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

Abstract

An embodiment of the present application provides a method and device for determining a road adhesion coefficient, a storage medium, and an electronic device, which are applied to the field of vehicle state observation, wherein the method includes: when a 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 the road adhesion coefficient of the target vehicle through a target algorithm, and determining a baseline road adhesion coefficient of the target vehicle based on the upper limit value and the lower limit value; minimizing a cost function corresponding to the target vehicle based on the initial road adhesion coefficient and the baseline road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle.

Description

Technical Field

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

[0002] Identifying road adhesion coefficients is a critical component of modern vehicle control technology, providing real-time road condition information to electronic control devices such as ABS (anti-lock braking systems) and ASR (anti-skid control systems) to optimize vehicle safety and stability. Currently, a common algorithm for identifying road adhesion coefficients stores friction characteristic curves for different road types in a computing device. The algorithm then compares theoretically calculated wheel deceleration with actual observed deceleration to identify the current road type using the most closely matching curve. However, this algorithm performs poorly in low-slip scenarios, where the characteristics of different road surfaces are less distinct, making it difficult to accurately distinguish them based on small changes in deceleration. This reduces identification accuracy and reliability.

[0003] In other words, the current road adhesion coefficient identification algorithm has key problems such as low accuracy and limited precision when it comes to effective estimation of untrained road surfaces and low slip rate conditions.

[0004] In the prior art, there is no effective solution to the problem of low accuracy of the current road adhesion coefficient identification algorithm when effectively estimating untrained road surfaces and low slip rate conditions.

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

[0006] The embodiments of the present application provide a method and device for determining a road adhesion coefficient, a storage medium, and an electronic device to at least solve the problem in the prior art of low accuracy of the current road adhesion coefficient identification algorithm when effectively estimating untrained road surfaces and low slip rate conditions.

[0007] According to one embodiment of the present application, a method for determining a road adhesion coefficient is provided, comprising: when a 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 the road adhesion coefficient of the target vehicle through a target algorithm, and determining a baseline road adhesion coefficient of the target vehicle based on the upper limit value and the lower limit value; and minimizing a cost function corresponding to the target vehicle based on the initial road adhesion coefficient and the baseline road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle.

[0008] In an exemplary embodiment, obtaining the initial road adhesion coefficient of the target vehicle includes: 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 the steering movement, obtaining the moment of inertia, angular acceleration, return torque and power assist torque of the steering system; determining the ground friction resistance torque based on the moment of inertia, angular acceleration, return torque and power assist torque of the steering system, and determining the initial road adhesion coefficient based on the ground friction resistance torque.

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

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

[0011] In an exemplary embodiment, obtaining the initial road 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 manually controlled to perform steering movement, obtaining the moment of inertia, angular acceleration, return torque, power assist torque of the steering system and the steering wheel torque of the target vehicle; determining the ground friction resistance torque based on the moment of inertia, angular acceleration, power assist torque of the steering system and the steering wheel torque of the target vehicle, and determining the initial road adhesion coefficient based on the ground friction resistance torque.

[0012] In an exemplary embodiment, determining the ground friction resistance torque based on the moment of inertia, angular acceleration, power torque of the steering system and the steering wheel torque of the target vehicle includes: determining the ground friction resistance torque using the following formula:

[0013] ,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.

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

[0015] ,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.

[0016] In an exemplary embodiment, the upper limit and lower limit of the road adhesion coefficient of the target vehicle are determined by a target algorithm, including: 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 based on the wheel end vertical load, wheel end longitudinal slip rate, tire side slip angle and the multiple friction coefficients; determining a longitudinal force curve based on the wheel end longitudinal force corresponding to each friction coefficient; and determining the upper limit and lower limit of the road adhesion coefficient of the target vehicle based on the longitudinal force curve.

[0017] In an exemplary embodiment, determining the upper and lower limits of the road adhesion coefficient of the target vehicle based on the longitudinal force curve includes: determining whether the longitudinal force curve has self-separation; in the case of self-separation of the longitudinal force curve, determining the target wheel-end longitudinal force of the target vehicle based on a dynamic equilibrium equation; determining the position of the target wheel-end longitudinal force in the longitudinal force curve, and determining the upper and lower limits of the road adhesion coefficient based on the position.

[0018] In an exemplary embodiment, after determining whether the longitudinal force curve has self-separation, the method further includes: determining the utilization adhesion coefficient of the target vehicle when the longitudinal force curve does not have self-separation; determining the distance 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; and determining the peak adhesion coefficient as the reference road surface adhesion coefficient.

[0019] 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, 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; determining the number of error values ​​that are greater than a preset error value; determining that the longitudinal force curve has self-separation if the number is greater than a preset threshold; and determining that the longitudinal force curve has not self-separation if 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 based on the initial road adhesion coefficient and the baseline road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle, the method also includes: establishing a measurement equation based on the dynamic model of the target vehicle; determining an observation error penalty of the cost function based on the measurement equation, determining a control quantity penalty of the cost function based on the control quantity of the target vehicle, and determining a baseline offset penalty of the cost function based on the state quantity of the target vehicle; and establishing the cost function based on the observation error penalty, the control quantity penalty and the baseline offset penalty.

