Method for simulating vehicle, computer program, and simulation device

Abstract

To provide a method for simulating a vehicle which can execute a vehicle travel simulation on the basis of the temperature change of a tire.SOLUTION: The present invention relates to a method for simulating a vehicle. The method includes the steps of: inputting a tire model 11a; inputting a vehicle body model 11b; inputting a road surface model 12; inputting first time-series data obtained by measuring travel data including the temperature of a tire; inputting a driver model 13; executing the vehicle travel simulation and acquiring second time-series data obtained by calculating a physical quantity that acts on the tire model 11a; specifying a relational expression of the temperature of the tire and the physical amount; and performing a second simulation by executing a vehicle travel simulation on the basis of the temperature change of the tire model 11a and acquiring predetermined data in time series.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a vehicle simulation method, a computer program, and a simulation device. [Background technology]

[0002] Conventionally, various methods for performing vehicle running simulations using a computer have been proposed (for example, see Patent Document 1 below). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-135085 A Summary of the Invention [Problem to be solved by the invention]

[0004] It is generally known that when a vehicle is running, the temperature of the tires rises due to friction with the road surface, and the physical quantities acting on the tires also change due to the influence of this change in tire temperature. Such changes in physical quantities directly affect the running performance of the vehicle. Therefore, it is important to take into account the change in tire temperature in a vehicle running simulation.

[0005] The present invention has been devised in consideration of the above-described circumstances, and has as its main object to provide a vehicle simulation method that is capable of performing a vehicle running simulation that takes into account tire temperature changes with a simple configuration. [Means for solving the problem]

[0006] The present invention is a vehicle simulation method, comprising the steps of: inputting a tire model, the characteristics of which can be adjusted by a first parameter set, into a computer; inputting a vehicle body model, the characteristics of which can be adjusted by a second parameter set, into the computer; inputting a road surface model that models a roadway, into the computer; inputting first time-series data obtained by measuring travel data including tire temperatures in a time series manner while the vehicle is actually travelling on the roadway, into the computer; and inputting a driver model that steers the vehicle model, the vehicle model including the tire model and the vehicle body model, into the computer so as to travel on the road surface model. The vehicle simulation method includes a first simulation step in which the computer performs a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquires second time series data obtained by calculating physical quantities acting on the tire model in a time series manner; a step in which the computer uses the first time series data and the second time series data to specify a relational equation between the tire temperature and the physical quantities; and a second simulation step in which the computer performs a vehicle running simulation using the vehicle model, the road surface model, the driver model, and the relational equation, taking into account temperature changes in the tire model, and acquires predetermined data in a time series manner therefrom. Effect of the Invention

[0007] By employing the above steps, the vehicle simulation method of the present invention makes it possible to carry out a vehicle running simulation that takes into account changes in tire temperature. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing an example of a tire simulation device (computer). [Diagram 2] 4 is a flowchart showing a processing procedure of a vehicle simulation method. [Diagram 3] FIG. 2 is a conceptual diagram showing a vehicle model and a road surface model. [Figure 4] FIG. 4 is a partially enlarged view of FIG. [Diagram 5] 4 is a graph showing an example of a vehicle running state included in first time series data. [Figure 6] 1 is a graph showing the relationship between longitudinal force of a tire model and tire temperature. [Figure 7] 1 is a graph showing the relationship between the temperature of a tire model and the mileage. [Figure 8] 13 is a graph showing lap times for each lap on a circuit road model. [Figure 9] 1 is a conceptual diagram of a trained model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. It should be understood that the drawings include exaggerated expressions and expressions different from the dimensional ratio of the actual structure in order to help understand the contents of the invention. Furthermore, the same or common elements are given the same reference numerals throughout the embodiments, and duplicated explanations are omitted. Furthermore, the specific configurations shown in the embodiments and drawings are for understanding the contents of the present invention, and the present invention is not limited to the specific configurations shown in the drawings.

[0010] In the vehicle simulation method (hereinafter sometimes referred to as the "simulation method") of this embodiment, a vehicle running simulation is carried out using a computer.

[0011] The vehicle is exemplified as a passenger car for racing intended for use on a circuit, but is not particularly limited thereto. The vehicle may be, for example, a truck, a bus, or a motorcycle. Furthermore, the vehicle is fitted with tires according to the type of vehicle. The tire is exemplified as a pneumatic tire, but may be, for example, an airless tire.

[0012] [Simulation device] The computer of this embodiment is configured as a tire simulation device (hereinafter, may be simply referred to as a "simulation device") 1A. Fig. 1 is a block diagram showing an example of the tire simulation device 1A (computer 1).

[0013] The simulation device 1A of this embodiment includes an input section 2 as an input device, an output section 3 as an output device, and a calculation processing device 4 that calculates physical quantities and the like of a tire.

[0014] For example, a keyboard or a mouse is used as the input unit 2. For example, a display device or a printer is used as the output unit 3. The arithmetic processing device 4 includes a calculation unit (CPU) 4A that performs various calculations, a storage unit 4B that stores data, programs, etc., and a working memory 4C.

[0015] The storage unit 4B is a non-volatile information storage device formed of, for example, a magnetic disk, an optical disk, an SSD, etc. The storage unit 4B includes a data unit 5 and a program unit 6.

[0016] The data section 5 is for storing data (information) necessary for carrying out a vehicle running simulation, calculation results, and the like. The data section 5 of this embodiment includes an initial data storage section 5a, a tire model storage section 5b, a vehicle body model storage section 5c, a road surface model storage section 5d, a first time series data storage section 5e, and a driver model storage section 5f. Furthermore, the data section 5 of this embodiment includes a second time series data storage section 5g, a relational expression storage section 5h, and a third time series data storage section 5i. Details of the data input to these data sections 5 will be described in each step of the simulation method described later. Note that the data section 5 is not limited to such an embodiment, and for example, some of these may be omitted, or a section for storing other data may be included.

[0017] [Program section] The program unit 6 is a program (computer program) necessary for carrying out a vehicle driving simulation. The program unit (program) 6 is executed by the calculation unit 4A to cause the computer 1 to function as a specific means. The program unit 6 may be downloadable from a server (cloud server) (not shown) via a communication network (not shown) such as a WAN (Wide Area Network), for example.

[0018] The program unit 6 of this embodiment includes a tire model input unit 6a, a vehicle body model input unit 6b, a road surface model input unit 6c, a first time-series data input unit 6d, and a driver model input unit 6e. Furthermore, the program unit 6 of this embodiment includes a first simulation unit 6f, a relational expression specification unit 6g, a second simulation unit 6h, a judgment unit 6i, and a second parameter set determination unit 6j. Details of the functions of these program units 6 will be described in each step of the simulation method described later.

[0019] [Vehicle simulation method (first embodiment)] Next, the processing procedure of the simulation method of this embodiment will be described. In the simulation method of this embodiment, as described above, a vehicle running simulation is performed. Then, based on the result of the vehicle running simulation, the characteristics of the vehicle (for example, toe angle, camber angle, vehicle height, and internal pressure) are determined. Fig. 2 is a flowchart showing the processing procedure of the vehicle simulation method.

[0020] [Enter tire model] In the simulation method of this embodiment, first, a tire model whose characteristics can be adjusted by a first parameter set is input to the computer 1 shown in Fig. 1 (step S1). Fig. 3 is a conceptual diagram showing a vehicle model 11 and a road surface model 12. Fig. 4 is a partially enlarged view of Fig. 3. In Fig. 4, the vehicle model 11 includes a tire model 11a and a vehicle body model 11b.

[0021] The first parameter set includes a plurality of parameters capable of identifying the characteristics of the tire. The first parameter set is preferably input to the initial data storage unit 5a shown in Fig. 1 prior to the execution of step S1. The first parameter set may be input to the initial data storage unit 5a when the step S1 is executed.

[0022] Examples of parameters that can specify the characteristics of a tire include a longitudinal spring constant, a lateral spring constant, a tire load, a tire load radius, a tire outer diameter, and a rolling resistance coefficient. Further examples of parameters include a longitudinal spring damping coefficient, a longitudinal friction coefficient, a lateral friction coefficient, a cornering force, a self-aligning torque, and a camber thrust. Of these parameters, at least one parameter is preferably included in the first parameter set. Such a first parameter set allows the definition of a tire model 11a (shown in FIG. 4) that is adjusted to the characteristics of any tire.

