Tire parameter optimization method and device and electronic equipment

Abstract

The invention discloses a tire parameter optimization method and device and electronic equipment. The method comprises the steps that performance indexes are determined according to a wetland braking model of a target tire, the wetland braking model is used for simulating the braking behavior of the target tire on a wetland road surface, and the performance indexes comprise indexes which are output by the wetland braking model and reflect the drainage capacity and the road gripping capacity of the target tire on the wetland road surface; design parameters, corresponding to the performance indexes, of the target tire are determined, and the design parameters comprise adjustable parameters influencing the wetland braking performance of the target tire; and optimizing the design parameters according to the performance indexes to obtain target design parameters. The technical problem that the research and development period is long due to the fact that evaluation of the wet land braking performance of the tire in the prior art depends on real vehicle testing is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tires, in particular to a tire parameter optimization method and device and electronic equipment. BACKGROUND

[0002] The tire is the only component of a vehicle that contacts the road surface, and the braking performance of the tire determines the safety of the vehicle driving. Especially on a wet road surface, a tire with good wet braking performance can effectively shorten the braking distance of the vehicle in an emergency braking, and fully ensure the safety of the vehicle driving. However, since the wet braking performance of the tire cannot be predicted at the design stage, the related technology needs to be judged by the data of real vehicle testing, and the tire production and testing are performed again after optimization according to experience, resulting in a long research and development cycle.

[0003] At present, no effective solution has been proposed for the above problems. SUMMARY

[0004] The embodiments of the present application provide a tire parameter optimization method, device and electronic equipment to at least solve the technical problem that the related technology relies on real vehicle testing to evaluate the wet braking performance of the tire, resulting in a long research and development cycle.

[0005] According to an aspect of an embodiment of the present application, a tire parameter optimization method is provided, including: determining a performance index according to a wet braking model of a target tire, wherein the wet braking model is used to simulate the braking behavior of the target tire on a wet road surface, and the performance index includes an index reflecting the drainage capacity and the grip capacity of the target tire on the wet road surface output by the wet braking model; determining a design parameter of the target tire corresponding to the performance index, wherein the design parameter includes an adjustable parameter affecting the wet braking performance of the target tire; and optimizing the design parameter according to the performance index to obtain a target design parameter.

[0006] In some embodiments of the present application, the wet braking model is constructed by: determining a first parameter corresponding to the target tire, wherein the first parameter quantifies the drainage capacity of the pattern of the target tire; determining a second parameter corresponding to the target tire, wherein the second parameter quantifies the grip capacity of the target tire; and determining the wet braking model according to the first parameter and the second parameter.

[0007] In some embodiments of the present application, the first parameter includes a critical slip speed of the target tire when the target tire is completely water-slipped during braking on a wet road surface; and the determination of the first parameter corresponding to the target tire includes: obtaining physical parameters corresponding to the wet braking environment and first structure parameters corresponding to the target tire, wherein the physical parameters are used to simulate the physical conditions in the wet environment, and the first structure parameters include the pattern design parameters of the target tire; and determining the first parameter according to the physical parameters and the first structure parameters.

[0008] In some embodiments of the present application, the second parameter comprises a braking force generated by the target tire in a driving direction when the target tire brakes on a wet road; determining the second parameter corresponding to the target tire comprises: obtaining a friction coefficient of the target tire on the wet road, a mechanical parameter for reflecting a mechanical property between the target tire and the wet road, and a second structure parameter corresponding to the target tire, wherein the second structure parameter comprises a footprint size parameter of the target tire; determining the second parameter according to the friction coefficient, the mechanical parameter, and the second structure parameter.

[0009] In some embodiments of the present application, the mechanical parameter comprises a radial load of a contact patch area of the target tire when the target tire brakes on the wet road; the radial load is determined by: obtaining an initial radial load of the target tire in a static load state and a first area of an initial contact patch area; partitioning the footprint of the target tire when braking on the wet road, and determining a contact patch area, a driving speed, and a second area of the contact patch area of the target tire when braking on the wet road; determining the radial load according to the initial radial load, the first area, the driving speed, and the second area.

[0010] In some embodiments of the present application, the design parameters are optimized according to the performance index to obtain target design parameters, comprising: determining a fitness corresponding to the performance index, wherein the fitness is used to quantitatively represent the wet braking performance of the target tire under the corresponding design parameter value; determining the fitness as a target function of a particle swarm optimization algorithm, and iteratively optimizing the design parameters according to the fitness in each iteration; in the case of meeting a preset condition, determining an output result of the particle swarm optimization algorithm as the target design parameter, wherein the output result comprises a group optimal solution.

[0011] In some embodiments of the present application, the design parameters are iteratively optimized according to the fitness in each iteration, comprising: determining an update speed corresponding to all particles in the particle swarm optimization algorithm according to the fitness, wherein the update speed is used to indicate the direction and speed of the particle moving from the current position to the next position, and each particle position represents a design parameter combination; determining an update position of the all particles corresponding to the update speed, wherein the update position is used to represent the updated design parameter combination.

[0012] In some embodiments of the present application, after determining the update position of the all particles corresponding to the update speed, the method further comprises: determining a first value range of each design parameter; comparing the value of the corresponding design parameter in the update position of the all particles with the first value range respectively to obtain a comparison result; in the case that the comparison result indicates that the value of the design parameter in the update position exceeds the first value range, updating the value to a boundary value of the first value range.

