Analysis method, analysis device, and analysis program

Abstract

To provide an analysis method, analysis device, and analysis program that can quickly analyze the undercarriage of a vehicle. [Solution] In PC1, a single suspension model 23 is created by connecting a tire model 21 and a suspension model 22 in series, simulating one suspension in an automobile. A segmented vehicle model 20 is created by setting tire characteristic values, suspension characteristic values, and segmented vehicle weights 24 to the created single suspension model 23. The acceleration changes when the created segmented vehicle model 20 is driven on the test course are displayed on the results display 70, and the user can analyze the behavior of the segmented vehicle model 20 by checking and verifying the results display 70. This allows for faster analysis of the behavior of a single suspension compared to creating a model of the entire automobile and analyzing the behavior of one suspension from that entire automobile model.

Description

Technical Field

[0001] The present invention relates to an analysis method, an analysis apparatus, and an analysis program.

Background Art

[0002] Patent Document 1 discloses creating a vehicle model by attaching a tire model that models a tire to a vehicle body model that models the entire vehicle body including suspension members, and performing various simulations on the created vehicle model. By means of the simulation using the vehicle model, it is possible to evaluate the behavior of the entire vehicle model and the performance of the tire modeled as the tire model.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, when changing parameters related to the running gear such as the tires and suspension members of the vehicle model, it is necessary to reconstruct the vehicle model using the changed parameters. Here, since the vehicle body model that constitutes the vehicle model models the entire vehicle body, even when only changing the parameters related to the running gear in the vehicle model, it is necessary to reconstruct the model of the entire vehicle body. Furthermore, in the simulation of the vehicle model, it is necessary to calculate in detail the behavior of each part of the vehicle body in the vehicle body model. As a result, there is a problem that it takes time to reconstruct the vehicle model with the changed parameters related to the running gear and to execute the simulation.

[0005] This invention was made to solve the above-mentioned problems and aims to provide an analysis method, analysis device, and analysis program that can quickly analyze the undercarriage of a vehicle. [Means for solving the problem]

[0006] To achieve this objective, the analysis method of the present invention comprises: a suspension creation step to create a single suspension model that simulates one suspension in a vehicle by combining a tire model that outputs information regarding the behavior of the tire in response to input to the tire and a suspension model that outputs information regarding the behavior of the suspension in response to input to the suspension; a divided vehicle creation step to create a divided vehicle model by setting tire characteristic values ​​in the tire model of the single suspension model created in the suspension creation step, setting suspension characteristic values ​​in the suspension model of the single suspension model, and further setting a divided vehicle weight, which is the vehicle weight applied to one suspension in the vehicle, in the single suspension model; an analysis step to analyze the behavior of the divided vehicle model created in the divided vehicle creation step; and an output step to output information regarding the behavior analyzed in the analysis step.

[0007] The analysis device of the present invention comprises: a suspension creation means for creating a single suspension model that simulates one suspension in a vehicle by combining a tire model that outputs information regarding the behavior of the tire in response to input to the tire and a suspension model that outputs information regarding the behavior of the suspension in response to input to the suspension; a divided vehicle creation means for creating a divided vehicle model by setting tire characteristic values ​​in the tire model of the single suspension model created by the suspension creation means, setting suspension characteristic values ​​in the suspension model of the single suspension model, and further setting a divided vehicle weight, which is the vehicle weight applied to one suspension in the vehicle, in the single suspension model; an analysis means for analyzing the behavior of the divided vehicle model created by the divided vehicle creation means; and an output means for outputting information regarding the behavior analyzed by the analysis means.

[0008] Furthermore, the analysis program of the present invention is a program that causes a computer to perform an analysis process to analyze the behavior of a vehicle, and causes the computer to perform the following steps: a suspension creation step to create a single suspension model that simulates one suspension of the vehicle by combining a tire model that outputs information about the behavior of the tire in response to input to the tire and a suspension model that outputs information about the behavior of the suspension in response to input to the suspension; a split vehicle creation step to create a split vehicle model by setting tire characteristic values ​​in the tire model of the single suspension model created in the suspension creation step, setting suspension characteristic values ​​in the suspension model of the single suspension model, and further setting the split vehicle weight, which is the vehicle weight applied to one suspension of the vehicle, in the single suspension model; an analysis step to analyze the behavior of the split vehicle model created in the split vehicle creation step; and an output step to output information about the behavior analyzed in the analysis step. [Effects of the Invention]

[0009] According to the analysis method described in claim 1, a single suspension model simulating one suspension system of a vehicle is created by combining a tire model and a suspension model, and a segmented vehicle model is created by setting tire characteristic values, suspension characteristic values, and segmented vehicle weights to the tire model in the created single suspension model. The behavior of the created segmented vehicle model is analyzed, and information regarding the analyzed behavior is output.

[0010] In other words, a segmented vehicle model is created that models one of the vehicle's undercarriages, and the behavior of this segmented vehicle model is analyzed. This has the effect of allowing for a faster analysis of the behavior of a single undercarriage compared to, for example, creating a model of the entire vehicle and analyzing the behavior of one undercarriage from that entire vehicle model.

[0011] Furthermore, by simply setting tire characteristic values, suspension characteristic values, and segmented vehicle weights in a single suspension model, segmented vehicle models can be created without having to recreate the tire and suspension models that make up the single suspension model. This means that even when sequentially creating segmented vehicle models with various tire characteristic values, suspension characteristic values, or segmented vehicle weights and analyzing their behavior, each segmented vehicle model can be created quickly, which also has the effect of enabling rapid analysis of these segmented vehicle models.

[0012] According to the analysis method of claim 2, in addition to the effects of the analysis method of claim 1, the tire model is composed of a model that does not contain shape information, which reduces the computational load when creating a single suspension model and a segmented vehicle model, and when analyzing the segmented vehicle model, compared to a model that contains shape information. This has the effect of enabling rapid analysis of the segmented vehicle model.