[0021] In an exemplary embodiment, before minimizing the cost function corresponding to the target vehicle based on 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 based on the actual observation value of the target vehicle and the current road adhesion coefficient, determining a control amount penalty term of the cost function based on the input value of the target vehicle, and determining a reference offset penalty of the cost function based on the predicted observation value of the target vehicle and the reference road adhesion coefficient; establishing the cost function based on the observation error penalty term, the control amount penalty term and the reference offset penalty term, and establishing constraints for the cost function, wherein the constraints include: a state quantity update constraint term, an observation model constraint term, and upper and lower limit constraints of the road adhesion coefficient.

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

[0023] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0024] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, 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 another embodiment of the present application, a computer program product is provided, including a computer program, which implements the steps of any of the above method embodiments when executed by a processor.

[0026] Through this application, when a target vehicle is in a target state, an initial road adhesion coefficient of the target vehicle is obtained; an upper and lower limit of the road adhesion coefficient of the target vehicle is determined using a target algorithm, and a baseline road adhesion coefficient of the target vehicle is determined based on the upper and lower limit values; and a cost function corresponding to the target vehicle is minimized based on the initial road adhesion coefficient and the baseline road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle. In this embodiment of the application, not only is an effective initial estimate and upper and lower limit ranges provided, but the introduction of a baseline value and the setting of an optimization target also ensure that the algorithm can continuously and accurately update the road adhesion coefficient estimate under various complex and changing driving conditions, providing more reliable data support for vehicle traction control, stability control, and other functions. Therefore, the problem of low accuracy of current road adhesion coefficient identification algorithms when effectively estimating under untrained road surfaces and low slip conditions can be resolved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

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

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

[0031] Figure 3 is a flow chart of estimating the adhesion coefficient using a steering system at rest according to an embodiment of the present application;

[0032] Figure 4 This is a flow chart of the uniform spreading method and the estimation of the upper and lower limits of the adhesion coefficient according to an embodiment of the present application;

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

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

[0035] Figure 7 It is a structural block diagram of a device for determining a road adhesion coefficient according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] The embodiments of the present application will be described in detail below 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 this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily 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 similar computing device. Taking running on a computer device as an example, Figure 1 This is a hardware structure block diagram of a computer device for determining a road adhesion coefficient according to an embodiment of the present application. Figure 1 As shown, the computer device may include one or more ( Figure 1Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data. The above-mentioned computer device may also include a transmission device 106 for communication functions and an input and output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer device. For example, the computer device may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

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

[0040] Memory 104 can be used to store computer programs, such as software programs and modules for application software, such as the computer program corresponding to the method for determining the road adhesion coefficient in the embodiments of the present application. Processor 102 executes the computer programs stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located relative to processor 102, and such remote memory may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0041] Transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a communications provider of the computer device. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

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

[0043] Step S202, when the target vehicle is in a target state, obtaining an initial road adhesion coefficient of the target vehicle;

[0044] When the target vehicle is in a target state (e.g., stationary or a specific initial driving state), an initial road adhesion coefficient is first obtained through a simple and fast method (e.g., based on steering resistance torque estimation). This provides a reasonable starting point for the cost function and avoids the problem of ineffective estimation due to insufficient discrimination in low-slip conditions.

[0045] Step S204, determining an upper limit value and a lower limit value of the 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;

[0046] A target algorithm (e.g., a uniformly distributed method combined with a vehicle dynamics model) is used to determine the upper and lower limits of the target vehicle's road adhesion coefficient. The method based on a theoretical model and actual observational data in step S204 can provide an estimated range even for untrained road surfaces or low slip scenarios, thus avoiding the limitations of algorithms that rely solely on a single model or signal feature.

[0047] Based on the upper and lower limits, a baseline road adhesion coefficient is determined. In low-slip scenarios, this baseline value can be obtained by comparing the currently utilized adhesion coefficient with the utilized adhesion coefficient of a characteristic road surface. In steering conditions, an adhesion coefficient estimate based on the steering return torque is used. The introduction of a baseline value provides a reference for the cost function, ensuring that the algorithm has a reliable basis for estimation even in low-resolution conditions.

[0048] Step S206 : 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.

[0049] Finally, the initial and baseline road adhesion coefficients are used as constraints, and combined with measured vehicle dynamic data (such as acceleration and yaw acceleration), a cost function is minimized using the MHE algorithm. This cost function not only accounts for observation errors and the smoothness of control inputs, but also introduces a penalty for deviations between the state estimate and the baseline. The MHE algorithm comprehensively considers multiple sources of information during estimation, including but not limited to front wheel steering torque, tire model predictions, and the vehicle kinematic model. This allows for more accurate road adhesion estimates under various operating conditions, particularly untrained surfaces and low slip scenarios.

[0050] Through the above steps, when the target vehicle is in the target state, the initial road adhesion coefficient of the target vehicle is obtained; the upper and lower limits of the road adhesion coefficient of the target vehicle are determined using a target algorithm, and the reference road adhesion coefficient of the target vehicle is determined based on the upper and lower limits; and the cost function corresponding to the target vehicle is minimized based on the initial road adhesion coefficient and the reference road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle. In this embodiment of the present application, not only is an effective initial estimate and upper and lower limits provided, but the introduction of a reference value and the setting of an optimization target also ensure that the algorithm can continuously and accurately update the road adhesion coefficient estimate under various complex and changing driving conditions, providing more reliable data support for vehicle traction control, stability control, and other functions. Therefore, the problem of low accuracy of current road adhesion coefficient identification algorithms when effectively estimating under untrained road surfaces and low slip conditions can be resolved.

