Method and device for determining road adhesion coefficient, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a road adhesion coefficient determination method and device, a storage medium and electronic equipment, and is applied to the field of vehicle state observation, and the method comprises the steps: obtaining an initial road adhesion coefficient of a target vehicle under the condition that the target vehicle is in a target state; determining an upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle through a target algorithm, and determining a reference road adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; and minimizing a cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient to determine a target road adhesion coefficient of the target vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle state observation, and more particularly, to a method and device for determining road surface adhesion coefficient, a storage medium, and an electronic device. Background Art

[0002] The identification of road surface adhesion coefficient is a key link in modern vehicle control technology, aiming to provide real-time road condition information for electronic control devices such as ABS (antilock braking system) and ASR (acceleration slip regulation) to optimize the safety and stability of vehicle driving. The currently common road surface adhesion coefficient identification algorithm is to store the friction characteristic curves under different road surface types in a computing device, and by comparing the theoretically calculated wheel deceleration with the actually observed deceleration, find the most matching curve to identify the current road surface type. However, this algorithm performs poorly in low slip rate scenarios because at this time, the characteristic differences between different road surfaces are not obvious, and it is difficult to accurately distinguish them through small changes in deceleration, reducing the accuracy and reliability of identification.

[0003] That is to say, when the current road surface adhesion coefficient identification algorithm faces the effective estimation of un-trained road surfaces and low slip rate working conditions, there are key problems such as low accuracy and limited precision.

[0004] In view of the problem of low accuracy of the current road surface adhesion coefficient identification algorithm in the effective estimation of un-trained road surfaces and low slip rate working conditions in the prior art, no effective solution has been proposed yet.

[0005] Therefore, it is necessary to improve the related technology to overcome the defects in the related technology. Summary of the Invention

[0006] The embodiments of the present application provide a method and device for determining road surface adhesion coefficient, a storage medium, and an electronic device, so as to at least solve the problem of low accuracy of the current road surface adhesion coefficient identification algorithm in the effective estimation of un-trained road surfaces and low slip rate working conditions in the prior art.

[0007] According to an embodiment of the present application, a method for determining road surface adhesion coefficient is provided, including: when a target vehicle is in a target state, obtaining an initial road surface adhesion coefficient of the target vehicle; determining an upper limit value and a lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determining a reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; minimizing a cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine a target road surface adhesion coefficient of the target vehicle.

[0008] In an exemplary embodiment, obtaining the initial road surface adhesion coefficient of the target vehicle includes: when the steering system of the target vehicle is in the first steering mode, controlling the steering system to output a target steering torque to control the front wheels of the target vehicle to perform a steering movement; after the front wheels of the target vehicle perform the steering movement, obtaining the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system; determining the ground friction resistance torque according to the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system, and determining the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0009] In an exemplary embodiment, determining the ground friction resistance torque according to the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system includes: determining the ground friction resistance torque through the following formula:

[0010] , where is the moment of inertia, is the angular acceleration, is the assist torque, is the self-aligning torque, is the ground friction resistance torque.

[0011] In an exemplary embodiment, obtaining the initial road surface adhesion coefficient of the target vehicle includes: when the steering system of the target vehicle is in the second steering mode and after manually controlling the front wheels of the target vehicle to perform a steering movement, obtaining the moment of inertia, angular acceleration, self-aligning torque, assist torque, and the steering wheel torque of the target vehicle of the steering system; determining the ground friction resistance torque according to the moment of inertia, angular acceleration, assist torque, and the steering wheel torque of the target vehicle of the steering system, and determining the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0012] In an exemplary embodiment, determining the ground friction resistance torque according to the moment of inertia, angular acceleration, assist torque, and the steering wheel torque of the target vehicle of the steering system includes: determining the ground friction resistance torque through the following formula:

[0013] , where is the moment of inertia, is the angular acceleration, is the steering wheel torque, is the assist torque, is the self-aligning torque, is the ground friction resistance torque.

[0014] In an exemplary embodiment, determining the initial road surface adhesion coefficient according to the ground friction resistance moment includes: determining the initial road surface adhesion coefficient through the following formula:

[0015] , where is the ground friction resistance moment, f is the initial road surface adhesion coefficient, G is the axle load of the target vehicle, and P is the tire inflation pressure of the target vehicle.

[0016] In an exemplary embodiment, determining the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm includes: obtaining the vertical load at the wheel end, the longitudinal slip ratio at the wheel end, the tire side slip angle, and a plurality of friction coefficients of the wheels of the target vehicle, where the plurality of friction coefficients are obtained by evenly spreading points at a certain interval within a target friction parameter range; calculating the longitudinal force at the wheel end corresponding to each friction coefficient according to the vertical load at the wheel end, the longitudinal slip ratio at the wheel end, the tire side slip angle, and the plurality of friction coefficients; determining a longitudinal force curve according to the longitudinal force at the wheel end corresponding to each friction coefficient; and determining the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle according to the longitudinal force curve.

[0017] In an exemplary embodiment, determining the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle according to the longitudinal force curve includes: determining whether the longitudinal force curve has self-separation; in the case where the longitudinal force curve has self-separation, determining the target longitudinal force at the wheel end of the target vehicle according to the dynamic equilibrium equation; determining the position of the target longitudinal force in the longitudinal force curve, and determining the upper limit value and the lower limit value of the road surface adhesion coefficient according to the position.

[0018] In an exemplary embodiment, after determining whether the longitudinal force curve has self-separation, the method further includes: in the case where the longitudinal force curve does not have self-separation, determining the utilization adhesion coefficient of the target vehicle; determining the distances between the utilization adhesion coefficient and the utilization adhesion coefficients corresponding to a plurality of characteristic road surfaces; determining the characteristic road surface with the smallest distance, and determining the peak adhesion coefficient of the characteristic road surface with the smallest distance; and determining the peak adhesion coefficient as the reference road surface adhesion coefficient.

[0019] In an exemplary embodiment, determining whether there is self-separation in the longitudinal force curve includes: determining the wheel-end longitudinal force corresponding to a target friction coefficient, where the target friction coefficient is one of the multiple friction coefficients; calculating the error values between the wheel-end longitudinal forces corresponding to the other friction coefficients and the wheel-end longitudinal force corresponding to the target friction coefficient, where the other friction coefficients are the friction coefficients among the multiple friction coefficients except the target friction coefficient; determining the number of error values greater than a preset error value; determining that there is self-separation in the longitudinal force curve when the number is greater than a preset threshold; and determining that there is no self-separation in the longitudinal force curve when the number is less than or equal to the preset threshold.

[0020] In an exemplary embodiment, before minimizing the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle, the method further includes: establishing a measurement equation according to the dynamic model of the target vehicle; determining the observation error penalty of the cost function according to the measurement equation, determining the control quantity penalty of the cost function according to the control quantity of the target vehicle, and determining the reference offset penalty of the cost function according to the state quantity of the target vehicle; and establishing the cost function according to the observation error penalty, the control quantity penalty, and the reference offset penalty.

[0021] In an exemplary embodiment, before minimizing the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle, the method further includes: determining the observation error penalty term of the cost function according to the actual observation value of the target vehicle and the current road surface adhesion coefficient, determining the control quantity penalty term of the cost function according to the input value of the target vehicle, and determining the reference offset penalty of the cost function according to the predicted observation value of the target vehicle and the reference road surface adhesion coefficient; establishing the cost function according to the observation error penalty term, the control quantity penalty term, and the reference offset penalty term, and establishing the constraint conditions of the cost function, where the constraint conditions include: a state quantity update constraint term, an observation model constraint term, and a road surface adhesion coefficient upper and lower limit constraint term.

