Vehicle torque control method, device, equipment and storage medium

Abstract

The present disclosure relates to a vehicle torque control method, device, equipment and storage medium. The method comprises: performing feedforward analysis on the vehicle torque according to a dynamic adhesion coefficient, a visual adhesion coefficient and a driver triggered torque to determine a first candidate torque of the vehicle; wherein the driver triggered torque is a vehicle torque determined through driving behavior information; performing prediction analysis on the vehicle torque according to the visual adhesion coefficient and the driver triggered torque to determine a second candidate torque of the vehicle; and determining a target torque of the vehicle based on the first candidate torque and the second candidate torque. According to the present disclosure, the wheel torque can be adjusted when the vehicle does not slip, the number and degree of wheel slip are reduced, the stability and drivability of the vehicle are improved, and the safety of the vehicle is ensured.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and in particular to a vehicle torque control method, device, equipment, and storage medium. Background Technology

[0002] With the development of vehicle engineering technology, vehicle safety has received increasing attention. During vehicle operation, tire slippage is a significant factor affecting vehicle safety.

[0003] In related technologies, when tire slippage is detected, the vehicle's torque is adjusted through feedback regulation to correct the vehicle's driving deviation and reduce the degree of tire slippage. However, tire slippage has already occurred before torque adjustment is performed through feedback, resulting in a lag in torque adjustment. This can lead to situations such as longer tire slippage distances and longer tire slippage recovery times, thus affecting vehicle safety. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a vehicle torque control method, apparatus, device, and storage medium.

[0005] In a first aspect, this disclosure provides a vehicle torque control method, the method comprising:

[0006] Based on the dynamic adhesion coefficient, visual adhesion coefficient, and driver-triggered torque, a feedforward analysis is performed on the vehicle torque to determine the first candidate torque of the vehicle; wherein, the driver-triggered torque is the vehicle torque determined through driving behavior information;

[0007] Based on the visual adhesion coefficient and the driver trigger torque, the vehicle torque is predicted and analyzed to determine the second candidate torque of the vehicle;

[0008] The target torque of the vehicle is determined based on the first candidate torque and the second candidate torque.

[0009] Secondly, this disclosure provides a vehicle torque control device, the device comprising:

[0010] The first analysis module is used to perform feedforward torque analysis on the vehicle based on the dynamic adhesion coefficient, the visual adhesion coefficient, and the driver trigger torque to determine the first candidate torque of the vehicle; wherein, the driver trigger torque is the vehicle torque determined through driving behavior information;

[0011] The second analysis module is used to perform predictive torque analysis on the vehicle based on the visual adhesion coefficient and the driver trigger torque, and to determine the second candidate torque of the vehicle.

[0012] The first determining module is used to determine the target torque of the vehicle based on the first candidate torque and the second candidate torque.

[0013] Thirdly, embodiments of this disclosure also provide a vehicle torque control device, comprising:

[0014] processor;

[0015] Memory, used to store executable instructions;

[0016] The processor is used to read executable instructions from memory and execute the executable instructions to implement the vehicle torque control method of the first aspect mentioned above.

[0017] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the vehicle torque control method of the first aspect described above.

[0018] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0019] This disclosure discloses a vehicle torque control method, apparatus, device, and storage medium. Based on the dynamic adhesion coefficient, visual adhesion coefficient, and driver-triggered torque, a feedforward analysis is performed on the vehicle torque to determine a first candidate torque. The driver-triggered torque is determined through driving behavior information. Based on the visual adhesion coefficient and driver-triggered torque, a predictive analysis is performed on the vehicle torque to determine a second candidate torque. Based on the first and second candidate torques, a target torque is determined. Therefore, even when the vehicle is not slipping, the visual adhesion coefficient can predict changes in road conditions in advance. Thus, the target torque determined based on this visual adhesion coefficient can adjust the vehicle torque, solving the problem of torque adjustment lag, improving the timeliness and effectiveness of vehicle torque control, thereby reducing the frequency and degree of wheel slippage, improving vehicle stability and drivability, and ensuring vehicle safety. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0021] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A schematic flowchart of a vehicle torque control method provided in an embodiment of this disclosure;

[0023] Figure 2 A schematic flowchart of another vehicle torque control method provided in this disclosure embodiment;

[0024] Figure 3 A schematic flowchart illustrating yet another vehicle torque control method provided in this disclosure embodiment;

[0025] Figure 4 This is a schematic diagram of vehicle force analysis provided in an embodiment of the present disclosure;

[0026] Figure 5 This is a schematic diagram of the structure of a vehicle torque control device provided in an embodiment of the present disclosure;

[0027] Figure 6 This is a schematic diagram of the structure of a vehicle torque control device provided in an embodiment of the present disclosure. Detailed Implementation

[0028] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0029] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0030] To address the aforementioned problems, this disclosure provides a vehicle torque control method, apparatus, device, and storage medium.

[0031] Figure 1 This is a flowchart illustrating a vehicle torque control method according to an embodiment of the present disclosure. The method can be executed by a vehicle torque control device, which can be implemented in software and / or hardware, and is generally integrated into an electronic device. Figure 1 As shown, the method includes:

[0032] Step 101: Based on the dynamic adhesion coefficient, visual adhesion coefficient and driver trigger torque, perform feedforward analysis on the vehicle torque to determine the first candidate torque of the vehicle; wherein, the driver trigger torque is the vehicle torque determined through driving behavior information.

[0033] The dynamic adhesion coefficient is the road surface adhesion coefficient obtained by performing dynamic analysis on the vehicle's state. The visual adhesion coefficient is the road surface adhesion coefficient obtained by performing image analysis on the road surface that the vehicle is about to travel over. The driver trigger torque is the vehicle torque determined through driving behavior information. This driving behavior information can characterize the driver's actions such as vehicle control and settings. In one optional embodiment, the driving behavior information includes: driver control information and vehicle torque distribution information, wherein the driver control information includes throttle information and the vehicle's current speed, and the vehicle torque distribution information includes the vehicle's current driving mode and the vehicle's lateral / longitudinal acceleration.

[0034] In this embodiment, the vehicle torque control device can perform force analysis on the vehicle torque based on the dynamic adhesion coefficient and the visual adhesion coefficient to determine the corresponding feedforward torque. Understandably, since the visual adhesion coefficient is the road adhesion coefficient of the surface the vehicle is about to travel on, the feedforward torque determined based on this visual adhesion coefficient can predict the road adhesion coefficient before the vehicle tires slip, thus achieving pre-adjustment of the torque. Furthermore, the determination of this feedforward torque also references the dynamic adhesion coefficient; therefore, the feedforward torque is determined by combining the current road adhesion coefficient (i.e., the dynamic adhesion coefficient) and the road adhesion coefficient of the surface the vehicle is about to travel on (i.e., the visual adhesion coefficient). After determining the feedforward torque, a first candidate torque can be determined based on this feedforward torque and the driver-triggered torque. There are various methods for determining the first candidate torque; for example, the larger of the feedforward torque and the driver-triggered torque can be selected as the first candidate torque.

[0035] Step 102: Based on the visual adhesion coefficient and the driver's trigger torque, predict and analyze the vehicle torque to determine the vehicle's second candidate torque.

