Vehicle control method, vehicle-mounted controller and vehicle

Abstract

The application discloses a vehicle control method, a vehicle-mounted controller and a vehicle. The method comprises the following steps: acquiring first vehicle data and corresponding measured road surface data in front of the vehicle when wheels work based on a first target torque; determining a target prediction working condition based on the first vehicle data and the measured road surface data; determining a second target torque corresponding to the wheels based on a torque distribution strategy corresponding to the target prediction working condition; and updating the second target torque to the first target torque, and repeatedly executing the step of acquiring the first vehicle data and the corresponding measured road surface data in front of the vehicle when the wheels work based on the first target torque. According to the scheme, the first vehicle data and the corresponding measured road surface data in front of the vehicle are analyzed in advance to determine a target prediction working condition into which the vehicle will enter, the target torque of the wheels is adjusted, active safety control of the vehicle is realized, the vehicle control process is changed from learning after knowing to knowing before controlling, and the stability and safety of vehicle driving are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a vehicle control method, a vehicle-mounted controller and a vehicle. BACKGROUND

[0002] In recent years, automobiles equipped with distributed drive systems have become a hot topic in the academic and industrial fields due to their better dynamic characteristics. Compared with centralized drive systems, distributed drive systems have the following advantages in dynamic control: first, distributed drive systems do not have complex transmission mechanisms, and can use wheel edge or hub motors as power sources, thereby saving power loss during transmission; second, distributed drive systems can independently control four wheels, have higher degrees of freedom, and have more flexible control potential.

[0003] The torque control process of existing distributed drive systems mainly focuses on longitudinal drive slip control and brake dynamics control of vehicles, as well as lateral stability control of vehicles. These control processes mainly use passive control, and mainly adjust the torque for situations that have already occurred, that is, the existing torque control process mainly uses a post-cognition control mode, which cannot guarantee the stability and safety of vehicle driving. SUMMARY

[0004] Embodiments of the present application provide a vehicle control method, a vehicle-mounted controller and a vehicle to solve the problem that the existing torque control process mainly uses a post-cognition control mode, which cannot guarantee the stability and safety of vehicle driving.

[0005] A vehicle control method, comprising:

[0006] In a process in which a wheel operates based on a first target torque, first vehicle data and corresponding measured road surface data in front of the vehicle are obtained;

[0007] Based on the first vehicle data and the measured road surface data, a target predicted working condition is determined;

[0008] Based on a torque distribution strategy corresponding to the target predicted working condition, a second target torque corresponding to the wheel is determined;

[0009] The second target torque is updated to the first target torque, and the process of obtaining the first vehicle data and the corresponding measured road surface data in front of the vehicle in the process in which the wheel operates based on the first target torque is repeatedly performed.

[0010] Preferably, the first vehicle data includes a current vehicle state and a measured vehicle speed;

[0011] The determination of the target predicted working condition based on the first vehicle data and the measured road surface data comprises:

[0012] determine an event detection result and a target road surface type based on the measured road surface data;

[0013] if the current vehicle state is a stable state, the event detection result is an impact excitation event, or the target road surface type is changed from a high adhesion road surface to a low adhesion road surface, the target predicted working condition is determined to be an unstable working condition;

[0014] if the measured vehicle speed is less than a preset vehicle speed, and the target road surface type is a sunken road surface, the target predicted working condition is determined to be a pit working condition.

[0015] Preferably, the measured road surface data includes radar point cloud data and a measured road surface image;

[0016] The event detection result and the target road surface type are determined based on the measured road surface data, including:

[0017] impact excitation event detection is performed based on the radar point cloud data and the measured road surface image to determine the event detection result;

[0018] The measured road surface image is identified using a road surface type identification model to determine the target road surface type.

[0019] Preferably, the impact excitation event detection is performed based on the radar point cloud data and the measured road surface image to determine the event detection result, including:

[0020] Road surface undulation detection is performed based on the radar point cloud data to determine a road surface undulation detection result;

[0021] Obstacle detection is performed based on the measured road surface image to determine an obstacle detection result;

[0022] If the road surface undulation detection result is that there is road surface undulation, or the obstacle detection result is that there is a road surface obstacle, the event detection result is determined to be an impact excitation event;

[0023] If the road surface undulation detection result is that there is no road surface undulation, and the obstacle detection result is that there is no road surface obstacle, the event detection result is determined to be an impact excitation event.

[0024] Preferably, the second target torque of the wheel is determined based on the torque distribution strategy corresponding to the target predicted working condition, including:

[0025] If the target predicted working condition is an unstable working condition, the first target torque is processed based on a torque transfer strategy to determine the second target torque of the wheel;

[0026] If the target working condition is a pit condition, the second vehicle data is processed based on a torque escape strategy to determine a second target torque of the wheel.

[0027] Preferably, the processing of the first target torque based on the torque transfer strategy to determine a second target torque of the wheel comprises:

[0028] The difference between the first target torque corresponding to the front axle wheel and the preset torque variable is determined as the second target torque corresponding to the front axle wheel.

[0029] The sum of the first target torque corresponding to the rear axle wheel and the preset torque variable is determined as the second target torque corresponding to the rear axle wheel.

[0030] Preferably, the second vehicle data comprises a vertical load of the wheel and an actual demand torque.

[0031] The processing of the second vehicle data based on the torque escape strategy to determine a second target torque of the wheel comprises:

[0032] The second target slip rate is updated to the first target slip rate, and the target torque constraint corresponding to the first target slip rate is updated, the second target slip rate being greater than the first target slip rate.

[0033] Under the target torque constraint, a torque distribution coefficient of the wheel is determined based on a vertical load of the wheel, and a second target torque of the wheel is determined based on the torque distribution coefficient of the wheel and an actual demand torque.

[0034] Preferably, before the first vehicle data and the measured road data corresponding to the front of the vehicle are acquired during the working of the wheel based on the first target torque, the vehicle control method further comprises:

[0035] A first target slip rate is determined based on third vehicle data, and a target torque constraint corresponding to the first target slip rate is acquired.

[0036] A current state of the vehicle is determined based on a stability evaluation of fourth vehicle data.

[0037] The first target torque of the wheel is determined based on the target torque constraint and a torque distribution strategy corresponding to the current state of the vehicle, and the wheel is controlled to work based on the first target torque.

[0038] Preferably, the fourth vehicle data comprises a measured steering wheel angle, a measured yaw rate, a measured wheel speed, and a measured acceleration.

[0039] The stability evaluation based on the fourth vehicle data to determine the current state of the vehicle comprises:

[0040] determining a yaw rate deviation value based on the measured steering wheel angle and the measured yaw rate;

[0041] determining a center of mass side slip angle deviation value based on the measured wheel speed and the measured acceleration;

[0042] if the yaw rate deviation value is greater than a first deviation threshold value or the center of mass side slip angle deviation value is greater than a second deviation threshold value, determining that the current state of the vehicle is an unstable state;

[0043] if the yaw rate deviation value is not greater than the first deviation threshold value and the center of mass side slip angle deviation value is not greater than the second deviation threshold value, determining that the current state of the vehicle is a stable state.

