Reference hand torque calculation method, apparatus, system and storage medium

Abstract

A reference hand torque calculation method, an apparatus, a system and a storage medium. The method comprises: acquiring preset parameters of a vehicle in the current period, the preset parameters comprising a front wheel steering angle and an estimated road adhesion coefficient value of the vehicle; on the basis of the preset parameters in the current period and the estimated road adhesion coefficient value, calculating an estimated rack force value in the current period; when the difference value between the estimated rack force value in the current period and a measured rack force value is greater than a preset value, on the basis of a preset feedback gain, adjusting the estimated rack force value, the preset feedback gain being used for adjusting the rack force to reduce the difference value between the estimated rack force value and the measured rack force value; and determining reference hand torque corresponding to the estimated rack force value, and outputting the reference hand torque to a hand torque closed-loop controller. Using the present solution can improve the accuracy of reference hand torque calculation, and reduce noise in calculation results to improve the stability of road feel feedback.

Description

Reference hand torque calculation method, device, system and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to a Chinese patent application filed with the Patent Office of China on April 3, 2024, with application number 202410406547.5 and application name “A reference hand torque calculation method, device, system and storage medium”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application relates to the field of vehicle control technology, and in particular to a reference hand torque calculation method, device, system and storage medium. Background Art

[0004] A car's steering system determines its lateral movement. Traditional steering systems are mechanical: the driver manipulates the steering wheel, transmitting steering intent to the steered wheels through a steering gear and tie rods, thereby achieving steering movement. Steer-by-wire systems eliminate the mechanical connection between the steering wheel and the steered wheels. Instead, they receive steering commands from the driver via a hand force simulator, and the front wheel actuator controls the steering wheel angle based on the input steering commands. Simultaneously, the front wheel actuator and hand force simulator calculate a reference hand torque based on road feedback and transmit this reference hand torque based on the vehicle's current state to the driver, allowing them to feel the current steering torque and achieve road feel feedback.

[0005] In the prior art, a commonly used method for calculating the reference hand torque is to calculate the reference hand torque based on the front wheel actuator model. Since the input quantities of the front wheel actuator model are the motor torque and rack position and / or rack speed of the front wheel actuator, and noise is generated during the operation of the motor and the gear transmission process, the calculation result of the reference hand torque has high noise, resulting in high-frequency fluctuations in the hand feel and unstable road feel feedback.

[0006] Therefore, how to provide a reference hand torque calculation method to improve the accuracy of the reference hand torque calculation, reduce the noise in the calculation results, and improve the smoothness of road feel feedback has become a technical problem that needs to be solved urgently. Summary of the Invention

[0007] The present application provides a reference hand torque calculation method, device, system and storage medium for improving the accuracy of reference hand torque calculation, while reducing noise in the calculation results and improving the smoothness of road feel feedback.

[0008] This application provides a reference hand torque calculation method, including:

[0009] Obtaining preset parameters of the vehicle for a current cycle, the preset parameters including a front wheel steering angle and an estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating a rack force based on the preset vehicle parameters obtained in a previous cycle and a front wheel actuator rack force of the current cycle;

[0010] Calculating an estimated rack force value for the current cycle based on preset parameters for the current cycle and an estimated road adhesion coefficient;

[0011] When the difference between the rack force estimation value and the rack force detection value in the current cycle is greater than a preset value, adjusting the rack force estimation value according to a preset feedback gain, wherein the preset feedback gain is used to reduce the difference between the rack force estimation value and the rack force detection value by adjusting the rack force;

[0012] A reference hand torque corresponding to the rack force estimation value is determined, and the reference hand torque is output to a hand torque closed-loop controller.

[0013] The beneficial effects of the present application are as follows: since the collected parameter value is the front wheel angle, the noise in the calculation is reduced; the smoothness of the road feel feedback is improved; in addition, by adjusting the rack force estimate, the difference with the detection value is reduced to improve the accuracy of the estimate, thereby improving the accuracy of the reference hand torque calculation.

[0014] In one embodiment, the front wheel angle of the vehicle in the preset parameters of the vehicle includes:

[0015] When the vehicle steering wheel is detected to be turning, the steering wheel angle is obtained;

[0016] The product of the steering wheel angle and the steering gear ratio is determined as the vehicle's front wheel angle.

[0017] In one embodiment, the preset parameters also include vehicle speed and rack force detection value. Obtaining the preset parameters of the vehicle includes:

[0018] The vehicle speed is obtained from the communication network, and the rack force detection value output by the front wheel actuator model is obtained.