[0036] In this embodiment, the vehicle torque control device can perform force analysis on the vehicle based on the visual adhesion coefficient to determine the corresponding predicted torque. Understandably, since the visual adhesion coefficient is the road adhesion coefficient of the surface the vehicle is about to travel on, the predicted torque determined based on this visual adhesion coefficient can predict the road adhesion coefficient before the vehicle tires slip, thus achieving pre-adjustment of the torque. Furthermore, unlike feedforward torque, this predicted torque is determined without referencing the dynamic adhesion coefficient; it is determined based on the road adhesion coefficient of the surface the vehicle is about to travel on (i.e., the visual adhesion coefficient). Therefore, in the calculation of this predicted torque, the surface the vehicle is about to travel on has a greater influence, while the surface the vehicle is currently traveling on has a smaller influence. After determining the predicted torque, a second candidate torque can be determined based on the predicted torque and the driver-triggered torque. There are various methods for determining the second candidate torque; for example, the larger of the predicted torque and the driver-triggered torque can be selected as the second candidate torque.

[0037] Step 103: Determine the target torque of the vehicle based on the first candidate torque and the second candidate torque.

[0038] The target torque is the final torque determined by the vehicle torque control device.

[0039] Furthermore, after determining the first candidate torque and the second candidate torque, the target torque corresponding to each wheel of the vehicle can be determined based on the first candidate torque and the second candidate torque. There are various methods for determining the target torque, and this embodiment does not impose any limitations. For example, the minimum value between the first candidate torque and the second candidate torque can be determined as the target torque. After determining the target torque of the vehicle, the target torque can be applied to the vehicle to control the vehicle's movement.

[0040] In this embodiment, a feedforward analysis is performed on the vehicle torque based on the dynamic adhesion coefficient, the visual adhesion coefficient, and the driver-triggered torque to determine a first candidate torque for the vehicle. The driver-triggered torque is the vehicle torque determined through driving behavior information. A predictive analysis is then performed on the vehicle torque based on the visual adhesion coefficient and the driver-triggered torque to determine a second candidate torque. Finally, a target torque for the vehicle is determined based on the first and second candidate torques. Therefore, even when the vehicle is not slipping, the visual adhesion coefficient can anticipate changes in road conditions. Consequently, the target torque determined based on this visual adhesion coefficient can adjust the vehicle torque, solving the problem of torque adjustment lag and improving the timeliness and effectiveness of vehicle torque control. This reduces the frequency and degree of wheel slippage, improves vehicle stability and drivability, and ensures vehicle safety.

[0041] In some embodiments, "performing feedforward torque analysis on the vehicle based on the dynamic adhesion coefficient, the visual adhesion coefficient, and the driver-triggered torque to determine a first candidate torque for the vehicle" includes: "performing force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle to obtain a first reference torque for the vehicle in the current cycle; processing the first reference torque and the driver-triggered torque of the current cycle based on the driving conditions of the vehicle in the current cycle to determine a first candidate torque for the vehicle in the current cycle"; "performing predictive torque analysis on the vehicle based on the visual adhesion coefficient and the driver-triggered torque to determine a second candidate torque for the vehicle" includes: "performing force analysis on the vehicle based on the visual adhesion coefficient of the current cycle to obtain a second reference torque for the vehicle in the current cycle; processing the second reference torque and the driver-triggered torque of the current cycle based on the driving conditions of the vehicle in the current cycle to determine a second candidate torque for the vehicle in the current cycle." Figure 2 A schematic flowchart of another vehicle torque control method provided in this disclosure embodiment is shown below. Figure 2 As shown, the method includes:

[0042] Step 201: Perform a force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle to obtain the first reference torque of the vehicle in the current cycle.

[0043] In this embodiment, the dynamic adhesion coefficient is the road surface adhesion coefficient obtained by performing dynamic analysis on the vehicle's state, and the visual adhesion coefficient in the current cycle is the road surface adhesion coefficient obtained by performing image analysis on the road surface that the vehicle is about to travel over. Parameters such as the cycle length can be configured according to the application scenario, and this embodiment does not impose any limitations. In this embodiment, there are multiple methods for obtaining the aforementioned dynamic adhesion coefficient and visual adhesion coefficient, and this embodiment does not impose any limitations. Examples are illustrated below:

[0044] In one optional implementation, the dynamic adhesion coefficient can be determined based on vehicle attitude information, specifically including the following steps:

[0045] First, the vehicle's attitude information is acquired. In this embodiment, the vehicle's attitude information includes the vehicle's body posture and wheel speeds, wheel accelerations, etc. There are various methods to acquire this attitude information; for example, it can be acquired through sensors.

[0046] Furthermore, based on the vehicle's attitude information, the vehicle's dynamic adhesion coefficient is determined. After obtaining the vehicle's attitude information, a modeling analysis can be performed on the vehicle based on this information, thereby determining the dynamic adhesion coefficient between the vehicle and the road surface based on the modeling results.

[0047] In one optional implementation, the visual adhesion coefficient can be determined based on road surface image information, specifically including the following steps:

[0048] First, the road surface image information ahead of the vehicle's direction of travel within the current period is acquired. In this embodiment, the road surface image information visually represents the characteristics of the road surface that the vehicle is about to travel over. There are various methods for acquiring this road surface image information, and this embodiment does not impose any limitations. For example, the road surface image information can be acquired by capturing the road surface ahead of the vehicle's direction of travel using 360-degree panoramic imaging.

[0049] Furthermore, neural network calculations are performed on the road surface image information, and the visual adhesion coefficient of the vehicle in the current cycle is determined based on the calculation results. In this embodiment, the neural network model used for neural network calculations on the road surface image information can be selected according to the application scenario, and this embodiment is not limited. For example, a convolutional neural network model can be used to process the road surface image information. In an optional implementation, a correspondence between road surface type and visual adhesion coefficient can be preset. The road surface type in the road surface image information is determined through neural network calculation, and the correspondence between road surface type and visual adhesion coefficient is retrieved based on the road surface type to determine the visual adhesion coefficient in the current cycle.

[0050] In this embodiment, the road surface adhesion coefficient between the vehicle and the road surface in the current cycle is determined based on the aforementioned dynamic adhesion coefficient and the visual adhesion coefficient in the current cycle. Based on this road surface adhesion coefficient, combined with data such as vehicle acceleration and body parameters, the vehicle can be subjected to force analysis, thereby obtaining the first reference torque corresponding to each wheel of the vehicle in the current cycle.

[0051] Step 202: Based on the vehicle's driving conditions in the current cycle, process the first reference torque of the current cycle and the driver trigger torque determined by driving behavior information to determine the first candidate torque of the vehicle in the current cycle.

[0052] In this step, the first candidate torque of the current cycle can be determined from two dimensions: the first reference torque and the driver trigger torque. The driver trigger torque is determined based on driving behavior information, which can characterize the driver's control, settings, and other behaviors on the vehicle. In one optional implementation, the driving behavior information includes: driver control information and vehicle torque distribution information, wherein the driver control information includes throttle information and the current vehicle speed, and the vehicle torque distribution information includes the current vehicle driving mode and the vehicle's lateral / longitudinal acceleration.