[0044] Preferably, the determining a yaw rate deviation value based on the measured steering wheel angle and the measured yaw rate comprises:

[0045] determining a measured front wheel angle based on the measured steering wheel angle;

[0046] determining an estimated yaw rate based on the measured front wheel angle and a target transfer function determined based on a two-degree-of-freedom model of the vehicle;

[0047] determining an expected yaw rate based on the estimated yaw rate and a maximum yaw rate;

[0048] determining a yaw rate deviation value based on the measured yaw rate and the expected yaw rate.

[0049] Preferably, the determining a center of mass side slip angle deviation value based on the measured wheel speed and the measured acceleration comprises:

[0050] determining a measured vehicle speed based on the measured wheel speed;

[0051] determining a target load of the wheel based on the measured acceleration and a pre-set static load;

[0052] determining an estimated center of mass side slip angle based on the measured vehicle speed, the wheel load and a two-degree-of-freedom model of the vehicle;

[0053] determining a center of mass side slip angle deviation value based on the estimated center of mass side slip angle and a pre-set expected center of mass side slip angle.

[0054] Preferably, the determining a first target torque of the wheel based on the target torque constraint and a torque distribution strategy corresponding to the current state of the vehicle, and controlling the wheel to work based on the first target torque comprises:

[0055] if the current state of the vehicle is the unstable state, processing the longitudinal force, the lateral force, the vertical force and the road adhesion coefficient of the wheel based on the target torque constraint and the stability distribution strategy to determine the first target torque of the wheel;

[0056] if the current state of the vehicle is the stable state, processing the current torque, the angular velocity and the wheel motor efficiency of the wheel based on the target torque constraint and the economy distribution strategy to determine the first target torque of the wheel.

[0057] Preferably, after the control wheel works based on the first target torque, the vehicle control method further comprises:

[0058] if the current state of the vehicle is the unstable state, determining the additional yaw moment based on the yaw rate deviation value and the center of mass side slip angle deviation value;

[0059] correcting the first target torque based on the additional yaw moment.

[0060] Preferably, the correcting the first target torque based on the additional yaw moment comprises:

[0061] determining the maximum center of mass side slip angle based on the measured vehicle speed and the road adhesion coefficient;

[0062] if the estimated center of mass side slip angle is less than the maximum center of mass side slip angle, applying the additional yaw moment to the wheel in a driving differential manner to correct the first target torque;

[0063] if the estimated center of mass side slip angle is not less than the maximum center of mass side slip angle, applying the additional yaw moment to the wheel in a braking differential manner to correct the first target torque.

[0064] A vehicle-mounted controller comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle control method.

[0065] A vehicle comprising the vehicle-mounted controller.

[0066] The vehicle control method, the vehicle-mounted controller and the vehicle disclosed by the application can analyze the target prediction working condition that the vehicle will enter in advance based on the first vehicle data collected in real time and the corresponding measured road surface data in front of the vehicle during the working process of the wheels based on the first target torque, so as to achieve the effect of knowing in advance; then the torque distribution strategy corresponding to the target prediction working condition is used to determine the second target torque corresponding to the wheels, the second target torque is updated as the new first target torque, and the working of the wheels is controlled based on the new first target torque, so as to achieve the effect of controlling in advance. In the scheme, the target torque of the wheels is adjusted by analyzing the target prediction working condition that the vehicle will enter in advance, active safety control of the vehicle is realized, the vehicle control process is changed from learning after knowing to knowing in advance and controlling in advance, and the stability and safety of the vehicle during driving are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the application. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0068] Figure 1 is a flowchart of a vehicle control method in an embodiment of the application;

[0069] Figure 2 is another flowchart of a vehicle control method in an embodiment of the application;

[0070] Figure 3 is another flowchart of a vehicle control method in an embodiment of the application;

[0071] Figure 4 is another flowchart of a vehicle control method in an embodiment of the application;

[0072] Figure 5 is another flowchart of a vehicle control method in an embodiment of the application;

[0073] Figure 6 is another flowchart of a vehicle control method in an embodiment of the application;

[0074] Figure 7 is another flowchart of a vehicle control method in an embodiment of the application;

[0075] Figure 8 is another flowchart of a vehicle control method in an embodiment of the application;

[0076] Figure 9 is another flowchart of a vehicle control method in an embodiment of the application;

[0077] Figure 10is another flow chart of the vehicle control method in an embodiment of the present application;

[0078] Figure 11 is another flow chart of the vehicle control method in an embodiment of the present application;

[0079] Figure 12 is another flow chart of the vehicle control method in an embodiment of the present application;

[0080] Figure 13 is another flow chart of the vehicle control method in an embodiment of the present application;

[0081] Figure 14 is another flow chart of the vehicle control method in an embodiment of the present application. DETAILED DESCRIPTION

[0082] 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, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0083] The vehicle control method provided by the embodiments of the present application can be applied in a vehicle-mounted controller of a vehicle, and is used to analyze in advance whether a situation affecting the stability and safety of the vehicle during driving will occur based on real-time collected vehicle data and road surface data, and to take corresponding control strategies in advance, so as to realize active safety control of the vehicle, and to change the vehicle control process from learning after the fact to knowing before controlling, so as to guarantee the driving safety of the vehicle.

[0084] In an embodiment, as shown in Figure 1 , a vehicle control method is provided, and the method is described by taking a vehicle-mounted controller as an example, and includes the following steps.

[0085] S101: In a process in which the wheels work based on a first target torque, first vehicle data and corresponding measured road surface data in front of the vehicle are acquired;

[0086] S102: A target predicted working condition is determined based on the first vehicle data and the measured road surface data;

[0087] S103: A second target torque corresponding to the wheels is determined based on a torque distribution strategy corresponding to the target predicted working condition;

[0088] S104: The second target torque is updated as the first target torque, and the process of acquiring the first vehicle data and the corresponding measured road surface data in front of the vehicle in the process in which the wheels work based on the first target torque is repeated.

[0089] wherein the first target torque is a torque used for controlling the wheels to work at the current time. The first vehicle data is data related to the vehicle collected at the current time, which is used for evaluating the target predicted working condition. The measured road surface data is data related to the road surface in front of the vehicle collected in real time.

[0090] As an example, in step S101, the vehicle controller can acquire the first vehicle data and the measured road surface data in front of the vehicle through the CAN bus or other communication methods while controlling the wheels to work based on the first target torque. The first vehicle data can be understood as data reflecting the current state of the vehicle, and the measured road surface data in front of the vehicle can be understood as data related to the road surface that the vehicle is about to enter.

[0091] As an example, in step S102, after acquiring the first vehicle data and the measured road surface data in front of the vehicle, the vehicle controller can call the pre-set working condition prediction logic to analyze the two input parameters, i.e., the first vehicle data and the measured road surface data, and determine the target predicted working condition as the output. The target predicted working condition can be understood as the working condition that the vehicle is about to enter.

[0092] As an example, in step S103, after determining the target predicted working condition, the vehicle controller can adopt a torque distribution strategy corresponding to the target predicted working condition, analyze the measured data required by the torque distribution strategy, and determine the second target torque corresponding to each wheel. The second target torque can be understood as the target torque needed to control the wheels to work at the next time. Generally, the second target torque determined based on the torque distribution strategy corresponding to the target predicted working condition can overcome the problems existing in the target predicted working condition that affect the stability and safety of the vehicle, so as to avoid the occurrence of related problems in advance.

