A method and system for optimizing a hard point of an automobile suspension

Abstract

The application discloses a kind of automobile suspension hard point optimization method and system, belong to automobile chassis design technical field, the optimization method includes: using suspension dynamics model to the initial hard point of target suspension carries out suspension K&C analysis, identifies the K characteristic to be optimized that does not meet target value range;Judge whether there is the same hard point performance matrix table of target suspension structure type, if it does not exist, then construct the matrix table;According to hard point performance matrix table and the K characteristic to be optimized, identify the hard point to be adjusted;According to the deviation of K characteristic to be optimized and target value, and the sign mark in hard point performance matrix table, determine the coordinate value increase direction of hard point to be adjusted;To the hard point to be adjusted is iteratively adjusted and suspension K&C analysis, until the key K characteristic of target suspension meets target value range.The present application can accurately and quickly optimize suspension hard point, improve suspension design efficiency, shorten development cycle.

Description

Technical Field

[0001] The invention belongs to the technical field of automobile chassis design, and in particular relates to an automobile suspension hard point optimization method and system. Background Art

[0002] Suspension hardpoints are a crucial design consideration in automotive chassis development. They directly impact the vehicle's handling stability and ride smoothness. Therefore, proper hardpoint design is crucial for achieving excellent chassis dynamic performance and forms the foundation of chassis suspension structural design.

[0003] The automotive industry uses suspension K&C (Kinematics & Compliance) characteristics to evaluate suspension performance, specifically its kinematics and compliance characteristics. The suspension's kinematics (K characteristics) describe the rigid-kinematic relationship of chassis components and are directly determined by the positional relationships of the suspension's hardpoints. The suspension's compliance characteristics (C characteristics) describe the mechanical deformation of chassis components, resulting from the deformation of chassis bushings, coil springs, and structural members under external forces. In engineering practice, the rationality of hardpoint design is primarily verified using the suspension's K characteristics. By decomposing the vehicle's dynamic performance objectives, target values ​​for key suspension K characteristics are set, and suspension hardpoints are analyzed and optimized to ensure that the suspension's K characteristics fall within the target range.

[0004] Due to the highly coupled nature of suspension components, current hardpoint design methods typically rely on engineers repeatedly performing manual adjustments. This lacks targeted optimization and relies heavily on individual experience. Traditional hardpoint optimization methods often compromise performance, requiring repeated adjustments and verification, resulting in low efficiency. Chassis designs for different vehicle models, even with identical suspension structures, require extensive repetitive hardpoint optimization. Therefore, accurately and rapidly optimizing suspension hardpoints to meet K-characteristic targets is a worthy research topic. Summary of the Invention

[0005] Based on this, the present application proposes a method and system for optimizing automobile suspension hard points, aiming to accurately and quickly optimize suspension hard points to make them meet K characteristic targets.

[0006] A first aspect of the present application provides a method for optimizing hard points of an automobile suspension, the method comprising:

[0007] Step S10: Using the suspension dynamics model, a suspension K&C analysis is performed on multiple initial hard points of the target suspension to obtain multiple initial key K characteristic eigenvalues. These eigenvalues ​​are compared with the corresponding K characteristic target values, and K characteristics that do not meet the target value range are identified as the K characteristics to be optimized for the target suspension.

[0008] Step S20: judging whether there is a hard point performance matrix table of the same structure type as the target suspension structure; if not, performing step S30, and if yes, performing step S40;

[0009] Step S30: constructing a hard point performance matrix table corresponding to the target suspension;

[0010] Step S40: identifying a hard point to be adjusted according to the hard point performance matrix table and the K characteristics to be optimized;

[0011] Step S50: determining the increasing or decreasing direction of the coordinate value of the hard point to be adjusted according to the deviation state of the K characteristic characteristic value to be optimized and the target value range and the corresponding symbol mark of the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table;

[0012] Step S60: iteratively adjusting the hard point to be adjusted according to the increasing or decreasing direction of the coordinate value and performing suspension K&C analysis until the K characteristic characteristic values of the target suspension are within the target value range.

[0013] Compared with the prior art, the automobile suspension hard point optimization method provided by the application can perform K&C analysis on a plurality of initial hard points through a suspension dynamics model, first identify the K characteristics that do not meet the target value range, i.e., the K characteristics to be optimized, and then facilitate subsequent targeted optimization. Further, the structure type of the target suspension is judged, and if there is a hard point performance matrix table of the same structure type as the target suspension structure, the table can be directly used for optimization, thereby avoiding repeated analysis work and saving time and resources. For the case where there is no hard point performance matrix table of the same structure type, a hard point performance matrix table is constructed to provide experience data support for subsequent hard point optimization schemes and ensure the accuracy and systematicness of the optimization process. Then, the hard point to be adjusted is quickly identified by using the hard point identification rule, unnecessary adjustment verification is reduced, and the optimization efficiency is improved. By analyzing the deviation state of the K characteristic characteristic value to be optimized and the target value range and the symbol mark in the hard point performance matrix table, the increasing or decreasing direction of the hard point coordinate value can be determined, which is helpful to guide the specific hard point optimization scheme operation. By iteratively adjusting the hard point coordinate value and performing suspension K&C analysis, the K characteristic characteristic values are gradually adjusted to be within the target value range, and finally the suspension performance meets the design requirements.

