Tire characteristic model identification method and tire characteristic model identification device

Abstract

To provide a feature capable of improving the estimation accuracy of a magic formula model used as a tire characteristic model.SOLUTION: This tire characteristic model identification method comprises: a pre-processing step; a first identification step; and a second identification step. In the pre-processing step, a first parameter group is identified in a basic formula of a magic formula model, and a second parameter group is derived using a detailed formula of the magic formula model on the basis of the first parameter group. In the first identification step, setting the second parameter group derived in the pre-processing step as an initial value and a representative parameter in the second parameter group variable, identification is carried out using the basic formula and the detailed formula. In the second identification step, setting the second parameter group updated in the first identification as an initial value and at least one of a load dependent parameter, a camber angle dependent parameter, and an inner pressure parameter variable, identification is carried out using the basic formula and the detailed formula.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a tire characteristic model identification method and a tire characteristic model identification device for identifying a group of parameters in a tire characteristic model configured using a magic formula formula. [Background technology]

[0002] For example, the magic formula is a widely known tire characteristic model that indicates tire characteristics such as tire slip angle, camber angle, tire lateral force against vertical load, etc. The magic formula is used by identifying a group of parameters in the formula based on actual measurement values ​​and analytical values ​​related to tire characteristics.

[0003] Patent Document 1 discloses a conventional method for designing a vehicle, including tires. In this design method, values ​​of parameters B through E obtained when the characteristic curves of tire lateral force and self-aligning torque are approximated by the "Magic Formula" are assigned to a vehicle model, and a running simulation is performed to evaluate performance. If the vehicle model does not satisfy predetermined performance in this performance evaluation, the values ​​of parameters B through E are modified and a running simulation is performed to evaluate vehicle performance. A tire characteristic curve defined by the modified values ​​of parameters B through E is calculated, and tire dynamic element parameters are derived from this characteristic curve based on a tire dynamic model configured using multiple tire dynamic element parameters. On the other hand, if the vehicle model satisfies the predetermined performance, the tire dynamic element parameters corresponding to parameters B through E are determined as the required tire characteristics. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-259975 Summary of the Invention [Problem to be solved by the invention]

[0005] The Magic Formula model representing the characteristics of a tire is configured using a group of nonlinear and complex parameters. The inventors have considered that, in the multi-stage identification of the complex parameter group in the Magic Formula model, the estimation accuracy of the Magic Formula model used as a tire characteristic model can be improved by devising the selection of parameters to be identified in each identification stage.

[0006] The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a tire characteristic model identification method and a tire characteristic model identification device that can improve the estimation accuracy of a magic formula model used as a tire characteristic model. [Means for solving the problem]

[0007] One aspect of the present invention is a tire characteristic model identification method, which identifies parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that expresses a first group of parameters included in the basic equation by a function using a second group of parameters, and includes a preprocessing step of calculating the first group of parameters by performing an optimization calculation using the basic equation, and deriving the second group of parameters by analysis using the detailed equation based on the calculated first group of parameters; a first identification step of setting parameter conditions under which the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter are fixed, performing an optimization calculation using the basic equation and the detailed equation to identify the representative parameter, and updating the second parameter group; and a second identification step of setting the second parameter group updated in the first identification step as an initial value, making one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter variable, setting parameter conditions under which the second parameter group other than the variable parameter is fixed, performing an optimization calculation using the basic equation and the detailed equation to identify the variable parameter, and updating the second parameter group.

[0008] Another aspect of the present invention is a tire characteristic model identification device that identifies parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that expresses a first group of parameters included in the basic equation by a function using a second group of parameters, the tire characteristic model identification device comprising: a pre-processing unit that performs an optimization calculation using the basic equation to calculate the first group of parameters and analytically derives the second group of parameters using the detailed equation based on the calculated first group of parameters; and a parameter identification unit that identifies the second group of parameters using the second group of parameters derived by the pre-processing unit, wherein the parameter identification unit uses the second group of parameters derived by the pre-processing unit as initial values ​​and performs an optimization calculation using the a first identification step in which a parameter condition is set under which a representative parameter in a second parameter group is made variable and a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter are fixed, an optimization calculation is performed using the basic equation and the detailed equation to identify the representative parameter, and the second parameter group is updated; and a second identification step is performed in which the second parameter group updated in the first identification step is used as an initial value, one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter is made variable, a parameter condition is set under which the second parameter group other than the variable parameter is fixed, an optimization calculation is performed using the basic equation and the detailed equation to identify the variable parameters, and the second parameter group is updated. [Effects of the Invention]

[0009] According to the present invention, it is possible to improve the estimation accuracy of the magic formula model used as a tire characteristic model. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a functional configuration of a tire characteristic model identification device according to an embodiment. [Figure 2] 10 is a diagram showing the processing content by the preprocessing unit. [Figure 3] 10 is a flowchart showing a procedure for parameter identification processing. [Figure 4] 10 is a diagram showing the processing contents in the first identification step and the second identification step. [Figure 5] 10 is a diagram showing the processing contents in the third identification step and the fourth identification step. [Figure 6] 10 is a diagram showing the processing content in the fifth identification step. [Figure 7] FIG. 10 is a schematic diagram showing an example of test data used for parameter identification. [Figure 8] 4 is a graph showing tire characteristics identified by the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present invention will be described below based on preferred embodiments with reference to Figures 1 to 8. The same or equivalent components and members shown in each drawing are designated by the same reference numerals, and duplicate descriptions will be omitted where appropriate. The dimensions of the members in each drawing are enlarged or reduced as appropriate to facilitate understanding. Some members that are not important for explaining the embodiments will be omitted from the drawings.

