Method and device for controlling vehicle torque

Abstract

This invention provides a method and apparatus for controlling vehicle torque, relating to the field of vehicle control technology. The method includes: acquiring a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of a plurality of wheels of the vehicle; and in each control cycle, performing the following steps: acquiring the road surface adhesion coefficient of the vehicle in the current control cycle; for each of the plurality of wheels, determining a first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model; constructing a target cost function for a model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient; and solving the target cost function to obtain the target torque of each of the plurality of wheels, so as to control the plurality of wheels to operate according to the target torque in the next control cycle.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method and apparatus for controlling vehicle torque. Background Technology

[0002] In the field of torque control for distributed electric drive vehicles, existing technologies largely rely on high-precision sensors to directly measure tire lateral forces and control based on simplified linear tire models. This not only significantly increases system cost and engineering implementation difficulty but also makes it difficult to accurately describe the mechanical characteristics of the tire in the nonlinear region. Especially on low-friction surfaces or under transient conditions, the estimation error of tire lateral forces directly degrades the accuracy and response speed of vehicle stability control. Furthermore, traditional control strategies often treat the road adhesion coefficient as a fixed parameter or an offline calibration value, which cannot be updated in real time during vehicle operation. This makes it difficult to dynamically adjust torque distribution according to changes in road conditions, further restricting the environmental adaptability and robustness of wheel motor torque control. Summary of the Invention

[0003] This invention provides a method and apparatus for controlling vehicle torque, which solves the technical problem in related technologies where the motor torque control adjustment is not timely due to the use of a fixed road surface adhesion coefficient, making it unable to adapt to the environment.

[0004] In a first aspect, embodiments of the present invention provide a method for controlling vehicle torque, the method comprising: Obtain a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of the multiple wheels of the vehicle; Within each control cycle, perform the following steps: Obtain the road adhesion coefficient of the vehicle in the current control cycle; For each of the plurality of wheels, the first tire lateral force of the wheel is determined by a segmented tire model based on the first lateral slip angle and the first vertical load; The target cost function of the predictive control strategy is constructed based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient. The target cost function is solved to obtain the target torque of each of the multiple wheels, so as to control the multiple wheels to work according to the target torque in the next control cycle.

[0005] Optionally, the tire characteristic curve diagram of the segmented tire model includes five tire curve segments, each tire curve segment including five lateral force coefficient intervals divided according to the lateral slip angle range; determining the first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using the segmented tire model includes: determining the target curve segment corresponding to the wheel from the five tire curve segments based on the first lateral slip angle; and determining the first tire lateral force corresponding to the wheel based on the slope and intercept of the target curve segment on the tire characteristic curve diagram.

[0006] Optionally, the step of constructing the target cost function of the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient includes: when all the plurality of wheels are effective, obtaining U control variables corresponding to U future control cycles, wherein each control variable is a hypothetical virtual torque sequence of the plurality of wheels in the future control cycle; starting from the first state and the road surface adhesion coefficient, predicting the second state of the vehicle within P future control cycles based on the U control variables and the segmented tire model, wherein P is a positive integer greater than U; for each future control cycle within the P future control cycles, determining the first cycle cost of the future control cycle according to the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, and using the sum of the first cycle costs of the P future control cycles as the target cost function.

[0007] Optionally, solving the target cost function to obtain the target torque of the plurality of wheels includes: within the current control cycle, solving the target cost function using the model predictive control strategy to obtain U optimal solutions corresponding to the U control variables, where each optimal solution is the torque of the plurality of wheels in the corresponding future control cycle; and taking the optimal solution of the control variable corresponding to the future control cycle most recent to the current control cycle among the U control variables as the target torque of the plurality of wheels in the next control cycle.

[0008] Optionally, the second state includes tire adhesion margin indices corresponding to the plurality of wheels; before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameters corresponding to the current control cycle, the method further includes: for each of the plurality of wheels, determining a second tire longitudinal force of the wheel based on the predicted motor torque of the wheel in the future control cycle; determining a second tire lateral force of the wheel based on the segmented tire model and the second tire longitudinal force; determining the resultant force of the wheel in the future control cycle based on the second tire lateral force and the second tire longitudinal force; determining the maximum adhesion force of the wheel in the future control cycle based on the road surface adhesion coefficient and the second vertical load of the wheel included in the second state in the future control cycle, and using the ratio of the resultant force to the maximum adhesion force as the tire adhesion margin index of the wheel, wherein the tire adhesion margin index characterizes the vehicle's anti-slip capability.

[0009] Optionally, before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, the method further includes: determining the target weight parameter from a preset set of weight parameters based on the road surface adhesion coefficient.

[0010] Optionally, the method further includes: if the steering wheel angle rate of the vehicle is detected to be greater than a first threshold or the lateral acceleration is detected to be greater than a second threshold, adjusting the initial weight parameters according to a preset first weight adjustment strategy to obtain the target weight parameters; if the vehicle is detected to be traveling on a highway, adjusting the initial weight parameters according to a preset second weight adjustment strategy to obtain the target weight parameters; if the yaw moment deviation of the vehicle is detected to be greater than a third threshold, adjusting the initial weight parameters according to a preset third weight adjustment strategy to obtain the target weight parameters.