[0019] In one embodiment, the rack force estimate for the current cycle is calculated based on the preset parameters and the estimated road adhesion coefficient for the current cycle, including:

[0020] Inputting the front wheel steering angle, vehicle speed and estimated road adhesion coefficient into a front axle lateral force calculation module to determine the front axle lateral force;

[0021] The front axle lateral force, vehicle tire parameters, suspension parameters and steering system parameters are input into the rack force calculation module to calculate the rack force estimate corresponding to the road adhesion coefficient estimate.

[0022] In one embodiment, adjusting the rack force estimate according to a preset feedback gain includes:

[0023] Aligning the rack force estimation value and the rack force detection value in the time domain;

[0024] determining a residual between the rack force estimate and the rack force detection value after the alignment operation;

[0025] Multiplying the residual by the preset feedback gain to obtain the road adhesion coefficient correction;

[0026] Correcting the estimated road adhesion coefficient value according to the road adhesion coefficient correction amount;

[0027] Updating the rack force estimate using the corrected road adhesion estimate;

[0028] The above steps are executed repeatedly until the difference between the rack force estimation value and the rack force detection value is less than the preset difference.

[0029] In one embodiment, determining a reference hand torque corresponding to the rack force estimate includes:

[0030] Performing a weighted summation on the rack force estimation value and the rack force detection value to obtain the final rack force;

[0031] The reference hand torque corresponding to the final rack force is determined to be the reference hand torque corresponding to the rack force estimate.

[0032] In one embodiment, the method further comprises,

[0033] Assign the estimated value of the road adhesion coefficient to the front axle tire model in the vehicle model;

[0034] When the rear axle tire model in the vehicle model is a nonlinear model, a preset road adhesion coefficient value is assigned to the rear axle tire model, wherein the preset road adhesion coefficient value is greater than a maximum value of the estimated road adhesion coefficient value.

[0035] The present application also provides a reference hand torque calculation device, comprising:

[0036] an acquisition module, configured to acquire preset parameters of the vehicle for a current cycle, the preset parameters including the front wheel steering angle and an estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating the rack force based on the preset vehicle parameters acquired in the previous cycle and the front wheel actuator rack force of the current cycle;

[0037] a calculation module, configured to calculate an estimated rack force value of the current cycle based on preset parameters of the current cycle and an estimated road adhesion coefficient;

[0038] an adjustment module, configured to adjust the rack force estimate value according to a preset feedback gain when the difference between the rack force estimate value and the rack force detection value in the current cycle is greater than a preset value, wherein the preset feedback gain is configured to reduce the difference between the rack force estimate value and the rack force detection value by adjusting the rack force;

[0039] The determination module is used to determine the reference hand torque corresponding to the rack force estimation value and output the reference hand torque to the hand torque closed-loop controller.

[0040] In one embodiment, the front wheel angle of the vehicle in the preset parameters of the vehicle includes:

[0041] When the vehicle steering wheel is detected to be turning, the steering wheel angle is obtained;

[0042] The product of the steering wheel angle and the steering gear ratio is determined as the vehicle's front wheel angle.

[0043] In one embodiment, the preset parameters also include vehicle speed and rack force detection values, and the acquisition module includes:

[0044] The acquisition submodule is used to obtain the vehicle speed from the communication network and obtain the rack force detection value output by the front wheel actuator model.

[0045] In one embodiment, the computing module includes:

[0046] a first determining submodule, configured to input the front wheel steering angle, the vehicle speed, and the estimated road adhesion coefficient into the front axle lateral force calculation module to determine the front axle lateral force;

[0047] The calculation submodule is used to input the front axle lateral force, vehicle tire parameters, suspension parameters and steering system parameters into the rack force calculation module to calculate the rack force estimation value corresponding to the road adhesion coefficient estimation value.

[0048] In one embodiment, the adjustment module includes:

[0049] an alignment submodule, configured to align the rack force estimation value with the rack force detection value in the time domain;

[0050] a determination submodule for determining a residual between the rack force estimation value and the rack force detection value after the alignment operation;

[0051] a multiplication submodule, configured to multiply the residual by a preset feedback gain to obtain a road adhesion coefficient correction;

[0052] A correction submodule, configured to correct the estimated road adhesion coefficient according to a road adhesion coefficient correction amount;

[0053] an updating submodule, configured to update the rack force estimation value by using the corrected road adhesion coefficient estimation value;

[0054] The loop submodule is used to loop through the above steps until the difference between the rack force estimation value and the rack force detection value is less than a preset difference.