[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, controlling the steering system to output a target steering torque to control the front wheels of the target vehicle to perform steering movement; after the front wheels of the target vehicle perform steering movement, obtaining the moment of inertia, angular acceleration, return torque and power assist torque of the steering system; determining the ground friction resistance torque based on the moment of inertia, angular acceleration, return torque and power assist torque of the steering system, and determining the initial road adhesion coefficient based on the ground friction resistance torque.

[0052] In this embodiment, the target vehicle's steering system is first detected as either steer-by-wire or EPS (Electric Power Steering), a first steering mode. If the steering system supports this control mode, the system automatically controls the steering system to output a target steering torque, causing the front wheels to execute steering movements. This creates a dynamic operating condition where road friction can be observed while the vehicle is stationary.

[0053] After the front wheels execute a steering motion, the vehicle's VCU (Vehicle Control Unit) collects key steering system dynamic parameters, including rotational inertia, angular acceleration, aligning torque, and assist torque. The moment of inertia reflects the steering system's ability to resist changes in angular acceleration, while angular acceleration is directly related to the effectiveness of the steering torque. The aligning torque and assist torque, respectively, reflect the road surface and steering system's resistance and assistance to the steering motion.

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

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

[0056] This process effectively derives the initial road adhesion coefficient from the steering system's dynamic characteristics when the vehicle is stationary, overcoming the low estimation accuracy of existing algorithms on untrained surfaces and in low-slip scenarios. This method is not only cost-effective but also provides a good starting point for subsequent road adhesion coefficient estimation during initial vehicle launch or under specific operating conditions, such as pivoting, thereby improving the robustness and accuracy of the overall estimation algorithm.

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

[0058] ,in, is the moment of inertia, is the angular acceleration, is the assist torque, is the 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 a steering system, it reflects the overall resistance of the steering wheel, steering shaft, steering knuckle arm and other components to angular acceleration. In the formula, It reflects the rotation effect of the steering system under the action of angular acceleration.

[0060] Angular acceleration is the rate of change of the angular velocity of the steering system per unit time. It describes the change in rotational speed when the steering system responds to steering torque.

[0061] When the front wheel rotates, the friction between the road surface and the tire generates a torque that attempts to restore the tire to a straight line, which is called the self-aligning torque. It is closely related to the friction characteristics of the road surface.

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

[0063] Ground friction torque is the resistance from the road surface encountered by the steering system during steering. It is determined by the adhesion characteristics between the tires and the road surface. When the vehicle is stationary, dynamic analysis of the steering system can indirectly estimate this resistance torque, and thus infer the road adhesion coefficient.

[0064] In an exemplary embodiment, obtaining the initial road 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 manually controlled to perform steering movement, obtaining the moment of inertia, angular acceleration, return torque, power assist torque of the steering system and the steering wheel torque of the target vehicle; determining the ground friction resistance torque based on the moment of inertia, angular acceleration, power assist torque of the steering system and the steering wheel torque of the target vehicle, and determining the initial road adhesion coefficient based on the ground friction resistance torque.

[0065] When the vehicle's steering system uses a secondary steering mode (non-wire control or electric power steering), the driver manually turns the steering wheel to drive the vehicle's front wheels. The steering system's moment of inertia, angular acceleration, assist torque, aligning torque, and driver-applied steering wheel torque are all measured and recorded in real time by the vehicle's sensors and control unit.

[0066] Under manual steering conditions, the total steering torque can be expressed as the sum of the driver's steering wheel torque and the power assist torque, which overcomes the resistance generated by the steering system's rotational inertia, the return torque, and the ground friction torque. The ground friction torque can be calculated by establishing a dynamic equilibrium equation. The specific equation is as follows:

[0067] ,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.

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

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

[0070] ,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.

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

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

[0073] The tire pressure of the target vehicle. Tire pressure has a direct impact on tire deformation and contact area. The high or low tire pressure will change the friction characteristics between the tire and the ground.

[0074] In a stationary state, the ground friction resistance torque is mainly generated by the friction between the tire and the road surface. The relationship between the friction 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 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 friction torque, friction torque T f It is equal to the friction force F multiplied by the contact radius r: .

[0077] Substituting the expressions for F and r into the above formula: .

[0078] To facilitate calculation and understanding, the above formula is further adjusted to obtain the following form: .

[0079] The embodiment of the present application effectively utilizes the steering system dynamics data of the vehicle in a stationary state and known vehicle parameters (such as axle load and tire pressure), calculates the ground friction resistance torque, and then estimates the initial road adhesion coefficient, providing an important starting value for subsequent more complex and accurate road adhesion coefficient estimation algorithms.

[0080] In this embodiment, a method for obtaining the initial road adhesion coefficient in a non-steer-by-wire system is provided by combining the driver's manual steering movements with steering system dynamic parameters and vehicle static parameters (axle load and tire pressure). This method overcomes the shortcomings of existing technologies in handling untrained roads and low-slip conditions, providing more reliable data support for vehicle stability control in complex road conditions.