[0011] Optionally, the step of constructing the target cost function of the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient includes: raising the failed wheel in the event of failure of any of the plurality of wheels; taking the assumed virtual torque sequence of each wheel other than the failed wheel in U future control cycles as U control variables; predicting the second state of the vehicle in P future control cycles based on the first state and the road surface adhesion coefficient, using the U control variables and the segmented tire model as a starting point, where P is a positive integer greater than U; and for each future control cycle in the P future control cycles, taking the tire load rate minimization function as the second cycle cost, and using the sum of the second cycle costs of the P future control cycles as the target cost function.

[0012] Secondly, embodiments of the present invention provide a vehicle torque control device, the device comprising: The acquisition module is used to acquire a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of the multiple wheels of the vehicle; The execution module is configured to perform the following steps in each control cycle: obtain the road surface adhesion coefficient of the vehicle in the current control cycle; for each of the plurality of wheels, determine the first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model; construct a target cost function for the model prediction control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient; solve the target cost function to obtain the target torque of each of the plurality of wheels, so as to control the plurality of wheels to operate according to the target torque in the next control cycle.

[0013] Thirdly, embodiments of the present invention provide a vehicle, including: processor; A memory for storing instructions to be executed by the processor, wherein the processor is configured to execute the instructions to implement the method as described in the first aspect.

[0014] Fourthly, embodiments of the present invention provide a storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the vehicle to perform the method as described in the first aspect.

[0015] Fifthly, embodiments of the present invention provide a computer program product, the program product comprising a computer program, the computer program being executed by a processor as described in the first aspect.

[0016] According to an embodiment of the present invention, a method for controlling vehicle torque is provided. The method involves obtaining a first state of the vehicle, including a first lateral slip angle and a first vertical load for each of multiple wheels. Within each control cycle, the following steps are performed: obtaining the road surface adhesion coefficient of the vehicle in the current control cycle; determining a first tire lateral force for each of the multiple wheels based on the first lateral slip angle and the first vertical load using a segmented tire model; constructing a target cost function for a model predictive control strategy based on the first tire lateral force of each wheel and the road surface adhesion coefficient; and solving the target cost function to obtain the target torque for each of the multiple wheels, thereby controlling the multiple wheels to operate according to the target torque in the next control cycle. By obtaining the road surface adhesion coefficient in real time within each control cycle and utilizing the target cost function of the model predictive control strategy, the controller can proactively optimize the vehicle dynamics for future control cycles based on the current actual road conditions and tire force states, ultimately outputting a target torque that meets stability and responsiveness requirements. Compared to existing technologies, the deep integration of real-time road surface recognition and model predictive control enables precise utilization and dynamic optimization of tire adhesion limits, effectively improving vehicle stability control and driving safety under extreme conditions such as low-adhesion roads or emergency obstacle avoidance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The flowchart of a vehicle torque control method provided in some embodiments of the present invention is shown; Figure 2 The present invention illustrates the concept of a tire characteristic curve diagram of a segmented tire model provided in some embodiments of the present invention; Figure 3 The structure of a vehicle torque control device provided in some embodiments of the present invention is shown; Figure 4 The present invention illustrates the concept of a vehicle torque control system provided by some embodiments thereof; Figure 5 The diagram shows a structural block diagram of a vehicle provided in some embodiments of this application. Detailed Implementation

[0019] As described in the background section, in the field of torque control for distributed electric drive vehicles, existing technologies largely rely on high-precision sensors to directly measure tire lateral forces and control based on simplified linear tire models. This not only significantly increases system cost and engineering implementation difficulty but also makes it difficult to accurately describe the mechanical characteristics of the tire in the nonlinear region. Especially on low-adhesion road surfaces or under transient conditions, the estimation error of tire lateral forces directly degrades the accuracy and response speed of vehicle stability control. Furthermore, traditional control strategies often treat the road adhesion coefficient as a fixed parameter or an offline calibration value, which cannot be updated in real time during vehicle operation. This makes it difficult to dynamically adjust torque distribution according to changes in road conditions, further restricting the environmental adaptability and robustness of wheel motor torque control.

[0020] According to an embodiment of the present invention, a method for controlling vehicle torque is provided. The method involves obtaining a first state of the vehicle, including a first lateral slip angle and a first vertical load for each of multiple wheels. Within each control cycle, the following steps are performed: obtaining the road surface adhesion coefficient of the vehicle in the current control cycle; determining a first tire lateral force for each of the multiple wheels based on the first lateral slip angle and the first vertical load using a segmented tire model; constructing a target cost function for a model predictive control strategy based on the first tire lateral force of each wheel and the road surface adhesion coefficient; and solving the target cost function to obtain the target torque for each of the multiple wheels, thereby controlling the multiple wheels to operate according to the target torque in the next control cycle. By obtaining the road surface adhesion coefficient in real time within each control cycle and utilizing the target cost function of the model predictive control strategy, the controller can proactively optimize the vehicle dynamics for future control cycles based on the current actual road conditions and tire force states, ultimately outputting a target torque that meets stability and responsiveness requirements. Compared to existing technologies, the deep integration of real-time road surface recognition and model predictive control enables precise utilization and dynamic optimization of tire adhesion limits, effectively improving vehicle stability control and driving safety under extreme conditions such as low-adhesion roads or emergency obstacle avoidance.

