Vehicle suspension control method, device, electronic device and storage medium

Abstract

The present invention relates to a vehicle suspension control method, device, electronic device and storage medium. The vehicle suspension control method includes: obtaining obstacle information and obstacle recognition confidence of a pre-aimed obstacle obtained by pre-aiming and identifying the vehicle's driving road; determining the optimal control force of the actuator of the suspension in the vehicle based on the obstacle information; determining the target confidence interval to which the obstacle recognition confidence belongs in a plurality of preset confidence intervals; and determining the actual control force of the suspension based on the target confidence interval and the optimal control force. The embodiment of the present application can perform graded control of the actual output force of the suspension based on different obstacle recognition confidences, so that the control of the actual output force of the suspension matches the obstacle recognition confidence, so that the control of the actual output force of the suspension is more in line with the actual driving conditions of the vehicle, thereby improving vehicle comfort, improving the smoothness of the vehicle when passing through obstacles, and saving energy consumption of suspension control.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle active suspension control, and in particular to a vehicle suspension control method, device, electronic device and storage medium. Background Art

[0002] When a vehicle travels on urban paved roads, individual impacts such as speed bumps, potholes, cracks, and road repairs can significantly impact ride comfort. Electronically controlled suspensions adjust suspension force in real time based on vehicle dynamics information input from sensors to mitigate road impacts and reduce vehicle body jolts. However, since these control actions are based on vehicle information that has already been altered by road input, there is a lag in the control. This lag is amplified by delays in sensor information transmission, controller calculations, and actuator response, resulting in poor control effectiveness and reduced ride comfort. To mitigate this lag, feedforward control is applied to the suspension. Active suspensions with visual preview use a visual preview system to detect road surface information ahead and provide input to the suspension controller, which then adjusts the suspension accordingly.

[0003] However, traditional preview suspension control systems may be unable to accurately identify obstacles. When they mistakenly identify existing obstacles, they have no control action on possible pulse road surfaces, and the preview control system cannot play its due role and performance. Alternatively, when they mistakenly identify non-existent obstacles, the use of fully active suspension actuation will produce reverse excitation, resulting in worse comfort, and it is impossible to balance the risk of misidentification and the need for smoothness. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a vehicle suspension control method, device, electronic device and storage medium.

[0005] In a first aspect, the present application provides a vehicle suspension control method, comprising:

[0006] Obtaining obstacle information and obstacle recognition confidence of the pre-aimed obstacles obtained by pre-aiming and identifying the road surface on which the vehicle is traveling;

[0007] determining an optimal control force of an actuator of a suspension in the vehicle according to the obstacle information;

[0008] Determining a target confidence interval to which the obstacle recognition confidence belongs among a plurality of preset confidence intervals;

[0009] An actual control force on the suspension is determined according to the target confidence interval and the optimal control force.

[0010] Optionally, the obstacle information includes: a longitudinal distance of the obstacle, a vertical height of the obstacle, a longitudinal width of the obstacle, and a time correction parameter. Determining the optimal control force of the actuator of the suspension in the vehicle based on the obstacle information includes:

[0011] Determining a road elevation curve according to the longitudinal distance of the obstacle, the vertical height of the obstacle, and the longitudinal width of the obstacle;

[0012] Converting the road elevation curve from the spatial domain to road excitation information in the time domain based on the current time correction parameter;

[0013] Inputting the road excitation information into a quarter model of the vehicle suspension system to obtain suspension dynamic performance parameters;

[0014] The optimal control force of the actuator is determined according to the suspension dynamic performance parameters and a preset optimal control algorithm.

[0015] Optionally, determining the actual control force of the suspension according to the target confidence interval and the optimal control force includes:

[0016] determining a first suspension control mode for the suspension according to the target confidence interval, and activating the first suspension control mode;

[0017] Acquiring a current suspension speed of the suspension, and determining a current maximum damping force and a current minimum damping force of a shock absorber of the suspension according to the current suspension speed;

[0018] When the first suspension control mode is a non-silent control mode, an actual control force of the suspension is determined based on the optimal control force, the maximum damping force, and the minimum damping force.

[0019] Optionally, if the target confidence interval is the first confidence interval, and the first suspension control mode is a fully active suspension control mode, determining the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes:

[0020] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force, and controlling the front wheel actuator of the vehicle to output an actuating force according to the difference between the optimal control force and the maximum damping force;

[0021] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force, and the front wheel actuator of the vehicle is controlled to output the actuating force according to the sum of the optimal control force and the maximum damping force.

[0022] Optionally, if the target confidence interval is a second confidence interval, a maximum boundary of the second confidence interval is smaller than a minimum boundary of the first confidence interval, and the first suspension control mode is a semi-active suspension control mode; determining the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes:

[0023] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force;

[0024] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force.

[0025] Optionally, if the target confidence interval is a third confidence interval, and a maximum boundary of the third confidence interval is smaller than a minimum boundary of the second confidence interval, the first suspension control mode is a low-gain semi-active suspension control mode; and determining the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes:

[0026] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the optimal control force and a preset confidence coefficient to obtain a first output force, and controlling the shock absorber of the suspension to output a damping force according to the first output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the maximum damping force and a preset confidence coefficient to obtain a second output force, and controlling the shock absorber of the suspension to output a damping force according to the second output force;

[0027] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the minimum damping force and a preset confidence coefficient is calculated to obtain a third output force, and the shock absorber of the suspension is controlled to output the damping force according to the third output force.

[0028] Optionally, if the target confidence interval is a fourth confidence interval, the maximum boundary of the fourth confidence interval is smaller than the minimum boundary of the third confidence interval, and the first suspension control mode is a high-gain semi-active suspension control mode; determining the actual control force of the vehicle suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes:

[0029] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the square of a preset confidence coefficient and the optimal control force to obtain a fourth output force, and controlling the shock absorber of the suspension to output a damping force according to the fourth output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the square of a preset confidence coefficient and the maximum damping force to obtain a fifth output force, and controlling the shock absorber of the suspension to output a damping force according to the fifth output force;

[0030] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the square of the preset confidence coefficient and the minimum damping force is calculated to obtain a sixth output force, and the shock absorber of the suspension is controlled to output the damping force according to the sixth output force.

[0031] Optionally, the method further includes:

[0032] obtaining a plurality of first unsprung accelerations of the front wheel lower control arm within a first window period;

[0033] determining whether the vehicle is traveling on a pulse road surface containing a pulse obstacle based on a plurality of the first unsprung accelerations;

[0034] If it is determined that the vehicle has traveled on a pulse road surface containing a pulse obstacle, performing spatiotemporal consistency matching between the preview obstacle and the pulse obstacle based on a plurality of first unsprung accelerations within the first window period and the obstacle information;

[0035] If the temporal and spatial consistency of the preview obstacle and the pulse obstacle is successfully matched, a time correction parameter is updated according to a plurality of first unsprung accelerations within the first window period, and the obstacle recognition confidence is updated according to a preset update rule.

[0036] Optionally, determining whether the vehicle has traveled on a pulse road surface containing a pulse obstacle based on the plurality of first unsprung accelerations includes:

[0037] extracting the first unsprung acceleration in a second window period from the plurality of first unsprung accelerations in the first window period, the second window period being within the first window period;

[0038] determining a first acceleration steady-state value within a long-term window based on the first unsprung acceleration within the first window period;

[0039] determining a second acceleration steady-state value within a short time window according to the first unsprung acceleration within the second window period;

[0040] determining whether a ratio of the first acceleration steady-state value to the second acceleration steady-state value is greater than or equal to a preset steady-state threshold;

[0041] If the ratio is greater than or equal to a preset threshold, it is determined that the front wheels of the vehicle are traveling on a pulse road surface.

