Parameter optimization control method for automobile body stability system based on adaptive tuning

Abstract

The application relates to the technical field of automobile control, and discloses a parameter optimization control method for an automobile body stability system based on adaptive tuning, which comprises the following steps: collecting driving state data of an automobile; the driving state data comprises the driving speed, the steering angle and the lateral displacement of the automobile; determining the driving scene of the automobile based on the driving state data; screening the driving state data based on the driving scene to obtain reference state data at the current moment; adaptively tuning the initial control vector of the automobile based on the reference state data to obtain a target control vector; and adjusting the automobile body stability system of the automobile based on the target control vector; the automobile body stability system comprises a tuned mass damper. The scheme can meet the control requirements of the tuned mass damper.

Description

Technical Field

[0001] This application relates to the field of automotive control technology, specifically to, but not limited to, a parameter optimization control method for an adaptive-tuned automotive body stability system. Background Technology

[0002] During vehicle operation, changes in road conditions and driving status can lead to dynamic changes in vehicle stability. In order to mitigate the negative impact of drastic changes in vehicle stability, related technologies have proposed improving driving and passenger comfort by controlling the state of the tuned mass damper (TMD) in the vehicle's stability system.

[0003] However, in practical applications, once the vehicle switches to driving mode, the TMD is controlled based on various data contained in the driving mode. Therefore, the above method cannot specifically meet the control requirements of the TMD. Summary of the Invention

[0004] Based on the above technical problems, this application provides a method for optimizing the control of vehicle body stability system parameters based on adaptive tuning, which can specifically meet the control requirements of TMD.

[0005] The technical solution provided in this application is as follows: This application provides a method for optimizing the parameters of an adaptive-tuned vehicle stability system, including: Collect vehicle driving status data; wherein, the driving status data includes the vehicle's driving speed, steering angle, and lateral displacement; The driving scenario of the vehicle is determined based on the driving status data; Based on the driving scenario, the driving status data is filtered to obtain the baseline status data at the current moment; The initial control vector of the vehicle is adaptively tuned based on the reference state data to obtain the target control vector; The vehicle's stability system is adjusted based on the target control vector; wherein the stability system includes a TMD (Transportation Management Device).

[0006] The adaptive tuning-based parameter optimization control method for vehicle stability systems provided in this application has at least the following beneficial effects: The adaptive tuning-based parameter optimization control method for vehicle stability systems provided in this application collects vehicle driving state data, including vehicle speed, steering angle, and lateral displacement, thus improving the comprehensiveness of the driving state data. Furthermore, it determines the vehicle's driving scenario based on the driving state data, thereby improving the correlation between the driving scenario and the vehicle's driving state data, and thus improving the accuracy of the driving scenario. Simultaneously, it filters the driving state data based on the driving scenario to obtain the current moment's baseline state data, achieving targeted filtering of the driving state data, thereby improving the correlation between the baseline state data and the driving scenario. Based on this, it automatically... By adapting and tuning the initial control vector of the vehicle to obtain the target control vector, automated and targeted tuning of the initial control vector is achieved. This not only improves the correlation between the target control vector and the driving scenario but also enhances the accuracy of the target control vector. On the other hand, the vehicle's stability control system (including the vehicle dynamics mode) is adjusted based on the target control vector, enabling targeted adjustments to the TMD. Furthermore, when driving state data changes, the driving scenario, baseline state data, and target control vector can also change accordingly, allowing for flexible and automated targeted adjustments to the TMD to specifically meet its control requirements. Attached Figure Description

[0007] Figure 1 A flowchart illustrating the adaptive tuning-based parameter optimization control method for vehicle stability systems provided in this application embodiment; Figure 2 A front view of the TMD provided in the embodiments of this application; Figure 3 A partial view of the TMD provided in the embodiments of this application; Figure 4 A top view of the TMD provided in the embodiments of this application; Figure 5 Another flowchart illustrating the adaptive tuning method for optimizing vehicle stability system parameters provided in this application embodiment. Detailed Implementation

[0008] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0009] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0010] During vehicle operation, rapid changes in road conditions and driving status can lead to dynamic changes in vehicle stability. Therefore, in order to mitigate the negative impact of these drastic changes in vehicle stability, related technologies have proposed improving driving and passenger comfort by controlling the state of the vehicle's stability mechanism (TMD).

[0011] However, in practical applications, once the vehicle switches to driving mode, the TMD is controlled based on various data contained in the driving mode. Therefore, the above method cannot specifically meet the control requirements of the TMD.