[0055] In one embodiment, the determination module includes:

[0056] A weighting module, configured to perform weighted summation on the rack force estimation value and the rack force detection value to obtain a final rack force;

[0057] The third determining submodule is configured to determine that the reference hand torque corresponding to the final rack force is the reference hand torque corresponding to the rack force estimation value.

[0058] In one embodiment, the apparatus further comprises:

[0059] An assignment module is used to assign an estimated road adhesion coefficient value to a front axle tire model in a vehicle model; and is also used to assign a preset road adhesion coefficient value to the rear axle tire model when the rear axle tire model in the vehicle model is a nonlinear model, wherein the preset road adhesion coefficient value is greater than the maximum value of the estimated road adhesion coefficient value.

[0060] The present application also provides a reference hand torque calculation system, comprising:

[0061] at least one processor; and,

[0062] a memory communicatively connected to at least one processor; wherein,

[0063] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor to implement the reference hand torque calculation method described in any of the above embodiments.

[0064] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the reference hand torque calculation system, the reference hand torque calculation system can implement the reference hand torque calculation method described in any of the above embodiments.

[0065] The present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the reference hand torque calculation method described in any of the above embodiments.

[0066] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0067] The technical solution of the present application is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0069] FIG1 is a flow chart of a method for calculating a reference hand torque in one embodiment of the present application;

[0070] FIG2 is a schematic structural diagram of a reference hand torque calculation device according to an embodiment of the present application;

[0071] FIG3 is a schematic diagram of the hardware structure of a reference hand torque calculation system in one embodiment of the present application. DETAILED DESCRIPTION

[0072] The following describes the embodiments of the present application in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0073] FIG1 is a flow chart of a method for calculating a reference hand torque in an embodiment of the present application. As shown in FIG1 , the method can be implemented as follows: S101 to S104:

[0074] In step S101, the preset parameters of the vehicle for the current cycle are obtained, the preset parameters including the front wheel steering angle and the estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating the rack force based on the preset vehicle parameters obtained in the previous cycle and the front wheel actuator rack force of the current cycle;

[0075] In step S102, the rack force estimation value of the current cycle is calculated based on the preset parameters of the current cycle and the estimated road adhesion coefficient;

[0076] In step S103, when the difference between the rack force estimation value and the rack force detection value in the current cycle is greater than a preset value, the rack force estimation value is adjusted according to a preset feedback gain, wherein the preset feedback gain is used to reduce the difference between the rack force estimation value and the rack force detection value by adjusting the rack force;

[0077] In step S104 , a reference hand torque corresponding to the rack force estimation value is determined, and the reference hand torque is output to a hand torque closed-loop controller.

[0078] In this application, preset parameters for the vehicle's current cycle are obtained. The preset parameters include the vehicle's front wheel angle and an estimated road adhesion coefficient. The estimated road adhesion coefficient is obtained by calculating the rack force based on the vehicle's preset parameters obtained in the previous cycle and the front wheel actuator rack force in the current cycle. In one embodiment of this application, the vehicle's front wheel angle in the preset parameters includes: obtaining the steering wheel angle when the vehicle's steering wheel is detected to be rotating; determining the product of the steering wheel angle and the steering gear ratio as the vehicle's front wheel angle. Alternatively, the corresponding front wheel angle is obtained by looking up the steering wheel angle in a table.

[0079] Since the vehicle's front wheel angle is used for calculation, the noise caused by the calculation of the motor torque and rack position and / or rack speed of the front wheel actuator is avoided, and the hand feel oscillation phenomenon in the road feel feedback is avoided. In addition, the preset parameters also include vehicle speed and rack force detection values, wherein the vehicle speed can be obtained by the ECU (Electronic Control Unit) that measures the vehicle speed. Since the front wheel actuator model calculates the reference hand torque, this method can accurately calculate the reference hand torque at all vehicle speeds. Therefore, the corresponding rack force obtained by the front wheel actuator model can be used as the rack force detection value.

[0080] The rack force estimate of the current cycle is calculated based on the preset parameters of the current cycle and the estimated road adhesion coefficient. First, the preset parameters are input into the road adhesion coefficient estimation module, and the road adhesion coefficient estimated value output by the road adhesion coefficient estimation module is obtained. Then, the rack force estimate of the current cycle is calculated based on the preset parameters of the current cycle and the estimated road adhesion coefficient.