[0021] The technical solution of the present invention and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the vehicle torque control method provided by the embodiments of the present invention can be executed by a target device. The target device can be a single electronic device or multiple electronic devices working together. The electronic device can be a server, such as an Electronic Control Unit (ECU) in a vehicle, or a cloud server capable of cloud computing.

[0022] Figure 1 The flowchart illustrates a vehicle torque control method provided by an embodiment of the present invention. For example... Figure 1 As shown, the vehicle torque control method provided in this embodiment of the invention includes steps 110 to 150.

[0023] Step 110: Obtain the first state of the vehicle, which includes the first lateral slip angle and the first vertical load of each of the multiple wheels of the vehicle.

[0024] In this embodiment of the invention, the first state of the vehicle can be calculated periodically based on data collected by various sensors on the vehicle, for example, every 5ms. The vehicle may include four wheel speed sensors (each with a 1kHz acquisition speed corresponding to the four wheels), an inertial acceleration sensor (IMU) with a 2kHz acquisition speed, a steering wheel angle sensor with a 500Hz acquisition speed, and four suspension displacement sensors with a 2kHz acquisition speed, each collecting corresponding sensor data. Subsequently, every 5ms, the first state of the vehicle is calculated using an Extended Kalman Filter (EKF). The EKF can be used to collect the vehicle's longitudinal slip ratio based on the sensor data. Lateral slip angle And the first vertical load corresponding to each wheel (represented as follows) ,in Let i represent the vertical load on the wheel, and i be the wheel number (1-4, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle, respectively). Then, the first longitudinal slip ratio of each wheel is obtained through the vehicle's longitudinal slip ratio. The lateral slip angle of the entire vehicle is considered as the first lateral slip angle of multiple wheels, i.e. .

[0025] In this embodiment of the invention, after obtaining the first state of multiple wheels of the vehicle every 5ms, steps 120 to 150 can be executed within each control cycle. The duration of the control cycle is not less than the period for calculating the first state of the vehicle; for example, the duration of the control cycle can be 20ms. Within each control cycle, the most recent first state can be used for calculation.

[0026] Step 120: Obtain the road adhesion coefficient of the vehicle in the current control cycle.

[0027] In this embodiment of the invention, the road surface adhesion coefficient can characterize the static friction coefficient between the tire and the road surface. The road surface adhesion coefficient can be determined by the recursive least squares (RLS) method based on the difference between the output torque of the motors corresponding to multiple wheels and the longitudinal acceleration of the vehicle. The longitudinal acceleration of the vehicle can also be obtained through sensor data.

[0028] Step 130: For each of the plurality of wheels, determine the first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model.

[0029] In this embodiment of the invention, since the output torque of each wheel motor is different, the lateral tire force of each wheel is also different. The first lateral tire force of each wheel can be determined using a segmented tire model. The segmented tire model can describe the lateral slip angle of the wheel. And the lateral force of the wheel The relationship between them, for example, a segmented tire model can be a Magic Formula tire model.

[0030] Step 140: Construct the target cost function of the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient.

[0031] In this embodiment of the invention, after determining the first tire lateral force of each of the multiple wheels, the objective cost function in the Model Predictive Control (MPC) strategy can be constructed based on the road adhesion coefficient. In MPC, the state of the vehicle in future control cycles can be predicted based on the first tire lateral forces of each of the multiple wheels, which characterize the vehicle's current first state, and the road adhesion coefficient, thereby allowing for adjustments to improve control accuracy. The future control cycle can be the control cycle following the current control cycle.

[0032] Step 150: Solve the target cost function to obtain the target torque of each of the plurality of wheels, so as to control the plurality of wheels to work according to the target torque in the next control cycle.

[0033] In this embodiment of the invention, the target cost function can be solved by the field-programmable gate array (FGPA) inside the vehicle to obtain the target torque of multiple wheels, and then commands can be issued to control the motors of each wheel to work according to the target torque in the next control cycle.

[0034] In this embodiment of the invention, the road surface adhesion coefficient is acquired in real time during each control cycle, and the target cost function of the model predictive control strategy is used to enable the controller to perform forward optimization of vehicle dynamics for multiple future control cycles based on the current actual road conditions and tire force state, ultimately outputting a target torque that meets stability and responsiveness requirements. This deep integration of real-time road surface identification and model predictive control achieves precise utilization and dynamic optimization of tire adhesion limits, effectively improving vehicle stability control performance and driving safety under extreme conditions such as low-adhesion roads or emergency obstacle avoidance.