[0014] As an optional implementation manner of the first aspect, the step S30 comprises:

[0015] Step S31: numbering the coordinate directions of the n initial hard points of the target suspension, taking the n hard point numbers as the first row of the matrix table and taking the m key K characteristics as the first column of the matrix table to obtain a hard point performance matrix table containing n*m boxes;

[0016] Step S32: sensitivity analysis is performed on the initial hard points, the coordinates of the n hard points are moved by a preset value one by one, and suspension K&C analysis is performed on the moved hard points respectively, to obtain m K characteristic characteristic values after n times of hard point movement;

[0017] Step S33: according to the hard point influence degree rule, the change degree of the K characteristic characteristic value after each hard point movement and the initial K characteristic characteristic value is represented by a symbol mark, and the symbol mark is filled in the n×m frame of the hard point performance matrix table corresponding to the column where the hard point is located and the row where the key K characteristic is located.

[0018] As an optional implementation of the first aspect, in the step S33, the hard point influence degree rule includes:

[0019] The change rate of the K characteristic characteristic value after the hard point movement and the initial K characteristic characteristic value is defined as: ηK i = ( K ， i –K i ) / K i , wherein is the initial i-th K characteristic characteristic value, is the i-th K characteristic characteristic value after movement, ηK i is the change rate of the i-th K characteristic characteristic value after movement.

[0020] According to the size of the change rate ηK i , a symbol mark is made:

[0021] When |ηKi|≥20% and ηKi≥0, the symbol mark “++” is used to represent;

[0022] When |ηKi|≥20% and ηKi<0, the symbol mark “--” is used to represent;

[0023] When 20%>|ηKi|≥5% and ηKi≥0, the symbol mark “+” is used to represent;

[0024] When 20%>|ηKi|≥5% and ηKi<0, the symbol mark “-” is used to represent;

[0025] When |ηKi|<5%, the symbol mark “0” is used to represent;

[0026] The symbol mark is sequentially filled in the n×m frame of the hard point performance matrix table corresponding to the column where the hard point is located and the row where the key K characteristic is located.

[0027] As an optional implementation of the first aspect, in the step S40, the hard point identification rule includes:

[0028] Step S41: determining the row where the K characteristic to be optimized is located in the hard point performance matrix table;

[0029] Step S42: determining the hard points in the row corresponding to the column marked with the symbol “++” or “--”; if there are none, determining the hard points in the column corresponding to the symbol marked with “+” or “-”;

[0030] Step S43: Based on the hard points determined in step S42, remove any hard points that significantly impact other K characteristics. The order of removal is: hard points marked with "++" or "--" corresponding to other K characteristics, and hard points marked with "+" or "-" corresponding to other K characteristics. Remove until only one hard point remains, which becomes the hard point to be adjusted.

[0031] As an optional implementation manner of the first aspect, in step S50, the hard point adjustment rule includes:

[0032] Determine the deviation relationship between the characteristic value of the K characteristic to be optimized and the target value range;

[0033] When the characteristic value of the K characteristic to be optimized is less than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is increased;

[0034] When the characteristic value of the K characteristic to be optimized is less than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", reduce the coordinate value of the hard point to be adjusted;

[0035] When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is reduced;

[0036] When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", the coordinate value of the hard point to be adjusted is increased.

[0037] A second aspect of an embodiment of the present application provides a vehicle suspension hard point optimization system, the system comprising:

[0038] The module for determining the K characteristics to be optimized is used to perform suspension K&C analysis on multiple initial hard points of the target suspension using the suspension dynamics model, obtain multiple initial key K characteristic eigenvalues, and compare them with the corresponding K characteristic target values. The K characteristics that do not meet the target value range are identified as the K characteristics to be optimized of the target suspension;

[0039] a hard point performance table determination module, configured to determine whether a hard point performance matrix table of the same type as the target suspension structure exists; if not, executing a hard point performance table construction module; and if so, executing a hard point adjustment direction determination module;

[0040] Construct a hard point performance table module to construct a hard point performance matrix table corresponding to the target suspension;

[0041] a module for determining hard points to be adjusted, configured to identify hard points to be adjusted using hard point identification rules according to the hard point performance matrix table and the K characteristics to be optimized;

[0042] a module for determining a hard point adjustment direction, configured to determine a direction of increase or decrease of a coordinate value of a hard point to be adjusted using a hard point adjustment rule based on a deviation between a characteristic value of the K characteristic to be optimized and a target value range, and symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table;

[0043] The K characteristic optimization module is used to iteratively adjust the hard point to be adjusted according to the direction of increase or decrease of the coordinate value and perform suspension K&C analysis until multiple key K characteristic feature values ​​of the target suspension are within the target value range.