[0013] According to the analysis method of claim 3, in addition to the effects of the analysis method of claim 1 or 2, the tire model is composed of a function that outputs information about the behavior of the tire in response to input to the tire, so that information about the behavior of the tire can be easily and quickly obtained. This has the effect of enabling rapid analysis of the segmented vehicle model.

[0014] According to the analysis method of claim 4, in addition to the effects of the analysis method of claim 1, the suspension model is composed of a model that does not contain shape information, which reduces the computational load when creating a single suspension model and a segmented vehicle model, and when analyzing the segmented vehicle model, compared to a model that contains shape information. This has the effect of enabling rapid analysis of the segmented vehicle model.

[0015] According to the analysis method of claim 5, in addition to the effects of the analysis method of claim 1 or 4, the suspension model is composed of a function that outputs information about the behavior of the tires in response to input to the suspension, so that information about the behavior of the suspension can be easily and quickly obtained. This has the effect of enabling rapid analysis of the segmented vehicle model.

[0016] According to the analysis method described in claim 6, in addition to the effects achieved by the analysis method described in claim 1 or 4, the suspension model is composed of a combination of component models that simulate the behavior of each component constituting the suspension. This has the effect that the individual components constituting the suspension can be analyzed by appropriately changing the characteristic values ​​of the component models.

[0017] According to the analysis method of claim 7, in addition to the effects of the analysis method of claim 1, multiple segmented vehicle models are created for a single undercarriage model, each with different tire characteristic values, suspension characteristic values, or segmented vehicle weights, and the behavior of the multiple segmented vehicle models is analyzed at once. This has the effect of efficiently analyzing the behavior of multiple segmented vehicle models with different tire characteristic values, suspension characteristic values, or segmented vehicle weights, compared to analyzing the behavior of each segmented vehicle model individually.

[0018] According to the analysis method described in claim 8, in addition to the effects of the analysis method described in claim 1, a search undercarriage model is created by combining a spring model that simulates a spring and a suspension model. A search segmented vehicle model is created by setting spring characteristic values, suspension characteristic values, and segmented vehicle weights for the spring model in the created search undercarriage model, and the behavior of the created search segmented vehicle model is analyzed. If the determination of the analyzed behavior is a predetermined result, the spring characteristic values ​​are converted into tire characteristic values. In this way, by using a spring model that has a simpler structure than a tire model and consists only of a spring, it is possible to quickly obtain tire characteristic values ​​of a tire model with desired characteristics.

[0019] When the determination of the behavior analyzed on one hand does not result in a predetermined outcome, the spring characteristic value is changed, and then the following creation of the search split vehicle model is executed again. As a result, there is also an effect that the tire characteristic values of a tire model having desired characteristics can be efficiently obtained.

[0020] According to the analysis method described in claim 9, in addition to the effect achieved by the analysis method described in claim 1, the behavior is analyzed by arranging the split vehicle model on the virtual road surface, and at that time, the split vehicle model is brought into contact with one point on the coordinates of the virtual road surface. As a result, compared with the case where the split vehicle model is in line contact or surface contact with the virtual road surface, the amount of calculation for analyzing the behavior can be reduced, so there is an effect that the analysis of the split vehicle model can be performed quickly.

[0021] According to the analysis device described in claim 10, the same effect as the analysis method described in claim 1 is achieved. Also, according to the analysis program described in claim 11, the same effect as the analysis method described in claim 1 is achieved.

Brief Description of the Drawings

[0022] [Figure 1] It is an external view of the PC. [Figure 2] (a) is a block diagram showing the electrical configuration of the PC, (b) is a diagram schematically representing characteristic value data, and (c) is a diagram schematically representing test data. [Figure 3] (a) is a flowchart of the main process, and (b) is a flowchart of the split model creation process. [Figure 4] (a) is a flowchart of the analysis process, and (b) is a diagram explaining the running of a plurality of split vehicle models on the test course. [Figure 5] It is a flowchart of the characteristic value search process.

Modes for Carrying Out the Invention

[0023] Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. First, the PC1 in this embodiment will be described with reference to Figure 1. Figure 1 is an external view of the PC1. The PC1 is an information processing device (computer) that analyzes the behavior of an automobile (vehicle) using a segmented vehicle model 20, which will be described later, to simulate an automobile (vehicle).

[0024] PC1 is equipped with a mouse 2 and a keyboard 3 for inputting user instructions, and a display device 4 for displaying analysis results, etc. Based on the user instructions input from the mouse 2 and keyboard 3, a segmented vehicle model 20 that mimics an automobile is created, and the results of analyzing the behavior of the created segmented vehicle model 20 (in this embodiment, the change in vertical acceleration) are shown on the result display 70 displayed on the display device 4.

[0025] The segmented vehicle model 20 is a virtual model that simulates the behavior of the undercarriage portion of an automobile, which consists of a single tire and suspension (hereinafter referred to as the "undercarriage portion"). The segmented vehicle model 20 consists of a tire model 21, a suspension model 22, and segmented vehicle weights 24.

[0026] The tire model 21 is a virtual model that mimics a tire. More specifically, the tire model 21 is composed of a function that outputs information related to the tire's behavior (e.g., vertical and horizontal acceleration) in response to various inputs to the tire (e.g., slip angle, slip ratio, air pressure, and vertical load). In this embodiment, "Magic Formula" is used as the tire model 21, but the tire model 21 may be constructed using other functions. Furthermore, the tire model 21 is composed of an intangible model that does not contain information about the shape of the tire.

[0027] By setting tire characteristic values, which are parameters related to the tire, in such a tire model 21, the user can simulate a tire with desired performance (for example, vibration characteristics and frictional resistance with the virtual road surface 50).

[0028] The suspension model 22 is a virtual model that simulates a suspension, and is composed of a combination of component models: a coil spring model 22a that simulates a coil spring, and a shock absorber model 22b that simulates a shock absorber (damper). The coil spring model 22a is also composed of a function that outputs force information related to the behavior of the coil spring in response to various inputs to the coil spring, and the shock absorber model 22b is also composed of a function that outputs information related to the behavior of the shock absorber in response to various inputs to the shock absorber. Both the coil spring model 22a and the shock absorber model 22b are composed of intangible models that do not contain information about the shape of the coil spring and shock absorber, respectively.