[0022] According to another embodiment of the present application, there is provided a device for determining a road surface adhesion coefficient, including: an acquisition module configured to acquire an initial road surface adhesion coefficient of the target vehicle when the target vehicle is in a target state; a first determination module configured to determine an upper limit value and a lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine a reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; a second determination module configured to minimize a cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient, so as to determine a target road surface adhesion coefficient of the target vehicle.

[0023] According to still another embodiment of the present application, there is also provided a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0024] According to still another embodiment of the present application, there is also provided an electronic device including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0025] According to still another embodiment of the present application, there is also provided a computer program product including a computer program, and the computer program realizes the steps in any one of the above method embodiments when being executed by a processor.

[0026] Through the present application, when the target vehicle is in a target state, an initial road surface adhesion coefficient of the target vehicle is acquired; an upper limit value and a lower limit value of the road surface adhesion coefficient of the target vehicle are determined through a target algorithm, and a reference road surface adhesion coefficient of the target vehicle is determined according to the upper limit value and the lower limit value; a cost function corresponding to the target vehicle is minimized according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient, so as to determine a target road surface adhesion coefficient of the target vehicle. In the embodiments of the present application, not only an effective initial estimate and upper and lower limit ranges are provided, but also through the introduction of a reference value and the setting of an optimization target, it is ensured that the algorithm can continuously and accurately update the estimate of the road surface adhesion coefficient under various complex and changing driving conditions, providing more reliable data support for the traction control, stability control, etc. of the vehicle. Therefore, the problem of low accuracy of the current road surface adhesion coefficient recognition algorithm in the face of effective estimation under un-trained road surfaces and low slip rate conditions can be solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0028] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0029] Figure 1 is the hardware structure block diagram of a computer device for a method of determining the road surface adhesion coefficient according to an embodiment of the present application;

[0030] Figure 2 is the flowchart of the method for determining the road surface adhesion coefficient according to an embodiment of the present application;

[0031] Figure 3 is the flowchart of estimating the adhesion coefficient using the steering system when stationary according to an embodiment of the present application;

[0032] Figure 4 is the flowchart of the uniform scatter point method and estimating the upper and lower limits of the adhesion coefficient according to an embodiment of the present application;

[0033] Figure 5 is the block diagram of the adhesion coefficient estimation fusion algorithm based on steering and vehicle dynamics model according to an embodiment of the present application;

[0034] Figure 6 is the force diagram of the vehicle according to an embodiment of the present application;

[0035] Figure 7 is the structure block diagram of the device for determining the road surface adhesion coefficient according to an embodiment of the present application. Detailed implementation manners

[0036] In the following, the embodiments of the present application will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0037] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence.

[0038] The method embodiments provided in the embodiments of the present application can be executed in a computer device or a similar computing device. Taking running on a computer device as an example, Figure 1 is the hardware structure block diagram of a computer device for a method of determining the road surface adhesion coefficient according to an embodiment of the present application. As Figure 1 shown, the computer device may include one or more ( Figure 1Only one processor 102 is shown (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data. Among them, the above computer device may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above computer device. For example, the computer device may further include more or fewer components than Figure 1 shown in, or have a different configuration from Figure 1 shown.

[0039] It should be noted that the above computer device can be understood as an in-vehicle device in a vehicle.

[0040] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method for determining the road surface adhesion coefficient in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely set relative to the processor 102, and these remote memories can be connected to the computer device through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer device. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (abbreviated as RF) module, which is used to communicate with the Internet wirelessly.

[0042] In this embodiment, a method for determining the road surface adhesion coefficient is provided. Figure 2 is a flowchart of the method for determining the road surface adhesion coefficient according to the embodiments of the present application. As Figure 2 shown, the process includes the following steps:

[0043] Step S202, when the target vehicle is in the target state, obtain the initial road surface adhesion coefficient of the target vehicle;

[0044] When the target vehicle is in a target state (such as stationary or a specific initial driving state), first obtain an initial road surface adhesion coefficient through a simple and fast method (such as the estimation based on the steering resistance torque). Furthermore, it provides a reasonable starting point for the cost function, avoiding the problem that it cannot be effectively estimated due to lack of sufficient discrimination under low slip rate conditions.

[0045] Step S204, determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value;

[0046] Use a target algorithm (for example, the uniform sampling method combined with the vehicle dynamics model) to determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle. The method based on the theoretical model and actual observation data in step S204 can provide an estimated range even in an untrained road surface or a low slip rate scenario, thus avoiding the limitation that the algorithm completely depends on a single model or signal feature.

[0047] Based on the upper limit value and the lower limit value, determine a reference road surface adhesion coefficient. In a low slip rate scenario, this reference value can be obtained by comparing the currently used adhesion coefficient with the used adhesion coefficient of the characteristic road surface. In a steering condition, the adhesion coefficient estimation based on the steering return torque is adopted. The introduction of the reference value provides a reference for the cost function, ensuring that the algorithm has a reliable basis for estimation even under low discrimination conditions.

[0048] Step S206, minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle.

[0049] Finally, take the initial road surface adhesion coefficient and the reference road surface adhesion coefficient as constraint conditions, combine the actually measured vehicle dynamic data (such as acceleration, yaw angular acceleration, etc.), and minimize the cost function through the MHE algorithm. The cost function not only considers the observation error and the smoothness of the control input, but also introduces the deviation penalty between the state estimation and the reference value. The MHE algorithm comprehensively considers various information sources during the estimation, including but not limited to the front wheel steering torque, the tire model prediction, and the vehicle kinematics model, so as to obtain a more accurate road surface adhesion coefficient estimation under various working conditions, especially in an untrained road surface and a low slip rate scenario.

[0050] Through the above steps, when the target vehicle is in the target state, obtain the initial road surface adhesion coefficient of the target vehicle; determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle. In the embodiments of the present application, not only an effective initial estimate and upper and lower limit ranges are provided, but also through the introduction of a reference value and the setting of an optimization target, it is ensured that the algorithm can continuously and accurately update the estimate of the road surface adhesion coefficient under various complex and changing driving conditions, providing more reliable data support for the traction control, stability control, etc. of the vehicle. Therefore, the problem of low accuracy of the current road surface adhesion coefficient identification algorithm in the face of effective estimation of an untrained road surface and a low slip rate working condition can be solved.

[0051] Optionally, step S202 can be implemented in the following manner: when the steering system of the target vehicle is in the first steering mode, control the steering system to output a target steering torque to control the front wheels of the target vehicle to perform a steering movement; after the front wheels of the target vehicle perform the steering movement, obtain the moment of inertia, angular acceleration, return torque, and assist torque of the steering system; determine the ground friction resistance torque according to the moment of inertia, angular acceleration, return torque, and assist torque of the steering system, and determine the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0052] In the embodiments of the present application, first, detect whether the steering system of the target vehicle is a steer-by-wire or EPS (electric power steering) system, that is, the first steering mode. If the steering system supports this control mode, the system will automatically control the steering system to output a target steering torque to prompt the front wheels to perform a steering movement. That is, create a dynamic working condition where the road surface friction can be observed when the vehicle is stationary.

[0053] After the front wheels perform the steering movement, collect key dynamic parameters such as the moment of inertia, angular acceleration, return torque, and assist torque of the steering system through the vehicle's VCU (vehicle control unit). The moment of inertia reflects the ability of the steering system to resist changes in angular acceleration, while the angular acceleration is directly related to the effect of the steering torque. The return torque and the assist torque respectively reflect the obstructive and helpful effects of the road surface and the steering system on the steering movement.

[0054] Based on the collected dynamic parameters, the system calculates the ground friction resistance torque. The magnitude of the friction resistance torque directly reflects the friction characteristics of the road surface, that is, the level of the road surface adhesion coefficient.

[0055] Finally, based on the calculated ground friction resistance torque and combined with an empirical formula or a mathematical model of the tire and the road surface, the initial road surface adhesion coefficient is determined. The initial road surface adhesion coefficient helps the algorithm quickly find a reasonable estimation range and reference value.