[0044] A third aspect of an embodiment of the present application provides a computer device, which includes a memory, a processor, and a processing program stored in the memory and executable on the processor. When the processing program is executed by the processor, the above-mentioned method for optimizing the hard points of a vehicle suspension is implemented.

[0045] A fourth aspect of an embodiment of the present application provides a storage medium, on which a processing program is stored. When the processing program is run by a processor, the above-mentioned automobile suspension hard point optimization method is executed.

[0046] Compared with the existing technology, the advantages and benefits of this application are as follows:

[0047] This application constructs a hard point matrix table to summarize and refine the performance impact of hard points of suspensions with the same structure, and expresses it intuitively in the form of a visual table, so that engineers can draw on hard point optimization experience and quickly formulate hard point adjustment plans, reducing trial and error costs, avoiding a lot of repetitive work, significantly improving chassis design efficiency, and shortening the R&D cycle.

[0048] The application is directed to the K characteristics which do not meet the target value, through the formulated hard point identification rule, hard point influence degree rule, hard point adjustment rule, the hard point to be adjusted is quickly locked and the coordinate value increasing and decreasing direction is judged, the accuracy and systematicness of the hard point optimization process are ensured, and the best hard point optimization scheme can be obtained. The implementation steps of the hard point optimization work are standardized, the optimization scheme with uneven performance formed due to the personal experience of engineers is avoided, and the automatic processing of the computer program is also easy to realize.

[0049] Additional aspects and advantages of the application will be described in the following description, become apparent from the following description, or be learned by practice of the embodiments of the application. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A flow chart of a hard point optimization method of an automobile suspension for the first embodiment of the application is provided.

[0051] Figure 2 A flow chart of a hard point performance matrix table construction method of a target suspension in the first embodiment of the application is provided.

[0052] Figure 3 A flow chart of a hard point optimization method of an automobile suspension for the second embodiment of the application is provided.

[0053] Figure 4 A flow chart of a hard point performance matrix table construction method of a target suspension in the second embodiment of the application is provided.

[0054] Figure 5 A structure schematic diagram of a McPherson suspension in the second embodiment of the application is provided.

[0055] Figure 6 A structure schematic diagram of a hard point optimization system of an automobile suspension for the third embodiment of the application is provided.

[0056] The following specific embodiments will further illustrate the application in combination with the above-mentioned drawings. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the application will be described clearly and completely in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0058] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of this application can be implemented in an order other than those illustrated or described herein. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0059] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0060] In the first embodiment, please refer to Figure 1 , is a flow chart of a method for optimizing automobile suspension hard points proposed in the first embodiment, the proposed method comprising:

[0061] Step S10: Use the suspension dynamics model to perform suspension K&C analysis on multiple initial hard points of the target suspension to obtain multiple initial key K characteristic eigenvalues, and compare them with the corresponding K characteristic target values ​​respectively. K characteristics that do not meet the target value range are identified, which are the K characteristics to be optimized for the target suspension.

[0062] Understandably, the design and optimization of suspension hardpoints is crucial for ensuring vehicle chassis performance, and the rationality of hardpoint design is primarily verified using the suspension's K-characteristics. The automotive industry generally uses Adams software to establish suspension dynamic models and perform suspension K&C analysis to determine the initial key K-characteristic eigenvalues ​​for the suspension. In automotive engineering, to meet vehicle performance targets, target value ranges are typically set for each key K-characteristic of the suspension. By comparing the initial key K-characteristic eigenvalues ​​with the preset K-characteristic target values, it is possible to identify K-characteristics that do not meet the target value range. These K-characteristics are the K-characteristics that require optimization for the target suspension.

[0063] Step S20: Determine whether there is a hard point performance matrix table of the same type as the target suspension structure; if not, execute step S30; if so, execute step S40.

[0064] It should be noted that the hardpoint performance matrix is ​​a crucial development tool during suspension hardpoint optimization. It summarizes and refines the performance impacts of hardpoints on suspensions with the same structure, intuitively expressing the influence of each hardpoint on suspension performance. Appropriate use of the hardpoint performance matrix allows for efficient and accurate hardpoint optimization. Before performing hardpoint optimization, the target suspension's structural type must be determined, and the vehicle suspension database must be queried to determine whether a hardpoint performance matrix with the same structural type exists. If one already exists, this matrix can be directly utilized, drawing on existing empirical data to guide the hardpoint optimization of the target suspension, proceeding to step S40. If not, step S30 is performed to reconstruct the hardpoint performance matrix for that suspension type. It is understood that a hardpoint matrix for suspensions of the same structural type only needs to be constructed once and can be used for subsequent optimization of suspension hardpoints of the same type.

