Regulating device and regulating method for clutch temperature estimation model

Abstract

The present invention relates to a regulating device and a regulating method for a clutch temperature estimation model. The regulating method can include generating n regulating genes, calculating a regulating value corresponding to a regulating variable by using information of each of the n regulating genes, calculating a temperature estimation precision by applying the calculated regulating value to the clutch temperature estimation model, extracting n regulating genes of the highest calculated precision, and regenerating m regulating genes by recombination of the extracted n regulating genes.

Description

Technical Field

[0001] This invention relates to an adjustment device and method for a clutch temperature estimation model. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not constitute prior art.

[0003] For example, when a vehicle begins to move, the temperature of a dry clutch may rise due to clutch friction. In particular, when starting a vehicle after it has been parked on a slope, the clutch temperature rises sharply due to hard friction. When the clutch temperature rises, the friction coefficient characteristics of the clutch friction material may deteriorate, and the driver may experience discomfort due to reduced starting acceleration.

[0004] Additionally, when this high temperature condition of the clutch is sustained or repeated, the clutch's durability and / or shift feel may deteriorate. Because drivers cannot easily identify this problem, a clutch temperature warning is displayed on the instrument cluster. The clutch temperature is typically calculated based on a clutch temperature model.

[0005] However, the applicant found that applying the clutch temperature estimation model to actual vehicles required a significant amount of time to evaluate and adjust the model. Furthermore, the accuracy of this clutch temperature estimation model, and the time required to adjust it, could largely depend on the skill or expertise of the person performing the adjustment.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The present invention provides a method and apparatus that reduces the average man-hours required for manual adjustment of temperature models and extracts highly accurate adjustment values ​​by quantifying the adjustment precision.

[0008] An exemplary method for adjusting a clutch temperature estimation model includes: generating a plurality of regulatory genes by a regulatory value generation module; calculating a regulatory value corresponding to a regulatory variable by the regulatory value generation module using information from each of the plurality of regulatory genes; calculating the temperature estimation accuracy by an accuracy calculation module by applying the calculated regulatory value to the clutch temperature estimation model; extracting a first number of regulatory genes with the highest calculated accuracy from the plurality of regulatory genes by the accuracy calculation module; and regenerating a second number of regulatory genes by a recombination module through recombination of the extracted first number of regulatory genes.

[0009] In one implementation, the calculation of regulatory values, the calculation of precision, and the extraction of the first number of regulatory genes can be further performed on the second number of regenerated regulatory genes.

[0010] The extraction of the first number of regulatory genes may include extracting the n regulatory genes with the highest precision from the n regulatory genes with high precision from the previous generation and the m regulatory genes from the subsequent generation.

[0011] An exemplary regulation method may further include: determining whether the current generation is the final generation while increasing the number of generations by repeating the following steps: regenerating a second number of regulatory genes by recombination of the extracted first number of regulatory genes, calculating the regulation value of the second number of regulatory genes, calculating the precision of the second number of regulatory genes, and extracting the first number of regulatory genes for the second number of regulatory genes; when the current generation is the final generation, ending the repetition and adjusting the clutch temperature estimation model with the regulation variable value having the highest precision.

[0012] The regeneration of the second number of regulatory genes may include: randomly selecting a third number of regulatory genes from the extracted first number of regulatory genes, extracting the regulatory gene components of a randomly assigned region from each of the third number of regulatory genes, and generating new regulatory genes by combining the extracted regulatory gene components in the corresponding regions.

[0013] The regeneration of a second number of regulatory genes may further include: regenerating mutated regulatory genes by setting a mutation probability for each chromosome in the new regulatory genes.

[0014] When regenerating a second number of regulatory genes, for each of the multiple sub-regulatory genes included in the first number of regulatory genes, the low-order mutation probability of the sub-regulatory gene can be set to higher, and the high-order mutation probability of the sub-regulatory gene can be set to lower.

[0015] In the calculation of accuracy, the accuracy of the pressure plate's maximum temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's maximum temperature, and the accuracy of the flywheel's cooling curve can be considered.

[0016] The accuracy calculation may include: calculating the accuracy of the clutch temperature estimation model by multiplying by the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve, and by multiplying by the mode weight value.