[0012] To address the above technical issues, this application provides a method for optimizing and controlling the parameters of an automotive vehicle stability system based on adaptive tuning. Figure 1 This is a flowchart illustrating the adaptive tuning-based parameter optimization control method for vehicle stability systems provided in this application embodiment. Figure 1 As shown, the process may include the following steps: Step 101: Collect vehicle driving status data.

[0013] The driving status data includes the vehicle's speed, steering angle, and lateral displacement.

[0014] In some embodiments, a vehicle may include a vehicle powered by electricity and / or fuel.

[0015] In some embodiments, after detecting that the vehicle has switched to driving mode, driving status data can be collected.

[0016] In some embodiments, the driving speed may include the instantaneous speed of the vehicle at the current moment, or the average driving speed of the vehicle within a first unit time period; for example, the first unit time period may be preset or flexibly adjusted, and this application embodiment does not limit it.

[0017] In some embodiments, the steering angle may include the change in the vehicle's steering angle over a second unit time period; for example, the second unit time period may be preset or flexibly adjusted, and this application embodiment does not limit this.

[0018] In some embodiments, lateral displacement may include the displacement distance generated by the vehicle's lateral movement within a third unit time; exemplaryly, the third unit time may be preset or flexibly adjusted, and this application embodiment does not limit this.

[0019] In some embodiments, the driving speed, steering angle, and lateral displacement in the driving state data may all be marked with time stamps. These time stamps are used to indicate the time corresponding to the driving speed, steering angle, and lateral displacement, respectively, and may also be used to indicate the duration corresponding to different driving speeds, steering angles, and lateral displacements.

[0020] In some embodiments, the driving state data may include a driving speed sequence, a steering angle sequence, and a lateral displacement sequence; wherein, the driving speed sequence may include a sequence of multiple driving speeds corresponding to different times, the steering angle sequence may include a sequence of multiple steering angles corresponding to different times, and the lateral displacement sequence may include a sequence of multiple lateral displacements corresponding to different times.

[0021] In some embodiments, driving status data can be collected in the following ways: By using a data acquisition device and / or equipment installed in the vehicle, driving speed, steering angle, and lateral displacement are collected at specified data acquisition time intervals, and the driving speed, steering angle, and lateral displacement are integrated to obtain driving status data.

[0022] For example, driving speed can be acquired by wheel speed sensor and / or inertial measurement unit, steering angle can be acquired by gyroscope and inertial measurement unit, and lateral displacement can be acquired by vision sensor and / or lidar. Accordingly, the acquisition device and / or acquisition equipment may include wheel speed sensor, inertial measurement unit, gyroscope, vision sensor and lidar, etc.

[0023] For example, the data acquisition time interval can be preset or changed or adjusted according to the gradient of driving status data. For instance, the data acquisition time interval can be shortened as the gradient of at least one of driving speed, steering angle and lateral displacement increases, or it can be increased as the gradient of at least one of the above data decreases.

[0024] Step 102: Determine the vehicle's driving scenario based on driving status data.

[0025] In one implementation, the driving scenario can be associated with the traffic state of the vehicle's current environment. For example, the traffic state can include a smooth flow state and a non-smooth flow state, and correspondingly, the driving scenario can include a smooth flow scenario and a non-smooth flow scenario. For example, a smooth flow state can include a stable state in terms of vehicle speed, steering angle, and lateral displacement, while a non-smooth flow state can include a non-smooth state in terms of vehicle speed, steering angle, and lateral displacement. The stable state can include a gradient of vehicle speed, steering angle, and lateral displacement within a unit time that is less than a gradient threshold, while the non-smooth driving state can include a gradient of at least one of vehicle speed, steering angle, and lateral displacement within a unit time that is greater than or equal to a gradient threshold.

[0026] Accordingly, the driving scenario can be determined in the following ways: The driving scenario can be determined by the changes in the data contained in the driving status data; for example, if the change in driving speed indicates that the rate of change of driving status within a specified time period is less than or equal to the rate of change threshold, it can be determined that the car is currently in a smooth driving scenario.

[0027] Step 103: Filter driving status data based on driving scenario to obtain the baseline status data at the current moment.

[0028] In some embodiments, the reference state data may include initial state data for adjusting the initial control vector of the vehicle; for example, the reference state data may be a portion of the driving state data.

[0029] In some embodiments, the baseline state data can be obtained in the following ways: The target factors are determined based on the driving scenario, and the data corresponding to the target factors in the driving status data are determined as the baseline status data. The target factors may include factors that trigger changes in the driving scenario. For example, for a smooth driving scenario, the target factors may include the rate of change of driving speed. In this case, the baseline status data may include driving speed.