[0012] (Embodiment) FIG. 1 is a block diagram showing the functional configuration of a tire characteristic model identification device 100 according to an embodiment. The tire characteristic model identification device 100 includes a storage unit 10, an operation unit 20, a display unit 30, and a calculation processing unit 40, and identifies a group of parameters of a magic formula model that represents tire characteristics. The magic formula model, for which the group of parameters has been identified, is a function that calculates the tire longitudinal force Fx relative to the tire slip ratio S, the tire lateral force Fy and moment Mz relative to the tire slip angle α, and the like, and is used, for example, in vehicle motion analysis. The independent variables in the magic formula model are the slip angle α and the slip ratio S. The magic formula model is also referred to as an MF model.

[0013] The tire characteristic model identification device 100 is an information processing device such as a PC (personal computer). Each unit in the tire characteristic model identification device 100 can be realized in terms of hardware by an electronic processing circuit made up of electronic elements such as a computer CPU, or mechanical parts, and in terms of software by a computer program, etc. However, here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

[0014] The storage unit 10 is a storage device configured, for example, by an SSD (Solid State Drive), a hard disk, a CD-ROM, a DVD, etc. The storage unit 10 stores test data 11 used for identifying the tire characteristic model, parameter group data 12, a computer program executed by the calculation processing unit 40, data used for executing the computer program, etc.

[0015] The test data 11 is data such as longitudinal force Fx versus slip ratio S of the tire, and lateral force Fy and moment Mz versus slip angle α, measured by a tire testing machine 90. The longitudinal force Fx, lateral force Fy, and moment Mz of the tire are measured at various measurement points (approximately 50 to 100 points) while varying the slip ratio S and slip angle α. Note that the number of measurement points at which the slip ratio S and slip angle α are varied is not limited to 50 to 100 points.

[0016] When measuring the tire's longitudinal force Fx, lateral force Fy, and moment Mz, the conditions for the tire load Fz, camber angle γ, and tire internal pressure P are set. Multiple conditions for the tire load Fz, camber angle γ, and tire internal pressure P are set. The tire's longitudinal force Fx, lateral force Fy, and moment Mz are defined based on the axial directions of the vehicle when the tire is mounted on the vehicle. The tire's longitudinal force Fx is the force that the tire receives from the road surface with which it contacts in the direction in which the vehicle travels straight (the longitudinal direction). The tire's lateral force Fy is the force that the tire receives from the road surface with which it contacts in the left-right direction of the vehicle. The moment Mz is a moment force that acts around a vertical axis that passes through the center of the tire. The tire center is defined, for example, as the center position of the tire in the width direction on the tire's rotation axis. The tire load Fz is a force that is applied to the tire in the vertical direction due to the vehicle weight, etc. The moment Mz may be considered to be a moment force acting around an axis that passes through the center of the tire and is perpendicular to the tire rotation axis and the front-to-rear direction.

[0017] The tire testing machine 90 is capable of measuring the longitudinal force Fx, lateral force Fy, and moment Mz of the tire by simulating the movement of the tire when braking and cornering a vehicle. The tire testing machine 90 may be a known testing machine that has a drive mechanism for rotating the tire at high speed, such as a drum type or flat belt type, and measures the forces and moments acting on the tire using a measuring means such as a six-component force meter.

[0018] The test data 11 may include data measured for a plurality of tires using the tire testing machine 90. The test data 11 may be stored by assigning tire identification information to each of the plurality of tires and linking the test data for the tire corresponding to each identification information so that the data can be read out.

[0019] The parameter group data 12 is a plurality of parameters that are updated in the multi-stage identification of the magic formula model 42a that represents tire characteristics, which will be described later, and is updated in the process of multiple identification calculations to reach a final determined value.

[0020] The operation unit 20 has operable input devices such as a touch panel, switches, keyboard, and mouse device, and accepts user operation input. The operation unit 20 accepts user operation input related to parameter identification of the Magic Formula model 42a. The display unit 30 has a display device such as a liquid crystal display, and displays various data in the parameter identification process of the Magic Formula model 42a and a screen for accepting user operation input.

[0021] The arithmetic processing unit 40 has a data acquisition unit 41, a tire characteristics calculation unit 42, a pre-processing unit 43, and a parameter identification unit 44. The arithmetic processing unit 40 is an electronic circuit that executes arithmetic processing, such as a CPU, and functions by reading and executing computer programs and data stored in the storage unit 10. The data acquisition unit 41, the tire characteristics calculation unit 42, the pre-processing unit 43, and the parameter identification unit 44 in the arithmetic processing unit 40 may be constructed as multiple program modules formed by a computer program.

[0022] The data acquisition unit 41 reads out the tire test data 11 from the storage unit 10 and outputs it to the pre-processing unit 43 and the parameter identification unit 44. The data acquisition unit 41 may also receive a user input from the operation unit 20 specifying a tire for which parameter identification is to be performed, and read out the test data corresponding to the specified tire from the test data 11 stored in the storage unit 10 based on the tire identification information.