[0029] Note that in Figure 1 and Figure 4(b) described later, the tire model 21, coil spring model 22a, and shock absorber model 22b are illustrated to resemble a tire, coil spring, and shock absorber, respectively. However, this is for illustrative purposes only, and the actual tire model 21, coil spring model 22a, and shock absorber model 22b do not contain any information regarding their shapes.

[0030] By setting coil spring characteristic values, which are parameters related to the coil spring (e.g., elastic properties), in the coil spring model 22a, a coil spring with the desired performance can be simulated by the user. Similarly, by setting shock absorber characteristic values, which are parameters related to the shock absorber (e.g., damping properties), in the shock absorber model 22b, a shock absorber with the desired performance can be simulated by the user. Hereinafter, the coil spring characteristic values ​​and shock absorber characteristic values ​​will be collectively referred to as "suspension characteristic values".

[0031] The coil spring model 22a and the shock absorber model 22b are connected in parallel to form the suspension model 22, and the suspension model 22 is connected in series on the tire model 21 described above to create a single undercarriage model 23.

[0032] The divided vehicle weight 24 is the weight added to one suspension section. In this embodiment, the automobile has four suspension sections, so the divided vehicle weight 24 is set to the weight obtained by dividing the total weight of the automobile by the number of suspension sections (i.e., 1 / 4 of the total weight of the automobile). These divided vehicle weights 24 are connected in series on a single suspension model 23. In this way, the divided vehicle model 20 is created.

[0033] Furthermore, the number of undercarriage components is not limited to four; it may be four or more depending on the number of undercarriage components of the automobile (vehicle). Also, the segmented vehicle weight 24 is not limited to the weight obtained by dividing the total vehicle weight by the number of undercarriage components; it may be set to a weight heavier or lighter than the weight obtained by dividing the total vehicle weight by the number of undercarriage components.

[0034] The behavior of the created segmented vehicle model 20 is analyzed by driving it on a virtual test course. Figure 1 illustrates a test course in which a step 50a is provided on a virtual road surface 50, which is a virtual road surface. In this embodiment, the segmented vehicle model 20 drives on the test course, and as an example of the behavior of the segmented vehicle model 20 while driving, vertical acceleration (i.e., vertical vibration) observed on the segmented vehicle weight 24 when it overcomes a step 50a, etc., is acquired, and the acquired acceleration trend (hereinafter referred to as "acceleration trend") is displayed on the result display 70 of the display device 4.

[0035] The results display 70 shows the desired (ideal) acceleration trend along with the acquired acceleration trend, allowing the user to quickly grasp the difference between the acquired acceleration and the desired acceleration, and use this information to readjust the tire characteristic values, coil spring characteristic values, or shock absorber characteristic values.

[0036] As described above, by connecting the tire model 21 and the suspension model 22 in series, a single suspension model 23 is created that simulates one suspension in an automobile. By setting tire characteristic values, suspension characteristic values, and divided vehicle weight 24 to the created single suspension model 23, a divided vehicle model 20 is created. When the created divided vehicle model 20 is driven on the test course, the acceleration changes are displayed on the results display 70, and the user can analyze the behavior of the divided vehicle model 20 by checking and verifying the results display 70.

[0037] In other words, a segmented vehicle model 20 is created that models one suspension system in an automobile, and the behavior of this segmented vehicle model 20 is analyzed. Therefore, compared to, for example, creating a model of the entire automobile and analyzing the behavior of one suspension system from that entire automobile model, the behavior of one suspension system can be analyzed more quickly.

[0038] Furthermore, a segmented vehicle model 20 can be created simply by setting tire characteristic values, suspension characteristic values, and segmented vehicle weight 24 in the single suspension model 23. In other words, a segmented vehicle model 20 can be created (reconstructed) without having to recreate the tire model 21 and suspension model 22 that make up the single suspension model 23. As a result, even when repeatedly creating segmented vehicle models 20 and analyzing their behavior by changing various tire characteristic values, suspension characteristic values, or segmented vehicle weight 24, each segmented vehicle model 20 can be created quickly, thus enabling rapid analysis of these segmented vehicle models.

[0039] Furthermore, since the tire model 21 is constructed using functions, information regarding the behavior of the tires can be easily and quickly obtained. This allows for rapid analysis of the segmented vehicle model 20. Moreover, because the tire model 21 is composed of an intangible model, the computational load required to create the single suspension model 23 and the segmented vehicle model 20, and to analyze the segmented vehicle model 20, can be reduced compared to when the tire model 21 is constructed using a model that contains information about its shape.

[0040] Furthermore, the tire model 21 makes contact with the virtual road surface 50 at point 60, which is a single point on the coordinate system, and as a result, the segmented vehicle model 20 also makes contact at point 60. This reduces the computational load required to analyze the segmented vehicle model 20 compared to cases where the tire model 21 makes contact with the virtual road surface 50 using a surface or a line.

[0041] Furthermore, the coil spring model 22a and the shock absorber model 22b are also constructed using functions. This allows for easy and rapid acquisition of information regarding the behavior of the coil springs and shock absorbers. Moreover, since the coil spring model 22a and the shock absorber model 22b are also constructed using intangible models, the computational load required to create the single suspension model 23 and the segmented vehicle model 20, and to analyze the segmented vehicle model 20, can be reduced compared to cases where these models are constructed using models that contain information about their shape.

[0042] Next, the electrical configuration of PC1 will be explained with reference to Figure 2. Figure 2(a) is a block diagram showing the electrical configuration of PC1. PC1 has a CPU 10, a hard disk drive (HDD) 11, and RAM 12, which are connected to input / output ports 14 via bus lines 13. The mouse 2, keyboard 3, and display device 4 described above are further connected to input / output ports 14.