[0030] For example, the relationship between driving scenarios and scenario factors can be established in advance, and after determining the driving scenario at the current moment, the scenario factors associated with the driving scenario corresponding to the driving scenario at the current moment in the relationship are determined as target factors based on the correspondence between the driving scenario at the current moment and the driving scenarios in the relationship.

[0031] Step 104: Adaptively tune the initial control vector of the vehicle based on the reference state data to obtain the target control vector.

[0032] In some embodiments, the initial control vector may be associated with a driving scenario and / or reference state data; for example, the values ​​of the vectors in the initial control vector may be preset according to the driving scenario, and the number of vectors in the initial control vector may be the same as the number of data types in the reference state data.

[0033] In some embodiments, the initial control vector may include a step or speed at which data in the reference state data is adjusted.

[0034] In some embodiments, the number of vectors in the target control vector may be the same as the number of vectors in the initial control vector, but the step represented by the target control vector may be different from the step in the reference state data.

[0035] In some embodiments, the target control vector can be obtained in the following way: The control strategy is determined based on the driving scenario, and then the initial control vector is adaptively tuned at least once according to the control strategy to obtain the target control vector; wherein the target control vector can match the control requirements represented by the control strategy.

[0036] For example, the control strategy may include a control rate for controlling at least one driving state data of the vehicle in a driving scenario, and may also include an effective control time for at least one driving state data. That is, the control strategy may include: adjusting the driving state data of the vehicle at a control rate within the effective control time to achieve a balance between rapid control of the driving state data and safe driving of the vehicle.

[0037] Step 105: Adjust the vehicle's stability system based on the target control vector.

[0038] The vehicle stability system includes TMD.

[0039] Figure 2 A front view of the TMD provided in an embodiment of this application. (e.g.) Figure 2 As shown, the TMD may include a sensor 201, a limiting device 202 on the side of the tuning mass, a tuning mass 203, and a vibration isolation support 204 composed of laminated rubber on the top and bottom of the tuning mass.

[0040] For example, the tuning mass block can be a power battery pack in an electric vehicle, or a fuel tank or other additional mass block in a gasoline vehicle. It can be arranged as a single large mass block or as a group of multiple mass blocks.

[0041] Figure 3 A partial view of the TMD provided in an embodiment of this application.

[0042] Figure 4 A top view of the TMD provided in an embodiment of this application.

[0043] If the car body vibrates during driving, the sensor can detect the direction and amplitude of the vibration. At this time, the tuned mass block will move in the opposite direction through the elastic deformation of the vibration isolation support, while the limiting device restricts its range of motion, ultimately achieving the purpose of mitigating the vibration of the car body.

[0044] For example, the aforementioned sensor can achieve real-time detection of the vehicle's displacement speed, steering angle, and lateral displacement.

[0045] In some embodiments, the vehicle stability system may further include an engine or transmission control unit, a brake pressure regulating unit, and auxiliary subsystems.

[0046] In some implementation sets, adjusting the vehicle stability system can be achieved in the following ways: Based on the driving scenario, the target to be adjusted is determined from the vehicle stability system, and the state of the target to be adjusted is gradually adjusted based on the control step represented by the target control vector.

[0047] For example, if the driving scenario represents a smooth driving scenario, the targets to be adjusted include the TMD and the engine or transmission control unit. Then, based on the control step represented by the target control vector, the vibration state of the TMD is controlled, the output torque of the engine is controlled, and the transmission gear is adjusted to reduce the probability of violent vibration of the car body caused by emergency braking in a smooth driving scenario.

[0048] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application collects vehicle driving state data, including vehicle speed, steering angle, and lateral displacement, thus improving the comprehensiveness of the driving state data. Furthermore, it determines the vehicle's driving scenario based on the driving state data, thereby improving the correlation between the driving scenario and the vehicle's driving state data, and thus improving the accuracy of the driving scenario. Simultaneously, it filters the driving state data based on the driving scenario to obtain the current reference state data, achieving targeted filtering of the driving state data, thereby improving the correlation between the reference state data and the driving scenario. Based on this, the reference state data... The initial control vector of the vehicle is adaptively tuned to obtain the target control vector, realizing automated and targeted tuning of the initial control vector. This not only improves the correlation between the target control vector and the driving scenario but also enhances the accuracy of the target control vector. On the other hand, the vehicle's stability control system, including the vehicle dynamics mode (TMD), is adjusted based on the target control vector, enabling targeted adjustments to the TMD. Furthermore, when driving state data changes, the driving scenario, baseline state data, and target control vector can also change accordingly, allowing for flexible and automated targeted adjustments to the TMD to specifically meet its control requirements.