[0017] An exemplary adjustment device for a clutch temperature estimation model includes: an adjustment value generation module configured to: randomly generate a plurality of first-generation adjustment genes and calculate an adjustment value corresponding to an adjustment variable by utilizing information from each of the plurality of first-generation adjustment genes; an accuracy calculation module configured to: apply the calculated adjustment value to the clutch temperature estimation model, calculate the accuracy of the temperature estimation, and extract the adjustment gene with the highest calculated accuracy among the plurality of first-generation adjustment genes; and a recombination module configured to regenerate a second-generation adjustment gene by recombination of the extracted first-generation adjustment gene.

[0018] The regulation value generation module can calculate the regulation value of the second-generation regulatory gene. The accuracy calculation module can apply the calculated regulation value of the second-generation regulatory gene to the clutch temperature estimation model, calculate the accuracy of the temperature estimation, and extract the third-generation regulatory gene with the highest accuracy from the first-generation and second-generation regulatory genes.

[0019] The recombination module can recombine third-generation regulatory genes to generate fourth-generation regulatory genes.

[0020] The recombination module can randomly select multiple regulatory genes from the extracted first-generation regulatory genes, extract the regulatory gene components of a randomly allocated region from each of the randomly selected multiple regulatory genes, and generate new regulatory genes by combining the extracted regulatory gene components in the corresponding regions.

[0021] The recombination module can regenerate the mutated regulatory gene by setting the mutation probability for each chromosome in the new regulatory gene.

[0022] For each of the multiple sub-regulatory genes included in the first-generation regulatory gene, the low-position mutation probability of the sub-regulatory gene can be set to higher, and the high-position mutation probability of the sub-regulatory gene can be set to lower.

[0023] The accuracy calculation module can calculate the adjustment accuracy by taking into account the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve.

[0024] The accuracy calculation module can calculate the accuracy of the clutch temperature estimation model by multiplying it by the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve, and by multiplying it by the mode weight value.

[0025] Based on the adjustment device and adjustment method for the clutch temperature estimation model according to the exemplary embodiment, automatic adjustment is achieved by utilizing a genetic algorithm, thereby significantly reducing the labor time required for adjustment and improving the adjustment accuracy.

[0026] Other areas of application will become apparent from the description provided herein. It should be understood that this specification and specific examples are intended for illustrative purposes only and are not intended to limit the scope of the invention. Attached Figure Description

[0027] To provide a good understanding of the invention, various embodiments of the invention will now be described by way of example with reference to the accompanying drawings, in which:

[0028] Figures 1 to 3 It is a graph showing the temperature change over time due to the heat generated between the flywheel and the clutch.

[0029] Figure 4 An adjustment device for a clutch temperature estimation model according to an exemplary embodiment is shown.

[0030] Figure 5 This is a flowchart illustrating an adjustment method for a clutch temperature estimation model according to an exemplary embodiment.

[0031] Figure 6 This is a table representing the first generation of regulatory genes.

[0032] Figure 7 The generation of second-generation regulatory genes is shown.

[0033] Figure 8 and Figure 9 It is a graph showing the estimated temperature and the actual measured temperature of the temperature estimation model according to each mode of the pressure plate.

[0034] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way.

[0035] Explanation of reference numerals in the attached figures

[0036] 1: Adjustment device

[0037] 10: Adjustment value generation module

[0038] 20: Precision Calculation Module

[0039] 30: Reorganization Module

[0040] 40: Storage module. Detailed Implementation

[0041] The following description is merely exemplary in nature and is not intended to limit the invention, application, or use. It should be understood that throughout the drawings, corresponding reference numerals denote the same or corresponding parts and features.

[0042] The exemplary embodiments disclosed in this specification will be described in detail below with reference to the accompanying drawings. In this specification, identical or similar components will be indicated by identical or similar reference numerals, and repeated descriptions will be omitted. The terms "module" and / or "unit" used for components in the following description are merely for the convenience of describing this specification. Therefore, these terms do not have a meaning or function that distinguishes them from each other. In describing exemplary embodiments of this specification, detailed descriptions of well-known techniques related to the invention will be omitted where it is determined that such descriptions may obscure the gist of the invention. The accompanying drawings are provided merely to facilitate understanding of the exemplary embodiments disclosed in this specification and should not be construed as limiting the spirit of the disclosure herein. It should be understood that the invention includes all modifications, equivalents, and substitutions without departing from the scope and spirit of the invention.