[0053] In this embodiment, the vehicle's driving condition can represent the vehicle's driving state, which includes, but is not limited to, any one of: driving condition, energy recovery condition, and braking condition. Furthermore, the obtained first reference torque and driver-triggered torque can be processed based on the vehicle's driving condition in the current cycle to obtain the vehicle's first candidate torque in the current cycle. There are various methods for obtaining the first candidate torque based on the first reference torque and driver-triggered torque, which can be set according to the scenario requirements. This embodiment does not impose any limitations. For example, when the driving condition is driving condition, the first candidate torque can be determined as the minimum torque between the first reference torque and the driver-triggered torque; when the driving condition is energy recovery condition, the first candidate torque can be determined as the maximum torque between the first reference torque and the driver-triggered torque.

[0054] Step 203: Perform a force analysis on the vehicle based on the visual adhesion coefficient of the current cycle to obtain the second reference torque of the vehicle in the current cycle.

[0055] The method for obtaining the visual adhesion coefficient in this step is similar to that in the steps described above, and will not be repeated here. In this embodiment, the road adhesion coefficient between the vehicle and the road surface in the current cycle is determined based on the visual adhesion coefficient of the current cycle. Based on this road adhesion coefficient, combined with data such as vehicle acceleration and body parameters, force analysis can be performed on the vehicle to obtain the second reference torque corresponding to each wheel of the vehicle in the current cycle.

[0056] Step 204: Process the second reference torque and driver trigger torque of the current cycle according to the vehicle's driving conditions in the current cycle to determine the second candidate torque of the vehicle in the current cycle.

[0057] In this step, the second candidate torque for the current cycle can be determined from two dimensions: the second reference torque and the driver trigger torque. In this embodiment, the obtained second reference torque and driver trigger torque can be processed according to the vehicle's driving conditions in the current cycle to obtain the second candidate torque for the current cycle. There are various methods for obtaining the second candidate torque based on the second reference torque and driver trigger torque, which can be set according to the scenario requirements. This embodiment does not impose any limitations. For example, when the driving condition is a drive condition, the second candidate torque can be determined as the minimum torque between the second reference torque and the driver trigger torque; when the driving condition is an energy recovery condition, the second candidate torque can be determined as the maximum torque between the second reference torque and the driver trigger torque.

[0058] Step 205: Determine the target torque of the vehicle based on the first candidate torque of the current cycle and the second candidate torque of the current cycle.

[0059] Furthermore, after determining the first candidate torque and the second candidate torque for the current cycle, the target torque corresponding to each wheel of the vehicle can be determined based on the two dimensions of the first candidate torque and the second candidate torque. There are various methods for determining the target torque, and this embodiment does not limit them. For example, the target torque can be determined as the minimum value between the first candidate torque and the second candidate torque, or the target torque can be determined as the weighted sum of the first candidate torque and the second candidate torque.

[0060] Understandably, in the above steps, the visual adhesion coefficient is the predicted road adhesion coefficient determined based on the road surface the vehicle is about to travel on, while the dynamic adhesion coefficient is the actual road adhesion coefficient determined based on the vehicle's driving state on the road surface. Therefore, the first reference torque determined based on the dynamic adhesion coefficient and the visual adhesion coefficient, combined with the vehicle's actual driving condition and predicted driving condition, leads to the first candidate torque determined based on this first reference torque, which is the torque determined through feedforward control. The second reference torque determined based on the visual adhesion coefficient is the torque obtained based on the vehicle's predicted driving condition, and thus the second candidate torque determined based on this second reference torque is the torque determined through predictive control. The target torque can be determined from both feedforward control and predictive control dimensions to determine the torque applied to each wheel of the vehicle.

[0061] In this embodiment, force analysis is performed on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle to obtain a first reference torque for the vehicle in the current cycle. The first reference torque of the current cycle and the driver-triggered torque determined by driving behavior information are processed based on the vehicle's driving conditions in the current cycle to determine a first candidate torque for the vehicle in the current cycle. Force analysis is performed on the vehicle based on the visual adhesion coefficient of the current cycle to obtain a second reference torque for the vehicle in the current cycle. The second reference torque and the driver-triggered torque of the current cycle are processed based on the vehicle's driving conditions in the current cycle to determine a second candidate torque for the vehicle in the current cycle. Based on the first candidate torque and the second candidate torque of the current cycle, the target torque for the vehicle is determined. Therefore, compared to the prior art where vehicle slippage is detected before adjusting wheel torque, this embodiment determines the first candidate torque through a feedforward control method and the second candidate torque through a predictive control method. This allows for wheel torque adjustment based on the target torque even when vehicle slippage has not occurred, solving the problem of torque adjustment lag, improving the timeliness and effectiveness of vehicle torque control, thereby reducing the number and degree of wheel slippage, improving vehicle stability and drivability, and ensuring vehicle safety.

[0062] Figure 3 This is a schematic flowchart of another vehicle torque control method provided in an embodiment of the present disclosure, as shown below. Figure 3 As shown, the method includes the following steps:

[0063] Step 301: Obtain the first confidence level corresponding to the dynamic adhesion coefficient and the second confidence level corresponding to the visual adhesion coefficient of the current cycle.

[0064] In this embodiment, the dynamic adhesion coefficient and the visual adhesion coefficient can be fused based on a confidence level. There are various methods for obtaining this confidence level, and this embodiment does not impose any limitations. For example, the second confidence level corresponding to the visual adhesion coefficient can be determined based on the confidence level of the road surface type obtained by convolution processing of the road surface image information, and the first confidence level can be set to the difference between 1 and the second confidence level. Alternatively, the correspondence between road surface type and the second confidence level can be pre-tested and calibrated. The road surface confidence level correspondence can be retrieved based on the road surface type of the current period to determine the second confidence level corresponding to the visual adhesion coefficient, and the first confidence level can be set to the difference between 1 and the second confidence level.

[0065] Step 302: Based on the first confidence level and the second confidence level, the dynamic adhesion coefficient and the visual adhesion coefficient are fused to obtain the fused adhesion coefficient.

[0066] Through fusion processing, a fused adhesion coefficient that integrates both dynamic and visual adhesion coefficients can be obtained. Since the dynamic adhesion coefficient represents the actual adhesion coefficient between the vehicle and the current road surface, and the visual adhesion coefficient represents the predicted adhesion coefficient between the vehicle and the road surface to be traveled, this fused adhesion coefficient can represent the adhesion coefficient of both the current road surface and the road surface to be traveled.

[0067] In this embodiment, there are multiple methods to obtain the fusion adhesion coefficient, which can be selected according to the application scenario. This embodiment does not impose any restrictions. For example, the first confidence level can be used as the weight of the dynamic adhesion coefficient, the second confidence level can be used as the weight of the visual adhesion coefficient, and the dynamic adhesion coefficient and the visual adhesion coefficient can be summed by weight to obtain the fusion adhesion coefficient.

[0068] Step 303: Perform a force analysis on the vehicle based on the fusion adhesion coefficient, the lateral adhesion coefficient of each wheel, and the vertical force to determine the first reference torque for each wheel in the current cycle.