[0081] To accurately estimate the reference hand torque and provide accurate feedback on road conditions when the road adhesion coefficient changes, this application uses the front wheel angle, vehicle speed, and rack force detection values ​​as inputs to the road adhesion coefficient estimation module to obtain an estimated road adhesion coefficient. Because the front wheel actuator has an inevitable lag in responding to the steering wheel angle, the vehicle model calculated based on the steering wheel angle will lead the front wheel actuator. Without phase lag processing, there will be time differences between the multiple inputs to the road adhesion coefficient estimation, preventing correct operation. In another embodiment of the present application, phase lag processing is performed on the internal state variables of the vehicle model and the vehicle model rack force. The phase lag is the phase lag between the actual rack position of the front wheel actuator and the steering wheel angle. For example, a frequency-dependent lag time can be used to accurately model the phase lag based on the measured response characteristics of the front wheel actuator to the steering wheel angle. Alternatively, a fixed lag characteristic setting, such as a specific lag time, can be used. The purpose of phase lag processing is to ensure that the phase of the vehicle model internal state and rack force is consistent with the phase of the front wheel actuator rack force. The road adhesion coefficient estimation module estimates the road adhesion coefficient of the front axle based on the front wheel actuator rack force, the phase-lagged vehicle model internal state, and the phase-lagged vehicle model rack force. In some embodiments, an extended Kalman filter is used for estimation, with the road adhesion coefficient as a state variable with a derivative of zero. The road adhesion coefficient state variable is estimated online based on the difference (residual) between the front wheel actuator rack force and the phase-lagged vehicle model rack force. In this process, the phase-lagged vehicle model internal state is used to calculate the feedback gain of the residual. The estimated road adhesion coefficient of the front axle is typically a value between 0.1 and 1.0. Other methods for estimating the road adhesion coefficient may also be used, such as the Romberg observer and the least squares method. Since the road surfaces on which the front and rear axles of the vehicle are located will not differ, in this application, the road adhesion coefficient estimated based on the rack force residual is only used to calculate the front axle lateral force.

[0082] Specifically, after obtaining the estimated road adhesion coefficient, the front wheel angle, vehicle speed, and estimated road adhesion coefficient are input into the front axle lateral force calculation module to determine the front axle lateral force. The front axle lateral force, vehicle tire parameters, suspension parameters, and steering system parameters are then input into the rack force calculation module to calculate the rack force estimate corresponding to the estimated road adhesion coefficient. The front axle lateral force calculation module and the rack force calculation module can be derived using a variety of calculation methods available in existing vehicle models, and this application does not limit this.

[0083] When the difference between the rack force estimation value and the rack force detection value in the current cycle is greater than a preset value, the rack force estimation value is adjusted according to a preset feedback gain, wherein the preset feedback gain is used to reduce the difference between the rack force estimation value and the rack force detection value by adjusting the rack force.

[0084] First, the rack force estimate and rack force detection values ​​are aligned in the time domain. As mentioned above, due to the inevitable lag in the front wheel actuator's response to the steering wheel angle, a phase lag is applied to the vehicle model's internal state variables and the vehicle model's rack force. The phase lag is the phase lag between the front wheel actuator's actual rack position and the steering wheel angle. Next, the residual between the rack force estimate and the rack force detection value after the alignment is multiplied by a preset feedback gain to obtain a road adhesion coefficient correction. Specifically, based on the phase-lag internal state variables of the vehicle model, the feedback gain corresponding to these internal state variables is calculated according to the principle of the extended Kalman filter, and the road adhesion coefficient estimate is adjusted for the current cycle. The road adhesion coefficient estimate is then corrected based on the road adhesion coefficient correction. The rack force estimate is then updated using the corrected road adhesion coefficient estimate. These steps are repeated until the difference between the rack force estimate and the rack force detection value is less than a preset difference. It should be noted that the size of the feedback gain can be adjusted by changing the design parameters of the extended Kalman filter. The design parameters of the extended Kalman filter are determined in the preliminary test to make the feedback gain of appropriate size so that the difference between the rack force estimation value and the rack force detection value after adjustment for at least a preset number of cycles is less than a preset value, thereby ensuring the smoothness of the adjustment process.