[0042] Optionally, performing spatiotemporal consistency matching on the preview obstacle and the pulse obstacle based on the plurality of first unsprung accelerations and the obstacle information within the first window period includes:

[0043] determining a pulse edge moment when a wheel travels onto a pulse obstacle based on a plurality of first unsprung accelerations within the first window period;

[0044] If the pulse edge moment is within a preset time range determined based on the obstacle information and the dynamic error of the obstacle longitudinal distance conforms to a normal distribution, determining a first concave-convex attribute of the preview obstacle based on the obstacle information, and determining a second concave-convex attribute of the pulse obstacle based on a plurality of first unsprung accelerations within the first window period;

[0045] determining whether the first concave-convex attribute is consistent with the second concave-convex attribute;

[0046] If the first concave-convex attribute is consistent with the second concave-convex attribute, it is determined that the spatiotemporal consistency matching between the preview obstacle and the pulse obstacle is successful.

[0047] Optionally, determining a first concave-convex attribute of the preview obstacle according to the obstacle information, and determining a second concave-convex attribute of the pulse obstacle according to a plurality of first unsprung accelerations within the first window period includes:

[0048] determining a first concave-convex attribute of the preview obstacle according to the obstacle type in the obstacle information;

[0049] calculating an average acceleration of the first unsprung acceleration within a second window period in the first window period;

[0050] A second concave-convex attribute of the pulse obstacle is determined according to the positive or negative sign of the acceleration mean value.

[0051] Optionally, updating the time correction parameter according to a plurality of first unsprung accelerations within the first window period includes:

[0052] determining a first moment when a wheel travels onto a pulse obstacle based on a plurality of first unsprung accelerations within the first window period;

[0053] estimating a second moment when the wheel travels onto the predicted obstacle based on the obstacle information;

[0054] Calculating a time difference between the first moment and the second moment, and updating the time correction parameter based on the time difference;

[0055] A confidence range corresponding to the obstacle recognition confidence is determined, and the obstacle recognition confidence is updated according to an update rule corresponding to the confidence range.

[0056] In a second aspect, the present application provides a vehicle suspension control device, comprising:

[0057] An acquisition module is used to obtain obstacle information and obstacle recognition confidence of the obstacle obtained by previewing the vehicle's driving road;

[0058] a first determining module, configured to determine an optimal control force of an actuator of a suspension in the vehicle according to the obstacle information;

[0059] A second determining module is configured to determine a target confidence interval to which the obstacle recognition confidence belongs from a plurality of preset confidence intervals;

[0060] A third determination module is configured to determine an actual control force on the suspension according to the target confidence interval and the optimal control force.

[0061] In a third aspect, the present application provides an electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0062] Memory for storing computer programs;

[0063] The processor is configured to implement any vehicle suspension control method described in the first aspect when executing a program stored in the memory.

[0064] In a fourth aspect, the present application provides a computer-readable storage medium, on which a program of a vehicle suspension control method is stored. When the program of the vehicle suspension control method is executed by a processor, the steps of the vehicle suspension control method described in any one of the first aspects are implemented.

[0065] Beneficial effects of the present invention:

[0066] The embodiment of the present application determines the optimal control force of the suspension actuator by utilizing obstacle information obtained by previewing and identifying the vehicle's driving road surface, determines a target confidence interval from multiple preset confidence intervals using the obstacle recognition confidence obtained by previewing and identifying, and then determines the actual control force of the suspension based on the target confidence interval and the optimal control force. By setting multiple candidate preset confidence intervals, hierarchical control of the actual output force of the suspension is achieved according to different obstacle recognition confidences, so that the control of the actual output force of the suspension is matched with the obstacle recognition confidence, so that the control of the actual output force of the suspension is more in line with the actual driving conditions of the vehicle, thereby improving vehicle comfort, improving the smoothness of the vehicle when passing through obstacles, and saving energy consumption of suspension control. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0069] Figure 1 A flow chart of a vehicle suspension control method provided in an embodiment of the present application;

[0070] Figure 2 A schematic diagram of determining a road elevation curve based on obstacle information provided in an embodiment of the present application;

[0071] Figure 3 A schematic diagram of a quarter model of a vehicle suspension system provided in an embodiment of the present application;

[0072] Figure 4 A schematic diagram of a multi-level threshold arbitration decision for obstacle recognition confidence provided in an embodiment of the present application;

[0073] Figure 5 A flowchart of another vehicle suspension control method provided in an embodiment of the present application;

[0074] Figure 6 A structural diagram of a vehicle suspension control device provided in an embodiment of the present application;

[0075] Figure 7 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0076] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0077] Because traditional preview suspension control systems may fail to accurately identify obstacles, if they mistakenly identify an existing obstacle, they will not take control action on a potentially impulsive road surface, failing to fully utilize the expected functions and performance of the preview control system. Alternatively, if they mistakenly identify a non-existent obstacle, the use of fully active suspension actuation will generate reverse excitation, resulting in a decrease in comfort, making it impossible to balance the risk of misidentification with the need for ride comfort. Therefore, embodiments of the present application provide a vehicle suspension control method, device, electronic device, and storage medium.

[0078] The present application provides a vehicle suspension control method, such as Figure 1 Shown, including:

[0079] Step S101, obtaining obstacle information and obstacle recognition confidence of the preview obstacle obtained by previewing the vehicle's driving road;

[0080] In an embodiment of the present application, a forward preview device is installed at a position with a good field of view in front of the vehicle, such as the glass in front of the rearview mirror, above the bumper, etc. The forward preview device may exemplarily include sensors such as cameras, radars, lidars, and machine learning models (such as CNN, RNN). The forward preview device is used to obtain pulse road surface information on the tire trajectory of the vehicle in the direction of travel, and use the machine learning model to identify road surface features (such as image texture, point cloud height difference), and generate a confidence score of the obstacle type while outputting the recognition result (for example, the CNN model identifies potholes on the road surface, outputs a "pothole" label and the corresponding confidence of the obstacle type), marks the identified pulse road surface as an obstacle, numbers the obstacle, and continues to track the obstacle until the rear axle of the vehicle drives a certain distance away from the obstacle and releases the obstacle information. The obstacle information and obstacle recognition confidence are obtained by preview recognition. Taking the preview information on the left wheel track of the vehicle as an example, the obstacle information includes: obstacle number i , obstacle type , longitudinal distance of obstacle , vertical height of obstacle and the longitudinal width of the obstacle wait.

[0081] Among them, obstacle number iThe number of obstacles that the forward preview device can identify and mark at the same time is limited. N , then wait until the marked obstacle information is released before marking it again, and so on. When the number overflows, the number counting starts again from 0.

[0082] Obstacle type , such as speed bumps, bumps, potholes, damaged roads, and other pulse road obstacles The road surface type is represented by natural numbers, where 0 represents a general random road surface, raised road surface types such as speed bumps, bumps, and raised manhole covers are represented by even numbers, and recessed road surface types such as potholes, damaged road surfaces, and recessed manhole covers are represented by odd numbers.

[0083] Longitudinal distance to obstacles The pre-aiming device calculates the longitudinal distance between the obstacle and the rear axle of the vehicle based on the information fusion of the visual camera or the visual camera combined with millimeter-wave radar, lidar and other sensors.