[0013] In some embodiments of the present application, the method further comprises: determining a second value range corresponding to each design parameter, and determining a range span value corresponding to each design parameter according to the second value range; and determining an initial speed of a particle in the particle swarm optimization algorithm according to the range span value.

[0014] In some embodiments of the present application, the design parameters include a footprint size parameter and a pattern parameter of the target tire, wherein the footprint size parameter includes a footprint length and a footprint width of the target tire on a wet road, and the pattern parameter includes a pattern groove depth and a pattern porosity of the target tire.

[0015] According to another aspect of the embodiments of the present application, a device for optimizing tire parameters is also provided, comprising: a construction module configured to determine a performance index according to a wet braking model of a target tire, wherein the wet braking model is configured to simulate a braking behavior of the target tire on a wet road, and the performance index includes an index output by the wet braking model and reflecting a drainage capacity and a grip capacity of the target tire on the wet road; a determination module configured to determine a design parameter of the target tire corresponding to the performance index, wherein the design parameter includes an adjustable parameter affecting a wet braking performance of the target tire; and an optimization module configured to optimize the design parameter according to the performance index to obtain a target design parameter.

[0016] According to still another aspect of the embodiments of the present application, an electronic device is also provided, comprising: a memory and a processor, the memory is configured to store program instructions; the processor is connected with the memory and is configured to execute the above-mentioned method for optimizing tire parameters.

[0017] According to still another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, comprising a stored computer program, wherein a device in which the non-volatile storage medium is located executes the above-mentioned method for optimizing tire parameters by running the computer program.

[0018] According to still another aspect of the embodiments of the present application, a computer program product is also provided, comprising computer instructions, which, when executed by a processor, implement the above-mentioned method for optimizing tire parameters.

[0019] In the embodiments of the present application, the drainage capacity and the grip capacity of the tire are quantified by constructing a wet braking model, and then the design parameters of the tire are optimized, so as to achieve the purpose of optimizing the design parameters of the tire in the tire design stage, thereby realizing the technical effects of accurately evaluating the wet braking performance of the tire and speeding up the development cycle, and further solving the technical problem that the evaluation of the wet braking performance of the tire in the related art depends on real vehicle testing, resulting in a long development cycle. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0021] Figure 1 Fig. 1 is a hardware structure block diagram of a computer terminal of a tire parameter optimization method according to an embodiment of the application;

[0022] Figure 2 Fig. 2 is a flow chart of a tire parameter optimization method according to an embodiment of the application;

[0023] Figure 3 Fig. 3 is a structural schematic diagram of a tire parameter optimization device according to an embodiment of the application. DETAILED DESCRIPTION

[0024] In order to enable persons skilled in the art to better understand the scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0027] Particle Swarm Optimization (PSO): A stochastic optimization algorithm based on swarm intelligence, which simulates the interaction between individuals and groups in the foraging behavior of bird flocks to optimize. In the embodiments of the present application, the PSO algorithm can be used to find the design parameters of the tire wet braking performance optimization, through the iterative update of the position and speed of the particle, the optimization of the tire design parameters is realized, in order to achieve the best wet braking performance.

[0028] Contact Length: The length of the tire contact area with the road surface in the direction of vehicle travel. In the embodiments of the present application, the contact length can be used as a design variable to affect the water film thickness change and drainage capacity in the tire wet braking performance.

[0029] Contact Width: The width of the tire footprint in the direction perpendicular to the direction of vehicle travel. In the embodiments of the present application, the contact width can also be used as a design variable to directly affect the tire wet braking performance, especially when calculating the radial pressure of the tire contact area, the optimization of the contact width helps to improve the braking performance of the tire.

[0030] Main groove depth / pattern groove depth: The depth of the main drainage groove in the tire pattern. In the embodiments of the present application, the pattern groove depth is a key factor in the tire drainage capacity, and optimizing the pattern groove depth can effectively improve the drainage speed of the tire on the wet road surface, thereby improving the wet braking performance.

[0031] Pattern porosity: The ratio of the void portion to the entire pattern area in the tire pattern. In the embodiments of the present application, the optimization of the pattern porosity aims to balance the drainage capacity and grip performance of the tire, and by adjusting the pattern porosity, the driving stability and safety of the tire on the wet road surface can be improved.

[0032] The wet braking performance of the tire is related to the structure design, pattern style and rubber performance of the tire. In order to balance various performances of the tire (rolling resistance, braking, wear resistance, handling, etc.), the tire has many combined components, complex structure, various pattern styles and rubber formulations, so the design parameters of the tire become more and more, and the tire becomes a complex system.

[0033] In the related art, the wet braking performance of the tire cannot be predicted during the design stage, and the tire engineer can only judge the wet braking performance of the tire through real vehicle test data after the tire is produced, and then use experience to optimize multiple schemes, and then produce and test the tire again, and select the tire with better wet braking performance from the multiple schemes. This design method has a long cycle, requires multiple rounds of tire production and testing, has high cost, and relies too much on real vehicle testing, making it difficult to form design experience.

[0034] To solve the above technical problems, the embodiments of the present application provide corresponding solutions, which are described in detail below.

[0035] The tire parameter optimization method embodiments provided by the embodiments of the present application can be executed in a mobile terminal, a computer terminal or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing the tire parameter optimization method is shown. As shown in Figure 1 The computer terminal 10 can include one or more processors (processors can include but are not limited to processing devices such as microprocessor MCU or programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication function through wired and / or wireless network connection. In addition, it can also include a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the I / O interface), a network interface, a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or less components than those shown in Figure 1 or have a different configuration from Figure 1 the structure shown.