[0023] The first parameter set of the present embodiment includes all of the above parameters, but is not limited to such an embodiment. For example, the first parameter set may omit some of the above parameters, or may further include other parameters.

[0024] The values ​​of the parameters in the first parameter set may be set to measured values ​​of the characteristics of the tire to be analyzed. This allows a tire model 11a adjusted to the characteristics of the tire to be analyzed to be defined. Alternatively, any value may be set to each parameter. This allows a tire model 11a having desired characteristics to be defined.

[0025] In step S1 of this embodiment, first, as shown in Fig. 1, the first parameter set input to the initial data storage unit 5a is input to the working memory 4C. Furthermore, a tire model input unit 6a included in the program unit 6 is read into the working memory 4C. This tire model input unit 6a is a program for inputting a tire model 11a (shown in Fig. 4). Then, the tire model input unit 6a is executed by the calculation unit 4A, whereby the computer 1 can function as a means for inputting the tire model 11a.

[0026] The tire model 11a can be appropriately defined as long as the characteristics are adjustable by the above-mentioned first parameter set. In step S1 of this embodiment, the tire model 11a is defined based on a predetermined theoretical formula.

[0027] The theoretical formula of the present embodiment uses a known magic formula, but is not limited to this. For example, the Fiala model or various other theoretical formulas may be used. The tire model 11a can be defined by substituting the first parameter set (multiple parameters) into such a theoretical formula.

[0028] The tire model 11a can be appropriately defined based on, for example, a known method. Examples of known methods are described in patent documents (JP Patent Publication No. 2005-28912 A) and patent documents (JP Patent Publication No. 2004-217185 A). For modeling the tire model 11a, for example, commercially available mechanical analysis software is used. Examples of mechanical analysis software include "Adams (registered trademark)" made by MSC Software Corporation and "CarSim (registered trademark)" made by Mechanical Simulation Corporation.

[0029] The tire model 11a of this embodiment has characteristics that can be adjusted by the first parameter set, and therefore does not take into account each component of the tire (e.g., carcass, etc.), unlike a tire model (not shown) based on the finite element method, for example. On the other hand, by changing the first parameter set of the tire model 11a, tire forces (tire generated forces) such as longitudinal force, lateral force, and cornering force generated by the tire can be easily changed. Therefore, in step S1 of this embodiment, the tire model 11a can be modeled easily and in a short time. The tire model 11a is input to the tire model storage unit 5b shown in FIG. 1.

[0030] [Enter vehicle body model] Next, in the simulation method of the present embodiment, a vehicle body model 11b (shown in FIG. 4) whose characteristics can be adjusted by a second parameter set is input to the computer 1 shown in FIG. 1 (step S2).

[0031] The second parameter set includes a plurality of parameters capable of identifying the characteristics of the vehicle. The second parameter set is preferably input to the initial data storage unit 5a shown in Fig. 1 prior to the execution of step S2. Note that the second parameter set may be input to the initial data storage unit 5a when the step S2 is executed.

[0032] The second parameter set of this embodiment preferably includes at least one of the toe angle, camber angle, vehicle height, and internal pressure. Such parameters relate to the settings of the vehicle and directly affect the physical quantities acting on the tires (longitudinal and lateral forces) and the lap time of the vehicle traveling on the circuit. Therefore, by adjusting these parameters, it is possible to shorten the lap time, etc.

[0033] The second parameters may include parameters related to vehicle specifications, such as the center of gravity position, moment of inertia, suspension characteristics, steering characteristics, engine characteristics, aerodynamic characteristics, and power train, in addition to parameters related to vehicle settings.

[0034] In this embodiment, it is preferable that at least one of the above parameters is included in the second parameter set, which allows the definition of a vehicle body model 11b (shown in FIG. 4) that is adjusted to the characteristics of an arbitrary vehicle.

[0035] The second parameter set of the present embodiment includes all of the above parameters, but is not limited to such an embodiment. For example, the second parameter set may omit some of the above parameters, or may further include other parameters.

[0036] The value of each parameter in the second parameter set may be set to a measured value or a design value of the characteristics of the vehicle to be analyzed. This makes it possible to define a vehicle body model 11b adjusted to the characteristics of the vehicle to be analyzed. Also, an arbitrary value may be set to each parameter. This makes it possible to define a vehicle body model 11b having desired characteristics.

[0037] In step S2 of this embodiment, first, as shown in Fig. 1, the second parameter set input to the initial data storage unit 5a is input to the work memory 4C. Furthermore, a vehicle body model input unit 6b included in the program unit 6 is read into the work memory 4C. This vehicle body model input unit 6b is a program for inputting the vehicle body model 11b shown in Fig. 4. Then, the vehicle body model input unit 6b is executed by the calculation unit 4A, whereby the computer 1 can function as a means for inputting the vehicle body model 11b.

[0038] The vehicle body model 11b can be appropriately defined as long as the characteristics are adjustable by the second parameter set. In step S2 of this embodiment, the vehicle body model is modeled based on multibody dynamics.

[0039] Multibody dynamics is a body system in which elements with mass, such as rigid bodies and elastic bodies, are connected by joint elements. The vehicle body model 11b can be defined by setting the above-mentioned second parameter set (multiple parameters) to this multibody dynamics. The vehicle body model 11b of this embodiment can be defined based on, for example, the center of gravity position of the vehicle, engine characteristics, aerodynamic characteristics, and powertrain, etc., of the second parameter set.

[0040] In step S2 of this embodiment, as shown in Fig. 4, a wheel rim model 11c and a suspension model 11d are input together with the vehicle body model 11b. The wheel rim model 11c is a model of a wheel rim (not shown). The suspension model 11d is a model of a suspension (not shown). The wheel rim model 11c and the suspension model 11d can be defined based on, for example, suspension characteristics (spring constant, etc.) of the second parameter set.

[0041] The tire model 11a and the wheel rim model 11c are defined in a state of being fixed to the vehicle body model 11b via the suspension model 11d. In this way, the vehicle model 11 can be set. For example, parameters related to the setting of the vehicle (toe angle, camber angle, vehicle height, and internal pressure) from the second parameter set are used for such a fixed definition.

[0042] The vehicle body model 11b (vehicle model 11) can be appropriately set based on conventional procedures such as those described in patent documents (JP Patent Publication No. 2005-28912 A) and patent documents (JP Patent Publication No. 2008-094241 A), etc. For example, the above-mentioned mechanical analysis software can be used for modeling such vehicle body model 11b (including the wheel rim model 11c and the suspension model 11d).

[0043] The vehicle body model 11b of this embodiment has characteristics that can be adjusted by the second parameter set, and therefore does not take into account each component of the vehicle (e.g., frame, etc.), unlike a vehicle body model (not shown) based on the finite element method, for example. On the other hand, by changing the second parameter set of the vehicle body model 11b, the vehicle's center of gravity height, wheelbase length, and suspension spring rate can be easily changed. Therefore, in step S2 of this embodiment, the vehicle body model 11b can be modeled easily and in a short time. The vehicle body model 11b (including the wheel rim model 11c and the suspension model 11d) is input to the vehicle body model storage unit 5c shown in FIG. 1.

[0044] [Enter road surface model] Next, in the simulation method of this embodiment, a road surface model 12 (shown in Figs. 3 and 4) that models a road is input to the computer 1 shown in Fig. 1 (step S3). In this embodiment, in step S4 described later, a road for actually running a vehicle to be evaluated is modeled, but the present invention is not limited to this. For example, any road may be modeled.

[0045] The road is not particularly limited as long as the vehicle can actually run on it. When the vehicle to be evaluated is a racing car as in this embodiment, for example, a circuit course may be modeled. The road surface model 12 is modeled using information about the road (circuit course). The information about the road includes, for example, coordinate values ​​of a GPS (Global Positioning System) of the road. Such information about the road is preferably input to the initial data storage unit 5a shown in FIG. 1 prior to the implementation of step S3. Note that the information about the road may be input to the initial data storage unit 5a when the step S3 is implemented.

