Torque distribution strategy for in-wheel motor based on dynamic boundary layer

Abstract

The application relates to a wheel hub motor torque distribution strategy based on a dynamic boundary layer. The method comprises the following steps: determining a sliding surface parameter of a vehicle based on vehicle inherent parameters and vehicle state parameters of the vehicle; determining a target boundary layer thickness model matched with the sliding surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models; determining a target yaw moment of the vehicle based on the target boundary layer thickness model; splitting the target yaw moment based on torque constraint conditions of the vehicle to obtain the torque of the motor in the four wheel hubs of the vehicle, and distributing the torque of the motor in the four wheel hubs of the vehicle to the corresponding wheel hub motor. The sliding surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle, and the plurality of preset candidate boundary layer thickness models represent the response model of the vehicle under different error scenarios. The method can improve the flexibility of the torque distribution mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a hub motor torque distribution strategy based on a dynamic boundary layer. BACKGROUND

[0002] With the rapid development of automobile electrification and intelligence, the response speed, robustness and execution mechanism coordination of vehicle lateral stability control are significantly improved.

[0003] In related technologies, when controlling the vehicle, a four-wheel independent control architecture driven by a hub motor is usually used to distribute torque to the four wheels of the vehicle to ensure the stability of the vehicle during driving.

[0004] However, the flexibility of the torque distribution method in the related art is relatively low. SUMMARY

[0005] Therefore, it is necessary to provide a hub motor torque distribution strategy based on a dynamic boundary layer to improve the flexibility of the torque distribution method.

[0006] In a first aspect, the present application provides a torque distribution method of a hub motor, applied to a controller in a vehicle, comprising:

[0007] determining a sliding mode surface parameter of the vehicle based on vehicle inherent parameters and vehicle state parameters of the vehicle; the sliding mode surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle;

[0008] determining a target boundary layer thickness model matched with the sliding mode surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models; the plurality of preset candidate boundary layer thickness models represent response models of the vehicle under different error scenarios;

[0009] determining a target yaw moment of the vehicle based on the target boundary layer thickness model;

[0010] splitting the target yaw moment to obtain the torque of the motor in the four hubs of the vehicle based on the torque constraint condition of the vehicle, and distributing the torque of the motor in the four hubs of the vehicle to the corresponding hub motor.

[0011] In one embodiment, the determination of the sliding mode surface parameter of the vehicle based on the vehicle inherent parameters and the vehicle state parameters of the vehicle comprises:

[0012] determining a yaw rate error of the vehicle based on the inherent parameters and the vehicle state parameters of the vehicle;

[0013] determining a center of mass side slip angle error of the vehicle based on the front track in the inherent parameters, the vehicle speed in the vehicle state parameters, the yaw angular acceleration and the lateral acceleration of the vehicle.

[0014] determine a slip surface parameter of the vehicle according to the yaw rate error and the center of mass side slip angle error.

[0015] In one of the embodiments, the yaw rate error of the vehicle is determined based on the intrinsic parameters of the vehicle and the state parameters of the vehicle, comprising:

[0016] determine a stability factor of the vehicle according to the intrinsic parameters of the vehicle;

[0017] determine a reference yaw rate of the vehicle based on the wheelbase of the vehicle, the stability factor and the state parameters of the vehicle in the intrinsic parameters of the vehicle;

[0018] determine the yaw rate error by calculating the difference between the reference yaw rate and the yaw rate of the vehicle.

[0019] In one of the embodiments, the center of mass side slip angle error of the vehicle is determined based on the front wheelbase in the intrinsic parameters, the vehicle speed in the state parameters of the vehicle, the yaw angular acceleration and the lateral acceleration of the vehicle, comprising:

[0020] calculate a first ratio between the lateral acceleration and the vehicle speed, and a second ratio between the front wheelbase of the vehicle and the vehicle speed;

[0021] determine the center of mass side slip angle error of the vehicle according to the arctangent function value of the first ratio, the fusion result of the second ratio and the yaw angular acceleration.

[0022] In one of the embodiments, each candidate boundary layer model corresponds to a slip surface parameter interval; and the target boundary layer thickness model matched with the slip surface parameter of the vehicle is determined from a plurality of preset candidate boundary layer thickness models, comprising:

[0023] determine the target slip surface interval matched with the slip surface parameter of the vehicle from the slip surface intervals corresponding to the candidate boundary layer thickness models;

[0024] determine the target boundary layer thickness model corresponding to the target slip surface interval as the target boundary layer thickness model.

[0025] In one of the embodiments, the candidate boundary layer models include a first candidate boundary layer thickness model, a second candidate boundary layer thickness model and a third candidate boundary layer thickness model; and the target slip surface interval matched with the slip surface parameter of the vehicle is determined from the slip surface intervals corresponding to the candidate boundary layer thickness models, comprising:

[0026] determine the instability risk quantification value of the vehicle according to the slip surface parameter and the preset slip surface threshold value;

[0027] If the instability risk quantization value is in the sliding surface interval corresponding to the first candidate boundary layer thickness model, the sliding surface interval corresponding to the first candidate boundary layer thickness model is determined as the target sliding surface interval; the first candidate boundary layer thickness model is in quadratic function correlation with the sliding surface parameter;

[0028] If the instability risk quantization value is in the sliding surface interval corresponding to the second candidate boundary layer thickness model, the sliding surface interval corresponding to the second candidate boundary layer thickness model is determined as the target sliding surface interval; the second candidate boundary layer thickness model is in quadratic function correlation with the sliding surface parameter;

[0029] If the instability risk quantization value is in the sliding surface interval corresponding to the third candidate boundary layer thickness model, the sliding surface interval corresponding to the third candidate boundary layer thickness model is determined as the target sliding surface interval; the third candidate boundary layer thickness model is in negative linear correlation with the sliding surface parameter.

[0030] In one of the embodiments, based on the target boundary layer thickness model, the target yaw moment of the vehicle is determined, comprising:

[0031] According to the sliding surface parameter and the preset sliding surface threshold value, the instability risk quantization value of the vehicle is determined;

[0032] The instability risk quantization value is input into the target boundary layer thickness model to obtain the target boundary layer thickness of the vehicle;

[0033] The ratio between the sliding surface parameter of the vehicle and the target boundary layer thickness is calculated, and the ratio is input into the saturation function to determine the saturation function value of the vehicle;

[0034] The product of the sliding surface parameter of the vehicle and the sliding film gain, the product of the saturation function value and the switching term gain are subtracted to obtain the target yaw moment of the vehicle.

[0035] In one of the embodiments, the torque constraint condition of the vehicle at least includes the following contents:

[0036] The product of the torque difference of the two front wheels of the vehicle and the front wheel track, and the product of the torque difference of the two rear wheels of the vehicle and the rear wheel track are consistent with the sum of the target yaw moment;

[0037] The sum of the torques of the motors in the four wheel hubs of the vehicle is consistent with the longitudinal demand force of the vehicle;

[0038] The torque of each wheel hub motor is less than or equal to the preset torque.