[0093] As an example, in step S104, after determining the second target torque, the vehicle controller can take the second target torque as the new first target torque to control the wheels to work based on the corresponding first target torque at the next time, and repeat the above step S104, so that when the wheels work based on the updated first target torque, the problems existing in the first vehicle data and the corresponding measured road surface data in front of the vehicle can be avoided, thereby ensuring the stability and safety of the vehicle.

[0094] In this embodiment, while the wheels are operating based on the first target torque, the target predicted operating condition that the vehicle is about to enter is analyzed in advance based on the first vehicle data collected in real time and the corresponding measured road surface data in front of the vehicle, thereby achieving a preemptive effect. A torque distribution strategy corresponding to the target predicted operating condition is then adopted to determine the second target torque corresponding to the wheel, and the second target torque is updated to the new first target torque, so that the wheel operation is controlled based on the new first target torque to achieve a preemptive control effect. In this solution, by analyzing the target predicted operating condition that the vehicle is about to enter in advance and adjusting the target torque of the wheel, active vehicle safety control is achieved, transforming the vehicle control process from after-knowledge and after-learning to preemptive control, thereby ensuring the stability and safety of vehicle driving.

[0095] In one embodiment, the first vehicle data includes a current vehicle state and a measured vehicle speed;

[0096] like Figure 2 As shown, step S102, i.e., determining a target predicted operating condition based on the first vehicle data and the measured road surface data, includes:

[0097] S201: performing event detection and road surface identification based on measured road surface data, and determining event detection results and target road surface type;

[0098] S202: If the current vehicle state is stable, and the event detection result indicates the presence of an impact excitation event or the target road surface type changes from a high-adhesion road surface to a low-adhesion road surface, then determining the target predicted operating condition as an unstable operating condition;

[0099] S203: If the measured vehicle speed is less than the preset vehicle speed and the target road surface type is a sunken road surface, the target predicted operating condition is determined to be a pothole operating condition.

[0100] The current vehicle state refers to the vehicle's current state, which can be either stable or unstable. A stable state refers to a state where the measured data used to assess handling stability reaches its corresponding preset threshold, while an unstable state refers to a state where the measured data used to assess handling stability does not reach its corresponding preset threshold. The measured vehicle speed refers to the vehicle's current speed, which can be calculated from the measured wheel speed or data collected by the acceleration sensor. The preset vehicle speed refers to the threshold used to assess whether the vehicle is in a low-speed or high-speed condition.

[0101] As an example, in step S201, after acquiring measured road surface data ahead of the vehicle, the onboard controller may employ pre-configured event detection logic to identify the measured road surface data and determine an event detection result. The event detection result may be either the presence or absence of an impact excitation event. An impact excitation event, as used herein, refers to an event that could affect the vehicle's stable driving. Furthermore, the pre-configured road surface type detection logic may be employed to identify the measured road surface data and determine a target road surface type. The target road surface type, as used herein, refers to the road surface type corresponding to the road surface that the vehicle is about to enter, as determined based on the measured road surface data ahead of the vehicle.

[0102] As an example, in step S202, when the vehicle is currently in a stable state, regardless of whether it is at low or high speed, if the event detection result determined based on the measured road surface data indicates the presence of an impact excitation event, or if the target road surface type changes from a high-adhesion road surface to a low-adhesion road surface, it can be determined that the vehicle will enter an unstable state if it continues to travel on the road ahead. In this case, the target predicted operating condition is determined to be an unstable condition, and a torque distribution strategy that matches the unstable condition is required to adjust the target torque of the wheels. A high-adhesion road surface here refers to a road surface with high adhesion. High-adhesion roads provide higher friction, allowing the vehicle to better grip the ground during driving and reducing the possibility of slipping. Conversely, a low-adhesion road surface refers to a road surface with low adhesion. Low-adhesion roads provide lower friction, making the vehicle more likely to slip during driving.

[0103] As an example, in step S203, the on-board controller determines that the vehicle is in a low-speed condition when the measured vehicle speed is less than the preset speed. At this time, if the target road surface type determined based on the measured road surface data is a sunken road surface, that is, it is identified that the vehicle is on a sunken road surface such as a snowy road surface or a muddy road surface where the tire adhesion and friction coefficient have a nonlinear relationship, it can be determined that when the vehicle continues to travel to the road ahead, its wheels are likely to be trapped in the snow or mud. At this time, the target predicted operating condition is determined to be a pothole condition, and it is necessary to adopt a torque distribution strategy that matches the pothole condition to adjust the target torque of the wheel.

[0104] In one embodiment, the measured road surface data includes radar point cloud data and measured road surface images;

[0105] like Figure 3 As shown, step S201, i.e., performing event detection and road surface identification based on measured road surface data, and determining event detection results and target road surface type, includes:

[0106] S301: performing impact excitation event detection based on radar point cloud data and measured road surface images, and determining event detection results;

[0107] S302: Identify the measured road surface image using the road surface type identification model to determine the target road surface type.

[0108] The radar point cloud data is point cloud data formed by radar real-time scanning of the road surface in front of the vehicle. The measured road surface image is a road surface image formed by real-time shooting of the road surface in front of the vehicle by the camera device.

[0109] As an example, in step S301, the vehicle-mounted controller can obtain radar point cloud data formed by radar scanning of the road surface in front of the vehicle, and obtain a measured road surface image formed by shooting of the road surface in front of the vehicle by the camera device; then, the pre-set event detection logic is used to process the two measured data of radar point cloud data and measured road surface image respectively, to evaluate whether the two measured data satisfy the corresponding evaluation conditions for evaluating the existence of impact excitation events, if any of the two measured data satisfies the corresponding evaluation condition for evaluating the existence of impact excitation events, the event detection result is determined to be an impact excitation event; if neither of the two measured data satisfies the corresponding evaluation condition for evaluating the existence of impact excitation events, the event detection result is determined to be no impact excitation event. In this example, impact excitation event detection is performed based on two dimensions of radar point cloud data and measured road surface image, to ensure the accuracy of the event detection result.

[0110] The road surface type identification model is a pre-trained neural network model for identifying road surface types. For example, the training road surface image and its corresponding road surface type label can be input into a convolutional neural network for training, and when the model converges, a trained neural network model for identifying road surface types can be obtained. In this example, the model can be trained based on different road surface types such as ordinary hard road surface, grassland, gravel ground, ice and snow ground, mud ground, thick sand ground, rock ground, and wading, and their corresponding training road surface images, to obtain the road surface type identification model.

[0111] As an example, in step S302, the vehicle-mounted controller can obtain a measured road surface image formed by shooting of the road surface in front of the vehicle by the camera device, and then identify the measured road surface image using the pre-trained road surface type identification model to obtain the target road surface type to which the road surface in front of the vehicle belongs, so as to subsequently perform working condition prediction based on the target road surface type. In this example, the measured road surface image is processed based on the road surface type identification model, and the target road surface type corresponding to the road surface to be driven into by the vehicle can be accurately determined by artificial intelligence.