[0046] In step S3 of this embodiment, first, as shown in Fig. 1, information about the roadway input to the initial data storage unit 5a (in this example, GPS coordinate values) is input to the working memory 4C. Furthermore, a road surface model input unit 6c included in the program unit 6 is read into the working memory 4C. This road surface model input unit 6c is a program for inputting a road surface model 12 (shown in Figs. 3 and 4). Then, the road surface model input unit 6c is executed by the calculation unit 4A, thereby allowing the computer 1 to function as a means for inputting the road surface model 12.

[0047] In step S3 of this embodiment, a three-dimensional road surface model 12 (circular road model 12A) is defined based on information about the road (in this example, GPS coordinate values).

[0048] The surface of the road surface model 12 may be defined as smooth, but it is preferable to define, for example, minute irregularities, irregular steps, depressions, undulations, or irregularities (not shown) approximating the actual road surface such as ruts, as in an actual asphalt road surface. Defining such irregularities improves the calculation accuracy of the vehicle travel simulation. The size of the irregularities is preferably set to, for example, 10 to 1000 mm in the front-rear, width, and depth directions.

[0049] For example, the above-mentioned mechanical analysis software can be used for modeling the road surface model 12. The road surface model 12 (circuit road model 12A) is input to the road surface model storage unit 5d shown in FIG.

[0050] [Enter the first time series data when the vehicle actually drives on the road] Next, in the simulation method of this embodiment, first time-series data obtained when a vehicle actually travels along a road is input to a computer 1 (shown in FIG. 1) (step S4). The first time-series data is travel data including tire temperature measured in a time series manner when the vehicle is actually traveling.

[0051] In step S4 of this embodiment, first, as shown in Fig. 1, a first time series data input section 6d included in the program section 6 is loaded into the working memory 4C. This first time series data input section 6d is a program for inputting first time series data obtained by measuring driving data including tire temperature in a time series manner. Then, the first time series data input section 6d is executed by the calculation section 4A, whereby the computer 1 can function as a means for inputting the first time series data.

[0052] The driving path in this embodiment is the above-mentioned circuit course. For example, a test driver drives a vehicle (in this example, a passenger car for racing) on ​​this circuit course. Then, driving data (including tire temperature) when the vehicle drives on the circuit course is measured in time series. In this embodiment, the driving data is measured in time series until the vehicle makes one lap on the circuit course, but is not particularly limited thereto. For example, the driving data may be measured in time series until the vehicle makes multiple laps on the circuit course.

[0053] In this embodiment, the first time-series data is acquired by measuring the running data at a predetermined time interval (each unit time). The time interval is appropriately set based on, for example, the calculation accuracy required for the vehicle running simulation, and can be set to, for example, 0.01 to 0.10 seconds.

[0054] The temperature of the tire can be measured appropriately. In this embodiment, a known non-contact thermometer 7 (shown in FIG. 1) is used to measure the temperature of the tire (including the temperature of the tread contact surface in this example) over time when the tire is actually traveling on a road.

[0055] 1, the thermometer 7 is connected to the simulation device 1A. As a result, the tire temperature measured by the thermometer 7 is acquired as first time-series data by the first time-series data input unit 6d, and further stored in the first time-series data storage unit 5e.

[0056] The travel data may include at least one of the following in addition to the tire temperature: travel speed, longitudinal acceleration, lateral acceleration, yaw rate, accelerator opening, brake opening, and steering angle. Furthermore, the travel data may further include camber angle, slip angle, longitudinal load, internal pressure, and GPS coordinates of the vehicle. These parameters make it possible to easily grasp the travel state (steering state) of the vehicle when actually traveling on the travel path, and the travel line and travel speed of the vehicle on the travel path. Here, the travel line means the position of the vehicle in the width direction of the road surface per unit time. FIG. 5 is a graph showing an example of the travel state of the vehicle included in the first time series data. In FIG. 5, the brake opening, accelerator opening, steering angle, travel speed, and yaw rate are shown as representatives.

[0057] The above-mentioned driving data (excluding tire temperature) can be measured as appropriate. In this embodiment, a known driving log measuring device 8 (shown in FIG. 1) is used to measure the driving data of the vehicle when it actually travels along a road in a time series manner. Examples of the driving log measuring device 8 include DigSpiceIV (DigiSpice 4) manufactured by DigiSpice Inc. and LT-8000GT manufactured by Qstars Inc.

[0058] 1, the running log measuring device 8 is connected to the simulation device 1A. As a result, the running data measured by the running log measuring device 8 is acquired as first time series data by the first time series data input unit 6d, and further stored in the first time series data storage unit 5e.

[0059] [Enter your driver model] Next, in the simulation method of this embodiment, a driver model 13 that steers the vehicle model 11 shown in Fig. 4 is input to the computer 1 (shown in Fig. 1) (step S5). The driver model 13 is for driving the vehicle model 11 on the road surface model 12. In this embodiment, the driver model 13 is defined using first time-series data obtained when the vehicle is actually driven on a road.

[0060] In step S5 of this embodiment, first, as shown in Fig. 1, the first time-series data input to the first time-series data storage unit 5e and a driver model input unit 6e included in the program unit 6 are input to the working memory 4C. The driver model input unit 6e is a program for inputting a driver model 13 that operates the vehicle model 11 shown in Fig. 4. Then, the driver model input unit 6e is executed by the calculation unit 4A, whereby the computer 1 can function as a means for inputting the driver model 13.

[0061] 4, the driver model 13 includes a determination unit 13a and an operation unit 13b. The determination unit 13a is for determining a driving line and a driving speed for traveling on the road surface model 12. The operation unit 13b is for operating the vehicle model 11 on the determined driving line and driving speed. For example, the above-mentioned mechanical analysis software can be used to define the driver model.

[0062] In step S5 of this embodiment, the GPS coordinate values ​​of the vehicle are used from among the travel data constituting the first time-series data described above, so that the travel line and travel speed of the vehicle when actually traveling on the travel road can be specified. Then, the specified travel line and travel speed of the vehicle are used to define the determination unit 13a. The determination unit 13a can determine the travel line and travel speed for traveling on the road surface model 12.

[0063] In step S5 of this embodiment, the steering angle, accelerator opening, and brake opening of the driving data constituting the above-mentioned first time-series data are used to identify the driving state of the test driver when the vehicle is actually driving on the driving road. These driving data are used to define the operation unit 13b. Such operation unit 13b makes it possible to operate the vehicle model 11 at the driving line and driving speed determined by the determination unit 13a according to the operation (steering angle, accelerator opening, and brake opening) when the vehicle is actually driving on the driving road. The driver model 13 is input to the driver model storage unit 5f shown in FIG. 1.

[0064] [Acquire second time-series data of physical quantities of tire model (first simulation step)] Next, in the simulation method of this embodiment, the computer 1 (shown in FIG. 1) acquires second time-series data by calculating the physical quantities acting on the tire model 11a (shown in FIG. 4) in a time-series manner (first simulation step S6). The second time-series data can be acquired by carrying out a vehicle running simulation using the vehicle model 11, the road surface model 12, and the driver model 13.

[0065] 1, in the first simulation step S6 of this embodiment, the tire model 11a inputted to the tire model storage unit 5b and the vehicle body model 11b inputted to the vehicle body model storage unit 5c are read into the working memory 4C. Furthermore, the road surface model 12 inputted to the road surface model storage unit 5d and the driver model 13 inputted to the driver model storage unit 5f are read into the working memory 4C.

[0066] In the first simulation step S6 of this embodiment, a first simulation section 6f included in the program section 6 is loaded into the working memory 4C. This first simulation section 6f is a program for acquiring second time-series data obtained by calculating a physical quantity acting on a tire model 11a (shown in FIG. 4) in a time-series manner. Then, the first simulation section 6f is executed by the calculation section 4A, whereby the computer 1 can function as a means for acquiring the second time-series data.

[0067] In the first simulation step S6 of this embodiment, first, the vehicle model 11 shown in Fig. 4 is defined. The vehicle model 11 is defined using the procedure described above.