[0039] In the second aspect, the application also provides a torque distribution device of a wheel hub motor, comprising:

[0040] The slip surface parameter determination module is configured to determine a slip surface parameter of the vehicle based on vehicle intrinsic parameters and vehicle state parameters of the vehicle, the slip surface parameter representing a deviation degree between a current driving state of the vehicle and a reference driving state of the vehicle.

[0041] The model determination module is configured to determine a target boundary layer thickness model matched with the slip surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models, the plurality of preset candidate boundary layer thickness models representing response models of the vehicle under different error scenarios.

[0042] The yaw moment determination module is configured to determine a target yaw moment of the vehicle based on the target boundary layer thickness model.

[0043] The torque determination module is configured to split the target yaw moment based on a torque constraint condition of the vehicle to obtain a torque of a motor in each of four wheel hubs of the vehicle, and to distribute the torque of the motor in each of the four wheel hubs to a corresponding wheel hub motor.

[0044] In a third aspect, the present application further provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing steps of the method in any one of the embodiments of the first aspect when executing the computer program.

[0045] In a fourth aspect, the present application further provides a computer readable storage medium, storing a computer program, and the computer program implementing steps of the method in any one of the embodiments of the first aspect when executed by a processor.

[0046] In a fifth aspect, the present application further provides a computer program product, including a computer program, and the computer program implementing steps of the method in any one of the embodiments of the first aspect when executed by a processor.

[0047] In the torque distribution strategy of the hub motor based on the dynamic boundary layer, the sliding mode surface parameter of the vehicle is determined based on the vehicle inherent parameters and the vehicle state parameters; a target boundary layer thickness model matched with the sliding mode surface parameter of the vehicle is determined from a plurality of preset candidate boundary layer thickness models; the target yaw moment of the vehicle is determined based on the target boundary layer thickness model; and the torque of the motor in the four hubs of the vehicle is obtained by splitting the target yaw moment based on the torque constraint condition of the vehicle, and the torque of the motor in the four hubs of the vehicle is distributed to the corresponding hub motor. The sliding mode surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle; and the plurality of preset candidate boundary layer thickness models represent the response models of the vehicle under different error scenarios. In the method, the sliding mode surface parameter is determined by combining the vehicle inherent parameters and the real-time state parameters, so as to accurately quantify the deviation between the current driving state and the reference state of the vehicle, the target boundary layer thickness model of the current error scenario is determined from the plurality of candidate models, so as to ensure that the yaw moment calculation adapts to different driving conditions, the target yaw moment is determined based on the target boundary layer thickness model, and finally the target yaw moment is split into the specific torque of the four hub motors and is distributed, which can dynamically adjust the control logic according to different driving error scenarios, adapt to the change of the vehicle condition, accurately calculate the yaw moment and split the torque, ensure that the driving state of the vehicle is stable and close to the reference trajectory, and at the same time, rely on the torque constraint to avoid the over-range output of the motor, and take into account the control effect and hardware safety. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creating any inventive labor.

[0049] Figure 1 An application environment diagram of the torque distribution method of the hub motor in an embodiment;

[0050] Figure 2 A flowchart of the torque distribution method of the hub motor in an embodiment;

[0051] Figure 3 A flowchart of the sliding mode surface parameter determination step in an embodiment;

[0052] Figure 4 A schematic diagram of a two-degree-of-freedom vehicle motion model in an embodiment;

[0053] Figure 5 A curve diagram of a boundary layer thickness model in an embodiment;

[0054] Figure 6 Flowchart for target yaw moment determination step in one embodiment;

[0055] Figure 7 Curve diagram for variation of sideslip angle of mass center with time in one embodiment;

[0056] Figure 8 Curve diagram for variation of sideslip angle of mass center with time in one embodiment;

[0057] Figure 9 Structural block diagram of torque distribution device of wheel hub motor in one embodiment;

[0058] Figure 10 Internal structure diagram of computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0060] It should be noted that the terms "first", "second" and the like used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application means two or more. The term "and / or" used in the present application means one of the options or any combination of a plurality of options.

[0061] With the rapid advancement of electric and intelligent vehicles, the requirements of vehicle lateral stability control on response speed, robustness and actuator coordination have been significantly improved. Although the four-wheel independent control architecture of wheel hub motor provides the possibility for precise regulation and control, the traditional sliding mode control has difficulty in balancing "chattering suppression under low error condition" and "robustness of instability correction under high error condition" when combining the yaw rate error and the sideslip angle error of the mass center, and it is not deeply coordinated with the torque saturation limit and efficiency characteristics of the wheel hub motor, which easily leads to the mismatch between the control command and the hardware capability.

[0062] Based on this, the application provides a wheel hub motor torque distribution strategy based on a dynamic boundary layer. The dynamic boundary layer is applied to a sliding mode control with yaw rate error and center of mass side slip angle error as inputs, low error chattering suppression and high error robustness dynamic adaptation are realized, and the yaw moment control is coordinated with the physical constraints and efficiency characteristics of the wheel hub motor during switching of different driving conditions (high-speed steering, low-attached road surface, etc.), thereby ensuring the safety of the control and the reliability of the instruction.

[0063] It should be noted that the beneficial effects or technical problems solved by the embodiments of the application are not limited to this, but also other implicit or related problems. For details, please refer to the description of the following embodiments.

[0064] The technical solutions of the application and how the technical solutions solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the application will be described below with reference to the drawings.

[0065] The torque distribution method of the wheel hub motor provided by the embodiments of the application can be applied in the application environment as shown in Figure 1 The controller 102 of the vehicle communicates with each wheel hub motor 104 in the vehicle through a connector. The controller 102 can also be connected with various sensing devices of the vehicle to obtain vehicle inherent parameters of the vehicle and vehicle dynamic state parameters of the vehicle in the driving state.

[0066] In an exemplary embodiment, as shown in Figure 2 A torque distribution method of a wheel hub motor is provided, which is applied to a controller in a vehicle and includes the following steps:

[0067] S201, determining a sliding mode surface parameter of the vehicle based on the vehicle inherent parameters and the vehicle state parameters of the vehicle.

[0068] The sliding mode surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle.

[0069] The vehicle refers to a four-wheel independent control architecture driven by a wheel hub motor. The controller can distribute torque to four wheel hub motors respectively to realize accurate regulation and control of the vehicle.

[0070] The vehicle inherent parameter refers to a fixed structural parameter of the vehicle in the driving process, including but not limited to: vehicle mass, wheelbase, front wheelbase to vehicle mass center, rear wheelbase to vehicle mass center, front wheel cornering stiffness, rear wheel cornering stiffness, etc.; the vehicle state parameter refers to a variable parameter of the vehicle in the driving process, including but not limited to: vehicle speed, vehicle mass center cornering angle value, lateral angular acceleration, front wheel rotation angle, etc.

[0071] In an actual application scenario, the vehicle inherent parameter can be directly determined from the factory certificate of the vehicle and pre-stored in the internal controller; the vehicle state parameter can be obtained by real-time communication between the controller and various sensors of the vehicle, for example, the controller receives the vehicle speed reported by the vehicle speed sensor, receives the lateral angular velocity reported by the vehicle lateral acceleration sensor, and receives the vehicle mass center cornering angle value reported by the vehicle mass center angle sensor.