[0036] It should be noted that the one or more processors and / or other data processing circuits described above can be referred to herein as "data processing circuits" in general. The data processing circuit can be embodied in whole or in part as software, hardware, firmware or any other combination. In addition, the data processing circuit can be a single independent processing module, or all or part of any one of the other elements combined into the computer terminal 10. As referred to in the embodiments of the present application, the data processing circuit serves as a processor to control (for example, the selection of the variable resistance terminal path connected to the interface).

[0037] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the tire parameter optimization method in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the tire parameter optimization method described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor, which can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0038] The transmission module 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (NIC) which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module used to communicate with the Internet in a wireless manner.

[0039] The display can be, for example, a touch screen type liquid crystal display (LCD) which can enable a user to interact with the user interface of the computer terminal 10.

[0040] It should be noted that, in some optional embodiments, the above-mentioned Figure 1 The computer terminal shown can include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that, Figure 1 is only one example of a particular implementation and is intended to illustrate the types of components that can be present in the above-described computer terminal.

[0041] Under the above-mentioned operating environment, the embodiments of the present application provide a tire parameter optimization method embodiment. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0042] Figure 2 is a flowchart of a tire parameter optimization method according to the embodiments of the present application, such as Figure 2As shown, the method comprises the following steps:

[0043] In step S202, a performance index is determined according to a wet road braking model of the target tire, wherein the wet road braking model is used to simulate the braking behavior of the target tire on a wet road surface, and the performance index includes an index output by the wet road braking model reflecting the drainage ability and the grip ability of the target tire on the wet road surface.

[0044] In the above step S202, the wet road braking model is used to simulate the braking behavior of the target tire on a wet road surface, including but not limited to the drainage process of the target tire and the change of the friction force between the tire and the wet road surface. The construction of the wet road braking model is based on the physical and mechanical principles of the contact between the tire and the road surface. By simulating the drainage process of the tire on the wet road surface and the generation of the grip force, the performance of the tire under wet and slippery conditions can be accurately predicted.

[0045] In some embodiments of the present application, the design target of the wet road braking performance of the tire can be determined as follows: when the tire brakes on a wet road surface (with a certain water film thickness), water will float on the tire, and a part of the water will immerse into the bottom of the tire, reducing the contact area between the tire and the road surface. When the tire reaches a certain driving speed, the tire will be completely water- floating, and the contact area between the tire and the road surface is 0. At this time, the speed is the critical slip speed of the tire. The greater the speed, the stronger the drainage ability of the tire pattern. At the same time, when the tire brakes on a wet road surface (with a certain water film thickness), the force generated by the tire in the longitudinal direction (driving direction) is the braking force. The greater the braking force, the greater the grip ability of the tire. Therefore, the drainage ability and the grip ability of the tire are used to characterize the wet road braking performance of the tire. The greater the critical slip speed and the braking force, the better the wet road braking performance of the tire.

[0046] For the drainage ability, in some embodiments of the present application, the drainage ability of the tire on the wet road can be evaluated according to a tire plane drainage mathematical calculation model. By calculating the curve of the water film thickness along the length direction of the tire print when the tire drives on the wet road, the model will consider factors such as fluid dynamics, tire shape and pattern design. Specifically, it is assumed that the wet road surface is flat without roughness, and the tire brakes on the wet road:

[0047]

[0048] In formula 1, υ is the dynamic viscosity of the fluid, F z is the radial load of the tire, l is the length of the tire ground print, b is the width of the tire ground print, V is the driving speed of the tire, h is the water film thickness variable, and x is the length variable of the print. Thus, the change of the water film thickness in the length direction (x direction) of the tire print when the tire drives on the wet road at a certain speed can be obtained.

[0049] For the grip ability, the grip ability of the tire can be evaluated in the wet braking model by calculating the radial pressure change and the friction coefficient of the tire contact area.

[0050] In some embodiments of the present application, the wet braking model can be constructed by: determining a first parameter corresponding to the target tire, wherein the first parameter quantifies the water drainage ability of the pattern of the target tire; determining a second parameter corresponding to the target tire, wherein the second parameter quantifies the grip ability of the target tire; and determining the wet braking model according to the first parameter and the second parameter.

[0051] The first parameter represents a quantitative indicator of the water drainage ability of the tire pattern, which reflects the influence of the tire pattern design (such as groove depth, pattern porosity, etc.) on the water film removal efficiency on wet road surface. The second parameter represents a quantitative indicator of the grip ability of the tire, which reflects the friction between the tire and the road surface on wet road surface, as well as the influence of the tire structure on this friction. Through the quantification of these two parameters, the wet braking model can more accurately simulate the dynamic behavior of the tire on wet road surface, thereby providing a scientific basis for the optimization of design parameters.

[0052] The first parameter includes the critical slip velocity of the target tire when the target tire is completely hydroplaning during wet road braking, and the first parameter corresponding to the target tire can be determined by: obtaining physical parameters corresponding to the wet braking environment and first structure parameters corresponding to the target tire, wherein the physical parameters are used to simulate the physical conditions under the wet environment, and the first structure parameters include the pattern design parameters of the target tire; and determining the first parameter according to the physical parameters and the first structure parameters.

[0053] Specifically, the first parameter can be the critical slip velocity (Critical Velocity for Hydroplaning, abbreviated as CVH) of the target tire when hydroplaning occurs during wet road braking, which directly reflects the water drainage efficiency of the tire pattern design. The higher the CVH, the higher the speed at which the tire can still maintain effective contact with the ground, avoiding the occurrence of hydroplaning, thereby having better wet braking performance.