[0081] Optionally, the upper limit and lower limit of the road adhesion coefficient of the target vehicle are determined by a target algorithm, including: 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 based on the wheel end vertical load, wheel end longitudinal slip rate, tire side slip angle and the multiple friction coefficients; determining a longitudinal force curve based on the wheel end longitudinal force corresponding to each friction coefficient; and determining the upper limit and lower limit of the road adhesion coefficient of the target vehicle based on the longitudinal force curve.

[0082] In this embodiment, key parameters of the target vehicle's wheels, including wheel-end vertical load, wheel-end longitudinal slip, and tire slip angle, are first collected. Vertical load reflects the vertical pressure at the tire's contact point with the road, longitudinal slip describes the relative slip between the tire and the road during longitudinal motion, and slip angle indicates the tire's deflection angle due to lateral forces.

[0083] Within the target friction parameter range (e.g., from 0.1 to 1.1), the system generates multiple friction coefficient values ​​at evenly spaced intervals. These friction coefficients are used in subsequent tire model calculations to determine the estimated road adhesion range by observing the differences in tire longitudinal forces at different friction coefficients. Each friction coefficient represents a hypothetical value for possible road friction characteristics.

[0084] Based on the collected wheel-end vertical load, wheel-end longitudinal slip rate, and tire slip angle, as well as multiple pre-set friction coefficients, the system uses a tire model (such as the HSRI model) to calculate the wheel-end longitudinal force corresponding to each friction coefficient. A tire model is a mathematical description of the tire's mechanical properties. By inputting tire load, slip rate, slip angle, and an assumed friction coefficient, it can output information such as longitudinal and lateral forces under different operating conditions.

[0085] Through these calculations, the system generates a set of wheel-end longitudinal forces corresponding to different friction coefficients, and then plots a longitudinal force curve. This curve reflects the dependence of tire longitudinal force on friction coefficient under specific load, slip rate, and sideslip angle conditions.

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

[0087] The above steps provide the vehicle's electronic control system with an estimated road adhesion coefficient range based on the tire model and vehicle dynamic parameters. This accurate road adhesion coefficient estimation helps the vehicle controller more quickly adjust traction and braking force distribution strategies, particularly during initial vehicle launch or under complex road conditions, preventing loss of control and ensuring safe driving.

[0088] In an exemplary embodiment, determining the upper and lower limits of the road adhesion coefficient of the target vehicle based on the longitudinal force curve includes: determining whether the longitudinal force curve has self-separation; in the case of self-separation of the longitudinal force curve, determining the target wheel-end longitudinal force of the target vehicle based on a dynamic equilibrium equation; determining the position of the target wheel-end longitudinal force in the longitudinal force curve, and determining the upper and lower limits of the road adhesion coefficient based on the position.

[0089] The tire's longitudinal force curve describes the relationship between the longitudinal force (traction or braking force) a tire can generate and the road's adhesion coefficient at different slip ratios. On low-adhesion surfaces, the tire's longitudinal force decreases as the slip ratio increases, while on high-adhesion surfaces, the longitudinal force changes more gradually. Therefore, if the longitudinal force curve shows a significant difference at a certain slip ratio, known as self-separation, it indicates that the tire's force performance at different adhesion coefficients begins to differ significantly.

[0090] The dynamic equilibrium equation is fundamental to analyzing vehicle kinematics and dynamics. Based on Newton's second law, it describes the relationship between tire longitudinal force, tire slip, wheel angular acceleration, and other parameters. After determining the self-separation of the longitudinal force curve, the dynamic equilibrium equation is used to calculate the target wheel-end longitudinal force for the target vehicle's current operating conditions.

[0091] Once the target wheel-end longitudinal force is determined, the next step is to find the location corresponding to this force value within a set of pre-defined tire longitudinal force curves. If this force value falls within a certain range of longitudinal force curves, the adhesion coefficient within this range is considered as the upper and lower limits of the estimated value.

[0092] Based on the position of the target wheel-end longitudinal force in the longitudinal force curve, the upper and lower limits of the road adhesion coefficient can be determined. Specifically, if the target wheel-end longitudinal force is located in a specific separation region of the curve, the adhesion coefficient range in this region serves as the estimated range of the target vehicle's road adhesion coefficient. For example, if the target longitudinal force is located exactly between the separation points of the 0.3 and 0.7 adhesion coefficient curves, the upper limit of the adhesion coefficient could be 0.7, and the lower limit could be 0.3.

[0093] In an exemplary embodiment, after determining whether the longitudinal force curve has self-separation, the method further includes: determining the utilization adhesion coefficient of the target vehicle when the longitudinal force curve does not have self-separation; determining the distance 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; and determining the peak adhesion coefficient as the reference road surface adhesion coefficient.

[0094] The effective adhesion coefficient is the actual adhesion between the tire and the road surface during driving, typically estimated by the ratio of the tire longitudinal force to the tire vertical load. Under conditions where the longitudinal force curve has not separated, the system first calculates and determines the target vehicle's current effective adhesion coefficient. This is determined based on real-time vehicle monitoring data (such as tire longitudinal force and tire load).