[0035] In an embodiment of the present invention, Figure 2 The diagram illustrates the concept of tire characteristic curves for a segmented tire model provided in some embodiments of the present invention. For example... Figure 2 As shown, the tire characteristic curve diagram of the segmented tire model ( Figure 2 The blue line differs from the traditional three-segment segmentation model. Figure 2 The red line indicates that the segmented tire model comprises five tire curve segments, each including five lateral force coefficient intervals divided according to the lateral slip angle range. Specifically, the Magic Formula tire model (… Figure 2 The area marked by the black dashed line (above) is divided into five regions within the range of the vehicle's sideslip angle α ∈ [-12°, 12°], which are the five tire curve segments. The parameters of each tire curve segment are adjusted according to the tire characteristics of each vehicle model and are determined by the wheel hardware. Figure 2 The tire characteristic curve diagram shown can be divided into the following five tire curve segments: Tire curve segment I (linear region): α∈[-3°,3°], slope =0.167, intercept =0; Tire curve segment II (strong nonlinear region, such as...) Figure 2 The yellow area shown is fitted in three segments: Tire curve segment II-1: α∈[-5°,-3°)∪(3°,5°], slope =0.09, intercept =0.5; Tire curve segment II-2: α∈[-7°,-5°)∪(5°,7°]), slope =0.0625, intercept =0.68; Tire curve segment II-3: α∈[-8°,-7°)∪(7°,8°], slope =0.01, intercept =0.81; Tire curve segment III (saturation region): α∈[-12°,-8°)∪(8°,12°], slope =0.0275, intercept =0.82.

[0036] In this embodiment of the invention, to better represent the five tire curve segments, binary variables can be used. (j=1,2,3,4,5) This implements the region switching of the tire curve segment, and the specific mapping relationship is as follows: =(0°≤|α|≤3°), =(3°<|α|≤5°), =(5°<|α|≤7°), =(7°<|α|≤8°), =(8°<|α|≤12°).

[0037] In this embodiment of the invention, step 130, in the process of determining the first tire lateral orientation of the wheel, can utilize, for example... Figure 2 The calculation of the tire characteristic curve diagram of the segmented tire model shown includes: determining the target curve segment corresponding to the wheel from the five tire curve segments based on the first lateral slip angle; and determining the first tire lateral force corresponding to the wheel based on the slope and intercept of the target curve segment on the tire characteristic curve diagram.

[0038] In this embodiment of the invention, the target curve segment corresponding to the wheel can be determined from five tire curve segments based on the wheel's first lateral slip angle, which is also the vehicle's lateral slip angle. Then, the first tire lateral force corresponding to the wheel is determined using the following formula:

[0039] in, The first tire lateral force of the i-th wheel, The vertical force (also called vertical load, i.e. the portion of the vehicle weight distributed to each wheel) is the force on the i-th wheel. The first lateral slip angle of the wheel, Let be the slope corresponding to the j-th tire curve segment (i.e., the target curve segment). Let be the intercept of the j-th tire curve segment (i.e., the target curve segment). This is the j-th tire curve segment (i.e., the target curve segment). For the previous one The largest lateral slip angle in the region (e.g.) Figure 2 The different curve segments and intervals j shown correspond to different slopes. and intercept , For the previous one (The largest sideslip angle in the region).

[0040] For example, if the vehicle's lateral slip angle is in the strongly nonlinear region (3° < α ≤ 5°), the first wheel for If the maximum value is 3° in the range (0° < α ≤ 3°), then the fitting formula is: .

[0041] In this embodiment of the invention, the process of constructing the target cost function of the model prediction control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient in step 140 includes: when all the plurality of wheels are effective, obtaining U control variables corresponding to U future control cycles, wherein each control variable is a hypothetical virtual torque sequence of the plurality of wheels in the future control cycle; starting from the first state and the road surface adhesion coefficient, predicting the second state of the vehicle within P future control cycles based on the U control variables and the segmented tire model, wherein P is a positive integer greater than U; for each future control cycle within the P future control cycles, determining the first cycle cost of the future control cycle according to the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, and using the sum of the first cycle costs of the P future control cycles as the target cost function.

[0042] In this embodiment of the invention, when multiple wheels are effective and the motor torques corresponding to the four wheels are allocated, U control variables corresponding to U future control cycles are obtained, where U is the control time domain in the model predictive control strategy and can be any positive integer, such as 3. Each control variable can be represented by u, where u includes the driving torques of the four wheels. The virtual torque sequence.

[0043] Subsequently, starting from the first state and the road surface adhesion coefficient, and based on the U control variables and the segmented tire model, the second state of the vehicle within P future control cycles is predicted, where P is a positive integer greater than U. P represents the prediction time domain in the model predictive control strategy, typically set to 10. That is, starting from the first state and the road surface adhesion coefficient of the current control cycle, and based on the U control variables and the segmented tire model, the second state of the vehicle within P future control cycles is predicted. Figure 2The five-stage tire model shown predicts the vehicle state over the next 10 steps (200ms), which is the second state. For example, in step 1 (t+20ms): if the torques T1=200Nm, T2=180Nm, T3=190Nm, and T4=170Nm, the overall yaw rate of the vehicle is predicted to be r=0.25rad / s and the lateral slip angle of the vehicle is predicted to be α=5°; steps 2 to 10: the second state under different torque combinations is predicted sequentially.

[0044] Finally, for each of the P future control cycles, the first cycle cost of the future control cycle is determined based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, and the sum of the first cycle costs of the P future control cycles is used as the target cost function.

[0045] In this embodiment of the invention, the first cycle cost can be expressed by the following formula:

[0046] in, , as well as For the target weight parameters, Let be the adhesion margin index for each wheel, representing the ratio of the current force of the i-th wheel to the friction circle limit. To account for the energy consumption of the motors for each wheel, This represents the difference in yaw moment of the vehicle.