[0049] Based on the foregoing embodiments, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application, which filters driving state data based on driving scenarios to obtain the current moment's baseline state data, can be achieved in the following way: If the driving scenario is an emergency steering scenario, the steering angle in the driving status data is determined as the baseline status data.

[0050] Accordingly, if the driving scenario is not an emergency steering scenario, the steering angle in the driving status data does not need to be determined as the baseline status data.

[0051] In some embodiments, an emergency steering scenario may include a scenario where the rate of change of steering angle per unit time is greater than or equal to a steering angle threshold.

[0052] In some embodiments, in an emergency steering scenario, the steering angle is determined as the baseline state data so that the target control vector can also be related to the steering angle. In this way, by controlling the TMD through the target control vector, the inertial centripetal force generated by the emergency steering can be weakened.

[0053] As can be seen from the above, in the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application embodiment, if the driving scenario is an emergency steering scenario, the steering angle in the driving state data is determined as the reference state data. Thus, in an emergency steering scenario, by determining the steering angle as the reference state data, the target control vector can be directly associated with the vehicle's steering angle. Compared to related technologies that determine various driving state data as the control basis, this solution can reduce control noise and thereby achieve targeted control of the angle-related states of the TMD (Traction Control Mode).

[0054] Based on the foregoing embodiments, in the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application embodiment, if the driving scenario is an emergency steering scenario, before determining the steering angle in the driving state data as the reference state data, the following operations may also be performed: Determine the first rate of change of steering angle and the second rate of change of driving speed in the driving status data. If the first rate of change is greater than or equal to the first threshold and the second rate of change is less than or equal to the second threshold, the driving scenario is determined as an emergency steering scenario.

[0055] Accordingly, if the first rate of change is less than the first threshold, or the second rate of change is greater than the second threshold, then the operation of identifying the driving scenario as an emergency steering scenario can be omitted.

[0056] In some embodiments, the first rate of change can characterize the speed of change of the steering angle; exemplarily, the first rate of change can be obtained by statistically analyzing the changes in the steering angle over a preset time interval; the first rate of change Specifically, it can be calculated using equation (1): (1) in, The turning angle is obtained from the kth data acquisition. The turning angle is obtained from the (k-1)th acquisition. The time interval between the k-th data acquisition and the (k-1)-th data acquisition is denoted as k, where k is an integer greater than or equal to 2.

[0057] In some embodiments, the second rate of change may include the acceleration of the driving speed; exemplarily, the second rate of change It can be calculated using equation (2): (2) in, The driving speed is obtained from the kth data collection. The driving speed is obtained from the (k-1)th data collection.

[0058] In some embodiments, a first rate of change greater than or equal to a first threshold and a second rate of change less than or equal to a second threshold can characterize that the vehicle's speed is in a stable state of change within a preset time interval, while the vehicle's steering angle undergoes a sudden change.

[0059] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application determines the first rate of change of the vehicle's steering angle and the second rate of change of its driving speed. If the first rate of change is greater than or equal to a first threshold and the second rate of change is less than or equal to the second threshold, then the driving scenario is determined to be an emergency steering scenario. Thus, by determining the vehicle's driving scenario through the rates of change of steering angle and driving speed, the correlation between emergency steering scenarios and the rates of change of steering angle and driving speed can be improved, thereby enabling a more accurate description of emergency steering scenarios and improving their accuracy.

[0060] Based on the foregoing embodiments, the method for optimizing and controlling the parameters of a vehicle stability system based on adaptive tuning provided in this application, which obtains the baseline state data at the current moment by filtering driving state data based on the driving scenario, can also be implemented in the following ways: If the driving scenario is a lateral risk scenario, the lateral displacement and steering angle in the driving status data are determined as the baseline status data.

[0061] Accordingly, if the driving scenario is not a lateral risk scenario, the operation of determining the lateral displacement and steering angle as the baseline state data can be omitted.

[0062] In some embodiments, lateral risk scenarios may include scenarios where the probability of a vehicle being struck or bumped on the side is greater than or equal to a probability threshold.

[0063] In some embodiments, if a vehicle is in a continuous lateral displacement within a preset time period and the steering angle is consistent with the direction of the lateral displacement, it can be determined that the vehicle is in a lateral risk scenario.