[0072] After determining the vehicle inherent parameter and the vehicle state parameter of the vehicle, the vehicle inherent parameter and the vehicle state parameter can be input into a preset sliding mode surface formula to calculate the sliding mode surface parameter of the vehicle, so as to quantify the deviation degree between the current driving state and the reference driving state of the vehicle.

[0073] Optionally, the reference vehicle state parameter of the vehicle can also be determined according to the vehicle inherent parameter, and then the sliding mode surface parameter of the vehicle is determined based on the vehicle state parameter and the reference vehicle state parameter.

[0074] S202, from a plurality of preset candidate boundary layer thickness models, determine a target boundary layer thickness model matched with the sliding mode surface parameter of the vehicle.

[0075] The plurality of preset candidate boundary layer thickness models represent response models of the vehicle under different error scenarios.

[0076] According to the sliding mode surface parameter of the vehicle, the error range of the vehicle is determined, and then according to the mapping relationship between the candidate boundary layer thickness model and the error range, the candidate boundary layer thickness model matched with the sliding mode surface parameter of the vehicle, i.e. the target boundary layer thickness model, is determined.

[0077] S203, determine the target yaw moment of the vehicle based on the target boundary layer thickness model.

[0078] The target boundary layer thickness model represents a functional relationship between the boundary layer thickness and the sliding mode surface parameter, and the boundary layer thickness refers to a control parameter representing the parameter change of the vehicle. In the embodiment of the application, the boundary layer thickness and the sliding mode surface parameter can be a quadratic function relationship, a linear relationship or an exponential relationship, etc.

[0079] The slip surface parameter is input into a target boundary layer thickness model to obtain a boundary layer thickness of the vehicle, and then the boundary layer thickness and the slip surface parameter are input into a preset yaw moment calculation formula to obtain a target yaw moment of the vehicle.

[0080] S204, based on the torque constraint condition of the vehicle, the target yaw moment is split to obtain the torque of the motor in the four wheel hubs of the vehicle, and the torque of the motor in the four wheel hubs of the vehicle is distributed to the corresponding wheel hub motor.

[0081] The torque constraint condition can include: the maximum torque of each wheel hub motor of the vehicle, the proportion of the front wheel motor torque and the rear wheel motor torque in the yaw moment, and the longitudinal moment corresponding to the four motor torques.

[0082] Under the torque constraint condition of the vehicle, the target yaw moment is split to obtain the torque of the motor in the four wheel hubs of the vehicle, and the torque of the motor in the four wheel hubs of the vehicle is distributed to the corresponding wheel hub motor.

[0083] In the embodiments of the present application, based on the vehicle inherent parameters and the vehicle state parameters of the vehicle, the slip surface parameters of the vehicle are determined; from a plurality of preset candidate boundary layer thickness models, a target boundary layer thickness model matched with the slip surface parameters of the vehicle is determined; based on the target boundary layer thickness model, a target yaw moment of the vehicle is determined; based on the torque constraint condition of the vehicle, the target yaw moment is split to obtain the torque of the motor in the four wheel hubs of the vehicle, and the torque of the motor in the four wheel hubs of the vehicle is distributed to the corresponding wheel hub motor. The slip surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle; the plurality of preset candidate boundary layer thickness models represent the response model of the vehicle under different error scenarios. In this method, the slip surface parameters are first determined by combining the vehicle inherent parameters and the real-time state parameters to accurately quantify the deviation between the current driving state and the reference state of the vehicle, and then the target boundary layer thickness model matched with the current error scenario is determined from the plurality of candidate models to ensure that the yaw moment calculation adapts to different driving conditions, and then the target yaw moment is determined based on the target boundary layer thickness model, and finally the target yaw moment is split into the specific torque of the four wheel hub motors and distributed, which can dynamically adjust the control logic according to different driving error scenarios to adapt to the change of the vehicle condition, and can also ensure the stability of the driving state of the vehicle by accurate yaw moment calculation and torque splitting to fit the reference trajectory, while relying on the torque constraint to avoid motor over-range output, and taking into account the control effect and hardware safety.

[0084] Next, the determination method of the slip surface parameter in the foregoing embodiments is further described. In an exemplary embodiment, as shown in Figure 3 the slip surface parameter of the vehicle is determined based on the vehicle inherent parameters and the vehicle state parameters of the vehicle, including:

[0085] S301, determine the yaw rate error of the vehicle based on the inherent parameters and the vehicle state parameters of the vehicle.

[0086] The inherent parameters and the vehicle state parameters of the vehicle are input into a preset yaw rate formula to calculate a reference yaw rate, and a difference between the reference yaw rate and an actual yaw rate of the vehicle is determined as the yaw rate error of the vehicle.

[0087] Next, the derivation process of the yaw rate formula is described.

[0088] Please refer to Figure 4 , Figure 4 for a schematic diagram of a two-degree-of-freedom vehicle model, Figure 4 in which a driving schematic diagram of a front wheel of the vehicle is represented by a steering angle, in the embodiment of the application, the vehicle is simplified as a rigid planar motion rigid body by ignoring the longitudinal dynamics of the vehicle (only considering lateral and yaw motion), suspension deformation and the nonlinear region of tire cornering characteristics (adopting a linear cornering model).

[0089] The definitions of the inherent parameters of the vehicle are as follows:

[0090] The wheelbase L=L f +L r , wherein L is the wheelbase (unit: m), L f is the distance from the front axle to the center of mass (unit: m), L r is the distance from the rear axle to the center of mass (unit: m); the total mass of the vehicle m (unit: kg); the moment of inertia IZ (unit: ) around the center of mass; the front wheel cornering stiffness C f (unit: N / rad), and the rear wheel cornering stiffness C r (unit: N / rad).

[0091] Based on the above parameter definitions, the force analysis and motion equations of the vehicle are as follows:

[0092] Lateral force balance:

[0093] The expression of the front wheel cornering force (F ) is as follows:

[0094]

[0095] wherein is the center of mass cornering angle (unit: rad), is the yaw rate (unit: rad / s), v is the vehicle speed (unit: m / s), is the front wheel steering angle.

[0096] The expression of the rear wheel cornering force (F The expression of the balance equation is as follows:

[0097]

[0098] The expression of the balance equation is as follows:

[0099]

[0100] wherein, is the rate of change of the centroid side slip angle.

[0101] The expression of the balance equation of the yaw moment is as follows:

[0102]

[0103] wherein, is the rate of change of the yaw angular velocity.

[0104] The above equations and the linearized motion equation are solved together to obtain a second-order linear differential equation group about the state variable .

[0105]

[0106] When the vehicle is in the steady-state steering (ωy=0),the expression of the yaw angular velocity is obtained by solving the above equation:

[0107] wherein, K is a stability factor, and the corresponding expression is as follows:

[0108]

[0109] In the embodiment of the application, the physical meaning of the stability factor is as follows: K>0: understeer characteristic (the yaw angular velocity decreases with the increase of the vehicle speed at high speed); K=0: neutral steering characteristic (the yaw angular velocity is proportional to the vehicle speed); K<0: oversteer characteristic (easily unstable at high speed).