[0065] Step S30: constructing a hard point performance matrix table corresponding to the target suspension.

[0066] It's important to note that this step creates a table for the target suspension hardpoint performance sensitivity. The hardpoint performance matrix correlates changes in hardpoint position with the suspension's key K characteristics, making it easy to visualize the impact of each hardpoint on suspension performance and providing empirical data support for subsequent hardpoint optimization solutions.

[0067] Specifically, see Figure 2 , the steps of constructing the hard point performance matrix table of the target suspension include the following steps:

[0068] Step S31: Number the coordinate directions of the n initial hard points of the target suspension, use the n hard point numbers as the first row of the matrix table, and use the m key K characteristics as the first column of the matrix table to obtain Hard point performance matrix table for each grid;

[0069] As you can understand, this step creates a table framework containing hard point numbers and key K characteristics. The hard point coordinates refer to the hard point's spatial orientation, typically divided into three axes: X, Y, and Z. By using these numbers, the impact of each hard point on suspension performance can be systematically recorded in the corresponding cell of the table, providing a foundation for subsequent hard point sensitivity analysis.

[0070] Step S32: performing sensitivity analysis on the initial hard points, moving the coordinates of n hard point numbers in sequence by preset values, and performing suspension K&C analysis on the hard points after the movement, to obtain m K characteristic eigenvalues ​​after the n hard point movements;

[0071] As you can understand, sensitivity analysis is a method for evaluating the sensitivity of suspension performance to changes in hardpoint position. In this step, the coordinates of each hardpoint are shifted by a preset value, and then a suspension K&C analysis is performed on each modified hardpoint. This sensitivity analysis identifies the hardpoints most sensitive to various K characteristics, allowing these hardpoints to be prioritized during hardpoint optimization.

[0072] Step S33: Based on the hard point influence degree rule, the degree of change between the K characteristic value after each hard point movement and the initial K characteristic value is represented by a symbol mark, and the symbol mark is filled in the n×m boxes of the hard point performance matrix corresponding to the hard point column and the key K characteristic row;

[0073] It can be understood that in this step, the K characteristic eigenvalue after the hard point is moved is compared with the initial K characteristic eigenvalue, and the degree of change is represented by specific symbolic marks. These symbolic marks can intuitively reflect the specific impact of each hard point movement on the suspension performance, making it easier for engineers to quickly identify the hard points that need to be adjusted.

[0074] Specifically, in step S33, the hard point influence degree rules include:

[0075] The change rate of the K characteristic value after the hard point moves and the initial K characteristic value is defined as: ηK i = ( K ， i –K i ) / K i ,in is the initial eigenvalue of the i-th K characteristic, is the eigenvalue of the K-th characteristic after the shift, ηK i is the rate of change of the eigenvalue of the i-th K characteristic after the shift;

[0076] According to the rate of change ηK i The values ​​are marked with symbols:

[0077] When |ηKi|≥20% and ηKi≥0, the symbol “++” is used to indicate it;

[0078] When |ηKi|≥20% and ηKi<0, the symbol “--” is used to indicate it;

[0079] When 20%>|ηKi|≥5% and ηKi≥0, the symbol “+” is used to indicate it;

[0080] When 20%>|ηKi|≥5% and ηKi<0, the symbol “-” is used to indicate it;

[0081] When |ηKi| is less than 5%, the symbol "0" is used to indicate it;

[0082] The symbol marks are sequentially filled into the n×m boxes of the hard point performance matrix table corresponding to the column where the hard point is located and the row where the key K characteristic is located.

[0083] As can be understood, the hard point influence rule in step S33 defines how to select the appropriate symbol based on the rate of change of the K characteristic eigenvalue before and after the hard point is moved. For example, the influence can be divided into three levels: Level 1 and Level 2 influence are relatively strong and can be further divided into positive and negative correlations. Level 3 influence is the weakest and does not distinguish between positive and negative correlations. Therefore, five symbolic symbols are used to represent the influence of hard points.

[0084] Step S40: Based on the hard point performance matrix table and the K characteristics to be optimized, hard points to be adjusted are identified using hard point identification rules.

[0085] It should be noted that the hard point identification rules in this step include:

[0086] Step S41: determining the row where the K characteristic to be optimized is located in the hard point performance matrix table;

[0087] Step S42: determining the hard points in the row corresponding to the column marked with the symbol “++” or “--”; if there are none, determining the hard points in the column corresponding to the symbol marked with “+” or “-”;

[0088] Step S43: Based on the hard points determined in step S42, remove any hard points that significantly impact other K characteristics. The order of removal is: hard points marked with "++" or "--" corresponding to other K characteristics, and hard points marked with "+" or "-" corresponding to other K characteristics. Remove until only one hard point remains, which becomes the hard point to be adjusted.