[0112] In an embodiment, step S301, i.e., impact excitation event detection based on radar point cloud data and measured road surface image to determine the event detection result, includes:

[0113] S401: detecting road unevenness based on the radar point cloud data to obtain a road unevenness detection result;

[0114] S402: detecting obstacles based on the measured road surface image to obtain an obstacle detection result;

[0115] S403: if the road unevenness detection result is that there is road unevenness, or the obstacle detection result is that there is road surface obstacle, determining that the event detection result is that there is impact excitation event.

[0116] S404: if the road unevenness detection result is that there is no road unevenness, and the obstacle detection result is that there is no road surface obstacle, determining that the event detection result is that there is impact excitation event.

[0117] As an example, in step S401, after obtaining the radar point cloud data formed by the radar scanning the road surface in front of the vehicle, the vehicle-mounted controller can detect road unevenness based on the radar point cloud data to determine the road unevenness detection result, which can be any one of existence of road unevenness and non-existence of road unevenness. Understandably, when the road unevenness detection result is that there is road unevenness, it means that the road in front of the vehicle is uneven, which will affect the stability and safety of the vehicle driving.

[0118] As an example, in step S402, after obtaining the measured road surface image formed by the camera equipment shooting the road surface in front of the vehicle, the vehicle-mounted controller can use a target detection algorithm to detect targets in the measured road surface image, and then evaluate whether the detected targets are road surface obstacles to obtain the obstacle detection result. The road surface obstacles can be, but are not limited to, speed bumps, manhole covers, potholes, and road surface residual concrete, etc. The existence of these obstacle detection results will affect the stability and safety of the vehicle driving.

[0119] As an example, in step S403, when the road unevenness detection result is that there is road unevenness, the vehicle-mounted controller can determine that according to the radar point cloud data scanned by the radar, it is determined that there is a "road unevenness" impact excitation event in the wheel track interval in front of the vehicle, which affects the stability and safety of the vehicle driving. Or, when the obstacle detection result is that there is road surface obstacle, the vehicle-mounted controller can determine that according to the measured road surface image shot by the camera equipment, it is determined that there is a "road surface obstacle" impact excitation event in the wheel track interval in front of the vehicle, which affects the stability and safety of the vehicle driving, so the event detection result is determined to be an impact excitation event.

[0120] As an example, in step S404, when the road surface undulation detection result is that there is no road surface undulation (i.e., the road surface is flat), the vehicle-mounted controller can determine that, according to the radar point cloud data scanned by the radar, there is no "road surface undulation", which is an impact excitation event affecting the driving stability and safety of the vehicle, in the wheel track interval in front of the vehicle, and when the obstacle detection result is that there is no road surface obstacle, the vehicle-mounted controller can determine that, according to the measured road surface image captured by the camera device, there is no "road surface obstacle", which is an impact excitation event affecting the driving stability and safety of the vehicle, in the wheel track interval in front of the vehicle, and thus the event detection result can be determined to be that there is no impact excitation event.

[0121] In an embodiment, as shown in FIG. 1, step S103, i.e., determining the second target torque of the wheel based on the torque distribution strategy corresponding to the target predicted working condition, includes: Figure 5

[0122] S501: If the target predicted working condition is the instability working condition, processing the first target torque based on the torque transfer strategy to determine the second target torque of the wheel.

[0123] S502: If the target predicted working condition is the pit working condition, processing the second vehicle data based on the torque escape strategy to determine the second target torque of the wheel.

[0124] The torque transfer strategy is the torque distribution strategy corresponding to the instability working condition, and specifically is a strategy for controlling the mutual transfer of the target torques of different wheels, which is set in advance.

[0125] As an example, in step S501, when the target predicted working condition is the instability working condition, i.e., when it is analyzed that the vehicle will soon enter the instability working condition, the vehicle-mounted controller can perform transfer processing on the first target torques of multiple wheels based on the torque transfer strategy set in advance, reduce the target torques of the front axle wheels (including the left front wheel and the right front wheel) in advance, and increase the target torques of the rear axle wheels (including the left rear wheel and the right rear wheel), so as to transfer the target torques of the front axle wheels to the rear axle wheels, thereby releasing the adhesion capacity of the front axle wheels, improving the steering ability of the vehicle, and achieving the purpose of preventing instability.

[0126] The torque escape strategy is the torque distribution strategy corresponding to the pit working condition, and specifically is a strategy for adjusting the target torques of different wheels to escape from the sinking road surface, such as the snow-covered road surface and the swamp road surface, on which the tire adhesion force and the friction coefficient have a nonlinear relationship. The second vehicle data is data collected in real time for calculating the second target torque.

[0127] ​As an example, in step S501, when the target predicted working condition is a pit working condition, that is, when it is analyzed that the vehicle will enter a pit working condition, the vehicle-mounted controller can analyze and process the second vehicle data required for the preset torque escape strategy based on the torque escape strategy, determine a second target torque that can make each wheel escape from the sunken road surface, and prevent the vehicle from sinking or assist the vehicle to escape.

[0128] In an embodiment, as shown in Figure 6 Step S501, that is, processing the first target torque based on the torque transfer strategy to determine the second target torque of the wheel, includes:

[0129] S601: determining the difference between the first target torque corresponding to the front axle wheel and the preset torque variable as the second target torque corresponding to the front axle wheel;

[0130] S602: determining the sum of the first target torque corresponding to the rear axle wheel and the preset torque variable as the second target torque corresponding to the rear axle wheel.

[0131] The preset torque variable is a preset torque variable for limiting the torque variable to be adjusted each time the torque transfer strategy is executed.

[0132] As an example, when the target predicted working condition is a loss of stability working condition, the vehicle-mounted controller needs to execute the torque transfer strategy to transfer the target torque corresponding to the front axle wheel and the rear axle wheel, specifically, the difference between the first target torque corresponding to the front axle wheel and the preset torque variable is determined as the second target torque corresponding to the front axle wheel, and the sum of the first target torque corresponding to the rear axle wheel and the preset torque variable is determined as the second target torque corresponding to the rear axle wheel, so as to transfer the target torque of the front axle wheel to the rear axle wheel, thereby releasing the adhesion capacity of the front axle wheel, improving the steering ability of the vehicle, and achieving the purpose of preventing loss of stability.

[0133] In an embodiment, the second vehicle data includes the vertical load of the wheel and the actual demand torque;

[0134] As shown in Figure 7 Step S502, that is, processing the second vehicle data based on the torque escape strategy to determine the second target torque of the wheel, includes:

[0135] S701: updating the second target slip ratio to the first target slip ratio, updating the target torque constraint corresponding to the first target slip ratio, and the second target slip ratio is greater than the first target slip ratio;

[0136] S702: determining the torque distribution coefficient of the wheel based on the vertical load of the wheel under the target torque constraint, and determining the second target torque of the wheel based on the torque distribution coefficient of the wheel and the actual demand torque.

[0137] wherein the first target slip ratio refers to a target slip ratio determined at a current time for calculating a target torque constraint, and the second target slip ratio refers to a target slip ratio determined at a next time for calculating a target torque constraint.