[0110] Based on the expression of the yaw angular velocity, the yaw angular velocity error of the vehicle is determined based on the inherent parameters and the state parameters of the vehicle, and the yaw angular velocity error includes:

[0111] According to the inherent parameters of the vehicle, the stability factor of the vehicle is determined; based on the wheelbase of the vehicle, the stability factor and the state parameters of the vehicle in the inherent parameters of the vehicle, the reference yaw angular velocity of the vehicle is determined; the difference between the reference yaw angular velocity and the yaw angular velocity of the vehicle is calculated to determine the yaw angular velocity error.

[0112]

[0113] The vehicle mass, vehicle wheelbase, front wheelbase, rear wheelbase, front wheel cornering stiffness and rear wheel cornering stiffness in the vehicle inherent parameters are substituted into the expression of the above stability factor to obtain the stability factor; then the vehicle wheelbase, the stability factor, the front wheel rotation angle in the vehicle state parameters and the vehicle speed in the vehicle state parameters are substituted into the expression of the above yaw rate to obtain the reference yaw rate of the vehicle; then the difference between the actual yaw rate and the reference yaw rate is determined as the yaw rate error of the vehicle, and the corresponding expression is as follows:

[0114]

[0115] wherein, the yaw rate error of the vehicle, the actual yaw rate value is obtained by a high-precision yaw rate sensor, the ideal yaw rate value is calculated based on a two-degree-of-freedom vehicle model.

[0116] In the embodiments of the present application, based on the inherent properties such as vehicle mass, center of mass position, wheelbase and front and rear axle cornering stiffness, it is ensured that the reference yaw rate can truly reflect the ideal driving state of the vehicle under the current driving state. On the other hand, the explicit yaw rate error accurately quantifies the deviation degree of the "actual driving trajectory and ideal trajectory" of the vehicle, provides a control basis for the subsequent controller, so that the subsequent control strategy can more specifically correct the error, and finally guarantees the steering stability and driving trajectory fitting degree of the vehicle under different working conditions.

[0117] S302, based on the front wheelbase in the inherent parameters, the vehicle speed in the vehicle state parameters, the yaw angular acceleration and the lateral acceleration of the vehicle, the center of mass cornering angle error of the vehicle is determined.

[0118] In one exemplary embodiment, a first ratio between the lateral acceleration and the vehicle speed, and a second ratio between the front wheelbase of the vehicle and the vehicle speed are calculated; the center of mass cornering angle error of the vehicle is determined according to the arctangent function value of the first ratio, the second ratio and the fusion result of the yaw angular acceleration.

[0119] The expression of the center of mass cornering angle error of the vehicle is as follows:

[0120]

[0121] wherein, the center of mass cornering angle error of the vehicle, the actual center of mass cornering angle value, the steady-state ideal center of mass cornering angle value, which is usually taken as 0, because in the ideal stable state, the center of mass cornering angle of the vehicle should tend to 0 to ensure driving stability.

[0122] Based on this, the expression of the vehicle's mass center side slip angle error is updated as follows:

[0123]

[0124] wherein a y is the lateral acceleration, which is collected by a lateral acceleration sensor, L f is the distance from the front axle to the mass center, and v is the vehicle speed, is the second-order derivative of the actual yaw rate with respect to time, representing the yaw angular acceleration.

[0125] The lateral acceleration, vehicle speed, and the distance from the front axle to the vehicle speed are substituted into the above updated mass center side slip angle error expression to obtain the vehicle's mass center side slip angle error. The small angle approximation error is corrected by the arctangent function, which is more consistent with the actual working condition. At the same time, the fusion result of the ratio of the front axle distance to the vehicle speed and the yaw angular acceleration is integrated, which compensates for the coupling effect of the yaw motion on the mass center side slip angle, ensuring that the calculated mass center side slip angle error can truly reflect the vehicle attitude deviation.

[0126] S303, according to the yaw rate error and the mass center side slip angle error, determine the vehicle's sliding mode surface parameter.

[0127] The product of the yaw rate error, the mass center side slip angle error, and the preset static weight is added to the integral of the yaw rate error with respect to time to obtain the vehicle's sliding mode surface parameter. The corresponding calculation formula is as follows:

[0128]

[0129] wherein s is the vehicle's sliding mode surface parameter, is the vehicle's yaw rate error, is the static weight, which is determined by frequency domain analysis and calibration, and the value is [0, 1], is the vehicle's mass center side slip angle error.

[0130] In the embodiments of the present application, the yaw rate error is calculated based on the vehicle's inherent parameters and real-time state parameters, which accurately reflects the degree of vehicle rotation motion deviation from the ideal trajectory. At the same time, the mass center side slip angle error is determined by combining the front axle distance, vehicle speed, yaw angular acceleration, and lateral acceleration, which effectively captures the core features of the vehicle attitude deviation from the stable state, which is equivalent to representing the vehicle error from the "motion state" and "attitude state" dimensions respectively, and then fusing the two types of errors into the sliding mode surface parameter, which converts the complex multi-dimensional vehicle dynamic deviation into a single quantitative index that can be directly used by the controller, providing a precise and robust control benchmark for the subsequent sliding mode control strategy.

[0131] After determining the slip surface parameter of the vehicle, a boundary layer thickness model that is currently adapted to the vehicle is further determined based on the slip surface parameter, so as to adjust the control parameter of the vehicle. In an exemplary embodiment, each candidate boundary layer model corresponds to a slip surface parameter interval; and a target boundary layer thickness model that matches the slip surface parameter of the vehicle is determined from a plurality of preset candidate boundary layer thickness models, including:

[0132] A target slip surface interval that matches the slip surface parameter of the vehicle is determined from the slip surface intervals corresponding to the candidate boundary layer thickness models; and the candidate boundary layer thickness model corresponding to the target slip surface interval is determined as the target boundary layer thickness model.

[0133] The slip surface parameter of the vehicle is matched with the slip surface intervals corresponding to the candidate boundary layer thickness models, a target slip surface interval including the slip surface parameter is determined, and the candidate boundary layer thickness model corresponding to the target slip surface interval is determined as the target boundary layer thickness model. The entire determination process is simple and direct, and the determination speed of the target boundary layer thickness model is improved.

[0134] In an exemplary embodiment, the candidate boundary layer models include a first candidate boundary layer thickness model, a second candidate boundary layer thickness model, and a third candidate boundary layer thickness model; and a target slip surface interval that matches the slip surface parameter of the vehicle is determined from the slip surface intervals corresponding to the candidate boundary layer thickness models, including:

[0135] A destabilization risk quantification value of the vehicle is determined according to the slip surface parameter and a preset slip surface threshold value; if the destabilization risk quantification value is in the slip surface interval corresponding to the first candidate boundary layer thickness model, the slip surface interval corresponding to the first candidate boundary layer thickness model is determined as the target slip surface interval; if the destabilization risk quantification value is in the slip surface interval corresponding to the second candidate boundary layer thickness model, the slip surface interval corresponding to the second candidate boundary layer thickness model is determined as the target slip surface interval; and if the destabilization risk quantification value is in the slip surface interval corresponding to the third candidate boundary layer thickness model, the slip surface interval corresponding to the third candidate boundary layer thickness model is determined as the target slip surface interval.