[0085] A reference hand torque corresponding to the rack force estimate is determined and output to a hand torque closed-loop controller. Specifically, a weighted summation of the rack force estimate and the rack force detection value is performed to obtain a final rack force. The reference hand torque corresponding to the final rack force is determined to be the reference hand torque corresponding to the rack force estimate.

[0086] An estimated road adhesion coefficient is assigned to the front axle tire model in the vehicle model. When the rear axle tire model in the vehicle model is a nonlinear model, a preset road adhesion coefficient value is assigned to the rear axle tire model. In this application, the estimated road adhesion coefficient is used only in the front axle tire model to calculate the front axle lateral force, making the front axle lateral force estimation result adaptive to changes in road adhesion conditions. The rear axle lateral force is calculated using a fixed road adhesion coefficient (e.g., μ ≥ 1.5) or a linear tire model that is independent of the road adhesion coefficient, ensuring that the vehicle model does not diverge under various road conditions and dynamic operating conditions. The accurately estimated front axle lateral force is then used to calculate the rack force, making the rack force estimation result adaptive to changes in road adhesion conditions. This ensures the accuracy of the rack force estimation result under various road conditions and dynamic operating conditions while also ensuring that the vehicle model calculation results do not diverge. Specifically, the front and rear axle tire slip angles are calculated based on the front wheel angle, vehicle speed, and internal state of the vehicle model and output to the front and rear axle tire models. The front axle tire model calculates the front axle lateral force based on the estimated road adhesion coefficient and the front axle tire slip angle, and outputs it to the vehicle model's internal state integrator. The front axle tire model must be a nonlinear tire model, such as the Dugoff tire model or the Magic formula tire model. In some embodiments, the Dugoff tire model is used, as it has a simple structure, requires minimal computation, and meets the accuracy requirements for rack force estimation. Because the road adhesion coefficient is estimated based on the difference between the vehicle model rack force and the front wheel actuator rack force, the front axle lateral force calculated based on this estimated road adhesion coefficient ensures that the vehicle model rack force is consistent with the front wheel actuator rack force in magnitude. This effectively reflects the actual road adhesion state on roads with varying adhesion. The rear axle tire model calculates the rear axle lateral force based on the rear axle tire slip angle. According to vehicle dynamics principles, vehicle instability is caused by the lateral force provided by the rear axle reaching saturation before the front axle. Therefore, the upper limit of the lateral force of the rear axle tire model is significantly greater than that of the front axle tire model, ensuring that the internal state does not diverge during computational integration of the vehicle model's internal state. In some embodiments, a linear tire model is used, that is, the tire lateral force increases linearly with the tire slip angle and is not limited by the road adhesion coefficient. A nonlinear tire model, such as the Dugoff tire model or the Magic formula tire model, may also be used. In this case, the adhesion coefficient of the nonlinear tire model is set to a relatively large fixed value (greater than the maximum value of the front axle road adhesion coefficient, a typical value is 1.5), making it difficult for it to reach a saturated state of lateral force, and also playing a role in avoiding the divergence of the integral of the internal state of the vehicle model. The vehicle model internal state integrator calculates the derivative value of the internal state (lateral velocity, yaw angular velocity) based on the lateral forces of the front and rear axles, combined with information such as vehicle mass and moment of inertia, and integrates it to obtain the internal state of the vehicle model.

[0087] The beneficial effects of the present application are: obtaining preset parameters of the vehicle, wherein the preset parameters include at least the front wheel steering angle of the vehicle; then, inputting the preset parameters into a road adhesion coefficient estimation module, and obtaining a road adhesion coefficient estimation value output by the road adhesion coefficient estimation module; further calculating a rack force estimation value of the current cycle based on the preset parameters and the road adhesion coefficient estimation value of the current cycle, and adjusting the rack force estimation value according to a preset feedback gain when the difference between the rack force estimation value and the rack force detection value is greater than a preset value, wherein the preset feedback gain is used to reduce the difference between the rack force estimation value and the rack force detection value by adjusting the rack force, determine a reference hand torque corresponding to the rack force estimation value, and output the reference hand torque to a hand torque closed-loop controller. Since the collected parameter value is the front wheel angle calculated based on the steering wheel angle, the noise in the reference hand torque calculation is reduced. In addition, the accuracy of the estimated value is improved by continuously adjusting the rack force estimate and the detection value, thereby improving the accuracy of the reference hand torque calculation. And because the rack force estimate is adjusted for a preset number of cycles, the sudden change of the reference hand torque caused by excessive adjustment is further avoided, thereby improving the smoothness of the road feel feedback.