[0138] As an example, when the target predicted working condition is a pit working condition, the vehicle-mounted controller needs to replace a smaller first target slip ratio with a larger second target slip ratio, so as to determine a new target torque constraint based on the new first target slip ratio, so as to ensure that the vehicle can effectively avoid entering the pit working condition under the condition of meeting the new target torque constraint. When the vehicle is under the target torque constraint, the vertical load corresponding to the front axle wheel and the rear axle wheel needs to be calculated and determined according to multiple parameters such as the mass, the mass center position and the acceleration of the vehicle; then, the torque distribution coefficient corresponding to the front axle wheel and the rear axle wheel is determined by querying a pre-set torque distribution coefficient table according to the vertical load corresponding to the front axle wheel and the rear axle wheel; finally, the second target torque corresponding to the front axle wheel and the rear axle wheel is respectively determined in combination with the actual demand torque of the driver and the torque distribution coefficient corresponding to the front axle wheel and the rear axle wheel.

[0139] In this example, when the target predicted working condition is determined to be a pit working condition, the target slip ratio is first increased, so as to achieve the purpose of adjusting the target torque constraint, so that the torque distribution based on the new target torque constraint, that is, the torque distribution based on the actual demand torque and the vertical load of the wheel, makes the second target torque corresponding to each wheel meet the corresponding target torque constraint, so as to prevent the vehicle from sinking or assist the vehicle to escape.

[0140] In an embodiment, as shown in FIG. 1, before step S101, that is, before the first vehicle data and the measured road data corresponding to the front of the vehicle are acquired during the working process of the wheel based on the first target torque, the vehicle control method further comprises: Figure 8

[0141] S801: determining the first target slip ratio based on the third vehicle data, and acquiring the target torque constraint corresponding to the first target slip ratio;

[0142] S802: performing stability evaluation based on the fourth vehicle data, and determining the current state of the vehicle;

[0143] S803: determining the first target torque of the wheel based on the target torque constraint and the torque distribution strategy corresponding to the current state of the vehicle, and controlling the wheel to work based on the first target torque.

[0144] wherein the third vehicle data refers to the data collected in real time for determining the target torque constraint. As an example, the third vehicle data includes but is not limited to measured accelerations such as longitudinal acceleration and lateral acceleration.

[0145] ​As an example, in step S801, the vehicle-mounted controller can obtain the third vehicle data through the CAN bus or other communication means, input the third vehicle data as an input parameter into the calculation model corresponding to the road adhesion coefficient, and determine the road adhesion coefficient corresponding to the third vehicle data. In this example, the inertial load corresponding to the wheel can be calculated based on the longitudinal acceleration and lateral acceleration as two input parameters; then, the target load corresponding to the wheel is determined based on the inertial load corresponding to the wheel and the pre-set static load; then, the target load corresponding to the wheel is input into the tire model established based on the magic formula, and the corresponding road adhesion coefficient can be estimated. Then, the vehicle-mounted controller can query the pre-set adhesion coefficient-slip ratio mapping table based on the road adhesion coefficient to determine the first target slip ratio corresponding to the road adhesion coefficient. Finally, after determining the first target slip ratio based on the third vehicle data, the vehicle-mounted controller can input the first target slip ratio as an input parameter into the pre-set torque constraint model, and determine the target torque constraint as the output result of the torque constraint model. In this example, the torque constraint model is pre-constructed according to the motor maximum torque map, the tire model, and the different target slip ratio map, and the first target slip ratio is input as an input parameter to determine the corresponding target torque constraint.

[0146] The fourth vehicle data refers to the data collected in real time for stability evaluation.

[0147] As an example, in step S802, the vehicle-mounted controller can obtain the fourth vehicle data through the CAN bus or other communication means, and then calculate the fourth vehicle data by using the pre-set evaluation condition for evaluating the stability of the vehicle. If the fourth vehicle data satisfies the evaluation condition, it is determined that the current state of the vehicle is a stable state; if the fourth vehicle data does not satisfy the evaluation condition, it is determined that the current state of the vehicle is an unstable state.

[0148] As an example, in step S803, after determining the target torque constraint and the current state of the vehicle, the vehicle-mounted controller needs to perform torque distribution based on the torque distribution strategy corresponding to the current state of the vehicle under the target torque constraint, to determine the first target torque corresponding to the wheel, so that the distributed first target torque satisfies the target torque constraint and meets the strategy target of the torque distribution strategy corresponding to the current state of the vehicle. For example, when the current state of the vehicle is a stable state, energy saving can be taken as the strategy target of the torque distribution strategy corresponding to the stable state, so that the first target torque determined based on the torque distribution strategy corresponding to the stable state can achieve the purpose of energy saving to the greatest extent; for another example, when the current state of the vehicle is an unstable state, handling stability can be taken as the strategy target of the torque distribution strategy corresponding to the unstable state, so that the first target torque determined based on the torque distribution strategy corresponding to the unstable state can meet the demand for vehicle stability, so as to ensure the stability and safety of the vehicle in driving.

[0149] In an embodiment, the fourth vehicle data comprises a measured steering wheel angle, a measured yaw rate, a measured wheel speed and a measured acceleration;

[0150] As shown in FIG. 8, the step S802 of performing stability evaluation based on the fourth vehicle data to determine the current state of the vehicle comprises: Figure 9

[0151] S901: determining a yaw rate deviation value based on the measured steering wheel angle and the measured yaw rate;

[0152] S902: determining a mass center side slip angle deviation value based on the measured wheel speed and the measured acceleration;

[0153] S903: if the yaw rate deviation value is greater than a first deviation threshold value or the mass center side slip angle deviation value is greater than a second deviation threshold value, determining that the current state of the vehicle is an unstable state;

[0154] S904: if the yaw rate deviation value is not greater than the first deviation threshold value and the mass center side slip angle deviation value is not greater than the second deviation threshold value, determining that the current state of the vehicle is a stable state.

[0155] The measured steering wheel angle is a real-time measured steering wheel angle, which can be a real-time measured data of a steering wheel angle sensor. The measured yaw rate is a real-time measured yaw rate, which can be a real-time measured data of a gyroscope or other device capable of measuring the yaw rate. The measured wheel speed is a real-time measured speed of a wheel, which can be a real-time measured data of a wheel speed sensor. The measured acceleration is a real-time measured acceleration, including longitudinal acceleration and lateral acceleration.

[0156] As an example, in the step S901, after obtaining the measured steering wheel angle and the measured yaw rate as the fourth vehicle data, the vehicle controller can first process the input parameter of the measured steering wheel angle based on a pre-set yaw rate estimation algorithm, and determine the output result of the measured steering wheel angle as an estimated yaw rate, so as to determine the yaw rate deviation value based on the estimated yaw rate and the measured yaw rate. Specifically, the difference between the estimated yaw rate and the measured yaw rate can be determined as the yaw rate deviation value. In this example, an estimated yaw rate is determined based on the measured steering wheel angle, and the estimated yaw rate is calculated with the measured yaw rate measured by the gyroscope or other device capable of measuring the yaw rate, so that the calculated yaw rate deviation value can reflect the difference between the yaw rates calculated in two dimensions, so as to further determine the current state of the vehicle based on the difference between the two.