[0056] Through the above process, it is possible to effectively obtain the initial road surface adhesion coefficient through the dynamic characteristics of the steering system when the vehicle is stationary, overcoming the problem of low estimation accuracy of existing algorithms in untested road surfaces and low slip rate scenarios. This method not only has a lower cost, but also can provide a good starting point for subsequent road surface adhesion coefficient estimation during the initial stage of vehicle startup or under specific working conditions, such as in-place turning, thereby improving the robustness and accuracy of the entire estimation algorithm.

[0057] Optionally, determining the ground friction resistance torque according to the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system includes: determining the ground friction resistance torque through the following formula:

[0058] , where is the moment of inertia, is the angular acceleration, is the assist torque, is the self-aligning torque, is the ground friction resistance torque.

[0059] The moment of inertia is a physical quantity that measures an object's resistance to changes in rotational acceleration. For the steering system, it reflects the overall resistance of components such as the steering wheel, steering gear shaft, and knuckle arm to angular acceleration. In the formula, reflects the rotational effect of the steering system under the action of angular acceleration.

[0060] The angular acceleration is the rate of change of angular velocity of the steering system per unit time, which describes the change in rotational speed of the steering system when responding to the steering torque.

[0061] When the front wheels rotate, the frictional force between the road surface and the tire generates a torque that attempts to return the tire to a straight state, i.e., the self-aligning torque. It is closely related to the frictional characteristics of the road surface.

[0062] The assist torque is provided by the EPS (Electric Power Steering) system or the steer-by-wire system and is the torque used to assist the driver in steering. In the steering assist test in the stationary state, it is the active torque applied to the steering system.

[0063] The ground friction resistance torque is the resistance from the road surface encountered by the steering system during the steering process, which is determined by the adhesion characteristics of the tire and the road surface. When the vehicle is stationary, through the dynamic analysis of the steering system, this resistance torque can be indirectly estimated, and then the road surface adhesion coefficient can be inferred.

[0064] In an exemplary embodiment, obtaining the initial road surface adhesion coefficient of the target vehicle includes: after the steering system of the target vehicle is in the second steering mode and the front wheels of the target vehicle are controlled to perform a steering movement manually, obtaining the moment of inertia, angular acceleration, return torque, assist torque of the steering system, and the steering wheel torque of the target vehicle; determining the ground friction resistance torque according to the moment of inertia, angular acceleration, assist torque of the steering system, and the steering wheel torque of the target vehicle, and determining the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0065] When the vehicle steering system adopts the second steering mode of non-by-wire or electric power steering, the driver manually turns the steering wheel to drive the front wheels of the vehicle to perform a steering movement. At this time, the moment of inertia, angular acceleration, assist torque, return torque in the steering system, and the steering wheel torque applied by the driver can all be measured and recorded in real time by vehicle sensors and the control unit.

[0066] In the manual steering condition, the total torque during steering can be expressed as the sum of the steering wheel torque applied by the driver and the assist torque, used to overcome the resistance generated by the moment of inertia of the steering system, the return torque, and the ground friction resistance torque. By establishing a dynamic equilibrium equation, the ground friction resistance torque can be calculated. The specific equation is as follows:

[0067] , where is the moment of inertia, is the angular acceleration, is the steering wheel torque, is the assist torque, is the return torque, is the ground friction resistance torque.

[0068] That is, the ground friction resistance torque can be determined by the steering wheel torque, assist torque, return torque, and the product of the moment of inertia and angular acceleration of the steering system.

[0069] Once the ground friction resistance torque is obtained, next, it can be converted into an estimate of the initial road surface adhesion coefficient through a known physical model or empirical formula. It is determined based on the following formula:

[0070] , where is the ground friction resistance torque, f is the initial road surface adhesion coefficient, G is the axle load of the target vehicle, and P is the tire pressure of the target vehicle.

[0071] It should be noted that the ground friction resistance moment is obtained by measuring the dynamic characteristics of the steering system in a stationary state, which mainly reflects the magnitude of the frictional moment between the tire and the road surface.

[0072] The axle load of the target vehicle can be estimated through the total vehicle mass and the load distribution. The magnitude of the axle load affects the pressure on the contact area between the tire and the ground, thereby indirectly affecting the magnitude of the frictional force.

[0073] The tire pressure of the target vehicle has a direct impact on the deformation and contact area of the tire. The level of tire pressure will change the frictional characteristics between the tire and the ground.

[0074] In a stationary state, the ground friction resistance moment is mainly generated by the frictional force between the tire and the road surface. The relationship between the frictional force F and the axle load G can be expressed as: ;

[0075] The relationship between the contact area A and the tire pressure P can be expressed as: , , where r is the contact radius and K is a constant related to the tire size and material properties.

[0076] Therefore, the relationship between the contact radius r and the tire pressure P can be expressed as: ; According to the definition of the frictional moment, the frictional moment T f is equal to the frictional force F multiplied by the contact radius r: .

[0077] Substitute the expressions of F and r into the above formula: .

[0078] For the convenience of calculation and understanding, the weights of the above formula are further adjusted to obtain the following form: .

[0079] In the embodiment of the present application, the dynamic data of the steering system of the vehicle in a stationary state and the known vehicle parameters (such as axle load and tire pressure) are effectively utilized. By calculating the ground friction resistance moment, the initial road surface adhesion coefficient is further estimated, providing an important starting value for subsequent more complex and accurate road surface adhesion coefficient estimation algorithms.

[0080] In the embodiment of the present application, through the manual steering movement of the driver, combined with the dynamic parameters of the steering system and the static parameters of the vehicle (axle load and tire pressure), a method for obtaining the initial road surface adhesion coefficient under a non-by-wire steering system is provided. At the same time, it overcomes the deficiencies of the prior art in the face of untrained road surfaces and low slip rate working conditions, providing more reliable data support for vehicle stability control under complex road conditions.

[0081] Optionally, determining the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm includes: obtaining the wheel-end vertical load, wheel-end longitudinal slip ratio, tire sideslip angle, and multiple friction coefficients of the wheels of the target vehicle, where the multiple friction coefficients are obtained by uniformly distributing points at a certain interval within a target friction parameter range; calculating the wheel-end longitudinal force corresponding to each friction coefficient according to the wheel-end vertical load, wheel-end longitudinal slip ratio, tire sideslip angle, and the multiple friction coefficients; determining a longitudinal force curve according to the wheel-end longitudinal force corresponding to each friction coefficient; and determining the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle according to the longitudinal force curve.

[0082] In the embodiments of the present application, first, key parameters such as the wheel-end vertical load, wheel-end longitudinal slip ratio, and tire sideslip angle of the wheels of the target vehicle need to be collected. The vertical load reflects the magnitude of the vertical pressure at the contact point between the tire and the road surface. The longitudinal slip ratio describes the relative sliding degree between the tire and the road surface during longitudinal movement, and the sideslip angle represents the deflection angle caused by the lateral force on the tire.

[0083] Within the target friction parameter range (such as from 0.1 to 1.1), the system will uniformly distribute points at a certain interval to obtain multiple friction coefficient values. These distributed friction coefficients are used for subsequent tire model calculations. The purpose is to determine the estimated range of the road surface adhesion coefficient by observing the differences in tire longitudinal forces under different friction coefficients. Each distributed friction coefficient represents a hypothetical value of the possible road surface friction characteristics.

[0084] Based on the collected wheel-end vertical load, wheel-end longitudinal slip ratio, and tire sideslip angle, and multiple preset friction coefficients, the system will use a tire model (such as the HSRI model) to calculate the wheel-end longitudinal force corresponding to each friction coefficient. The tire model is a mathematical description based on the mechanical characteristics of the tire. By inputting the load, slip ratio, sideslip angle of the tire, and the assumed friction coefficient, information such as the longitudinal force and lateral force of the tire under different working conditions can be output.