[0069] In this embodiment, the lateral force is defined as the ratio of the lateral force to the longitudinal force of the wheel, and the vertical force is defined as the force acting on the wheel in the perpendicular direction. In this embodiment, there are no restrictions on the number of wheels of the vehicle; a four-wheeled vehicle is used as an example to illustrate the force analysis process. Figure 4 This is a schematic diagram of vehicle force analysis provided in an embodiment of the present disclosure, such as... Figure 4 As shown in the figure, m represents the mass of the vehicle, g represents the acceleration due to gravity, and a x a represents longitudinal acceleration. y Fz represents lateral acceleration. F Fz represents the vertical force on the front wheel. R Fz represents the vertical force on the rear wheel. L Fz represents the vertical force on the left wheel.R ′ represents the vertical force on the right wheel. Figure 4 Parameters not listed include: vehicle wheelbase l, distance l between vehicle center of gravity and front axle. F The distance between the vehicle's center of gravity and the rear axle l R Front axle track w F Rear axle track w R The height of the center of gravity, h. Based on the above parameters, the vertical force Fz of each wheel of the vehicle can be determined. FL Fz represents the vertical force on the left front wheel. FR This represents the vertical force Fz of the right front wheel. RL This represents the vertical force Fz of the left rear wheel. RR This indicates the vertical force on the right rear wheel:

[0070]

[0071]

[0072]

[0073]

[0074] Furthermore, based on the vehicle's lateral acceleration and mass, the lateral forces on each wheel can be determined. For a four-wheeled vehicle, the lateral forces include the lateral force Fy of the left front wheel. FL The lateral force Fy of the right front wheel FR lateral force Fy of the left rear wheel RL lateral force Fy of the right rear wheel RR The vehicle's fusion adhesion coefficient is Mu. XX Laterally, the adhesion coefficient is MuUsedY XX The first reference torque is MTra_Static XX ,in:

[0075]

[0076]

[0077] The subscript XX in the above formula can be replaced with FL, FR, RL, or RR according to the calculation requirements, where FL represents the left front wheel, FR represents the right front wheel, RL represents the left rear wheel, and RR represents the right rear wheel. In the embodiments described later, the meaning of the subscript XX is the same as here, and will not be repeated. Taking the calculation of the first reference torque corresponding to the left front wheel as an example, the corresponding calculation formula is:

[0078]

[0079]

[0080] Step 304: Based on the vehicle's driving conditions in the current cycle, process the first reference torque of the current cycle and the driver trigger torque determined by driving behavior information to determine the first candidate torque of the vehicle in the current cycle.

[0081] In this embodiment, the vehicle's power supply method is not limited, and the method includes, but is not limited to, wheel-mounted motor power supply or centralized motor power supply. In wheel-mounted motor power supply, each motor corresponds to one wheel, and the wheel motor includes, but is not limited to, wheel-side motors or wheel-in-hub motors. Taking different power supply methods as examples, the method for obtaining the first candidate torque is explained as follows:

[0082] When the vehicle is powered by a wheeled electric motor:

[0083] If the vehicle is driven by a wheel motor, the minimum torque between the first reference torque of the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque for the current cycle.

[0084] Continuing with the example of the four-wheeled vehicle mentioned above, if the first reference torque is MTra_Static XX The driver trigger torque is MPropDrvReq XX For the first candidate torque MTr_Dynamic XX :

[0085] MTar_Dynamic XX =Min(MTar_Static XX MPropDrvReq XX )

[0086] Min() represents taking the minimum value.

[0087] If the vehicle is operating under an energy recovery mode based on wheel motor power supply, the maximum torque among the reverse torque of the first reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque for the current cycle.

[0088] Continuing with the example of the four-wheeled vehicle mentioned above, if the reverse torque of the first reference torque is -MTar S tatic XX The driver trigger torque is MPropDrvReq XX For the first candidate torque MTr_Dynamic XX :

[0089] MTar_Dynamic XX =Max(-MTar_StaticXX MPropDrvReq XX )

[0090] Max() represents taking the maximum value.

[0091] When the vehicle is powered by a centralized motor:

[0092] If the vehicle is driven by a centralized motor, the maximum torque among the first reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first coaxial torque; the minimum torque among N times the first coaxial torque and the driver trigger torque is obtained as the first candidate torque in the current cycle; where N is a positive number.

[0093] Continuing with the example of the four-wheeled vehicle, the value of N can be set according to the number of wheels on the same axle, etc. This embodiment does not impose any restrictions. For example, N can be set to 2, and the maximum torque in the first reference torque corresponding to the wheel and its co-axial wheel is Max(MTar_Static). XL MTar_Static XR If the first coaxial torque is Max(MTar_Static), then the first coaxial torque is Max(MTar_Static). XL MTar_Static XP The driver trigger torque is MPropDrvReq. XX For the first candidate torque MTr_Dynamic XA :

[0094] MTar_Dynamic XA =Min(2Max(MTar_Static) XL MTar_Static XR ),MPropDrvReq XA )

[0095] XA can be set to FL, FR, RL, or RR according to calculation requirements. Here, X represents the axle to which the wheel belongs, and A represents the side to which the wheel belongs. For example, XL represents the left wheel of the X-axis, and XR represents the right wheel of the X-axis. In the following embodiments, the meanings of XA, XL, and XR are similar to those here and will not be repeated.

[0096] For example, if we calculate the first candidate torque MRar_Dynamic for the left front wheel... FL Then we have:

[0097] MTar_Dynamic FL =Min(2Max(MTar_Static) FL MTar_StaticFR ),MPropDrvReq FL )

[0098] Optionally, if the vehicle uses a drive mode based on centralized motor power supply, the vehicle torque control method further includes: obtaining a first reference torque difference between the coaxial wheels of the wheels as a first torque difference; and determining a first braking torque of the wheels based on the first torque difference corresponding to the wheels.

[0099] In this embodiment, the first torque difference corresponding to each wheel can also be calculated. When the braking torque is less than the first torque difference, no braking torque is applied to the wheel, thereby avoiding the vehicle from slipping on the low-friction side of the road surface. Specifically, the difference between the first reference torque of the coaxial wheel and the first reference torque of the wheel can be obtained as the first torque difference. The first torque difference is then processed by dead zone calculation and other methods to obtain the first braking torque applied to the wheel.

[0100] Continuing with the example of a four-wheeled vehicle, if the left wheel XL is the low-tether wheel and XR is the right wheel on the same axle as that wheel, then the first braking torque is MbTar_Dynamic. XL for:

[0101] MbTar_Dynamic XL =DeadZone(MTar_Static XR -MTar_Static XL )

[0102] DeadZone() represents the calculation of the dead zone.

[0103] If the right wheel XR is the low-tether wheel, and XL is the left wheel on the same axle as that wheel, then the first braking torque is MbTar_Dynamic. XR for:

[0104] MbTar_Dynamic XR =DeadZone(MTar_Static XL -MTar_Static XR )

[0105] If the vehicle is operating under an energy recovery mode based on centralized motor power supply, the accumulated torque of the first reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first accumulated torque; the maximum torque among the reverse torque of the first accumulated torque and the driver trigger torque is obtained as the first candidate torque in the current cycle.

