Vehicle control method, medium, product, device, and vehicle

Abstract

The application relates to a vehicle control method, medium, product, equipment and vehicle, and the method comprises the following steps: when the vehicle is in a target driving state, the corresponding actuator is controlled according to the control parameter of at least one actuator of the vehicle, so as to assist the vehicle to drive; the at least one actuator comprises an aerodynamic component of the vehicle. According to the method, the control parameters of multiple actuators including the aerodynamic component are adjusted, so that active auxiliary control of the vehicle is realized, and the control effect of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle control method, medium, product, equipment and vehicle. Background Art

[0002] Currently, many vehicles have Track Mode features, but these modes often fail to meet drivers' needs for racetrack driving scenarios. For example, Track Mode aims to meet drivers' controllability needs by maximizing vehicle output. However, depending on driving conditions, this approach struggles to achieve the desired driving state, resulting in poor vehicle control. Summary of the Invention

[0003] The embodiments of the present application provide a vehicle control method, medium, product, device and vehicle to solve the above-mentioned problems.

[0004] In order to achieve the above object, according to a first aspect of the present application, a vehicle control method is provided, the method comprising:

[0005] When the vehicle is in a target driving state, a corresponding actuator is controlled according to a control parameter of at least one actuator of the vehicle to assist the vehicle in driving; the at least one actuator includes an aerodynamic component of the vehicle.

[0006] Optionally, the method further includes:

[0007] A control parameter of the at least one actuator is determined according to the vehicle state information.

[0008] Optionally, the vehicle status information includes at least one of the slip rate, yaw angular velocity, and pedal depth of the vehicle.

[0009] Optionally, the at least one actuator further includes a power system of the vehicle, and determining the control parameters of the at least one actuator according to the vehicle state information includes:

[0010] When a difference between a slip ratio of the vehicle and an ideal slip ratio is greater than or equal to a first preset difference threshold, at least one of a target torque of the power system and a target posture of an aerodynamic component is used as the control parameter.

[0011] Optionally, the target posture includes the height and rotation angle of the target aerodynamic component, and / or the target torque includes at least one of the left front wheel driving torque, the right front wheel driving torque, the left rear wheel driving torque, the right rear wheel driving torque, and the longitudinal driving torque.

[0012] Optionally, determining the control parameters of the at least one actuator according to the vehicle state information includes:

[0013] differences in relative poses of a plurality of aerodynamic components of the vehicle as the control parameter when a yaw rate of the vehicle is greater than an ideal yaw rate.

[0014] Optionally, the differences in relative poses of the plurality of aerodynamic components include a difference in relative pose between a first aerodynamic component and a second aerodynamic component, the first aerodynamic component and the second aerodynamic component being located on two sides of the vehicle respectively.

[0015] Optionally, the determining the control parameter of the at least one actuator according to the vehicle state information comprises:

[0016] a rotation angle of a target aerodynamic component of the vehicle as the control parameter when a pedal depth of the vehicle is greater than an ideal pedal depth.

[0017] Optionally, the controlling the corresponding actuator according to the control parameter of the at least one actuator of the vehicle comprises:

[0018] processing a deviation of the vehicle state information according to a gain coefficient of a preset control algorithm, determining a control amount of the control parameter, and controlling the corresponding actuator based on the control amount of the control parameter.

[0019] Optionally, the gain coefficient of the preset control algorithm includes at least two of a proportional coefficient, a differential coefficient, and an integral coefficient.

[0020] Optionally, the method further comprises:

[0021] determining a compensation value according to the control amount of the preset control parameter and the controlled vehicle state information;

[0022] updating the control amount of the control parameter according to the compensation value to obtain an updated control amount of the control parameter.

[0023] Optionally, the updating the control amount of the control parameter according to the compensation value to obtain an updated control amount of the control parameter comprises:

[0024] updating the control amount of the control parameter at the current time according to the compensation value obtained at each time to obtain an updated control amount of the control parameter at the current time.

[0025] Optionally, the compensation value includes a first compensation value and a second compensation value, and the updating the control amount of the control parameter according to the compensation value to obtain an updated control amount of the control parameter comprises:

[0026] calculating a difference between the control amount of the control parameter and the first compensation value;

[0027] determine a control amount of the updated control parameter according to a ratio of the difference value and the second compensation value.

[0028] Optionally, the processing of the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter comprises:

[0029] updating the preset gain coefficient according to the deviation of the vehicle state information to obtain the gain coefficient of the preset control algorithm;

[0030] processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to obtain the control amount of the control parameter.

[0031] Optionally, the preset gain coefficient comprises a gain coefficient at a previous time, and the updating of the preset gain coefficient according to the deviation of the vehicle state information to obtain the gain coefficient of the preset control algorithm comprises:

[0032] determining a fuzzy coefficient at a current time according to a deviation of vehicle state information at the current time and a preset fuzzy rule; different deviations correspond to different fuzzy coefficients;

[0033] updating the gain coefficient at the previous time according to the fuzzy coefficient obtained at the current time to obtain a gain coefficient at the current time.

[0034] Optionally, the processing of the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter comprises:

[0035] processing a difference between the slip rate of the vehicle and an ideal slip rate according to the gain coefficient of the preset control algorithm to obtain a change amount of the target torque distribution ratio and a change amount of the target aerodynamic component pose.

[0036] Optionally, the processing of the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter comprises:

[0037] processing a difference between the yaw rate of the vehicle and an ideal yaw rate according to the gain coefficient of the preset control algorithm to obtain a relative pose difference of a plurality of aerodynamic components.

[0038] Optionally, the processing of the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter comprises:

[0039] processing a difference between the longitudinal acceleration of the vehicle and an ideal longitudinal acceleration according to the gain coefficient of the preset control algorithm to obtain a change amount of a rotation angle of the target aerodynamic component.

[0040] Optionally, the deviation of the gain coefficient according to the preset control algorithm from the vehicle state information is processed to determine the control amount of the control parameter, including:

[0041] The difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration is processed according to the gain coefficient of the preset control algorithm to obtain a torque value of the longitudinal driving torque.

[0042] Optionally, the method further includes:

[0043] When the vehicle is in a steering state, the brake torque of the target wheel is taken as the control parameter.

[0044] Optionally, the method further includes:

[0045] When the vehicle is in a race track mode and the vehicle is in a straight state, it is determined that the vehicle is in a target driving state.

[0046] Optionally, the aerodynamic assembly includes an active separation grid and / or an active separation tail.

[0047] According to a second aspect of the present application, the embodiments of the present application further provide an electronic device, including:

[0048] a memory having a computer program stored thereon;

[0049] a processor configured to execute the computer program in the memory to implement the steps of any of the methods provided by the embodiments of the present application.

[0050] According to a third aspect of the present application, the embodiments of the present application further provide a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of any of the methods provided by the embodiments of the present application.

[0051] According to a fourth aspect of the present application, the embodiments of the present application further provide a computer program product including a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the steps of any of the methods provided by the embodiments of the present application.

[0052] According to a fifth aspect of the present application, the embodiments of the present application further provide a vehicle including the vehicle control device, or the electronic device, or executing the steps of any of the methods provided by the embodiments of the present application.

[0053] Some embodiments of the present specification at least include the following beneficial effects: determining the control parameter of the vehicle according to the vehicle state information, overcoming the problem that the control parameter is fixed and difficult to match the driving condition in the related art. By obtaining the vehicle state information in real time, the control parameter of intelligent driving can be dynamically adjusted, so as to better match different driving conditions and improve the safety and stability of driving.

[0054] Other features and advantages of the present application will be described in detail in the following detailed description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0056] In order to more completely understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, wherein the same reference numerals in the following description represent the same parts.

[0057] Figure 1 is an application scenario of a vehicle control method according to some embodiments of the present specification;

[0058] Figure 2 is an exemplary flowchart of a vehicle control method according to some embodiments of the present specification;

[0059] Figure 3 is an exemplary schematic diagram of determining the control parameter of the control amount according to some embodiments of the present specification;

[0060] Figure 4 is an exemplary schematic diagram of determining the driving condition according to some embodiments of the present specification;

[0061] Figure 5 is an exemplary schematic diagram of vehicle state information according to some embodiments of the present specification;

[0062] Figure 6 is an exemplary schematic diagram of drive slip control according to some embodiments of the present specification;

[0063] Figure 7 is an exemplary schematic diagram of unintended yaw control according to some embodiments of the present specification;

[0064] Figure 8 is an exemplary schematic diagram of deceleration control according to some embodiments of the present specification;

[0065] Figure 9 is an exemplary schematic diagram of a tail fin mounting position according to some embodiments of the present specification;

[0066] Figure 10 is an exemplary schematic diagram of a tail fin according to some embodiments of the present specification;

[0067] Figure 11 is an exemplary schematic diagram of a tail fin lifting column according to some embodiments of the present specification;

[0068] Figure 12 is an exemplary schematic diagram of a tail fin blade angle according to some embodiments of the present specification;

[0069] Figure 13 is a structural schematic diagram of an electronic device according to some embodiments of the present specification;

[0070] Figure 14 is an exemplary schematic diagram of a vehicle according to some embodiments of the present specification. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0072] In order to facilitate the understanding of the implementation scheme provided by the embodiments of the present application, the related application background of the vehicle control method provided by the embodiments of the present application is described first.