[0023] The tire characteristic calculation unit 42 has a magic formula model 42a, and calculates the tire longitudinal force Fx, lateral force Fy, and moment Mz based on the magic formula model 42a. The magic formula model 42a has a basic formula using coefficients B, C, D, and E. The basic formula of the magic formula model 42a is also called the basic Pacejka formula after its creator, and is written in a format including the following functions: y=D sin[C arctan{B xE(B x-arctan(B x))}] ···(1)

[0024] The x on the right side of equation (1) represents an independent variable, which is either one of the slip angle α and the slip ratio S itself, or one of the slip angle α and the slip ratio S added or multiplied by a coefficient, etc. This means that the slip angle α and the slip ratio S are treated as independent variables on the right side of equation (1). Note that the notation "B x" on the right side of equation (1) represents the multiplication of B and x.

[0025] The basic equation of the magic formula model 42a includes a term y expressed in equation (1), and is defined independently for the tire longitudinal force Fx, lateral force Fy, and moment Mz, and is expressed by including terms that are further added to y and terms that are further multiplied by y. In the basic equation of the magic formula model 42a, coefficients are also set for the terms that are further added to y and terms that are further multiplied by y. A feature of the basic equation of the magic formula model 42a is that by including calculations using sine functions and arctan functions, an empirical equation that provides a good fit to tire characteristics obtained from tire test data 11 can be obtained.

[0026] The parameters in the basic equation of the magic formula model 42a, including the coefficients B, C, D, and E on the right-hand side of equation (1), are referred to as the first parameter group. A detailed equation of the magic formula model 42a is defined, which expresses the first parameter group, including the coefficients B, C, D, and E, etc., using a second parameter group, which further includes multiple coefficients, taking into account the load conditions, camber angle conditions, and internal pressure conditions. The magic formula model 42a is configured to include the basic equation and the detailed equation.

[0027] For example, the shape of the curve representing tire characteristics, such as tire longitudinal force Fx relative to slip ratio S, changes depending on tire load Fz, camber angle γ, and tire internal pressure P. If coefficients B, C, D, and E on the right-hand side of equation (1) were determined uniquely (to constant values) through identification, the conditions of tire load Fz, camber angle γ, and tire internal pressure P would not be taken into consideration, and the tire characteristics model would be insufficient to be used in the dynamic analysis of a vehicle or the like.

[0028] For this reason, the detailed formula of the magic formula model 42a defines the coefficients B, C, D, and E using a second parameter group that describes their dependence on the tire load Fz, camber angle γ, and tire internal pressure P. The second parameter group includes a representative parameter that provides values ​​for the coefficients B, C, D, and E under conditions of a reference tire load Fzo, a reference camber angle γo, and a reference tire internal pressure Po. The second parameter group includes a load-dependent parameter, a camber angle-dependent parameter, and an internal tire pressure-dependent parameter that are prepared to change the values ​​of the coefficients B, C, D, and E when the tire load Fz, the camber angle γ, and the tire internal pressure P change from their reference values. By including the representative parameter, the load-dependent parameter, the camber angle-dependent parameter, and the internal tire pressure-dependent parameter, the second parameter group provides the dependence of the coefficients B, C, D, and E on the tire load Fz, the camber angle γ, and the tire internal pressure P.

[0029] For example, a load-dependent parameter is a coefficient by which the amount of change in tire load Fz from the reference tire load Fzo is multiplied. A camber angle-dependent parameter is a coefficient by which the amount of change in camber angle γ from the reference camber angle γo is multiplied. An internal pressure-dependent parameter is a coefficient by which the amount of change in tire internal pressure P from the reference tire internal pressure Po is multiplied. The reference tire load Fzo is determined taking into account the weight of the vehicle on which the tire is to be mounted, etc. The reference camber angle γo is a design value assumed when mounting the tire on a vehicle, and the reference tire internal pressure Po is a set value for tire air pressure that is set taking into account the weight of the vehicle, etc.

[0030] The tire characteristics calculation unit 42 calculates the tire longitudinal force Fx, lateral force Fy, and moment Mz by providing the values ​​of the second parameter group to the magic formula model 42a. The tire characteristics calculation unit 42 updates each parameter based on a multi-stage identification process that changes the conditions for the parameter group by the parameter identification unit 44, and is equipped with the magic formula model 42a described using the finally determined second parameter group.

[0031] The pre-processing unit 43 calculates the first parameter group through identification processing and analytically derives the second parameter group as a preliminary process before the parameter identification unit 44 performs identification processing of the second parameter group, which will be described later. Fig. 2 is a diagram showing the processing content by the pre-processing unit 43. The pre-processing unit 43 performs identification processing of the first parameter group through optimization calculations using the basic equations of the magic formula model 42a for the tire longitudinal force Fx, lateral force Fy, and moment Mz.

[0032] In the identification process, the preprocessing unit 43 sets parameter conditions that make the first parameter group, including the coefficients B, C, D, and E in the above-described equation (1), variable. The preprocessing unit 43 determines optimal values ​​for the first parameter group using values ​​calculated by the magic formula model 42a and an objective function calculated based on the test data 11. The objective function is, for example, a function that calculates the sum of squares of the error between the value calculated by the magic formula model 42a and the test data 11, and the closer the calculated value is to 0, the smaller the error of the magic formula model 42a. Various known identification processing methods can be used for the identification process by optimization calculation in the preprocessing unit 43. For example, it is known that in analysis tools such as MATLAB (registered trademark), identification process by optimization calculation can be performed using functions such as fmincon and lsqnonlin.