[0054] The physical parameters are used to simulate the specific physical conditions of the interaction between the tire and the road surface under the wet environment, including but not limited to water flow density, initial water film thickness, road roughness, etc. The physical parameters are indispensable in calculating the CVH, and determine the dynamic changes of the water film between the tire and the wet road surface, thereby affecting the water drainage ability of the tire.

[0055] In some embodiments of the present application, the calculation formula of the first parameter is as follows:

[0056]

[0057] In formula 2, p i is the average tire contact pressure, p is the water flow density, h0 is the initial water film thickness, h R is the road roughness, P T is the groove depth, and y is the pattern porosity, and n is the number of pattern grooves.

[0058] The critical slip speed is the boundary speed at which the tire begins to exhibit water sliding on a wet road surface. By simulating the drainage effect of tires with different pattern designs under specific physical conditions, the ability to prevent water sliding can be evaluated. Physical parameters may include, but are not limited to, the degree of wetness of the road surface, the contact pressure of the tire with the road surface, the material properties of the tire, and the like, while the first structural parameter focuses on the pattern design of the tire, such as the width and depth of the pattern groove and the shape of the pattern block. Through comprehensive analysis of multiple parameters, the drainage capacity of the tire can be more accurately determined, thereby optimizing the pattern design and improving the braking performance of the tire on wet ground.

[0059] The second parameter includes the braking force generated by the target tire in the driving direction when the target tire brakes on a wet road. The second parameter corresponding to the target tire can be determined by the following steps: obtaining the friction coefficient of the target tire on a wet road, the mechanical parameter reflecting the mechanical properties between the target tire and the wet road, and the second structural parameter corresponding to the target tire, wherein the second structural parameter includes the footprint size parameter of the target tire; determining the second parameter according to the friction coefficient, the mechanical parameter and the second structural parameter.

[0060] Specifically, the second parameter can be the longitudinal grip force (LGF) generated by the target tire in the driving direction when the target tire brakes on a wet road. The LGF directly reflects the wet ground grip ability of the tire. The greater the LGF, the better the stability of the tire when braking on a wet road, and the shorter the braking distance.

[0061] The friction coefficient refers to the resistance coefficient between the tire and the road surface during braking on a wet road, and is a key factor affecting the wet braking performance. The friction coefficient directly determines the grip force and braking effect of the tire on a wet road. The mechanical parameter includes, but is not limited to, the average contact pressure of the tire, the stiffness of the pattern block, the radial load of the tire, and the like, and describes the mechanical state of the tire when braking on a wet road.

[0062] In some embodiments of the present application, the calculation formula of the second parameter is as follows:

[0063]

[0064] In formula 3, b is the footprint width, l c is the footprint length of the tire contact area, l his the length of the footprint of the adhesion zone, C x is the longitudinal block stiffness of the tire, F c is the radial load of the tire contact patch, μ dw is the dynamic friction coefficient of the tire on a wet road.

[0065] The magnitude of the braking force is directly related to the braking effect of the tire on a wet surface, and the comprehensive consideration of the friction coefficient, mechanical parameters, and footprint size parameters can more comprehensively evaluate the grip ability of the tire. The friction coefficient reflects the friction characteristics between the tire material and the wet road surface, the mechanical parameters such as the radial load of the contact patch reveal the mechanical state of the tire during braking on a wet surface, and the footprint size parameters are related to the actual contact area between the tire and the road surface. Through accurate measurement and analysis of multiple parameters, it is helpful to optimize the material and structure of the tire to improve its grip performance on a wet surface and solve the problem of excessive braking distance on a wet surface.

[0066] It should be noted that the mechanical parameters include the radial load of the contact patch of the target tire during braking on a wet road surface, and the radial load can be determined by the following method: obtaining the initial radial load of the target tire under the static load condition and the first area of the initial contact patch; dividing the footprint of the target tire during braking on a wet road surface, and determining the contact patch area, the running speed and the second area of the contact patch area of the target tire during braking on a wet road surface; determining the radial load according to the initial radial load, the first area, the running speed and the second area.

[0067] Specifically, the contact patch area refers to the effective load bearing area formed by the contact between the tire and the road surface, and its shape and size will change due to the pressure distribution and running speed of the tire. The first area refers to the initial contact patch area of the tire under the static load condition, and the second area refers to the actual contact patch area of the tire under the wet road braking condition.

[0068] Due to the dynamic changes of the contact patch area, the calculation of the radial load is complex, especially in the case of high-speed braking, the second area of the contact patch area may change rapidly, making it difficult to calculate the real-time radial load. In order to solve this problem, a dynamic simulation model of tire wet braking can be established, the variation law of the contact patch area under different running speeds is input, and the second area of the contact patch area is calculated by computer simulation to finely reproduce the dynamic changes of the contact patch area during wet braking.

[0069] In some embodiments of the present application, the calculation formula of the radial load is:

[0070]

[0071] In formula 4, F c is the radial load of the tire contact patch during braking on a wet road surface, Fz is the radial load of the tire under static load (initial radial load), A is the footprint area of the tire under static load (first area), A w is the area of the tire in contact with the road during braking on a wet road (second area), and V is the running speed of the tire.

[0072] During wet braking, the contact patch area of the tire with the road surface changes due to changes in speed, pattern design, and road conditions, which directly affects the tire's grip. By accurately measuring and calculating the radial load of the contact patch, the mechanical state of the tire on a wet road can be more accurately evaluated, providing key data for optimizing design parameters. This evaluation method based on radial load solves the problem of quantifying wet braking mechanical properties in traditional tire design, and helps to improve the braking performance of the tire on a wet road.