[0047] Specifically, ( This indicates maximizing tire force margin, which avoids single-wheel traction saturation (such as slippage). On low-traction surfaces (ice / snow / rain), stability must be prioritized. Taking a larger value (such as 0.75) can reduce the energy consumption to 0.6 on dry roads, thus balancing energy demand. () indicates minimizing energy consumption, which means reducing motor efficiency loss. With motor speed Motor torque The relationship is non-linear (e.g., copper losses increase at low speeds and high torque). , These represent the voltage and output current of the high-voltage battery, respectively. In this embodiment of the invention, the motor efficiency MAP is fitted to... Represented as a quadratic function of torque: (The coefficients a, b, and c were calibrated through bench testing.) This indicates vehicle stability control, designed to track the ideal yaw moment. ,in Calculated using a two-degree-of-freedom vehicle model: ( This is the proportionality coefficient. (Yaw rate) Acquired directly from sensor data. When >5% At that time, it is necessary to improve (e.g., increasing from 0.1 to 0.2) to suppress vehicle drift / understeer. The target weight parameter must meet the normalization condition ( ).

[0048] In this embodiment of the invention, the constraint condition of the objective cost function may include the peak torque of the wheel motor, for example, ±300 Nm. To prevent vehicle slippage, the constraint condition may also include the adhesion margin of any wheel. <0.75, and yaw rate ≤0.5rad / s.

[0049] In this embodiment of the invention, after determining the target cost function in step 140, the implementation process of solving the target cost function in step 150 to obtain the target torque of the plurality of wheels may include: within the current control cycle, solving the target cost function through the model predictive control strategy to obtain U optimal solutions corresponding to the U control variables, each of the optimal solutions being the torque of the plurality of wheels in the corresponding future control cycle; and taking the optimal solution of the control variable corresponding to the future control cycle most recent to the current control cycle among the U control variables as the target torque of the plurality of wheels in the next control cycle.

[0050] In this embodiment of the invention, the Field Programmable Gate Array (FPGA) can employ a Sequential Quadratic Programming (SQP) algorithm to find the minimized objective cost function while satisfying constraints. Specifically, it can iterate through U=3 torque combinations (T1~T4 within ±300Nm) to find the torque sequence that minimizes the cost of the first cycle. For example, in step 1, T1=195Nm, T2=175Nm, T3=185Nm, T4=165Nm (attachment margin M_i=0.68, optimal energy consumption, and minimum yaw deviation), and so on for steps 2 and 3. Furthermore, the optimal solution of the control variable corresponding to the most recent future control cycle among the U control variables is taken as the target torque for the multiple wheels in the next control cycle. That is, only the torque command of step 1 is output to the wheel motor controller; the torques of the remaining two steps are only used as a reference for the next optimization and are not executed.

[0051] In this embodiment of the invention, as described above, the second state includes the tire adhesion margin index corresponding to the plurality of wheels. Before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, the tire adhesion margin index of the wheel can be determined through the following steps: For each of the plurality of wheels, the second tire longitudinal force of the wheel is determined based on the predicted motor torque of the wheel in the future control cycle; the second tire lateral force of the wheel is determined based on the segmented tire model and the second tire longitudinal force; the resultant force of the wheel in the future control cycle is determined based on the second tire lateral force and the second tire longitudinal force; the maximum adhesion force of the wheel in the future control cycle is determined based on the road surface adhesion coefficient and the second vertical load of the wheel included in the second state in the future control cycle, and the ratio of the resultant force to the maximum adhesion force is used as the tire adhesion margin index of the wheel, wherein the tire adhesion margin index characterizes the vehicle's anti-slip capability.

[0052] In this embodiment of the invention, the tire adhesion margin index can be determined by the following formula:

[0053] in, , These are the longitudinal force and lateral force of the second tire of the wheel in the second state, respectively, calculated with reference to the longitudinal force and lateral force of the first tire. During the calculation of the first cycle cost, control... <0.75 to retain a safety margin The torque is obtained from the motor of the i-th wheel. Thus, the ratio of the current tire force to the friction circle limit value is quantified by the tire adhesion margin index; the smaller the value, the greater the adhesion margin and the stronger the anti-slip capability.

[0054] In this embodiment of the invention, the target weight parameter in the first cycle cost can be adjusted in real time according to the road surface adhesion coefficient and driving conditions. Before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, the method further includes: determining the target weight parameter from a preset set of weight parameters based on the road surface adhesion coefficient.