[0095] The system pre-stores the usable adhesion coefficients of various characteristic road surfaces, including dry asphalt, wet asphalt, snow, and ice. Each surface has a specific usable adhesion coefficient range. The system compares the target vehicle's usable adhesion coefficient with these preset characteristic road surfaces and calculates the distance between them. The "distance" can be Euclidean distance, Mahalanobis distance, or other distances, which quantifies the similarity between the target vehicle's usable adhesion coefficient and the characteristic road surface's usable adhesion coefficient.

[0096] After calculating the distances to all characteristic road surfaces, the system selects the characteristic road surface with the shortest distance as the reference that most closely matches the current vehicle's driving conditions. This means the target vehicle's current effective adhesion coefficient is closest to that of the characteristic road surface, reflecting the type of road surface the vehicle is likely to be traveling on under the current operating conditions.

[0097] Finally, the system determines the peak adhesion coefficient corresponding to the characteristic road surface with the shortest distance as the reference road adhesion coefficient. The peak adhesion coefficient is the maximum adhesion capacity that the tire can provide between the tire and the road surface under ideal conditions, which is usually higher than the actual adhesion coefficient used in actual driving.

[0098] In the embodiment of the present application, even in challenging conditions where the longitudinal force curve does not self-separate (such as low slip or cornering conditions), by comparing the adhesion coefficient with the characteristic road surface, a reasonable baseline road adhesion coefficient can still be effectively determined, providing key input for the vehicle's electronic stability control system and optimizing the vehicle's driving performance and safety 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, 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; determining the number of error values ​​that are greater than a preset error value; determining that the longitudinal force curve has self-separation if the number is greater than a preset threshold; and determining that the longitudinal force curve has not self-separation if the number is less than or equal to the preset threshold.

[0100] First, a target friction coefficient is selected from among multiple preset friction coefficients. This selection can be random or based on a rule (such as the median or maximum value). The target friction coefficient is the basis for subsequent error calculations and curve self-separation determination.

[0101] For a selected target friction coefficient, the corresponding wheel-end longitudinal force is determined. For other friction coefficients, the wheel-end longitudinal force is compared to the target friction coefficient and an error is calculated. This error can be calculated as either an absolute or relative error, quantifying the difference in longitudinal force under different adhesion assumptions.

[0102] The number of error values ​​greater than a preset error threshold is counted. If the number of error values ​​greater than the preset error threshold exceeds a preset threshold (i.e., the preset separation judgment criterion), the longitudinal force curve is determined to have self-separated. This means that under the current vehicle operating conditions, the differences in tire longitudinal forces under different adhesion coefficient assumptions begin to become significant, and the curves begin to show a separation trend, providing important clues 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, the longitudinal force curve is considered to have not self-separated. In this case, the difference between the curves is insufficient to effectively distinguish different adhesion coefficients through longitudinal force curve separation, and other strategies (such as estimation based on the utilization of the adhesion coefficient) may be necessary to determine the initial 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 self-separated based on the changing characteristics of the tire longitudinal force. This helps the system use other information sources such as the steering system and vehicle dynamics model to assist in estimating the road adhesion coefficient in operating conditions where the tire model has limited visibility into the adhesion coefficient (such as low slip ratio scenarios), thereby improving the accuracy and reliability of the road adhesion coefficient estimation.

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

[0105] Optionally, the observation error penalty term of the cost function is determined based on the actual observation value of the target vehicle and the current road adhesion coefficient, the control amount penalty term of the cost function is determined based on the input value of the target vehicle, and the baseline offset penalty of the cost function is determined based on the predicted observation value of the target vehicle and the baseline road adhesion coefficient; the cost function is established based on the observation error penalty term, the control amount penalty term and the baseline offset penalty term, and the constraints of the cost function are established, wherein the constraints include: state quantity update constraints, observation model constraints, and upper and lower limit constraints of road adhesion coefficient.

[0106] The cost function and the constraints of the cost function include:

[0107] ;

[0108] ;

[0109]

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

[0111] The measurement equation forms the basis for comparing the model's predicted output with the actual observed values. It describes the mathematical relationship between the system's state variables and the observed variables. In this embodiment, the measurement equation is based on the target vehicle's dynamic model, including the vehicle's longitudinal acceleration, lateral acceleration, and yaw angular acceleration. These measurements can be acquired using the vehicle's inertial measurement unit (IMU) or other sensors.

[0112] The observation error penalty reflects the difference between the model prediction value and the actual observation value, and is an important component of the cost function. In an embodiment of the present application, the observation error penalty is calculated based on the deviation between the measured value predicted in the measurement equation and the actual measured value. The greater the deviation, the greater the observation error penalty, which prompts the optimization algorithm to get as close as possible to the actual observation data when looking for the optimal solution, thereby improving 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 penalty is another key component of the cost function. It is calculated based on the vehicle's control variables (such as wheel-end driving force and braking force). The primary purpose of the control penalty is to prevent overly aggressive control strategies and reduce unnecessary control actions, thereby avoiding vehicle instability and wasted resources. In the optimization algorithm, a smaller control penalty helps maintain smooth vehicle operation and improve driving comfort. This penalty term is typically multiplied by a weight matrix to adjust the importance of the control variable in the optimization objective.