[0084] Vertical height of the obstacle and vertical width The preview device calculates the height information of the uneven road surface based on the information fusion of sensors such as visual cameras, millimeter-wave radars, and lidars.

[0085] Confidence of obstacle events , the reliability of detecting and identifying abnormal road bumps is expressed in 0~1.

[0086] In addition, the main information that can be obtained through sensors, CAN communication or calculation processing includes: unsprung acceleration information, suspension height, suspension speed, and vehicle speed information for use in subsequent embodiments.

[0087] Step S102, determining an optimal control force of an actuator of a suspension in the vehicle according to the obstacle information;

[0088] In this step, a road elevation curve can be determined based on the longitudinal distance of the obstacle, the vertical height of the obstacle, and the longitudinal width of the obstacle; the road elevation curve is converted from the spatial domain into road excitation information in the time domain based on the current time correction parameter; the road excitation information is input into a 1 / 4 model of the vehicle suspension system to obtain suspension dynamic performance parameters; and the optimal control force of the actuator (including the actuator and shock absorber) is determined based on the suspension dynamic performance parameters and a preset optimal control algorithm.

[0089] Furthermore, the suspension controller can calculate an approximate road elevation curve based on the longitudinal distance, vertical height and longitudinal width of the obstacle obtained through preview identification. Here, the road elevation curve is information on the change of vertical height on the vehicle tire trajectory in the vehicle's driving direction with longitudinal distance. Then, based on the preset time delay parameter and the current time correction parameter, the road elevation information is converted from spatial domain description information to time domain road excitation information for the wheel, and the time domain road excitation information of the left and right wheels is input into the suspension control system. The suspension control system inputs the road excitation information into the 1 / 4 model of the vehicle suspension system to obtain suspension dynamic performance parameters. The suspension dynamic performance parameters include: body acceleration representing ride comfort, wheel dynamic deflection representing handling stability, and suspension dynamic deflection representing driving safety. The suspension dynamic performance parameters are weightedly calculated using the optimal control algorithm, namely the LQR control algorithm, and the integral value of the weighted square sum of the above three suspension dynamic performance parameters in the time domain is used as the target performance indicator to solve and obtain the optimal suspension force input.

[0090] In practical applications, the displacement information of the front pulse road input can be calculated first by the obstacle type, distance, height and width information. The preview distance in the preview information is the distance from the rear wheel to the obstacle, so according to the preview distance and vehicle wheelbase Obtain the distance information from the front wheel to the obstacle, and further obtain the road surface excitation input function of the front wheel .

[0091] Pulse road surfaces such as bumps, speed bumps, and potholes are all quasi-sinusoidal fluctuations and are described by the cosine function because its slope is zero near the starting and ending points. Figure 2 As shown in scene 1:

[0092] (1)

[0093] i Obstacle number ， Obstacle type, Longitudinal distance to the obstacle, The vertical height of the obstacle, Vertical width, Vehicle wheelbase

[0094] The pulse pavement such as raised manhole cover, sunken manhole cover, ground cracks has a trapezoidal cross section, the slope near the starting and ending points suddenly changes, and it is relatively flat in the middle. Figure 2 As shown in Scenario 2 in . To simplify the description, a step function is used to describe it:

[0095] (2)

[0096] The road elevation input related to the preview distance input is used as the front suspension control input. On the one hand, the measured comprehensive delay between the information receiving program and the actuator action is considered. , the preview control state should be entered in advance when the front wheels approach the pulse road surface. On the other hand, considering the unavoidable errors in other aspects such as information transmission delay, there is a systematic error (i.e. time correction parameter) between the time to reach the obstacle estimated based on the preview distance and the actual time to reach the obstacle. , this time correction parameter Its initial value can be obtained by calibration and corrected in subsequent embodiments.

[0097] Consider delays and systematic error , then we get the distance judgment formula for the front wheel to enter the preview control state:

[0098] (3)

[0099] in is the longitudinal acceleration.

[0100] Corrected preview distance information

[0101] (4)

[0102] Substituting the corrected preview distance into equations (1) and (2) yields the road surface excitation input for front wheel preview control: .

[0103] After obtaining the road excitation input, the suspension system control force is obtained according to the vehicle suspension system 1 / 4 model and the LQR control algorithm.

[0104] Among them, the 1 / 4 model of the vehicle suspension system is as follows Figure 3 As shown, Figure 3 middle and They are stiffness, is the passive damping coefficient of the suspension system, is the sprung mass, is the unsprung mass, is the sprung mass displacement, is the unsprung mass displacement, is the road excitation displacement. The dynamic differential equation of the suspension system is established:

[0105] (5)

[0106] (6)

[0107] Take the state vector , the pulse road excitation Input vehicle suspension model to get output variables

[0108] Convert the differential equation of the vehicle suspension system into the system state equation and output equation:

[0109] (7)

[0110] (8)

[0111] Where: ; ; ; ; ;

[0112] ;

[0113] In the output variable Y, The three variables are vehicle acceleration, which represents ride comfort; wheel dynamic deflection, which represents handling stability; and suspension dynamic deflection, which represents driving safety. Using the LQR control algorithm, the integral value of the weighted square sum of these three performance evaluation indicators in the time domain is used as the target performance indicator, expressed as follows:

[0114] (9)

[0115] Where, is the initial control time; are the weighting coefficients of vehicle body acceleration, wheel dynamic deflection and suspension dynamic deflection respectively.

[0116] Substituting formula (8) into formula (9), the performance index can be converted into:

[0117] (10)

[0118] Where: ; ; ; .

[0119] According to formula (10), the optimal control matrix of the actuator is:

[0120] (11)

[0121] Where K is the optimal state feedback gain matrix; P is the solution of the following Riccati equation:

[0122] (12)

[0123] The LQR control algorithm is used to perform weighted calculation on the output variable Y to obtain the pulse road elevation input Optimal control force under .

[0124] This application considers the hysteresis caused by various factors in the control system. When the distance to the obstacle exceeds the threshold, the input preview distance information is switched to a state updated based on the vehicle's longitudinal acceleration prediction. This reduces the actuator's response hysteresis to the input and improves the control system's accuracy.

[0125] Step S103, determining a target confidence interval to which the obstacle recognition confidence belongs from a plurality of preset confidence intervals;

[0126] In this step, the obstacle recognition confidence can be compared with the upper and lower boundaries of each preset confidence interval respectively, and the preset confidence interval in which the obstacle recognition confidence is greater than the lower boundary and less than the upper boundary is determined as the target confidence interval.

[0127] Multiple preset confidence intervals can be divided into five levels of confidence decision thresholds:

[0128] High confidence interval: Activate full active control (height, stiffness + damping combined adjustment);

[0129] Higher confidence interval: Activate semi-active control (damping adjustment only);

[0130] Medium confidence interval: Activate conservative strategy semi-active control (damping adjustment only, and output damping force);

[0131] Lower confidence interval: Activate energy-saving strategy semi-active control (damping adjustment only, and damping force output);

[0132] Low confidence interval: The preview control module does not output valid control signals.

[0133] Step S104 : determining an actual control force on the suspension according to the target confidence interval and the optimal control force.