[0085] Through the above calculations, the system will obtain a set of wheel-end longitudinal forces corresponding to different friction coefficients, and then draw a longitudinal force curve. This curve reflects the dependence relationship between the tire longitudinal force and the friction coefficient under specific load, slip ratio, and sideslip angle conditions.

[0086] By analyzing the longitudinal force curve, the system can determine which scatter points have friction coefficients closest to the actual value of the current tire longitudinal force. Under low slip rate conditions, the curve may be relatively flat, and the differences in longitudinal forces corresponding to different friction coefficients are not obvious. At this time, the upper and lower limits of the adhesion coefficient may be relatively wide. As the slip rate increases, the curve begins to separate, and the differences in longitudinal forces corresponding to different friction coefficients gradually increase. The system can more accurately determine the range of friction coefficients that best match the current tire longitudinal force, thereby determining the upper and lower limit values of the road surface adhesion coefficient.

[0087] Through the above steps, it is possible to provide an estimated range of road surface adhesion coefficient for the vehicle's electronic control system based on the tire model and vehicle dynamic parameters. Especially at the initial stage of vehicle startup or under complex road conditions, accurate estimation of the road surface adhesion coefficient can help the vehicle controller adjust strategies such as traction force distribution and braking force distribution more quickly, avoid vehicle out-of-control, and ensure safe driving.

[0088] In an exemplary embodiment, determining the upper and lower limit values of the road surface adhesion coefficient of the target vehicle according to the longitudinal force curve includes: determining whether there is self-separation in the longitudinal force curve; in the case where there is self-separation in the longitudinal force curve, determining the target wheel-end longitudinal force of the target vehicle according to the dynamic equilibrium equation; determining the position of the target wheel-end longitudinal force in the longitudinal force curve, and determining the upper and lower limit values of the road surface adhesion coefficient according to the position.

[0089] The tire longitudinal force curve describes the relationship between the longitudinal force (traction force or braking force) that the tire can generate at different slip rates and the road surface adhesion coefficient. On a road surface with a low adhesion coefficient, the longitudinal force of the tire decreases as the slip rate increases, while the change in longitudinal force on a road surface with a high adhesion coefficient is relatively gentle. Therefore, if there is a significant difference in the longitudinal force curve at a certain slip rate, that is, the self-separation phenomenon, it indicates that the force performance of the tire under different adhesion coefficients begins to be significantly different.

[0090] The dynamic equilibrium equation is the basis for analyzing vehicle kinematics and dynamics. It is based on Newton's second law and describes the relationship between parameters such as tire longitudinal force, tire slip rate, and wheel angular acceleration. After determining the self-separation of the longitudinal force curve, the target wheel-end longitudinal force under the current working condition of the target vehicle is calculated through the dynamic equilibrium equation.

[0091] Once the target wheel-end longitudinal force is obtained, the next step is to find the position corresponding to this force value in a preset set of tire longitudinal force curves. If this force value falls between the longitudinal force curves within a certain range, then the adhesion coefficients within this range are the estimated upper and lower limits that can be considered.

[0092] Based on the position of the target wheel-end longitudinal force in the longitudinal force curve, the upper and lower limit values of the road surface adhesion coefficient can be determined. Specifically, if the target wheel-end longitudinal force is located in a specific separation area of the curve, then the adhesion coefficient range in this area is the estimated range of the road surface adhesion coefficient of the target vehicle. For example, if the target longitudinal force is just between the separation points of the adhesion coefficient curves of 0.3 and 0.7, then the upper limit value of the adhesion coefficient can be 0.7 and the lower limit value is 0.3.

[0093] In an exemplary embodiment, after determining whether there is self-separation in the longitudinal force curve, the method further includes: in the case where there is no self-separation in the longitudinal force curve, determining the utilization adhesion coefficient of the target vehicle; determining the distances between the utilization adhesion coefficient and the utilization adhesion coefficients corresponding to multiple characteristic road surfaces; determining the characteristic road surface with the smallest distance, and determining the peak adhesion coefficient of the characteristic road surface with the smallest distance; determining the peak adhesion coefficient as the reference road surface adhesion coefficient.

[0094] The utilization adhesion coefficient is the adhesion ability between the tire and the road surface actually exerted by the vehicle during driving, and is usually estimated by the ratio of the tire longitudinal force to the tire vertical load. Under the condition that the longitudinal force curve does not self-separate, the system first calculates and determines the current utilization adhesion coefficient value of the target vehicle. This is determined based on the vehicle's real-time monitoring data (such as tire longitudinal force, tire load, etc.).

[0095] The utilization adhesion coefficients of multiple characteristic road surfaces are pre-stored in the system. These characteristic road surfaces may include dry asphalt, wet asphalt, snow, ice, etc., and each road surface has its specific utilization adhesion coefficient range. The system compares the utilization adhesion coefficient of the target vehicle with these preset utilization adhesion coefficients of the characteristic road surfaces and calculates the distances between them. The "distance" can be the Euclidean distance, Mahalanobis distance, etc., which is used to quantify the similarity between the utilization adhesion coefficient of the target vehicle and the utilization adhesion coefficients of the characteristic road surfaces.

[0096] After calculating the "distances" from all the characteristic road surfaces, the system will select the characteristic road surface with the smallest distance as the reference closest to the current vehicle driving conditions. That is, the current utilization adhesion coefficient of the target vehicle is closest to the utilization adhesion coefficient of this characteristic road surface, which also reflects the road surface type on which the vehicle may be driving under the current working conditions.

[0097] Finally, the system will determine the peak adhesion coefficient corresponding to the characteristic road surface with the smallest distance as the reference road surface adhesion coefficient. The peak adhesion coefficient is the maximum adhesion ability that the tire and the road surface can provide under ideal conditions, and is usually higher than the utilization adhesion coefficient during actual driving.

[0098] In an embodiment of the present application, even under challenging working conditions where the longitudinal force curve does not self-separate (such as low slip rate or steering conditions), by utilizing the comparison between the adhesion coefficient and the characteristic road surface, a reasonable reference road surface adhesion coefficient can still be effectively determined, providing a key input for the vehicle's electronic stability control system and optimizing the driving performance and safety of the vehicle under complex road conditions.

[0099] In an exemplary embodiment, determining whether the longitudinal force curve has self-separation includes: determining the wheel-end longitudinal force corresponding to the target friction coefficient, where the target friction coefficient is one of the multiple friction coefficients; calculating the error values between the wheel-end longitudinal forces corresponding to the other friction coefficients and the wheel-end longitudinal force corresponding to the target friction coefficient, where the other friction coefficients are the friction coefficients other than the target friction coefficient among the multiple friction coefficients; determining the number of error values greater than a preset error value; and determining that the longitudinal force curve has self-separation when the number is greater than a preset threshold; and determining that the longitudinal force curve does not have self-separation when the number is less than or equal to the preset threshold.

[0100] First, select one from a preset multiple of friction coefficients as the "target friction coefficient". This selection can be random or based on a certain rule (such as selecting the median or the maximum value). The target friction coefficient is the basis for subsequent error calculation and curve self-separation judgment.

[0101] For the selected target friction coefficient, determine the corresponding wheel-end longitudinal force. For the remaining other friction coefficients, compare with the wheel-end longitudinal force under the target friction coefficient and calculate the error values. The calculation of the error values can be the absolute error or the relative error, and its purpose is to quantify the differences in longitudinal forces under different adhesion coefficient assumptions.