[0114] The baseline deviation penalty is another element in the cost function. It measures the deviation between a state variable (such as the road adhesion coefficient) and a baseline value (i.e., the initial or baseline road adhesion coefficient). This penalty term is introduced to constrain the system state within a reasonable range and prevent the estimated value from deviating too much from the actual value. The baseline deviation penalty term is typically multiplied by a weight matrix to adjust the weight of the baseline deviation in the optimization objective, ensuring that the estimated peak adhesion coefficient is close to the observed data and consistent with prior knowledge or initial estimates.

[0115] The cost function combines the above-mentioned observation error penalty, control amount penalty, and reference offset penalty to guide the optimization direction of the MHE (mobile time domain estimation) algorithm. The cost function is expressed as follows:

[0116] .

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

[0118] The constraints of the cost function are: ;

[0119] ;

[0120] .

[0121] In order to better understand the process of the above-mentioned method for determining the road adhesion coefficient, the implementation method flow of the above-mentioned method for determining the road adhesion coefficient is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of this application.

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

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

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

[0125] Step 11: Determine whether the vehicle is in P gear. If the vehicle is in 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, proceed to step 13; if it is a non-steer-by-wire system, proceed to step 15.

[0127] Step 13: The steering system is steer-by-wire, which automatically controls the forward steering system to output steering torque to drive the steering system to move, and is used to test the road's steering resistance;

[0128] Step 14: VCU is based on the forward rotation process, using the formula: Calculate the ground friction torque, where is the moment of inertia, is the angular acceleration, is the assist torque, is the aligning torque, is the ground friction resistance torque.

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

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

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

[0132] VCU is based on the user's steering process and uses the formula: Calculate the ground friction torque, where 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.

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

[0134] Step 17: Calculate the ground friction torque Finally, the initial road adhesion coefficient is calculated using a semi-empirical formula, which is: ,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.

[0135] The axle load can be approximately estimated by the vehicle's equipment mass and the front and rear load configuration, and the tire pressure can be obtained through the tire pressure sensor.

[0136] It should be noted that the driving torque on the steering system is the torque of the driver's hand turning the steering wheel. With forward assist torque ; The resistance torque of the steering system is the steering system return torque Friction torque with the ground ; The moment of inertia of the forward rotation system is , the angular acceleration of the forward rotation system is α;

[0137] Ignoring the internal transmission efficiency of the transmission system, the following approximate theoretical formula is obtained based on the angular momentum theorem of the rotating system: ;

[0138] There is a definite relationship between the ground friction resistance torque, road adhesion, and forward turning angular velocity. If the user rotates the forward turning system with the same torque, the higher the road adhesion, the greater the ground friction resistance torque, and the corresponding smaller the forward turning angular acceleration. The road adhesion is indirectly determined by the magnitude of the forward turning angular acceleration.

[0139] For the front steering system, the control system is wire-controlled steering. When the vehicle is stationary after starting, the front steering system can be automatically controlled to steer to realize automatic recognition of road adhesion. Compared with the theoretical formula of non-wire-controlled steering, the driver's manual steering torque is is 0, and the assist torque is directly the steering torque, and the corresponding approximate theoretical formula is as follows: .

[0140] Step 2: Determine the upper and lower limits of the estimate using the uniform scattering method;

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

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

[0143] Within the friction parameter range of 0.1-1.1, different peak adhesion coefficients are selected at fixed intervals (e.g. Seeds=0.1, 0.3, 0.5, 0.7, 0.9, 1.1). These adhesion coefficient points are then brought into the HSRI tire model to calculate the vertical load at the current wheel end. , wheel end longitudinal slip rate , tire slip angle Peak adhesion coefficient of wheel end at the spreading point under road conditions situation.

[0144] Step 22: Determine the adhesion coefficient estimation range at low slip ratio;

[0145] When the target wheel's longitudinal slip is less than 3%, the wheel-end longitudinal forces calculated at each seeding point are nearly identical. This means that there's no significant difference in wheel-end longitudinal forces regardless of whether the peak adhesion coefficient is 0.1 or 1.1. Because the seeding method can't effectively distinguish between different adhesion coefficients at low slip rates, the seeding method lacks the ability to distinguish between upper and lower bounds. Consequently, the output adhesion coefficient estimate defaults to the range [0.1, 1.1].

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

[0147] By comparing the wheel end longitudinal forces at various spreading points (where i = 0.1, ..., 0.9) and the longitudinal force calculated when the adhesion coefficient is 1.1 When the relative error exceeds the preset threshold, it indicates that the adhesion coefficient of the scattering point begins to separate.

[0148] Self-separation typically occurs at low adhesion coefficient points and gradually spreads to high adhesion coefficient points. When more than two self-separations occur, the seeding curve is considered to have significant self-separation, allowing for more accurate upper and lower bounds of the peak adhesion coefficient.

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

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

[0151] Step 25: Adjust the consistency 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, the upper and lower limits of the peak adhesion coefficient for the front and rear wheels on the same side must be consistent. By calculating the variance of the front and rear wheel seeding curves at the same moment in real time, the upper and lower limits of the curve with the larger variance are selected as the adhesion limit values ​​for that side. This indicates that the self-separation of the curve is more obvious, which helps to more accurately estimate the upper and lower limits.