[0043] Terms including ordinal numbers (e.g., first, second, etc.) will only be used to describe various components and should not be interpreted as limiting these components. These terms are only used to distinguish one component from other components.

[0044] It should be understood that when a component is referred to as "connected" or "linked" to another component, it can be directly connected or linked to the other component, or it can be connected or linked to the other component through other components inserted in between. Furthermore, it should be understood that when a component is referred to as "directly connected" or "directly linked" to another component, it can be directly connected or linked to the other component without any other components inserted in between.

[0045] It will be further understood that the terms “comprising” and “having” as used in this specification specify the presence of the stated features, values, steps, operations, components, parts or combinations thereof, but do not exclude the presence or addition of one or more other features, values, steps, operations, components, parts or combinations thereof.

[0046] First, the adjustment of the clutch temperature estimation model will be explained.

[0047] Figures 1 to 3 It is a graph showing the temperature change over time due to the heat generated between the flywheel and the clutch.

[0048] The basic principle of the clutch temperature model is to treat the heat generated during clutch slippage as an energy source. The goal is to adjust how much energy is distributed to the flywheel-clutch assembly. Additionally, a cooling coefficient is set and adjusted to determine if the cooled clutch / flywheel temperature matches the evaluation results during cooling.

[0049] like Figure 1As shown, ClutchTrqTune2 represents the flywheel torque compensation value and is a variable adjusted when the overall temperature of the flywheel and / or clutch is high or low. The actual thermal energy is calculated by multiplying the flywheel torque by a coefficient of ClutchTrqTune2. Figure 1 As the dashed line in the diagram increases, the value of ClutchTrqTune2 increases, leading to increased heat generation. Therefore, the flywheel / clutch temperature is higher than that based on the single-dotted line. For example... Figure 1 The solid line in the diagram indicates that as the value of ClutchTrqTune2 decreases, the heat energy also decreases, resulting in a lower flywheel / clutch temperature compared to the flywheel / clutch temperature based on the dashed line.

[0050] refer to Figure 1 The dashed line in the diagram represents the increase in heat energy as the value of ClutchTrqTune2 increases. Therefore, the flywheel / clutch temperature becomes higher than the reference value shown by the dashed line. (Reference) Figure 1 The solid line in the diagram represents the decrease in heat energy as the value of ClutchTrqTune2 decreases. Consequently, the flywheel / clutch temperature becomes lower than the reference value for flywheel / clutch temperature shown by the dashed line.

[0051] like Figure 2 As shown, QtrfC2 is a variable that determines how much of the heat energy generated during the clutch heating process is allocated to each component. Figure 2 As shown by the dashed line in the graph on the right, when QtfC2 is high, the temperature rises above the reference value (single-dot line) because more heat is distributed to the clutch components. Additionally, as... Figure 2 As shown by the dashed line in the left-hand curve graph, because less heat energy is distributed to the flywheel side, less heat energy is received on the flywheel side, and the temperature drops below the reference value (single-dotted line). Figure 2 As shown by the solid line in the graph on the right, when QtfC2 is low, the temperature drops below the reference value (dashed line) because less heat is distributed to the clutch components. Additionally, as... Figure 2 As shown by the solid line in the graph on the left, the flywheel receives more heat and its temperature rises above the baseline value (dashed line) because more heat is distributed to the flywheel side.

[0052] refer to Figure 3BaseHTCTune is a variable used to adjust the amount of clutch cooling, and an increase in the value of BaseHTCTune may lead to a greater temperature drop during clutch cooling. ReD represents the Reynolds number at each radius and can be calculated as the product of AIRNU (e.g., 62914.41), engine speed * DCTR (effective radius of the clutch housing), and "DCTR * π(pi) * 2". For evaluation, the temperature trend is monitored while the clutch is cooled after reaching its maximum temperature, and the adjustment is mainly related to the time interval after reaching the maximum temperature. However, as... Figure 3 The dashed line shown indicates that as the BaseHTCTune value increases, the cooling capacity increases, and the maximum temperature becomes lower than the base value (dashed line). Conversely, as... Figure 3 The solid line shown indicates that as this value decreases, the cooling capacity decreases, and the maximum temperature becomes higher than the baseline value (dashed line). Therefore, this effect should be considered when making adjustments. When combining the three main control variables, many situations can arise, and manual adjustment by considering the effect of each variable is not easy.