[0064] As can be seen from the above, in the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application embodiment, if the driving scenario is a lateral risk scenario, the lateral displacement and steering angle in the driving state data are determined as the reference state data. Thus, by associating the lateral displacement and steering angle with the lateral risk scenario, targeted filtering of the vehicle's driving state data can be achieved, the correlation between the reference state data and the lateral risk scenario can be improved, and a fine-grained characterization of the lateral risk scenario can be achieved, thereby improving the accuracy of the lateral risk scenario.

[0065] Based on the foregoing embodiments, in the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application embodiment, if the driving scenario is a lateral risk scenario, before determining the lateral displacement and steering angle in the driving state data as the reference state data, the following operations can also be performed: Determine the first rate of change of steering angle and the third rate of change of lateral displacement in the driving status data; if the first rate of change is greater than or equal to the first threshold and the third rate of change is greater than or equal to the third threshold, the driving scenario is determined as a lateral risk scenario.

[0066] Accordingly, if the first rate of change is less than the first threshold, or the third rate of change is less than the third threshold, then the operation of identifying the driving scenario as a lateral risk scenario can be omitted.

[0067] In some embodiments, the third rate of change may include the rate of change of lateral displacement within a preset time interval; exemplarily, the third rate of change... It can be calculated using equation (3): (3) in, The lateral displacement is obtained from the kth acquisition. The lateral displacement is obtained from the (k-1)th acquisition.

[0068] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application determines the first rate of change of the steering angle and the third rate of change of the lateral displacement. If the first rate of change is greater than or equal to a first threshold and the third rate of change is greater than or equal to a third threshold, the driving scenario is determined to be a lateral risk scenario. Thus, by associating the lateral risk scenario with the rate of change of the vehicle's steering angle and the rate of change of its lateral displacement, a fine-grained quantification of the lateral risk scenario can be achieved based on the rate of change of the vehicle's steering angle and the rate of change of its lateral displacement, thereby improving the accuracy of the lateral risk scenario.

[0069] Based on the foregoing embodiments, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application, which adaptively tunes the initial control vector of the vehicle based on reference state data to obtain the target control vector, can be achieved through the following steps: Step A1: Based on the initial vector, predict the reference state data using the vehicle's motion state equation to obtain the predicted state data.

[0070] In some embodiments, the equation of motion can mathematically describe the causal relationship between the current motion state of the vehicle, the control vector, and the motion state at the next moment. Essentially, it transforms the mechanical motion laws of the vehicle into a computable mathematical equation to quantitatively predict changes in the vehicle's motion.

[0071] For example, the equation of motion can be shown as in equation (4): (4) in, Let the motion state of the car be at time k+1. Let K be the motion state of the car at time k. Let k be the control vector for the car at time k. This is a state matrix used to describe how the car's state at time k affects its state at time k+1. The input matrix is ​​used for control. The strength of the effect on the state at time k.

[0072] It should be noted that in the embodiments of this application, the kth time can correspond to the kth time, and the (k+1)th time can correspond to the (k+1)th time.

[0073] For example, the initial control vector can be The baseline state data can be Furthermore, the reference state data may include a portion of the driving state data, and the initial control vector may be... Predicted state data can be .

[0074] Step A2: Determine the risk level of the vehicle.

[0075] In some embodiments, the risk level can be used to quantify the degree of risk of the vehicle's current driving state; for example, the risk level can increase as the data value in the baseline state data increases; correspondingly, the risk level can be determined in the following ways: The data values ​​in the baseline state data are compared with a preset range set to quantify the baseline state data. If the baseline state data is in the high-risk range represented by the range set, the risk level is determined to be high-risk. If the baseline state data is in the low-risk range represented by the range set, the risk level is determined to be low-risk.

[0076] For example, the first mapping relationship between the value range of the interval range set and the risk interval can be established in advance, or it can be flexibly adjusted according to different vehicles and different driving scenarios. This application embodiment does not limit this.

[0077] Step A3: Using the vehicle's objective function, the initial control vector is adaptively adjusted based on the predicted state data and risk level to obtain the target control vector.

[0078] In some embodiments, the objective function can be transformed into a mathematical calculation process to balance the vehicle's driving safety, comfort, and energy consumption levels, based on the actual driving needs of the car. For example, the objective function can be shown in equation (5): (5) in, It is the control error of the objective function; the objective of vehicle control is... The value of is the smallest. It is the expected state of the next moment after the k-th moment corresponding to the actual driving needs. N is an integer greater than 1, used to represent the number of prediction steps, that is, the cumulative number of control errors.