[0106] Continuing with the example of the four-wheeled vehicle mentioned above, the cumulative torque of the first reference torque of the wheel and its coaxial wheel, i.e., the first cumulative torque, is MTra_Static. XL +MTar_Static XR The driver trigger torque is MPropDrvReq XA For the first candidate torque MTr_Dynamic XA :

[0107] MTar_Dynamic XA =Max(-(MTar_Static) XL +MTar_Static XR ),MPropDrvReq XA )

[0108] It should be noted that if the vehicle is in braking condition, and the vehicle's power supply method includes wheel power supply or centralized power supply, the first candidate torque can be obtained according to the following method. Specifically, the maximum torque among the reverse torque of the first reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the first candidate torque for the current cycle.

[0109] Continuing with the example of the four-wheeled vehicle, if the first reference torque corresponding to the wheel in the current period is MTra_Static XX Driving behavior information determined by the driver's application of force to the brake pedal includes master cylinder pressure pMC and wheel cylinder braking performance Cp. XX The driver trigger torque determined based on this driving behavior information is pMC·Cp XX For the first candidate torque MbTar_Dynamic XX :

[0110] MbTar_Dynamic XX =Max(-MTar_Static XX , -pMC·Cp XX )

[0111] Step 305: Perform a force analysis on the vehicle based on the visual adhesion coefficient of the current cycle, the lateral adhesion coefficient of each wheel, and the vertical force to determine the second reference torque for each wheel in the current cycle.

[0112] In this embodiment, the number of wheels of the vehicle is not limited, and the force analysis process is explained using a four-wheeled vehicle as an example. Figure 3The force analysis is performed, and the calculation methods for the lateral adhesion coefficient and vertical force are similar to those described above, so they will not be repeated here. In this embodiment, the visual adhesion coefficient is MU_Pre_XX, and the lateral adhesion coefficient is MuUsedY. XX The vertical force is Fz XX Then the second reference torque MTr_Static_Pre XX for:

[0113]

[0114] Step 306: Process the second reference torque and driver trigger torque of the current cycle according to the vehicle's driving conditions in the current cycle to determine the second candidate torque of the vehicle in the current cycle.

[0115] In this embodiment, the vehicle's power supply method is not limited. For example, the vehicle's power supply method includes, but is not limited to, wheel-mounted motor power supply or centralized motor power supply. Taking different power supply methods as examples, the method for obtaining the first candidate torque is explained as follows:

[0116] When the vehicle is powered by a wheeled electric motor:

[0117] If the vehicle is driven by a wheel motor, the minimum torque between the second reference torque of the wheel and the driver trigger torque in the current cycle is obtained as the first minimum torque; based on the first minimum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0118] In this embodiment, when calculating the second candidate torque for the current cycle, the second reference torque of the previous cycle can be referenced, thereby making the change of the second reference torque between adjacent cycles more gradual and improving the riding experience of the passengers in the vehicle. Specifically, a preset weighting parameter can be set, which can be set according to user needs, etc. This embodiment does not impose any restrictions. The second reference torque of the previous cycle is weighted according to the preset weighting parameter, and the second reference torque of the current cycle is determined based on the second reference torque of the previous cycle after the weighting process.

[0119] Continuing with the example of the four-wheeled vehicle mentioned above, if the second reference torque corresponding to the wheel in the current period is MTra_Static_Pre XX The driver trigger torque is MPropDrvReq XX The preset weighting parameter is α, and the second candidate torque of the previous cycle is MTr_Dynamic_Pre_K1. XX Then the second candidate torque MTr_Dynamic_Pre in the current cycle XX for:

[0120] MTar_Dynamic_Pre XX

[0121] =α·Min(MTar_Static_Pre XX MPropDrvReq XX )+(1-α)·MTar_Dynamic_Pre_K1 XX

[0122] If the vehicle is operating under an energy recovery mode powered by a wheel motor, the maximum torque among the reverse torque of the second reference torque corresponding to the wheel in the current cycle and the driver trigger torque is obtained as the first maximum torque; based on the first maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0123] Continuing with the example of the four-wheeled vehicle mentioned above, if the reverse torque of the second reference torque is -MTar_Static_Pre XX The driver trigger torque is MPropDrvReq XX The preset weighting parameter is α, and the second candidate torque of the previous cycle is MTr_Dynamic_Pre_K1. XX Then the second candidate torque MTr_Dynamic_Pre in the current cycle XX for:

[0124] MTar_Dynamic_Pre XX

[0125] =α·Max(-MTar_Static_Pre XX MPropDrvReq XX )+(1-α)·MTar_Dynamic_Pre_K1 XX

[0126] When the vehicle is powered by a centralized electric motor:

[0127] If the vehicle is driven by a centralized motor, the maximum torque among the second reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second coaxial torque; the minimum torque among M times the second coaxial torque and the driver trigger torque is obtained as the second minimum torque; the second candidate torque for the current cycle is determined based on the preset weighted parameters, the second minimum torque and the second candidate torque of the previous cycle; where M is a positive number.

[0128] Continuing with the example of the four-wheeled vehicle, the value of M can be set according to the number of wheels on the same axle, etc. This embodiment does not impose any restrictions. For example, M can be set to 2, where the maximum torque in the second reference torque corresponding to the wheel and its coaxial wheel, i.e., the second coaxial torque, is Max(MTar_Static_Pre). XL MTar_Static_Pre XR The driver trigger torque is MPropDrvReq. XA Then, the minimum value between M times the second coaxial torque and the driver trigger torque, i.e., the second minimum torque, is Min(2Max(MTar_Static_Pre)). XL MTar_Static_Pre XR ),MPropDrvReq XA ), with 1-α as the second candidate torque MTr_Dynamic_Pre_K1 in the previous cycle. XA The weights, with α as the weight of the second minimum torque, are used to determine the second candidate torque MTr_Dynamic_Pre for the current cycle. XA for:

[0129] MTar_Dynamic_Pre XA

[0130] =αMin(2Max(MTar_Static_Pre XL MTar_Static_Pre XR ),MPropDrvReq XA )+(1-α)·MTar_Dynamic_Pre_K1 XA

[0131] Optionally, if the vehicle uses a drive mode based on centralized motor power supply, the vehicle torque control method further includes: obtaining a second reference torque difference between the coaxial wheels of the wheels as a second torque difference; and determining the second braking torque of the wheels in the current cycle based on the second torque difference and the second braking torque of the previous cycle.

[0132] In this embodiment, the second torque difference corresponding to each wheel can also be calculated. When the braking torque is less than the second torque difference, no braking torque is applied to the wheel, thereby avoiding vehicle slippage on the low-friction side of the road surface. Specifically, the difference between the second reference torque of the coaxial wheel and the second reference torque of the wheel can be obtained as the second torque difference. Dead zone calculation and other processing are performed on the second torque difference. The second torque difference after dead zone calculation and the second braking torque MbTar_Dynamic_Pra_K1 of the previous cycle are calculated according to the preset weighting parameter α.XA The weighted summation process is performed to determine the second braking torque applied to each wheel in the current cycle.