[0073] At present, adjustable aerodynamic components (such as active tail fin, variable angle tail fin, etc.) are gradually applied to high-performance vehicles and new energy vehicle models. By changing the airflow distribution around the vehicle body, functions such as downforce optimization, wind resistance reduction or lateral stability enhancement are realized. Related technologies control aerodynamic components or other actuators (such as brake systems, powertrains, etc.) as independent modules, which are not effectively coordinated with other actuators of the vehicle, and cannot be dynamically adjusted according to the real-time driving state, resulting in low adaptability in different driving conditions.

[0074] In view of this, some embodiments of the present specification provide a vehicle control method, which dynamically adjusts the control parameters of multiple actuators including aerodynamic components based on real-time collected vehicle state information (such as vehicle speed, acceleration, steering angle, yaw rate, etc.), so as to realize active auxiliary control of the vehicle driving state and improve the control effect of the vehicle.

[0075] Figure 1 is an application scenario of a vehicle control method according to some embodiments of the present specification.

[0076] In some embodiments, the vehicle control method of the embodiments of the present specification can be applied to specific scenarios such as racing sports, professional driving training, etc.

[0077] In some embodiments, the vehicle includes an electric motor. It can be understood that the number and position of the electric motor is one possible case, and the embodiments of the present application do not limit this.

[0078] As shown in Figure 1 , the vehicle is a front-rear axle independent drive vehicle, a front axle and rear axle two-wheel independent drive vehicle, or a four-wheel drive vehicle. For example, when the vehicle is a front-rear axle independent drive vehicle, the front axle and the rear axle of the vehicle can each have an independent power source for driving. When the vehicle is a front axle and rear axle two-wheel independent drive vehicle, the two wheels of the front axle can be driven by independent power sources, and the two wheels of the rear axle can be driven by independent power sources. When the vehicle is a four-wheel drive vehicle, it can be a full-time four-wheel drive vehicle, a manual lockable four-wheel drive vehicle, an automatic lockable four-wheel drive vehicle, or an all-terrain four-wheel drive vehicle, etc.

[0079] The vehicle can be a vehicle driven by electric energy. For example, when the vehicle is a vehicle driven by electric energy, it can be a new energy vehicle, such as a pure electric vehicle, a range-extended electric vehicle, a hybrid electric vehicle, a fuel cell electric vehicle, etc.

[0080] For example, when the vehicle includes three electric motors, one of the three electric motors is located at the front axle of the vehicle, and the other two electric motors are located at the rear axle of the vehicle. For example, when the vehicle includes four electric motors, two of the four electric motors are located at the front axle of the vehicle and are used to drive the left front wheel and the right front wheel, respectively. The other two electric motors are located at the rear axle of the vehicle and are used to drive the left rear wheel and the right rear wheel, respectively. For example, when the vehicle includes front-rear dual electric motors, one of the front-rear dual electric motors is located at the front axle of the vehicle, and the other electric motor is located at the rear axle of the vehicle.

[0081] It should be understood that the vehicle control method provided by the embodiments of the present application is applied to the vehicle in the above Figure 1 , and is specifically applied to any one electronic control unit (Electronic Control Unit, ECU, also referred to as electronic device) in the vehicle. The embodiments of the present application will be described below with the execution subject of the method being any one electronic device in the vehicle as an example.

[0082] In some embodiments, the method according to the embodiments of the present application is implemented by taking the electronic device as an example of the control module, and the application scenario can also include a collection module.

[0083] The collection module is mainly configured to collect vehicle state information of the vehicle during driving of the vehicle. The vehicle state information is configured to represent an operating state of the vehicle during driving of the vehicle, such as an engine speed, a vehicle speed, and an operating state or a value of some components during driving of the vehicle. After the collection module collects the vehicle state information, the vehicle state information can be sent to the control module, so that the control module determines the control parameter of the at least one actuator according to the vehicle state information. Optionally, the vehicle state information includes a vehicle speed, a vehicle mode, a wheel speed, a steering wheel angle, an acceleration of the wheel, a wheel steering angle, and the like.

[0084] In some embodiments, the collection module includes a plurality of sensors, such as a vehicle speed sensor, a gear sensor, a wheel speed sensor, an acceleration sensor, a steering wheel angle sensor, a wheel cylinder pressure sensor, and the like.

[0085] The vehicle speed sensor is configured to collect a vehicle speed. The gear sensor is configured to collect a current gear of the vehicle. The wheel cylinder pressure sensor is mainly configured to collect wheel cylinder pressures of four wheels. The acceleration sensor is configured to collect lateral and longitudinal accelerations of the vehicle as a whole during driving of the vehicle. The steering wheel angle sensor is configured to collect a steering wheel angle of the vehicle.

[0086] The control module can further obtain driving environment information around the vehicle collected by the collection module, and identify road information of the vehicle according to an out-of-vehicle road image collected by a road image sensor.

[0087] After the scenario of the embodiments of the present application and the structure of the electronic device relied on are introduced, a vehicle control method provided by the embodiments of the present application is introduced.

[0088] It is worth noting that the application scenario diagram of the vehicle control method is provided only for illustrative purposes, and is not intended to limit the scope of the present specification. For those of ordinary skill in the art, various changes and modifications can be made according to the description of the present specification. For example, the application scenario can also include a database, an information source, and the like. For another example, the application scenario can be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not deviate from the scope of the present specification.

[0089] Figure 2 is an exemplary flowchart of the vehicle control method according to some embodiments of the present specification. In some embodiments, the flowchart 200 can be executed based on an electronic device. As shown in Figure 2 the flowchart 200 includes the following steps.

[0090] At step 210, when the vehicle is in the target driving state, a corresponding actuator is controlled according to a control parameter of at least one actuator of the vehicle to assist the vehicle to drive; the at least one actuator includes an aerodynamic component of the vehicle.

[0091] The target driving state refers to a certain specific operation mode or driving scene in which the vehicle is currently located. For example, the target driving state can include a straight driving state, a turning state, etc.

[0092] In some embodiments, the target driving state can be determined based on driving information of the vehicle and driving intention, etc. For example, the turning state of the vehicle can be determined according to the turning angle of the vehicle by detecting the turning angle of the vehicle. For another example, whether the vehicle is in the driving state can be determined by driving state information (such as vehicle speed, acceleration, etc.) obtained by a wheel speed sensor, an acceleration sensor, etc., or by determining whether the vehicle is in the driving state by the running state of the engine or motor. For example, when the engine or motor is started and outputs a state signal, the state signal is transmitted to the electronic device, and the electronic device can determine whether the vehicle is in the driving state according to the state signal.

[0093] In some embodiments, when the vehicle is in the track mode and the vehicle is in the straight driving state, it is determined that the vehicle is in the target driving state.

[0094] The track mode is a driving mode or function for providing a more aggressive driving experience. For example, in a predetermined track mode, the vehicle can be considered to be in a state with low (or no) stability control, at which time the driver can perform an adventurous driving manner without being restricted by the vehicle stability control system.

[0095] In some embodiments, the track mode can be enabled by detecting and obtaining the corresponding operation of the user. The user can input an instruction to enable the track mode by clicking a button, checking an option, inputting a command, voice, etc. When the electronic device detects the instruction to enable the track mode, the track mode is enabled.

[0096] Specifically, when the vehicle control system receives the instruction to enable the track mode, the vehicle control system can first determine whether the vehicle satisfies the track driving condition. The track driving condition can be set based on the protection of the personal safety of the driver and other personnel, for example, the track driving condition can include whether the vehicle is in a preset closed road section (such as a track), whether the driver and the passenger are buckled up, and whether the door is closed, and the like. Of course, the track driving condition can also be set based on other factors. The vehicle control system can detect according to the preset track driving condition, and when the vehicle satisfies the track driving condition, the vehicle control system controls the vehicle to enter the track mode. When the vehicle does not satisfy the track driving condition, the vehicle does not enter the track mode, and the driver is prompted. For example, the vehicle control system can send a prompt instruction to the display terminal of the vehicle, and the display terminal of the vehicle displays that the track mode cannot be entered, and prompts the driver to perform corresponding operations to satisfy the track driving condition.

[0097] In some embodiments, after receiving the instruction to enable the track mode and controlling the vehicle to enter the track mode, the vehicle control method further includes: determining whether the vehicle is in a straight running state; and determining that the vehicle is in the target running state when the vehicle is in the track mode and the vehicle is in the straight running state.

[0098] In some embodiments, the vehicle control system can determine whether the vehicle is in the straight running state based on the steering wheel angular velocity and the vehicle steering acceleration.

[0099] In some embodiments, under the premise that the steering wheel angular velocity and the vehicle steering acceleration are equal to 0, the vehicle control system can further determine whether the vehicle is in the straight running state based on the obtained steering wheel angle and the vehicle steering speed. If the obtained steering wheel angle and the vehicle steering speed are both 0, that is, the steering wheel is not turned and the vehicle is not deflected, it can be considered that the vehicle is currently in the straight running state.