[0033] The preprocessing unit 43 substitutes each parameter of the first parameter group calculated by the identification process into the detailed equation of the Magic Formula model 42a and analytically derives each parameter of the second parameter group. By substituting each parameter of the first parameter group calculated by the identification process into the detailed equation of the Magic Formula model 42a, a simultaneous equation is derived, and the preprocessing unit 43 can calculate the values ​​of the parameters included in the second parameter group by solving the simultaneous equation. The preprocessing unit 43 outputs the calculated values ​​of each parameter of the second parameter group to the parameter identification unit 44.

[0034] The parameter identification unit 44 includes a condition setting unit 44a and an identification calculation unit 44b. The condition setting unit 44a sets conditions (hereinafter referred to as parameter conditions) for fixed parameters and variable parameters at each stage of the multi-stage identification process. The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group input from the pre-processing unit 43 as initial values ​​in the identification process.

[0035] The identification calculation unit 44b varies the parameters in accordance with the parameter conditions set by the condition setting unit 44a and determines optimal parameters at each stage based on optimization calculations. The identification calculation unit 44b provides the parameter conditions set by the condition setting unit 44a to the magic formula model 42a of the tire characteristics calculation unit 42, and calculates the tire longitudinal force Fx, lateral force Fy, and moment Mz as dependent variables.

[0036] The identification calculation unit 44b determines optimal values ​​for the parameter group using values ​​calculated by the Magic Formula model 42a and an objective function calculated based on the test data 11. As with the identification process in the pre-processing unit 43, the objective function is a function that calculates, for example, the sum of squares of the error between the value calculated by the Magic Formula model 42a and the test data 11, and the closer the calculated value is to 0, the smaller the error of the Magic Formula model 42a. As with the identification process in the pre-processing unit 43, various known identification processing methods can be used for the identification process by optimization calculation in the identification calculation unit 44b, and as described above, functions in an analysis tool such as MATLAB (registered trademark) can be used.

[0037] The identification calculation unit 44b stores the data of the optimum parameter group determined at each stage of the multi-stage identification process in the storage unit 10 as parameter group data 12.

[0038] Next, the operation of parameter identification by the tire characteristic model identification device 100 will be described. Fig. 3 is a flowchart showing the procedure of the parameter identification process. The data acquisition unit 41 of the tire characteristic model identification device 100 reads out test data 11 related to a target tire for which parameters of the Magic Formula model 42a are to be identified from the storage unit 10 (S1). The pre-processing unit 43 performs identification processing of a first group of parameters through optimization calculations using basic equations of the Magic Formula model 42a for the tire longitudinal force Fx, lateral force Fy, and moment Mz (S2).

[0039] The preprocessing unit 43 substitutes each parameter of the first parameter group calculated by the identification process in step S2 into the detailed formula of the magic formula model 42a, and analytically derives each parameter of the second parameter group (S3). The condition setting unit 44a of the parameter identification unit 44 sets a variable k, which counts the number of process repetitions, to 1 (S4), and sets parameter conditions for the first stage of identification process (first identification step) (S5).

[0040] The identification calculation unit 44b performs an optimization calculation for the parameter group in the magic formula model 42a based on the parameter conditions set in step S5 (S6). The identification calculation unit 44b stores the parameter group obtained as a result of the optimization calculation in step S6 in the storage unit 10 as parameter group data 12 (S7).

[0041] The identification calculation unit 44b determines whether the variable k has reached the number of repetitions N (=5) (S8), and if it determines that it has not (S8: NO), adds 1 to the variable k (S9). The condition setting unit 44a sets the parameter conditions for the next identification step based on the parameter group data 12 determined by the identification calculation unit 44b (S10), and returns the process to step S6. By returning the process to step S6, the identification process from the second identification step onwards is executed. The identification steps proceed up to the fifth identification step. If it is determined in step S8 that the variable k has reached the number of repetitions N (S8: YES), the process ends.

[0042] [First identification step] 4 is a diagram showing the processing contents in the first identification step and the second identification step. The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group calculated by the preprocessing unit 43 as initial values ​​in the first identification step. In the first identification step, the condition setting unit 44a sets parameter conditions that make the representative parameter in the second parameter group variable and fix the load-dependent parameters, camber angle-dependent parameters, and internal pressure-dependent parameters to their initial values.

[0043] The identification calculation unit 44b performs an optimization calculation using the basic and detailed equations of the magic formula model 42a under the parameter conditions set by the condition setting unit 44a, and performs identification processing on the second parameter set. The identification calculation unit 44b updates the values ​​of the representative parameters included in the second parameter set as a processing result.

[0044] In the first identification step, test data 11 is used in which the tire load Fz is varied under the conditions of a reference tire internal pressure Po and a reference camber angle γo (e.g., 0°). The identification calculation unit 44b performs identification calculation for each piece of test data 11 corresponding to each tire load Fz. The identification calculation unit 44b averages the representative parameters calculated for each tire load Fz to determine the representative parameter as the processing result of the first identification step.

[0045] [Second identification step] The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group calculated in the first identification step as initial values ​​in the second identification step. In the second identification step, the condition setting unit 44a sets parameter conditions that make the load-dependent parameters in the second parameter group variable and fix the representative parameters, camber angle-dependent parameters, and internal pressure-dependent parameters to their initial values.