[0068] Next, in the first simulation step S6 of the present embodiment, the determination unit 13a of the driver model 13 determines the driving line and driving speed of the vehicle model 11. Furthermore, the operation unit 13b of the driver model 13 operates the vehicle model 11 at the determined driving line and driving speed.

[0069] As described above, the driver model 13 is defined using the first time-series data when the vehicle actually travels on a road. By using such a driver model 13 in a vehicle travel simulation, the vehicle model 11 traveling on the road surface model 12 that models the road can reproduce the vehicle operation when the vehicle actually travels on the road. In this way, the vehicle travel simulation can be performed.

[0070] In the vehicle running simulation, the vehicle model 11 runs on the road surface model 12, and thus the physical quantities acting on the tire model 11a can be calculated at predetermined time intervals (per unit time). This allows second time-series data in which the physical quantities acting on the tire model 11a are calculated in a time-series manner to be acquired. The time intervals can be set appropriately based on the calculation accuracy required for the vehicle running simulation, etc. The time intervals in this embodiment can be set to be the same as the time intervals at which the above-mentioned running data is measured.

[0071] The physical quantity can be calculated as appropriate as long as it acts on the tire model 11a. The physical quantity in this embodiment preferably includes at least one of the longitudinal force and the lateral force (in this example, both the longitudinal force and the lateral force). Such longitudinal force and lateral force change from moment to moment depending on the steering angle, accelerator opening, and brake opening of the driver model 13, and affect the speed (lap time) of the vehicle model 11. Therefore, by acquiring such longitudinal force and lateral force at each time interval, the relationship between the longitudinal force and lateral force acting on the tire model 11a and the speed (lap time) of the vehicle model 11 can be easily understood.

[0072] In the first simulation step S6 of the present embodiment, the physical quantities (longitudinal and lateral forces) acting on the tire model 11a are calculated in a time series manner in a specific section or all sections (i.e., one lap of the circuit road model) of the circuit road model 12A, but are not particularly limited thereto. For example, the physical quantities may be calculated in a time series manner until the vehicle model 11 travels all sections of the road surface model 12 multiple times (i.e., multiple laps of the circuit road model 12A). Note that, unlike the second simulation step S8 described later, the first simulation step S6 does not calculate the physical quantities of the tire model 11a that change from moment to moment in response to temperature changes of the tire model 11a. Therefore, in the first simulation step S6, when the same driver model 13 is used in each lap of the circuit road model 12A, the same physical quantities (longitudinal and lateral forces) are calculated, and therefore the lap times of each lap are also the same.

[0073] The vehicle running simulation (calculation of physical quantities acting on the tire model 11a) can be easily performed by using the above-mentioned mechanism analysis software. The second time-series data is input to the second time-series data storage unit 5g shown in FIG.

[0074] [Identify the relationship between tire temperature and physical quantities] Next, in the simulation method of the present embodiment, the computer 1 (shown in FIG. 1) specifies a relational expression between the tire temperature and the physical quantity of the tire model (step S7). The first time-series data and the second time-series data are used to specify the relational expression.

[0075] In step S7 of this embodiment, first, as shown in FIG. 1, the first time series data input to the first time series data storage unit 5e and the second time series data input to the second time series data storage unit 5g are read into the working memory 4C. Furthermore, a relational equation specification unit 6g included in the program unit 6 is read into the working memory 4C. This relational equation specification unit 6g is a program for specifying a relational equation between the tire temperature and the physical quantity of the tire model using the first time series data and the second time series data. Then, the relational equation specification unit 6g is executed by the calculation unit 4A, whereby the computer 1 can function as a means for specifying the relational equation.

[0076] The relational equation can be appropriately specified as long as it can show the relationship between the tire temperature and the physical quantities (longitudinal and lateral forces) of the tire model 11a. Incidentally, the tire temperature is considered to change depending on the running state of the vehicle (such as the above-mentioned lateral acceleration and yaw rate). Similarly, the physical quantities of the tire model 11a are considered to change depending on the running state of the vehicle (such as the above-mentioned lateral acceleration and yaw rate) of the vehicle (vehicle model 11). In step S7 of this embodiment, the relational equation between the tire temperature and the physical quantities of the tire model, including the running state of the vehicle, is specified.

[0077] In step S7 of the present embodiment, the temperature of the tire model (estimated tire temperature) is determined based on the following formulas (1) to (3): In the following formulas (1) to (3), the temperature of the tire model (estimated tire temperature) is determined taking into consideration the physical quantities (longitudinal and lateral forces) of the tire model and the actual heat generation of the tire associated with the generation of the physical quantities. TE = F × Vs × k … (1) F = √(FX 2 +FY 2 )…(2) Vs = √((Vcosα-Reω) 2 +V 2 ×sin 2 α)…(3) Where: TE: Tire temperature (estimated temperature of the tire model) F: Physical quantity of the tire Vs: Tire sliding speed k: Heat transfer coefficient FX: Front and rear tire forces FY: Lateral force of tire V: Traveling speed α: Slip angle Re: Effective rolling radius ω: Tire angular velocity

[0078] The heat transfer coefficient k in the above formula (1) is a coefficient for adjusting the temperature of the tire model (estimated temperature TE of the tire model) that rises based on the physical quantities of the tire model calculated by simulation to match the tire temperature (temperature of the tread contact surface) Tt during actual driving input to the first time-series data storage unit 5e. Here, when the temperature difference between the estimated temperature TE of the tire model and the tire temperature Tt is ΔT (i.e., Tt-TE), it is desirable that ΔT is 10 degrees or less. This is because, normally, when the tire temperature changes by 10 degrees, the physical properties of the tire change, and accordingly, the physical quantities of the tire may change.

[0079] The physical quantity F of the tire in the above formula (1) is calculated as a resultant force of the tire longitudinal force FX and the tire lateral force FY based on the above formula (2). The tire longitudinal force FX and the tire lateral force FY are specified by the calculated values ​​(physical quantities) output by acquiring the second time-series data (first simulation step S6).

[0080] The above equation (3) is the equation that shows the tire slip speed (in this example, Vs = √(Vsx 2 +Vsy 2 )) is transformed using other variables. The traveling speed V in the above formula (3) can be determined for each time interval from the first time series data. Also, the slip angle α, effective rolling radius Re, and tire angular velocity ω in the above formula (3) can be determined for each time interval from the first time series data.

[0081] Next, in step S7 of this embodiment, a relational expression between the longitudinal force of the tire model and the running state of the vehicle is identified based on the following equation (4). FX=a×CA+b×SA+c×Fz+d×TE+e×IP+f×V+g×Gy+h×YawRate+i …(4) Where: FX: Front and rear forces of tire model CA: Camber angle SA: Slip angle Fz: Vertical load TE: Tire temperature (estimated temperature of the tire model) IP: Internal pressure V: Traveling speed Gy: Lateral acceleration YawRate: Yaw rate a~h: Multiple regression coefficients i: multiple regression intercept

[0082] In the above formula (4), the longitudinal force FX of the tire model can be determined for each time interval (each unit time) from the second time series data. Meanwhile, the camber angle CA, slip angle SA, longitudinal load Fz, internal pressure IP, running speed V, lateral acceleration Gy, and yaw rate YawRate can be determined for each time interval from the first time series data. Also, the tire temperature (estimated temperature of the tire model) TE is given by the above formula (1). By aligning the measurement time of the first time series data and the calculation time of the second time series data, multiple regression analysis can be performed to fit the coefficients a to i.

[0083] In the above formula (4), a longitudinal force FX of the tire model 11a can be calculated by substituting an arbitrary running state (in this example, the camber angle CA, etc.) and the estimated temperature TE of the tire model.