[0043] The CPU 10 is a processing unit that controls the various parts connected to the bus line 13 and input / output ports 14. The HDD 11 is a rewritable, non-volatile storage device that stores the analysis program 11a, model data 11b, characteristic value data 11c, and test data 11d. When the CPU 10 executes the analysis program 11a, the main processing described later is executed as shown in Figure 3(a).

[0044] Model data 11b stores information about the tire model 21 and the coil spring model 22a and shock absorber model 22b that make up the suspension model 22, specifically functions that represent the tire model 21, etc. In addition to the above models, model data 11b also stores information about the spring model that simulates a spring, which will be described later in Figure 5.

[0045] Figure 2(a) is a schematic representation of the characteristic value data 11c. As shown in Figure 2(a), the characteristic value data 11c stores the tire characteristic values, suspension characteristic values, and segmented vehicle weight 24 as associated values. Of these, the suspension characteristic values ​​store the coil spring characteristic values ​​and shock absorber characteristic values ​​together.

[0046] The characteristic value data 11c stores combinations of tire characteristic values, suspension characteristic values, and segmented vehicle weight 24 set through experiments using actual automobiles, the characteristic value search process described later in Figure 5, and other simulations.

[0047] The tire characteristic values, suspension characteristic values, and segmented vehicle weight 24, which are stored in association with characteristic value data 11c, are acquired. The acquired tire characteristic values ​​are applied to the tire model 21, and the acquired suspension characteristic values ​​are applied to the suspension model 22, respectively, to create a single suspension model 23. By connecting the acquired segmented vehicle weight 24 to the created single suspension model 23, a segmented vehicle model 20 is created.

[0048] Figure 2(b) is a schematic representation of the test data 11d. As shown in Figure 2(b), the test data 11d stores the above-mentioned test course and the normal vibration characteristics, which are the desired (ideal) vertical acceleration transition when the segmented vehicle model 20 travels along that test course at a predetermined speed (e.g., 30 km / h).

[0049] The standard vibration characteristics also include a desired vertical acceleration progression when a segmented vehicle model 20, positioned in a weightless state at a predetermined height (e.g., 1 m) from a virtual road surface 50, is dropped under gravity (1G), and the tire model 21 of the segmented vehicle model 20 comes into contact with the virtual road surface 50.

[0050] The test course specified by the user via mouse 2 or keyboard 3 and the corresponding standard vibration characteristics are obtained from test data 11d. By running the segmented vehicle model 20 on the acquired test course, the vertical acceleration changes observed in the segmented vehicle model 20 are obtained.

[0051] The acceleration transitions in the acquired standard vibration characteristics and the acceleration transitions in the standard vibration characteristics are displayed in the results display 70, and the user can determine the validity of the tire characteristic values, suspension characteristic values, and divided vehicle weight 24 set for the divided vehicle model 20 by checking and verifying the results display 70.

[0052] Next, we will explain the processes executed by the CPU 10 with reference to Figures 3-5. Figure 3(a) is a flowchart of the main process. The main process is executed when the user inputs an instruction to run the analysis program 11a via the mouse 2 or keyboard 3.

[0053] The main process first checks whether the user has given an instruction via mouse 2 or keyboard 3 to add tire characteristic values, suspension characteristic values, and segmented vehicle weight 24 to the characteristic value data 11c, or whether an instruction has been given to update the tire characteristic values, suspension characteristic values, and segmented vehicle weight 24 stored in the characteristic value data 11c (S1).

[0054] If, during the processing of S1, there is an instruction to add or update tire characteristic values, suspension characteristic values, and divided vehicle weight 24 (S1:Yes), the tire characteristic values, suspension characteristic values, and divided vehicle weight 24 instructed by the user are added to the characteristic value data 11c, or the tire characteristic values, suspension characteristic values, and divided vehicle weight 24 instructed by the user are updated in the characteristic value data 11c (S2). On the other hand, if, during the processing of S1, there is no instruction to add or update tire characteristic values, suspension characteristic values, and divided vehicle weight 24 (S1:No), the processing of S2 is skipped.

[0055] After processing S1 and S2, the operating mode of the analysis program 11a is checked (S3). In this embodiment, the operating modes of the analysis program 11a include "analysis," which verifies the acceleration transition by running the segmented vehicle model 20 on a test course, and "characteristic value search," which searches for tire characteristic values ​​using a spring model. In processing S3, it is determined whether the operating mode instructed by the user via the mouse 2 or keyboard 3 is analysis or characteristic value search.

[0056] In the S3 process, if the operating mode is analysis (S3: "Analysis"), the partitioned model creation process (S4) described later is executed in Figure 3(b), and then the analysis process (S5) described later is executed in Figure 4(a). On the other hand, in the S3 process, if the operating mode is characteristic value search (S3: "Characteristic Value Search"), the characteristic value search process (S6) described later is executed in Figure 5.

[0057] After processing S5 and S6, check if the user has given an instruction to terminate the analysis program 11a via mouse 2 or keyboard 3 (S7). If no instruction to terminate the analysis program 11a has been given in processing S7 (S7: No), repeat the processing from S1 onwards. On the other hand, if an instruction to terminate the analysis program 11a has been given in processing S7 (S7: Yes), terminate the main processing.

[0058] The division model creation process for S4 will now be explained with reference to Figure 3(b). Figure 3(b) is a flowchart of the division model creation process. The division model creation process is the process of creating multiple division vehicle models 20 using the tire model 21 and suspension model 22 stored in the model data 11b, and the tire characteristic values, suspension characteristic values, and division vehicle weights 24 stored in the characteristic value data 11c.

[0059] The split model creation process first obtains information about the tire model 21 and the suspension model 22 from the model data 11b (S10). After the process in S10, the single undercarriage model 23 described above in Figure 1 is created from the obtained information about the tire model 21 and the suspension model 22 (S11).

[0060] After processing in S11, the counter variable N is set to 1 (S12). The counter variable N is a numerical value that represents the order of the combinations of tire characteristic values, suspension characteristic values, and divided vehicle weights stored in the characteristic value data 11c. Specifically, when the counter variable N is "1" (1st), it refers to the first combination of tire characteristic values, etc. in the characteristic value data 11c ("No. 1" in Figure 2(b)), and when the counter variable N is "2", it refers to the second combination of tire characteristic values, etc. in the characteristic value data 11c ("No. 2" in Figure 2(b)). Hereafter, "N" in "Nth" refers to the value of the counter variable N.