[0053] During initial tuning, tuning variables QtrfC2, cltchTrqTune2, and BaseHTCTune are randomly generated. The random generation of tuning variables can vary depending on the type of transmission. For example, considering the clutch structure, for intelligent manual transmissions (iMT) and automated manual transmissions (AMT), QtrfC2, cltchTrqTune2, and BaseHTCTune can be set as tuning variables; for dual clutch transmissions (DCT), QtrfC1 and CltchTrqTune1 can be added as tuning variables.

[0054] Figure 4 An adjustment device for a clutch temperature estimation model according to an exemplary embodiment is shown.

[0055] Figure 5 This is a flowchart illustrating an adjustment method for a clutch temperature estimation model according to an exemplary embodiment.

[0056] like Figure 4 As shown, the adjustment device 1 includes an adjustment value generation module 10, an accuracy calculation module 20, a recombination module 30, and a storage module 40.

[0057] The regulating device 1 according to the exemplary embodiment utilizes a genetic algorithm and may perform or include the following steps.

[0058] In step S1, the adjustment value generation module 10 receives the weight values ​​and adjustment variables for each mode. The input information can be stored in the storage module 40.

[0059] After completing the data input for each mode, in step S2, the regulation value generation module 10 randomly generates n regulatory genes.

[0060] In step S3, the regulatory value generation module 10 uses the information of each regulatory gene to calculate the regulatory value corresponding to the regulatory variable. At this time, a predetermined number of regulatory variables are selected from all regulatory variables, and the regulatory values ​​of the selected regulatory variables can be calculated in step S3.

[0061] In step S4, the accuracy calculation module 20 applies the calculated adjustment value to the clutch temperature estimation model to calculate the maximum temperature difference and the cooling curve area difference, and uses the calculation results to calculate the accuracy of the estimated temperature through the clutch temperature estimation model.

[0062] In step S5, the precision calculation module 20 extracts n regulatory genes (e.g., 10 genes) in a high-precision order.

[0063] In step S6, the recombination module 30 recombines the extracted n regulatory genes to generate m regulatory genes (e.g., 30 regulatory genes).

[0064] Subsequently, steps S3, S4, and S5 were performed on m regulatory genes (completing the second generation).

[0065] Step S5 can be performed on the n regulatory genes with high accuracy in the previous generation (generation i) and the m regulatory genes in the current generation (generation i+1). That is, the accuracy of the clutch temperature estimation model is calculated using the n regulatory genes in the first generation and the m regulatory genes in the second generation, and the regulatory genes with the highest accuracy are extracted from the total n+m regulatory genes.

[0066] Thus, by repeating steps S3 to S6, the number of generations (first generation, second generation, third generation, ... and final generation) increases. Furthermore, after executing step S5, step S7 determines whether the current generation is the final generation. When the current generation is the final generation (step S7 - yes), steps S3-S6 are no longer repeated, and adjustment is completed in step S8. The clutch temperature estimation model can be adjusted using the adjustment variable with the highest accuracy in the final generation.

[0067] Subsequently, reference Figure 6 This section will describe a variable generation method using a genetic algorithm according to an exemplary embodiment. In the following, the exemplary embodiment uses three moderating variables as an example. However, the number of moderating variables can be varied according to the design.

[0068] Figure 6 This is a table representing the first generation of regulatory genes.

[0069] For example, such as Figure 6 As shown, the regulatory genes generated in step S2 comprise a total of 17 chromosomes. The number of chromosomes, 17, is exemplary and can be changed according to the design. The regulatory genes can be expressed in binary. Of the 17 chromosomes included in the regulatory genes, chromosomes 1 to 5 represent the regulatory values ​​of QtrfC2, chromosomes 6 to 11 represent the regulatory values ​​of CltchTrqTune2, and chromosomes 12 to 17 represent the regulatory values ​​of BaseHTCTune. However, the invention is not limited to this, and the number and location of all chromosomes can be changed according to the design. Hereinafter, the multiple chromosomes representing regulatory values ​​are collectively referred to as sub-regulatory genes.