[0136] The first candidate boundary layer thickness model is in a quadratic function relationship with the slip surface parameter; the second candidate boundary layer thickness model is in a quadratic function relationship with the slip surface parameter; and the third candidate boundary layer thickness model is in a negative linear relationship with the slip surface parameter.

[0137] The ratio of the slip surface parameter and the preset slip surface threshold value is determined as the destabilization risk quantification value of the vehicle, and the corresponding expression is as follows:

[0138]

[0139] where s is the slip surface parameter, smax To preset the sliding surface threshold, The value is used to quantify the risk of vehicle instability, with a range of [0,1].

[0140] Furthermore, the expressions for the three candidate boundary layer thickness models are as follows:

[0141]

[0142] Where c is the threshold for smooth vehicle operation, which can be 0.2, and q is the threshold for the corresponding transitional operation, which can be 0.8. Please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of the boundary layer thickness model. Figure 5 The horizontal axis represents the quantified value of the vehicle's instability risk, and the vertical axis represents the boundary layer thickness.

[0143] Combination Figure 5 It can be seen that, in the quantitative value of instability risk When the value is less than c, the vehicle is determined to be in the low error region, corresponding to the first candidate boundary model. .in, Set to the maximum boundary layer thickness for stable vehicle driving conditions (such as straight-line constant speed, small-angle steering, etc.), expand the continuous control area, and suppress sliding mode control chattering. As an intermediate transitional value to balance control performance under different operating conditions. When the vehicle yaw rate and center of gravity sideslip angle errors are small, the normalized value of the sliding surface error is... It is usually less than this threshold.

[0144] Within this range, the boundary layer thickness varies. The thickness is increased by a quadratic function, the control switching frequency is reduced, the number of actuator actions is reduced, the motor current fluctuation is reduced from the hardware level, and the motor service life is extended.

[0145] Instability risk quantification value When the condition is [c, q], the vehicle is determined to be in the mean error region, corresponding to the second candidate boundary model. Here, k serves as a linear variation coefficient, adjusting the boundary layer thickness when vehicle driving conditions change (such as moderate-angle steering, speed changes, etc.). q corresponds to the transition condition threshold, representing the normalized value of the sliding surface error when the vehicle experiences a moderate change in driving conditions. It falls within this range.

[0146] Within this range, the boundary layer thickness varies. Increasing linearly decreases achieves a balance between suppressing chattering and ensuring the system's response speed to errors. Increasing or decreasing the boundary layer enables the control system to respond to errors promptly, ensuring control accuracy and maintaining stable vehicle operation.

[0147] in the case where the instability risk quantization value is greater than q, it is determined that the vehicle is in a high error zone, corresponding to a third candidate boundary model, wherein, is the minimum boundary layer thickness, when the vehicle faces a high-risk instability working condition (such as high-speed emergency obstacle avoidance, limit turning on low adhesion road surface, etc.), the boundary layer thickness is reduced to enhance the switching control effect of the sliding mode control. λ is an exponential decay coefficient, which determines the rate at which the boundary layer thickness decreases as increases.

[0148] in this interval, increases, the boundary layer thickness decreases rapidly in an exponential form, the continuous control region is reduced, the control system is prompted to generate a larger yaw moment quickly, the instability trend of the vehicle is corrected, the dangerous phenomena such as spin and skid are suppressed, and the driving safety of the vehicle is ensured.

[0149] In the embodiments of the present application, the instability risk quantization value of the vehicle is calculated through the sliding surface parameters and the preset threshold value, and then the target sliding surface interval is matched and determined from the three candidate boundary layer thickness models according to the interval to which the quantization value belongs, so as to accurately correspond to the vehicle instability risk scene under the correlation characteristics of different sliding surface parameters, provide accurate interval definition basis for subsequent targeted assessment or control of vehicle instability risk, and improve the adaptability and accuracy of instability risk judgment.

[0150] In an exemplary embodiment, as Figure 6 shown, based on the target boundary layer thickness model, the target yaw moment of the vehicle is determined, comprising:

[0151] S601, determining the instability risk quantization value of the vehicle according to the sliding surface parameters and the preset sliding surface threshold value.

[0152] The ratio of the sliding surface parameters and the preset sliding surface threshold value is determined as the instability risk quantization value of the vehicle, and the corresponding expression is as follows:

[0153]

[0154] wherein, s is the sliding surface parameter, s max is the preset sliding surface threshold value, is the instability risk quantization value of the vehicle, and the value interval is [0, 1].

[0155] S602, inputting the instability risk quantization value into the target boundary layer thickness model to obtain the target boundary layer thickness of the vehicle.

[0156] The instability risk quantization value is input into the expression corresponding to the target boundary layer thickness model, and the target boundary layer thickness of the vehicle is calculated.

[0157] S603, a ratio between the vehicle's sliding surface parameter and the target boundary layer thickness is calculated, and the ratio is input into a saturation function to determine a saturation function value of the vehicle.

[0158] The saturation function refers to a piecewise nonlinear function, and the core feature is that when the input is within a certain preset linear interval, the output is linearly related to the input; when the input exceeds the upper limit or lower limit of the interval (i.e., the saturation threshold), the output is fixed at a preset maximum or minimum value and no longer changes with the input, and is in a saturated state.

[0159] In the embodiments of the present application, the expression of the saturation function value is: wherein s is the vehicle's sliding surface parameter, is the target boundary layer thickness, and the saturation function.

[0160] S604, the product of the vehicle's sliding surface parameter and the sliding film gain, and the product of the saturation function value and the switching term gain are subtracted to obtain the vehicle's target yaw moment.

[0161] The expression of the target yaw moment is as follows:

[0162]

[0163] wherein, is the target yaw moment, is the sliding film gain, is the switching term gain, and cooperates with the dynamic boundary layer ( ) to suppress chattering.

[0164] In the embodiments of the present application, the vehicle's instability risk is accurately quantified in combination with the sliding surface parameter and the threshold value, and then the instability risk quantification value is limited according to the target boundary layer thickness model and the saturation function. Finally, the target yaw moment is obtained through the cooperative calculation of the sliding film gain and the switching term gain, which can accurately match the vehicle's instability risk to output an adaptive yaw moment control amount, avoid excessive fluctuation of the yaw moment control amount beyond the physical limit of the motor, and effectively balance the accuracy, stability and execution safety of the vehicle's instability control.

[0165] In the case of determining the target yaw moment, the distribution mode of the moment is described: in an exemplary embodiment, the torque constraint condition of the vehicle at least includes the following contents:

[0166] The product of the torque difference of the two front wheels of the vehicle and the front wheel track is consistent with the sum of the product of the torque difference of the two rear wheels of the vehicle and the rear wheel track and the target yaw moment; the sum of the torques of the motors in the four wheel hubs of the vehicle is consistent with the longitudinal demand force of the vehicle; the torque of each motor in the wheel hub is less than or equal to the preset torque.