[0061] After processing in S12, the Nth combination of tire characteristic value, suspension characteristic value, and divided vehicle weight 24 is obtained from the characteristic value data 11c (S13). After processing in S13, the tire characteristic value and suspension characteristic value obtained in processing S12 are applied to the single suspension model 23 created in processing S11 using the method described above in Figure 1, and the divided vehicle weight 24 obtained in processing S12 is then connected to the single suspension model 23 to create the Nth divided vehicle model 20 (S14).

[0062] After processing in S14, 1 is added to the counter variable N (S15), and it is checked whether the counter variable N is greater than the number of data points in the characteristic value data 11c (i.e., the number of combinations of tire characteristic values ​​etc. stored in the characteristic value data 11c) (S16).

[0063] In the S16 process, if the counter variable N is less than or equal to the number of data points in the characteristic value data 11c (S16: No), the process from S13 onwards is repeated. On the other hand, in the S16 process, if the counter variable N is greater than the number of data points in the characteristic value data 11c (S16: Yes), the split model creation process is terminated.

[0064] This segmented model creation process generates segmented vehicle models 20 using the tire characteristic values, suspension characteristic values, and segmented vehicle weights 24 stored in the characteristic value data 11c. In other words, as many segmented vehicle models 20 as there are combinations of tire characteristic values, etc., stored in the characteristic value data 11c are created. The multiple segmented vehicle models 20 created are used in the analysis process of S5 described later.

[0065] Next, we will explain the analysis process in S5 with reference to Figure 4. Figure 4(a) is a flowchart of the analysis process. The analysis process involves driving multiple segmented vehicle models 20, created in the segmented model creation process of S4, on the test course, and obtaining and displaying the acceleration changes of each.

[0066] The analysis process first acquires a combination of a test course and its corresponding standard vibration characteristics from the test data 11d, as specified by the user via mouse 2 or keyboard 3 (S20). After the processing in S20, all of the multiple segmented vehicle models 20 created in the segmented model creation process in S4 are placed on the acquired test course (S21). Specifically, at the time of processing in S21, the test course is in a zero-gravity state, and each segmented vehicle model 20 is placed at a predetermined height (e.g., 1m) above the virtual road surface 50, with the tire models 21 of the segmented vehicle models 20 facing the virtual road surface 50.

[0067] After processing in S21, gravity (1G) is set on the test course (S22), and the vertical acceleration of each segmented vehicle model 20 when it falls and comes into contact with the virtual road surface 50 is obtained (S23). After processing in S23, the acceleration changes of each segmented vehicle model 20 obtained and the acceleration changes when it comes into contact with the virtual road surface 50 in the standard vibration characteristics obtained in processing S20 are displayed on the display device 4 (S24).

[0068] In this case, the acceleration changes of each acquired segmented vehicle model 20 and the acceleration changes when in contact with the virtual road surface 50 in the standard vibration characteristics may be displayed together in one result display 70 as described above in Figure 1, or a result display 70 may be provided for each segmented vehicle model 20, and each result display 70 may display the acceleration changes of the corresponding segmented vehicle model 20 and the acceleration changes when in contact with the virtual road surface 50 in the standard vibration characteristics.

[0069] In this way, by placing multiple segmented vehicle models 20 at predetermined heights on a test course in a zero-gravity state, setting gravity, and acquiring the observed acceleration, it is possible to simultaneously set gravity on the test course and acquire the acceleration progression when the segmented vehicle models 20 are dropped from a predetermined height.

[0070] After the processing in S24, the multiple segmented vehicle models 20 created in the segmented model creation process in S4 are driven on the test course acquired in the processing in S20 (S25). The driving of the multiple segmented vehicle models 20 on the test course in the processing of S25 will be explained with reference to Figure 4(b).

[0071] Figure 4(b) illustrates the operation of multiple segmented vehicle models 20 on a test course. In this embodiment, multiple segmented vehicle models 20 are run simultaneously on the test course, and the acceleration changes of each are acquired. Figure 4(b) shows an example of a test course similar to that in Figure 1, in which the segmented vehicle models 20 are made to overcome a step 50a. The step 50a is provided in the horizontal direction of the paper.

[0072] On this test course, first, multiple segmented vehicle models 20 are arranged in a single line horizontally in front of the step 50a. Then, the multiple segmented vehicle models 20 are started to move simultaneously at a predetermined speed (for example, 30 km / h). As a result, the multiple segmented vehicle models 20 move simultaneously to overcome the step 50a.

[0073] In this way, by running multiple segmented vehicle models 20 simultaneously on the same test course, the acceleration transitions of multiple segmented vehicle models 20, each with different tire characteristics, suspension characteristics, and segmented vehicle weights 24, can be acquired at once. This makes it possible to acquire the acceleration transitions of multiple segmented vehicle models 20 more efficiently compared to running each segmented vehicle model 20 individually with different tire characteristics, suspension characteristics, and segmented vehicle weights 24.

[0074] Furthermore, by storing various variations of the characteristic values ​​of the parts to be evaluated (tires, suspension) using the segmented vehicle model 20 in the characteristic value data 11c, the acceleration changes of the segmented vehicle model 20 with various characteristic values ​​can be obtained all at once. For example, if you want to evaluate the tires, you can store various variations of the tire characteristic values, along with the fixed suspension characteristic values ​​and segmented vehicle weights, in the characteristic value data 11c.

[0075] Furthermore, if you want to evaluate the coil springs in the suspension, you can store the suspension characteristics values, which are obtained by varying the coil spring characteristics and the fixed shock absorber characteristics, along with the fixed tire characteristics and the divided vehicle weight, in the characteristic value data 11c.

