torque at the time of the emptying early warning trigger is not greater than the maximum motor output torque, and the motor output torque is suppressed; When the actual speed is higher than or equal to the maximum safe speed, the hierarchical trigger response is as follows: First-level response control instruction: reduce the motor output torque, and collect the actual speed within a first set time; when the actual speed is reduced to below the maximum safe speed, the current motor output torque is maintained; when the actual speed is not reduced to below the maximum safe speed, a second-level response is started; Second-level response control instruction: directly reduce the speed, and collect the actual speed within a second set time; when the actual speed is reduced to below the maximum safe speed, the current motor output torque and speed are maintained; when the actual speed is not reduced to below the maximum safe speed, a third-level response is started; Third-level response control instruction: control the motor to brake and stop, and send an alarm information.

10. A high-speed lift suppression control system for an electric vehicle in a low-gravity environment, characterized by, The low-gravity environment electric vehicle high-speed emptying suppression method of any one of claims 1-9 comprises: a data acquisition module for acquiring front terrain data of the electric vehicle; a terrain prediction module for extracting terrain parameters according to the front terrain data; an emptying prediction module for deploying a low-gravity corrected two-degree-of-freedom vertical motion model, calculating the normal force of each wheel of the electric vehicle according to the terrain parameters, and performing emptying risk prediction according to the normal force to obtain a risk prediction result, and determining whether to trigger an emptying early warning, and starting a two-stage controller if the emptying early warning is triggered; an inertial measurement unit for collecting vehicle attitude data and actual speed; a two-stage controller for triggering an emptying suppression strategy according to the vehicle attitude data, the actual speed, the terrain parameters and the front terrain data; a vehicle drive motor controller for regulating and controlling the output torque and speed of the vehicle drive motor according to the emptying suppression strategy.

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