[0133] Continuing with the example of a four-wheeled vehicle, if the left wheel XL is the low-tether wheel and XR is the right wheel on the same axle as that wheel, then the second torque difference value is MTr_Static_Pre XR -MTar_Static_Pre XL The second braking torque MbTar_Dynamic_Pre XL for:

[0134] MbTar_Dynamic_Pre XL

[0135] =α·DeadZone(MTar_Static_Pre XR -MTar_Static_Pre XL )+(1-α)·MbTar_Dynamic_Pre_K1 XL

[0136] If the right wheel XR is the low-attached wheel, and XL is the left wheel on the same axle as that wheel, then the second torque difference value is MTr_Static_Pre. XL -MTar_Static_Pre XR The second braking torque MbTar_Dynamic_Pre XR for:

[0137] MbTar_Dynamic_Pre XR

[0138] =α·DeadZone(MTar_Static_Pre XL -MTar_Static_Pre XR )+(1-α)·MbTar_Dynamic_Pre_K1 XR

[0139] If the vehicle is operating under an energy recovery mode based on centralized motor power supply, the accumulated torque of the second reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second accumulated torque. The maximum torque among the reverse torque of the second accumulated torque and the driver trigger torque is obtained as the second maximum torque. Based on the preset weighted parameters, the second maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0140] Continuing with the example of the four-wheeled vehicle mentioned above, the cumulative torque of the second reference torque of the wheel and its coaxial wheel, i.e., the second cumulative torque, is MTra_Static_Pre.XL +MTar_Static_Pre XR The driver trigger torque is MPropDrvReq XA The second candidate torque in the previous cycle was MTr_Dynamic_Pre_K1 XA The preset weighting parameter is α. The second maximum torque and the second candidate torque from the previous cycle are weighted and summed based on the preset weighting parameter α to determine the second candidate torque MTra_Dynamic_Pre for the current cycle. XA :

[0141] MTar_Dynamic_Pre XA

[0142] =αMax(-(MTar_Static_Pre XL +MTar_Static_Pre XR ),MPropDrvReq XA )+(1-α)·MTar_Dynamic_Pre_K1 XA

[0143] It should be noted that if the vehicle is in braking condition, and the vehicle's power supply method includes wheel power supply or centralized power supply, the second candidate torque can be obtained according to the following method. Specifically, the maximum torque among the reverse torque of the second reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the third maximum torque; the second candidate torque of the current cycle is determined based on the third maximum torque and the second candidate torque of the previous cycle.

[0144] Continuing with the example of the four-wheeled vehicle mentioned above, if the second reference torque corresponding to the wheel in the current period is MTra_Static_Pre XX Driving behavior information determined by the driver's application of force to the brake pedal includes master cylinder pressure pMC and wheel cylinder braking performance Cp. XX The driver trigger torque determined based on this driving behavior information is pMC·Cp XX Then, the maximum value between the reverse torque of the second reference torque and the reverse torque of the driver trigger torque in the current cycle, i.e., the third maximum torque, is Max(-MTar_Static_Pre). XX , -pMC·Cp XX The second candidate torque MbTar_Dynamic_Pre is determined by weighting the third maximum torque and the second candidate torque from the previous cycle based on α. XX :

[0145] MbTar_Dynamic_PreXX

[0146] =α·Max(-MTar_Static_Pre XX , -pMC·Cp XX )+(1-α)·MbTar_Dynamic_Pre_K1 XX

[0147] Step 307: Determine the target torque of the vehicle based on the first candidate torque and the second candidate torque of the current cycle.

[0148] After determining the first candidate torque and the second candidate torque for the current cycle, the first and second candidate torques can be processed by taking the minimum value, thereby determining the target torque for each wheel of the vehicle.

[0149] Therefore, this embodiment provides a timely and effective vehicle torque control method, which is applicable to various operating conditions such as power supply, energy recovery, and braking, and to various power supply situations such as wheel motor power supply and centralized motor power supply. It covers common daily driving conditions and vehicle power supply types, providing a torque control method with relatively comprehensive application scenarios. Furthermore, when determining the second candidate torque of the current cycle, the second candidate torque of the previous cycle is referenced, which reduces the difference between the second candidate torques of adjacent cycles, making the change of the second candidate torque more gradual. This, in turn, makes the change of the target torque determined based on the second candidate torque more gradual, thereby improving the riding comfort of the occupants.

[0150] Figure 5 A schematic diagram of the structure of a vehicle torque control device provided in an embodiment of this disclosure is shown.

[0151] In some embodiments of this disclosure, Figure 5 The vehicle torque control device shown can be executed by electronic devices or a server. The electronic devices can include, but are not limited to, mobile terminals such as smartphones, laptops, personal digital assistants (PDAs), tablets (PADs), portable multimedia players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), and wearable devices, as well as fixed terminals such as digital TVs, desktop computers, and smart home devices. The server can be a server cluster or a cloud server.

[0152] like Figure 5 As shown, the vehicle torque control device 500 may include: a first analysis module 501, a second analysis module 502, and a first determination module 503.

[0153] The first analysis module 501 is used to perform feedforward torque analysis on the vehicle based on the dynamic adhesion coefficient, the visual adhesion coefficient, and the driver trigger torque to determine the first candidate torque of the vehicle; wherein, the driver trigger torque is the vehicle torque determined through driving behavior information.

[0154] The second analysis module 502 is used to perform predictive torque analysis on the vehicle based on the visual adhesion coefficient and the driver trigger torque, and determine the second candidate torque of the vehicle.

[0155] The first determining module 503 is used to determine the target torque of the vehicle based on the first candidate torque and the second candidate torque.

[0156] In some embodiments of this disclosure, the first analysis module 501 includes:

[0157] The first analysis submodule is used to perform force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle, and obtain the first reference torque of the vehicle in the current cycle.

[0158] The first processing submodule is used to process the first reference torque and the driver trigger torque of the current cycle according to the driving conditions of the vehicle in the current cycle, and determine the first candidate torque of the vehicle in the current cycle.

[0159] In some embodiments of this disclosure, the apparatus further includes:

[0160] The first acquisition module is used to acquire road surface image information in front of the vehicle's driving direction within the current period;

[0161] The second determining module is used to perform neural network calculations on the road surface image information, and determine the visual adhesion coefficient of the vehicle in the current cycle based on the calculation results; and,

[0162] The third acquisition module is used to acquire the attitude information of the vehicle;

[0163] The third determining module is used to determine the dynamic adhesion coefficient of the vehicle based on the vehicle's attitude information.

[0164] In some embodiments of this disclosure, the first analysis submodule is configured to:

[0165] Obtain the first confidence level corresponding to the dynamic adhesion coefficient, and the second confidence level corresponding to the visual adhesion coefficient of the current cycle;

[0166] Based on the first confidence level and the second confidence level, the dynamic adhesion coefficient and the visual adhesion coefficient are fused to obtain a fused adhesion coefficient;

[0167] Based on the fusion adhesion coefficient, the lateral adhesion coefficient of each wheel, and the vertical force, the force analysis of the vehicle is performed to determine the first reference torque corresponding to each wheel in the current cycle.

[0168] In some embodiments of this disclosure, the first processing submodule is configured to:

[0169] If the vehicle uses a drive mode powered by wheel motors, the minimum torque between the first reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque for the current cycle; or...

[0170] If the vehicle is operating under an energy recovery mode based on wheel motor power supply, the maximum torque between the reverse torque of the first reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque of the current cycle.