[0076] Similarly, if you want to evaluate the shock absorber in the suspension, you can store the suspension characteristic values, which are obtained by varying the shock absorber characteristic values ​​and the fixed coil spring characteristic values, along with the fixed tire characteristic values ​​and the divided vehicle weight, in the characteristic value data 11c. This allows for a detailed and rapid evaluation of the coil springs or shock absorbers that make up the suspension.

[0077] Return to Figure 4(a). After processing in S25, the vertical acceleration transitions of each of the multiple segmented vehicle models 20 observed by driving on the test course are acquired (S26). After processing in S26, the acquired acceleration transitions of each segmented vehicle model 20 and the desired acceleration transitions obtained in processing S20 when driving on the test course are displayed on the display device 4 (S27).

[0078] In this case, the acceleration trends of each of the acquired segmented vehicle models 20 and the desired acceleration trends when driving on the test course may be displayed together in a single result display 70, or a result display 70 may be provided for each segmented vehicle model 20, and the acceleration trends of the segmented vehicle model 20 corresponding to each result display 70 and the desired acceleration trends when driving on the test course in the standard vibration characteristics may be displayed.

[0079] After processing S27, the analysis process will be terminated.

[0080] The user can confirm and verify the display resulting from the S24 and S27 processes in the analysis process, and reset the tire characteristic values, suspension characteristic values, or segmented vehicle weight 24 as needed. The user then reflects (updates) the reset tire characteristic values, suspension characteristic values, or segmented vehicle weight 24 in the characteristic value data 11c using the S1 and S2 processes described above in Figure 3(a), and then executes the segmented model creation process in S4 and the analysis process in S5 again. This allows the user to confirm the validity of the reset tire characteristic values, suspension characteristic values, or segmented vehicle weight 24.

[0081] Next, we will explain the characteristic value search process of S6 with reference to Figure 5. Figure 5 is a flowchart of the characteristic value search process. The characteristic value search process is a process that uses a spring model to search for tire characteristic values ​​to be applied to the tire model 21.

[0082] The spring model is a model in which the elastic properties of the simulated spring change according to the applied spring constant (spring characteristic value), and, similar to the tire model 21 described above, is configured to be able to travel on a virtual road surface 50.

[0083] The characteristic value search process first sets the initial value of the spring constant applied to the spring model (S30). The initial value of the spring constant set in process S30 may be a value entered by the user via mouse 2 or keyboard 3, or a fixed value pre-stored in HDD 11 may be used.

[0084] After processing in S30, the spring model and suspension model 22 are obtained from the model data 11b (S31). After processing in S31, the suspension characteristic values ​​and divided vehicle weights for application to the search-type divided vehicle model, which are specified by the user via the mouse 2 or keyboard 3, are obtained from the characteristic value data 11c (S32). Note that the suspension characteristic values ​​and divided vehicle weights for application to the search-type divided vehicle model are not limited to those stored in the characteristic value data 11c; for example, suspension characteristic values ​​and divided vehicle weights for the search-type divided vehicle model that have been previously stored in the HDD 11 may be used.

[0085] After processing in S32, a test course on which the search-type segmented vehicle model is driven, specified by the user via the mouse 2 or keyboard 3, is obtained from the test data 11d as a combination of the test course and the corresponding standard vibration characteristics (S33). Note that the combination of the test course on which the search-type segmented vehicle model is driven and the corresponding standard vibration characteristics is not limited to those stored in the test data 11d; for example, a test course for the search-type segmented vehicle model and the corresponding standard vibration characteristics that have been pre-stored in the HDD 11 may be used.

[0086] After processing in S33, the suspension model 22 obtained in processing S32 is connected in series to the spring model obtained in processing S31 to create a search suspension model (S34). In other words, the search suspension model is a model that uses a spring model instead of a tire model 21 in the single suspension model 23 (see Figure 1).

[0087] After processing in S34, the spring constant set in processing S30, or reset in processing S41 described later, is applied to the spring model in the search suspension model, the suspension characteristic values ​​obtained in processing S32 are applied to the suspension model 22 in the search suspension model, and the divided vehicle weight 24 obtained in processing S32 is connected to the search suspension model to create a search divided vehicle model (S35).

[0088] After processing in S35, the created search segmented vehicle model is placed on the test course acquired in processing S33, and gravity (1G) is set on the test course (S36). In processing S36, similar to the processing in S21 and S22 described above in Figure 3(b), the search segmented vehicle model is placed at a predetermined height (for example, 1m) from the virtual road surface 50, and the spring model of the search segmented vehicle model is positioned facing the virtual road surface 50, and gravity is set in that state.

[0089] After processing in S36, the search segmented vehicle model is driven on the test course (S37), and the vertical acceleration transition of the search segmented vehicle model observed during driving on the test course is obtained (S38). After processing in S38, the obtained acceleration transition of the search segmented vehicle model and the desired acceleration transition when driving on the test course in the standard vibration characteristics obtained in processing in S33 are displayed on the display device 4 (S39).

[0090] The user checks and verifies the acceleration transition displayed in process S39, determines whether the spring constant set in process S30 or reset in process S41 (described later) is acceptable, and inputs whether the result was satisfactory via mouse 2 or keyboard 3.

[0091] After processing in S39, it is checked whether the input judgment result was good (S40). If the input judgment result was not good in processing S40 (S40: No), the spring constant is reset (S41), and the processing from S35 onwards is repeated. The resetting of the spring constant in processing S41 may be done, for example, using a spring constant entered by the user via mouse 2 or keyboard 3, or by adding, subtracting, multiplying, or dividing a predetermined value by the spring constant before resetting.

[0092] On the other hand, in the S40 process, if the input judgment result is good (S40: Yes), the spring constant is converted to a tire characteristic value using a known method (S42). After the S42 process, the converted tire characteristic value, the suspension characteristic value obtained in the S32 process, and the divided vehicle weight 24 are associated and saved in the characteristic value data 11c (S43), and the characteristic value search process is terminated.