[0167] wherein the torque constraint condition of the vehicle can be described by the following expression:

[0168]

[0169] wherein r is the hub radius, d f is the front wheel track, d r is the rear wheel track, r is the wheel radius, F xtotal is the vehicle demand longitudinal force, T max is the maximum available torque of the motor (preset torque), T fr is the torque of the right front wheel motor of the vehicle, T fl is the torque of the left front wheel motor of the vehicle, T rr is the torque of the right rear wheel motor of the vehicle, T rl is the torque of the left rear wheel motor of the vehicle.

[0170] In the embodiments of the present application, under the premise of the three core constraints of “the sum of the product of the front wheel torque difference and the wheel track and the product of the rear wheel torque difference and the wheel track matches the target yaw moment”, “the total torque of the four motors meets the longitudinal demand force”, and “each motor torque does not exceed the preset limit value”, the torque of each hub motor is obtained by quadratic programming, and is distributed to the corresponding hub motor, which can accurately realize the yaw moment required for vehicle instability control and the longitudinal force required for driving, avoid damage of a single motor due to torque overload, realize the collaborative adaptation of vehicle dynamics control requirements and motor physical performance limitations, and guarantee the accuracy of control execution, the stability of driving, and the safety of motor operation.

[0171] In one exemplary embodiment, a torque distribution method of a hub motor is provided, applied to a controller in a vehicle, comprising the following steps:

[0172] (1) According to the inherent parameters of the vehicle, the stability factor of the vehicle is determined, and based on the wheelbase of the vehicle, the stability factor and the state parameters of the vehicle in the inherent parameters of the vehicle, the reference yaw rate of the vehicle is determined; then the difference between the reference yaw rate and the yaw rate of the vehicle is calculated to determine the yaw rate error.

[0173] (2) Calculate the first ratio between the lateral acceleration of the vehicle and the speed of the vehicle, and the second ratio between the front wheelbase of the vehicle and the speed of the vehicle; according to the inverse tangent function value of the first ratio, the second ratio and the fusion result of the yaw angular acceleration, the error of the vehicle's center of mass side slip angle is determined.

[0174] (3) The product of the yaw rate error, the center of mass side slip angle error and the preset static weight is added to the integral of the yaw rate error with respect to time to obtain the sliding mode surface parameter of the vehicle.

[0175] The sliding surface parameter represents a deviation degree between a current driving state of the vehicle and a reference driving state.

[0176] (4) A risk quantification value of instability of the vehicle is determined according to a ratio between the sliding surface parameter and a preset sliding surface threshold value.

[0177] (5) A target sliding surface interval matching the risk quantification value of instability of the vehicle is determined from a plurality of preset candidate boundary layer thickness models, based on the sliding surface interval corresponding to each candidate boundary layer thickness model, and a target boundary layer thickness model corresponding to the target sliding surface interval is determined as the target boundary layer thickness model.

[0178] The candidate boundary layer model includes a first candidate boundary layer thickness model, a second candidate boundary layer thickness model, and a third candidate boundary layer thickness model; the first candidate boundary layer thickness model is related to the sliding surface parameter in a quadratic function; the second candidate boundary layer thickness model is related to the sliding surface parameter in a quadratic function; and the third candidate boundary layer thickness model is related to the sliding surface parameter in a negative linear function.

[0179] (6) The target boundary layer thickness of the vehicle is obtained by inputting the risk quantification value of instability into the target boundary layer thickness model.

[0180] (7) A ratio between the sliding surface parameter of the vehicle and the target boundary layer thickness is calculated, and the ratio is input into a saturation function to determine a saturation function value of the vehicle.

[0181] (8) A target yaw moment of the vehicle is obtained by subtracting a product of the sliding surface parameter of the vehicle and a sliding surface gain from a product of the saturation function value and a switching term gain.

[0182] (9) The target yaw moment is split to obtain the torque of the motor in the four wheel hubs of the vehicle based on the torque constraint condition of the vehicle, and the torque of the motor in the four wheel hubs of the vehicle is distributed to the corresponding wheel hub motor.

[0183] The torque constraint condition of the vehicle at least includes the following: a product of a torque difference between two front wheels of the vehicle and a front wheel track is consistent with a sum of a product of a torque difference between two rear wheels of the vehicle and a rear wheel track; a sum of the torque of the motor in the four wheel hubs of the vehicle is consistent with a longitudinal demand force of the vehicle; and the torque of each wheel hub motor is less than or equal to a preset torque.

[0184] In the embodiments of the present application, a torque distribution strategy of an in-wheel motor based on a dynamic boundary layer is provided. The dynamic boundary layer is applied to a sliding mode control with yaw rate error and center of mass side slip angle error as inputs, so as to realize dynamic adaptation of low error chattering suppression and high error robustness, and ensure that the yaw moment control is coordinated with the physical constraints and efficiency characteristics of the in-wheel motor in the switching of different driving conditions (high-speed steering, low-attached road surface, etc.), thereby guaranteeing the safety of control and the reliability of instructions.

[0185] On the basis of the above torque distribution strategy, simulation verification is further made. Please refer to Figure 7 and Figure 8 , Figure 7 FIG. 1 is a schematic view of a curve of a yaw rate of a vehicle changing with time, wherein the horizontal axis is time (unit: s) and the vertical axis is the yaw rate (unit: rad / s), Figure 7 FIG. 1 is a schematic view of a curve of a yaw rate of a vehicle changing with time, wherein the horizontal axis is time (unit: s) and the vertical axis is the yaw rate (unit: rad / s),

[0186] Figure 8 FIG. 2 is a schematic view of a curve of a center of mass side slip angle of a vehicle changing with time, wherein the horizontal axis is time (unit: s) and the vertical axis is the center of mass side slip angle (unit: rad), Figure 8 FIG. 2 is a schematic view of a curve of a center of mass side slip angle of a vehicle changing with time, wherein the horizontal axis is time (unit: s) and the vertical axis is the center of mass side slip angle (unit: rad),

[0187] From the simulation results of Figure 7 and Figure 8 , it can be seen that the torque distribution method provided in the present application can make the yaw rate of the vehicle most close to the reference yaw rate, and the center of mass side slip angle of the vehicle is controlled to be the smallest, so that the overall effect is best and the process is reliable.

[0188] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in the other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0189] Based on the same inventive concept, the embodiments of the present application also provide a torque distribution device of an in-wheel motor for implementing the torque distribution method of the in-wheel motor involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more torque distribution device embodiments of the in-wheel motor provided below can refer to the limitations of the torque distribution method of the in-wheel motor described above, which will not be repeated here.