[0079] It should be noted that in equation (5), the starting state of the adaptive adjustment process is the state corresponding to the baseline state data. .

[0080] In some embodiments, the risk level can be directly related to the number of prediction steps. For example, the number of prediction steps is proportional to the risk level; for instance, the number of prediction steps can increase as the risk level increases.

[0081] Specifically, constraints can be set based on the TMD damping force limit, the vehicle's body slip angle safety boundary, and the dynamic parameter range, and the iterative adaptive adjustment between formulas (4) and (5) can be achieved. The process continues until the control error of the objective function is minimized; at this point, the optimal control vector obtained can be considered the objective control vector. .

[0082] For example, the above-described recursive adaptive adjustment of the initial control vector can be used as the parameter optimization process for a vehicle's Model Predictive Control (MPC). Through this process, state data prediction, control vector optimization, and state control can be integrated during vehicle operation, thereby enabling dynamic, adaptive, and intelligent control of the vehicle's driving state.

[0083] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application predicts the reference state data based on the initial control vector using the vehicle's motion state equation, thus achieving the reuse of the vehicle's motion state equation and enabling accurate prediction of the predicted state data. Furthermore, after determining the risk level of the vehicle, the initial control vector is adaptively adjusted based on the predicted state data and the risk level using the vehicle's objective function to obtain the target control vector. This allows for targeted adjustment of the initial control vector, thereby improving the correlation between the target control vector and the reference state data and the risk level, and enhancing the accuracy and targeting of the target control vector.

[0084] Based on the foregoing embodiments, the method for optimizing and controlling the parameters of a vehicle stability system based on adaptive tuning provided in this application can determine the risk level of the vehicle in the following ways: The baseline risk data is determined based on the steering angle and lateral displacement, and the risk level is determined based on the baseline risk data.

[0085] In some embodiments, the baseline risk data may include reference data for determining the risk level, or the baseline risk data may include data whose impact on the risk level is greater than or equal to a threshold.

[0086] In some embodiments, the baseline risk data may be determined in any of the following ways: The first rate of change is obtained by statistically analyzing the steering angle, and the third rate of change is obtained by statistically analyzing the lateral displacement. Then, the first rate of change and the third rate of change are statistically averaged to obtain the baseline risk data.

[0087] The sum of the squares of the first rate of change and the third rate of change is used as the baseline risk data.

[0088] In some embodiments, the risk level can be determined in the following ways: The first and third rates of change are statistically analyzed to obtain the maximum values ​​of the angle rate of change and the lateral displacement rate of change. The first sum of squares between the maximum values ​​of the angle rate of change and the lateral displacement rate of change is determined, and the ratio of the second sum of squares to the first sum of squares is determined as the risk value. Then, the risk value is quantified to obtain the risk level. The second sum of squares may include the sum of squares of the first and third rates of change at the current moment. Specifically, it can be shown in equation (6): (6) in, It can be a risk value. The first rate of change, The third rate of change, The maximum rate of change of angle. This represents the maximum rate of change of lateral displacement. For the first sum of squares, This is the second sum of squares.

[0089] For example, quantifying risk values ​​to obtain risk levels can be achieved in the following ways: Obtain a set of risk thresholds, and quantify the risk value based on the relationship between the risk value and the thresholds in the risk threshold set to obtain the risk level; for example, if the risk value is located in the high-risk range in the risk threshold set, the risk level can be a high-risk level.

[0090] As can be seen from the above, in the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application embodiment, the baseline risk data is determined based on the steering angle and lateral displacement, which improves the correlation between the steering angle, lateral displacement and baseline risk data, and also enables the baseline risk data to characterize the risk state from the dimensions of the vehicle's steering angle and lateral displacement; furthermore, the risk level is determined based on the baseline risk data, realizing the quantification and simplification of the baseline risk data.

[0091] Based on the foregoing embodiments, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application obtains the target control vector by adaptively adjusting the initial control vector based on predicted state data and risk level using the vehicle's objective function. This can be achieved in the following way: The prediction time window is determined based on the risk level; the prediction step number is determined based on the prediction time window; the initial control vector is adaptively adjusted based on the prediction state data and the prediction step number through the objective function to obtain the target control vector.

[0092] In some embodiments, the prediction time window may include the time difference between the current time and the time corresponding to the desired target control vector.