[0153] Step 26: Fusion using adhesion coefficients to optimize the estimated lower bound ;

[0154] The estimated value of the utilized adhesion coefficient is further considered to ensure that the lower limit of the peak adhesion estimate is not less than the actual utilized adhesion coefficient value of the current vehicle. The lower limit value determined by the Seeding method is compared with the current utilized adhesion coefficient value, and the larger value is used as the final estimated lower limit value to improve the rationality and reliability of the peak adhesion coefficient estimate.

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

[0156] Based on the self-separation of the scattered point curve and the estimation of the upper and lower limits determined by the scattered point method, the adhesion coefficient and the semi-empirical tire-road mathematical model are used in real-time calculation:

[0157] .

[0158] As shown in Table 1, the parameter values ​​(C1, C2, and C3) and peak adhesion coefficients for the six road types were obtained. A reasonable baseline peak adhesion coefficient was determined by comparing the currently calculated utilization coefficient with the utilization coefficients of each characteristic road surface. Step 3 only determines the baseline adhesion coefficient value for scenarios with low slip rates and no separation in the scatter curve. If separation is present in the scatter curve, the average of the upper and lower limits of the peak adhesion coefficient is used as the baseline adhesion coefficient.

[0159] Table 1

[0160]

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

[0162] The measurement equation is established based on the three-degree-of-freedom dynamic model of the whole vehicle.

[0163] Among them, Figure 6 As shown in Figure 2, the three-degree-of-freedom dynamic model of the vehicle is:

[0164] ;

[0165] ;

[0166] ;

[0167] in is the longitudinal acceleration of the vehicle, is the lateral acceleration, Yaw angular acceleration, is the turning angle of the front wheel, is the turning angle of the rear wheel, a and b are the distance from the front and rear axles to the center of mass and the left and right wheelbase respectively; It should be noted that in actual applications, the rear wheels usually do not turn, so the steering angle of the rear wheels is Actually it is zero, so in Figure 6 Not shown The state quantity to be estimated is the peak adhesion coefficient on the left and right sides , the observed quantity is ; Define the sampling window length N of MHE. Assume that the measurement value at each sampling time i (i=kN…k) is and the state quantity to be optimized , that is, there is the following relationship between x(i): ;

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

[0169] The optimal problem cost function and constraints are listed as follows:

[0170]

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

[0172] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, 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 a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0173] This embodiment also provides a device for determining a road adhesion coefficient, which is used to implement the aforementioned embodiments and preferred implementations. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented in software, implementation using 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 a road adhesion coefficient according to an embodiment of the present application, such as Figure 7 As shown, the device includes:

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

[0176] a first determining module 74 for determining an upper limit value and a lower limit value of the road adhesion coefficient of the target vehicle by using 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;

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

[0178] Using the above-described device, when a target vehicle is in a target state, an initial road adhesion coefficient of the target vehicle is obtained; an upper limit and a lower limit of the road adhesion coefficient of the target vehicle are determined using a target algorithm, and a baseline road adhesion coefficient of the target vehicle is determined based on the upper limit and the lower limit; and a cost function corresponding to the target vehicle is minimized based on the initial road adhesion coefficient and the baseline road adhesion coefficient to determine the target road adhesion coefficient of the target vehicle. In this embodiment of the present application, not only is an effective initial estimate and upper and lower limit ranges provided, but the introduction of a baseline value and the setting of an optimization target also ensure that the algorithm can continuously and accurately update the road adhesion coefficient estimate under various complex and changing driving conditions, providing more reliable data support for vehicle traction control, stability control, and other functions. Therefore, the problem of low accuracy of current road adhesion coefficient identification algorithms when effectively estimating under untrained road conditions and low slip conditions can be resolved.

[0179] In an exemplary embodiment, an acquisition module is used to control the steering system to output a target steering torque when the steering system of the target vehicle is in a first steering mode, so as to control the front wheels of the target vehicle to perform steering movement; after the front wheels of the target vehicle perform steering movement, the moment of inertia, angular acceleration, return torque and power assist torque of the steering system are acquired; the ground friction resistance torque is determined according to the moment of inertia, angular acceleration, return torque and power assist torque of the steering system, and the initial road adhesion coefficient is determined according to the ground friction resistance torque.

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

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

[0182] In an exemplary embodiment, an acquisition module is used to obtain the moment of inertia, angular acceleration, self-aligning torque, power-assist torque and steering wheel torque of the steering system and the target vehicle 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 steering movement; determine the ground friction resistance torque based on the moment of inertia, angular acceleration, power-assist torque and steering wheel torque of the steering system and determine the initial road adhesion coefficient based on the ground friction resistance torque.

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

[0184] ,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.

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

[0186] ,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.

[0187] In an exemplary embodiment, a first determination module is used to obtain 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; calculate the wheel-end longitudinal force corresponding to each friction coefficient based on the wheel-end vertical load, wheel-end longitudinal slip rate, tire side slip angle and the multiple friction coefficients; determine the longitudinal force curve based on the wheel-end longitudinal force corresponding to each friction coefficient; and determine the upper limit and lower limit of the road adhesion coefficient of the target vehicle based on the longitudinal force curve.

[0188] In an exemplary embodiment, a first determination module is used to determine whether the longitudinal force curve has self-separation; in the case of self-separation of 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 and lower limits of the road adhesion coefficient according to the position.