[0190] In an exemplary embodiment, as shown in Figure 9 A torque distribution device of an in-wheel motor is provided, comprising: a sliding surface parameter determination module 901, a model determination module 902, a yaw moment determination module 903, and a torque determination module 904, wherein:

[0191] The sliding surface parameter determination module 901 is configured to determine a sliding surface parameter of the vehicle based on vehicle inherent parameters and vehicle state parameters of the vehicle; the sliding surface parameter represents a deviation degree between a current driving state and a reference driving state of the vehicle;

[0192] The model determination module 902 is configured to determine a target boundary layer thickness model matched with the sliding surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models; the plurality of preset candidate boundary layer thickness models represent response models of the vehicle under different error scenarios;

[0193] The yaw moment determination module 903 is configured to determine a target yaw moment of the vehicle based on the target boundary layer thickness model;

[0194] The torque determination module 904 is configured to split the target yaw moment to obtain torques of motors in four in-wheel motors of the vehicle based on torque constraint conditions of the vehicle, and distribute the torques of the motors in the four in-wheel motors of the vehicle to corresponding in-wheel motors.

[0195] In an example embodiment, the sliding mode surface parameter determination module 901 comprises: an angular velocity error determination unit, a center of mass side slip angle error determination unit, and an error fusion unit, wherein:

[0196] The angular velocity error determination unit is configured to determine a yaw angular velocity error of the vehicle based on intrinsic parameters of the vehicle and vehicle state parameters;

[0197] The center of mass side slip angle error determination unit is configured to determine a center of mass side slip angle error of the vehicle based on a front track of the intrinsic parameters, a vehicle speed of the vehicle state parameters, a yaw angular acceleration of the vehicle, and a lateral acceleration of the vehicle;

[0198] The error fusion unit is configured to determine a sliding mode surface parameter of the vehicle according to the yaw angular velocity error and the center of mass side slip angle error.

[0199] In an example embodiment, the angular velocity error determination unit is further configured to determine a stability factor of the vehicle according to the intrinsic parameters of the vehicle, determine a reference yaw angular velocity of the vehicle based on a vehicle track of the intrinsic parameters, the stability factor, and the vehicle state parameters, and determine the yaw angular velocity error by calculating a difference between the reference yaw angular velocity and a yaw angular velocity of the vehicle.

[0200] In an example embodiment, the center of mass side slip angle error determination unit is further configured to calculate a first ratio between the lateral acceleration and the vehicle speed, and a second ratio between a front track of the vehicle and the vehicle speed, and determine the center of mass side slip angle error of the vehicle according to an arctangent function value of the first ratio, a fusion result of the second ratio and the yaw angular acceleration.

[0201] In an example embodiment, each candidate boundary layer model corresponds to a sliding mode surface parameter interval; the model determination module 902 comprises: an interval determination unit and a model matching unit, wherein:

[0202] The interval determination unit is configured to determine a target sliding mode surface interval matching the sliding mode surface parameter of the vehicle from the sliding mode surface intervals corresponding to the candidate boundary layer thickness models;

[0203] The model matching unit is configured to determine a candidate boundary layer thickness model corresponding to the target sliding mode surface interval as a target boundary layer thickness model.

[0204] In an example embodiment, the candidate boundary layer models comprise: a first candidate boundary layer thickness model, a second candidate boundary layer thickness model, and a third candidate boundary layer thickness model; the interval determination unit further comprises a quantization value determination subunit, a first interval determination subunit, a second interval determination subunit, and a third interval determination subunit, wherein:

[0205] The quantification value determination subunit is configured to determine a quantification value of the instability risk of the vehicle according to the sliding surface parameter and a preset sliding surface threshold value.

[0206] The first interval determination subunit is configured to determine the sliding surface interval corresponding to the first candidate boundary layer thickness model as the target sliding surface interval if the quantification value of the instability risk is in the sliding surface interval corresponding to the first candidate boundary layer thickness model; the first candidate boundary layer thickness model is in a quadratic function relationship with the sliding surface parameter. The second interval determination subunit is configured to determine the sliding surface interval corresponding to the second candidate boundary layer thickness model as the target sliding surface interval if the quantification value of the instability risk is in the sliding surface interval corresponding to the second candidate boundary layer thickness model; the second candidate boundary layer thickness model is in a quadratic function relationship with the sliding surface parameter. The third interval determination subunit is configured to determine the sliding surface interval corresponding to the third candidate boundary layer thickness model as the target sliding surface interval if the quantification value of the instability risk is in the sliding surface interval corresponding to the third candidate boundary layer thickness model; the third candidate boundary layer thickness model is in a negative linear relationship with the sliding surface parameter.

[0207] In an exemplary embodiment, the yaw moment determination module 903 is further configured to determine a quantification value of the instability risk of the vehicle according to the sliding surface parameter and a preset sliding surface threshold value; input the quantification value of the instability risk into the target boundary layer thickness model to obtain the target boundary layer thickness of the vehicle; calculate a ratio between the sliding surface parameter of the vehicle and the target boundary layer thickness, and input the ratio into a saturation function to determine a saturation function value of the vehicle; and subtract a product of the sliding surface parameter of the vehicle and a sliding film gain from a product of the saturation function value and a switching term gain to obtain the target yaw moment of the vehicle.

[0208] In an exemplary embodiment, the torque constraint condition of the vehicle at least includes the following contents:

[0209] The product of the torque difference between the two front wheels of the vehicle and the front wheel track is consistent with the sum of the product of the torque difference between the two rear wheels of the vehicle and the rear wheel track and the target yaw moment;

[0210] The sum of the torques of the motors in the four wheel hubs of the vehicle is consistent with the longitudinal demand force of the vehicle;

[0211] The torque of each motor in the wheel hub is less than or equal to a preset torque.

[0212] The above-mentioned modules in the torque distribution device of the wheel motor can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0213] In an example embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in FIG. 1. Figure 10 The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store torque distribution data of the in-wheel motor. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a torque distribution method of an in-wheel motor.

[0214] Those skilled in the art can understand that Figure 10 The structure shown in FIG. 1 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0215] In an example embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0216] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0217] In an embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0218] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0219] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0220] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0221] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A torque distribution method of an in-wheel motor, characterized by, A controller applied to a vehicle, the method comprising: determining a yaw rate error of the vehicle based on intrinsic parameters and vehicle state parameters of the vehicle; determining a center of mass side slip angle error of the vehicle based on a front track of the intrinsic parameters, a vehicle speed of the vehicle state parameters, a yaw angular acceleration and a lateral acceleration of the vehicle; determining a sliding mode surface parameter of the vehicle according to the yaw rate error and the center of mass side slip angle error; the sliding mode surface parameter representing a deviation degree between a current driving state and a reference driving state of the vehicle; determining a target boundary layer thickness model matched with the sliding mode surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models; the plurality of preset candidate boundary layer thickness models representing response models of the vehicle under different error scenarios; determining a destabilization risk quantization value of the vehicle according to the sliding mode surface parameter and a preset sliding mode surface threshold value; inputting the destabilization risk quantization value into the target boundary layer thickness model to obtain a target boundary layer thickness of the vehicle; calculating a ratio between the sliding mode surface parameter of the vehicle and the target boundary layer thickness, and inputting the ratio into a saturation function to determine a saturation function value of the vehicle; determining a target yaw moment of the vehicle by subtracting a product of the sliding mode surface parameter of the vehicle and a sliding mode gain from a product of the saturation function value and a switching term gain; based on a torque constraint condition of the vehicle, splitting the target yaw moment to obtain a torque of a motor in four wheel hubs of the vehicle, and distributing the torque of the motor in the four wheel hubs of the vehicle to corresponding wheel motors.