[0089] As you can understand, this step determines the order of hard point selection for optimization based on the hard point influence levels (i.e., symbolic labels) determined in step S33. This hard point identification rule effectively identifies hard points that significantly influence the K characteristic to be optimized, while simultaneously eliminating hard points that significantly affect other K characteristics. This method considers the coupled nature of hard point influences, avoiding optimizing one K characteristic while causing other K characteristics to vary significantly beyond the target range, thus ensuring performance balance during the optimization process.

[0090] Step S50: Based on the deviation between the characteristic value of the K characteristic to be optimized and the target value range, and the symbol marks corresponding to the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table, the hard point adjustment rule is used to determine the increase or decrease direction of the coordinate value of the hard point to be adjusted.

[0091] Specifically, the hard point adjustment rules in this step include:

[0092] Determine the deviation relationship between the characteristic value of the K characteristic to be optimized and the target value range;

[0093] When the characteristic value of the K characteristic to be optimized is less than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is increased;

[0094] When the characteristic value of the K characteristic to be optimized is less than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", reduce the coordinate value of the hard point to be adjusted;

[0095] When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is reduced;

[0096] When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", the coordinate value of the hard point to be adjusted is increased.

[0097] As can be understood, the deviation relationship between the K characteristic value to be optimized and the target value range is first determined. If the K characteristic value to be optimized is less than the target value range, the K characteristic value needs to be increased to approach the target value. In this case, if the symbols for the hard point to be adjusted and the K characteristic to be optimized are marked with "++" or "+", the two are directly proportional. To increase the K characteristic value, the coordinate value of the hard point to be adjusted needs to be increased. If the symbols are marked with "--" or "-", the two are inversely proportional. To increase the K characteristic value, the coordinate value of the hard point to be adjusted needs to be decreased. Conversely, if the K characteristic value to be optimized is greater than the target value range, the K characteristic value needs to be decreased to approach the target value. In this case, if the symbols for the hard point to be adjusted and the K characteristic to be optimized are marked with "++" or "+", the two are directly proportional. To decrease the K characteristic value, the coordinate value of the hard point to be adjusted needs to be decreased. If the symbols are marked with "--" or "-", the two are inversely proportional. To decrease the K characteristic value, the coordinate value of the hard point to be adjusted needs to be increased.

[0098] Step S60: performing iterative adjustment and suspension K&C analysis on the hard point to be adjusted in the direction of increase or decrease of the coordinate value until multiple key K characteristic feature values ​​of the target suspension are within the target value range.

[0099] As you can understand, the optimal hard point adjustment solution has been identified through the preceding steps. In this step, simply adjust the hard point to be adjusted in the direction of the coordinate values ​​determined in the previous step to bring the K characteristic eigenvalues ​​to the target range. After the hard point adjustment, re-perform the suspension K&C analysis to determine whether the K characteristic eigenvalues ​​are optimized. If the K characteristic eigenvalues ​​still deviate from the target, iteratively adjust the hard points again until all key K characteristic eigenvalues ​​are within the target range, completing the target suspension hard point optimization.

[0100] In the second embodiment of this application, please refer to Figure 3 , which is a flow chart of a vehicle suspension hard point optimization method proposed in the second embodiment, the proposed method includes:

[0101] Step S100: Using Adams software to establish a dynamic model of the target suspension, performing suspension K&C analysis on the initial hard points of the target suspension, obtaining 14 key K characteristic eigenvalues ​​of the initial hard point solution, and comparing the results with the corresponding K characteristic target values. It is identified that the "toe angle change rate during flat jump" does not meet the corresponding target value range, and therefore the "toe angle change rate during flat jump" is the K characteristic to be optimized.

[0102] Specifically, Adams software was used to establish a dynamic model of the target suspension, and suspension K&C analysis was performed on the initial hard points of the target suspension. The eigenvalues ​​of 14 key K characteristics for the initial hard point solution were obtained, as shown in Table 1. Comparing these results with the corresponding K characteristic target values, the eigenvalue of the "jump toe angle change rate" was identified as -18.29° / m, which falls within the corresponding target value range of [-10, -5]° / m. Therefore, this "jump toe angle change rate" was the K characteristic to be optimized.

[0103] Table 1 Key K characteristic eigenvalues ​​of the target suspension initial hard point solution

[0104]

[0105] Step S200: judging the target suspension as a McPherson type according to the suspension structure type, and finding that there is no hard point performance matrix table with the same structure type in the automobile suspension database, so the hard point performance matrix table of the target suspension needs to be reconstructed.

[0106] Step S300: constructing a hard point performance matrix table corresponding to the target suspension.