[0093] In some embodiments, a second mapping relationship can be pre-established between multiple risk levels and multiple time windows. In this way, after determining the risk level at the current moment, the time window corresponding to the risk level that matches the risk level at the current moment in the second mapping relationship can be determined as the prediction time window.

[0094] Table 1 shows the statistical results of the second mapping relationship provided in the embodiments of this application.

[0095] Table 1 It should be noted that the length of the time window in the second mapping relationship is for illustrative purposes only and can be flexibly adjusted in specific applications.

[0096] In some embodiments, the number of prediction steps can be calculated in the following way: The quotient of the prediction time window and the data collection time interval is determined as the prediction step number, as shown in equation (7): (7) Where N is the number of prediction steps, To predict the time window, This represents the data collection time interval.

[0097] For example, after calculating the number of predicted steps, the initial control vector can be recursively adjusted based on the predicted state data and the number of predicted steps using equation (5), combined with equation (4), to obtain the target control vector.

[0098] It should be noted that in the embodiments of this application, the kth time can correspond to the kth time, and the (k+1)th time can correspond to the (k+1)th time. In this case, Can Equivalent to.

[0099] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application determines the prediction time window based on the risk level, and then determines the prediction step number based on the prediction time window. In this way, the correlation between the prediction step number and the risk level is realized. On this basis, the initial control vector is adaptively adjusted based on the predicted state data and the prediction step number through the objective function to obtain the target control vector. This enables targeted adaptive adjustment of the initial control vector, thereby improving the targeting and accuracy of the target control vector.

[0100] Based on the foregoing embodiments, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application can further perform the following steps: Step B1: Determine the first rate of change of steering angle, the second rate of change of driving speed, and the third rate of change of lateral displacement in the driving status data.

[0101] Step B2: If the first rate of change is less than the first threshold, the second rate of change is less than the second threshold, and the third rate of change is less than the third threshold, then the driving scenario is determined to be a normal scenario.

[0102] Accordingly, if the first rate of change is greater than or equal to the first threshold, the second rate of change is greater than or equal to the second threshold, or the third rate of change is greater than or equal to the third threshold, then the operation of determining the driving scenario as a normal scenario can be omitted.

[0103] In some embodiments, the general scenarios may include a set of scenarios excluding emergency steering scenarios and lateral risk scenarios.

[0104] Step B3: Determine the driving status data as the baseline status data.

[0105] In some embodiments, after determining the baseline state data, the risk level of the vehicle can be determined by the method provided in the foregoing embodiments. Then, a prediction time window is determined based on the risk level, and the prediction step number is determined based on the prediction time window. Next, the baseline state data is predicted based on the initial control vector using the vehicle's motion state equation to obtain the predicted state data. Then, the initial control vector is adaptively adjusted based on the predicted state data and the prediction step number using the objective function to obtain the target control vector.

[0106] As can be seen from the above, the adaptive tuning-based vehicle stability system parameter optimization control method provided in this application determines the first rate of change of steering angle, the second rate of change of driving speed, and the third rate of change of lateral displacement in the driving state data. If the first rate of change is less than a first threshold, the second rate of change is less than a second threshold, and the third rate of change is less than a third threshold, then the driving scenario is determined to be a normal scenario. In this way, not only is the rate of change of various data in the driving state data tracked and statistically analyzed, but the normal scenario is also associated with the rate of change of various data in the driving state data, thereby improving the precision of the normal scenario determination process. On this basis, the driving state data is determined as the baseline state data, thus improving and supplementing the method of determining the baseline state data when the vehicle is in a normal scenario. Combined with the aforementioned embodiments, the baseline state data can be determined comprehensively and specifically in diverse driving scenarios, thereby improving the robustness and comprehensiveness of the technical solution provided in this application.

[0107] Figure 5 Another flowchart illustrating the adaptive tuning vehicle stability system parameter optimization control method provided in this application embodiment is shown below. Figure 5As shown, the process may include the following steps: Step 501, Begin.

[0108] For example, at this time, the car can switch to driving mode.

[0109] Step 502: Collect driving status data.

[0110] Step 503: Classification of driving scenarios.

[0111] For example, driving scenarios can be classified based on the first rate of change of steering angle, the second rate of change of driving speed, and the third rate of change of lateral displacement in the driving status data to determine whether the vehicle is currently in an emergency steering scenario, a lateral risk scenario, or a normal scenario.

[0112] Step 504: Determine the risk level.

[0113] For example, the risk baseline data can be determined based on the steering angle and lateral displacement using the method provided in the foregoing embodiments, and then the risk level can be determined based on the risk baseline data.

