A method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance

Abstract

This invention relates to the field of vehicle suspension technology, and specifically to a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance. The method involves: acquiring a suspension multi-body model; identifying hard points and bushings in the suspension multi-body model, naming the coordinates of each hard point and the stiffness of each bushing; displaying the hard point coordinates and bushing stiffness to the user to identify influencing factors; modifying each influencing factor to determine first modified data, second modified data, and baseline data; performing operating condition simulations based on the first modified data, second modified data, and baseline data to determine the corresponding K&C index values ​​for each simulation result; and performing polynomial fitting for each K&C index based on the first modified data, second modified data, baseline data, and the corresponding values ​​of the K&C index under each simulation result to determine the sensitivity of the influencing factors. This method significantly reduces the number of times the suspension multi-body model is run, thus accelerating the sensitivity analysis of the influencing factors.

Description

Technical Field

[0001] This manual relates to the field of vehicle suspension technology, and in particular to a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance. Background Technology

[0002] Against the backdrop of the wave of intelligent automotive technology and increasingly fierce market competition, intelligent chassis has become a key development direction for improving the overall performance of vehicles. Excellent suspension and steering K&C performance is the physical foundation for precise chassis control and wide-range adjustment, directly affecting the vehicle's handling stability and ride comfort. However, suspension systems have numerous K&C performance indicators, and each indicator is influenced by dozens or even hundreds of factors (such as hardpoint coordinates and bushing stiffness). How to quickly and accurately identify key influencing factors in the early stages of design to guide subsequent structural optimization and chassis tuning has become a core pain point in the vehicle development process.

[0003] Currently, the industry primarily employs sensitivity analysis methods based on multibody dynamics. Common techniques include using Adams / Insight modules or integrating Adams, Insight, and Matlab for simulation. In terms of experimental design, two-level DOE methods are mainly used, such as Plackett-Burman, partial factorial design, and full factorial design. While the Plackett-Burman method requires fewer runs (8-48), it is considered a "coarse screening" method and often requires secondary analysis in conjunction with other methods, resulting in a still significant overall computational load. Partial factorial design (8-2000 runs) requires fewer runs (20-3000 runs). n-1 ) and fully factorial design (running 2 times) n When dealing with dozens of influencing factors, the number of simulations increases exponentially. Even with compromise solutions in engineering, the number of model runs often needs to be set to the thousands or even hundreds of thousands, resulting in high computational costs. In addition, although the Latin hypercube method in response surface methodology provides a more flexible sampling strategy, it still requires an extremely high number of calculations to ensure analytical accuracy when there are many influencing factors.

[0004] As the modeling accuracy and detail of suspension multibody models continue to improve, the time required for a single simulation increases significantly. How to effectively reduce the number of model runs required for sensitivity analysis while ensuring analysis accuracy has become a technical challenge that urgently needs to be solved to improve chassis development efficiency and shorten the R&D cycle.

[0005] Therefore, this specification provides a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance. Summary of the Invention

[0006] This specification provides a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance, in order to solve the aforementioned problems existing in the prior art.

[0007] The following technical solution is adopted in this specification: This manual provides a method for rapid sensitivity analysis of multiple influencing factors on suspension K&C performance, including the following steps: S1. Obtain the multibody model of the suspension; S2. Identify the hard points and bushings in the suspension multibody model, and name and store the coordinates of each hard point in the hard point set, and name and store the stiffness of each bushing in the bushing set. S3. Display the hard point coordinates in the hard point set and the bushing stiffness in the bushing set to the user, and determine the influencing factors of the suspension multibody model in response to the user's selection operation. S4. For each influencing factor, change the influencing factor according to the preset change method, determine the first change data and the second change data of the influencing factor, and use the influencing factor as the basic data; S5. Based on the first changed data, the second changed data, and the basic data, perform preset working condition simulations on the suspension multibody model respectively; S6. Determine the simulation results corresponding to the first changed data, the second changed data, and the basic data, and determine the K&C index values ​​corresponding to each simulation result; S7. For each K&C index, based on the first changed data, the second changed data, the basic data, and the values ​​of the K&C index under each simulation result, perform polynomial fitting to determine the mathematical model; S8. Based on the mathematical model, determine the sensitivity of the factors affecting the K&C index.

[0008] Based on the aforementioned technical methods, this solution establishes a polynomial mathematical model between each selected influencing factor and various K&C indicators by performing only three simulations (basic data, first changed data, and second changed data) for each selected influencing factor, thereby quickly obtaining sensitivity. Compared to traditional sensitivity analysis methods that require numerous simulations or experiments, this significantly reduces computational resources and time costs, greatly reduces the number of times the suspension multibody model is run, and accelerates the sensitivity analysis of indicators. The more influencing factors there are, the more significant the difference in computation time becomes. Users can interactively select the influencing factors to be analyzed from the hardpoint set and bushing set of the suspension multibody model, making the analysis process more targeted and enabling rapid focus on key design parameters based on actual engineering needs. This method supports the simultaneous analysis of multiple K&C indicators (such as indicators corresponding to various operating conditions, such as wheel bounce, lateral force, and self-centering torque), and can provide the sensitivity of each influencing factor under each indicator, which helps to comprehensively understand the performance characteristics of the suspension system.