[0046] The identification calculation unit 44b performs an optimization calculation using the basic and detailed equations of the magic formula model 42a under the parameter conditions set by the condition setting unit 44a, and performs identification processing on the second parameter group. The identification calculation unit 44b updates the values ​​of the load-dependent parameters included in the second parameter group as a processing result.

[0047] The second identification step is a process for identifying the load dependency of the magic formula model 42a, and therefore uses test data 11 measured by changing the tire load Fz under the conditions of a reference tire internal pressure Po and a reference camber angle γo. The identification calculation unit 44b may determine the load-dependent parameter as the processing result of the second identification step by averaging the multiple load-dependent parameters calculated by the identification process.

[0048] [Third Identification Step] 5 is a diagram showing the processing contents in the third identification step and the fourth identification step. The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group calculated in the second identification step as initial values ​​in the third identification step. In the third identification step, the condition setting unit 44a sets parameter conditions that make the camber angle-dependent parameters in the second parameter group variable and fix the representative parameters, load-dependent parameters, and internal pressure-dependent parameters to their initial values.

[0049] The identification calculation unit 44b performs an optimization calculation using the basic and detailed equations of the magic formula model 42a under the parameter conditions set by the condition setting unit 44a, and performs identification processing on the second parameter group. The identification calculation unit 44b updates the values ​​of the camber angle dependent parameters included in the second parameter group as processing results.

[0050] The third identification step is a process for identifying the camber angle dependency of the Magic Formula model 42a, and therefore uses test data 11 measured while varying the camber angle γ under the condition of a reference tire internal pressure Po. The identification calculation unit 44b may determine the camber angle dependent parameter as the processing result of the third identification step by averaging multiple camber angle dependent parameters calculated by the identification process. Note that, since the identification process related to the load dependency of the Magic Formula model 42a is performed in the second identification step, the identification calculation in the third identification step may be performed including test data 11 measured while varying the tire load Fz.

[0051] [Fourth identification step] The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group calculated in the third identification step as initial values ​​in the fourth identification step. In the fourth identification step, the condition setting unit 44a sets parameter conditions that make the internal pressure-dependent parameters in the second parameter group variable and fix the representative parameters, load-dependent parameters, and camber angle-dependent parameters to their initial values.

[0052] The identification calculation unit 44b performs an optimization calculation using the basic and detailed equations of the magic formula model 42a under the parameter conditions set by the condition setting unit 44a, and performs identification processing on the second parameter group. The identification calculation unit 44b updates the values ​​of the internal pressure-dependent parameters included in the second parameter group as processing results.

[0053] The fourth identification step is a process of identifying the dependency of the magic formula model 42a on the tire internal pressure, but since the second and third identification steps perform identification processing related to the load dependency and camber angle dependency of the magic formula model 42a, the identification calculation in the fourth identification step may be performed using all test data 11 including test data measured while changing the tire internal pressure P. The identification calculation unit 44b may determine the camber angle dependent parameter as the processing result of the fourth identification step by averaging multiple internal pressure dependent parameters calculated by the identification processing.

[0054] [5th ​​identification step] 6 is a diagram showing the processing content in the fifth identification step. The parameter identification unit 44 uses the values ​​of each parameter in the second parameter group calculated in the fourth identification step as initial values ​​in the fifth identification step. In the fifth identification step, the condition setting unit 44a sets parameter conditions that make all parameters in the second parameter group (representative parameters, load-dependent parameters, camber angle-dependent parameters, and internal pressure-dependent parameters) variable.

[0055] The identification calculation unit 44b performs an optimization calculation using the basic and detailed equations of the magic formula model 42a under the parameter conditions set by the condition setting unit 44a, and performs identification processing on the second parameter set. The identification calculation unit 44b updates the values ​​of all parameters included in the second parameter set as a processing result.

[0056] In the fifth identification step, an identification calculation is performed including all test data 11 measured while changing the tire load Fz, camber angle γ, and tire internal pressure P, and the values ​​of the parameters included in the second parameter group obtained as the processing result are determined as final values. Note that the identification calculation unit 44b may determine the second parameter group as the processing result of the fifth identification step by averaging the multiple parameters calculated by the identification process.

[0057] The parameter identification unit 44 may change the order in which the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter are identified in the second identification step to the fourth identification step. The parameter identification unit 44 may identify one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter in the second identification step, identify one of the remaining two parameters in the third identification step, and identify the last remaining parameter in the fourth identification step.

[0058] Next, the test data 11 used for parameter identification by the tire characteristic model identification device 100 will be described. FIG. 7 is a schematic diagram showing an example of the test data 11 used for parameter identification. The test data 11 may include, for example, braking test data that simulates tire braking and cornering test data that simulates tire cornering. The braking test data is used for parameter identification of the magic formula model 42a that represents the tire longitudinal force Fx. The cornering test data is used for parameter identification of the magic formula model 42a that represents the tire lateral force Fy and moment Mz.

[0059] The braking test data is data obtained by, for example, fixing the camber angle γ at one condition (γ=G2°) and varying the tire load Fz under four conditions, and measuring the tire longitudinal force Fx, lateral force Fy, and moment Mz using a tire testing machine 90. For example, the braking test data is obtained by varying the tire load Fz to values ​​obtained by multiplying it by ratios R1, R2, R3, and R4 relative to the load index LI, which is determined by the weight of the vehicle when stationary, and storing the data measured by the tire testing machine 90 in test data files BT1, BT2, BT3, and BT4, respectively, to form a total of four test data files. The ratios R1, R2, R3, and R4 are, for example, values ​​in the range of 0.1 to 1.5, but are not limited to this range.