[0070] The first daughter regulatory gene TG1 is located on chromosomes 1 through 5. The first daughter regulatory gene TG1 can have a regulatory value based on a decimal number converted from the binary number corresponding to each of chromosomes 1 through 5. For example, when chromosomes 1 through 5 in the first row are 01100, the converted value is 0*2. 4 +1*2 3 +1*2 2 +0*2 1 +0*2 0 =12. After the final conversion, the adjustment value of QtrfC2 can be determined as 0.01*12+0.4=0.52 (final value). The weight value of 0.01 used to obtain the final value by multiplying by the conversion value 12, and the constant 0.4 added to the weight value*conversion value can be changed according to design factors.

[0071] The second sub-regulatory gene TG2 is located on chromosomes 6 through 11. The second sub-regulatory gene TG2 can have a regulation value based on a decimal number converted from the binary number corresponding to each chromosome from 6 to 11. For example, when chromosomes 6 through 11 in the first row are 100000, the conversion value is 1*2. 5 +0*2 4 +0*2 3 +0*2 2 +0*2 1 +0*2 0 =32. After the final conversion, the adjustment value of CltchTrqTune2 can be determined as 0.01*32+0.6=0.92 (final value). The weight value of 0.01 used to obtain the final value by multiplying by the conversion value 32, and the constant 0.6 added to the weight value*conversion value can be changed according to design factors.

[0072] The third daughter regulatory gene TG3 is located on chromosomes 12 to 17.

[0073] The third daughter regulatory gene TG3 can have a regulatory value based on a decimal number converted from the binary number corresponding to each of chromosomes 12 through 17. For example, when chromosomes 12 through 17 in the first row are 101101, the conversion value is 1*2. 5 +0*2 4 +1*2 3 +1*2 2 +0*2 1 +1*2 0 =45. After the final transformation, the adjustment value of BaseHTCTune can be determined as 0.01*45+0.3=0.75 (final value). The weight value of 0.01 used to obtain the final value and multiply it by the transformation value 45, as well as the constant 0.3 added to the weight value*transformation value, can be changed according to the design factor.

[0074] In the above method, a total of N regulatory genes were randomly generated in the first generation, and the regulatory values ​​of each regulatory variable QtrfC2, CltchTrqTune2 and BaseHTCTune were calculated based on each regulatory gene (N is a natural number).

[0075] The clutch temperature estimation model is evaluated based on the N sets of regulatory variables QtrfC2, CltchTrqTune2, and BaseHTCTune calculated in this way, to extract n regulatory genes (n is a natural number) with high regulatory accuracy from the N sets of regulatory accuracy. Second-generation regulatory genes can be generated by gene crossover of the extracted n regulatory genes. That is, new second-generation regulatory genes are generated by combining n fragments with high accuracy from the first-generation regulatory genes. An example of the combination method is shown below.

[0076] Step 1) Recombination module 30 randomly selects k fragments from n fragments with high precision in the first-generation regulatory genes. However, all k first-generation regulatory genes must be distinct.

[0077] Step 2) The recombination module 30 randomly assigns a predetermined region to each of the k first-generation regulatory genes and extracts the regulatory gene from the assigned region.

[0078] Step 3) Recombination module 30 generates regulatory genes by combining the extracted regulatory genes corresponding to each region.

[0079] Step 4) Recombination module 30 regenerates the mutated regulatory gene by setting a mutation probability for each chromosome in the regulatory gene. At this point, the mutation probability of the lower position of the sub-regulatory genes of the first-generation regulatory gene can be set to high, and the mutation probability of the higher position can be set to low. For example, the mutation probability of 2 corresponding to each sub-regulatory gene of the first-generation regulatory gene... 2 2 1 and 2 0 The mutation probability of chromosomes 3, 4, 5, 9, 10, 11, 15, 16, and 17 can be set to 20%, and the mutation probability of the remaining chromosomes can be set to 2%.

[0080] Recombination module 30 repeats steps 1 to 4 to generate m second-generation regulatory genes.

[0081] The regulation value generation module 10 calculates the regulation value of each of the regulation variables QtrfC2, CltchTrqTune2, and BaseHTCTune based on m second-generation regulatory genes. The accuracy calculation module 20 evaluates the clutch temperature estimation model based on the m sets of regulation variables QtrfC2, CltchTrqTune2, and BaseHTCTune calculated in this way.