[0088] In one embodiment, the front wheel angle of the vehicle in the preset parameters of the vehicle in step S101 includes:

[0089] In step A1, when the vehicle steering wheel is detected to be rotating, the steering wheel angle is obtained;

[0090] In step A2, the product of the steering wheel angle and the steering gear ratio is determined as the vehicle front wheel angle.

[0091] In one embodiment, the preset parameters further include vehicle speed and rack force detection value, and the above step S101 can be implemented as follows:

[0092] The vehicle speed is obtained from the communication network, and the rack force detection value output by the front wheel actuator model is obtained.

[0093] In one embodiment, the above step S104 may be implemented as the following steps B1-B2:

[0094] In step B1, the front wheel steering angle, vehicle speed and estimated road adhesion coefficient are input into a front axle lateral force calculation module to determine the front axle lateral force;

[0095] In step B2, the front axle lateral force, vehicle tire parameters, suspension parameters and steering system parameters are input into the rack force calculation module to calculate the rack force estimation value corresponding to the road adhesion coefficient estimation value.

[0096] In one embodiment, the above step S105 may be implemented as the following steps C1-C6:

[0097] In step C1, the rack force estimation value and the rack force detection value are aligned in the time domain;

[0098] In step C2, a residual difference between the rack force estimation value and the rack force detection value after the alignment operation is determined;

[0099] In step C3, the residual error is multiplied by a preset feedback gain to obtain a road adhesion coefficient correction value;

[0100] In step C4, the estimated road adhesion coefficient value is corrected according to the road adhesion coefficient correction amount;

[0101] In step C5, the rack force estimate is updated using the corrected road adhesion coefficient estimate.

[0102] In step C6 , the above steps C1 - C5 are executed in a loop until the difference between the rack force estimation value and the rack force detection value is smaller than a preset difference.

[0103] In one embodiment, the above step S106 may be implemented as the following steps D1-D2:

[0104] In step D1, a weighted sum is performed on the rack force estimation value and the rack force detection value to obtain a final rack force;

[0105] In step D2, the reference hand torque corresponding to the final rack force is determined as the reference hand torque corresponding to the rack force estimation value.

[0106] In one embodiment, the method may also be implemented as follows:

[0107] In step E1, the estimated value of the road adhesion coefficient is assigned to the front axle tire model in the vehicle model;

[0108] In step E2, when the rear axle tire model in the vehicle model is a nonlinear model, a preset road adhesion coefficient value is assigned to the rear axle tire model, wherein the preset road adhesion coefficient value is greater than the maximum value of the estimated road adhesion coefficient value.

[0109] It should be noted that the rear axle tire model can be either linear or nonlinear. The rear axle tire model calculates the rear axle lateral force based on the rear tire slip angle. According to vehicle dynamics principles, vehicle instability is caused by the lateral force provided by the rear axle reaching saturation before that of the front axle. Therefore, the lateral force upper limit of the rear axle tire model is significantly greater than that of the front axle tire model, ensuring that the internal state of the vehicle model does not diverge during computational integration.

[0110] In this application, when a linear tire model is selected as the rear axle tire model, the tire lateral force increases linearly with the tire slip angle and is not limited by the road adhesion coefficient. When a nonlinear tire model is selected, a preset road adhesion coefficient value is assigned to the rear axle tire model. The preset road adhesion coefficient value is greater than the maximum estimated road adhesion coefficient value. For example, setting the preset road adhesion coefficient value to 1.5 makes it difficult for the rear axle tire model to reach lateral force saturation and also helps to prevent divergence of the vehicle model's internal state integral.

[0111] FIG2 is a schematic diagram of the structure of a reference hand torque calculation device in one embodiment of the present application. As shown in FIG2 , the device includes:

[0112] an acquisition module 201 for acquiring preset parameters of the vehicle for a current cycle, wherein the preset parameters include a front wheel steering angle and an estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating a rack force based on the preset vehicle parameters acquired in a previous cycle and a front wheel actuator rack force of the current cycle;

[0113] A calculation module 202 is configured to calculate an estimated rack force value for the current cycle based on preset parameters and an estimated road adhesion coefficient for the current cycle;

[0114] an adjustment module 203 configured to adjust the rack force estimate value according to a preset feedback gain when the difference between the rack force estimate value and the rack force detection value in the current cycle is greater than a preset value, wherein the preset feedback gain is configured to reduce the difference between the rack force estimate value and the rack force detection value by adjusting the rack force;

[0115] The determination module 204 is configured to determine a reference hand torque corresponding to the rack force estimation value, and output the reference hand torque to a hand torque closed-loop controller.