[0060] The cornering test data is data obtained by measuring the tire longitudinal force Fx, lateral force Fy, and moment Mz using a tire testing machine 90, while varying the tire load Fz under four conditions (R1×LI, R2×LI, R3×LI, and R4×LI) and the camber angle γ under three conditions (G1°, G2°, and G3°). Regarding the camber angle γ, for example, G1 may take a value in the range of -7 to -1, G2 may take a value in the range of -1 to 1, and G3 may take a value in the range of 1 to 7, but these ranges are not limited to these. When the camber angle γ is G1°, the tire load Fz is varied under four conditions, and the data measured by the tire testing machine 90 is stored in test data files CT1, CT2, CT3, and CT4, respectively. When the camber angle γ is G2 degrees, the tire load Fz is changed under four conditions, and the data measured by the tire testing machine 90 is stored in test data files CT5, CT6, CT7, and CT8, respectively. When the camber angle γ is G3 degrees, the tire load Fz is changed under four conditions, and the data measured by the tire testing machine 90 is stored in test data files CT9, CT10, CT11, and CT12, respectively.

[0061] The tire characteristic model identification device 100 can perform identification processes in the pre-processing, first identification step, and second identification step by using four test data files BT1, BT2, BT3, and BT4 for braking test data. As described above, in the pre-processing, the basic equations of the Magic Formula model 42a are used to perform identification processes for the first parameter group. In the first identification step, the detailed equations of the Magic Formula model 42a are used to perform identification processes for the representative parameters in the second parameter group. In the second identification step, the detailed equations of the Magic Formula model 42a are used to perform identification processes for the load-dependent parameters in the second parameter group.

[0062] Furthermore, with regard to braking test data, by varying the camber angle γ and tire internal pressure P and measuring the data using the tire testing machine 90, it is possible to perform identification processing for the camber angle-dependent parameters and internal pressure-dependent parameters in the second parameter group.

[0063] The tire characteristic model identification device 100 can perform identification processes in pre-processing, a first identification step, a second identification step, and a third identification step by using 12 test data files CT1 to CT12 for the cornering test data. The tire characteristic model identification device 100 performs identification processes for the first parameter group, a representative parameter in the first parameter group, a load-dependent parameter, and a camber angle-dependent parameter through the pre-processing and the first to third identification steps, respectively. Furthermore, with regard to the cornering test data, the tire internal pressure P is changed and data is measured by the tire testing machine 90, thereby performing identification processes for the internal pressure-dependent parameters in the second parameter group.

[0064] Fig. 8 is a graph showing tire characteristics identified by the present embodiment. In Fig. 8, the solid line is a graph showing tire characteristics identified by the method of the present embodiment, and the dashed line is a graph showing tire characteristics identified by a conventional method. The conventional method identifies parameters in a single stage without relying on the method of the present embodiment. It can be seen that the method of the present embodiment performs a multi-stage identification process, thereby making it possible to obtain a graph of tire characteristics that approximates the values ​​measured by the tire testing machine 90.

[0065] The tire characteristic model identification method in this embodiment identifies parameters for a basic equation of the Magic Formula model 42a that represents tire characteristics and a detailed equation of the Magic Formula model 42a that expresses a first group of parameters included in the basic equation by a function using a second group of parameters, based on test data 11. As a preprocessing step, the tire characteristic model identification method calculates the first group of parameters by performing an optimization calculation using the basic equation of the Magic Formula model 42a, and derives the second group of parameters by analysis using the detailed equation based on the calculated first group of parameters.

[0066] The first identification step in the tire characteristic model identification method sets parameter conditions in which the second parameter group derived in the preprocessing step is used as an initial value, the representative parameters in the second parameter group are variable, and the load-dependent parameters, camber angle-dependent parameters, and internal pressure-dependent parameters are fixed, and performs optimization calculations using the basic and detailed equations of the magic formula model 42a to identify the representative parameters and update the second parameter group.

[0067] In the second identification step, the second parameter group updated in the first identification step is set as an initial value, one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter is made variable, and parameter conditions are set in which the second parameter group other than the variable parameters is fixed, and an optimization calculation is performed using the basic equation and detailed equation of the magic formula model 42a to identify the variable parameters, and the second parameter group is updated. This tire characteristic model identification method can improve the estimation accuracy by the magic formula model used as the tire characteristic model.

[0068] In the second identification step, the tire characteristic model identification method varies the load-dependent parameters and identifies the load-dependent parameters based on test data 11 measured while changing the tire load under conditions of a reference tire internal pressure and a reference camber angle. This allows the tire characteristic model identification method to identify the second parameter group sequentially starting from the load-dependent parameters for the load-dependent parameters, camber angle-dependent parameters, and internal pressure-dependent parameters.

[0069] In addition, in the tire characteristic model identification method, in the second identification step, the load-dependent parameters calculated for each tire load are determined by averaging. This allows the tire characteristic model identification method to identify the load-dependent parameters by multiple identification processes and derive more accurate parameters by averaging the multiple calculated load-dependent parameters.