[0082] The precision calculation module 20 extracts the regulatory precision of m groups based on the second-generation regulatory genes and the n groups with higher precision from the top n highest regulatory precisions of the first-generation regulatory genes. The regulatory precisions of the remaining regulatory genes, excluding those corresponding to the top n highest regulatory precisions, are stored in the storage module 40 and can be used later when the regulatory precision based on the same regulatory gene is needed. For example, when a new regulatory gene generated by the recombination module 30 is the same as a previously generated regulatory gene, the precision calculation module 20 can utilize the regulatory precision stored in the storage module 40.

[0083] Figure 7 The generation of second-generation regulatory genes is shown.

[0084] First, select the second, fourth, fifth, and eighth regulatory genes from the n genes with high precision in the first generation of regulatory genes.

[0085] For each of the four first-generation regulatory genes, a predetermined region is randomly assigned, and the regulatory gene components of the assigned region are extracted. For example, chromosome numbers 1 to 8 are extracted from RG1 "01100100" of the fifth regulatory gene, chromosome numbers 9 to 10 are extracted from RG2 "00" of the eighth regulatory gene, chromosome numbers 11 to 16 are extracted from RG3 "010111" of the fourth regulatory gene, and chromosome number 17 is extracted from RG4 "1" of the fourth regulatory gene.

[0086] Subsequently, a new regulatory gene “01100100000101111” was generated by combining the genes extracted from each region.

[0087] Genes are generated to regulate gene variation based on predetermined mutation probabilities on each chromosome. Although Figure 7 The probability of mutations in the genes shown is small, but it helps to find regulatory values ​​with new information not present in regulatory genes generated so far. Regarding Figure 7 The sub-regulatory genes of the first-generation regulatory genes shown (chromosomes 1-5, 6-11, and 12-17) will correspond to the lowest 3 positions 2. 2 2 1 2 0 The mutation probability of chromosomes (i.e., chromosomes 3 to 5, 9 to 11, and 12 to 17) is set to 20%, and the mutation probability of the remaining chromosomes is set to 2%. For each chromosome of the new regulatory gene, mutation occurs when the mutation probability is lower than the predetermined mutation probability baseline value; otherwise, mutation does not occur.

[0088] For example, if the predetermined mutation probability baseline for chromosome 3 is 20%, then since the mutation probability of 0.015652 is less than 20%, a mutation occurs in chromosome 3, changing from "1" to "0". Mutations occur in chromosomes 5, 6, 9, 15, and 17 in the same manner as described above, changing from "1" to "0", and vice versa.

[0089] In this way, m second-generation regulatory genes were generated. The regulatory value generation module 10 calculates the regulatory values ​​of the regulatory variables QtrfC2, CltchTrqTune2, and BaseHTCTune based on the three sub-regulatory genes TG5, TG6, and TG7 of the m second-generation regulatory genes.

[0090] The accuracy calculation module 20 can take into account variables such as the maximum temperature of the pressure plate (PP), the cooling curve of the pressure plate (PP), the maximum temperature of the flywheel (CP), and the cooling curve of the flywheel (CP) to calculate accuracy.

[0091] The method for calculating the precision of each variable is as follows.

[0092] The accuracy of the maximum PP temperature is based on a comparison between the maximum PP temperature estimated by the temperature estimation model and the highest PP temperature in the actual PP temperature data. For example, the accuracy of the maximum PP temperature can be calculated as "1 - absolute value {(maximum PP temperature in the temperature estimation model - actual maximum PP temperature) / (actual maximum PP temperature)}".

[0093] The accuracy of the PP cooling profile is based on the area on the PP temperature curve when cooled after the actual PP maximum temperature and the area on the temperature curve when cooled after the PP maximum temperature in the temperature estimation model.

[0094] For example, the accuracy of the PP cooling curve can be calculated as "1-{∑absolute value(area of ​​PP temperature curve when the temperature estimation model is cooled - area of ​​actual PP temperature curve when the temperature is cooled)} / (area of ​​actual PP temperature curve when the temperature is cooled)}".