[0134] In this step, the first suspension control mode for the suspension can be determined according to the target confidence interval, and the first suspension control mode can be activated; the current suspension speed of the suspension is obtained, and the current maximum damping force and minimum damping force of the shock absorber of the suspension are determined according to the current suspension speed; when the first suspension control mode is a non-silent control mode, the actual control force of the suspension is determined based on the optimal control force, the maximum damping force and the minimum damping force.

[0135] In the embodiment of the present application, the actual control force includes: at least one of the actuating force that the actuator actually needs to output and the damping force that the shock absorber needs to output.

[0136] For front wheel suspension control, a confidence-based hierarchical arbitration mechanism is used to determine the control method. The suspension controller receives information about obstacle i and applies a mean filter to the confidence information to reduce instability. Based on the confidence level, the controller determines whether to adopt fully active or semi-active control. When the confidence level is high, the suspension adopts fully active control. When the confidence level is relatively high, the suspension adopts semi-active control. When the confidence level is medium, the suspension adopts semi-active control and multiplies the output by the confidence level. When the confidence level is low, the suspension adopts semi-active control and multiplies the output by the square of the confidence level. When the confidence level is low, the suspension preview control module does not control the actuator.

[0137] The specific decision-making mechanism is shown in the attached figure of the specification. Figure 4 As shown, it includes five-level decision threshold settings to make decisions through arbitration module 1:

[0138] In one embodiment of the present application, if the target confidence interval is a first confidence interval (i.e., a high confidence interval, such as: ), the first suspension control mode is a fully active suspension control mode, and step S104 determines the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force, including:

[0139] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force, and controlling the front wheel actuator of the vehicle to output an actuating force according to the difference between the optimal control force and the maximum damping force;

[0140] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force, and the front wheel actuator of the vehicle is controlled to output the actuating force according to the sum of the optimal control force and the maximum damping force.

[0141] In practical applications, for high confidence intervals : Activate full active control (combined adjustment of actuation and damping forces), switch to Figure 4 The execution mode of module 2;

[0142] According to the suspension speed, if the required suspension force and damping force If the direction is consistent, the damping force is adjusted to the maximum, and the suspension is used as the driving force. Only acts as suspension damping force Not enough to provide full suspension force This is because the output damping force requires less energy than the output actuation force. The controller adjusts the speed of the suspension in real time. The real-time maximum damping force is obtained from the shock absorber characteristic curve If the required suspension force , then the damping force of the suspension shock absorber is sufficient to provide the required suspension force, and the suspension actuator does not need to work. , suspension damping force ;like , suspension damping force , the damping force of the suspension shock absorber is not enough to provide the required suspension force, the suspension working force ;

[0143] like and damping force In the opposite direction, adjust the damping force to the actual speed of the suspension. Minimum damping force ,Right now , .

[0144] In one embodiment of the present application, if the target confidence interval is the second confidence interval (i.e., a higher confidence interval, such as: ), the maximum boundary of the second confidence interval is smaller than the minimum boundary of the first confidence interval, and the first suspension control mode is a semi-active suspension control mode; step S104 determines the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force, including:

[0145] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force;

[0146] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force.

[0147] In practical applications, for higher confidence intervals : Activate semi-active control (damping adjustment only, actuation force ), switch to Figure 4The execution mode of module 3;

[0148] exist and damping force If the direction is consistent, , the damping force can provide all the required suspension force, then the suspension damping force ,like At this time, the shock absorber cannot output enough damping force to meet the suspension force requirements, and can only adjust the damping force to the maximum ;

[0149] exist and damping force In the opposite direction, If this cannot be achieved, adjust the damping force to the lowest value. , reduce the difference between the actual output force of the suspension and the required suspension force, that is, .

[0150] In one embodiment of the present application, if the target confidence interval is the third confidence interval (i.e., the medium confidence interval, such as: ), the maximum boundary of the third confidence interval is smaller than the minimum boundary of the second confidence interval, and the first suspension control mode is a low-gain semi-active suspension control mode; step S104 determines the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force, including:

[0151] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the optimal control force and a preset confidence coefficient to obtain a first output force, and controlling the shock absorber of the suspension to output a damping force according to the first output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the maximum damping force and a preset confidence coefficient to obtain a second output force, and controlling the shock absorber of the suspension to output a damping force according to the second output force;

[0152] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the minimum damping force and a preset confidence coefficient is calculated to obtain a third output force, and the shock absorber of the suspension is controlled to output the damping force according to the third output force.

[0153] In practical applications, for medium confidence intervals, : Activate conservative strategy semi-active control (damping adjustment only, actuation force ), switch to Figure 4 The execution mode of module 4 in .

[0154] The output target damping force is the theoretical damping force multiplied by the confidence coefficient. Here, the theoretical damping force With higher confidence Same, then , because the general variable damping shock absorber tends to have higher energy consumption when the damping is higher, this move is to reduce the energy consumption of the suspension system.

[0155] In one embodiment of the present application, if the target confidence interval is the fourth confidence interval (i.e., a lower confidence interval, such as: ), the maximum boundary of the fourth confidence interval is smaller than the minimum boundary of the third confidence interval, and the first suspension control mode is a high-gain semi-active suspension control mode; step S104 determines the actual control force of the vehicle suspension based on the optimal control force, the maximum damping force, and the minimum damping force, including:

[0156] In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the square of a preset confidence coefficient and the optimal control force to obtain a fourth output force, and controlling the shock absorber of the suspension to output a damping force according to the fourth output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the square of a preset confidence coefficient and the maximum damping force to obtain a fifth output force, and controlling the shock absorber of the suspension to output a damping force according to the fifth output force;

[0157] Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the square of the preset confidence coefficient and the minimum damping force is calculated to obtain a sixth output force, and the shock absorber of the suspension is controlled to output the damping force according to the sixth output force.

[0158] In practical applications, for lower confidence intervals : Activate energy-saving strategy semi-active control (damping adjustment only, force ), switch to the execution mode of module 5.

[0159] The output target damping force is the theoretical damping force multiplied by the square of the confidence coefficient. Here, the theoretical damping force With higher confidence Same, then , the purpose here is also to reduce energy consumption.

[0160] In one embodiment of the present application, if the target confidence interval is the fifth confidence interval (i.e., the low confidence interval ), the maximum boundary of the fifth confidence interval is less than the minimum boundary of the fourth confidence interval, and the first suspension control mode is the preview control silent mode; at this time, the preview control module does not output a valid control signal, and the actuator output force is based on the output of other control modules. 、 Switch to Figure 4 The execution mode of module 6 in .

[0161] In summary, for the suspension control of the front wheel position, a confidence-based hierarchical arbitration mechanism is used to decide the control method to be adopted. The suspension controller receives information about obstacles. The controller needs to judge the confidence at this time and decide whether to adopt a fully active control method or a semi-active control method. When the confidence is in a high confidence range, the suspension adopts a fully active control method. When the confidence is in a high or medium confidence range, the suspension adopts a semi-active control method and the lower the confidence, the lower the output force. When the confidence is in a low confidence range, the suspension preview control module does not control the actuator. Based on the confidence hierarchical control strategy, the actuating force output by the actuator under different confidence conditions can be obtained. and the damping force output by the shock absorber .