[0084] Next, in step S7 of this embodiment, a relational expression between the lateral force of the tire model 11a and the running state of the vehicle is identified based on the following equation (5). FY=a×CA+b×SA+c×Fz+d×TE+e×IP+f×V+g×Gy+h×YawRate+i …(5) Where: FY: Lateral force of tire model CA: Camber angle SA: Slip angle Fz: Vertical load TE: Tire model temperature (estimated temperature) IP: Internal pressure V: Traveling speed Gy: Lateral acceleration YawRate: Yaw rate a~h: Multiple regression coefficients i: multiple regression intercept

[0085] In the above formula (5), the lateral force FY of the tire model can be determined for each time interval (each unit time) from the second time series data. Meanwhile, the camber angle CA, slip angle SA, longitudinal load Fz, internal pressure IP, running speed V, lateral acceleration Gy, and yaw rate YawRate can be determined for each time interval from the first time series data. Also, the temperature (estimated temperature) TE of the tire model is given by formula (1). By aligning the measurement time of the first time series data and the calculation time of the second time series data, multiple regression analysis can be performed to fit the coefficients a to i.

[0086] In the above equation (5), the lateral force FY of the tire model can be calculated by substituting an arbitrary running state (in this example, the camber angle CA, etc.) and the estimated temperature TE of the tire model.

[0087] In the above formulas (4) and (5), the estimated temperature TE of the tire model calculated by the above formula (1) is substituted for the estimated temperature TE of the tire model, thereby making it possible to calculate the physical quantities (longitudinal force FX and lateral force FY) of the tire model taking into account the temperature change of the tire (tire model) that cannot be calculated in a vehicle running simulation based on a theoretical formula (such as the magic formula). The above formulas (1) to (5) can be input to the relational equation storage unit 5h shown in FIG. 1.

[0088] Fig. 6 is a graph showing the relationship between the longitudinal force of the tire model 11a and the temperature of the tire. As shown in Fig. 6, as the tire temperature increases, the longitudinal force of the tire model 11a increases, but when the tire temperature exceeds P°C, the longitudinal force of the tire model 11a starts to decrease. As such, the longitudinal force of the tire model 11a has temperature dependency, so it is important to carry out a vehicle running simulation taking into account the temperature change of the tire model 11a.

[0089] The lateral force of tire model 11a increases as the tire temperature increases, similar to the longitudinal force of tire model 11a, but starts to decrease when the tire temperature exceeds Q° C. In this way, since the lateral force of tire model 11a has temperature dependency, it is important to carry out a vehicle running simulation taking into account the temperature change of tire model 11a.

[0090] [Conducting a vehicle running simulation that takes into account temperature changes in the tire model (second simulation process)] Next, in the simulation method of this embodiment, the computer 1 (shown in FIG. 1) performs a vehicle running simulation that takes into account temperature changes in the tire model 11a, and acquires predetermined data therefrom in a time series manner (second simulation step S8).

[0091] In the second simulation step S8, similarly to the first simulation step S6, a vehicle running simulation is performed using the vehicle model 11, the road surface model 12, and the driver model 13, as shown in Figures 3 and 4. Furthermore, in the second simulation step S8, unlike the first simulation step S6, a vehicle running simulation is performed using the relational expressions (the above formulas (1) to (5)) between the tire temperature and the physical quantities of the tire model 11a.

[0092] In the second simulation step S8 of this embodiment, as shown in Fig. 1, the tire model 11a (shown in Fig. 4) inputted to the tire model storage unit 5b is read into the work memory 4C. Furthermore, the vehicle body model 11b (shown in Fig. 4) inputted to the vehicle body model storage unit 5c and the road surface model 12 (shown in Fig. 4) inputted to the road surface model storage unit 5d are read into the work memory 4C. Furthermore, the driver model 13 (shown in Fig. 4) inputted to the driver model storage unit 5f and the relational expressions (the above formulas (1) to (5)) inputted to the relational expression storage unit 5h are read into the work memory 4C.

[0093] In the second simulation step S8 of this embodiment, a second simulation section 6h included in the program section 6 is loaded into the working memory 4C. This second simulation section 6h is a program for performing a vehicle running simulation that takes into account temperature changes in a tire model 11a (shown in FIG. 4) and acquiring predetermined data in a time series therefrom. The second simulation section 6h is executed by the calculation section 4A, thereby allowing the computer 1 to function as a means for acquiring predetermined data in a time series.

[0094] In the second simulation step S8 of this embodiment, first, similarly to the first simulation step S6, the vehicle model 11 is steered on the road surface model 12 based on the driver model 13 as shown in Figures 3 and 4. This allows a vehicle travel simulation to be performed that reproduces the vehicle's operation when actually traveling on a road.

[0095] In the vehicle running simulation, the vehicle model 11 runs on the road surface model 12, and thus the physical quantities (longitudinal force and lateral force) acting on the tire model 11a can be calculated at predetermined time intervals (for each unit time). At this time, the temperature of the tire model 11a is calculated using the first time-series data and the above formulas (1) to (5), and the physical quantities (longitudinal force and lateral force) acting on the tire model 11a are corrected based on the temperature. Thus, in the second simulation step S8, unlike the first simulation step S6, the physical quantities (longitudinal force and lateral force) of the tire model 11a that take into account the temperature change of the tire model can be calculated for each time interval.

[0096] In this manner, in this embodiment, by using the above-mentioned relational expressions (1) to (5) between the tire temperature and the physical quantities, it is possible to calculate the physical quantities (longitudinal force and lateral force) taking into account the temperature change of the tire model 11a, for example, without performing a simulation based on the finite element method. Therefore, the simulation method of this embodiment makes it possible to perform a vehicle running simulation taking into account the temperature change of the tire with a simple configuration.

[0097] In addition, in the second simulation step S8 of this embodiment, unlike the first simulation step S6, the physical quantities (longitudinal force and lateral force) of the tire model 11a that change from moment to moment in response to a temperature change in the tire model 11a are calculated. Therefore, in the second simulation step S8, even if the same driver model is used for each lap of the road surface model 12, different values ​​are calculated for the physical quantities (longitudinal force and lateral force) of the tire model 11a, and therefore different values ​​can be calculated for the lap times for each lap.

[0098] In the second simulation step S8 of the present embodiment, at least one of the temperature of the tire model 11a and the physical quantity of the tire model 11a (in this example, both) can be acquired in time series as predetermined data by performing a vehicle running simulation.

[0099] Fig. 7 is a graph showing the relationship between the temperature of the tire model 11a and the mileage. Fig. 7 shows the relationship between the temperature and the mileage for Case 1 and Case 2, which are different from each other in the second parameter set in terms of the camber angle and the internal pressure. The graph in Fig. 7 shows that the temperature of the tire model 11a increases with the mileage in both Case 1 and Case 2. On the other hand, the temperature of the tire model 11a increases more greatly in Case 2 than in Case 1.

[0100] In this way, the temperature of the tire model 11a (physical quantity of the tire model 11a) directly affects the settings (toe angle, camber angle, vehicle height, internal pressure, etc.) of the vehicle model 11 included in the second parameter set. Therefore, by acquiring the temperature of the tire model 11a and the physical quantity of the tire model 11a in chronological order, it becomes possible to evaluate the second parameter set (toe angle, camber angle, vehicle height, internal pressure, etc.).

[0101] In the second simulation step S8 of the present embodiment, the lap time of a specific section or all sections (all sections in this example) of the circuit road model 12A by the vehicle model 11 may be acquired as the predetermined data. This makes it possible to evaluate a second set of parameters (toe angle, camber angle, vehicle height, internal pressure, etc.) that directly affect the lap time.

[0102] In the second simulation step S8, the lap time for each lap may be acquired until the vehicle model 11 completes a plurality of laps around the entire section of the circuit road model 12A. This makes it possible to evaluate the lap time for each lap taking into account the temperature and physical quantity changes of the tire model 11a.

[0103] 8 is a graph showing the lap time for each lap on the circuit road model 12A. In FIG. 8, the tendency of the lap time for each lap is shown for the above-mentioned cases 1 and 2.

[0104] As shown in Fig. 8, up to the fifth lap, the lap time is shorter in Case 2 than in Case 1. This is because, as shown in Fig. 7, the rate of increase in temperature of tire model 11a is larger in Case 2 than in Case 1, and the temperature of tire model 11a reaches the temperature (temperature P in Fig. 6) at which the longitudinal force and lateral force peak earlier.

[0105] On the other hand, from the sixth lap onwards, the lap time is smaller in Case 1 than in Case 2. This is because, as shown in Fig. 7, the rate of increase in temperature of tire model 11a is smaller in Case 1 than in Case 2, and the temperature of tire model 11a from the sixth lap onwards is maintained near the temperature at which the longitudinal force and lateral force peak (temperature P in Fig. 6).