[0093] In the characteristic value search process, a search undercarriage model is created by combining the spring model and the suspension model 22. A search segmented vehicle model is then created by setting the spring constant, suspension characteristic values, and segmented vehicle weight to the created search undercarriage model. The created search segmented vehicle model is driven on a test course, and the acceleration changes during this time are displayed. The user then checks and verifies the acceleration changes, and if the results are satisfactory, the spring constant is converted into tire characteristic values.

[0094] Here, the spring model, being a model that simulates a spring, has a simpler structure than the tire model 21, which simulates a tire. By using such a search-type suspension model, the processing load on PC1 during model construction and test course driving is reduced compared to the segmented vehicle model 20 composed of the tire model 21, and these can be implemented quickly. As a result, tire characteristic values ​​based on spring constants that yield good judgment results for the user can be quickly searched for.

[0095] Furthermore, if the user's judgment result in the S40 process is deemed unsatisfactory, the spring constant is changed and the creation of the search-use segmented vehicle model and subsequent steps are executed again. In other words, the spring constant is reset based on the spring constant that the user's judgment result deemed unsatisfactory, and the creation of the search-use segmented vehicle model and subsequent steps are executed again, allowing for efficient searching of tire characteristic values ​​based on spring constants that the user's judgment result deemed satisfactory.

[0096] In this process, the spring constant is changed in various ways, and the creation of a segmented vehicle model for exploration and the repeated running of the segmented vehicle model on the test course are performed. Even in such cases, the simple structure of the spring model allows for the rapid creation of the segmented vehicle model for exploration and the repeated running of the segmented vehicle model on the test course.

[0097] Although the present invention has been described above based on embodiments, it can be easily inferred that the present invention is not limited in any way to the embodiments described above, and that various improvements and modifications are possible without departing from the spirit of the present invention.

[0098] In the above embodiment, the tire model 21 is constructed as an intangible model represented by a function, but the invention is not limited to this. As the tire model 21, other models that do not use functions or have a shape, such as a model based on the finite element method, may be used. Similarly, the coil spring model 22a and the shock absorber model 22b are constructed as intangible models represented by functions, but the invention is not limited to this, and other models that do not use functions or have a shape, such as a model based on the finite element method, may be used.

[0099] In the above embodiment, the suspension model 22 is composed of a coil spring model 22a and a shock absorber model 22b, but it is not limited to this. Models of other components that make up the suspension, such as suspension arms and bushings, may be added to the suspension model 22.

[0100] In this case, the characteristic values ​​of the other added parts should be included in the suspension characteristic values. For example, to add a suspension arm model to the suspension model 22, the coil spring model 22a and shock absorber model 22b should be connected in parallel, and the suspension arm model should be connected in series.

[0101] In the above embodiment, the tire model 21 is in contact with the virtual road surface 50 at point 60, which is a single point on the coordinate system (see Figure 1), but it is not limited to this. For example, the tire model 21 may be in contact with the virtual road surface 50 by a line, or by a surface.

[0102] In the above embodiment, in the process of S25 in Figure 4(a), multiple segmented vehicle models were arranged in a single horizontal line in front of the step 50a (see Figure 4(b)), and the multiple segmented vehicle models 20 were started to travel simultaneously at a predetermined speed. However, the embodiment is not limited to this. For example, multiple segmented vehicle models may be arranged in a single vertical line in front of the step 50a before starting to travel. This allows the multiple segmented vehicle models arranged vertically to travel in a manner that causes them to successively overcome the step 50a.

[0103] Furthermore, the running speeds of each of the multiple segmented vehicle models 20 may be different. In this case, the desired vertical acceleration transitions for each running speed should be set in the standard vibration characteristics of the test data 11d, and the acceleration transitions in the standard vibration characteristics corresponding to the running speed of the segmented vehicle model 20 should be displayed in the processing of S27. Moreover, the timing at which each of the multiple segmented vehicle models 20 starts running may be different.

[0104] In the above embodiment, the behavior of the divided vehicle model 20 was obtained by acquiring the vertical acceleration transition observed on the divided vehicle weight 24, but it is not limited to this. For example, the frequency characteristics of the acceleration obtained by performing a fast Fourier transform on the acquired acceleration transition may also be used as the behavior of the divided vehicle model 20. Alternatively, instead of acceleration, the velocity transition or the frequency characteristics of the velocity observed on the divided vehicle weight 24 may be used as the behavior of the divided vehicle model 20, or values ​​other than acceleration and velocity that relate to the movement of the divided vehicle model 20 may also be used as the behavior of the divided vehicle model 20.

[0105] Alternatively, the behavior of the segmented vehicle model 20 is not limited to obtaining vertical acceleration, etc., but may also be obtained horizontal acceleration, etc., or acceleration, etc., in other directions. By obtaining horizontal acceleration, etc., as part of the behavior of the segmented vehicle model 20, the cornering characteristics when the segmented vehicle model 20 drives around corners on a test course can be evaluated.

[0106] Furthermore, the behavior of the segmented vehicle model 20 is not limited to acquiring acceleration, etc., only in the vertical direction or vertical direction, i.e., in only one dimension. For example, it may also acquire acceleration, etc., in two dimensions (vertical and horizontal), or in two or more dimensions (for example, vertical, horizontal, and roll direction).

[0107] Furthermore, the position for observing the acceleration of the divided vehicle model 20 is not limited to the divided vehicle weight 24, but may be any position on the divided vehicle model 20, for example, on the tire model 21 or the suspension model 22.

[0108] In the above embodiment, the acquired acceleration trend and the acceleration trend in the standard vibration characteristics were displayed on the results display 70, and the user verified their validity, but the embodiment is not limited to this. For example, the difference between the acquired acceleration trend and the acceleration trend in the standard vibration characteristics may be calculated, the PC1 may determine whether the magnitude of the calculated difference is within a predetermined range, and the result may be displayed on the display device 4.

[0109] In the above embodiment, the spring constant was used as the spring characteristic value applied to the spring model in the characteristic value search process in Figure 5, but this is not the only option. For example, values ​​representing the characteristics of the spring, such as the damping constant which indicates the degree of damping of the spring, may be used as the spring characteristic value.