[0157] ​As an example, in step S902, after determining the measured wheel speed and the measured acceleration, the vehicle-mounted controller can process the two input parameters of the measured wheel speed and the measured acceleration based on the pre-set mass center side slip angle estimation algorithm, and determine the output result as the estimated mass center side slip angle, which can be understood as the mass center side slip angle calculated according to the measured wheel speed and the measured acceleration. Then, the difference between the estimated mass center side slip angle and the pre-set expected mass center side slip angle is determined as the mass center side slip angle deviation value. The expected mass center side slip angle refers to the mass center side slip angle pre-set according to the specific conditions of the vehicle, including but not limited to the vehicle type, shape, size, etc. Generally, the expected mass center side slip angle can be set to 0 or other values close to 0.

[0158] The first deviation threshold refers to a threshold for evaluating whether the yaw rate deviation value reaches a large standard, which is a critical value for dividing the stable state and the unstable state. The second deviation threshold refers to a threshold for evaluating whether the mass center side slip angle deviation value reaches a large standard, which is a critical value for dividing the stable state and the unstable state. In the present example, the first deviation threshold and the second deviation threshold can be the same or different.

[0159] As an example, in step S903, the vehicle-mounted controller can compare the yaw rate deviation value with the first deviation threshold, and compare the mass center side slip angle deviation value with the second deviation threshold. When the yaw rate deviation value is greater than the first deviation threshold, or the mass center side slip angle deviation value is greater than the second deviation threshold, it is determined that at least one of the two dimensional data analyzed by analyzing the yaw rate and the mass center side slip angle may exist in an unstable state, and therefore, it can be determined that the current state of the vehicle is an unstable state.

[0160] As an example, in step S904, the vehicle-mounted controller can compare the yaw rate deviation value with the first deviation threshold, and compare the mass center side slip angle deviation value with the second deviation threshold. When the yaw rate deviation value is not greater than the first deviation threshold, and the mass center side slip angle deviation value is not greater than the second deviation threshold, it is determined that both of the two dimensional data analyzed by analyzing the yaw rate and the mass center side slip angle are in a stable state, and therefore, it can be determined that the current state of the vehicle is a stable state.

[0161] In an embodiment, as shown in FIG. 10, step S901, i.e., determining the yaw rate deviation value based on the measured steering wheel angle and the measured yaw rate, includes: Figure 10

[0162] S1001: determining the measured front wheel angle based on the measured steering wheel angle;

[0163] ​S1002: determining an estimated yaw rate based on the measured front wheel steering angle and a target transfer function determined based on a two-degree-of-freedom model of the vehicle;

[0164] S1003: determining a desired yaw rate based on the estimated yaw rate and a maximum yaw rate;

[0165] S1004: determining a yaw rate deviation value based on the measured yaw rate and the desired yaw rate.

[0166] As an example, in step S1001, after obtaining the measured steering wheel steering angle, the vehicle controller can estimate the wheel steering angles corresponding to each wheel based on the measured steering wheel steering angle, and determine the measured front wheel steering angles related to the yaw rate therefrom. For example, the vehicle controller can query a two-dimensional data table pre-set to reflect the mapping relationship between different steering wheel steering angles and different wheel steering angles based on the measured steering wheel steering angle, and determine the wheel steering angles corresponding to the measured steering wheel steering angle therefrom, where the wheel steering angles include two measured front wheel steering angles and two rear wheel steering angles.

[0167] As an example, in step S1002, after determining the measured front wheel steering angle, the vehicle controller can perform pull-type transformation on a two-degree-of-freedom model of the vehicle pre-constructed by the resultant force of the vehicle along the longitudinal direction and the moment of force around the center of mass, to determine a target transfer function for reflecting the mapping relationship between different front wheel steering angles and different yaw rates; and then substitute the measured front wheel steering angle into the target transfer function to determine the estimated yaw rate corresponding to the measured front wheel steering angle. The estimated yaw rate can be understood as the yaw rate estimated based on the measured front wheel steering angle.

[0168] The maximum yaw rate refers to the maximum value of the yaw rate allowed to be reached by the vehicle in stable driving. As an example, the maximum yaw rate is a pre-set maximum value, or can be a maximum value determined according to the real-time measured road adhesion coefficient.

[0169] As an example, in step S1003, after determining the estimated yaw rate, the vehicle controller can compare and analyze the estimated yaw rate and the maximum yaw rate to determine the desired yaw rate. For example, the estimated yaw rate and the maximum yaw rate can be compared, and if the estimated yaw rate is less than the maximum yaw rate, the estimated yaw rate is determined as the desired yaw rate; and if the estimated yaw rate is not less than the maximum yaw rate, the maximum yaw rate is determined as the desired yaw rate, so as to avoid the desired yaw rate determined finally exceeding the maximum yaw rate allowed thereby affecting the stability and safety of the vehicle driving.

[0170] As an example, in step S1004, the vehicle-mounted controller can determine the yaw rate deviation value according to the expected yaw rate and the measured yaw rate, and specifically, can determine the difference between the expected yaw rate and the measured yaw rate as the yaw rate deviation value. In this example, the measured yaw rate is calculated with the expected yaw rate, so that the calculated yaw rate deviation value can reflect the difference between the two calculated yaw rates in two dimensions under the constraint of the maximum yaw rate, so as to further determine the current state of the vehicle according to the difference between the two, and ensure the accuracy of the current state of the vehicle.

[0171] In an embodiment, as shown in Figure 11 Step S902, i.e., determining the mass center side slip angle deviation value based on the measured wheel speed and the measured acceleration, includes:

[0172] S1101: determining the measured vehicle speed based on the measured wheel speed;

[0173] S1102: determining the target load of the wheel based on the measured acceleration and the pre-set static load;

[0174] S1103: determining the estimated mass center side slip angle based on the measured vehicle speed, the wheel load and the two-degree-of-freedom model of the vehicle;

[0175] S1104: determining the mass center side slip angle deviation value based on the estimated mass center side slip angle and the pre-set expected mass center side slip angle.

[0176] As an example, in step S1101, after determining the measured wheel speed corresponding to each wheel, the vehicle-mounted controller can dynamically calculate the measured vehicle speed corresponding to each wheel based on the measured wheel speed; or can determine the measured vehicle speed based on the measured wheel speed and the positioning data collected by the GPS positioning module or the Beidou positioning module in real time, and this process can be determined by using the existing algorithm, which will not be described here.

[0177] As an example, in step S1102, the vehicle-mounted controller can obtain two measured accelerations, i.e., the longitudinal acceleration and the lateral acceleration; then, based on the longitudinal acceleration and the lateral acceleration, determine the inertial load corresponding to each wheel; and then, in combination with the pre-set static load and the inertial load corresponding to each wheel, determine the target load corresponding to each wheel.

[0178] As an example, in step S1103, after determining the measured vehicle speed and the target load corresponding to each wheel, the vehicle-mounted controller can utilize the two-degree-of-freedom model of the vehicle pre-constructed by the resultant force of the vehicle along the longitudinal direction and the moment around the mass center, to process the measured vehicle speed and the target load corresponding to each wheel, and determine the estimated mass center side slip angle according to the processing result.