[0107] like Figure 4 FIG. 1 is a flow chart of a method for constructing a hard point performance matrix table of a target suspension, wherein the method comprises:

[0108] Step S301: numbering the coordinate directions of the target suspension hard points;

[0109] like Figure 5 The figure shows the structure of the McPherson suspension. There are 6 hard points on the suspension: the top of the shock absorber tower, the ball head point of the lower arm, the outer point of the steering rod, the inner point of the steering rod, the front point of the lower arm and the rear point of the lower arm. These hard points are marked as A, B, C, D, E, and F. At the same time, the three coordinate directions of X, Y, and Z are set for each hard point, resulting in 18 hard point numbers, namely A X 、A Y 、A Z 、B X 、B Y 、B Z 、C X 、C Y 、C Z 、D X 、D Y 、D Z 、E X 、E Y 、E Z 、F X 、F Y 、F Z , as the first row of the hardpoint performance matrix. Furthermore, the 14 key K characteristics of the target suspension are used as the first column of the hardpoint performance matrix: toe angle change rate, camber angle change rate, wheel center longitudinal change rate, wheel center lateral change rate, roll center height, braking anti-nod percentage, kingpin longitudinal offset, kingpin lateral offset, tire radius, caster trail, caster angle, kingpin inclination angle, turning diameter, and 20-degree turning angle Ackerman percentage. This creates a hardpoint performance matrix containing 18 x 14 grids.

[0110] Step S302: Using the suspension dynamics model in Adams software, perform sensitivity analysis on the hard points.

[0111] The coordinates of 18 hard points are moved in the positive direction by 10 mm in sequence, and K&C analysis is performed again on the hard points after the movement. The adjustment can obtain 14 K characteristic eigenvalues ​​after the movement.

[0112] Step S303: Based on the hard point influence rule, the K characteristic eigenvalues ​​after each hard point movement are compared with the initial K characteristic eigenvalues, and the change rate nKi of each K characteristic eigenvalue after each movement is calculated. The values ​​are then marked with symbols in the cells of the hard point performance matrix corresponding to the hard point column and the key K characteristic row.

[0113] Specifically, the K characteristic values ​​after each hard point movement are compared with the initial K characteristic values, and the formula ηK is used. i = ( K ，i – K i ) / K i Calculate the rate of change ηK of each K characteristic value after each movement i , a total of 18×14 K characteristic value change rates need to be recorded. According to each ηK i The value is represented by the symbolic marking rules for the degree of hard point influence as set in Table 2. The degree of change in the K characteristic after each hard point movement is represented by a symbolic mark. The symbolic mark is then filled in the 18 × 14 boxes of the hard point performance matrix corresponding to the column where the hard point is located and the row where the key K characteristic is located. This completes the construction of the hard point performance matrix for the target suspension, as shown in Table 3.

[0114] Table 2 Symbol marking rules for hard point influence degree

[0115]

[0116] Table 3 Hard point performance matrix of McPherson suspension

[0117]

[0118] Step S400: Based on the established hard point performance matrix of the McPherson suspension and the “flat jump toe angle change rate” characteristic to be optimized, a hard point identification rule is used to identify that the hard point to be adjusted is Cz.

[0119] The specific identification process is as follows: In the hard point performance matrix in Table 3, find the row containing the "Flat Jump Toe Angle Change Rate" characteristic. The hard points with the "++" and "--" symbols in this row are Bz, Cz, Dz, and Ez, indicating that these hard points have the highest sensitivity to the "Flat Jump Toe Angle Change Rate" characteristic. Among these four hard points, the columns containing Bz, Dz, and Ez also contain other "++" and "--" symbols, indicating that these three hard points also have high sensitivities to certain other K characteristics and need to be eliminated one by one. Ultimately, the only remaining hard point is Cz, indicating that the Z direction of hard point C is the hard point to be adjusted.

[0120] Step S500: Determine the direction of increase or decrease of the coordinate value of the hard point to be adjusted based on the deviation between the "level jump toe angle change rate" characteristic and the target value range, and the "level jump toe angle change rate" characteristic and the symbol "--" corresponding to the hard point to be adjusted Cz.

[0121] Specifically, the target value range for the "Flat Jump Toe Angle Change Rate" characteristic is [-10, -5]° / m. However, the K characteristic analysis results for the initial hard point solution show a value of -18.29° / m, which is below the target range. Furthermore, according to the hard point performance matrix, the sign relationship between the Cz hard point and the K characteristic is "--," indicating an inversely proportional relationship. Therefore, to increase the K characteristic's eigenvalue closer to the target value, the coordinate value of the Cz hard point should be reduced.

[0122] Step S600: Reduce and adjust the coordinate value of the Cz hard point and re-perform the suspension K&C analysis to ensure that the "flat jump toe angle change rate" characteristic reaches the target value range while ensuring that other K characteristics also meet the target requirements.