[0095] The accuracy of the maximum CP temperature is based on a comparison between the maximum CP temperature estimated by the temperature estimation model and the highest CP temperature in the actual CP temperature data. For example, the accuracy of the maximum CP temperature can be calculated as "1 - absolute value {(maximum CP temperature in the temperature estimation model - actual maximum CP temperature) / (actual maximum CP temperature)}".

[0096] The accuracy of the CP cooling profile is based on the area on the CP temperature curve when cooling after the actual CP maximum temperature and the area on the temperature curve when cooling after the CP maximum temperature in the temperature estimation model.

[0097] For example, the accuracy of the CP cooling curve can be calculated as "1-{∑absolute value(area of ​​CP temperature curve when the temperature estimation model is cooled - area of ​​the actual CP temperature curve when the temperature is cooled)} / (area of ​​the actual CP temperature curve when the temperature is cooled)}".

[0098] The accuracy calculation module 20 calculates the final accuracy by multiplying the weighted values ​​of each of the PP maximum temperature, PP cooling curve, CP maximum temperature, and CP cooling curve by the calculated accuracy, and by multiplying by the mode weight value. The weighted values ​​of each variable and the mode weight value can be input through the user interface.

[0099] The mode weight value represents the weight value of a mode defined according to driving conditions. For example, driving conditions can be defined in various ways depending on design factors such as the level of heat generated by the clutch, cooling methods, and the vehicle's driving environment. The mode weight value represents the weight value assigned to each mode.

[0100] Figure 8 and Figure 9 It is a graph showing the estimated temperature and the actual measured temperature of the temperature estimation model according to each mode of the pressure plate.

[0101] like Figure 8 and Figure 9 As shown, the clutch temperature estimation model according to the exemplary embodiment is adjusted so that the two temperature curves are very similar, so as to show a very small error rate between the two curves.

[0102] Traditional clutch temperature model evaluation processes include: installing temperature sensors in the vehicle's transmission or clutch; evaluating the vehicle and measuring data for each mode; adjusting the temperature model in a spreadsheet (e.g., an Excel file) after data conversion; re-evaluating the vehicle after inputting adjustment variables; and determining if additional adjustments are needed. When adjusting the temperature model using an Excel file that applies the same equations as the vehicle temperature model, calculating one adjustment value for one mode typically takes more than 10 seconds. If adjustment variables are input and there are nine modes, a total of nine Excel spreadsheets are required. Therefore, adjusting a single variable takes at least 90 seconds (1 minute 30 seconds), and approximately 5 minutes per variable if comparison and analysis time is considered. Thus, if a variable is changed 10 times for adjustment, adjusting a single variable using the same process takes approximately one hour. Furthermore, variations in adjustment time and accuracy may occur depending on the expertise of the adjuster.

[0103] Thus, based on the adjustment device and adjustment method for the clutch temperature estimation model according to the exemplary embodiment, automatic adjustment is achieved by utilizing a genetic algorithm, thereby significantly reducing the time required for adjustment and improving the adjustment accuracy.

[0104] Although the invention has been described in conjunction with exemplary embodiments now considered practical, it should be understood that the invention is not limited to the disclosed embodiments. Rather, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. An adjustment method for a clutch temperature estimation model, the adjustment method comprising: Multiple regulatory genes are generated by the regulatory value generation module; The regulation value generation module calculates the regulation value corresponding to the regulation variable by utilizing the information of each of the plurality of regulatory genes; The accuracy calculation module calculates the temperature estimation accuracy by applying the calculated adjustment value to the clutch temperature estimation model; The precision calculation module extracts a first number of regulatory genes with the highest calculated precision from the plurality of regulatory genes; The recombination module regenerates a second number of regulatory genes by recombinizing the extracted first number of regulatory genes. Among them, the second number of regulatory genes to be generated include: A third number of regulatory genes are randomly selected from the first number of extracted regulatory genes; Extract the regulatory gene components from a randomly assigned region of each of the third number of regulatory genes; New regulatory genes are generated by combining the extracted regulatory gene components from the corresponding regions; The mutated regulatory gene is regenerated by setting a mutation probability for each chromosome in the new regulatory gene. When regenerating a second number of regulatory genes, for each of the plurality of sub-regulatory genes included in the first number of regulatory genes, the mutation probability of the lower bits of the binary number of the sub-regulatory gene is set to be higher than the mutation probability of the higher bits of the binary number of the sub-regulatory gene, the sub-regulatory gene corresponding to a plurality of chromosomes representing regulatory values ​​and having a regulatory value based on a value in a decimal number converted according to the binary number corresponding to each of the plurality of chromosomes.