[0110] Furthermore, while the characteristic value search process performed the search for tire characteristic values ​​using a spring model for the search segmented vehicle model, it is not limited to this. For example, the search for tire characteristic values ​​could be performed using a search segmented vehicle model that uses tire model 21 instead of the spring model, or the search for tire characteristic values ​​could be performed using a search segmented vehicle model that uses other models.

[0111] In the above embodiment, an automobile was used as an example of the vehicle that serves as the base for the segmented vehicle model 20, but it is not limited to this, and other vehicles such as bicycles or motorcycles may also be used. For example, if a bicycle is used as the base vehicle for the segmented vehicle model 20, the suspension model 22 may be omitted from the segmented vehicle model 20.

[0112] In the above embodiment, a PC1 was given as an example of an information processing device that executes the analysis program 11a, but the invention is not limited to this, and the analysis program 11a may be executed on other information processing devices such as smartphones and tablet terminals. Furthermore, the present invention may be applied to a dedicated device (analysis device) that stores the analysis program 11a in ROM or the like and executes only the analysis program 11a. [Explanation of symbols]

[0113] 1 PC (Analysis device, computer) 11a Analysis Program 20-part vehicle model 21 Tire Models 22 Suspension Models 22a Coil spring model (part model) 22b Shock absorber model (parts model) 23 Single-Suspension Models 24 division vehicle weight 50 Virtual road surface S11 Suspension construction steps, suspension construction methods S14 Step for creating a divided vehicle, means for creating a divided vehicle S26 Analysis step, analysis means S27 Output step, output means S34 Steps to create the undercarriage for exploration S35 Steps for creating a split vehicle for exploration S40 Decision step, part of the iteration step S41 Part of the iteration step S42 Conversion Step

Claims

1. A suspension creation step involves creating a single suspension model that simulates one suspension system in a vehicle by combining a tire model that outputs information about the behavior of the tire in response to input to the tire, and a suspension model that outputs information about the behavior of the suspension in response to input to the suspension, A divided vehicle creation step involves creating a divided vehicle model by setting tire characteristic values ​​for the tire model in the single suspension model created in the suspension creation step, setting suspension characteristic values ​​for the suspension model in the single suspension model, and further setting the divided vehicle weight, which is the vehicle weight applied to one suspension of the vehicle, in the single suspension model. The analysis step involves analyzing the behavior of the segmented vehicle model created in the segmented vehicle creation step, An analysis method characterized by comprising an output step that outputs information about the behavior analyzed in the analysis step.

2. The analysis method according to claim 1, characterized in that the tire model is a model that does not have information about its shape.

3. The analysis method according to claim 1 or 2, characterized in that the tire model is composed of a function that outputs information regarding the behavior of the tire in response to input to the tire.

4. The analysis method according to claim 1, characterized in that the suspension model is a model that does not have information about shape.

5. The analysis method according to claim 1 or 4, characterized in that the suspension model is composed of a function that outputs information regarding the behavior of the tire in response to input to the suspension.

6. The analysis method according to claim 1 or 4, characterized in that the suspension model is composed of a combination of component models that simulate the behavior of each component constituting the suspension.

7. The aforementioned split vehicle creation step involves creating multiple split vehicle models, each with different tire characteristic values, suspension characteristic values, or split vehicle weights, based on the single suspension model created in the suspension creation step. The analysis method according to claim 1, characterized in that the analysis step performs an analysis of the behavior of multiple divided vehicle models created in the divided vehicle creation step all at once.

8. A step to create an exploration undercarriage model, which involves combining a spring model that simulates a spring with the suspension model, A step to create a search-type divided vehicle model, which involves setting spring characteristic values ​​in the spring model created in the search-type The exploration analysis step involves analyzing the behavior of the exploration segmented vehicle model created in the exploration segmented vehicle creation step, The determination step involves determining the behavior analyzed in the exploration analysis step, If the determination in the determination step results in a predetermined outcome, a conversion step is performed to convert the spring characteristic value into the tire characteristic value, The analysis method according to claim 1, further comprising an iterative step in which, if the determination in the determination step is not a predetermined result, the spring characteristic value is changed and the search segment vehicle creation step, the search analysis step, and the determination step are executed again.

9. The aforementioned analysis step involves placing a virtual road surface on the segmented vehicle model created in the segmented vehicle creation step and analyzing its behavior. The analysis method according to claim 1, characterized in that the segmented vehicle model is in contact with a point on the coordinates of the virtual road surface.

10. A suspension creation means for creating a single suspension model that simulates one suspension system in a vehicle by combining a tire model that outputs information regarding the behavior of the tire in response to input to the tire, and a suspension model that outputs information regarding the behavior of the suspension in response to input to the suspension, A segmented vehicle creation means creates a segmented vehicle model by setting tire characteristic values ​​in the tire model of the single suspension model created by the suspension creation means, setting suspension characteristic values ​​in the suspension model of the single suspension model, and further setting the segmented vehicle weight, which is the vehicle weight applied to one suspension of the vehicle, to the single suspension model. An analysis means for analyzing the behavior of the segmented vehicle model created by the segmented vehicle creation means, An analysis device characterized by comprising an output means for outputting information about the behavior analyzed by the analysis means.

11. An analysis program that causes a computer to perform an analysis process to analyze the behavior of a vehicle, A suspension creation step to create a single suspension model that simulates one suspension system in the vehicle by combining a tire model that outputs information regarding the behavior of the tire in response to input to the tire and a suspension model that outputs information regarding the behavior of the suspension in response to input to the suspension, A divided vehicle creation step involves creating a divided vehicle model by setting tire characteristic values ​​for the tire model in the single suspension model created in the suspension creation step, setting suspension characteristic values ​​for the suspension model in the single suspension model, and further setting the divided vehicle weight, which is the vehicle weight applied to one suspension of the vehicle, in the single suspension model. The analysis step involves analyzing the behavior of the segmented vehicle model created in the segmented vehicle creation step, An analysis program characterized by causing the computer to execute an output step that outputs information about the behavior analyzed in the analysis step.

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