[0073] Step S204, determining the design parameters of the target tire corresponding to the performance indicators, wherein the design parameters include adjustable parameters that affect the wet braking performance of the target tire.

[0074] In the above step S204, when selecting the target tire design parameters for optimization, the following aspects can be considered comprehensively:

[0075] (1) Parameter correlation analysis: that is, to identify which design parameters have a significant impact on the critical slip speed and braking force, for example, the pattern groove depth and pattern porosity are directly related to the drainage capacity, thereby affecting the critical slip speed; while the footprint length and width of the tire more affect the contact area and pressure distribution, and then affect the braking force.

[0076] (2) Parameter controllability evaluation: that is, to evaluate the adjustability and controllability of the design parameters, only those parameters that can be freely adjusted in the design stage and can bring obvious performance improvement after adjustment are worth optimizing. For example, the pattern design parameters of the tire can usually be flexibly adjusted in the design stage, while some inherent properties of the tire material may be difficult to change greatly in the design stage.

[0077] (3) Interaction between parameters: that is, to consider the interaction between design parameters, the optimization of some parameters may have positive or negative effects on other parameters, which needs to be considered when building the model to ensure that optimization does not sacrifice other important performance, for example, increasing the pattern groove depth can improve the drainage capacity, but may affect the wear resistance or noise level of the tire.

[0078] In some embodiments of the present application, for parameters directly related to the critical slip speed and braking force (first target parameters), such as groove depth, pattern porosity, indentation length and width, can be the focus of optimization; for parameters indirectly affecting or having potential impact but easy to adjust (second target parameters), can also be included in the optimization range, with lower weight and priority than the directly related parameters; for those parameters with limited performance improvement after adjustment or high adjustment cost and great technical difficulty (third target parameters), can be temporarily not optimized, and reserved as fixed values or default values.

[0079] In some embodiments of the present application, the design parameters can include ground contact footprint size parameters and pattern parameters of the target tire, wherein the ground contact footprint size parameters include the ground contact footprint length and the ground contact footprint width of the target tire on a wet road, and the pattern parameters include the groove depth and the pattern porosity of the target tire.

[0080] Specifically, in the wet braking model (for example, the wet braking model determined by the first parameters and the second parameters described above), the water flow density is p, the initial water film thickness is h0, the water film thickness is h, the road roughness is n, the friction coefficient is μ, the tire contact area is P, the tire radial load is F, the tire longitudinal pattern block stiffness is C, the groove depth is b, and the pattern porosity is ψ. R The road roughness is n, the friction coefficient is μ dw The road roughness is n, the friction coefficient is μ c The tire radial load is F, the tire contact area is P i The tire radial load is F, the tire contact area is P x The tire longitudinal pattern block stiffness is C, the groove depth is b, and the pattern porosity is ψ T The tire longitudinal pattern block stiffness is C, the groove depth is b, and the pattern porosity is ψ

[0081] X=[l,b,P T ,ψ] T ……Formula 5

[0082] Step S206, optimizing the design parameters according to the performance indicators to obtain target design parameters.

[0083] In the above step S206, the target design parameters refer to a set of design parameter values after optimization, which can effectively improve the wet braking performance of the tire, including but not limited to the groove depth, the pattern porosity, the indentation length and the indentation width of the tire, etc.

[0084] In some embodiments of this application, design parameters can be optimized using the Response Surface Methodology (RSM). Specifically: two levels, high and low, are set for each design parameter to construct a response surface; an experimental matrix is ​​generated based on the high and low levels of the design parameters, including experimental points for different combinations of design parameters, with each point representing a specific numerical combination of design parameters; actual experiments or advanced simulation techniques are performed on each point in the generated experimental matrix to collect corresponding critical slip velocity and contact area grip data, resulting in a performance dataset; a response surface model is constructed using multiple regression analysis, with design parameters as independent variables and performance indicators (critical slip velocity and braking force) as dependent variables; and an optimization algorithm (such as quadratic programming or genetic algorithm) is used to find the optimal values ​​of the performance indicators predicted by the response surface model in the design parameter space, thus obtaining the target design parameters.

[0085] To quickly find the optimal combination of design parameters, a particle swarm optimization algorithm can be used to determine the target design parameters. Specifically: the fitness corresponding to the performance index is determined, where fitness is used to quantify the wet braking performance of the target tire under the corresponding design parameter values; the fitness is determined as the objective function of the particle swarm optimization algorithm, and the design parameters are iteratively optimized based on the fitness in each iteration; under the condition of satisfying preset conditions, the output result of the particle swarm optimization algorithm is determined as the target design parameters, where the output result includes the swarm optimal solution.

[0086] In particle swarm optimization, fitness is a measure of how well a particle (i.e., a combination of design parameters) performs in relation to the optimization objective. Higher fitness indicates better wet braking performance of the tires under the given design parameter combination. In some embodiments of this application, the fitness function can be determined as:

[0087] f(x i ) = F x (x i )+v crit (x i )...Formula 6

[0088] In Formula 6, x i For the design parameter combination, F x (x i The primary performance indicator reflecting the target tire's grip on wet surfaces (refer to Formula 3), v crit (x i The first performance indicator is a second performance index reflecting the target tire's drainage capacity on wet roads (refer to Formula 2). It should be noted that different weights can be assigned to the first and second performance indicators to balance their relative importance.