[0106] In this way, the lap time directly affects the settings (toe angle, camber angle, vehicle height, internal pressure, etc.) of the vehicle model 11 included in the second parameter set. Therefore, by acquiring the lap time in time series, it becomes possible to evaluate the second parameter set (toe angle, camber angle, vehicle height, internal pressure, etc.).

[0107] In the second simulation step S8, the data acquired in a time series manner (the temperature of the tire model 11a, the physical quantities of the tire model 11a, and the lap time) is input to the third time series data storage unit 5i shown in FIG.

[0108] [Judge whether the lap time meets the criteria] Next, in the simulation method of this embodiment, the computer 1 (shown in FIG. 1) determines whether or not the lap time of the vehicle model shown in FIG. 8 satisfies a predetermined standard (step S9).

[0109] In the case where the vehicle model 11 is made to run multiple laps around all sections of the circuit road model 12A and the lap time for each lap is acquired as in this embodiment, it may be determined whether the lap time for any lap satisfies the criteria. In particular, when the vehicle to be evaluated is a racing car, the lap time for the final lap (the tenth lap in FIG. 8) tends to be important. In this case, it is determined whether the lap time for the final lap satisfies the criteria.

[0110] The lap time standard can be appropriately determined depending on the performance required for the vehicle. It is preferable that such a standard is input to the initial data storage unit 5a shown in Fig. 1 prior to the implementation of step S9. Note that the standard may be input to the initial data storage unit 5a when the implementation of step S9 is performed.

[0111] In step S9 of this embodiment, first, as shown in FIG. 1, the lap time standard of the second parameter set inputted to the initial data storage unit 5a is inputted to the working memory 4C. Furthermore, the lap time (shown in FIG. 8) inputted to the third time-series data storage unit 5i is inputted to the working memory 4C. Furthermore, a determination unit 6i included in the program unit 6 is read into the working memory 4C. This determination unit 6i is a program for determining whether or not the lap time of the vehicle model 11 satisfies the standard. Then, the determination unit 6i is executed by the calculation unit 4A, thereby making it possible to cause the computer 1 to function as a means for determining whether or not the lap time satisfies the standard.

[0112] In step S9, if the lap time of vehicle model 11 satisfies a predetermined standard ("Yes" in step S9), it can be determined that the second parameter set (toe angle, camber angle, vehicle height, internal pressure, etc.) defined for vehicle model 11 is good. In this case, in the simulation method of this embodiment, the vehicle is set based on the second parameter set (step S10). This makes it possible to set the vehicle so that it can actually run on a road with a lap time that satisfies the standard.

[0113] In step S9, if the lap time of the vehicle model 11 does not satisfy the predetermined criterion ("No" in step S9), step S11 is performed to determine a second parameter set so that the lap time of the vehicle model 11 satisfies the criterion.

[0114] [Determine the second set of parameters for which the lap time meets the criteria] Next, in the simulation method of this embodiment, the computer 1 (shown in FIG. 1) determines a second parameter set so that the lap time of the vehicle model 11 shown in FIG. 4 satisfies a predetermined standard (step S11). The second parameter set may be determined based on, for example, the experience of an operator. In this embodiment, the second parameter set may be determined based on an optimization algorithm.

[0115] The optimization algorithm is for determining optimal design parameters that satisfy an arbitrary objective function under certain constraint conditions. In this embodiment, a second parameter set that satisfies a lap time standard of the vehicle model 11 under certain constraint conditions of the second parameter set is determined.

[0116] The second parameter set determined in step S11 of this embodiment can be appropriately selected from the above parameters. In this embodiment, the parameters related to the vehicle settings (toe angle, camber angle, vehicle height, and internal pressure) are determined so that the lap time of the vehicle model 11 satisfies the standard.

[0117] The constraint conditions are set for each parameter (toe angle, camber angle, vehicle height, and internal pressure) included in the second parameter set. These constraint conditions are set within a changeable range of each parameter in the vehicle that is the modeling target of the vehicle model 11. The constraint conditions are preferably inputted into the initial data storage unit 5a shown in Fig. 1 prior to the implementation of step S11. The constraint conditions may be inputted into the initial data storage unit 5a when the step S11 is implemented.

[0118] In step S11 of this embodiment, first, as shown in FIG. 1, the constraint conditions of the second parameter set and the lap time standard input to the initial data storage unit 5a are input to the working memory 4C. Furthermore, a second parameter set determination unit 6j included in the program unit 6 is read into the working memory 4C. This second parameter set determination unit 6j is a program for determining a second parameter set based on an optimization algorithm so that the lap time of the vehicle model 11 satisfies a predetermined standard. Then, the second parameter set determination unit 6j is executed by the calculation unit 4A, whereby the computer 1 can function as a means for determining the second parameter set.

[0119] A publicly known optimization algorithm may be used. Examples of the optimization algorithm include optimization methods that simulate biology (neural networks, genetic algorithms, particle swarm optimization, etc.). Further examples of the optimization algorithm include statistical optimization methods (experimental design and Taguchi method, etc.), optimization methods that simulate physical phenomena (simulated annealing, etc.), and optimization methods that use artificial intelligence.

[0120] In this embodiment, the second parameter set is determined based on particle swarm optimization (PSO), which requires a relatively short calculation time. In particle swarm optimization, a plurality of second parameter sets (first generation) are created, an objective function (lap time) of each second parameter set is calculated, and each second parameter set is updated (generational change) so as to approach the most desirable objective function. Random numbers are used to update each second parameter set, and an optimal second parameter set is searched for in a wide range. Details of particle swarm optimization (PSO) are described in various documents, such as "Particle Swarm Optimization and Nonlinear Systems" in IEICE Fundamentals Review (Institute of Electronics, Information and Communication Engineers), Vol. 5, No. 2, August 2011.

[0121] In step S11 of this embodiment, a plurality of second parameter sets are created (determined) based on particle swarm optimization (PSO). Next, in this embodiment, vehicle running simulations based on these plurality of second parameter sets are respectively performed, and lap times are respectively obtained (second simulation step S8). Then, it is determined whether each lap time satisfies a criterion (step S9), and if the lap times based on all the second parameter sets do not satisfy the criterion ("No" in step S9), the second parameter sets are further updated in step S11.

[0122] In the simulation method of this embodiment, the second parameter set is updated based on an optimization algorithm until the lap time satisfies the criterion. As a result, in the simulation method of this embodiment, the second parameter set can be reliably determined so that the lap time satisfies the predetermined criterion, regardless of the experience of the operator, etc.

[0123] [Vehicle simulation method (second embodiment)] In step S5 (shown in FIG. 2) of the above embodiment, the determination unit 13a and the operation unit 13b of the driver model 13 shown in FIG. 4 are defined using the first time-series data obtained when the vehicle actually travels along the travel path, but the present invention is not limited to such an embodiment. For example, the determination unit 13a and / or the operation unit 13b may be a trained model that has been trained by machine learning in advance. In the present embodiment, a case is illustrated in which both the determination unit 13a and the operation unit 13b are trained models, but only one of the determination unit 13a and the operation unit 13b may be a trained model.

[0124] Fig. 9 is a conceptual diagram of the trained model 21. In Fig. 9, the trained model 21 of the determination unit 13a (shown in Fig. 4) of the driver model 13 is representatively shown.

[0125] The trained model 21 is configured as a neural network defined based on a radial basis function (RBF). The trained model (RBF network) 21 includes an input layer 22, an output layer 23, and an intermediate layer 24, and is defined as an approximate response surface expressed by overlapping Gaussian functions.

[0126] In the trained model 21 of the determination unit 13a (shown in FIG. 4), the input layer 22 can receive GPS coordinate values ​​(position on the circuit) of the vehicle that has traveled on the circuit. The coordinate values ​​include, for example, longitude and latitude. Meanwhile, the output layer 23 is configured to be able to output the travel line and travel speed. The intermediate layer 24 is a basis function (Gaussian function) and is generated by machine learning.