[0100] It should be understood that the steering wheel angle and the vehicle steering speed are both 0, which means that the steering wheel angle and the vehicle steering speed are allowed to fluctuate within a threshold range close to 0. Since the vehicle also needs to fine-tune the direction in the straight running state, it can be considered that the vehicle is currently in the straight running state when the obtained steering wheel angle and the vehicle steering speed are both 0 or maintained within a small range close to 0.

[0101] The predetermined angle and the predetermined yaw rate can be set in advance for the above determination. The predetermined angle is a threshold close to 0 for the steering wheel angle, and the predetermined yaw rate is a threshold close to 0 for the vehicle steering speed. For example, if the absolute value of the current steering wheel angle is less than the predetermined angle, it is considered to be equal to 0. For example, if the absolute value of the current vehicle steering speed is less than the predetermined yaw rate, it is considered to be equal to 0.

[0102] In some embodiments, the absolute value of the obtained steering wheel angle and the absolute value of the vehicle steering speed are also used to determine whether the vehicle is in a steering state, in consideration of different road conditions during actual driving of the vehicle. Specifically, if the absolute value of the obtained steering wheel angle is greater than a predetermined angle, and the absolute value of the obtained vehicle steering speed is greater than a predetermined yaw rate, it is determined that the vehicle is in a steering state.

[0103] In some embodiments of the present specification, enabling / disabling the track mode helps users to start the track mode according to the actual situation, improves the flexibility of driving, and improves the driving experience of users.

[0104] Actuators are devices in vehicles for converting control signals into physical actions to change the state or position of corresponding components of the vehicle to achieve a specific control target. For example, actuators can include aerodynamic components (such as active spoilers, active air intakes, active front spoilers), suspension systems (such as active suspension systems), braking systems, power systems, etc.

[0105] Aerodynamic components are adjustable components for adjusting the aerodynamic characteristics of a vehicle. By changing the position, angle, etc., the aerodynamic characteristics of the vehicle can be optimized.

[0106] In some embodiments, the aerodynamic component can include a lifting motor and a turning motor, so that the aerodynamic component is lifted, lowered or turned.

[0107] In some embodiments, the lifting motor can feed back a motor position signal to the electronic device in real time during operation, and the electronic device can determine whether to continue to control the lifting motor according to the motor position signal. The turning motor can feed back a motor rotation angle signal to the electronic device in real time during operation, and the electronic device can determine whether to continue to control the turning motor according to the motor rotation angle signal.

[0108] In some embodiments, the aerodynamic component is built-in with a position sensor or an angle sensor to collect the position or rotation angle of the aerodynamic component. The rotation angle can be the angle between the horizontal plane of the spoiler and the ground, where the ground refers to the relative ground, specifically the relative ground parallel to the axis from the front to the rear of the vehicle.

[0109] In some embodiments, the aerodynamic component includes an active separation grid and / or an active separation spoiler.

[0110] The active separation spoiler refers to an aerodynamic component installed on a vehicle. For example, as shown in FIG. 1, the spoiler is installed on the rear of the vehicle. Figure 9 wherein Figure 9 (a) is a top view of the spoiler installation position, and Figure 9(b) is a rear view of the rear wing installation position. The active detachable rear wing can be installed at the rear of the vehicle.

[0111] In some embodiments, the active split rear wing may include a rear wing located on the left side of the vehicle body and a rear wing located on the right side of the vehicle body, such as Figure 10 The tail wing shown, where Figure 10 (a) is a front view of one side of the rear wing (looking from the rear of the car to the front of the car). One side of the rear wing can be composed of two lifting columns and a blade that can be flipped. Figure 10 (b) is a side view of one side of the rear wing (looking from one side of the vehicle to the other side), which shows the side profile of the rear wing and the shape of the lifting column. The blades of the rear wing are streamlined, with a certain curvature at the front and rear ends, which helps to reduce wind resistance and optimize airflow. Figure 10 (c) is a top view of one side of the tail wing (from top to bottom). The tail wing blades may include the tail wing blade body and the decorative panels on both sides. Among them, the left and right tail wings can each have independent driving components, such as Figure 11 As shown, it can be in different rotation angles, deployment heights or other spatial postures at the same time. The tail wing on each side can include one or more blades with adjustable angles and heights, such as Figure 12 The angle of the tail blade is shown, where Figure 12 The tail blades shown in (a) are parallel to the ground. Figure 12 The tail blades shown in (b) are perpendicular to the ground, meaning they can rotate up to 90°. The tail blade's rotation angle is the angle of inclination of the tail blade relative to the vehicle's reference plane and is expressed in degrees. For example, 0° represents horizontal, a positive value indicates downward tilt, and a negative value indicates upward tilt.

[0112] An active splitter grille is an aerodynamic component mounted on the front or side of a vehicle, typically used to control the amount and direction of airflow into the vehicle's interior, such as the engine compartment, brake system, or battery cooling system.

[0113] In some embodiments, the active split grille includes a grille on the left side of the vehicle and a grille on the right side. Each grille has independent drive components, allowing it to be in different opening and closing angles or deployed states at the same time. Each grille can be composed of one or more adjustable blades.

[0114] The opening and closing angle of the grid refers to the inclination angle of the blade of the grid relative to the reference plane of the vehicle body or the plane of the air inlet, which can be expressed in degrees. For example: 0° indicates that the blade is completely closed and blocks the air inlet; a positive value (such as 30°, 60°) indicates that the blade is positively deflected; a negative value (such as -15°) may indicate that the blade is negatively deflected; by adjusting the opening and closing angle, the air flow into the vehicle body can be dynamically controlled, thereby affecting the heat dissipation efficiency, the wind resistance coefficient, and the aerodynamic characteristics of the whole vehicle.

[0115] The control parameter refers to the relevant parameter of the corresponding actuator when it is running, which can be adjusted according to actual conditions. For example, for the aerodynamic component, the control parameter can include the current of the motor, the motor speed, and the motor rotor position when the aerodynamic component is running. The operating parameter can also include the operating position of the aerodynamic component when it is running.

[0116] For example, the control parameter of the aerodynamic component includes a lifting control parameter and an angle control parameter, wherein the lifting control parameter is used to control the height of lifting or lowering, which includes a lifting control signal and a lowering control signal; the angle control parameter is used to control the adjustment angle.

[0117] In some embodiments, the control parameter can be determined in various ways based on vehicle state information, for example, the control parameter can be determined based on a preset table or vector database constructed based on historical data. The preset table / vector database can be a table or database representing the correspondence between vehicle state information and control parameters. For example, by processing the vehicle state information through a preset control algorithm (such as a PID control algorithm, a fuzzy control algorithm, a neural network prediction, etc.) configured in the vehicle, the control parameter is determined.

[0118] It should be noted that in the vehicle control system, different actuators require different control parameters due to their different functions and effects. For example, the aerodynamic component can include a main wing, a main front spoiler, a main diffuser, etc., wherein the control parameter of the aerodynamic component can include: angle / position, speed of moving the adjustment component (for example, time required from closed state to fully open); for example, the control parameter of the suspension system can include: stiffness, damping coefficient, etc.; the control parameter of the steering system can include: assist size, return torque, steering ratio, etc.; for example, the control parameter of the brake system can include: brake force distribution ratio; for example, the control parameter of the power system can include: torque distribution ratio, etc.

[0119] In some embodiments of the present specification, by dynamically adjusting the control parameter of the actuator according to different driving scenarios, the vehicle response is more sensitive and accurate, and the driver's control experience is improved; not only limited to the aerodynamic component, but also can be linked with other actuators (such as suspension, brake, power system) for control, to realize the overall optimization of the whole vehicle performance.

[0120] In some embodiments, the method further comprises:

[0121] determining the control parameter of the at least one actuator according to the vehicle state information.

[0122] The vehicle state information is a set of parameters describing the current running condition of the vehicle. For example, the driving state information can include at least one of the vehicle speed, the longitudinal acceleration, the yaw rate, the gear of the vehicle.

[0123] In some embodiments, the vehicle state information includes at least one of the slip ratio, the yaw rate, the pedal depth of the vehicle.

[0124] In some embodiments, the vehicle state information includes the driving state information and / or the driving environment information. The driving state information can include, but is not limited to, the information related to the kinetic parameters such as the vehicle speed, the longitudinal acceleration, the lateral acceleration, the yaw rate, etc. The driving environment information can include, but is not limited to, the road surface information (such as the friction coefficient of the road surface, the degree of wetness, etc.), the road type, etc. In some embodiments, the vehicle state information can also include the driving intention, which can include, but is not limited to, the accelerator pedal depth, the brake pedal depth, the steering wheel angle, the selected driving mode (such as the economy mode, the comfort mode, the sport mode, etc.).

[0125] The yaw rate is used to reflect the speed of the vehicle rotating around its longitudinal axis at the current time, which can be in units of degrees / second or radians / second. The vehicle speed is used to indicate the driving speed of the vehicle. The steering wheel angle is used to reflect the steering wheel turning angle of the vehicle at the current time. The acceleration represents the rate of change of the vehicle speed with time, i.e. the degree of increase or decrease of the speed per unit time, and the acceleration can be linear acceleration (longitudinal acceleration, lateral acceleration, etc.).