[0089] By converting multiple performance indicators into one fitness function, the optimization process can be simplified, allowing the particle swarm optimization algorithm to directly iterate on design parameters without needing to consider each performance indicator separately.

[0090] In the particle swarm optimization algorithm, each particle represents a combination of design parameters, and the position and velocity of the particle are updated with each iteration. The fitness function is used as a standard to evaluate the position of the particle and guide it to move towards better design parameter combinations. The global optimal solution and the individual optimal solution of the particle are updated based on the fitness, gradually approaching the global optimal solution.

[0091] The particle swarm optimization algorithm is an efficient optimization method that simulates group intelligence behavior to quickly find the best design parameter combination. The determination of fitness is to convert the wetland braking performance indicators into a numerical form that can be processed by the algorithm, allowing the algorithm to identify and evaluate different design parameter combinations. In each iteration, the algorithm adjusts the speed and position of each individual (i.e., design parameter combination) in the current group based on its fitness, ultimately finding the individual with the highest fitness, which is the global optimal solution. This optimization method not only solves the limitations of traditional optimization algorithms in handling multi-parameter optimization problems, but also quickly converges, improves optimization efficiency, and ensures that the found design parameter combination can significantly improve the wetland braking performance of the tire.

[0092] In some embodiments of the present application, the design parameters can be iteratively optimized by the following steps: determining the update speed of each particle in the particle swarm optimization algorithm according to the fitness, wherein the update speed indicates the direction and rate of movement of the particle from the current position to the next position, and each particle position represents a design parameter combination; determining the update position of the all particles corresponding to the update speed, wherein the update position represents the updated design parameter combination.

[0093] Each particle in the particle swarm optimization algorithm has a velocity vector that determines the direction and step length of the particle movement. The calculation of the update speed is based on the historical optimal position of the particle and the historical optimal position of the group, as well as the current speed and position of the particle.

[0094] The initial speed of the particle can be determined by the following method: determining the second value range corresponding to each design parameter, and determining the range span value corresponding to each design parameter based on the second value range; determining the initial speed of the particle in the particle swarm optimization algorithm based on the range span value.

[0095] The range span value is the difference between the maximum and minimum values of the second value range. For example, if the pattern depth is between 6.5 mm and 7.5 mm as the second value range, the range span value is 1 mm. The initial velocity of the particle can be set to a certain proportion (such as 10%) of the range span value, and is usually randomly generated within this proportion range to increase the diversity of exploration. For example, if the range span value of the pattern depth is 1 mm, the initial velocity of the particle can be randomly selected within the interval of ([-0.1, 0.1]) mm.

[0096] Based on the updated velocity, the position vector of the particle is also updated accordingly, representing the iterative improvement of the design parameter combination. The updated position reflects the adjustment of the design parameters, aiming to optimize the wet braking performance of the target tire.

[0097] The determination of the updated velocity is based on the current fitness of the particle and the fitness of the group optimal solution, which can guide the particle to move towards a better design parameter combination. The calculation of the updated position is based on the updated velocity, which adjusts the position of the particle in the design parameter space, thereby forming a new design parameter combination. This updating mechanism based on fitness can ensure that the algorithm constantly approaches a better solution during iteration, and finally finds a design parameter combination that maximizes the wet braking performance.

[0098] In some embodiments of the present application, after determining the updated positions of all particles corresponding to the updated velocities, the following steps can be performed: determining a first value range of each design parameter; comparing the values of the corresponding design parameters in the updated positions of all particles with the first value range, respectively, to obtain comparison results; and in the case where the comparison results indicate that the values of the design parameters in the updated positions exceed the first value range, updating the values to the boundary values of the first value range.

[0099] Specifically, at the end of each iteration of the particle swarm optimization algorithm, the updated position of each particle, i.e. the new design parameter combination, is checked to see if it is still within the first value range. This can be done through a simple numerical comparison operation, such as checking whether the pattern groove depth is between 6 mm and 8 mm. If it is found that the value of a certain design parameter in the updated position of the particle exceeds the first value range, such as the pattern groove depth being greater than 8 mm or less than 6 mm, the value of the design parameter is adjusted to the boundary value of the first value range closest to it, in order to maintain the feasibility of the design parameter combination.

[0100] The determination of the value range can be based on physical limitations, cost considerations, and performance requirements of tire design and manufacturing, etc. For example, the groove depth cannot be less than a certain value to ensure drainage effect, while it cannot be too large to avoid increasing the rolling resistance. By comparing the numerical value of the design parameter in the updated position with the value range, unreasonable design in the optimization process can be avoided, and it is ensured that the final design parameter combination is feasible in the actual tire design.

[0101] To facilitate understanding of the above-mentioned determination process of the target design parameter, some specific embodiments are described below. Taking the response surface method as an example, the following steps can be included:

[0102] (1) Initialization of design parameters.

[0103] The design parameters are selected as the groove depth, the pattern porosity, the footprint length, and the footprint width as the design variables.

[0104] For each design parameter, a high level and a low level are set. For example, for the groove depth, the high level is set to 8 mm and the low level is set to 6 mm; the high level of the pattern porosity is 1 and the low level is 0; the high level of the footprint length is 140 mm and the low level is 110 mm; the high level of the footprint width is 200 mm and the low level is 160 mm.