[0127] In step S5, the basis functions of the intermediate layer 24 are adjusted (learned) so as to reduce the difference between the output for the input and the true output (teaching data). In this embodiment, the GPS coordinate values ​​of the vehicle are used as the input for the driving data of each lap when the vehicle travels multiple laps around the circuit. As a result, the driving line and driving speed estimated for these inputs are output (estimated). As the true output, the driving line and driving speed corresponding to the GPS coordinate values ​​of the vehicle (position of the circuit) are used for the driving data of each lap. Then, the basis functions of the intermediate layer 24 are adjusted so as to reduce the difference between the output (estimated) driving line and driving speed and the actual driving line and driving speed. For example, a gradient method or the like is used as the learning method. As a result, a trained model 21 capable of estimating the optimal driving line and driving speed for the vehicle from the GPS coordinate values ​​of the vehicle is created.

[0128] The trained model 21 is configured as an RBF network, and therefore can accurately express even if the input and output have a highly nonlinear relationship. Furthermore, even if abnormal data is included in the teacher data (the GPS coordinate values ​​of the vehicle, the driving line, and the driving speed), the trained model 21 can generate a response function without being affected by the abnormal data because the superposition of Gaussian functions is performed by the least squares method. Furthermore, unlike a normal neural network, the trained model 21 does not require backpropagation, and therefore the calculation cost can be reduced. The trained model 21 can be constructed, for example, by using commercially available computer software (for example, MATLAB made by The MathWorks, Inc., modeFRONTIER made by ESTECO, Inc., etc.).

[0129] In the trained model 21 (not shown) of the operation unit 13b (shown in FIG. 4) of the driver model 13, the input layer 22 is configured to be able to input the GPS coordinate values ​​of the vehicle (position on the circuit course). Meanwhile, the output layer 23 is configured to be able to output the steering angle, accelerator opening, and brake opening. The intermediate layer 24 is a basis function (Gaussian function) and is generated by machine learning. The training procedure, etc. are as described above.

[0130] The determination unit 13a in this embodiment can predict, for example, an optimal driving line and driving speed for the vehicle model 11 from the coordinate values ​​(corresponding to GPS coordinate values) of the vehicle model 11 on the circuit road model 12A by complementing the driving line and driving speed included in the teacher data. Furthermore, the operation unit 13b can predict, for example, an optimal steering angle, accelerator opening, and brake opening for the vehicle model 11 from the coordinate values ​​of the vehicle model 11 by complementing the steering angle, accelerator opening, and brake opening included in the teacher data.

[0131] The driver model 13 of this embodiment makes it possible to make the vehicle model 11 run on the road surface model 12 at an optimal running line and running speed, without being influenced by the actual operation of a test driver who actually runs the vehicle on a running road. By performing a vehicle running simulation based on such a driver model 13, a second parameter set capable of further shortening the lap time can be determined.

[0132] Although a particularly preferred embodiment of the present invention has been described in detail above, the present invention is not limited to the illustrated embodiment and can be modified and carried out in various forms. EXAMPLES

[0133] [Example A] A vehicle simulation method was carried out based on the processing procedure shown in Fig. 2. In this vehicle simulation method, a relational expression between the tire temperature and the physical quantity was identified using first time-series data obtained by measuring driving data including the tire temperature in a time series manner and second time-series data obtained by calculating the physical quantity acting on the tire model in a time series manner.

[0134] Next, vehicle models were set based on two cases (case 1 and case 2) in which the camber angle and the internal pressure were different from each other among the second parameter set. For these vehicle models, a vehicle running simulation was performed taking into account the temperature change of the tire model, and predetermined data was obtained in a time series therefrom.

[0135] The camber angle and the internal pressure in Case 1 and Case 2 are as follows: In Case 2, the camber angle is set larger than in Case 1, while the internal pressure is set smaller. Case 1: Camber angle: Front left and right wheels: -2.0 degrees Left rear wheel, right rear wheel: -2.2 degrees internal pressure: All wheels: 180kPa Case 2: Camber angle: Front left and right wheels: -2.0 degrees Left rear wheel, right rear wheel: -3.2 degrees internal pressure: Left front wheel, right front wheel: 160kPa Left rear wheel, right rear wheel: 180kPa

[0136] Fig. 7 is a graph showing the relationship between the temperature of the tire model and the distance traveled. Fig. 8 is a graph showing the lap time for each lap on the circuit road model. Fig. 8 shows the tendency of the lap time for each lap for the above-mentioned cases 1 and 2.

[0137] In general, when the camber angle is set large and the internal pressure is set small, the load on the tire is large. Therefore, the load on the tire model in Case 2 is larger than the load on the tire model in Case 1. For this reason, as shown in FIG. 7, the temperature of the tire model 11a in the tire model in Case 2 is increased more significantly than that in the tire model in Case 1.

[0138] As shown in Fig. 8, up to the fifth lap, the lap time is shorter in Case 2 than in Case 1. This is because, as shown in Fig. 7, the rate of increase in the temperature of the tire model is greater in Case 2 than in Case 1, and the temperature of the tire model reaches the temperature at which the longitudinal force and lateral force peak (temperature P in Fig. 6) earlier.

[0139] As shown in Fig. 8, from the sixth lap onwards, the lap time is smaller in Case 1 than in Case 2. This is because, as shown in Fig. 7, the rate of increase in the temperature of the tire model is smaller in Case 1 than in Case 2, and the temperature of the tire model from the sixth lap onwards is maintained near the temperature at which the longitudinal force and lateral force peak (temperature P in Fig. 6).

[0140] In this way, in the embodiment, for example, it is possible to calculate physical quantities (longitudinal force and lateral force) that take into account temperature changes in the tire model without performing a simulation based on the finite element method. Therefore, in the embodiment, it is possible to perform a vehicle running simulation that takes into account temperature changes in the tire with a simple configuration.

[0141] [Example B] 2, a second parameter set was determined (Case 3) based on the optimization algorithm for the tire model of Case 1 above so that the fastest lap time would be shorter. Also, for comparison, a second parameter set was determined (Case 4) based on the operator's experience for the tire model of Case 1 above so that the fastest lap time would be shorter.

[0142] Then, for the tire models of Case 3 and Case 4, a vehicle running simulation was carried out taking into account the temperature change of the tire model, and lap times were obtained in time series therefrom.

[0143] As a result of the test, the fastest lap time in case 3 was reduced by 9% compared to the fastest lap time in case 4. Therefore, in case 3, an optimization algorithm was used to determine a second parameter set that could reduce the lap time.

[0144] [Note] The present invention includes the following aspects.