[0162] This embodiment of the present application arbitrates the suspension preview control mode based on the confidence level of image-based road obstacle identification. When the confidence level exceeds a first confidence threshold, the preview suspension control system adopts a fully active suspension control mode, leveraging the fully active suspension system's ability to suppress vehicle jolts by collaboratively controlling the suspension system's damping and actuating forces. When the confidence level is below the first confidence threshold but above a second confidence threshold, the preview suspension control system adopts a semi-active suspension control mode, improving the vehicle's ride comfort when passing over obstacles by controlling the suspension system's damping to reduce the impact on the vehicle body when approaching the obstacle and the aftershocks after leaving the obstacle. When the confidence level exceeds a fourth confidence threshold but falls below the second confidence threshold, the preview suspension control system still adopts the semi-active suspension control mode, but multiplies the final damping force output by a coefficient associated with the confidence level, reducing the damping force output by the control algorithm. The associated damping coefficient is switched based on the third confidence threshold. When the confidence level falls below the fourth confidence threshold, no valid control signal is output. In the decision-making of the preview control mode of the suspension of the vehicle's front axle, the confidence of the preview recognition is the main basis.

[0163] In the conventional preview control method, if the recognition confidence is lower than a certain threshold, it is judged to be unreliable and the obstacle data will not be sent. However, there is a possibility that the confidence of obstacle recognition in the same scene is reduced due to changes in external light or environment. If there is an obstacle in front and the data is discarded because the recognition confidence is not high, the missed recognition does not trigger the preview control, and the vehicle comfort is reduced. Therefore, the purpose of this application is to optimize the problem that relying on a single recognition confidence in the preview control active suspension cannot balance comfort and the risk of misidentification well. Different control modes are adopted under multi-level preview recognition confidence thresholds. Since the fully active suspension actuator will quickly and significantly change the suspension height when actuated, if the flat ground is mistakenly identified as an obstacle, it will cause the vehicle to shake unnecessary. Therefore, the fully active suspension control mode is adopted under high confidence conditions to give full play to the performance of the fully active suspension, avoid triggering the active action of the fully active suspension when misidentified as much as possible, and minimize the impact of the vehicle on the pulse road surface to improve comfort. When confidence is low, the semi-active suspension control mode is used. In this mode, only the suspension system damping force is adjusted. The damping force is a passive force, generating a reaction force based on the relative velocity of the sprung and unsprung surfaces. This has the advantage that if an impulsive road surface is present, meaning an obstacle is present, adjusting the damping can reduce the impact force and suppress vehicle body sway. If the recognition error indicates that there is no impulsive road surface or the recognition information is inaccurate, meaning that the obstacle is flat and the confidence level is low, the damping force acts in the opposite direction of the suspension's motion, dissipating energy without inputting additional energy. Adjusting the damping has no effect and does not generate a reverse excitation effect. When the recognition level is medium or low, the optimal theoretical damping force calculated by the algorithm is de-gained, and when the recognition level is low, the preview control output is disabled. The effect is that the lower the recognition confidence, the lower the suspension system damping value and the lower the current driving the shock absorber. This fully utilizes the adjustable damping shock absorber's ability to improve comfort while reducing energy loss.

[0164] The embodiment of the present application determines the optimal control force of the suspension actuator by utilizing obstacle information obtained by previewing and identifying the vehicle's driving road surface, determines a target confidence interval from multiple preset confidence intervals using the obstacle recognition confidence obtained by previewing and identifying, and then determines the actual control force of the suspension based on the target confidence interval and the optimal control force. By setting multiple candidate preset confidence intervals, hierarchical control of the actual output force of the suspension is achieved according to different obstacle recognition confidences, so that the control of the actual output force of the suspension is matched with the obstacle recognition confidence, so that the control of the actual output force of the suspension is more in line with the actual driving conditions of the vehicle, thereby improving vehicle comfort, improving the smoothness of the vehicle when passing through obstacles, and saving energy consumption of suspension control.

[0165] Traditional visual preview systems (such as cameras and lidars) are easily affected by ambient lighting, occlusion, or sensor noise when detecting the distance to speed bumps, resulting in large distance measurement errors. The preview distance error will directly cause the suspension actuator to be triggered early or late, and it will not be able to accurately offset the impact of the road surface, resulting in an increase in the vertical acceleration of the vehicle body and reduced comfort. Simply relying on visual sensor filtering algorithms or increasing sensor redundancy (such as multi-camera fusion) is costly and cannot dynamically compensate for real-time errors. Existing suspension control methods do not fully utilize the physical feedback signals (such as acceleration) when the wheels pass through speed bumps for closed-loop correction. To this end, in one embodiment of the present application, if Figure 5 As shown, the vehicle suspension control method further includes:

[0166] Step S201, obtaining a plurality of first unsprung accelerations of the front wheel lower control arm within a first window period;

[0167] The acceleration sensor is installed on the front wheel lower arm, one on each left and right lower arm, to obtain the unsprung acceleration of the two wheels and transmit it to the suspension controller.

[0168] Step S202, determining whether the vehicle has traveled on a pulse road surface containing a pulse obstacle based on the plurality of the first unsprung accelerations;

[0169] In this step, the suspension controller determines whether the vehicle is passing through a pulsed road surface by windowing and extracting features of the vertical acceleration under the front wheel spring.

[0170] Furthermore, the first unsprung acceleration within a second window period can be extracted from the multiple first unsprung accelerations within the first window period, the second window period being within the first window period; a first acceleration steady-state value within a long-time window is determined based on the first unsprung acceleration within the first window period; a second acceleration steady-state value within a short-time window is determined based on the first unsprung acceleration within the second window period; and it is determined whether a ratio of the first acceleration steady-state value to the second acceleration steady-state value is greater than or equal to a preset steady-state threshold value; if the ratio is greater than or equal to the preset threshold value, it is determined that the front wheels of the vehicle have traveled on a pulsed road surface.

[0171] In practical applications, the detection of the vertical acceleration under the front wheel spring can be used to determine whether the vehicle has passed through a pulse road surface. The short-term energy input of a pulse road surface is much higher than that of a general random road surface, and the root mean square value (RMS) of the data can reflect the vibration energy level to a certain extent. If the RMS value of the acceleration data changes suddenly, it is determined that the wheel has traveled through a pulse road surface. The controller records the past The unsprung vertical acceleration value at the sampling moment , and take it Calculate the RMS value of acceleration at the sampling moment:

[0172] (6)

[0173] By using the sliding window weighted average method, the past The iterative update formula of the acceleration root mean square value at each sampling moment is as follows:

[0174] (7)

[0175] The sudden change of RMS value can be calculated by the ratio of the RMS value in the short window period to the RMS value in the long window period. P To reflect that if the wheel runs on the pulse road, the RMS Values ​​compared to the steady state within a long time window RMS The value suddenly changes. For the pulse road judgment based on the acceleration sensor, there is a sudden change ratio of the pulse edge acceleration root mean square. P The judgment formula (here the short time window is defined as the past moments, the long time window is defined as the past moments):

[0176] (8)

[0177] In this formula, is a constant threshold, m、n It is also a constant, and its value is obtained through calibration.

[0178] If the ratio P is greater than or equal to the preset threshold , it is determined that the RMS value has undergone a mutation. The mutation of the RMS value also reflects the mutation of the energy of the forced vibration of the unsprung mass, indicating that the front wheel of the vehicle has traveled over a pulse road surface, that is, the wheel has traveled onto an obstacle.