[0126] In some embodiments, the vehicle state information can also include the overall vehicle mass, the vehicle size, the wheel driving torque, etc.

[0127] In some embodiments, the driving state information, the driving environment information and the driving intention of the vehicle can be actively acquired (for example, in real time, at intervals or under certain conditions) under certain conditions in communication with the acquisition module configured on the vehicle. The acquisition module includes at least one sensor, such as a vehicle speed sensor, an acceleration sensor, a yaw rate sensor, a steering wheel angle sensor, a tire pressure sensor, an image acquisition device, a radar sensor, etc.

[0128] In some embodiments, the driving environment information can be obtained through visual recognition algorithm based on the road image captured by the camera on the vehicle; or the driving environment information can be determined through high-precision map matching.

[0129] In some embodiments, the control parameter of the vehicle can be determined based on a deviation between the vehicle state information and the ideal state information.

[0130] The ideal state information refers to information corresponding to an ideal motion state that the vehicle is expected to have in the target driving state. For example, the ideal state information can include, but is not limited to, an ideal yaw rate, an ideal longitudinal acceleration, an ideal vehicle speed, etc.

[0131] In some embodiments, the ideal state information can be obtained in various ways. For example, the ideal state information can be matched with a preset ideal state information according to the current target driving state through a vehicle dynamics model, through a table lookup method, or by invoking corresponding ideal state information through a driving mode (such as a sports mode, an energy-saving mode).

[0132] In some embodiments, the ideal state information of the vehicle can be determined through a large number of experiments. For example, a plurality of vehicles are experimented on to determine the yaw rate of each vehicle when the vehicle is turning in place; an average value of the plurality of yaw rates determined through the experiments is calculated; and the average value is taken as the ideal state information.

[0133] In some embodiments, the ideal state information can also be determined according to the vehicle model information. For different vehicle models, there are differences in subjective feelings of the driver or the occupant when the vehicle is turning. Therefore, different ideal state information can be set for different vehicle models. Specifically, for a plurality of vehicles of the same vehicle model, experiments are performed to determine the vehicle state information of each vehicle under various working conditions; an average value of the plurality of vehicle state information determined through the experiments is calculated; and the average value is taken as the ideal state information of the vehicle of the vehicle model. The same processing is performed for vehicles of other vehicle models to obtain ideal state information of different vehicle models. A mapping relationship reflecting the vehicle model and the corresponding ideal state information is established. The vehicle model matched with the obtained vehicle model information is looked up in the mapping relationship; and the ideal state information corresponding to the looked-up vehicle model in the mapping relationship is taken as the ideal state information in the embodiments of the present application.

[0134] In the embodiments of the present application, various sensors can be arranged on the vehicle body, and the current vehicle state information can be obtained based on the sensors. Of course, the current vehicle state information can also be calculated based on other parameters of the vehicle. The embodiments of the present application do not limit this.

[0135] The deviation refers to the difference between the current vehicle state information and the ideal state information. For example, the deviation can include a difference between the actual yaw rate and the ideal yaw rate, a difference between the actual longitudinal acceleration and the ideal longitudinal acceleration, a difference between the actual vehicle speed and the ideal vehicle speed, etc.

[0136] In some embodiments, the control parameter of the vehicle can be determined according to the deviation between the vehicle state information and the ideal state information by a preset control algorithm (such as a PID control algorithm, a fuzzy control algorithm, etc.).

[0137] In some embodiments, the at least one actuator further comprises a power system of the vehicle, and the control parameter of the at least one actuator is determined according to the vehicle state information, comprising:

[0138] When the difference between the slip ratio of the vehicle and the ideal slip ratio is greater than or equal to a first preset difference threshold, at least one of the target torque of the power system and the target pose of the aerodynamic component is taken as the control parameter.

[0139] The power system refers to a system in the vehicle for generating and transmitting driving force. For example, when the vehicle includes multiple driving motors, the power system includes a front axle driving motor, a rear axle driving motor, and a power distribution controller.

[0140] The first preset difference threshold is a critical value for evaluating whether to trigger the coordinated control strategy of the power system and the aerodynamic component.

[0141] The first preset difference threshold can be a system default value, an empirical value, a human pre-set value, etc. or any combination thereof, which can be set according to actual needs, and the present specification does not limit this.

[0142] The slip ratio refers to the difference between the actual speed of the wheel and the theoretical speed of the wheel during the driving process of the vehicle, which is usually used to measure the adhesion state between the wheel and the road surface.

[0143] In some embodiments, the speed of each wheel can be measured in real time by a wheel speed sensor, the speed of the vehicle can be obtained by a speed sensor, or the lateral acceleration can be obtained by an inertial measurement unit (IMU), and the slip ratio of each wheel (such as the slip ratio of the left front wheel, the slip ratio of the right front wheel, the slip ratio of the left rear wheel, the slip ratio of the right rear wheel, etc.) can be calculated based on the speed of each wheel and the speed of the vehicle.

[0144] The ideal slip ratio refers to the slip ratio corresponding to the best adhesion state between the tire and the road surface under a specific road surface condition.

[0145] In some embodiments, the road surface type can be determined by a road surface recognition system (such as a camera, a radar, a tire noise sensor, a vehicle preview system, etc.), different road surface types correspond to different road surface adhesion coefficients, and the ideal slip ratio under the current road surface adhesion coefficient can be matched by a table lookup method or an empirical model.

[0146] The target torque refers to the ideal driving torque expected to be applied to the driving wheel under the current driving state.

[0147] The target pose refers to the position and / or attitude of the aerodynamic component in space, and the attitude includes a pitch angle, etc.

[0148] In some embodiments, the corresponding control instructions can be generated based on the control parameters, and sent to the controller of the power system through the communication bus (such as CAN bus or vehicle Ethernet, etc.) of the vehicle; the controller of the power system adjusts the output torque of the motor according to the control parameters.

[0149] In some embodiments, the target pose includes at least one of the height and the rotation angle of the target aerodynamic component, and / or the target torque includes at least one of the left front wheel driving torque, the right front wheel driving torque, the left rear wheel driving torque, the right rear wheel driving torque, and the longitudinal driving torque.

[0150] The height of the aerodynamic component refers to the vertical position of the aerodynamic component (such as a tail wing, a spoiler, etc.) relative to the reference surface of the vehicle body.

[0151] The rotation angle of the aerodynamic component refers to the angle of rotation of the aerodynamic component around its axis, for adjusting the windward area, the direction and strength of lift / down pressure.

[0152] The left front wheel driving torque, the right front wheel driving torque, the left rear wheel driving torque, and the right rear wheel driving torque respectively refer to the independent driving torque obtained by each of the four wheels of the vehicle, for realizing torque vectoring control.

[0153] The longitudinal driving torque refers to the total driving torque required by the vehicle as a whole in the longitudinal direction (i.e. the forward or backward direction), which can be output by the power system (such as an electric motor) and distributed to the driving wheels through the transmission system.

[0154] In some embodiments, when the difference between the actual slip ratio and the ideal slip ratio of the vehicle is greater than or equal to the first preset difference threshold, the corresponding control instructions can be generated according to at least one of the above target pose and target torque, and issued to the corresponding actuators.

[0155] It can be understood that when the difference between the slip ratio and the ideal slip ratio of the vehicle is greater than or equal to the first preset difference threshold, it indicates that the vehicle has the risk of slipping, such as the driving slip state, that is, the driving wheel (such as the front wheel or the rear wheel) of the vehicle slips obviously when accelerating, and the tire speed is much higher than the vehicle speed.

[0156] In some embodiments of the present specification, the height and rotation angle of the aerodynamic component are precisely controlled to optimize the aerodynamic performance of the vehicle; the driving torque of each wheel is independently controlled to optimize the traction force distribution, reduce tire slip, and improve the traction force and stability of the vehicle on low adhesion road surfaces; the power system and the aerodynamic component work cooperatively to realize optimal adjustment of the overall vehicle performance.

[0157] In some embodiments, the control parameter of the at least one actuator is determined according to the vehicle state information, comprising:

[0158] The relative pose difference of the plurality of aerodynamic components of the vehicle is used as the control parameter when the yaw rate of the vehicle is greater than the ideal yaw rate.

[0159] The ideal yaw rate refers to the yaw rate value that the vehicle is expected to reach.

[0160] The yaw rate of the vehicle can be the yaw rate of the vehicle at the current time. The current time usually refers to the time point when the processor collects data, processes and makes decisions, for example, the current time can be the time point when the processor collects data, updates control instructions or makes decisions for the last time.

[0161] In some embodiments, the ideal yaw rate can be calculated based on the vehicle dynamics model, and the actual yaw rate of the vehicle can be detected by the yaw rate sensor on the vehicle.

[0162] The ideal yaw rate is a critical value for evaluating whether to trigger the differential control strategy of different aerodynamic components.

[0163] In some embodiments, the relative pose difference of the plurality of aerodynamic components includes the relative pose difference between the first aerodynamic component and the second aerodynamic component, and the first aerodynamic component and the second aerodynamic component are respectively located on the two sides of the vehicle.