[0145] [Invention 1] 1. A method for simulating a vehicle, comprising: inputting a tire model, the tire characteristics of which can be adjusted by a first parameter set, into a computer; inputting a vehicle body model, the characteristics of which can be adjusted by a second parameter set, into the computer; A step of inputting a road surface model, which is a model of a road, into the computer; a step of actually driving the vehicle on the driving road and measuring driving data including tire temperatures in a time series manner, and inputting the first time series data into the computer; inputting a driver model for steering the vehicle model including the tire model and the vehicle body model into the computer so as to run the vehicle model on the road surface model; a first simulation step in which the computer performs a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquires second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; specifying a relational expression between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; a second simulation step of the computer performing a vehicle running simulation taking into consideration a temperature change of the tire model by using the vehicle model, the road surface model, the driver model, and the relational expression, and acquiring predetermined data in a time series therefrom. Vehicle simulation methods. [Invention 2] The vehicle simulation method according to aspect 1, wherein the data in the second simulation step includes at least one of a temperature of the tire model and a physical quantity of the tire model. [Invention 3] the road surface model is a circuit road model, 3. The vehicle simulation method according to claim 1 or 2, wherein the data in the second simulation step is a lap time for a specific section or all sections of the circuit road model by the vehicle model. [Invention 4] The vehicle simulation method according to aspect 3, further comprising a step in which the computer determines the second parameter set based on an optimization algorithm so that the lap time satisfies a predetermined criterion. [Invention 5] 5. The vehicle simulation method according to any one of claims 1 to 4, wherein the driver model steers the vehicle model in accordance with a steering angle, an accelerator opening, and a brake opening during the actual running of the vehicle. [Invention 6] A simulation method for a vehicle described in any one of claims 1 to 5, wherein the driver model includes a determination unit that determines a driving line and driving speed for driving on the road surface model, and an operation unit that steers the vehicle model on the determined driving line and driving speed. [Invention 7] A vehicle simulation method as described in the present invention 6, wherein the decision unit and / or the operation unit are trained models that have been machine-learned in advance. [Invention 8] A vehicle simulation method according to any one of the present inventions 1 to 7, wherein the first parameter set includes at least one of a vertical spring constant, a lateral spring constant, a tire load, a tire load radius, a tire outer diameter, a rolling resistance coefficient, a vertical spring damping coefficient, a longitudinal friction coefficient, a lateral friction coefficient, a cornering force, a self-aligning torque, and a camber thrust. [The present invention 9] 9. A vehicle simulation method according to any one of claims 1 to 8, wherein the second parameter set includes at least one of a toe angle, a camber angle, a vehicle height, and an internal pressure. [The present invention 10] A vehicle simulation method according to any one of claims 1 to 9, wherein the driving data includes, in addition to the tire temperature, at least one of driving speed, longitudinal acceleration, lateral acceleration, yaw rate, accelerator opening, brake opening, and steering angle. [The present invention 11] 11. The vehicle simulation method according to any one of claims 1 to 10, wherein the physical quantity includes at least one of a longitudinal force and a lateral force acting on the tire model. [The present invention 12] 1. A computer program for performing a simulation of a vehicle, comprising: Computer, A means for inputting a tire model whose characteristics can be adjusted by a first parameter set; A means for inputting a vehicle body model whose characteristics can be adjusted by a second parameter set; A means for inputting a road surface model that models a road; a means for inputting first time series data obtained by measuring travel data including tire temperatures in a time series manner while the vehicle is actually travelling along the travel route; a means for inputting a driver model for operating the vehicle model including the tire model and the vehicle body model so as to run the vehicle model on the road surface model; a means for performing a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquiring second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; a means for identifying a relational expression between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; performing a vehicle running simulation taking into consideration a temperature change of the tire model by using the vehicle model, the road surface model, the driver model, and the relational expression, and functioning as a means for acquiring predetermined data in a time series therefrom; Computer program. [The present invention 13] An apparatus having a processor for performing a simulation of a vehicle, the apparatus comprising: The arithmetic processing device includes: a tire model input unit for inputting a tire model whose characteristics can be adjusted by a first parameter set; a vehicle body model input unit for inputting a vehicle body model whose characteristics can be adjusted by a second parameter set; a road surface model input unit for inputting a road surface model that models a road; a first time series data input unit that inputs first time series data obtained by actually driving the vehicle on the driving road and measuring driving data including tire temperatures in a time series manner; a driver model input unit for inputting a driver model for operating the vehicle model, the driver model including the tire model and the vehicle body model, so as to run the vehicle model on the road surface model; a first simulation unit that performs a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquires second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; a relational equation determination unit that determines a relational equation between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; a second simulation unit that uses the vehicle model, the road surface model, the driver model, and the relational expression to perform a vehicle running simulation taking into account a temperature change of the tire model, and acquires predetermined data in a time series therefrom. Vehicle simulation device. [Explanation of symbols]

[0146] 11a Tire Model 11b Vehicle body model 12 Road surface model 13 Driver Model

Claims

1. 1. A method for simulating a vehicle, comprising: inputting a tire model, the tire model having adjustable characteristics according to a first set of parameters, into a computer; inputting a vehicle body model, the characteristics of which can be adjusted by a second parameter set, into the computer; A step of inputting a road surface model, which is a model of a road, into the computer; a step of actually driving the vehicle on the driving road and measuring driving data including tire temperatures in a time series manner, and inputting the first time series data into the computer; inputting a driver model for steering the vehicle model including the tire model and the vehicle body model into the computer so as to run the vehicle model on the road surface model; a first simulation step in which the computer executes a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquires second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; specifying a relational expression between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; a second simulation step of the computer performing a vehicle running simulation taking into consideration a temperature change of the tire model by using the vehicle model, the road surface model, the driver model, and the relational expression, and acquiring predetermined data in a time series therefrom. Vehicle simulation methods.

2. The vehicle simulation method according to claim 1 , wherein the data in the second simulation step includes at least one of a temperature of the tire model and a physical quantity of the tire model.

3. the road surface model is a circuit road model, 2. The vehicle simulation method according to claim 1, wherein the data in the second simulation step is a lap time for a specific section or all sections of the circuit road model by the vehicle model.

4. 4. The vehicle simulation method according to claim 3, further comprising the step of: determining, by the computer, the second parameter set based on an optimization algorithm such that the lap time satisfies a predetermined criterion.

5. 2. The vehicle simulation method according to claim 1, wherein the driver model steers the vehicle model in accordance with a steering angle, an accelerator depression, and a brake depression during the actual running of the vehicle.

6. 2. The vehicle simulation method according to claim 1, wherein the driver model includes a determination unit that determines a driving line and a driving speed for driving on the road surface model, and an operation unit that steers the vehicle model along the determined driving line and driving speed.

7. The vehicle simulation method according to claim 6 , wherein the determination unit and / or the operation unit is a trained model that has been trained by machine learning in advance.

8. 2. The vehicle simulation method of claim 1, wherein the first parameter set includes at least one of a vertical spring constant, a lateral spring constant, a tire load, a tire load radius, a tire outer diameter, a rolling resistance coefficient, a vertical spring damping coefficient, a longitudinal friction coefficient, a lateral friction coefficient, a cornering force, a self-aligning torque, and a camber thrust.

9. The vehicle simulation method according to claim 1 , wherein the second set of parameters includes at least one of a toe angle, a camber angle, a vehicle height, and an internal pressure.

10. 2. The vehicle simulation method according to claim 1, wherein the driving data includes at least one of a driving speed, a longitudinal acceleration, a lateral acceleration, a yaw rate, an accelerator opening, a brake opening, and a steering angle, in addition to the tire temperature.

11. The vehicle simulation method according to claim 1 , wherein the physical quantity includes at least one of a longitudinal force and a lateral force acting on the tire model.

12. 1. A computer program for performing a simulation of a vehicle, comprising: Computer, A means for inputting a tire model whose characteristics can be adjusted by a first parameter set; A means for inputting a vehicle body model whose characteristics can be adjusted by a second parameter set; A means for inputting a road surface model that models a road; a means for inputting first time series data obtained by measuring travel data including tire temperatures in a time series manner while the vehicle is actually travelling along the travel road; a means for inputting a driver model for operating the vehicle model including the tire model and the vehicle body model so as to run the vehicle model on the road surface model; a means for performing a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquiring second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; a means for determining a relational expression between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; performing a vehicle running simulation taking into consideration a temperature change of the tire model by using the vehicle model, the road surface model, the driver model, and the relational expression, and functioning as a means for acquiring predetermined data in a time series therefrom; Computer program.

13. An apparatus having a processor for performing a simulation of a vehicle, the apparatus comprising: The arithmetic processing device includes: a tire model input unit for inputting a tire model whose characteristics can be adjusted by a first parameter set; a vehicle body model input unit for inputting a vehicle body model whose characteristics can be adjusted by a second parameter set; a road surface model input unit for inputting a road surface model that models a road; a first time-series data input unit that inputs first time-series data obtained by actually driving the vehicle on the driving road and measuring driving data including tire temperatures in a time series manner; a driver model input unit for inputting a driver model for operating the vehicle model, the driver model including the tire model and the vehicle body model, so as to run the vehicle model on the road surface model; a first simulation unit that performs a vehicle running simulation using the vehicle model, the road surface model, and the driver model, and acquires second time-series data obtained by calculating a physical quantity acting on the tire model in a time-series manner; a relational equation specifying unit that specifies a relational equation between the tire temperature and the physical quantity by using the first time-series data and the second time-series data; a second simulation unit that uses the vehicle model, the road surface model, the driver model, and the relational expression to perform a vehicle running simulation taking into account a temperature change of the tire model, and acquires predetermined data in a time series therefrom. Vehicle simulation device.

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