[0179] When the vehicle reaches the edge of the pulse road, the unsprung mass will oscillate under the action of the tire elastic force and the suspension spring force. The unsprung mass acceleration value will oscillate at a frequency close to the natural frequency of the unsprung mass. Due to the damping effect of the suspension and tire, the oscillation amplitude will decay at a relatively fixed ratio. The acceleration extreme point will appear when the road excitation suddenly increases. For bumps, P The maximum value will appear at the first extreme point, while the maximum value will appear at the second extreme point for the pit. Record this maximum value. . After that, within T1 time, if , it is not recognized as a new pulse road edge, and the attenuation ratio K1 and the silent time T1 are both calibrated and determined.

[0180] Step S203 , if it is determined that the vehicle has traveled on a pulse road surface containing a pulse obstacle, performing spatiotemporal consistency matching between the preview obstacle and the pulse obstacle based on the plurality of first unsprung accelerations within the first window period and the obstacle information;

[0181] In some embodiments of the present invention, when acceleration detection identifies a pulsed road surface, the obstacle information identified through preview is compared with the pulsed road surface information derived from unsprung acceleration. Based on the preview distance information and the real-time vehicle speed, a theoretical range of the front wheel arrival time is estimated. If the pulse edge event time identified through acceleration detection is within the error range of the arrival time calculated based on the preview information, the previewed obstacle event and the acceleration-identified pulsed road surface edge event are considered to share the same obstacle event characteristics.

[0182] Furthermore, the concave-convex properties of the obstacle type identified in preview are checked against the concave-convex properties determined by unsprung acceleration detection. If the identifications are consistent, the preview obstacle event and the acceleration-identified pulse road edge event are considered to be aligned, and the pulse road edge event is marked as being in an alignable state.

[0183] If the pulse edge event time identified by acceleration detection is not within the expected time range of any preview obstacle event, or if the bump properties of the obstacle type identified by preview and the bump properties determined by unsprung acceleration detection conflict, the pulse road edge event is marked as unalignable. This pulse road edge event is not aligned with any preview obstacle event and is not used as a reference for error compensation correction of the preview obstacle distance.

[0184] In this step, a pulse edge moment when the wheel reaches the pulse obstacle can be determined based on multiple first unsprung accelerations within the first window period. If the pulse edge moment is within a preset time range determined based on the obstacle information and the dynamic error of the obstacle's longitudinal distance conforms to a normal distribution, a first concave-convex attribute of the preview obstacle is determined based on the obstacle information, and a second concave-convex attribute of the pulse obstacle is determined based on the multiple first unsprung accelerations within the first window period. It is determined whether the first concave-convex attribute is consistent with the second concave-convex attribute. If the first concave-convex attribute is consistent with the second concave-convex attribute, it is determined that the temporal and spatial consistency of the preview obstacle and the pulse obstacle is matched.

[0185] When the acceleration detection identifies the pulse road surface, the longitudinal distance of the obstacle identified by the preview is determined Dynamic error Is it in line with normal distribution? , where the dynamic error of the preview recognition distance can be obtained in advance based on experimental tests , and obtain the 99% confidence interval of its dynamic error , here It can be regarded as the system steady-state error, and the 99% confidence interval of the preview distance is ,Right now Within the 99% confidence interval of the preview distance.

[0186] The preset time range can be determined based on the dynamic error range and the real-time vehicle speed. The theoretical range of the estimated front wheel arrival time is the preset time range. , if the pulse edge moment identified by acceleration detection is within the preset time range, that is .

[0187] Furthermore, a first concave-convex attribute of the preview obstacle can be determined based on the obstacle information, and a second concave-convex attribute of the pulse obstacle can be determined based on multiple first unsprung accelerations within the first window period, including: determining the first concave-convex attribute of the preview obstacle based on the obstacle type in the obstacle information; calculating the average acceleration of the first unsprung accelerations within the second window period in the first window period; and determining the second concave-convex attribute of the pulse obstacle based on the positive or negative sign of the average acceleration. In other words, whether the pulse road surface is a convex obstacle or a concave obstacle is determined by the vertical acceleration direction when driving onto the edge of the pulse road surface.

[0188] Calculate the past m The average unsprung acceleration at each moment:

[0189] (9)

[0190] like , the pulse road surface is identified as a raised road surface;

[0191] like , the pulse road surface is identified as a concave road surface;

[0192] like , then take the past m-1 The average unsprung acceleration at each moment Make a judgment, where l is the calibration value and l is less than m.

[0193] In summary, the suspension controller can determine whether the pulse road surface is a convex obstacle or a concave obstacle by the vertical acceleration direction of the edge of the pulse road surface.

[0194] When determining whether the first concave-convex attribute is consistent with the second concave-convex attribute, the obstacle type identified by the preview can be The concave-convex properties of are tested with the concave-convex properties determined by the unsprung acceleration detection, namely:

[0195] (10)

[0196] If the recognition is consistent, the preview obstacle event and the acceleration recognition pulse road edge event are considered to be aligned, and the pulse road edge event is marked as aligned. It should be noted that the purpose of spatiotemporal consistency alignment is to correct the preview information used by the rear wheel control and the next front wheel control.

[0197] If the actual pulse edge event time is identified based on acceleration detection If the pulse road edge event does not occur within the expected event range of any preview obstacle event, or if a conflict is detected between the bump properties of the obstacle type identified by preview and the bump properties determined by unsprung acceleration detection, the pulse road edge event is marked as unalignable. This pulse road edge event is not aligned with any preview obstacle event and is not used as a reference for error compensation in the preview obstacle distance.

[0198] In step S204 , if the temporal and spatial consistency of the preview obstacle and the pulse obstacle is successfully matched, a time correction parameter is updated according to the multiple first unsprung accelerations within the first window period, and the obstacle recognition confidence is updated according to a preset update rule.

[0199] In the embodiment of the present application, the actual time difference between the preview obstacle event and the pulse obstacle event can be calculated, and this difference can be used as the correction value for the time when the rear wheel reaches the obstacle, and this difference data can be used to update the system error of the time to reach the obstacle based on the preview distance. When the alignment state is possible and the concave and convex attributes are consistent, it can be considered that the credibility of the preview obstacle information is high, and the confidence of the preview obstacle information will be updated. i When the front wheels are in the same confidence-based hierarchical control strategy, the same confidence-based hierarchical control strategy as the front wheels is adopted.

[0200] In this step, the first moment when the wheel reaches the pulse obstacle can be determined based on the multiple first unsprung accelerations in the first window period, that is, when the wheel reaches the pulse road surface, the threshold is broken for the first time. Moment Identifying a pulse edge moment; estimating a second moment when the wheel will travel onto the predicted obstacle based on the obstacle information; calculating a time difference between the first moment and the second moment, and updating the time correction parameter based on the time difference; determining a confidence range corresponding to the obstacle recognition confidence, and updating the obstacle recognition confidence according to an update rule corresponding to the confidence range.

[0201] In the embodiment of the present application, the approximate estimation of the actual time error of the pulse edge event is , is the time when the pulse road edge event occurs estimated based on the preview distance and vehicle speed, is the actual pulse road moment obtained based on acceleration detection, The value is used as the compensation value for the arrival time of the new data to update the preview obstacle. Since the pre-aiming system is a time-varying system affected by factors such as light and vibration, a sliding window is used to limit the scope of historical data influence, and the The mean value within the window period is used as the compensation value Reference value, set time window U , each time you add back:

[0202] Mean update: (11)

[0203] Time correction parameters (12)

[0204] The rear wheel suspension control is determined based on pulse road edge events. When a pulse road edge event occurs, if the vehicle is in an alignable state and the bump attributes are recognized consistently, the information from the preview obstacle recognition is considered to be highly reliable, and the rear wheel suspension will be controlled in the same manner as the front wheel.