[0171] In some embodiments of this disclosure, the first processing submodule is configured to:

[0172] If the vehicle uses a drive mode based on centralized motor power supply, the maximum torque among the first reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first coaxial torque; the minimum torque among N times the first coaxial torque and the driver trigger torque is obtained as the first candidate torque in the current cycle; where N is a positive number; or...

[0173] If the vehicle is operating under an energy recovery mode based on centralized motor power supply, the accumulated torque of the first reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first accumulated torque; the reverse torque of the first accumulated torque and the maximum torque among the driver trigger torque are obtained as the first candidate torque of the current cycle.

[0174] In some embodiments of this disclosure, if the vehicle uses a drive mode based on centralized motor power supply, the device further includes:

[0175] The first braking module is used to obtain a first reference torque difference between the wheel and the coaxial wheel of the wheel as a first torque difference; and to determine the first braking torque of the wheel based on the first torque difference corresponding to the wheel.

[0176] In some embodiments of this disclosure, the first processing submodule is configured to:

[0177] If the vehicle is in braking condition, the maximum torque among the reverse torque of the first reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the first candidate torque of the current cycle.

[0178] In some embodiments of this disclosure, the second analysis module 502 includes:

[0179] The second analysis submodule is used to perform force analysis on the vehicle based on the visual adhesion coefficient of the current cycle, and obtain the second reference torque of the vehicle in the current cycle.

[0180] The second processing submodule is used to process the second reference torque and the driver trigger torque of the current cycle according to the driving conditions of the vehicle in the current cycle, and determine the second candidate torque of the vehicle in the current cycle.

[0181] In some embodiments of this disclosure, the second analysis submodule is configured to:

[0182] Based on the visual adhesion coefficient of the current cycle, the lateral adhesion coefficient of each wheel, and the vertical force, the force analysis of the vehicle is performed to determine the second reference torque corresponding to each wheel in the current cycle.

[0183] In some embodiments of this disclosure, the second processing submodule is configured to:

[0184] If the vehicle uses a drive mode powered by a wheel motor, the minimum torque among the second reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first minimum torque; based on the first minimum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined; or...

[0185] If the vehicle is operating under an energy recovery mode powered by a wheel motor, the reverse torque of the second reference torque corresponding to the wheel in the current cycle and the maximum torque of the driver trigger torque are obtained as the first maximum torque; based on the first maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0186] In some embodiments of this disclosure, the second processing submodule is configured to:

[0187] If the vehicle uses a drive mode based on centralized motor power supply, the maximum torque among the second reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second coaxial torque; the minimum torque among M times the second coaxial torque and the driver trigger torque is obtained as the second minimum torque; based on the second minimum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined; the difference between the second reference torque of the wheel and its coaxial wheel is obtained as the second torque difference; the second braking torque of the wheel in the current cycle is determined based on the second torque difference and the second braking torque of the previous cycle; wherein, M is a positive number; or,

[0188] If the vehicle is operating under an energy recovery mode based on a centralized motor, the accumulated torque of the second reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second accumulated torque. The reverse torque of the second accumulated torque and the maximum torque among the driver trigger torques are obtained as the second maximum torque. Based on the second maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0189] In some embodiments of this disclosure, if the vehicle uses a drive mode based on centralized motor power supply, the device further includes:

[0190] The second braking module is configured to acquire a second reference torque difference between the wheel and the coaxial wheel of the wheel as a second torque difference; and determine the second braking torque of the wheel in the current cycle based on the second torque difference and the second braking torque of the previous cycle. In some embodiments of this disclosure, the second processing submodule is configured to:

[0191] If the vehicle is in braking condition, the maximum torque among the reverse torque of the second reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the third maximum torque; based on the third maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

[0192] The vehicle torque control device provided in this disclosure can execute the vehicle torque control method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of executing the method.

[0193] Figure 6 A schematic diagram of the hardware circuit structure of a vehicle torque control device provided in an embodiment of this disclosure is shown.

[0194] like Figure 6As shown, the vehicle torque control device 600 may include a controller 601 and a memory 602 storing computer program instructions.

[0195] Specifically, the controller 601 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0196] Memory 602 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the integrated gateway device. In a particular embodiment, memory 602 is a non-volatile solid-state memory. In a particular embodiment, memory 602 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0197] The controller 601 reads and executes computer program instructions stored in the memory 602 to perform the steps of the vehicle torque control method provided in the embodiments of this disclosure.

[0198] In one example, the vehicle torque control device 600 may also include a transceiver 603 and a bus 604. Wherein, as Figure 6 As shown, the controller 601, memory 602 and transceiver 603 are connected via bus 604 and communicate with each other.

[0199] Bus 604 includes hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 604 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0200] The following are embodiments of a computer-readable storage medium provided in this disclosure. This computer-readable storage medium belongs to the same inventive concept as the vehicle torque control methods of the above embodiments. For details not described in detail in the embodiments of the computer-readable storage medium, please refer to the embodiments of the lane keeping method described above.

[0201] This embodiment provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform a vehicle torque control method. The method includes: performing force analysis on the vehicle based on a dynamic adhesion coefficient and a visual adhesion coefficient to obtain a first reference torque for the vehicle; processing the first reference torque and a driver-triggered torque determined through driving behavior information based on the vehicle's driving conditions to determine a first candidate torque for the vehicle; performing force analysis on the vehicle based on the visual adhesion coefficient to obtain a second reference torque for the vehicle; processing the second reference torque and the driver-triggered torque based on the vehicle's driving conditions to determine a second candidate torque for the vehicle; and determining a target torque for the vehicle based on the first candidate torque and the second candidate torque.

[0202] Of course, the computer-executable instructions provided in the embodiments of this disclosure are not limited to the above-described method operations, but can also execute related operations in the vehicle torque control method provided in any embodiment of this disclosure.

[0203] Based on the above description of the implementation methods, those skilled in the art can clearly understand that this disclosure can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer cloud platform (which may be a personal computer, server, or network cloud platform, etc.) to execute the vehicle torque control method provided in the various embodiments of this disclosure.

[0204] Note that the above description is merely a preferred embodiment and the technical principles employed in this disclosure. Those skilled in the art will understand that this disclosure is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this disclosure. Therefore, although this disclosure has been described in detail through the above embodiments, it is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this disclosure, and the scope of this disclosure is determined by the scope of the appended claims.

Claims

1. A vehicle torque control method, characterized in that, include: Based on the dynamic adhesion coefficient, visual adhesion coefficient, and driver-triggered torque, a feedforward analysis is performed on the vehicle torque to determine the first candidate torque of the vehicle; wherein, the driver-triggered torque is the vehicle torque determined through driving behavior information; Based on the visual adhesion coefficient and the driver trigger torque, the vehicle torque is predicted and analyzed to determine the second candidate torque of the vehicle; The target torque of the vehicle is determined based on the first candidate torque and the second candidate torque; The step of performing feedforward analysis on vehicle torque based on dynamic adhesion coefficient, visual adhesion coefficient, and driver trigger torque to determine the first candidate torque of the vehicle includes: The force analysis of the vehicle is performed based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle to obtain the first reference torque of the vehicle in the current cycle. Based on the vehicle's driving conditions in the current cycle, the first reference torque and the driver's trigger torque for the current cycle are processed to determine the vehicle's first candidate torque for the current cycle.