2. The adjustment method according to claim 1, wherein, Further operations are performed on the second number of regulatory genes that are regenerated: calculating regulatory values, calculating temperature estimation accuracy, and extracting the first number of regulatory genes.

3. The adjustment method according to claim 2, wherein, Extracting the first number of regulatory genes involves extracting the regulatory genes with the highest accuracy from the previous generation of high-precision regulatory genes and the regulatory genes of the subsequent generation.

4. The adjustment method according to claim 2, further comprising: While increasing the number of generations by repeating the following steps, determine whether the current generation is the final generation: regenerate a second number of regulatory genes by recombination of the extracted first number of regulatory genes, calculate the regulatory value of the second number of regulatory genes, calculate the precision of the second number of regulatory genes, and extract the first number of regulatory genes from the second number of regulatory genes. When the current generation is the final generation, the repetitive operation ends and the clutch temperature estimation model is adjusted by adjusting the variable values ​​with the highest accuracy.

5. The adjustment method according to claim 1, wherein, The accuracy is calculated based on the highest temperature of the pressure plate, the pressure plate cooling curve, the highest temperature of the flywheel, and the flywheel cooling curve.

6. The adjustment method according to claim 5, wherein, The calculation accuracy includes: multiplying by the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve, and multiplying by the mode weight value to calculate the accuracy of the clutch temperature estimation model.

7. An adjustment device for a clutch temperature estimation model, the adjustment device comprising: The regulation value generation module is configured to: randomly generate multiple first-generation regulatory genes and use the information of each of the multiple first-generation regulatory genes to calculate the regulation value corresponding to the regulatory variable; The accuracy calculation module is configured to apply the calculated regulation value to the clutch temperature estimation model, calculate the accuracy of the temperature estimation, and extract the regulation gene with the highest calculated accuracy from multiple first-generation regulation genes. as well as The recombination module is configured to generate second-generation regulatory genes by recombinizing the extracted first-generation regulatory genes. The recombination module is configured to: randomly select multiple regulatory genes from the extracted first-generation regulatory genes; extract regulatory gene components from randomly allocated regions of each of the randomly selected multiple regulatory genes; generate new regulatory genes by combining the extracted regulatory gene components in the corresponding regions; and generate mutated regulatory genes by setting mutation probabilities for each chromosome in the new regulatory genes. For each of the plurality of sub-regulatory genes included in the first-generation regulatory genes, the mutation probability of the lower bits of the binary number of the sub-regulatory gene is set to be higher than the mutation probability of the higher bits of the binary number of the sub-regulatory gene, the sub-regulatory gene corresponding to a plurality of chromosomes representing regulatory values ​​and having a regulatory value based on a value in a decimal number converted according to the binary number corresponding to each of the plurality of chromosomes.

8. The adjustment device for the clutch temperature estimation model according to claim 7, wherein: The regulatory value generation module is configured to calculate the regulatory value of the second-generation regulatory gene; The accuracy calculation module is configured to: apply the calculated regulation value of the second-generation regulatory gene to the clutch temperature estimation model, calculate the accuracy of the temperature estimation, and extract the third-generation regulatory gene with the highest accuracy from the first-generation and second-generation regulatory genes.

9. The adjustment device for the clutch temperature estimation model according to claim 8, wherein, The recombination module is configured to recombine the extracted third-generation regulatory genes to regenerate fourth-generation regulatory genes.

10. The adjustment device for the clutch temperature estimation model according to claim 7, wherein, The accuracy calculation module is configured to calculate the adjustment accuracy based on the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve.

11. The adjustment device for a clutch temperature estimation model according to claim 10, wherein, The accuracy calculation module is configured to calculate the accuracy of the clutch temperature estimation model by multiplying the accuracy of the pressure plate's highest temperature, the accuracy of the pressure plate's cooling curve, the accuracy of the flywheel's highest temperature, and the accuracy of the flywheel's cooling curve, and by multiplying by the mode weight value.

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