[0102] Count the number of error values greater than the preset error threshold among all error values. If the number of error values greater than the preset error threshold exceeds the preset threshold (i.e., the preset separation judgment criterion), it is determined that the longitudinal force curve has a self-separation phenomenon. This means that under the current vehicle working conditions, the differences in tire longitudinal forces under different adhesion coefficient assumptions start to be significant, and the curve begins to show a separation trend, which provides an important clue for the subsequent estimation of the peak adhesion coefficient. Conversely, if the number of error values greater than the preset error threshold is less than or equal to the preset threshold, it is considered that the longitudinal force curve does not have self-separation. At this time, the differences between the curves are not sufficient to effectively distinguish different adhesion coefficients through the separation of the longitudinal force curve, and other strategies (such as based on the estimation of the utilization adhesion coefficient) may need to be adopted to determine the preliminary estimation range of the peak adhesion coefficient.

[0103] Through the embodiments of the present application, the system can effectively determine whether the longitudinal force curve has a self-separation phenomenon based on the variation characteristics of the tire longitudinal force. This helps the system to assist in the estimation through other information sources such as the steering system and the vehicle dynamics model under the conditions where the observability of the tire model for the adhesion coefficient is weak (such as in the low slip rate scenario), improving the accuracy and reliability of the road surface adhesion coefficient estimation.

[0104] In an exemplary embodiment, before minimizing the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle, the method further includes: establishing a measurement equation according to the dynamics model of the target vehicle; determining the observation error penalty of the cost function according to the measurement equation, determining the control quantity penalty of the cost function according to the control quantity of the target vehicle, and determining the reference offset penalty of the cost function according to the state quantity of the target vehicle; establishing the cost function according to the observation error penalty, the control quantity penalty and the reference offset penalty.

[0105] Optionally, determining the observation error penalty term of the cost function according to the actual observation value of the target vehicle and the current road surface adhesion coefficient, determining the control quantity penalty term of the cost function according to the input value of the target vehicle, and determining the reference offset penalty of the cost function according to the predicted observation value of the target vehicle and the reference road surface adhesion coefficient; establishing the cost function according to the observation error penalty term, the control quantity penalty term and the reference offset penalty term, and establishing the constraint conditions of the cost function, where the constraint conditions include: state quantity update constraint term, observation model constraint term, road surface adhesion coefficient upper and lower limit constraint term.

[0106] Among them, the cost function and the constraint conditions of the cost function include:

[0107] ;

[0108] ;

[0109]

[0110] , where, is the cost function, is the actual observation value of the target vehicle, is the observation value of the target vehicle predicted according to the current road surface adhesion coefficient x(i) of the target vehicle, is the initial road surface adhesion coefficient, , N is the time window, i is the sampling moment, R is the weight matrix of the observation error penalty, is the input value for the target vehicle, Q is the weight matrix for control quantity penalty, is the reference road surface adhesion coefficient, P is the weight matrix for reference offset penalty, is the observation noise, is the lower limit value of the road surface adhesion coefficient of the first tire of the target vehicle, is the upper limit value of the road surface adhesion coefficient of the first tire of the target vehicle, is the lower limit value of the road surface adhesion coefficient of the second tire of the target vehicle, is the upper limit value of the road surface adhesion coefficient of the second tire of the target vehicle. The upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle include: the lower limit value and the upper limit value of the road surface adhesion coefficient of the first tire of the target vehicle, and the lower limit value and the upper limit value of the road surface adhesion coefficient of the second tire of the target vehicle.

[0111] The measurement equation is the basis for comparing the output predicted by the model with the actual observed value, and it describes the mathematical relationship between the system state quantity and the observed quantity. In the embodiment of the present application, the measurement equation is established based on the dynamic model of the target vehicle, including the longitudinal acceleration of the whole vehicle, the lateral acceleration, and the yaw angular acceleration. These measurement values can be obtained through the vehicle's inertial measurement unit (IMU) sensor or other sensors.

[0112] The observation error penalty reflects the difference between the model prediction value and the actual observed value, and it is an important part of the cost function. In the embodiment of the present application, the observation error penalty is calculated based on the deviation between the predicted measurement value and the actual measurement value in the measurement equation. The larger the deviation, the larger the observation error penalty, which prompts the optimization algorithm to approximate the actual observed data as much as possible when searching for the optimal solution, and improves the accuracy of the model prediction. The observation error penalty term is usually multiplied by a weight matrix to adjust the relative importance of different observation errors in the optimization process.

[0113] The control quantity penalty is another key part of the cost function, and it is calculated based on the control quantity of the vehicle (such as the driving force and braking force at the wheel end, etc.). The main purpose of the control quantity penalty is to avoid overly aggressive control strategies, reduce unnecessary control actions, and thus avoid unstable vehicle behavior or resource waste. In the optimization algorithm, a smaller control quantity penalty helps to keep the vehicle running smoothly and improve driving comfort. This penalty term is usually multiplied by a weight matrix to adjust the importance of the control quantity in the optimization objective.

[0114] The reference offset penalty is another element in the cost function. It measures the deviation between the state variables (such as the road surface adhesion coefficient) and the reference value (i.e., the initial road surface adhesion coefficient or the reference road surface adhesion coefficient). The introduction of this penalty term is to constrain the system state within a reasonable range and prevent the estimated value from deviating too much from the actual value. The reference offset penalty term is usually multiplied by a weight matrix to adjust the weight of the reference offset in the optimization objective, ensuring that the estimated result of the peak adhesion coefficient is both close to the observed data and in line with prior knowledge or the initial estimate.

[0115] The cost function combines the above-mentioned observation error penalty, control variable penalty, and reference offset penalty to guide the optimization direction of the MHE (Moving Horizon Estimation) algorithm. The expression of the cost function is as follows:

[0116] 。

[0117] The cost function finds the optimal state variable x and control variable u by minimizing This optimization process aims to improve the model prediction accuracy, maintain the smoothness of the control strategy, and ensure that the estimated value of the state variable is consistent with the reference state variable. By dynamically adjusting the weight matrix, according to the real-time working conditions and control requirements of the vehicle, the proportion of each penalty term in the cost function can be flexibly adjusted to ensure that the algorithm can effectively estimate the target road surface adhesion coefficient in different scenarios, thereby optimizing the vehicle's dynamic control strategy and improving driving safety and comfort.

[0118] The constraint conditions of the cost function are: ;

[0119] ;

[0120] 。

[0121] To better understand the process of the above method for determining the road surface adhesion coefficient, the following further describes the implementation method flow of the above method for determining the road surface adhesion coefficient in combination with optional embodiments, but it is not used to limit the technical solutions of the embodiments of the present application.

[0122] In this embodiment, a method for determining the road surface adhesion coefficient is provided. As Figure 5 shown, the specific steps are as follows:

[0123] Step 1: Estimate the adhesion coefficient using the power steering system when the vehicle is stationary.

[0124] The specific implementation method is as Figure 3 shown, including:

[0125] Step 11: Determine whether the vehicle is in the P gear. If the vehicle is in the P gear, proceed to the subsequent step 12; otherwise, the function exits.

[0126] Step 12: Determine whether the current steering system is a steer-by-wire system. If it is a steer-by-wire system, go to Step 13; if it is a non-steer-by-wire system, go to Step 15;

[0127] Step 13: The steering system is a steer-by-wire system. Automatically control the front steering system to output a steering torque to drive the steering system to move, for probing the road surface steering resistance;

[0128] Step 14: Based on the front steering rotation process, the VCU uses the formula: to calculate the ground friction resistance torque, where, is the moment of inertia, is the angular acceleration, is the assist torque, is the return torque, is the ground friction resistance torque.

[0129] It should be noted that the front and rear steering angles, the steering angle change rate, and the steering torque can be collected and sent by the front and rear steering controllers. The return torque is calibrated by experience. The moment of inertia of the rotating system is measured from the actual vehicle, and the angular acceleration of the steering system is obtained by differentiating the steering angle change rate.