2. The method of claim 1, wherein, The determination of the yaw rate error of the vehicle based on the intrinsic parameters and the vehicle state parameters of the vehicle comprises: determining a stability factor of the vehicle according to the vehicle intrinsic parameters; determining a reference yaw rate of the vehicle based on a vehicle track of the vehicle intrinsic parameters, the stability factor and the vehicle state parameters; calculating a difference between the reference yaw rate and a yaw rate of the vehicle to determine a yaw rate error.

3. The method of claim 1, wherein, The determination of the center of mass side slip angle error of the vehicle based on the front track of the intrinsic parameters, the vehicle speed of the vehicle state parameters, the yaw angular acceleration and the lateral acceleration of the vehicle comprises: calculating a first ratio between the lateral acceleration and the vehicle speed, and a second ratio between the front track of the vehicle and the vehicle speed; determining the center of mass side slip angle error of the vehicle according to an inverse tangent function value of the first ratio, a fusion result of the second ratio and the yaw angular acceleration.

4. The method according to any one of claims 1 to 3, characterized in that, Each candidate boundary layer model corresponds to a sliding mode surface parameter interval; The determination of the target boundary layer thickness model matched with the sliding mode surface parameter of the vehicle from the plurality of preset candidate boundary layer thickness models comprises: determining a target sliding mode surface interval matched with the sliding mode surface parameter of the vehicle from the sliding mode surface intervals corresponding to the candidate boundary layer thickness models; determining the candidate boundary layer thickness model corresponding to the target sliding mode surface interval as the target boundary layer thickness model.

5. The method of claim 4, wherein, The candidate boundary layer model includes a first candidate boundary layer thickness model, a second candidate boundary layer thickness model, and a third candidate boundary layer thickness model; the target sliding surface interval matched with the sliding surface parameter of the vehicle is determined from the sliding surface interval corresponding to each of the candidate boundary layer thickness models, including: According to the sliding surface parameter and the preset sliding surface threshold, the instability risk quantization value of the vehicle is determined; If the instability risk quantization value is in the sliding surface interval corresponding to the first candidate boundary layer thickness model, the sliding surface interval corresponding to the first candidate boundary layer thickness model is determined as the target sliding surface interval; the first candidate boundary layer thickness model is related to the sliding surface parameter as a quadratic function; If the instability risk quantization value is in the sliding surface interval corresponding to the second candidate boundary layer thickness model, the sliding surface interval corresponding to the second candidate boundary layer thickness model is determined as the target sliding surface interval; the second candidate boundary layer thickness model is related to the sliding surface parameter as a quadratic function; If the instability risk quantization value is in the sliding surface interval corresponding to the third candidate boundary layer thickness model, the sliding surface interval corresponding to the third candidate boundary layer thickness model is determined as the target sliding surface interval; the third candidate boundary layer thickness model is related to the sliding surface parameter as a negative linear function.

6. The method according to any one of claims 1 to 3, characterized in that, The torque constraint condition of the vehicle at least includes the following contents: The product of the torque difference of the two front wheels of the vehicle and the front wheel track is consistent with the sum of the product of the torque difference of the two rear wheels of the vehicle and the rear wheel track and the target yaw moment; The sum of the torques of the motors in the four wheel hubs of the vehicle is consistent with the longitudinal demand force of the vehicle; The torque of each wheel hub motor is less than or equal to the preset torque.

7. A torque distribution device of an in-wheel motor characterized by comprising: The device includes: A sliding surface parameter determination module is configured to determine a yaw rate error of the vehicle based on inherent parameters and vehicle state parameters of the vehicle, determine a mass center side slip angle error of the vehicle based on a front track in the inherent parameters, a vehicle speed in the vehicle state parameters, a yaw angular acceleration, and a lateral acceleration of the vehicle, and determine a sliding surface parameter of the vehicle according to the yaw rate error and the mass center side slip angle error; the sliding surface parameter represents the deviation degree between the current driving state and the reference driving state of the vehicle; A model determination module is configured to determine a target boundary layer thickness model matched with the sliding surface parameter of the vehicle from a plurality of preset candidate boundary layer thickness models; the plurality of preset candidate boundary layer thickness models represent response models of the vehicle in different error scenarios; The yaw moment determination module is configured to determine a risk of instability quantization value of the vehicle according to the slip surface parameter and a preset slip surface threshold value, input the risk of instability quantization value into the target boundary layer thickness model to obtain a target boundary layer thickness of the vehicle, calculate a ratio between the slip surface parameter of the vehicle and the target boundary layer thickness, and input the ratio into a saturation function to determine a saturation function value of the vehicle, and obtain a target yaw moment of the vehicle by subtracting a product of the slip surface parameter of the vehicle and a slip film gain from a product of the saturation function value and a switching term gain. The torque determination module is configured to split the target yaw moment based on a torque constraint condition of the vehicle to obtain a torque of a motor in each wheel hub of the vehicle, and distribute the torque of the motor in each wheel hub of the vehicle to a corresponding wheel hub motor.

8. The apparatus of claim 7, wherein, Each candidate boundary layer model corresponds to a slip surface parameter interval; The model determination module comprises: The interval determination unit is configured to determine a target slip surface interval matched with the slip surface parameter of the vehicle from the slip surface intervals corresponding to the candidate boundary layer thickness models. The model matching unit is configured to determine the candidate boundary layer thickness model corresponding to the target slip surface interval as the target boundary layer thickness model.

9. The apparatus of claim 8, wherein, The candidate boundary layer models comprise a first candidate boundary layer thickness model, a second candidate boundary layer thickness model and a third candidate boundary layer thickness model; and the interval determination unit further comprises: The quantization value determination subunit is configured to determine a risk of instability quantization value of the vehicle according to the slip surface parameter and a preset slip surface threshold value. The first interval determination subunit is configured to determine the slip surface interval corresponding to the first candidate boundary layer thickness model as the target slip surface interval if the risk of instability quantization value is in the slip surface interval corresponding to the first candidate boundary layer thickness model; the first candidate boundary layer thickness model is in a quadratic function relationship with the slip surface parameter. The second interval determination subunit is configured to determine the slip surface interval corresponding to the second candidate boundary layer thickness model as the target slip surface interval if the risk of instability quantization value is in the slip surface interval corresponding to the second candidate boundary layer thickness model; the second candidate boundary layer thickness model is in a quadratic function relationship with the slip surface parameter. The third interval determination subunit is configured to determine the slip surface interval corresponding to the third candidate boundary layer thickness model as the target slip surface interval if the risk of instability quantization value is in the slip surface interval corresponding to the third candidate boundary layer thickness model; the third candidate boundary layer thickness model is in a negative linear relationship with the slip surface parameter. 10.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-9. The processor, when executing the computer program, implements the steps of the method in any one of claims 1 to 6.

Images