[0123] Specifically, through iterative adjustment of the Cz hard point in this step, the initial hard point coordinates were adjusted to the optimized hard point coordinates shown in Table 4. Ultimately, the 14 key K characteristic eigenvalues ​​of the target suspension were all within the target value range. The key K characteristic eigenvalues ​​before and after hard point optimization are shown in Table 5.

[0124] Table 4 Comparison of hard point coordinates before and after optimization of the McPherson suspension

[0125]

[0126] Table 5 Comparison of key K characteristics before and after optimization of the McPherson suspension

[0127]

[0128] In the third embodiment, please refer to Figure 6 , is a schematic structural diagram of an automobile suspension hard point optimization system proposed in the third embodiment, the optimization system comprising:

[0129] Determine K characteristics to be optimized module 01, which is used to perform suspension K&C analysis on multiple initial hard points of the target suspension using the suspension dynamics model, obtain multiple initial key K characteristic eigenvalues, and compare them with the corresponding K characteristic target values. K characteristics that do not meet the target value range are identified as the K characteristics to be optimized of the target suspension;

[0130] A hard point performance table determination module 02 is used to determine whether a hard point performance matrix table of the same type as the target suspension structure exists; if not, a hard point performance table construction module 03 is executed; if so, a hard point adjustment direction determination module 04 is executed;

[0131] Build hard point performance table module 03, used to build a hard point performance matrix table corresponding to the target suspension;

[0132] A module 04 for determining hard points to be adjusted is configured to identify hard points to be adjusted using hard point identification rules according to the hard point performance matrix table and the K characteristics to be optimized;

[0133] The hard point adjustment direction determination module 05 is used to determine the direction of increase or decrease of the coordinate value of the hard point to be adjusted using the hard point adjustment rule according to the deviation state of the characteristic value of the K characteristic to be optimized and the target value range, and the symbol mark corresponding to the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table;

[0134] The K characteristic optimization module 06 is used to iteratively adjust the hard point to be adjusted according to the direction of increase or decrease of the coordinate value and perform suspension K&C analysis until multiple key K characteristic feature values ​​of the target suspension are within the target value range.

[0135] On the other hand, the present application also proposes a computer device, which includes a memory, a processor, and a processing program stored in the memory and capable of running on the processor. When the processing program is executed by the processor, the above-mentioned automobile suspension hard point optimization method is implemented.

[0136] On the other hand, the present application further proposes a storage medium, on which a processing program is stored. When the processing program is run by a processor, the above-mentioned automobile suspension hard point optimization method is executed.

[0137] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0138] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of this application.

[0139] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A method for optimizing automobile suspension hard points, characterized in that: The optimization method comprises: Step S10: Using the suspension dynamics model, a suspension K&C analysis is performed on multiple initial hard points of the target suspension to obtain multiple initial key K characteristic eigenvalues. These eigenvalues ​​are compared with the corresponding K characteristic target values, and K characteristics that do not meet the target value range are identified as the K characteristics to be optimized for the target suspension. Step S20: Determine whether there is a hard point performance matrix table of the same type as the target suspension structure; if not, execute step S30; if so, execute step S40; Step S30: constructing a hard point performance matrix table corresponding to the target suspension; Step S31: Number the coordinate directions of the n initial hard points of the target suspension, use the n hard point numbers as the first row of a matrix table, and use the m key K characteristics as the first column of the matrix table to obtain a hard point performance matrix table containing n×m grids; Step S32: Perform sensitivity analysis on the initial hard points, shift the coordinates of n hard points sequentially by preset values, and perform suspension K&C analysis on each of the shifted hard points to obtain m K characteristic eigenvalues ​​after the n-times hard point shift. Step S33: Based on the hard point influence degree rule, the degree of change between the K characteristic value after each hard point movement and the initial K characteristic value is represented by a symbol mark, and the symbol mark is filled in the n×m boxes of the hard point performance matrix corresponding to the hard point column and the key K characteristic row; Step S40: identifying hard points to be adjusted using hard point identification rules based on the hard point performance matrix table and the K characteristics to be optimized; Step S50: Determine the direction of increase or decrease of the coordinate value of the hard point to be adjusted by applying the hard point adjustment rule based on the deviation between the characteristic value of the K characteristic to be optimized and the target value range, and the symbol marks corresponding to the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table; Step S60: performing iterative adjustment and suspension K&C analysis on the hard point to be adjusted in the direction of increase or decrease of the coordinate value until multiple key K characteristic feature values ​​of the target suspension are within the target value range.