[0070] Furthermore, the third identification step in the tire characteristic model identification method sets parameter conditions under which the second parameter group updated in the second identification step is used as an initial value, the camber angle-dependent parameters are variable, and the second parameter group other than the camber angle-dependent parameters is fixed, and performs optimization calculations using the basic equations and detailed equations of the magic formula model 42a to identify the camber angle-dependent parameters and update the second parameter group. This allows the tire characteristic model identification method to perform identification processing for the camber angle-dependent parameters after identifying the load-dependent parameters.

[0071] Furthermore, a fourth identification step in the tire characteristic model identification method sets parameter conditions such that the second parameter group updated in the third identification step is used as an initial value, the internal pressure-dependent parameters are variable, and the second parameter group other than the internal pressure-dependent parameters is fixed, and performs optimization calculations using the basic equations and detailed equations of the magic formula model 42a to identify the internal pressure-dependent parameters and update the second parameter group. This allows the tire characteristic model identification method to perform identification processing for the load-dependent parameters, the camber angle-dependent parameters, and the internal pressure-dependent parameters in that order.

[0072] Furthermore, in the fifth identification step of the tire characteristic model identification method, the second parameter group updated in the fourth identification step is set as initial values, parameter conditions are set to make all parameters in the second parameter group variable, and an optimization calculation is performed using the basic equation and detailed equation of the magic formula model 42a to identify all parameters in the second parameter group and update the second parameter group. As a result, the tire characteristic model identification method performs an identification process as the final identification process in which the parameters updated in the fourth identification step are set as initial values ​​and the second parameter group is variable, thereby enabling more accurate parameters to be derived.

[0073] The tire characteristic model identification device 100 identifies parameters based on test data for a basic equation of the Magic Formula model 42a that represents tire characteristics and a detailed equation of the Magic Formula model 42a that represents a first group of parameters included in the basic equation by a function using a second group of parameters. A pre-processing unit 43 of the tire characteristic model identification device 100 performs optimization calculations using the basic equation of the Magic Formula model 42a to calculate the first group of parameters, and derives the second group of parameters by analysis using the detailed equation based on the calculated first group of parameters.

[0074] The tire characteristic model identification device 100 includes a parameter identification unit 44 that identifies the second parameter group using the second parameter group derived by the pre-processing unit 43. As a first identification step, the parameter identification unit 44 sets parameter conditions under which the second parameter group derived by the pre-processing unit 43 is used as an initial value, a representative parameter in the second parameter group is variable, and a load-dependent parameter, a camber angle-dependent parameter, and an inner pressure-dependent parameter are fixed, and performs optimization calculations using the basic equations and detailed equations of the magic formula model 42a to identify the representative parameters and update the second parameter group.

[0075] In the second identification step, the parameter identification unit 44 sets a parameter condition in which the second parameter group updated in the first identification step is set as an initial value, one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter is made variable, and the second parameter group other than the variable parameters is fixed, and performs an optimization calculation using the basic equation and detailed equation of the magic formula model 42a to identify the variable parameters and update the second parameter group. This enables the tire characteristic model identification device 100 to improve the estimation accuracy by the magic formula model used as a tire characteristic model.

[0076] The technical ideas embodied in the above embodiments can be generalized to include the technical ideas described in the following items.

[0077] The first item is a tire characteristic model identification method for identifying parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that represents a first group of parameters included in the basic equation by a function using a second group of parameters, the method comprising: a pre-processing step of calculating the first group of parameters by performing an optimization calculation using the basic equation; and deriving the second group of parameters by analysis using the detailed equation based on the calculated first group of parameters; and a pre-processing step of setting the second group of parameters derived in the pre-processing step as initial values, making representative parameters in the second group of parameters variable, and adjusting load-dependent parameters, camber angle-dependent parameters, and the like. a first identification step of setting parameter conditions under which a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter are fixed, performing an optimization calculation using the basic equation and the detailed equation to identify the representative parameter, and updating the second parameter group; and a second identification step of setting parameter conditions under which the second parameter group updated in the first identification step is used as an initial value, making one of a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter variable, and fixing the second parameter group other than the variable parameter, performing an optimization calculation using the basic equation and the detailed equation to identify the variable parameter, and updating the second parameter group.

[0078] A second item is the tire characteristic model identification method according to the first item, in which the second identification step makes the load-dependent parameter variable, and identifies the load-dependent parameter based on the test data measured while changing the tire load under conditions of a reference tire internal pressure and a reference camber angle.

[0079] A third item is the tire characteristic model identification method according to the second item, wherein the second identifying step determines the load-dependent parameter by averaging the load-dependent parameters calculated for each tire load.

[0080] A fourth item is a tire characteristic model identification method according to the second or third item, further comprising a third identification step of setting parameter conditions under which the second group of parameters updated in the second identification step is set as an initial value, the camber angle dependent parameters are variable, and the second group of parameters other than the camber angle dependent parameters are fixed, performing an optimization calculation using the basic equations and the detailed equations to identify the camber angle dependent parameters, and updating the second group of parameters.

[0081] A fifth item is a tire characteristic model identification method according to the fourth item, further including a fourth identification step of setting parameter conditions under which the second parameter group updated in the third identification step is set as an initial value, the internal pressure-dependent parameter is set as a variable, and the second parameter group other than the internal pressure-dependent parameter is fixed, performing an optimization calculation using the basic equation and the detailed equation to identify the internal pressure-dependent parameter, and updating the second parameter group.