[0205] One difference from the front wheel is that the confidence of the preview obstacle information will be updated.

[0206] (13)

[0207] Another difference from the front wheel is that the time difference is handled differently. The processing is as shown in formula (12). For the rear wheel, since the alignment event has already occurred on the front wheel, we can directly obtain:

[0208] (14)

[0209] The rear wheel passes an obstacle i When the confidence-based The hierarchical control strategy determines the working mode of the suspension, and the hierarchical control strategy is consistent with the calculation logic of module 3. , combined with its vehicle status sensor, the rear wheel suspension output force is calculated through the same control algorithm in module 2 and module 3 .

[0210] If alignment is not possible, there is no credible preview obstacle information as a control reference for this pulse road event. In this case, the preview control module will not make effective suspension force control for this pulse road event, and the suspension force will be controlled by other algorithm modules.

[0211] This embodiment of the present application detects the RMS acceleration within a longer window period and a shorter window period. The ratio of these two RMS values ​​determines whether the road input energy has abruptly changed. This method detects the edge of a pulsed road surface, determines the time a wheel travels on the pulsed road surface, and determines the concavity and convexity of the pulsed road surface based on the sign of the mean vertical acceleration of the unsprung mass within the short window period. The timing and concavity of the pulsed road surface edge event are compared with the preview obstacle information. Taking into account the error of the preview obstacle, the pulsed road surface edge that falls within the temporal error range of a preview obstacle event and has the same concavity and convexity is temporally and spatially aligned with the preview obstacle. The difference between the aligned events is recorded, the mean of the differences is calculated, and the standard deviation of the differences is updated to provide a correction for the next obstacle event. For preview obstacle events that align with the pulsed road surface edge event, their confidence is increased, and the aligned preview obstacle information is used for rear wheel control. If the pulsed road surface edge event fails to align with the preview obstacle event, the inter-axle preview pulsed road surface control method is applied to the rear wheels.

[0212] The embodiment of the present application detects the edge of the pulse road surface through an acceleration sensor, and can make corrections to the preview recognition information in the current obstacle scene and use it for rear wheel control. It can also make systematic corrections to the preview recognition information in subsequent obstacle scenes, thereby improving the accuracy of the input preview information and thus improving the accuracy of the entire preview control system.

[0213] In another embodiment of the present application, Figure 6 As shown, a vehicle suspension control device is also provided, comprising:

[0214] An acquisition module 11 is used to obtain obstacle information and obstacle recognition confidence of the obstacle obtained by performing a preview recognition on the vehicle's driving road;

[0215] A first determining module 12 is configured to determine an optimal control force of an actuator of a suspension in the vehicle according to the obstacle information;

[0216] A second determining module 13 is configured to determine a target confidence interval to which the obstacle recognition confidence belongs from among a plurality of preset confidence intervals;

[0217] The third determination module 14 is configured to determine an actual control force on the suspension according to the target confidence interval and the optimal control force.

[0218] In another embodiment of the present application, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0219] Memory for storing computer programs;

[0220] The processor is configured to implement the vehicle suspension control method described in any of the aforementioned method embodiments when executing the program stored in the memory.

[0221] In an electronic device provided by an embodiment of the present invention, a processor determines an optimal control force of a suspension actuator by executing a program stored in a memory using obstacle information obtained by previewing and identifying a vehicle's driving road surface, determines a target confidence interval from a plurality of preset confidence intervals using the obstacle recognition confidence obtained by the preview recognition, and then determines an actual control force of the suspension based on the target confidence interval and the optimal control force. By setting a plurality of candidate preset confidence intervals, hierarchical control of the actual output force of the suspension is achieved based on different obstacle recognition confidences, so that the control of the actual output force of the suspension matches the obstacle recognition confidence, and the control of the actual output force of the suspension is more consistent with the actual driving conditions of the vehicle, thereby improving vehicle comfort, improving the smoothness of the vehicle when passing through obstacles, and saving energy consumption of suspension control.

[0222] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0223] The communication interface 1120 is used for communication between the electronic device and other devices.

[0224] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0225] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0226] In another embodiment of the present application, a computer-readable storage medium is also provided, on which a program of a vehicle suspension control method is stored. When the program of the vehicle suspension control method is executed by a processor, the steps of the vehicle suspension control method described in any of the aforementioned method embodiments are implemented.

[0227] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0228] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present 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 present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A vehicle suspension control method, characterized in that: include: Obtaining obstacle information and obstacle recognition confidence of a preview obstacle obtained by previewing and identifying the vehicle's driving road surface, wherein the obstacle information includes a time correction parameter used to convert a road elevation curve from a spatial domain into road excitation information in a time domain; determining an optimal control force of an actuator of a suspension in the vehicle according to the obstacle information; Determining a target confidence interval to which the obstacle recognition confidence belongs among a plurality of preset confidence intervals; determining an actual control force on the suspension according to the target confidence interval and the optimal control force; The method further comprises: obtaining a plurality of first unsprung accelerations of the front wheel lower control arm within a first window period; determining whether the vehicle is traveling on a pulse road surface containing a pulse obstacle based on a plurality of the first unsprung accelerations; If it is determined that the vehicle has traveled on a pulse road surface containing a pulse obstacle, performing spatiotemporal consistency matching between the preview obstacle and the pulse obstacle based on a plurality of first unsprung accelerations within the first window period and the obstacle information; Performing spatiotemporal consistency matching on the preview obstacle and the pulse obstacle based on the plurality of first unsprung accelerations within the first window period and the obstacle information includes: determining a pulse edge moment when a wheel travels onto a pulse obstacle based on a plurality of first unsprung accelerations within the first window period; If the pulse edge moment is within a preset time range determined based on the obstacle information and the dynamic error of the obstacle longitudinal distance conforms to a normal distribution, determining a first concave-convex attribute of the preview obstacle based on the obstacle information, and determining a second concave-convex attribute of the pulse obstacle based on a plurality of first unsprung accelerations within the first window period; determining whether the first concave-convex attribute is consistent with the second concave-convex attribute; If the first concave-convex attribute is consistent with the second concave-convex attribute, it is determined that the spatiotemporal consistency matching between the preview obstacle and the pulse obstacle is successful; If the temporal and spatial consistency of the preview obstacle and the pulse obstacle is successfully matched, a time correction parameter is updated according to a plurality of first unsprung accelerations within the first window period, and the obstacle recognition confidence is updated according to a preset update rule.

2. The vehicle suspension control method according to claim 1, characterized in that: The obstacle information includes: a longitudinal distance of the obstacle, a vertical height of the obstacle, a longitudinal width of the obstacle, and a time correction parameter. Determining the optimal control force of the actuator of the suspension in the vehicle based on the obstacle information includes: Determining a road elevation curve according to the longitudinal distance of the obstacle, the vertical height of the obstacle, and the longitudinal width of the obstacle; Converting the road elevation curve from the spatial domain to road excitation information in the time domain based on the current time correction parameter; Inputting the road excitation information into a quarter model of the vehicle suspension system to obtain suspension dynamic performance parameters; The optimal control force of the actuator is determined according to the suspension dynamic performance parameters and a preset optimal control algorithm.