[0116] In one embodiment, the front wheel angle of the vehicle in the preset parameters of the vehicle includes:

[0117] When the vehicle steering wheel is detected to be turning, the steering wheel angle is obtained;

[0118] The product of the steering wheel angle and the steering gear ratio is determined as the vehicle's front wheel angle.

[0119] In one embodiment, the preset parameters also include vehicle speed and rack force detection values, and the acquisition module includes:

[0120] The acquisition submodule is used to obtain the vehicle speed from the communication network and obtain the rack force detection value output by the front wheel actuator model.

[0121] In one embodiment, the computing module includes:

[0122] a first determining submodule, configured to input the front wheel steering angle, the vehicle speed, and the estimated road adhesion coefficient into the front axle lateral force calculation module to determine the front axle lateral force;

[0123] The calculation submodule is used to input the front axle lateral force, vehicle tire parameters, suspension parameters and steering system parameters into the rack force calculation module to calculate the rack force estimation value corresponding to the road adhesion coefficient estimation value.

[0124] In one embodiment, the adjustment module includes:

[0125] an alignment submodule, configured to align the rack force estimation value with the rack force detection value in the time domain;

[0126] a multiplication submodule, configured to multiply the residual of the rack force estimation value and the rack force detection value after the alignment operation by a preset feedback gain to obtain a road adhesion coefficient correction value;

[0127] an adjustment submodule, configured to adjust the estimated road adhesion coefficient value of the previous cycle according to the road adhesion coefficient correction amount, and determine the adjusted road adhesion coefficient as the estimated road adhesion coefficient value of the current cycle;

[0128] The second determining submodule is configured to determine a rack force estimation value corresponding to a road adhesion coefficient estimation value of a current cycle, and adjust the current rack force estimation value to the rack force estimation value corresponding to the road adhesion coefficient estimation value of the current cycle.

[0129] In one embodiment, the determination module includes:

[0130] A weighting module, configured to perform weighted summation on the rack force estimation value and the rack force detection value to obtain a final rack force;

[0131] The third determining submodule is configured to determine that the reference hand torque corresponding to the final rack force is the reference hand torque corresponding to the rack force estimation value.

[0132] In one embodiment, the apparatus further comprises:

[0133] An assignment module is used to assign an estimated road adhesion coefficient value to a front axle tire model in a vehicle model; and is also used to assign a preset road adhesion coefficient value to the rear axle tire model when the rear axle tire model in the vehicle model is a nonlinear model, wherein the preset road adhesion coefficient value is greater than the maximum value of the estimated road adhesion coefficient value.

[0134] FIG3 is a schematic diagram of the hardware structure of a reference hand torque calculation system in one embodiment of the present application. As shown in FIG3 , the reference hand torque calculation system includes:

[0135] at least one processor 320; and,

[0136] A memory 304 in communication with at least one processor 320; wherein,

[0137] The memory 304 stores instructions that can be executed by at least one processor 320. The instructions are executed by at least one processor 320 to implement the reference hand torque calculation method described in any of the above embodiments.

[0138] 3 , the reference hand torque calculation system 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power supply component 306 , and an input / output (I / O) interface 312 .

[0139] The processing component 302 generally controls the overall operation of the reference hand torque calculation system 300. The processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 302 may include one or more modules to facilitate interaction between the processing component 302 and other components.

[0140] The memory 304 is configured to store various types of data to support the operation of the reference hand torque calculation system 300. Examples of such data include instructions for any application or method operating on the reference hand torque calculation system 300, such as text, images, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0141] The power supply assembly 306 provides power to various components of the reference hand torque calculation system 300. The power supply assembly 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the onboard control system 300.

[0142] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0143] In an exemplary embodiment, the reference hand torque calculation system 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the reference hand torque calculation method described in any of the above embodiments.

[0144] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the reference hand torque calculation system, the reference hand torque calculation system can implement the reference hand torque calculation method described in any of the above embodiments.

[0145] The present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the reference hand torque calculation method described in any of the above embodiments.