[0114] Step 505: Dynamically adjust the prediction time window.

[0115] For example, the prediction time window can be determined based on the risk level using the method provided in the foregoing embodiments.

[0116] Step 506: MPC parameter optimization.

[0117] For example, the method provided in the foregoing embodiments can be used to determine the number of prediction steps based on the prediction time window, and then recursively and adaptively adjust the initial state vector through the vehicle's motion state equation and objective function, thereby achieving optimization of the vehicle's MPC parameters.

[0118] Step 507, Status Control.

[0119] For example, the TMD can be controlled according to the target state vector to improve the stability of vehicle driving.

[0120] For example, after step 507 is completed, steps 502 to 507 can continue to be executed to achieve continuous automated high-precision following control of vehicle driving stability.

[0121] Through the above process, the automated and intelligent execution of driving status data collection, driving scenario classification, MPC parameter optimization, and state control is realized, so that the target state vector on which the state control is based can be matched with the driving status data, thereby improving the relevance and accuracy of the target state vector.

[0122] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0123] The methods disclosed in the various method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict.

[0124] The features disclosed in the various product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0125] The features disclosed in the various method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0126] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0127] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0128] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware nodes. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0130] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A parameter optimization control method for a vehicle body stability system based on adaptive tuning, characterized in that, The method includes: Collect vehicle driving status data; wherein, the driving status data includes the vehicle's driving speed, steering angle, and lateral displacement; The driving scenario of the vehicle is determined based on the driving status data; Based on the driving scenario, the driving status data is filtered to obtain the baseline status data at the current moment; The initial control vector of the vehicle is adaptively tuned based on the reference state data to obtain the target control vector; The vehicle's stability system is adjusted based on the target control vector; wherein the vehicle stability system includes a tuned mass damper (TMD).

2. The method according to claim 1, characterized in that, The step of filtering the driving state data based on the driving scenario to obtain the baseline state data at the current moment includes: If the driving scenario is an emergency steering scenario, the steering angle in the driving status data is determined as the reference status data.

3. The method according to claim 2, characterized in that, If the driving scenario is an emergency steering scenario, before determining the steering angle in the driving state data as the reference state data, the method further includes: Determine the first rate of change of the steering angle and the second rate of change of the driving speed in the driving status data; If the first rate of change is greater than or equal to the first threshold and the second rate of change is less than or equal to the second threshold, the driving scenario is determined to be the emergency steering scenario.

4. The method according to claim 1, characterized in that, The step of filtering the driving state data based on the driving scenario to obtain the baseline state data at the current moment includes: If the driving scenario is a lateral risk scenario, the lateral displacement and the steering angle in the driving state data are determined as the baseline state data.

5. The method according to claim 4, characterized in that, If the driving scenario is a lateral risk scenario, before determining the lateral displacement and steering angle in the driving state data as the reference state data, the method further includes: Determine the first rate of change of the steering angle and the third rate of change of the lateral displacement in the driving state data; If the first rate of change is greater than or equal to the first threshold and the third rate of change is greater than or equal to the third threshold, the driving scenario is determined to be the lateral risk scenario.

6. The method according to claim 1, characterized in that, The process of adaptively tuning the initial control vector of the vehicle based on the reference state data to obtain the target control vector includes: The predicted state data is obtained by predicting the reference state data based on the initial control vector using the vehicle's motion state equation; Determine the risk level of the vehicle; The target control vector is obtained by adaptively adjusting the initial control vector based on the predicted state data and the risk level using the vehicle's objective function.

7. The method according to claim 6, characterized in that, Determining the risk level of the vehicle includes: Based on the steering angle and the lateral displacement, baseline risk data is determined; The risk level is determined based on the baseline risk data.

8. The method according to claim 6, characterized in that, The step of adaptively adjusting the initial control vector based on the predicted state data and the risk level using the vehicle's objective function to obtain the target control vector includes: The prediction time window is determined based on the aforementioned risk level; The number of prediction steps is determined based on the prediction time window; The target control vector is obtained by adaptively adjusting the initial control vector based on the predicted state data and the predicted number of steps using the objective function.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: Determine the first rate of change of the steering angle, the second rate of change of the driving speed, and the third rate of change of the lateral displacement in the driving state data; If the first rate of change is less than the first threshold, the second rate of change is less than the second threshold, and the third rate of change is less than the third threshold, the driving scenario is determined to be a normal scenario. The driving status data is determined as the baseline status data.

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