[0189] In an exemplary embodiment, a first determination module is used to determine the utilization adhesion coefficient of the target vehicle when there is no self-separation in the longitudinal force curve; determine the distance 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 used to determine the wheel-end longitudinal force corresponding to a target friction coefficient, wherein the target friction coefficient is one of the multiple friction coefficients; calculate 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 number of error values ​​greater than a preset error value; if the number is greater than a preset threshold, determine that the longitudinal force curve has self-separation; if the number is less than or equal to the preset threshold, determine that the longitudinal force curve does not have self-separation.

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

[0192] In an exemplary embodiment, the second determination module is used to determine the observation error penalty term of the cost function based on the actual observation value of the target vehicle and the current road adhesion coefficient, determine the control amount penalty term of the cost function based on the input value of the target vehicle, and determine the baseline offset penalty of the cost function based on the predicted observation value of the target vehicle and the baseline road adhesion coefficient; establish the cost function based on the observation error penalty term, the control amount penalty term and the baseline offset penalty term, and establish the constraints of the cost function, wherein the constraints include: state quantity update constraint term, observation model constraint term, and upper and lower limit constraints of road adhesion coefficient.

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

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

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

[0196] S1, when the target vehicle is in a target state, obtaining an initial road adhesion coefficient of the target vehicle;

[0197] S2, 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;

[0198] S3 , 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.

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

[0200] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, 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 electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

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

[0203] S1, when the target vehicle is in a target state, obtaining an initial road adhesion coefficient of the target vehicle;

[0204] S2, 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;

[0205] S3 , 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.

[0206] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0207] An embodiment of the present application further provides another computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0208] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a 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 performs the steps of any of the above method embodiments.

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

[0210] S1, when the target vehicle is in a target state, obtaining an initial road adhesion coefficient of the target vehicle;

[0211] S2, 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;

[0212] S3 , 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.

[0213] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0214] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0215] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included within the scope of protection 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, wherein the target state includes one of the following: a stationary state and a preset initial driving state; 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; 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; Wherein, determining the reference road adhesion coefficient of the target vehicle according to the upper limit value and the lower limit value includes: obtaining the wheel end vertical load, wheel end longitudinal slip rate and 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, wheel end longitudinal slip rate and tire side slip angle and the multiple friction coefficients; determining a longitudinal force curve according to the wheel end longitudinal force corresponding to each friction coefficient; determining the upper limit value and the lower limit value of the road adhesion coefficient of the target vehicle according to the longitudinal force curve; and in the case that the longitudinal force curve has self-separation, determining the reference road adhesion coefficient according to the average of the upper limit value and the lower limit value.

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, aligning 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 resistance torque according to the moment of inertia, angular acceleration, aligning 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, wherein 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, aligning torque, power assist 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 a ground friction resistance torque according to the moment of inertia, angular acceleration, power assist 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 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 and lower limit of the road adhesion coefficient are determined according to the position.

8. The method according to claim 7, 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 in the longitudinal force curve; determining distances between the utilization adhesion coefficient and the utilization 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.

9. The method according to claim 7, characterized in that Determining whether the longitudinal force curve exhibits self-separation includes: 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 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; When 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.

10. The method according to claim 1, characterized in that Before determining a target road adhesion coefficient of the target vehicle by minimizing a cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient, the method further includes: Establishing a measurement equation based on a dynamic 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.

11. The method according to claim 10, characterized in that Before determining a target road adhesion coefficient of the target vehicle by minimizing a cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient, the method further includes: determining an observation error penalty term of the cost function based on an actual observation value of the target vehicle and a current road adhesion coefficient, determining a control amount penalty term of the cost function based on an input value of the target vehicle, and determining a reference offset penalty term of the cost function based on a predicted observation value of the target vehicle and the reference road adhesion coefficient; The cost function is established based on the observation error penalty term, the control quantity penalty term and the reference offset penalty term, and the constraints of the cost function are established, wherein the constraints include: state quantity update constraint term, observation model constraint term, and upper and lower limit constraints of road adhesion coefficient.

12. A device for determining a road adhesion coefficient, characterized in that: include: an acquisition module, configured to acquire an initial road adhesion coefficient of the target vehicle when the target vehicle is in a target state, wherein the target state includes one of the following: a stationary state and a preset initial driving state; a first determining module, configured to determine an upper limit value and a lower limit value of a road adhesion coefficient of the target vehicle by using 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; a second determining module, configured to minimize a cost function corresponding to the target vehicle according to the initial road adhesion coefficient and the reference road adhesion coefficient, so as to determine a target road adhesion coefficient of the target vehicle; Among them, the first determination module is also used to obtain 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; calculate the wheel-end longitudinal force corresponding to each friction coefficient based on the wheel-end vertical load, wheel-end longitudinal slip rate, tire side slip angle and the multiple friction coefficients; determine the longitudinal force curve based on the wheel-end longitudinal force corresponding to each friction coefficient; determine the upper limit and lower limit of the road adhesion coefficient of the target vehicle based on the longitudinal force curve; in the event of self-separation of the longitudinal force curve, determine the reference road adhesion coefficient based on the average of the upper limit and lower limit.

13. 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 11 when executed.

14. 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 11 through the computer program.

15. 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 11 is implemented.

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