[0164] The first aerodynamic component and the second aerodynamic component refer to adjustable components arranged on the left and right sides (or symmetric areas on the front and rear sides) of the vehicle. For example, the first aerodynamic component can be a spoiler or a spoiler located on the left side of the rear of the vehicle body, and the second aerodynamic component is a spoiler or a spoiler arranged on the right side of the rear of the vehicle body. The first aerodynamic component and the second aerodynamic component have independent driving components and can be adjusted in height, angle or deployment degree according to the control instructions.

[0165] The pose refers to the position and attitude of the aerodynamic component in three-dimensional space, including its height, rotation angle and deployment length relative to the reference surface of the vehicle body. For example, a certain spoiler can be set to 10 cm high and 5 degrees downward, while the spoiler component on the other side can remain in place or be adjusted at a different angle to form a height difference or an angle difference.

[0166] Relative pose difference refers to the difference in spatial position or attitude between two aerodynamic components. For example, when the left tail fin is set to a higher position while the right tail fin remains at a lower position, there is a relative pose difference in height between the two; for another example, if the blade of the left tail fin is rotated downward by 10 degrees while the blade of the right tail fin is only rotated by 5 degrees, a relative pose difference in rotation angle is formed between the two. By adjusting the relative pose difference between multiple aerodynamic components, an asymmetric aerodynamic effect is generated, thereby exerting a corrective yaw moment on the vehicle to assist the vehicle in restoring a stable driving state.

[0167] It can be understood that when the yaw angular velocity of the vehicle is greater than the ideal yaw angular velocity, it indicates that the vehicle is at risk of yaw instability (e.g., fishtailing or skidding, etc.).

[0168] In some embodiments of the present specification, by differentially controlling the left and right or front and rear aerodynamic components, an additional yaw moment is generated to suppress the tendency of oversteering or understeering of the vehicle and improve the yaw stability.

[0169] In some embodiments, the control parameter of the at least one actuator is determined according to the vehicle state information, including:

[0170] When the pedal depth of the vehicle is greater than the ideal pedal depth, the rotation angle of the target aerodynamic component of the vehicle is used as the control parameter.

[0171] The pedal depth refers to the displacement of the pedal when the driver applies pressure to the accelerator pedal or brake pedal with the foot. The pedal depth reflects the current driving intention of the driver, such as acceleration demand or deceleration intention. In some embodiments, the pedal depth can be obtained in real time by a pedal position sensor (e.g., a pressure sensor or an angle sensor, etc.).

[0172] The ideal pedal depth refers to the pedal depth that the vehicle is expected to reach.

[0173] In some embodiments, in a specific driving state, the ideal pedal depth can be obtained according to the driving mode, environmental perception information, and vehicle dynamics model. For example, in the adaptive cruise control mode, the ideal acceleration pedal depth or brake pedal depth can be automatically calculated according to the distance to the vehicle in front; in the energy-saving driving mode, a relatively gentle pedal operation can be adopted to improve the energy efficiency performance.

[0174] The target aerodynamic component can include any one or combination of the tail fins on the left and right sides of the tail of the vehicle.

[0175] The ideal pedal depth is a critical value for evaluating whether to adjust the rotation angle control strategy of the aerodynamic component.

[0176] It can be understood that when the brake pedal depth of the vehicle is greater than the ideal brake pedal depth, it indicates that the vehicle is in a deceleration state.

[0177] In some embodiments of the present specification, when it is determined that the vehicle is in a braking state, the stability of the vehicle is further enhanced by adjusting the angle of the aerodynamic component, which helps the vehicle to brake more smoothly.

[0178] In some embodiments, the corresponding actuator is controlled according to the control parameter of at least one actuator of the vehicle, including:

[0179] According to the deviation of the vehicle state information based on the gain coefficient of the preset control algorithm, the control amount of the control parameter is determined, so as to control the corresponding actuator based on the control amount of the control parameter.

[0180] The control parameter refers to a parameter variable type used to control the vehicle. The control amount refers to a specific numerical value of the control parameter. For example, the control amount can include a specific value or a change value of the driving torque of the wheel, a specific value or a change value of the pose of the aerodynamic component, etc.

[0181] The deviation of the vehicle state information refers to the deviation of the vehicle state information from the ideal state information. For example, the deviation includes at least one of the difference between the slip rate of the vehicle and the ideal slip rate, the difference between the yaw rate of the vehicle and the ideal yaw rate, and the difference between the acceleration of the vehicle and the ideal acceleration.

[0182] The preset control algorithm refers to a control strategy or algorithm used to calculate the control amount of the control parameter based on the vehicle state deviation. For example, the preset control algorithm can include but is not limited to a PID (Proportional Integral Derivative Control) control algorithm, a model predictive control (MPC), a fuzzy control algorithm, etc.

[0183] In some embodiments, as shown in Figure 3 The corresponding actuator can be controlled based on the control amount of the control parameter, and the state (such as the pose, the torque value, etc.) of the actuator can be monitored in real time based on the sensor configured in the actuator to determine whether the actuator achieves the expected control effect.

[0184] In some embodiments, as shown in Figure 4As shown, after the vehicle state information is collected, the working condition of the vehicle can be determined according to the deviation of the vehicle state information from the ideal state information, so as to determine the control parameter of the actuator to be controlled and the control amount of the control parameter. For example, when the working condition of the vehicle is the unexpected yaw control, the control parameter includes the difference between the heights of the two side flaps and the difference between the turning angles (also referred to as rotation angles) of the two side flap blades. For another example, when the working condition of the vehicle is the drive slip control, the control parameter includes the four-wheel drive torque, the heights of the two side flaps, and the turning angles of the two side flap blades. For another example, when the working condition of the vehicle is the deceleration control, the control parameter includes the turning angle of the flap blade.

[0185] In some embodiments of the present specification, the control amounts of the multiple actuators are coordinated by the gain coefficient of the preset control algorithm, so as to achieve the optimal adjustment of the overall vehicle performance.

[0186] In some embodiments, the gain coefficient of the preset control algorithm includes at least two of the proportional coefficient, the integral coefficient, and the differential coefficient.

[0187] The gain coefficient used by the preset control algorithm includes at least two of the proportional coefficient (Kp), the integral coefficient (Ki), and the differential coefficient (Kd). Different combinations of the gain coefficients constitute a PID control algorithm or a simplified form thereof (such as a PI control or a PD control).

[0188] In some embodiments, the control amount of the control parameter of the vehicle can be calculated by the following PID control algorithm (also referred to as a PID controller):

[0189] u(t) = Kp * e(t) + Ki * ∫e(t)dt + Kd * de(t) / dt;

[0190] wherein u(t) represents the control amount of the control parameter of the vehicle, e(t) represents the deviation of the current vehicle state information of the vehicle, Kp, Ki, and Kd are respectively the proportional coefficient, the integral coefficient, and the differential coefficient, and are used to adjust the performance of the PID controller.

[0191] The gain coefficient of the preset control algorithm can be a fixed value or a dynamically changing value. The gain coefficient of the preset control algorithm is used to control the size of the control amount used each time.

[0192] In some embodiments of the present specification, by determining the gain coefficient, different driving working conditions (such as wet and slippery road surface, high-speed cruising, etc.) can be adapted.

[0193] In some embodiments, the method further includes:

[0194] determine a compensation value according to the control amount of the preset control parameter and the vehicle state information after control;

[0195] update the control amount of the control parameter according to the compensation value to obtain an updated control amount of the control parameter.

[0196] The control amount of the preset control parameter can be a control amount of the preset control parameter, or a control amount of the control parameter used at the previous moment. The vehicle state information after control refers to vehicle response information after at least one actuator is controlled based on the control amount of the preset control parameter,

[0197] The control amount of the control parameter refers to a parameter value calculated according to the deviation and a preset control algorithm (such as a PID control algorithm) and used to drive the actuator to act. For example, the control amount of the control parameter can be an adjustment amount of the rotation angle of a tail wing, a change value of the output torque of a motor, etc.

[0198] The control parameter is different, and the vehicle state information to be collected is different. The vehicle state information after control reflects the dynamic performance of the vehicle after the control amount at a certain moment is applied. On this basis, the electronic device can further determine whether there is insufficient control effect, overshoot, delay or other situations by analyzing the relationship between the ideal state information and the actual vehicle response information (i.e., the vehicle state information after control). If so, the control amount of the control parameter at the current moment is compensated based on the compensation value to control the vehicle.

[0199] The compensation value refers to the change amount of the control amount of the control parameter before and after updating.

[0200] In some embodiments, the control amount of the control parameter can be updated according to the compensation value to generate an updated control amount of the control parameter. The update can be linear superposition or non-linear mapping, depending on the design of the control strategy. For example: the updated control amount of the control parameter = the original control amount of the control parameter + the compensation value, the updated control amount of the control parameter = a x the original control amount of the control parameter + β x the compensation value (where a and β are weighting coefficients that can be dynamically adjusted according to control requirements), or non-linear function mapping, such as the updated control amount of the control parameter = f (the original control amount of the control parameter, the compensation value).

[0201] In some embodiments of the present specification, by introducing the compensation value, the control amount of the control parameter calculated based on the deviation is dynamically corrected, the response accuracy and adaptability of the control are improved, and the ability of the vehicle to cope with complex working conditions can be enhanced.