3. The vehicle suspension control method according to claim 1, characterized in that: Determining an actual control force on the suspension according to the target confidence interval and the optimal control force includes: determining a first suspension control mode for the suspension according to the target confidence interval, and activating the first suspension control mode; Acquiring a current suspension speed of the suspension, and determining a current maximum damping force and a current minimum damping force of a shock absorber of the suspension according to the current suspension speed; When the first suspension control mode is a non-silent control mode, an actual control force of the suspension is determined based on the optimal control force, the maximum damping force, and the minimum damping force.

4. The vehicle suspension control method according to claim 3, characterized in that: If the target confidence interval is the first confidence interval, and the first suspension control mode is the fully active suspension control mode, determining the actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes: In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force, and controlling the front wheel actuator of the vehicle to output an actuating force according to the difference between the optimal control force and the maximum damping force; Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force, and the front wheel actuator of the vehicle is controlled to output the actuating force according to the sum of the optimal control force and the maximum damping force.

5. The vehicle suspension control method according to claim 3, characterized in that: If the target confidence interval is a second confidence interval, a maximum boundary of the second confidence interval is smaller than a minimum boundary of the first confidence interval, and the first suspension control mode is a semi-active suspension control mode; Determining an actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes: In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, controlling the shock absorber of the suspension to output the damping force according to the optimal control force; or, if the optimal control force is greater than the maximum damping force, controlling the shock absorber of the suspension to output the maximum damping force; Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the shock absorber of the suspension is controlled to output the minimum damping force.

6. The vehicle suspension control method according to claim 3, characterized in that: If the target confidence interval is a third confidence interval, and the maximum boundary of the third confidence interval is smaller than the minimum boundary of the second confidence interval, the first suspension control mode is a low-gain semi-active suspension control mode; Determining an actual control force of the suspension based on the optimal control force, the maximum damping force, and the minimum damping force includes: In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the optimal control force and a preset confidence coefficient to obtain a first output force, and controlling the shock absorber of the suspension to output a damping force according to the first output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the maximum damping force and a preset confidence coefficient to obtain a second output force, and controlling the shock absorber of the suspension to output a damping force according to the second output force; Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the minimum damping force and a preset confidence coefficient is calculated to obtain a third output force, and the shock absorber of the suspension is controlled to output the damping force according to the third output force.

7. The vehicle suspension control method according to claim 3, characterized in that: If the target confidence interval is a fourth confidence interval, and the maximum boundary of the fourth confidence interval is smaller than the minimum boundary of the third confidence interval, the first suspension control mode is a high-gain semi-active suspension control mode; Determining an actual control force of the suspension of the vehicle based on the optimal control force, the maximum damping force, and the minimum damping force includes: In a case where the optimal control force and the damping force of the suspension are in the same direction, if the optimal control force is less than or equal to the maximum damping force, calculating the product of the square of a preset confidence coefficient and the optimal control force to obtain a fourth output force, and controlling the shock absorber of the suspension to output a damping force according to the fourth output force; or, if the optimal control force is greater than the maximum damping force, calculating the product of the square of a preset confidence coefficient and the maximum damping force to obtain a fifth output force, and controlling the shock absorber of the suspension to output a damping force according to the fifth output force; Alternatively, when the optimal control force is in opposite directions to the damping force of the suspension, the product of the square of the preset confidence coefficient and the minimum damping force is calculated to obtain a sixth output force, and the shock absorber of the suspension is controlled to output the damping force according to the sixth output force.

8. The vehicle suspension control method according to claim 1, characterized in that: Determining whether the vehicle has traveled on an impulsive road surface containing an impulsive obstacle based on the plurality of the first unsprung accelerations includes: extracting the first unsprung acceleration in a second window period from the plurality of first unsprung accelerations in the first window period, the second window period being within the first window period; determining a first acceleration steady-state value within a long-term window based on the first unsprung acceleration within the first window period; determining a second acceleration steady-state value within a short time window according to the first unsprung acceleration within the second window period; determining whether a ratio of the first acceleration steady-state value to the second acceleration steady-state value is greater than or equal to a preset steady-state threshold; If the ratio is greater than or equal to a preset threshold, it is determined that the front wheels of the vehicle are traveling on a pulse road surface.

9. The vehicle suspension control method according to claim 1, characterized in that: Determining a first concave-convex attribute of the preview obstacle based on the obstacle information, and determining a second concave-convex attribute of the pulse obstacle based on a plurality of first unsprung accelerations within the first window period, includes: determining a first concave-convex attribute of the preview obstacle according to the obstacle type in the obstacle information; calculating an average acceleration of the first unsprung acceleration within a second window period in the first window period; A second concave-convex attribute of the pulse obstacle is determined according to the positive or negative sign of the acceleration mean value.

10. The vehicle suspension control method according to claim 1, wherein: Updating a time correction parameter according to a plurality of first unsprung accelerations within the first window period includes: determining a first moment when a wheel travels onto a pulse obstacle based on a plurality of first unsprung accelerations within the first window period; estimating a second moment when the wheel travels onto the predicted obstacle based on the obstacle information; Calculating a time difference between the first moment and the second moment, and updating the time correction parameter based on the time difference; A confidence range corresponding to the obstacle recognition confidence is determined, and the obstacle recognition confidence is updated according to an update rule corresponding to the confidence range.

11. A vehicle suspension control device, characterized in that: include: An acquisition module is used to obtain obstacle information and obstacle recognition confidence of the obstacle obtained by previewing the vehicle's driving road; a first determining module, configured to determine an optimal control force of an actuator of a suspension in the vehicle based on the obstacle information, wherein the obstacle information includes a time correction parameter, wherein the time correction parameter is used to convert a road elevation curve from a spatial domain into road excitation information in a time domain; A second determining module is configured to determine a target confidence interval to which the obstacle recognition confidence belongs from a plurality of preset confidence intervals; a third determining module, configured to determine an actual control force on the suspension according to the target confidence interval and the optimal control force; The vehicle suspension control device is further used to: obtain a plurality of first unsprung accelerations of the front wheel lower control arm within a first window period; determine whether the vehicle has traveled on a pulse road surface containing a pulse obstacle based on the plurality of first unsprung accelerations; if it is determined that the vehicle has traveled on a pulse road surface containing a pulse obstacle, perform spatiotemporal consistency matching on the preview obstacle and the pulse obstacle based on the plurality of first unsprung accelerations within the first window period and the obstacle information; perform spatiotemporal consistency matching on the preview obstacle and the pulse obstacle based on the plurality of first unsprung accelerations within the first window period and the obstacle information, including: determining a pulse edge moment when the wheel travels on the pulse obstacle based on the plurality of first unsprung accelerations within the first window period; if the pulse edge moment The method comprises the steps of: determining a first concave-convex attribute of the preview obstacle based on the obstacle information and determining a dynamic error of the longitudinal distance of the obstacle that conforms to a normal distribution; determining a second concave-convex attribute of the pulse obstacle based on a plurality of first unsprung accelerations within a first window period; determining whether the first concave-convex attribute is consistent with the second concave-convex attribute; determining that a spatiotemporal consistency match between the preview obstacle and the pulse obstacle is successful if the first concave-convex attribute is consistent with the second concave-convex attribute; and updating a time correction parameter based on the plurality of first unsprung accelerations within the first window period and updating the obstacle recognition confidence level according to a preset update rule.

12. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the vehicle suspension control method according to any one of claims 1 to 10 when executing a program stored in the memory.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program of a vehicle suspension control method, and when the program of the vehicle suspension control method is executed by a processor, the steps of the vehicle suspension control method according to any one of claims 1 to 10 are implemented.

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