[0055] In this embodiment of the invention, Table 1 shows the correspondence between the road surface adhesion coefficient μ and the weight parameters. Within the current control cycle, after determining the road surface adhesion coefficient, a weight parameter corresponding to the magnitude of the road surface adhesion coefficient can be determined from a set of preset weight parameters, and used as the target weight parameter. Table 1 is shown below: Table 1. Correspondence between road surface adhesion coefficient and weighting parameters

[0056] In this embodiment of the invention, the target weight parameters are further adjusted according to emergency conditions. For example, within the current control cycle, if the vehicle's steering wheel angle rate is detected to be greater than a first threshold (e.g., 100° / s) or the vehicle's IMU lateral acceleration is detected to be greater than a second threshold (e.g., 0.4g), it indicates that the vehicle has entered an emergency obstacle avoidance condition. The initial weight parameters are then adjusted according to a preset first weight adjustment strategy. The target weight parameter is temporarily increased to 0.8 to ensure priority for response, thereby obtaining the target weight parameter. =0.8. The initial weight parameter can be the weight parameter determined based on the road surface adhesion coefficient in the previous control cycle, as described above; the initial weight parameter can also be a preset initial parameter in the model predictive control strategy. Furthermore, when the vehicle is detected to be traveling on a highway, indicating that the vehicle has entered a high-speed cruise condition, the initial weight parameter can be adjusted according to a preset second weight adjustment strategy. Increase it to 0.3, prioritize reducing energy consumption, and obtain the target weight parameter, i.e. =0.3. When the yaw moment deviation of the vehicle is detected to be greater than the third threshold (e.g., 5%M_ideal), it indicates that the vehicle has entered a yaw instability condition. The initial weight parameters can be adjusted according to a preset third weight adjustment strategy. The target weight parameter is increased to 0.2 to suppress vehicle fishtailing / understeer, thus obtaining the target weight parameter. =0.2.

[0057] In this embodiment of the invention, if any of the plurality of wheels fails, the failed wheel can be raised. Simultaneously, step 140, which involves constructing the target cost function of the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient, may include: using the assumed virtual torque sequences of each wheel (excluding the failed wheel) over U future control cycles as U control variables; starting from the first state and the road surface adhesion coefficient, predicting the second state of the vehicle over P future control cycles based on the U control variables and the segmented tire model, where P is a positive integer greater than U; for each of the P future control cycles, using the tire load rate minimization function as the second cycle cost, and summing the second cycle costs of the P future control cycles as the target cost function.

[0058] In this embodiment of the invention, when the motor controller detects a current deviation >20A or a speed fluctuation >50rpm for a certain motor, it can be considered that the motor has failed, and further, the wheel corresponding to that motor has failed. In the event of any wheel failure, an active suspension adjustment strategy is executed, raising the failed wheel by 10mm within 50ms and reducing the vertical load to below 30% of its original value. The torque is temporarily increased to 0.85. Furthermore, in the model predictive control strategy, U control variables are obtained, each including the torque of all wheels except the failed wheel, and prediction and control are performed to reconstruct the torque of the remaining three motors. Within each future control cycle, the torque is reallocated at the cost of minimizing the tire load rate function, which is shown below:

[0059] The constraints still include the motor peak torque (±300Nm) and friction circle limitation.

[0060] For example, if a failure of the left front wheel is detected during normal driving, the remaining three motors will need to handle the original total torque of 1800 N·m. Original allocation ratio: Front axle 55% (left 45% + right 55%), Rear axle 45% (left 50% + right 50%) After the second cycle cost reconstruction: the right front wheel torque is increased by 40% (from 544.5 N·m to 762.3 N·m), and the rear wheel distribution ratio is adjusted to 55:45, ensuring that the yaw moment balance error is <5%. The solution reduces the left front wheel torque from 280 Nm to 220 Nm. Reduced to 3.5kN, The torque of the right front wheel is increased to 250Nm (to balance the yaw moment).

[0061] In an embodiment of the present invention, Figure 3 An embodiment of the present invention provides a vehicle torque control device 300. For example... Figure 3 As shown, the vehicle torque control 300 provided in this embodiment of the invention includes: The acquisition module 310 is used to acquire the current door control mode of the vehicle; when the door control mode indicates that the door can be controlled by gesture, it acquires the weight information of multiple seats in the vehicle. The execution module 320 is used to determine the target seat carrying the target occupant from the plurality of seats based on the weight information.

[0062] It should be noted that the embodiments of the vehicle torque control device in this specification and the embodiments of the vehicle torque control method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the corresponding embodiments of the vehicle torque control method above, and the repeated parts will not be described again.

[0063] In this embodiment of the invention, to better understand the vehicle torque control method provided by this embodiment, examples are given below. Figure 4 The architecture of a vehicle torque control system provided by some embodiments of the present invention is illustrated. For example... Figure 4 As shown, the vehicle torque control system provided in this embodiment of the invention may include a sensing layer: four 1kHz wheel speed sensors, a 2kHz IMU inertial acceleration sensor, a 500Hz steering wheel angle sensor, and four 2kHz suspension displacement sensors; a calculation layer: a main MCU (200MHz) running a fast-layer EKF algorithm; and an FPGA coprocessor accelerating MPC solving; and an execution layer: a four-motor controller (response time <10ms) and an active suspension actuator (adjustment speed 20mm / s). Simultaneously, within the calculation layer, a dual-layer time-scale sensor fusion architecture is included. The fast layer (5ms cycle) performs sensor fusion, fusing 1kHz wheel speed signals, 2kHz IMU six-axis data (acceleration ±16g, angular velocity ±2000° / s), and a 500Hz steering wheel angle signal. The core states are estimated using an extended Kalman filter (EKF): longitudinal slip ratio s (estimation error <±0.02), lateral slip angle α (estimation error <±0.5°), and the vertical load on each wheel. (Load transfer model based on suspension displacement sensor). Prediction layer (20ms cycle): Real-time estimation of road adhesion coefficient μ: Recursive least squares (RLS) method is used, calculated based on the difference between motor output torque and longitudinal acceleration, with a convergence time of <100ms; Model predictive control (MPC) planning: prediction time domain P=10 steps (200ms look-ahead), control time domain U=3 steps, solved by FPGA hardware acceleration.