[0202] In some embodiments, updating the control amount of the control parameter according to the compensation value to obtain an updated control amount of the control parameter includes:

[0203] The control amount of the control parameter at the current time is updated according to the compensation value obtained at each time, to obtain the updated control amount of the control parameter at the current time.

[0204] In some embodiments, each control period corresponds to a time, and at each time, a real-time compensation value is calculated based on the control amount of the control parameter used at the previous time and the current vehicle state information (as the controlled vehicle state information), to correct the control amount of the control parameter calculated at the current time based on the PID control algorithm, and to generate the updated control amount of the control parameter at the current time, so as to realize dynamic optimization and closed-loop adjustment of the control amount of the control parameter.

[0205] The control period is a time period for updating the control amount of the control parameter, which can be a preset time interval (for example, every 10 milliseconds or every 50 milliseconds).

[0206] In some embodiments, for each control period, the control amount of the control parameter in the previous control period controls at least one actuator of the vehicle, and at the beginning of the current control period, the current vehicle state information is obtained as the controlled vehicle state information. Based on the deviation between the current vehicle state information and the ideal state information, it is determined whether the actual vehicle response meets the expected effect, such as eliminating the deviation or having response lag, overshoot, etc. If there is, a neural network prediction model is used to calculate the compensation value of the current control period, to correct the control amount of the control parameter calculated at the current control period, so as to control the vehicle based on the updated control amount of the control parameter at the current control period.

[0207] In some embodiments, the compensation value includes a first compensation value and a second compensation value, and the control amount of the control parameter is updated according to the compensation value to obtain the updated control amount of the control parameter, including:

[0208] Calculating the difference between the control amount of the control parameter and the first compensation value;

[0209] Determining the updated control amount of the control parameter according to the ratio of the difference to the second compensation value.

[0210] For example, the preset control algorithm includes an adaptive neural network PID control algorithm, and the first compensation value and the second compensation value are obtained by constructing a neural network prediction model.

[0211] The neural network prediction model is an algorithm or model for predicting the first compensation value and the second compensation value.

[0212] In some embodiments, the neural network prediction model is a machine learning model. For example, the neural network prediction model can include any one or combination of a convolutional neural network (CNN), a recurrent neural network (RNN), a deep neural network (DNN), or other custom model structure.

[0213] In some embodiments, the input of the neural network prediction model includes the control amount of the preset control parameter and the corresponding vehicle state information, and the output can include a compensation value (e.g., a first compensation value and a second compensation value, etc.). The control amount of the preset control parameter can be the control amount of the control parameter adopted at the previous time, or the control amount of the control parameter calculated by the PID controller at the current time, which can be determined according to actual conditions.

[0214] In some embodiments, the neural network prediction model can be trained based on a large number of labeled training samples through various feasible ways. For example, the parameters can be updated based on the gradient descent method. An exemplary training process includes: inputting a plurality of labeled training samples into an initial neural network prediction model, constructing a loss function based on the labels and the results of the initial neural network prediction model, and iteratively updating the parameters of the initial neural network prediction model based on the loss function through gradient descent or other methods. When a preset condition is met, the model training is completed, and a trained neural network prediction model is obtained. The preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0215] In some embodiments, the training sample at least includes the control amount of the sample control parameter and the corresponding sample vehicle state information. The training sample can be obtained based on historical data. For example, the training sample at least includes the control amount of the sample control parameter at the first time and the sample vehicle state information at the second time, and the first time is earlier than the second time.

[0216] In some embodiments, the label can include the actual compensation value corresponding to the training sample. The label can be obtained by automatic or manual labeling.

[0217] In some embodiments of the present specification, the neural network prediction model helps to efficiently and accurately obtain the compensation value.

[0218] For example, a nonlinear function can be approximated by training the u' and y data and to construct a neural network model, and the training data of the neural network model is , and a trained multi-input multi-output neural network prediction model is obtained, which can be used to determine the compensation value at a certain time real-time input, output and , and the final output of the PID controller is:

[0219] .

[0220] wherein, represents the control quantity of the updated control parameter, represents the control quantity of the control parameter before updating, represents the first compensation value, represents the second compensation value.

[0221] In some embodiments, the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm is processed to determine the control quantity of the control parameter, comprising:

[0222] According to the gain coefficient of the preset control algorithm, the difference between the slip rate of the vehicle and the ideal slip rate is processed to obtain the change amount of the target torque distribution ratio and the change amount of the target aerodynamic component pose.

[0223] The target torque distribution ratio refers to the proportional relationship of the driving torque between the front and rear axles or the left and right wheels in a multi-axle driving vehicle (such as a four-wheel drive vehicle) or a vehicle with torque vectoring capability.

[0224] The change amount of the target aerodynamic component pose refers to the adjustment amplitude of the pose of the aerodynamic component.

[0225] In some embodiments, when the difference between the actual slip rate and the ideal slip rate of the vehicle is greater than or equal to a first preset difference threshold, the change amount of the target torque distribution ratio and the change amount of the target aerodynamic component pose are determined based on the difference between the actual slip rate and the ideal slip rate of the vehicle through a preset control algorithm.

[0226] For example, as shown in Figure 5 , the acquisition module includes a vehicle-mounted sensor and a vehicle model size data storage for obtaining vehicle state information, including: yaw rate , longitudinal driving torque , left front wheel driving torque , right front wheel driving force , left rear wheel driving force , right rear wheel driving force , left front wheel speed , right front wheel speed , left rear wheel speed , right rear wheel speed , left front wheel angular velocity , right front wheel angular velocity , left rear wheel angular velocity , right rear wheel angular velocity , vehicle mass , vehicle longitudinal speed , longitudinal acceleration , brake pedal depth .

[0227] In some embodiments, the road adhesion coefficient can be estimated according to the vehicle state information, and the actual slip ratio of the tire is determined ; secondly, according to the mapping relationship between the tire adhesion coefficient and the slip ratio, the ideal slip ratio of the tire is obtained by table lookup ; and the difference of the slip ratio is calculated .

[0228] In some embodiments, the road surface type can be determined by using the vehicle preview system, and then the road adhesion coefficient can be obtained according to the road surface type. It should be noted that the way of determining the road surface type by using the vehicle preview system is not limited to the description of the above embodiments, and other ways can also be used in actual application. Ideal slip ratio: . Wherein, r represents the radius of the tire.

[0229] In some embodiments, if , output ; otherwise, output . Wherein, is the slip ratio threshold, that is, the first preset difference threshold, which can be obtained according to the vehicle type calibration.

[0230] In some embodiments, as shown in Figure 6 , if , the required driving torque is calculated by using the fuzzy PID control algorithm according to (ideal longitudinal acceleration , actual longitudinal acceleration ). Specifically, by using the Fuzzy logic controller, the fuzzy rules are formulated according to the real vehicle test data, (such as the fuzzy inference shown in Figure 6 ), the values of P and I in the PID controller can be determined based on the changes of and . The PID controller calculates the control amount of the control parameter, that is, the required longitudinal driving torque, according to the difference between the ideal longitudinal acceleration and the actual longitudinal acceleration.

[0231] In some embodiments, based on , the adaptive neural network PID algorithm (such as the neural network estimator shown in Figure 6 ) can be used to determine the distribution ratio of the front and rear wheel driving torque, the height of the tail wing, the rotation angle of the tail wing, etc., to perform driving anti-skid control. For example, by represents the vehicle state information at the time t, i.e., the vector of the time-varying front-rear torque distribution ratio, the height of the two side wings, and the rotation angle of the two side wings, where, represents the front-rear torque distribution ratio, represents the height of the two side wings, represents the rotation angle of the two side wings. The control amount of the control parameter of the initial output of the PID controller is , (i = 1, r), represents the vector of the change amount of the control amount of the control parameter, where, represents the change value of the front-rear torque distribution ratio, represents the change value of the wing height, represents the change value of the wing rotation angle. The neural network prediction model has a 5-layer structure, the input layer has 8 neurons, the middle three hidden layers have 20, 15, and 10 neurons respectively, and the output layer has 4 neurons. The obtained multi-input multi-output neural network prediction model can output and according to the real-time input at a certain time . For example, at a certain time, the output is . Therefore, the final output of the PID controller is:

[0232] ;

[0233] In some embodiments, the PID controller final output can be used as the control amount of the updated control parameter at the current time, and the driving motor of the vehicle wheel and the wing can be controlled in the control period corresponding to the current time.

[0234] In some embodiments of the present specification, by adjusting the change amount of the target torque distribution ratio, the torque output of each driving wheel can be optimized in real time, the driving force of the wheel with too high slip rate is reduced, and the driving force of the wheel with strong adhesion is increased, thereby improving the traction and driving stability of the whole vehicle; by adjusting the change amount of the target aerodynamic component pose, the wheel pressure can be increased, the adhesion between the tire and the ground is improved, and the slip is further inhibited.

[0235] In some embodiments, the deviation of the vehicle state information is processed according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter, including:

[0236] The difference between the yaw rate of the vehicle and the ideal yaw rate is processed according to the gain coefficient of the preset control algorithm to obtain the relative pose difference of the plurality of aerodynamic components.