[0082] A sixth item is the tire characteristic model identification method according to the fifth item, further including a fifth identification step of setting a parameter condition under which the second parameter group updated in the fourth identification step is set as an initial value, all parameters in the second parameter group are variable, performing an optimization calculation using the basic equation and the detailed equation to identify all parameters in the second parameter group, and updating the second parameter group.

[0083] A seventh item is a tire characteristic model identification device that identifies parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that represents a first group of parameters included in the basic equation by a function using a second group of parameters, the device comprising: a pre-processing unit that performs an optimization calculation using the basic equation to calculate the first group of parameters, and that analytically derives the second group of parameters using the detailed equation based on the calculated first group of parameters; and a parameter identification unit that identifies the second group of parameters using the second group of parameters derived by the pre-processing unit, wherein the parameter identification unit uses the second group of parameters derived by the pre-processing unit as initial values ​​and determines a substitute value in the second group of parameters. The tire characteristic model identification device performs a first identification step of setting parameter conditions under which a table parameter is variable and a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter are fixed, performing an optimization calculation using the basic equations and the detailed equations to identify the representative parameter, and updating the second parameter group; and a second identification step of setting the second parameter group updated in the first identification step as an initial value, setting parameter conditions under which one of the load-dependent parameter, the camber angle-dependent parameter, and the internal pressure-dependent parameter is variable, and fixing the second parameter group other than the variable parameters, performing an optimization calculation using the basic equations and the detailed equations to identify the variable parameters, and updating the second parameter group.

[0084] The present invention has been described above based on the embodiments. These embodiments are merely examples, and it will be understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and that such modifications and changes also fall within the scope of the claims of the present invention. Therefore, the descriptions and drawings in this specification should be treated as illustrative rather than restrictive. [Explanation of symbols]

[0085] 11 Test data, 42a Magic formula model, 43 Preprocessing section, 44 Parameter identification unit, 100 Tire characteristic model identification device.

Claims

1. A tire characteristic model identification method for identifying parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that represents a first group of parameters included in the basic equation by a function using a second group of parameters, comprising: a pre-processing step of calculating the first group of parameters by performing an optimization calculation using the basic equations, and deriving the second group of parameters by analysis using the detailed equations based on the calculated first group of parameters; a first identification step of setting parameter conditions in which the second parameter group derived in the preprocessing step is set as an initial value, a representative parameter in the second parameter group is set as variable, and a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter are set as fixed, and performing optimization calculations using the basic equations and the detailed equations to identify the representative parameters and update the second parameter group; a second identification step of setting the second parameter group updated in the first identification step as an initial value, varying one of a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter, and setting parameter conditions under which the second parameter group other than the parameters that have been varied is fixed, performing an optimization calculation using the basic equations and the detailed equations to identify the parameters that have been varied, and updating the second parameter group.

2. 2. The tire characteristic model identification method according to claim 1, wherein the second identification step makes the load-dependent parameter variable, and identifies the load-dependent parameter based on the test data measured while changing the tire load under conditions of a reference tire internal pressure and a reference camber angle.

3. 3. The tire characteristic model identifying method according to claim 2, wherein the second identifying step determines the load-dependent parameter by averaging the load-dependent parameters calculated for each tire load.

4. 3. The tire characteristic model identification method according to claim 2, further comprising a third identification step of setting parameter conditions under which the second parameter group updated in the second identification step is set as an initial value, the camber angle dependent parameters are variable, and the second parameter group other than the camber angle dependent parameters is fixed, performing an optimization calculation using the basic equations and the detailed equations to identify the camber angle dependent parameters, and updating the second parameter group.

5. 5. The tire characteristic model identification method according to claim 4, further comprising a fourth identification step of setting parameter conditions under which the second parameter group updated in the third identification step is set as an initial value, the internal pressure-dependent parameter is set as a variable, and the second parameter group other than the internal pressure-dependent parameter is fixed, performing an optimization calculation using the basic equation and the detailed equation to identify the internal pressure-dependent parameter, and updating the second parameter group.

6. 6. The tire characteristic model identification method according to claim 5, further comprising a fifth identification step of setting the second parameter group updated in the fourth identification step as an initial value, setting a parameter condition under which all parameters in the second parameter group are variable, performing an optimization calculation using the basic equations and the detailed equations to identify all parameters in the second parameter group, and updating the second parameter group.

7. A tire characteristic model identification device that identifies parameters based on test data for a basic equation of a Magic Formula model that represents tire characteristics and a detailed equation of the Magic Formula model that represents a first group of parameters included in the basic equation by a function using a second group of parameters, a pre-processing unit that calculates the first group of parameters by performing an optimization calculation using the basic equations, and that analytically derives the second group of parameters using the detailed equations based on the calculated first group of parameters; a parameter identification unit that identifies the second parameter group using the second parameter group derived by the preprocessing unit, The parameter identification unit a first identification step of setting parameter conditions in which the second parameter group derived by the preprocessing unit is used as an initial value, a representative parameter in the second parameter group is variable, and a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter are fixed, performing optimization calculations using the basic equations and the detailed equations to identify the representative parameters, and updating the second parameter group; and a second identification step of setting a parameter condition under which the second parameter group updated in the first identification step is set as an initial value, one of a load-dependent parameter, a camber angle-dependent parameter, and an internal pressure-dependent parameter is made variable, and the second parameter group other than the parameters that have been made variable is fixed, performing an optimization calculation using the basic equations and the detailed equations to identify the parameters that have been made variable, and updating the second parameter group.

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