2. The automobile suspension hard point optimization method according to claim 1, characterized in that: In step S33, the hard point influence degree rules include: The change rate of the K characteristic value after the hard point moves and the initial K characteristic value is defined as: ηK i = ( K ， i – K i ) / K i ,in is the initial eigenvalue of the i-th K characteristic, is the eigenvalue of the K-th characteristic after the shift, ηK i is the rate of change of the eigenvalue of the i-th K characteristic after the shift; According to the rate of change ηK i The values ​​are marked with symbols: When |ηKi|≥20% and ηKi≥0, the symbol "++" is used to indicate it; When |ηKi|≥20% and ηKi<0, the symbol "--" is used to indicate it; When 20%>|ηKi|≥5% and ηKi≥0, the symbol "+" is used to indicate it; When 20%>|ηKi|≥5% and ηKi<0, the symbol "-" is used to indicate it; When |ηKi|<5%, the symbol "0" is used to indicate it; The symbol marks are sequentially filled into the n×m boxes of the hard point performance matrix table corresponding to the column where the hard point is located and the row where the key K characteristic is located.

3. The automobile suspension hard point optimization method according to claim 1, characterized in that: In step S40, the hard point identification rules include: Step S41: determining the row where the K characteristic to be optimized is located in the hard point performance matrix table; Step S42: determining the hard points in the row corresponding to the column marked with the symbol "++" or "--", and if there are none, determining the hard points in the column corresponding to the symbol "+" or "-"; Step S43: Based on the hard points determined in step S42, remove hard points that have a significant impact on other K characteristics. The priority for removal is: hard points marked with "++" or "--" corresponding to other K characteristics, and hard points marked with "+" or "-" corresponding to other K characteristics. Remove until only one hard point remains, which is the hard point to be adjusted.

4. The method for optimizing hard points of automobile suspension according to claim 1, characterized in that: In step S50, the hard point adjustment rules include: Determine the deviation relationship between the characteristic value of the K characteristic to be optimized and the target value range; When the characteristic value of the K characteristic to be optimized is less than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is increased; When the characteristic value of the K characteristic to be optimized is smaller than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", reduce the coordinate value of the hard point to be adjusted; When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "++" or "+", the coordinate value of the hard point to be adjusted is reduced; When the characteristic value of the K characteristic to be optimized is greater than the target value range, and the symbols corresponding to the K characteristic to be optimized and the hard point to be adjusted are marked as "--" or "-", the coordinate value of the hard point to be adjusted is increased.

5. An automobile suspension hard point optimization system, characterized in that: The optimization system comprises: The module for determining the K characteristics to be optimized is used to perform suspension K&C analysis on multiple initial hard points of the target suspension using the suspension dynamics model, obtain multiple initial key K characteristic eigenvalues, and compare them with the corresponding K characteristic target values. The K characteristics that do not meet the target value range are identified as the K characteristics to be optimized of the target suspension; a hard point performance table determination module, configured to determine whether a hard point performance matrix table of the same type as the target suspension structure exists; if not, executing a hard point performance table construction module; and if so, executing a hard point adjustment direction determination module; A hard point performance table construction module is used to construct a hard point performance matrix table corresponding to the target suspension, specifically comprising: numbering the coordinate directions of n initial hard points of the target suspension, using the n hard point numbers as the first row of the matrix table, and using m key K characteristics as the first column of the matrix table, to obtain a hard point performance matrix table containing n×m grids; performing sensitivity analysis on the initial hard points, shifting the coordinates of the n hard points sequentially by preset values, and performing suspension K&C analysis on each of the shifted hard points to obtain m K characteristic eigenvalues ​​after n hard point shifts; and using symbols to represent the degree of change between the K characteristic eigenvalue after each hard point shift and the initial K characteristic eigenvalue according to a hard point influence degree rule, and filling the symbols into the n×m grids of the hard point performance matrix table corresponding to the hard point column and the key K characteristic row. A module for determining hard points to be adjusted is used to identify hard points to be adjusted using hard point identification rules based on a hard point performance matrix table and K characteristics to be optimized; A module for determining the direction of hard point adjustment is used to determine the direction of increase or decrease of the coordinate value of the hard point to be adjusted using the hard point adjustment rule according to the deviation state of the characteristic value of the K characteristic to be optimized and the target value range, as well as the symbol mark corresponding to the K characteristic to be optimized and the hard point to be adjusted in the hard point performance matrix table; The K characteristic optimization module is used to iteratively adjust the hard points to be adjusted according to the direction of increase or decrease of coordinate values ​​and perform suspension K&C analysis until multiple key K characteristic eigenvalues ​​of the target suspension are within the target value range.

6. A computer device, characterized in that: The computer device includes a memory, a processor, and a processing program stored in the memory and executable on the processor. When the processing program is executed by the processor, a vehicle suspension hard point optimization method according to any one of claims 1 to 4 is implemented.

7. A storage medium, characterized in that: The storage medium stores a processing program, and when the processing program is executed by the processor, the method for optimizing the hard points of an automobile suspension according to any one of claims 1 to 4 is implemented.

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