[0237] ​In some embodiments, when the vehicle's yaw acceleration is greater than the ideal yaw acceleration, a preset control algorithm is used to determine the relative position differences of multiple aerodynamic components based on the difference between the vehicle's yaw acceleration and the ideal yaw acceleration. For example, the relative position differences of the multiple aerodynamic components may include the difference in lift heights of the two rear fins and the difference in rotation angles of the two rear fins.

[0238] In some embodiments, if , then the output ; Otherwise, output .in, is the actual yaw angular velocity, is the yaw rate threshold, which can be obtained according to vehicle model calibration.

[0239] In some embodiments, as Figure 7 As shown, if , then according to , that is, the difference between the ideal yaw acceleration and the vehicle's yaw acceleration, using an adaptive neural network PID control algorithm (such as Figure 7 The neural network estimator shown in FIG2 determines the difference in the lift height of the two tail wings and the difference in the rotation angle of the two tail wings to perform yaw control. For example, by express The vehicle status information at the time of the vehicle is the vector of the height of the two side tail wings and the rotation angle of the two side tail wings that change with time. The control quantity of the control parameter initially output by the PID controller is , respectively representing the difference in tail height on both sides and the difference in tail rotation angle on both sides. The neural network prediction model has a 4-layer structure, with 6 neurons in the input layer, 15 and 10 neurons in the middle two hidden layers, and 3 neurons in the output layer. The obtained multi-input and multi-output neural network prediction model can be based on the Real-time input and output and For example, at a certain moment the output Therefore, the final output of the PID controller is:

[0240] ;

[0241] In some embodiments, the height and rotation angle of the rear wings on both sides of the vehicle wheels can be controlled in the control cycle corresponding to the current moment based on the control quantity finally output by the PID controller as the updated control parameter at the current moment.

[0242] For example, when the lateral acceleration deviation indicates that the vehicle is about to over-steer (spin), the height and rotation angle of the outboard spoiler can be increased to generate a correction moment to the outboard side, thereby preventing the vehicle from spinning.

[0243] In some embodiments of the present disclosure, the lateral acceleration deviation reflects the dynamic change trend of the vehicle rotating around the vertical axis. By adjusting the relative pose difference of the aerodynamic components (such as the angle difference of the left and right spoilers), asymmetric downforce or airflow resistance can be generated on the left and right sides of the vehicle, thereby generating a correction yaw moment. The yaw moment can effectively suppress the over-steering or under-steering trend of the vehicle, and improve the stability and handling of the vehicle in various driving conditions.

[0244] In some embodiments, the deviation of the vehicle state information is processed according to a gain coefficient of a preset control algorithm to determine a control amount of a control parameter, including:

[0245] The preset gain coefficient is updated according to the deviation of the vehicle state information to obtain the gain coefficient of the preset control algorithm.

[0246] The deviation of the vehicle state information is processed according to the gain coefficient of the preset control algorithm to obtain the control amount of the control parameter.

[0247] In some embodiments, the preset gain coefficient includes a gain coefficient at a previous time, and the preset gain coefficient is updated according to the deviation of the vehicle state information to obtain the gain coefficient of the preset control algorithm, including:

[0248] A fuzzy coefficient at the current time is determined according to the deviation of the vehicle state information at the current time and a preset fuzzy rule. Different deviations correspond to different fuzzy coefficients.

[0249] The gain coefficient at the previous time is updated according to the fuzzy coefficient obtained at the current time to obtain the gain coefficient at the current time.

[0250] In the embodiments of the present disclosure, the difference between the ideal longitudinal acceleration and the actual longitudinal acceleration and the change rate of the difference can be used as one of the parameters to be determined. Based on the above, the control amount of the control parameter matching the current state of the vehicle can be determined.

[0251] In some embodiments, the gain coefficient at the current time is determined according to the difference and the change rate of the difference. The gain coefficient includes a proportional coefficient, a differential coefficient, and an integral coefficient. Based on this, the gain coefficient at the current time includes the proportional coefficient at the current time, the differential coefficient at the current time, and the integral coefficient at the current time.

[0252] In some embodiments, the difference, the difference change rate and the change amount of the gain coefficient have a corresponding relationship. The change amount of the gain coefficient at the current time can be determined according to the corresponding relationship. In the case that the gain coefficient at the current time includes a proportional coefficient at the current time, a differential coefficient at the current time and an integral coefficient at the current time, the change amount of the gain coefficient at the current time is specifically: a proportional coefficient change amount at the current time, a differential coefficient change amount at the current time and an integral coefficient change amount at the current time.

[0253] In the embodiments of the present application, the above-mentioned corresponding relationship can be obtained by the R&D personnel of the vehicle based on simulation experiments. For example, △Kp is the change amount of the proportional coefficient in the gain coefficient, △Ki is the change amount of the integral coefficient in the gain coefficient, and △Kd is the change amount of the differential coefficient in the gain coefficient; △Kp, △Ki and △Kd can be obtained by inputting the difference and the difference change rate into a fuzzy controller, for example, the specific values of △Kp, △Ki and △Kd can be determined according to the preset fuzzy rules and membership functions. Wherein, the difference and the difference change rate can use the same membership function.

[0254] In some embodiments, the gain coefficient at the current time can be determined according to the gain coefficient at the last time and the change amount of the gain coefficient at the current time. For example, in the case that the gain coefficient includes a proportional coefficient and an integral coefficient, the sum of the proportional coefficient at the last time and the proportional coefficient change amount at the current time is taken as the proportional coefficient at the current time, and the sum of the integral coefficient at the last time and the integral coefficient change amount at the current time is taken as the integral coefficient at the current time.

[0255] It can be understood that there is no gain coefficient at the last time at the initial time of the vehicle. Therefore, the gain coefficient at the initial time of the vehicle can be set to a preset value. That is, the control parameter at the initial time when the vehicle enters the track mode is a preset value.

[0256] In some embodiments, the control amount of the control parameter at the current time can be determined according to the difference and the gain coefficient at the current time.

[0257] In some embodiments, the difference, the difference change rate and the gain coefficient at the current time can be input into a PID controller to determine the control amount of the control parameter.

[0258] In some embodiments of the present specification, by analyzing the deviation between the vehicle state information and the ideal state information in real time, the gain coefficient of the control algorithm is dynamically updated to optimize the control output and improve the stability of the vehicle control system.

[0259] In some embodiments, the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm is processed to determine the control amount of the control parameter, including:

[0260] According to the gain coefficient of the preset control algorithm, a difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration is processed to obtain a change amount of the rotation angle of the target aerodynamic component.

[0261] In some embodiments, when the brake pedal depth of the vehicle is greater than the ideal brake pedal depth, a change amount of the rotation angle of the target aerodynamic component is determined based on a difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration by a preset control algorithm. For example, if , an output is ; otherwise, an output is . Wherein, is the actual brake pedal depth, is the brake pedal depth threshold, which can be calibrated according to the vehicle type.

[0262] In some embodiments, if , the rotation angle of the tail wing is determined by a fuzzy PID control algorithm according to (Wherein, is the ideal longitudinal acceleration, is the actual longitudinal acceleration).

[0263] In some embodiments, as shown in Figure 8 , if , for example, by to represent the vehicle state information at , that is, the vector of the rotation angle of the two side tail wings changing over time. A fuzzy logic controller can be used to determine the preset fuzzy rules (such as the fuzzy inference shown in Figure 8 ) according to the real vehicle test data, and the values of P and I in the PID controller can be determined based on the change rates of and ; the PID controller calculates the control amount of the control parameter according to the difference between the ideal longitudinal acceleration and the actual longitudinal acceleration to control the rotation angle of the two side tail wings. For example, when deceleration control is performed, the rotation angle of the two side tail wings is the maximum angle.

[0264] In some embodiments of the present specification, the longitudinal acceleration deviation reflects the difference between the actual state and the ideal state of the vehicle during acceleration or braking. By adjusting the rotation angle of the aerodynamic component (such as the active tail wing), the air resistance and downforce on the vehicle can be effectively changed. For example, when accelerating, reducing air resistance can improve acceleration performance; when braking, increasing downforce can improve tire adhesion, shorten braking distance, and enhance braking stability.

[0265] In some embodiments, the deviation of the vehicle state information is processed according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter, including:

[0266] According to the gain coefficient of the preset control algorithm, the difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration is processed to obtain the torque value of the longitudinal driving torque.

[0267] For more details about determining the torque value of the longitudinal driving torque, please refer to the relevant description in the morning.

[0268] In some embodiments, the method further comprises:

[0269] When the vehicle is in the steering state, the braking torque of the target wheel is taken as the control parameter.

[0270] The target wheel refers to a specific wheel selected to apply a braking torque during the steering control process. The selection of the target wheel can be determined based on information such as the current steering state of the vehicle.

[0271] In some embodiments of the present specification, when the vehicle is in the steering state, by taking the braking torque of the target wheel as the control parameter, precise control of the steering dynamics of the vehicle can be achieved, and the steering stability of the vehicle can be improved.

[0272] It should be noted that the above description of the process is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the process under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0273] The specific implementation of each operation can be referred to the previous embodiments, which will not be repeated here.