[0105] An experimental matrix is generated. Taking the Box-Behnken design as an example, an experimental matrix containing different combinations of design parameters is generated. This means that in addition to the central point, the experimental matrix also includes experiments at the edge and intermediate points in the design parameter space, a total of 27 experimental points (3 parameters, 3 levels for each parameter, forming a Box-Behnken design experimental matrix).

[0106] (2) Data collection.

[0107] The 27 experimental points are tested for wet braking performance. In some embodiments of the present application, advanced simulation can be performed under certain conditions (such as a certain speed, water film thickness, temperature, etc.) to obtain the critical slip speed and braking force data corresponding to each set of design parameters. For example, for each experimental point, the above-mentioned formula 2 and formula 3 are used for calculation to obtain the simulated critical slip speed and braking force.

[0108] All simulation results are sorted to form a performance data set containing critical slip speed and braking force.

[0109] (3) Model construction and preliminary analysis.

[0110] Using multiple regression analysis, the design parameters are used as independent variables, and the performance indicators (critical slip speed and braking force) are used as dependent variables, to establish a response surface model. For example, the model can be a quadratic polynomial, which is used to capture the complex non-linear relationship between design parameters and performance indicators.

[0111] By checking the R 2 value, residual analysis chart and ANOVA analysis of the response surface model, the accuracy and reliability of the model are verified, for example, the R 2 value of the model is 0.95, indicating that the model explains 95% of the variation and is a relatively reliable model.

[0112] (4) Model optimization.

[0113] Based on the constructed response surface model, a quadratic programming algorithm is used to find the combination that can maximize ([v crit , F x ]) in the design parameter space, and the final confirmed design parameter combination is determined as the target design parameter, which can provide the best wet braking performance of the tire.

[0114] (5) Model verification and result confirmation.

[0115] The optimal design parameter combination is selected for actual experiment, for example, under the 205 / 55R16 tire specification, the wet braking performance test is conducted on the optimized design parameters, and the critical slip speed is measured as 95Km / h, and the braking force is measured as 1567.8N, while the critical slip speed of the tire before optimization is 90.79Km / h, and the braking force is 1230.75N. It can be seen that after optimization, the critical slip speed of the tire is increased by 4.21Km / h, and the braking force is increased by 337.05N, and the tire drainage capacity is increased by 104% compared with the initial scheme, and the tire drainage capacity is increased by 127% compared with the initial scheme.

[0116] Through the above steps S202 to S206, the drainage capacity and grip capacity of the tire are quantified by constructing a wet braking model, and then the design parameters of the tire are optimized, so as to achieve the purpose of optimizing the tire design parameters in the tire design stage, thereby realizing the technical effects of accurately evaluating the wet braking performance of the tire and speeding up the development cycle, and further solving the technical problems that the evaluation of the wet braking performance of the tire by related technologies depends on real vehicle test, resulting in long development cycle.

[0117] The traditional 'design-simulation-test' mode is often accompanied by high time and financial costs, and the optimization design method breaks this tradition, allowing the optimal design parameter combination to be identified through efficient calculation at the design stage, reducing the number of subsequent simulations and actual tests, thereby greatly shortening the development cycle of the tire from concept to finished product, reducing the development cost, and improving the market response speed. The construction of the wet braking model is based on the physical and mechanical principles of tire and road surface contact, and through the simulation of the drainage process and the generation of the tire grip on the wet road surface, the performance of the tire under wet and slippery conditions can be accurately predicted. The process of optimizing the design parameters is essentially an improvement of the tire design, aiming to find the best combination of tire design parameters to improve the wet braking performance and solve the technical problems of long braking distance and poor safety performance on wet and slippery roads.

[0118] Figure 3 is a structural diagram of a tire parameter optimization device according to an embodiment of the present application, as shown in Figure 3 , the device comprises:

[0119] The construction module 302 is configured to determine a performance index according to a wet braking model of a target tire, wherein the wet braking model is used to simulate the braking behavior of the target tire on a wet road surface, and the performance index includes an index reflecting the drainage capacity and grip capacity of the target tire on the wet road surface output by the wet braking model.

[0120] The determination module 304 is configured to determine a design parameter of the target tire corresponding to the performance index, wherein the design parameter includes an adjustable parameter affecting the wet braking performance of the target tire.

[0121] The optimization module 306 is configured to optimize the design parameter according to the performance index to obtain a target design parameter.

[0122] It should be noted that Figure 3 The tire parameter optimization device shown in Figure 2 is used to execute the tire parameter optimization method shown in Figure 2 , so Figure 3 The related explanations in the tire parameter optimization method in also apply to the tire parameter optimization device shown in

[0123] , which will not be described here.

[0124] For example, the processor performs the following functions by executing program instructions stored in the memory: determining a performance index according to a wet road braking model of the target tire, wherein the wet road braking model is used to simulate braking behavior of the target tire on a wet road surface, and the performance index includes an index output by the wet road braking model and reflecting drainage and grip capabilities of the target tire on the wet road surface; determining a design parameter of the target tire corresponding to the performance index, wherein the design parameter includes an adjustable parameter affecting wet road braking performance of the target tire; and optimizing the design parameter according to the performance index to obtain a target design parameter.

[0125] The embodiments of the present application further provide a non-volatile storage medium, which comprises a stored computer program, wherein a device in which the non-volatile storage medium is located performs the steps of the optimization method of the tire parameter in the embodiments of the present application by running the computer program.

[0126] The embodiments of the present application further provide a computer program product, which comprises computer instructions, and the computer instructions implement the steps of the optimization method of the tire parameter in the embodiments of the present application when executed by a processor.