[0130] Step 15: If the steering system is a non-steer-by-wire system, prompt the driver to turn the steering wheel through the HMI to estimate the road surface adhesion;

[0131] Step 16: The user manually turns the steering wheel;

[0132] Based on the user's process of turning the steering wheel, the VCU uses the formula: to calculate the ground friction resistance torque, where, is the moment of inertia, is the angular acceleration, is the steering wheel torque, is the assist torque, is the return torque, is the ground friction resistance torque.

[0133] The front and rear steering angles, the steering angle change rate, the torque of the driver manually turning the steering wheel, and the steering torque can be collected and sent by the front and rear steering controllers. The return torque is estimated by experience calibration. The moment of inertia of the rotating system is measured from the actual vehicle, and the angular acceleration of the steering system is obtained by differentiating the steering angle change rate.

[0134] Step 17: After calculating the ground friction resistance torque , calculate the initial road surface adhesion coefficient through a semi-empirical formula. The semi-empirical formula is: , where, is the ground friction resistance moment, f is the initial road surface adhesion coefficient, G is the axle load of the target vehicle, and P is the tire pressure of the target vehicle.

[0135] The axle load can be approximately estimated by the vehicle's curb weight and the front and rear load distribution, and the tire pressure can be obtained through a tire pressure sensor.

[0136] It should be noted that: the driving torque received by the steering system is the torque applied by the driver's hand on the steering wheel and the front assist torque ; the resistance torque received by the steering system is the steering system return torque and the ground friction resistance torque ; the moment of inertia of the front steering system is , and the angular acceleration of the front steering system is α;

[0137] Ignoring the internal transmission efficiency of the transmission system, according to the momentum theorem of the rotating system, there is the following approximate theoretical formula: ;

[0138] Among them, there is a definite relationship between the ground friction resistance moment and the road surface adhesion and the front angular velocity. If the user rotates the front steering system with the same torque, the higher the road surface adhesion, the greater the ground friction resistance moment, and the corresponding smaller the front angular acceleration. The road surface adhesion is indirectly judged by the magnitude of the front angular acceleration.

[0139] For the front steering system control system with steer-by-wire, in the stationary condition after the vehicle starts, it can automatically control the front steering system to steer to achieve automatic recognition of the road surface adhesion. Compared with the theoretical formula of non-steer-by-wire, the torque applied by the driver's hand on the steering wheel is 0, and the assist torque is directly the steering torque, and the corresponding approximate theoretical formula is as follows: .

[0140] Step 2: Determination of the upper and lower limits of the estimate by the uniform sampling method;

[0141] The specific implementation method is as Figure 4 shown, including:

[0142] Step 21: Initialization and calculation of the sampling friction coefficient;

[0143] Within the friction parameter range of 0.1 - 1.1, select different peak adhesion coefficients by sampling at fixed intervals (such as Seeds = 0.1, 0.3, 0.5, 0.7, 0.9, 1.1), and then substitute these adhesion coefficient points into the HSRI tire model for calculation to calculate the current wheel-end vertical load , wheel-end longitudinal slip ratio , and tire side slip angle and the wheel end under the peak adhesion coefficient road surface condition of the spreading points condition.

[0144] Step 22: Determination of the adhesion coefficient estimation interval at low slip ratios;

[0145] When the longitudinal slip ratio of the target wheel is less than 3%, the longitudinal wheel end forces calculated at each spreading point are almost equal, that is, regardless of whether the peak adhesion coefficient adopted is 0.1 or 1.1, there is no significant difference in the performance of the longitudinal wheel end force. Since the spreading point method cannot effectively distinguish the differences in different adhesion coefficients at low slip ratios, at this time, the Seeding method does not have the ability to distinguish the upper and lower limits, and the default estimated range of the adhesion coefficient output is [0.1, 1.1].

[0146] Step 23: Self-separation detection and analysis of the Seeding curve;

[0147] By comparing the longitudinal wheel end forces (where (i = 0.1,..., 0.9)) at each spreading point with the longitudinal force calculated when the adhesion coefficient is 1.1 When the error exceeds the preset threshold, it indicates that the adhesion coefficient of this spreading point starts to self-separate.

[0148] The self-separation phenomenon usually appears before the low adhesion coefficient point and gradually spreads to the high adhesion coefficient point. When the number of self-separations that appear exceeds 2, it is considered that there is an obvious self-separation in the Seeding curve. At this time, more accurate upper and lower limits of the peak adhesion coefficient can be given.

[0149] Step 24: Judgment of the upper and lower limits of the peak adhesion coefficient based on the self-separation phenomenon;

[0150] If it is determined that there is self-separation in the Seeding curve, then the longitudinal wheel end force calculated according to the dynamic balance equation of single-wheel drive or braking torque, combined with the distribution of the Seeding curve, to determine reasonable upper and lower limits of the peak adhesion coefficient.

[0151] Step 25: Consistency adjustment of the upper and lower limits of the peak adhesion coefficient of the front and rear wheels on the same side of the four-wheel drive vehicle.

[0152] For four-wheel drive vehicles, it is necessary to ensure the consistency of the upper and lower limits of the peak adhesion coefficient of the front and rear wheels on the same side. By calculating the variance of the Seeding curves of the front and rear wheels at the same moment in real time, the upper and lower limits of the curve with the larger variance are selected as the Limit value for the adhesion on this side, because this indicates that the self-separation of this curve is more obvious and helps to more accurately estimate the upper and lower limits.

[0153] Step 26: Fusion and utilization of the adhesion coefficient to optimize the estimated lower limit ;

[0154] Further consider using the estimated value of the adhesion coefficient to ensure that the lower limit value of the peak adhesion estimation is not less than the adhesion coefficient value actually utilized by the current vehicle. Compare the lower limit value determined by the Seeding method with the currently utilized adhesion coefficient value, and take the larger value as the final estimated lower limit value to improve the rationality and reliability of the peak adhesion coefficient estimation.

[0155] Step 3: Determine the reference adhesion coefficient based on the utilized adhesion coefficient ;

[0156] Based on the determination of the self-separation and estimation upper and lower limits of the scattering curve by the scattering point method, through real-time calculation of the utilized adhesion coefficient and the semi-empirical tire-road mathematical model:

[0157] .

[0158] As shown in Table 1, obtain the parameter values (C1, C2, C3) and peak adhesion coefficients of 6 types of road surfaces. By comparing the closeness of the currently calculated utilized adhesion coefficient to the utilized adhesion coefficients of each characteristic road surface, obtain a reasonable reference peak adhesion coefficient. Step 3 only aims at determining the reference adhesion coefficient value when the slip ratio is low and the scattering curve does not show a separation scenario; if the scattering curve shows a separation, use the average value of the peak adhesion coefficient upper and lower limits as the reference adhesion coefficient.

[0159] Table 1

[0160]

[0161] Step 4: Estimate the peak adhesion coefficients of the left and right wheels based on the MHE algorithm;

[0162] Establish a measurement equation based on the vehicle's three-degree-of-freedom dynamics model.

[0163] Among them, as Figure 6 shown, the vehicle's three-degree-of-freedom dynamics model is:

[0164] ;

[0165] ;

[0166] ;

[0167] Among them is the vehicle's longitudinal acceleration, is the lateral acceleration, the yaw angular acceleration, is the steering angle of the front wheels, is the steering angle of the rear wheels, a, b are the distances from the front and rear axles to the center of mass, and the left and right wheelbases respectively; They are the longitudinal and lateral tire forces of the four wheels respectively. It should be noted that in actual applications, the rear wheels usually do not steer, so the steering angle of the rear wheels is actually zero, so in Figure 6 it is not shown . The state variables to be estimated are the peak adhesion coefficients on the left and right sides , and the observed variables are ; Define the sampling window length N of the MHE. Assume that the measurement values at each sampling time i (i = k - N... k) and the state variables to be optimized , that is, there is the following relationship between x(i): ;

[0168] where is the measurement noise, is the measurement equation, which is used to establish the relationship between the estimated state variable x(k) and the observed variable y(k) at time k, and can be written based on the aforementioned three-degree-of-freedom dynamic equation. The MHE optimization problem is to find the optimal control variable within the window time N, so that the state variable makes the total cost function calculated within a period of time the smallest. The selection of the optimization window is adjusted according to the computing power of the controller and the actual situation. Taking a sampling period of 10 ms as an example, the general reference N is 50 - 80.