[0064] For example, in the case of double lane change operation on icy and snowy roads (μ=0.3), the known conditions include vehicle parameters and sensor inputs. The vehicle parameters include a four-motor SUV, a curb weight of 2200kg, a wheelbase of 1.6m, and a peak torque of 300Nm per motor. Sensor inputs include wheel speed, IMU accelerometer, and steering wheel angle. The wheel states estimated using EKF are shown in Table 2 below. Table 2 Examples of EKF estimation of the first state of a vehicle

[0065] In scenarios involving icy and snowy roads (μ=0.3), stability must be prioritized; therefore, the following settings are used: , , Table 2 shows that the left front wheel There is a risk of slippage, and its longitudinal force needs to be reduced. Therefore, it is optimized using Sequential Quadratic Programming (SQP).

[0066] Set up U control variables, where each control variable is the torque of the four wheels. .

[0067] The constraints include: Friction circle: (Left front wheel) The required current is reduced from 4.2kN to ≤3.8kN.

[0068] Motor torque external characteristics: | |≤300Nm.

[0069] The model predictive control strategy includes the following steps: Step 1: Predict the state in the time domain in 10 steps (200ms). Based on a five-segment tire model and vehicle dynamics model, the vehicle state within the next 200ms is predicted cycle by cycle, accurately identifying risk points: Step 1 (20ms): Wheel slip angle 4.2°, adhesion margin 0.67, safe condition. Step 2 (40ms): Wheel slip angle 4.5°, adhesion margin 0.69, safe condition. Step 3 (60ms): Wheel slip angle 5.1°, adhesion margin 0.72, close to saturation threshold. Step 4 (80ms): Wheel slip angle 5.8°, adhesion margin 0.76, exceeding the safety threshold, tire is about to slip. Steps 5-10 (100ms-200ms): Tire saturation continues, vehicle yaw rate overshoot exceeds 15%, posing a risk of fishtailing and instability. Step 2: Optimal torque solution in the control time domain (3 steps, 60ms) Expanding the objective cost function constructed using the cost of the first cycle:

[0070] Based on the 10-step time-domain prediction results, with "maximizing adhesion margin, optimizing energy consumption, and achieving stability" as the multi-objective function, the optimal torque sequence for the four wheels in the first three steps is solved to avoid the risk of subsequent instability. Step 1 Control Command: Fine-tune the torque of the four wheels to 195Nm, 175Nm, 185Nm, and 165Nm respectively, slightly reducing power output and suppressing the increase of the sideslip angle. Step 2 Reference Instructions: Four-wheel torque 190Nm, 170Nm, 180Nm, 160Nm, maintain a stable state. Step 3 Reference Instructions: Four-wheel torque 185Nm, 165Nm, 175Nm, 155Nm, with allowance for adjustment. Step 3: Optimize the execution logic in a rolling manner (core closed-loop process) Only the optimal torque command of step 1 (20ms) is output to the four-motor controller. The execution layer completes the response within 10ms. The single-cycle response time of the entire link is 30ms, which is far below the 50ms limit. The torque in steps 2 and 3 is not executed directly, but is only used as an optimization reference and does not occupy the communication and control resources of the execution layer; After the 20ms period expires, the fast layer updates the vehicle status and road adhesion coefficient. The MPC controller repeats the "10-step prediction - 3-step solution - single-step execution" process to achieve rolling time-domain optimization and adapt to road status fluctuations in real time.

[0071] In this embodiment of the invention, a total response time of 48ms (5ms state estimation + 20ms optimization + 10ms actuator response + 13ms communication delay) is achieved through a dual-layer time architecture, which is 26% faster than the traditional single MPC architecture (65ms). In the double lane change test on an icy and snowy road surface with μ=0.3, the yaw rate overshoot is reduced compared to the regular method, achieving a synergistic optimization of fast response and small overshoot. The five-segment tire model improves the force estimation accuracy in the 3°~8° slip angle range by 62.5%, and significantly improves the adhesion margin. The dynamic weighting strategy within the objective cost function reduces energy consumption while ensuring stability, achieving synergistic optimization of energy consumption and safety. Furthermore, under single motor failure conditions, it can significantly reduce the yaw rate tracking error caused by suspension-torque coordinated control, enhancing fault tolerance.

[0072] Figure 5 This is a structural block diagram of a vehicle provided as an embodiment of this application. Figure 5 As shown, the vehicle provided in this embodiment includes a processor 510 and a memory 520, wherein the memory is used to store instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the above method.

[0073] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0074] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory including instructions that can be executed by a processor of a device to perform the described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. This non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enables the vehicle to perform... Figure 1 and / or Figure 2 The method shown.