[0179] As an example, in step S1104, after determining the estimated centroid side slip angle, the vehicle-mounted controller can determine the difference between the estimated centroid side slip angle and the preset expected centroid side slip angle as the centroid side slip angle deviation value, so as to subsequently determine whether the vehicle is in the unstable state based on the comparison result of the centroid side slip angle deviation value and the second deviation threshold value. In this example, the estimated centroid side slip angle is calculated with the expected centroid side slip angle, so that the calculated centroid side slip angle deviation value can reflect the difference between the estimated centroid side slip angle and the expected centroid side slip angle determined in the vehicle calibration process, so as to further determine the current state of the vehicle according to the difference between the two, and ensure the accuracy of the current state of the vehicle.

[0180] In an embodiment, step S803, i.e., determining the first target torque of the wheel based on the target torque constraint and the torque distribution strategy corresponding to the current state of the vehicle, includes:

[0181] S1201: If the current state of the vehicle is the unstable state, the longitudinal force, lateral force, vertical force and road adhesion coefficient of the wheel are processed based on the target torque constraint and the stability distribution strategy to determine the first target torque of the wheel.

[0182] S1202: If the current state of the vehicle is the stable state, the current torque, angular velocity and wheel motor efficiency of the wheel are processed based on the target torque constraint and the economic distribution strategy to determine the first target torque of the wheel.

[0183] The stability distribution strategy is a distribution strategy with handling stability as the strategy target.

[0184] As an example, in step S1201, when the current state of the vehicle is the unstable state, the vehicle-mounted controller can determine the torque distribution strategy corresponding to the stability distribution strategy, and process the longitudinal force, lateral force, vertical force and road adhesion coefficient of the wheel based on the stability distribution strategy under the target torque constraint to determine the first target torque corresponding to each wheel. In this example, based on the stability distribution strategy, the torque can be distributed according to the following optimization target: wherein, is the longitudinal force of the i-th wheel, is the lateral force of the i-th wheel, is the vertical force of the i-th wheel, is the friction coefficient, i.e., the road adhesion coefficient, and J is the first target torque of the i-th wheel. By using the above stability distribution strategy, the first target torque corresponding to the wheel is distributed in the whole torque with the minimum road adhesion consumption, so as to ensure that the road adhesion consumption is minimized under the conditions of meeting the driving demand torque and yaw torque demand, and to ensure the handling stability of the vehicle and further improve the safety capability of the vehicle.

[0185] As an example, in step S1202, when the current vehicle state is a stable state, the vehicle controller can determine the economy allocation as the corresponding torque allocation strategy, and based on the economy allocation strategy, the current torques, angular velocities and wheel motor efficiencies of the wheels are processed under the target torque constraint to determine the first target torques of the wheels. In this example, based on the economy allocation strategy, the torques can be allocated according to the following optimization objective: wherein, is the torque of the i-th wheel, is the angular velocity of the i-th wheel, is the wheel motor efficiency of the i-th wheel, and J is the first target torque of the i-th wheel. By using the above economy allocation strategy, the front wheels and the rear wheels can be allocated according to the highest driving efficiency, and the left and right wheels can be allocated equally to achieve the purpose of energy saving.

[0186] In an embodiment, as shown in Figure 13 after step S803, i.e., after controlling the wheels to work based on the first target torques, the vehicle control method further includes:

[0187] S1301: If the current vehicle state is an unstable state, an additional yaw moment is determined based on the yaw rate deviation value and the center of mass side slip angle deviation value;

[0188] S1302: The first target torques are corrected based on the additional yaw moment.

[0189] As an example, in step S1301, when the current vehicle state determined based on the yaw rate deviation value and the center of mass side slip angle deviation value is an unstable state, the vehicle controller needs to further determine an additional yaw moment based on the yaw rate deviation value and the center of mass side slip angle deviation value. Specifically, the yaw rate deviation value and the center of mass side slip angle deviation value can be weighted to determine the additional yaw moment, so that the additional yaw moment is related to the yaw rate deviation value and the center of mass side slip angle deviation value, which are two data affecting the stability of the vehicle.

[0190] As an example, in step S1302, after determining the additional yaw moment, the vehicle controller can correct the first target torques determined based on the target torque constraint and the torque allocation strategy corresponding to the current vehicle state based on the additional yaw moment, so that the corrected first target torques can guarantee the stability and safety of the vehicle during driving.

[0191] In an embodiment, as shown in Figure 14 step S1302, i.e., correcting the first target torques based on the additional yaw moment, includes:

[0192] S1401: Determine the maximum center of mass side slip angle based on the measured vehicle speed and the road adhesion coefficient;

[0193] S1402: If the estimated side slip angle of the mass center is less than the maximum side slip angle of the mass center, an additional yaw moment is applied to the wheels in a drive differential manner, and the first target torque is corrected;

[0194] S1403: If the estimated side slip angle of the mass center is not less than the maximum side slip angle of the mass center, an additional yaw moment is applied to the wheels in a brake differential manner, and the first target torque is corrected.

[0195] As an example, in step S1401, the vehicle-mounted controller can query a pre-set vehicle speed-friction coefficient-maximum side slip angle of the mass center Map based on the measured vehicle speed and the road adhesion coefficient (i.e. the friction coefficient), determine the corresponding maximum side slip angle of the mass center, and the maximum side slip angle of the mass center is the side slip angle of the mass center that determines the vehicle stability boundary.

[0196] As an example, in step S1402, when the estimated side slip angle of the mass center is less than the maximum side slip angle of the mass center, i.e. when the estimated side slip angle of the mass center determined according to the measured wheel speed and the measured acceleration is less than the maximum side slip angle of the mass center, it is determined that it is within the vehicle stability boundary, at this time, an additional yaw moment can be applied to the wheels in a drive differential manner to correct the first target torque of each wheel, so as to adjust the first target torque of the wheel without losing speed, and to ensure the stability of the vehicle.

[0197] As an example, in step S1403, when the estimated side slip angle of the mass center is not less than the maximum side slip angle of the mass center, i.e. when the estimated side slip angle of the mass center determined according to the measured wheel speed and the measured acceleration is greater than or equal to the maximum side slip angle of the mass center, it is determined that it is not within the vehicle stability boundary, at this time, an additional yaw moment can be applied to the wheels in a brake differential manner to correct the first target torque of each wheel, and the first target torque of the wheel is adjusted by reducing the speed to ensure the stability of the vehicle.

[0198] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0199] In an embodiment, a vehicle-mounted controller is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the vehicle control method in the above embodiments when executing the computer program, for example Figure 1 S101-S104, or Figures 2 to 14 To avoid repetition, it will not be repeated here.

[0200] In an embodiment, a vehicle is provided, which includes the vehicle-mounted controller of the above embodiments.