[0169] Write the cost function and constraints of the optimal problem as follows:

[0170]

[0171] Finally, minimize the cost function to obtain the target road surface adhesion coefficient .

[0172] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0173] In this embodiment, a device for determining the road surface adhesion coefficient is further provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0174] Figure 7 is a structural block diagram of a device for determining the road surface adhesion coefficient according to an embodiment of the present application. As Figure 7 shown, the device includes:

[0175] An acquisition module 72, configured to acquire the initial road surface adhesion coefficient of the target vehicle when the target vehicle is in a target state;

[0176] A first determination module 74, configured to determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value;

[0177] A second determination module 76, configured to minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient, so as to determine the target road surface adhesion coefficient of the target vehicle.

[0178] Through the above device, when the target vehicle is in a target state, the initial road surface adhesion coefficient of the target vehicle is acquired; the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle are determined through a target algorithm, and the reference road surface adhesion coefficient of the target vehicle is determined according to the upper limit value and the lower limit value; the cost function corresponding to the target vehicle is minimized according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient, so as to determine the target road surface adhesion coefficient of the target vehicle. In the embodiment of the present application, not only an effective initial estimate and upper and lower limit ranges are provided, but also through the introduction of a reference value and the setting of an optimization target, it is ensured that the algorithm can continuously and accurately update the estimate of the road surface adhesion coefficient under various complex and changing driving conditions, providing more reliable data support for the traction control, stability control, etc. of the vehicle. Therefore, the problem of low accuracy of the current road surface adhesion coefficient recognition algorithm in the face of effective estimation under untested road surfaces and low slip rate conditions can be solved.

[0179] In an exemplary embodiment, an acquisition module is configured to, when the steering system of the target vehicle is in a first steering mode, control the steering system to output a target steering torque to control the front wheels of the target vehicle to perform a steering movement; after the front wheels of the target vehicle perform the steering movement, acquire the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system; determine the ground friction resistance torque according to the moment of inertia, angular acceleration, self-aligning torque, and assist torque of the steering system, and determine the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0180] In an exemplary embodiment, the acquisition module is configured to determine the ground friction resistance torque through the following formula:

[0181] , where is the moment of inertia, is the angular acceleration, is the assist torque, is the self-aligning torque, is the ground friction resistance torque.

[0182] In an exemplary embodiment, the acquisition module is configured to, when the steering system of the target vehicle is in a second steering mode and after manually controlling the front wheels of the target vehicle to perform a steering movement, acquire the moment of inertia, angular acceleration, self-aligning torque, assist torque, and the steering wheel torque of the target vehicle of the steering system; determine the ground friction resistance torque according to the moment of inertia, angular acceleration, assist torque, and the steering wheel torque of the target vehicle of the steering system, and determine the initial road surface adhesion coefficient according to the ground friction resistance torque.

[0183] In an exemplary embodiment, the acquisition module is configured to determine the ground friction resistance torque through the following formula:

[0184] , where is the moment of inertia, is the angular acceleration, is the steering wheel torque, is the assist torque, is the self-aligning torque, is the ground friction resistance torque.

[0185] In an exemplary embodiment, the acquisition module is configured to determine the initial road surface adhesion coefficient through the following formula:

[0186] , where is the ground friction resistance moment, f is the initial road surface adhesion coefficient, G is the axle load of the target vehicle, and P is the tire inflation pressure of the target vehicle.

[0187] In an exemplary embodiment, a first determination module is configured to obtain the wheel-end vertical load, wheel-end longitudinal slip ratio, tire sideslip angle, and multiple friction coefficients of the wheels of the target vehicle, where the multiple friction coefficients are obtained by uniformly distributing points at a certain interval within a target friction parameter range; calculate the wheel-end longitudinal force corresponding to each friction coefficient according to the wheel-end vertical load, wheel-end longitudinal slip ratio, tire sideslip angle, and the multiple friction coefficients; determine a longitudinal force curve according to the wheel-end longitudinal force corresponding to each friction coefficient; and determine an upper limit value and a lower limit value of the road surface adhesion coefficient of the target vehicle according to the longitudinal force curve.

[0188] In an exemplary embodiment, a first determination module is configured to determine whether self-separation exists in the longitudinal force curve; in the case where self-separation exists in the longitudinal force curve, determine the target wheel-end longitudinal force of the target vehicle according to the dynamic equilibrium equation; determine the position of the target wheel-end longitudinal force in the longitudinal force curve, and determine the upper limit value and the lower limit value of the road surface adhesion coefficient according to the position.

[0189] In an exemplary embodiment, a first determination module is configured to, in the case where self-separation does not exist in the longitudinal force curve, determine the utilization adhesion coefficient of the target vehicle; determine the distances between the utilization adhesion coefficient and the utilization adhesion coefficients corresponding to multiple characteristic road surfaces; determine the characteristic road surface with the smallest distance, and determine the peak adhesion coefficient of the characteristic road surface with the smallest distance; and determine the peak adhesion coefficient as the reference road surface adhesion coefficient.

[0190] In an exemplary embodiment, a first determination module is configured to determine the wheel-end longitudinal force corresponding to a target friction coefficient, where the target friction coefficient is one of the multiple friction coefficients; calculate the error values between the wheel-end longitudinal forces corresponding to the other friction coefficients and the wheel-end longitudinal force corresponding to the target friction coefficient, where the other friction coefficients are the friction coefficients other than the target friction coefficient among the multiple friction coefficients; determine the number of error values greater than a preset error value; in the case where the number is greater than a preset threshold, determine that self-separation exists in the longitudinal force curve; and in the case where the number is less than or equal to the preset threshold, determine that self-separation does not exist in the longitudinal force curve.

[0191] In an exemplary embodiment, a second determination module is configured to establish a measurement equation according to the dynamic model of the target vehicle; determine the observation error penalty of the cost function according to the measurement equation, determine the control quantity penalty of the cost function according to the control quantity of the target vehicle, and determine the reference offset penalty of the cost function according to the state quantity of the target vehicle; and establish the cost function according to the observation error penalty, the control quantity penalty, and the reference offset penalty.

[0192] In an exemplary embodiment, a second determination module is configured to determine an observation error penalty term of the cost function according to the actual observation value of the target vehicle and the current road surface adhesion coefficient, determine a control quantity penalty term of the cost function according to the input value of the target vehicle, and determine a reference offset penalty of the cost function according to the predicted observation value of the target vehicle and the reference road surface adhesion coefficient; establish the cost function according to the observation error penalty term, the control quantity penalty term, and the reference offset penalty, and establish constraint conditions of the cost function, where the constraint conditions include: a state quantity update constraint term, an observation model constraint term, and a road surface adhesion coefficient upper and lower limit constraint term.

[0193] It should be noted that the above-mentioned respective modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned respective modules are separately located in different processors in any combination form.

[0194] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0195] Optionally, in this embodiment, the above storage medium can be configured to store program codes for executing the following steps:

[0196] S1. When the target vehicle is in a target state, obtain the initial road surface adhesion coefficient of the target vehicle;

[0197] S2. Determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value;

[0198] S3. Minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle.