[0075] This application also provides a computer program product, including a computer program, which, when executed by a processor, performs... Figure 1 and / or Figure 2 The method shown.

[0076] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0077] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other modifications and adjustments to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all modifications and adjustments falling within the scope of the invention.

[0078] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention is also intended to include these modifications and variations.

Claims

1. A control method of vehicle torque, characterized by, The method includes: Obtain a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of the multiple wheels of the vehicle; Within each control cycle, perform the following steps: Obtain the road adhesion coefficient of the vehicle in the current control cycle; For each of the plurality of wheels, the first tire lateral force of the wheel is determined by a segmented tire model based on the first lateral slip angle and the first vertical load; The target cost function of the predictive control strategy is constructed based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient. The target cost function is solved to obtain the target torque of each of the multiple wheels, so as to control the multiple wheels to work according to the target torque in the next control cycle.

2. The method of claim 1, wherein, The tire characteristic curve diagram of the segmented tire model includes five tire curve segments, and each tire curve segment includes five lateral force coefficient intervals divided according to the lateral slip angle range. The determination of the first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model includes: Based on the first lateral slip angle, the target curve segment corresponding to the wheel is determined from the five tire curve segments; The first tire lateral force corresponding to the wheel is determined based on the slope and intercept of the target curve segment on the tire characteristic curve diagram.

3. The method of claim 1, wherein, The objective cost function for constructing the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road adhesion coefficient includes: When all the wheels are effective, obtain U control variables corresponding to U future control cycles, where each control variable is a hypothetical virtual torque sequence of the multiple wheels in the future control cycle; Starting from the first state and the road surface adhesion coefficient, based on the U control variables and the segmented tire model, predict the second state of the vehicle within P future control cycles, where P is a positive integer greater than U; For each of the P future control cycles, the first cycle cost of the future control cycle is determined based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, and the sum of the first cycle costs of the P future control cycles is used as the target cost function.

4. The method of claim 3, wherein, Solving the target cost function to obtain the target torque of each of the plurality of wheels includes: Within the current control cycle, the target cost function is solved by the model predictive control strategy to obtain U optimal solutions corresponding to the U control variables, where each optimal solution is the torque of the multiple wheels in the corresponding future control cycle; The optimal solution of the control variable among the U control variables, corresponding to the most recent future control cycle, is taken as the target torque of the multiple wheels in the next control cycle.

5. The method according to claim 3, characterized in that, The second state includes the tire adhesion margin index corresponding to the plurality of wheels; Before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, the method further includes: For each of the plurality of wheels, a second tire longitudinal force is determined based on the predicted motor torque of the wheel within the future control cycle. The second tire lateral force of the wheel is determined based on the segmented tire model and the longitudinal force of the second tire; The resultant force of the wheel in the future control cycle is determined based on the second tire lateral force and the second tire longitudinal force; The maximum adhesion force of the wheel in the future control cycle is determined based on the road surface adhesion coefficient and the second vertical load of the wheel in the second state during the future control cycle. The ratio of the resultant force to the maximum adhesion force is used as the tire adhesion margin index of the wheel, which characterizes the vehicle's anti-slip capability.

6. The method according to claim 3, characterized in that, Before determining the first cycle cost of the future control cycle based on the second state corresponding to the future control cycle and the target weight parameter corresponding to the current control cycle, the method further includes: The target weight parameter is determined from a set of preset weight parameters based on the road surface adhesion coefficient.

7. The method according to claim 3, characterized in that, The method further includes: If the steering wheel angle rate of the vehicle is detected to be greater than the first threshold or the lateral acceleration is detected to be greater than the second threshold, the initial weight parameters are adjusted according to the preset first weight adjustment strategy to obtain the target weight parameters; When the vehicle is detected to be traveling on a highway, the initial weight parameters are adjusted according to a preset second weight adjustment strategy to obtain the target weight parameters; If the yaw moment deviation of the vehicle is detected to be greater than the third threshold, the initial weight parameters are adjusted according to the preset third weight adjustment strategy to obtain the target weight parameters.

8. The method according to claim 1, characterized in that, The objective cost function for constructing the model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road adhesion coefficient includes: If any one of the plurality of wheels fails, the failed wheel shall be raised. The hypothetical virtual torque sequences of each wheel, excluding the failed wheel, over U future control cycles are used as U control variables. Starting from the first state and the road surface adhesion coefficient, based on the U control variables and the segmented tire model, predict the second state of the vehicle within P future control cycles, where P is a positive integer greater than U; For each of the P future control cycles, the tire load rate minimization function is used as the second cycle cost, and the sum of the second cycle costs of the P future control cycles is used as the target cost function.

9. A vehicle torque control device, characterized in that, The device includes: The acquisition module is used to acquire a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of the multiple wheels of the vehicle; The execution module is configured to perform the following steps in each control cycle: obtain the road surface adhesion coefficient of the vehicle in the current control cycle; for each of the plurality of wheels, determine the first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model; construct a target cost function for the model prediction control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient; solve the target cost function to obtain the target torque of each of the plurality of wheels, so as to control the plurality of wheels to operate according to the target torque in the next control cycle.

10. A vehicle, characterized in that, include: processor; A memory for storing instructions to be executed by the processor, wherein the processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 8.

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