2. The method according to claim 1, characterized in that, Before performing force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle, the method further includes: Obtain road surface image information in front of the vehicle's direction of travel within the current period; The road surface image information is subjected to neural network calculations, and the visual adhesion coefficient of the vehicle in the current cycle is determined based on the calculation results; and, Obtain the attitude information of the vehicle; The dynamic adhesion coefficient of the vehicle is determined based on the vehicle's attitude information.

3. The method according to claim 1, characterized in that, The step of performing force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle to obtain the first reference torque of the vehicle in the current cycle includes: Obtain the first confidence level corresponding to the dynamic adhesion coefficient, and the second confidence level corresponding to the visual adhesion coefficient of the current cycle; Based on the first confidence level and the second confidence level, the dynamic adhesion coefficient and the visual adhesion coefficient are fused to obtain a fused adhesion coefficient; Based on the fusion adhesion coefficient, the lateral adhesion coefficient of each wheel, and the vertical force, the force analysis of the vehicle is performed to determine the first reference torque corresponding to each wheel in the current cycle.

4. The method according to claim 1, characterized in that, The step of processing the first reference torque and the driver trigger torque of the current cycle based on the vehicle's driving conditions in the current cycle to determine the first candidate torque of the vehicle in the current cycle includes: If the vehicle uses a drive mode powered by wheel motors, the minimum torque between the first reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque for the current cycle; or... If the vehicle is operating under an energy recovery mode based on wheel motor power supply, the maximum torque between the reverse torque of the first reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first candidate torque of the current cycle.

5. The method according to claim 1, characterized in that, The step of processing the first reference torque and the driver trigger torque of the current cycle based on the vehicle's driving conditions in the current cycle to determine the first candidate torque of the vehicle in the current cycle includes: If the vehicle uses a drive mode based on centralized motor power supply, the maximum torque among the first reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first coaxial torque; the minimum torque among N times the first coaxial torque and the driver trigger torque is obtained as the first candidate torque in the current cycle; where N is a positive number; or... If the vehicle is operating under an energy recovery mode based on centralized motor power supply, the accumulated torque of the first reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the first accumulated torque; the reverse torque of the first accumulated torque and the maximum torque among the driver trigger torque are obtained as the first candidate torque of the current cycle.

6. The method according to claim 5, characterized in that, If the vehicle uses a drive system based on a centralized motor power supply, the method further includes: Obtain a first reference torque difference between the coaxial wheel and the wheel as a first torque difference; determine the first braking torque of the wheel based on the first torque difference corresponding to the wheel.

7. The method according to claim 1, characterized in that, The step of processing the first reference torque and the driver trigger torque of the current cycle based on the vehicle's driving conditions in the current cycle to determine the first candidate torque of the vehicle in the current cycle includes: If the vehicle is in braking condition, the maximum torque among the reverse torque of the first reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the first candidate torque of the current cycle.

8. The method according to claim 1, characterized in that, The step of predicting and analyzing the vehicle torque based on the visual adhesion coefficient and the driver trigger torque to determine the second candidate torque of the vehicle includes: The force analysis of the vehicle is performed based on the visual adhesion coefficient of the current cycle to obtain the second reference torque of the vehicle in the current cycle; Based on the vehicle's driving conditions in the current cycle, the second reference torque and the driver's trigger torque for the current cycle are processed to determine the vehicle's second candidate torque for the current cycle.

9. The method as described in claim 8, characterized in that, The step of performing force analysis on the vehicle based on the visual adhesion coefficient of the current cycle to obtain the second reference torque of the vehicle in the current cycle includes: Based on the visual adhesion coefficient of the current cycle, the lateral adhesion coefficient of each wheel, and the vertical force, the force analysis of the vehicle is performed to determine the second reference torque corresponding to each wheel in the current cycle.

10. The method as described in claim 8, characterized in that, The step of processing the second reference torque and the driver trigger torque for the current cycle based on the vehicle's driving conditions in the current cycle to determine the second candidate torque for the vehicle in the current cycle includes: If the vehicle uses a drive mode powered by a wheel motor, the minimum torque among the second reference torque corresponding to the wheel and the driver trigger torque in the current cycle is obtained as the first minimum torque; based on the first minimum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined; or... If the vehicle is operating under an energy recovery mode powered by a wheel motor, the reverse torque of the second reference torque corresponding to the wheel in the current cycle and the maximum torque of the driver trigger torque are obtained as the first maximum torque; based on the first maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

11. The method as described in claim 8, characterized in that, The step of processing the second reference torque and the driver trigger torque for the current cycle based on the vehicle's driving conditions in the current cycle to determine the second candidate torque for the vehicle in the current cycle includes: If the vehicle uses a drive mode based on centralized motor power supply, the maximum torque among the second reference torques corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second coaxial torque; the minimum torque among M times the second coaxial torque and the driver trigger torque is obtained as the second minimum torque; based on the second minimum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined; where M is a positive number; or, If the vehicle is operating under an energy recovery mode based on a centralized motor, the accumulated torque of the second reference torque corresponding to the wheel and its coaxial wheel in the current cycle is obtained as the second accumulated torque. The reverse torque of the second accumulated torque and the maximum torque among the driver trigger torques are obtained as the second maximum torque. Based on the second maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

12. The method according to claim 11, characterized in that, If the vehicle uses a drive system based on a centralized motor power supply, the method further includes: The second reference torque difference between the coaxial wheel and the wheel is obtained as the second torque difference; the second braking torque of the wheel in the current cycle is determined based on the second torque difference and the second braking torque of the previous cycle.

13. The method as described in claim 8, characterized in that, The step of processing the second reference torque and the driver trigger torque for the current cycle based on the vehicle's driving conditions in the current cycle to determine the second candidate torque for the vehicle in the current cycle includes: If the vehicle is in braking condition, the maximum torque among the reverse torque of the second reference torque corresponding to the wheel and the reverse torque of the driver trigger torque in the current cycle is obtained as the third maximum torque; based on the third maximum torque and the second candidate torque of the previous cycle, the second candidate torque of the current cycle is determined.

14. A vehicle torque control device, characterized in that, include: The first analysis module is used to perform feedforward torque analysis on the vehicle based on the dynamic adhesion coefficient, the visual adhesion coefficient, and the driver trigger torque to determine the first candidate torque of the vehicle; wherein, the driver trigger torque is the vehicle torque determined through driving behavior information; The second analysis module is used to perform predictive torque analysis on the vehicle based on the visual adhesion coefficient and the driver trigger torque, and to determine the second candidate torque of the vehicle. The first determining module is used to determine the target torque of the vehicle based on the first candidate torque and the second candidate torque; The first analysis module includes: The first analysis submodule is used to perform force analysis on the vehicle based on the dynamic adhesion coefficient and the visual adhesion coefficient of the current cycle, and obtain the first reference torque of the vehicle in the current cycle. The first processing submodule is used to process the first reference torque and the driver trigger torque of the current cycle according to the driving conditions of the vehicle in the current cycle, and determine the first candidate torque of the vehicle in the current cycle.

15. A vehicle torque control device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the vehicle torque control method according to any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, The storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the vehicle torque control method according to any one of claims 1-13.

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