[0127] The embodiments of the present application further provide a computer program, which implements the steps of the optimization method of the tire parameter in the embodiments of the present application when executed by a processor.

[0128] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0129] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0130] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the unit embodiment described above is only illustrative, and for example, the division of units can be a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0131] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0132] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0133] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0134] The above is only the preferred embodiment of the present application. It should be pointed out that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method of optimization of tire parameters, characterized in that, The method comprises: determining a performance index according to a wet road braking model of a target tire, wherein the wet road braking model is used to simulate braking behavior of the target tire on a wet road surface, and the performance index comprises an index output by the wet road braking model and reflecting drainage capacity and grip capacity of the target tire on the wet road surface; determining a design parameter of the target tire corresponding to the performance index, wherein the design parameter comprises an adjustable parameter affecting wet road braking performance of the target tire; optimizing the design parameter according to the performance index to obtain a target design parameter.

2. The method of claim 1, wherein, The wet road braking model is constructed by: determining a first parameter corresponding to the target tire, wherein the first parameter quantifies drainage capacity of a pattern of the target tire; determining a second parameter corresponding to the target tire, wherein the second parameter quantifies grip capacity of the target tire; determining the wet road braking model according to the first parameter and the second parameter.

3. The method of claim 2, wherein, The first parameter comprises a critical slip speed of the target tire when the target tire is completely water-slipped during braking on a wet road surface; determining a first parameter corresponding to the target tire comprises: obtaining physical parameters corresponding to a wet road braking environment and first structure parameters corresponding to the target tire, wherein the physical parameters are used to simulate physical conditions in a wet environment, and the first structure parameters include pattern design parameters of the target tire; determining the first parameter according to the physical parameters and the first structure parameters.

4. The method of claim 2, wherein, The second parameter comprises braking force generated by the target tire in a driving direction when the target tire brakes on a wet road surface; determining a second parameter corresponding to the target tire comprises: obtaining a friction coefficient of the target tire on a wet road surface, mechanical parameters reflecting mechanical properties between the target tire and the wet road surface, and second structure parameters corresponding to the target tire, wherein the second structure parameters include footprint size parameters of the target tire; determining the second parameter according to the friction coefficient, the mechanical parameters, and the second structure parameters.

5. The method of claim 4, wherein, The mechanical parameters include a radial load of a contact patch area of the target tire during braking on the wet road surface; the radial load is determined by: obtaining an initial radial load of the target tire in a static load state and a first area of an initial contact patch area; partitioning a footprint of the target tire during braking on the wet road surface, and determining a contact patch area, a driving speed, and a second area of the contact patch area of the target tire during braking on the wet road surface; determining the radial load according to the initial radial load, the first area, the driving speed, and the second area.

6. The method of claim 1, wherein, optimizing the design parameter according to the performance index to obtain a target design parameter comprises: determining a fitness corresponding to the performance index, wherein the fitness is used to quantify wet road braking performance of the target tire under a corresponding design parameter value; Determine the fitness as an objective function of a particle swarm optimization algorithm, and iteratively optimize the design parameters according to the fitness in each iteration; In the case that a preset condition is met, determine the particle swarm optimization algorithm output result as the target design parameters, wherein the output result includes a group optimal solution.

7. The method of claim 6, wherein, In each iteration, iteratively optimize the design parameters according to the fitness, including: Determine the update speed corresponding to each particle in the particle swarm optimization algorithm according to the fitness, wherein the update speed is used to indicate the direction and rate of movement of the particle from the current position to the next position, and each particle position represents a design parameter combination; Determine the update position of each particle corresponding to the update speed, wherein the update position is used to represent the updated design parameter combination.

8. The method of claim 7, wherein, After determining the update position of each particle corresponding to the update speed, the method further includes: Determine the first value range of each design parameter; Compare the numerical value of the corresponding design parameter in the update position of each particle with the first value range to obtain a comparison result; In the case that the comparison result indicates that the numerical value of the design parameter in the update position exceeds the first value range, update the numerical value to the boundary value of the first value range.

9. The method of claim 6, wherein, The method further includes: Determine the second value range corresponding to each design parameter, and determine the range span value corresponding to each design parameter according to the second value range; Determine the initial speed of the particle in the particle swarm optimization algorithm according to the range span value.

10. The method of claim 1, wherein, The design parameters include the footprint size parameters and the pattern parameters of the target tire, wherein the footprint size parameters include the footprint length and the footprint width of the target tire on a wet road, and the pattern parameters include the pattern groove depth and the pattern porosity of the target tire.

11. An apparatus for optimizing a tire parameter, characterized in that, Including: A construction module for determining a performance index according to a wet braking model of a target tire, wherein the wet braking model is used to simulate the braking behavior of the target tire on a wet road, and the performance index includes an index reflecting the drainage capacity and the grip capacity of the target tire on the wet road output by the wet braking model; A determination module for determining the design parameters of the target tire corresponding to the performance index, wherein the design parameters include adjustable parameters affecting the wet braking performance of the target tire; An optimization module for optimizing the design parameters according to the performance index to obtain target design parameters.

12. An electronic device, comprising: Including: A memory and a processor, the memory is used to store program instructions; the processor is connected with the memory, and is used to execute the tire parameter optimization method of any one of claims 1 to 10.

13. A non-volatile storage medium, comprising: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the tire parameter optimization method of any one of claims 1 to 10 by running the computer program.

14. A computer program product comprising computer instructions, characterized in that, The computer instructions, when executed by a processor, implement the method for optimizing a tire parameter of any one of claims 1 to 10.

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