[0201] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0202] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0203] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A vehicle control method characterized by, The method comprises: during working of the wheel based on the first target torque, acquiring first vehicle data and corresponding measured road surface data in front of the vehicle; based on the first vehicle data and the measured road surface data, determining a target predicted working condition; based on a torque distribution strategy corresponding to the target predicted working condition, determining a second target torque corresponding to the wheel; updating the second target torque as the first target torque, and repeatedly performing, during working of the wheel based on the first target torque, acquiring first vehicle data and corresponding measured road surface data in front of the vehicle; after controlling the wheel to work based on the first target torque, if the current state of the vehicle is an unstable state, determining a yaw rate deviation value based on a measured steering wheel angle and a measured yaw rate, determining a mass center side slip angle deviation value based on a measured wheel speed and a measured acceleration, and determining an additional yaw moment based on the yaw rate deviation value and the mass center side slip angle deviation value; wherein the unstable state refers to a state in which measured data for evaluating handling stability does not reach a corresponding preset threshold value; determining a maximum mass center side slip angle based on a measured vehicle speed and a road surface adhesion coefficient; if an estimated mass center side slip angle is less than the maximum mass center side slip angle, applying the additional yaw moment to the wheel in a driving differential manner to correct the first target torque; wherein the estimated mass center side slip angle is a mass center side slip angle calculated according to the measured wheel speed and the measured acceleration; if the estimated mass center side slip angle is not less than the maximum mass center side slip angle, applying the additional yaw moment to the wheel in a braking differential manner to correct the first target torque.

2. The vehicle control method according to claim 1, characterized by, The first vehicle data comprises a current vehicle state and a measured vehicle speed; The method of determining a target predicted working condition based on the first vehicle data and the measured road surface data comprises: performing event detection and road surface identification based on the measured road surface data to determine an event detection result and a target road surface type; if the current vehicle state is a stable state, the event detection result is that there is an impact excitation event, or the target road surface type is changed from a high adhesion road surface to a low adhesion road surface, then determining that the target predicted working condition is an unstable working condition; if the measured vehicle speed is less than a preset vehicle speed, and the target road surface type is a sunken road surface, then determining that the target predicted working condition is a pothole working condition.

3. The vehicle control method according to claim 2, characterized by, The measured road surface data comprises radar point cloud data and a measured road surface image; The method of performing event detection and road surface identification based on the measured road surface data to determine an event detection result and a target road surface type comprises: performing impact excitation event detection based on the radar point cloud data and the measured road surface image to determine an event detection result; identifying the measured road surface image using a road surface type identification model to determine a target road surface type.

4. The vehicle control method according to claim 3, characterized by The method of performing impact excitation event detection based on the radar point cloud data and the measured road surface image to determine an event detection result comprises: performing road surface undulation detection based on the radar point cloud data to determine a road surface undulation detection result; performing obstacle detection based on the measured road surface image to determine an obstacle detection result; If the road surface fluctuation detection result is that there is road surface fluctuation, or the obstacle detection result is that there is road surface obstacle, it is determined that the event detection result is that there is impact excitation event; If the road surface fluctuation detection result is that there is no road surface fluctuation, and the obstacle detection result is that there is no road surface obstacle, it is determined that the event detection result is that there is impact excitation event.

5. The vehicle control method according to claim 2, characterized by The second target torque of the wheel is determined based on the torque distribution strategy corresponding to the target predicted working condition, comprising: If the target predicted working condition is an unstable working condition, the first target torque is processed based on a torque transfer strategy to determine the second target torque of the wheel; If the target predicted working condition is a pit working condition, the second vehicle data is processed based on a torque escape strategy to determine the second target torque of the wheel.

6. The vehicle control method according to claim 5, characterized by The second target torque of the wheel is determined based on the torque distribution strategy corresponding to the target predicted working condition, comprising: The difference between the first target torque corresponding to the front axle wheel and the preset torque variable is determined as the second target torque corresponding to the front axle wheel; The sum of the first target torque corresponding to the rear axle wheel and the preset torque variable is determined as the second target torque corresponding to the rear axle wheel.

7. The vehicle control method according to claim 5, characterized by The second vehicle data includes the vertical load of the wheel and the actual demand torque; The second target torque of the wheel is determined based on the torque distribution strategy corresponding to the target predicted working condition, comprising: The second target slip rate is updated to the first target slip rate, and the target torque constraint corresponding to the first target slip rate is updated, the second target slip rate being greater than the first target slip rate; Under the target torque constraint, the torque distribution coefficient of the wheel is determined based on the vertical load of the wheel, and the second target torque of the wheel is determined based on the torque distribution coefficient of the wheel and the actual demand torque.

8. The vehicle control method according to claim 1, characterized by Before the wheel works based on the first target torque, the vehicle control method further comprises: Based on the third vehicle data, a first target slip rate is determined, and a target torque constraint corresponding to the first target slip rate is obtained; Based on the fourth vehicle data, a stability evaluation is performed to determine the current state of the vehicle; Based on the target torque constraint and the torque distribution strategy corresponding to the current state of the vehicle, the first target torque of the wheel is determined, and the wheel is controlled to work based on the first target torque.

9. The vehicle control method according to claim 8, characterized by, The fourth vehicle data includes the measured steering wheel angle, the measured yaw rate, the measured wheel speed and the measured acceleration; Based on the fourth vehicle data, a stability evaluation is performed to determine the current state of the vehicle, comprising: Based on the measured steering wheel angle and the measured yaw rate, a yaw rate deviation value is determined; Based on the measured wheel speed and the measured acceleration, a center of mass side slip angle deviation value is determined; If the yaw rate deviation value is greater than a first deviation threshold, or the center of mass side slip angle deviation value is greater than a second deviation threshold, it is determined that the current state of the vehicle is an unstable state; If the yaw rate deviation value is not greater than the first deviation threshold, and the center of mass side slip angle deviation value is not greater than the second deviation threshold, it is determined that the current state of the vehicle is a stable state.

10. The vehicle control method according to claim 9, characterized by The determining the yaw rate deviation value based on the measured steering wheel angle and the measured yaw rate comprises: determining a measured front wheel angle based on the measured steering wheel angle; determining an estimated yaw rate based on the measured front wheel angle and a target transfer function determined based on a two-degree-of-freedom model of the vehicle; determining an expected yaw rate based on the estimated yaw rate and a maximum yaw rate; determining the yaw rate deviation value based on the measured yaw rate and the expected yaw rate.

11. The vehicle control method according to claim 9, characterized by, The determining the center of mass side slip angle deviation value based on the measured wheel speed and the measured acceleration comprises: determining a measured vehicle speed based on the measured wheel speed; determining a target load of the wheel based on the measured acceleration and a pre-set static load; determining an estimated center of mass side slip angle based on the measured vehicle speed, the wheel load and the two-degree-of-freedom model of the vehicle; determining the center of mass side slip angle deviation value based on the estimated center of mass side slip angle and a pre-set expected center of mass side slip angle.

12. The vehicle control method according to claim 9, characterized by, The determining the first target torque of the wheel based on the target torque constraint and the torque distribution strategy corresponding to the current state of the vehicle, and controlling the wheel to work based on the first target torque comprises: if the current state of the vehicle is an unstable state, processing the longitudinal force, the lateral force, the vertical force and the road adhesion coefficient of the wheel based on the target torque constraint and a stability distribution strategy, and determining the first target torque of the wheel; if the current state of the vehicle is a stable state, processing the current torque, the angular velocity and the wheel-side motor efficiency of the wheel based on the target torque constraint and an economic distribution strategy, and determining the first target torque of the wheel.

13. An in-vehicle controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the vehicle control method in any one of claims 1 to 12.

14. A vehicle characterized by comprising: The vehicle-mounted controller comprises the vehicle control method in claim 13. The vehicle-mounted controller comprises the vehicle control method in claim 13.

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