[0274] Figure 13 is a structural schematic diagram of an electronic device according to some embodiments of the present specification. As shown in Figure 13 The electronic device 1300 can include a processor 1301 and a memory 1302. The electronic device 1300 can also include one or more of a multimedia component 1303, an input / output (I / O) component 1304, and a communication component 1305. In this embodiment, the electronic device 1300 can be a device that implements the vehicle control method provided in the present embodiment.

[0275] The processor 1301 is configured to control overall operations of the electronic device 1300 to complete all or part of the steps of the vehicle control method described above. The memory 1302 is configured to store various types of data to support operations of the electronic device 1300, which can include, for example, instructions for operating any application or method on the electronic device 1300, and application-related data. The memory 1302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or an optical disk. The multimedia component 1303 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 1302 or transmitted through the communication component 1305. The audio component also includes at least one speaker configured to output audio signals. The I / O component 1304 provides an interface between the processor 1301 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 1305 is configured to perform wired or wireless communication between the electronic device 1300 and other devices. The wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, narrow band internet of things (NB-IOT), enhanced machine type communication (eMTC), or other 5G, and the like, or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 1305 can include a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0276] In an exemplary embodiment, the electronic device 1300 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for executing the vehicle control method described above.

[0277] In another exemplary embodiment, a computer readable storage medium is also provided, which stores a computer program. The program instructions are executed by a processor to implement the steps of the vehicle control method described above. For example, the computer readable storage medium can be the memory 1302 described above which stores the program instructions. The program instructions can be executed by the processor 1301 of the electronic device 1300 to implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0278] Alternatively, the instructions are executed by a computer to implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0279] In another exemplary embodiment, a computer program product is also provided, which includes a computer program or instructions. The computer program or instructions are executed by a processor to implement the steps of the vehicle control method described above. For example, the computer program product can be the memory 1302 described above which stores the computer program. The computer program can be executed by the processor 1301 of the electronic device 1300 to implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0280] Alternatively, the instructions are executed by a computer to implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0281] Figure 14 is an exemplary schematic view of a vehicle according to some embodiments of the present description.

[0282] As Figure 14As shown, the present application also provides a vehicle, wherein the vehicle is provided with the electronic device provided in any of the above embodiments, and the electronic device is used to execute the vehicle control method provided in any of the above embodiments. The vehicle can be a fuel automobile, a plug-in hybrid electric vehicle, a new energy vehicle, etc., which are not specifically limited in the present specification.

[0283] In one embodiment, the vehicle can be configured in a fully or partially autonomous driving mode. For example, the vehicle can control itself while in the autonomous driving mode, and can determine a current state of the vehicle and its surrounding environment, determine a possible behavior of at least one other vehicle in the surrounding environment, and determine a confidence level corresponding to a likelihood that the other vehicle performs the possible behavior, based on the determined information, to control the vehicle. When the vehicle is in the autonomous driving mode, the vehicle can be configured to operate without human interaction.

[0284] In the description of the present application, the terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0285] The embodiments, implementation manners and related technical features of the present application can be combined, replaced with each other without conflict.

[0286] The above is only the preferred embodiment of the present application, and does not limit the present application in any form. Although the description of each embodiment in the present application has its own emphasis, the parts not described in detail in a certain embodiment can be referred to the related embodiments of other embodiments. Any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments, as long as it does not deviate from the technical solution content of the present application, still belongs to the scope of the technical solution of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: When the vehicle is in a target driving state, controlling a corresponding actuator according to a control parameter of at least one actuator of the vehicle to assist the vehicle in driving; the at least one actuator includes an aerodynamic component of the vehicle and a power system of the vehicle; The method further comprises: determining a control parameter of the at least one actuator based on the vehicle state information; The vehicle state information includes a slip ratio of the vehicle, and determining the control parameter of the at least one actuator based on the vehicle state information includes: When the difference between the slip rate of the vehicle and the ideal slip rate is greater than or equal to a first preset difference threshold, at least one of the target torque of the power system and the target posture of the aerodynamic component is used as the control parameter to control the corresponding actuator based on the control amount of the control parameter. The control amount of the control parameter includes a change in a target torque distribution ratio and / or a change in a target aerodynamic component posture.

2. The method according to claim 1, characterized in that The vehicle state information further includes at least one of a yaw rate and a pedal depth of the vehicle.

3. The method according to claim 1, characterized in that The target posture includes the height and rotation angle of the target aerodynamic component, and / or the target torque includes at least one of the left front wheel driving torque, the right front wheel driving torque, the left rear wheel driving torque, the right rear wheel driving torque, and the longitudinal driving torque.

4. The method according to claim 2, characterized in that The determining, based on the vehicle state information, the control parameter of the at least one actuator includes: When the yaw rate of the vehicle is greater than an ideal yaw rate, relative posture differences of multiple aerodynamic components of the vehicle are used as the control parameters.

5. The method according to claim 4, characterized in that The relative position differences of the plurality of aerodynamic components include a relative position difference between a first aerodynamic component and a second aerodynamic component, wherein the first aerodynamic component and the second aerodynamic component are respectively located on two sides of the vehicle.

6. The method according to claim 2, characterized in that The determining, based on the vehicle state information, the control parameter of the at least one actuator includes: When the pedal depth of the vehicle is greater than an ideal pedal depth, a rotation angle of a target aerodynamic component of the vehicle is used as the control parameter.

7. The method according to claim 1, characterized in that The controlling the corresponding actuator according to the control parameter of at least one actuator of the vehicle includes: The deviation of the vehicle state information is processed according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter, so as to control the corresponding actuator based on the control amount of the control parameter.

8. The method according to claim 7, characterized in that The gain coefficient of the preset control algorithm includes at least two of a proportional coefficient, a differential coefficient, and an integral coefficient.

9. The method according to claim 7, characterized in that The method further comprises: Determine the compensation value based on the control amount of the preset control parameters and the vehicle state information after control; The control amount of the control parameter is updated according to the compensation value to obtain the updated control amount of the control parameter.

10. The method according to claim 9, characterized in that The updating of the control amount of the control parameter according to the compensation value to obtain the updated control amount of the control parameter includes: The control amount of the control parameter at the current moment is updated according to the compensation value obtained at each moment to obtain the control amount of the control parameter after update at the current moment.

11. The method according to claim 9, characterized in that The compensation value includes a first compensation value and a second compensation value, and updating the control amount of the control parameter according to the compensation value to obtain the updated control amount of the control parameter includes: Calculating a difference between a control amount of the control parameter and a first compensation value; The control amount of the updated control parameter is determined according to the ratio of the difference to the second compensation value.

12. The method according to claim 7, characterized in that The step of processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter includes: updating a preset gain coefficient according to the deviation of the vehicle state information to obtain a gain coefficient of the preset control algorithm; The deviation of the vehicle state information is processed according to the gain coefficient of the preset control algorithm to obtain the control amount of the control parameter.

13. The method according to claim 12, characterized in that The preset gain coefficient includes the gain coefficient at the previous moment, and the updating of the preset gain coefficient according to the deviation of the vehicle state information to obtain the gain coefficient of the preset control algorithm includes: Determine the fuzzy coefficient at the current moment based on the deviation of the vehicle state information at the current moment and the preset fuzzy rules; different deviations correspond to different fuzzy coefficients; The gain coefficient at the previous moment is updated according to the fuzzy coefficient obtained at the current moment to obtain the gain coefficient at the current moment.

14. The method according to claim 7, wherein: The step of processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter includes: The difference between the slip rate of the vehicle and the ideal slip rate is processed according to the gain coefficient of the preset control algorithm to obtain the change in the target torque distribution ratio and the change in the target aerodynamic component posture.

15. The method according to claim 7, characterized in that The step of processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter includes: The difference between the yaw rate of the vehicle and the ideal yaw rate is processed according to the gain coefficient of the preset control algorithm to obtain the relative posture difference of multiple aerodynamic components.

16. The method according to claim 7, characterized in that The step of processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter includes: The difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration is processed according to the gain coefficient of the preset control algorithm to obtain a change in the rotation angle of the target aerodynamic component.

17. The method according to claim 7, characterized in that The step of processing the deviation of the vehicle state information according to the gain coefficient of the preset control algorithm to determine the control amount of the control parameter includes: The difference between the longitudinal acceleration of the vehicle and the ideal longitudinal acceleration is processed according to the gain coefficient of the preset control algorithm to obtain a torque value of the longitudinal driving torque.

18. The method according to claim 1, wherein The method further comprises: When the vehicle is in a turning state, the braking torque of the target wheel is used as the control parameter.

19. The method according to any one of claims 1 to 18, characterized in that The method further comprises: When the vehicle is in the track mode and the vehicle is in a straight-moving state, it is determined that the vehicle is in a target driving state.

20. The method according to any one of claims 1 to 18, characterized in that The aerodynamic component comprises an active breakaway grille and / or an active breakaway rear wing.

21. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 20.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 20 are implemented.

23. A computer program product, characterized in that The method comprises a computer program or instructions, which implement the steps of the method according to any one of claims 1 to 20 when executed by a processor.

24. A vehicle, characterized in that: The electronic device according to claim 21 or the steps of implementing the method according to any one of claims 1 to 20.

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