[0199] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media that can store computer programs such as USB flash drives, read-only memory (ROM for short), random access memory (RAM for short), mobile hard disks, magnetic disks, or optical discs.

[0200] An embodiment of the present application also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0201] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0202] Optionally, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0203] S1. When the target vehicle is in a target state, obtain the initial road surface adhesion coefficient of the target vehicle;

[0204] S2. Determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value;

[0205] S3. Minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient to determine the target road surface adhesion coefficient of the target vehicle.

[0206] An embodiment of the present application also provides a computer program product. The above computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps in any one of the above method embodiments.

[0207] An embodiment of the present application also provides another computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps in any one of the above method embodiments.

[0208] An embodiment of the present application also provides a computer program. The computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; the processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in any one of the above method embodiments.

[0209] Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

[0210] S1. When the target vehicle is in a target state, obtain the initial road surface adhesion coefficient of the target vehicle;

[0211] S2. Determine the upper limit value and the lower limit value of the road surface adhesion coefficient of the target vehicle through a target algorithm, and determine the reference road surface adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value;

[0212] S3. Minimize the cost function corresponding to the target vehicle according to the initial road surface adhesion coefficient and the reference road surface adhesion coefficient, so as to determine the target road surface adhesion coefficient of the target vehicle.

[0213] Specific examples in this embodiment may refer to the examples described in the above-mentioned embodiments and exemplary embodiments, and will not be elaborated herein.

[0214] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described herein can be executed in a different order, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0215] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a road adhesion coefficient, characterized in that: include: When the target vehicle is in a target state, obtaining an initial road adhesion coefficient of the target vehicle; Determining an upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle by a target algorithm, and determining a reference road adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; A cost function corresponding to the target vehicle is minimized according to the initial road adhesion coefficient and the reference road adhesion coefficient to determine a target road adhesion coefficient of the target vehicle.

2. The method according to claim 1, characterized in that Obtaining the initial road adhesion coefficient of the target vehicle, including: When the steering system of the target vehicle is in a first steering mode, controlling the steering system to output a target steering torque to control the front wheels of the target vehicle to perform a steering movement; After the front wheels of the target vehicle perform a steering motion, obtaining the moment of inertia, angular acceleration, aligning torque and assist torque of the steering system; The ground friction resistance torque is determined according to the moment of inertia, angular acceleration, return torque and assist torque of the steering system, and the initial road adhesion coefficient is determined according to the ground friction resistance torque.

3. The method according to claim 2, characterized in that Determining the ground friction torque according to the moment of inertia, angular acceleration, return torque and assist torque of the steering system includes: The ground friction torque is determined by the following formula: ,in, is the moment of inertia, is the angular acceleration, is the assist torque, is the aligning torque, is the ground friction resistance torque.

4. The method according to claim 1, characterized in that: Obtaining the initial road adhesion coefficient of the target vehicle, including: After the steering system of the target vehicle is in the second steering mode and the front wheels of the target vehicle are manually controlled to perform a steering movement, the moment of inertia, angular acceleration, return torque, power torque of the steering system and the steering wheel torque of the target vehicle are obtained; The ground friction resistance torque is determined according to the moment of inertia, angular acceleration, power-assist torque of the steering system and the steering wheel torque of the target vehicle, and the initial road adhesion coefficient is determined according to the ground friction resistance torque.

5. The method according to claim 4, characterized in that Determining the ground friction torque according to the moment of inertia, angular acceleration, power torque of the steering system and the steering wheel torque of the target vehicle includes: The ground friction torque is determined by the following formula: ,in, is the moment of inertia, is the angular acceleration, is the steering wheel torque, is the assist torque, is the aligning torque, is the ground friction resistance torque.

6. The method according to any one of claims 2 to 5, characterized in that Determining the initial road adhesion coefficient according to the ground friction resistance torque includes: The initial road adhesion coefficient is determined by the following formula: ,in, is the ground friction resistance torque, f is the initial road adhesion coefficient, G is the axle load of the target vehicle, and P is the tire pressure of the target vehicle.

7. The method according to claim 1, characterized in that Determining the upper limit and lower limit of the road adhesion coefficient of the target vehicle by a target algorithm includes: Obtaining the wheel end vertical load, wheel end longitudinal slip rate, tire side slip angle and multiple friction coefficients of the wheels of the target vehicle, wherein the multiple friction coefficients are obtained by evenly distributing points at a certain interval within the target friction parameter range; Calculating the wheel end longitudinal force corresponding to each friction coefficient according to the wheel end vertical load, the wheel end longitudinal slip rate, the tire sideslip angle and the multiple friction coefficients; Determining a longitudinal force curve according to the wheel end longitudinal force corresponding to each friction coefficient; An upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle are determined according to the longitudinal force curve.

8. The method according to claim 7, characterized in that Determining an upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle according to the longitudinal force curve includes: determining whether the longitudinal force curve exhibits self-separation; In the case where the longitudinal force curve has self-separation, determining the target wheel-end longitudinal force of the target vehicle according to a dynamic equilibrium equation; The position of the target wheel-end longitudinal force in the longitudinal force curve is determined, and the upper limit value and the lower limit value of the road adhesion coefficient are determined according to the position.

9. The method according to claim 8, characterized in that After determining whether the longitudinal force curve has self-separation, the method further includes: determining a utilized adhesion coefficient of the target vehicle in the absence of self-separation of the longitudinal force curve; Determining the distances between the utilized adhesion coefficient and the utilized adhesion coefficients corresponding to the plurality of characteristic road surfaces; Determining a characteristic road surface with a minimum distance, and determining a peak adhesion coefficient of the characteristic road surface with a minimum distance; The peak adhesion coefficient is determined as the reference road adhesion coefficient.

10. The method according to claim 8, characterized in that Determining whether the longitudinal force curve has self-separation, comprising: Determining a wheel end longitudinal force corresponding to a target friction coefficient, wherein the target friction coefficient is one of the multiple friction coefficients; Calculating the error values ​​between the wheel end longitudinal forces corresponding to other friction coefficients and the wheel end longitudinal forces corresponding to the target friction coefficient, wherein the other friction coefficients are friction coefficients other than the target friction coefficient among the multiple friction coefficients; Determine the amount by which the error value is greater than a preset error value; In the case where the number is greater than a preset threshold, determining that the longitudinal force curve has self-separation; When the number is less than or equal to a preset threshold, it is determined that the longitudinal force curve does not have self-separation.

11. The method according to claim 1, characterized in that: Before minimizing the cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle, the method further includes: Establishing a measurement equation according to the dynamics model of the target vehicle; Determining an observation error penalty of the cost function according to the measurement equation, determining a control amount penalty of the cost function according to a control amount of the target vehicle, and determining a reference offset penalty of the cost function according to a state amount of the target vehicle; The cost function is established according to the observation error penalty, the control amount penalty and the reference deviation penalty.

12. The method according to claim 11, characterized in that Before minimizing the cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle, the method further includes: Determining an observation error penalty term of the cost function according to an actual observation value of the target vehicle and a current road adhesion coefficient, determining a control amount penalty term of the cost function according to an input value of the target vehicle, and determining a reference offset penalty of the cost function according to a predicted observation value of the target vehicle and the reference road adhesion coefficient; The cost function is established according to the observation error penalty term, the control quantity penalty term and the reference offset penalty term, and the constraint conditions of the cost function are established, wherein the constraint conditions include: state quantity update constraint term, observation model constraint term, and upper and lower limit constraints of road adhesion coefficient.

13. A device for determining a road adhesion coefficient, characterized in that: include: An acquisition module, used for acquiring an initial road adhesion coefficient of the target vehicle when the target vehicle is in a target state; A first determination module, used to determine an upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle through a target algorithm, and determine a reference road adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value; The second determination module is used to minimize the cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 12 when executed.

15. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 12 through the computer program.

16. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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