[0146] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0147] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0148] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0150] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A reference hand torque calculation method, characterized in that: include: Obtaining preset parameters of the vehicle for a current cycle, the preset parameters including a front wheel steering angle and an estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating a rack force based on the preset vehicle parameters obtained in a previous cycle and a front wheel actuator rack force of the current cycle; Calculating an estimated rack force value of the current cycle based on the preset parameters and the estimated road adhesion coefficient of the current cycle; When the difference between the rack force estimation value and the rack force detection value in the current cycle is greater than a preset value, adjusting the rack force estimation value according to a preset feedback gain, wherein the preset feedback gain is used to reduce the difference between the rack force estimation value and the rack force detection value by adjusting the rack force; A reference hand torque corresponding to the rack force estimation value is determined, and the reference hand torque is output to a hand torque closed-loop controller.

2. The method according to claim 1, wherein The vehicle front wheel angle in the preset parameters of the vehicle includes: When the vehicle steering wheel is detected to be turning, the steering wheel angle is obtained; The product of the steering wheel angle and the steering gear ratio is determined as the vehicle's front wheel angle.

3. The method according to any one of claims 1-2, characterized in that The preset parameters also include vehicle speed and rack force detection value, and obtaining the preset parameters of the vehicle includes: The vehicle speed is obtained from the communication network, and the rack force detection value output by the front wheel actuator model is obtained.

4. The method according to any one of claims 1 to 3, characterized in that: The calculating the rack force estimation value of the current cycle according to the preset parameters of the current cycle and the road adhesion coefficient estimation value includes: Inputting the front wheel steering angle, vehicle speed and estimated road adhesion coefficient into a front axle lateral force calculation module to determine the front axle lateral force; The front axle lateral force, vehicle tire parameters, suspension parameters and steering system parameters are input into the rack force calculation module to calculate the rack force estimate corresponding to the road adhesion coefficient estimate.

5. The method according to any one of claims 1 to 4, characterized in that: The adjusting the rack force estimation value according to the preset feedback gain includes: Aligning the rack force estimation value and the rack force detection value in the time domain; determining a residual between the rack force estimate and the rack force detection value after the alignment operation; Multiplying the residual by a preset feedback gain to obtain a road adhesion coefficient correction amount; Correcting the estimated road adhesion coefficient value according to the road adhesion coefficient correction amount; Updating the rack force estimate using the corrected road adhesion estimate; The above steps are executed repeatedly until the difference between the rack force estimation value and the rack force detection value is less than the preset difference.

6. The method according to any one of claims 1 to 5, characterized in that: Determining a reference hand torque corresponding to the rack force estimation value includes: Performing a weighted summation on the rack force estimation value and the rack force detection value to obtain the final rack force; The reference hand torque corresponding to the final rack force is determined to be the reference hand torque corresponding to the rack force estimate.

7. The method according to any one of claims 1 to 6, wherein: The method further comprises: Assign the estimated value of the road adhesion coefficient to the front axle tire model in the vehicle model; When the rear axle tire model in the vehicle model is a nonlinear model, a preset road adhesion coefficient value is assigned to the rear axle tire model, wherein the preset road adhesion coefficient value is greater than a maximum value of the estimated road adhesion coefficient value.

8. A reference hand torque calculation device, characterized in that: include: an acquisition module, configured to acquire preset parameters of the vehicle for a current cycle, the preset parameters including a front wheel steering angle and an estimated road adhesion coefficient of the vehicle, wherein the estimated road adhesion coefficient is obtained by calculating a rack force based on the preset vehicle parameters acquired in a previous cycle and a front wheel actuator rack force of the current cycle; a calculation module, configured to calculate an estimated rack force value of the current cycle based on the preset parameters and the estimated road adhesion coefficient of the current cycle; an adjustment module, configured to adjust the rack force estimate value according to a preset feedback gain when the difference between the rack force estimate value and the rack force detection value in the current cycle is greater than a preset value, wherein the preset feedback gain is configured to reduce the difference between the rack force estimate value and the rack force detection value by adjusting the rack force; The determination module is used to determine a reference hand torque corresponding to the rack force estimation value, and output the reference hand torque to a hand torque closed-loop controller.

9. A reference hand torque calculation system, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the reference hand torque calculation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor corresponding to the reference hand torque calculation system, the reference hand torque calculation system can implement the reference hand torque calculation method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the reference hand torque calculation method according to any one of claims 1 to 7 are implemented.

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