[0009] Furthermore, the S7 specifically includes: A first change amount is determined based on the first changed data and the basic data; a second change amount is determined based on the second changed data and the basic data; and a third change amount is determined based on the basic data and the basic data. For each K&C index, the K&C index value corresponding to the simulation result corresponding to the first changed data is taken as the first index value, the K&C index value corresponding to the simulation result corresponding to the second changed data is taken as the second index value, and the K&C index value corresponding to the simulation result corresponding to the basic data is taken as the third index value. A fourth change is determined based on the first indicator value and the third indicator value; a fifth change is determined based on the second indicator value and the third indicator value; and a sixth change is determined based on the third indicator value and the third indicator value. Using the first, second, and third changes as independent variables, and the fourth, fifth, and sixth changes as dependent variables, a polynomial fitting is performed to determine the mathematical model.

[0010] Furthermore, S8 specifically includes: After differentiating the mathematical model, the slope of the tangent line is calculated when the independent variable is the third variable. The slope of the tangent line is used as the sensitivity of the K&C index to the influencing factors.

[0011] Furthermore, it also includes step S9: Determine the sensitivity of each influencing factor corresponding to the K&C index; Based on the sensitivity of each influencing factor corresponding to the K&C index, the overall sensitivity of the influencing factors of the K&C index is determined.

[0012] Based on the above technical means, by calculating the total sensitivity of the factors affecting a K&C index, we can know how "sensitive" the K&C index is. If this value is small, it means that the K&C index is less affected by external interference in the suspension multibody model.

[0013] Furthermore, the method also includes: For each influencing factor, calculate the ratio of the sensitivity of that influencing factor to the total sensitivity of the influencing factors of that K&C index, and determine the relative sensitivity of that influencing factor to that K&C index.

[0014] By using the aforementioned technical means, the degree of influence of each influencing factor on the K&C index can be determined by calculating the proportion of the sensitivity of each influencing factor to the total sensitivity of the influencing factors of a K&C index.

[0015] Furthermore, in S2, the coordinates of each hard point are named and stored in the hard point set, and the stiffness of each bushing is named and stored in the bushing set, specifically including: Determine the coordinates of each hard point in the X, Y, and Z directions, and store them in the hard point set after naming them in the form of hard point name-X, hard point name-Y, and hard point name-Z respectively. Determine the translational stiffness and oscillation stiffness of each bushing in the X, Y, and Z directions, and store them in the bushing set after naming them in the form of bushing name-TX, bushing name-TY, bushing name-TZ, bushing name-RX, bushing name-RY, and bushing name-RZ.

[0016] Furthermore, S4 specifically includes: For each influencing factor, if the influencing factor is selected by the user from the set of hard points, the influencing factor is used as the basic data; Subtract a preset amount of change from the coordinates of the influencing factor to determine the first change data of the influencing factor; and add the amount of change to the coordinates of the influencing factor to determine the second change data of the influencing factor.

[0017] Furthermore, S4 specifically includes: For each influencing factor, if the influencing factor is selected by the user from the bushing set, the influencing factor is used as the basic data; Divide the stiffness of the influencing factor by a preset scaling factor to determine the first change data of the influencing factor; and multiply the stiffness of the influencing factor by the scaling factor to determine the second change data of the influencing factor.

[0018] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance.

[0019] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance.

[0020] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This method establishes a polynomial mathematical model between each selected influencing factor and various K&C indicators by performing only three simulations (basic data, first changed data, and second changed data) for each selected influencing factor, thereby quickly obtaining sensitivity. Compared to traditional sensitivity analysis methods that require numerous simulations or experiments, this significantly reduces computational resources and time costs, greatly reduces the number of times the suspension multibody model is run, and accelerates the sensitivity analysis of indicators. The difference in computation time becomes more pronounced with the number of influencing factors. Users can interactively select the influencing factors to be analyzed from the hardpoint set and bushing set of the suspension multibody model, making the analysis process more targeted and enabling rapid focus on key design parameters based on actual engineering needs. This method supports the simultaneous analysis of multiple K&C indicators (such as indicators corresponding to various operating conditions, such as wheel hop, lateral force, and self-centering torque), and can provide the sensitivity of each influencing factor for each indicator, which helps to comprehensively understand the performance characteristics of the suspension system. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance, provided in an embodiment of this specification. Figure 2 This specification provides a corresponding Figure 1 A schematic diagram of the structure of an electronic device. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0023] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0024] In this specification, K&C stands for Kinematics and C stands for Compliance. In vehicle engineering, kinematics and compliance are usually studied together (i.e., K&C characteristics) because the driving dynamics of a vehicle are the result of the superposition of these two states.

[0025] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A flowchart illustrating a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance, provided in this specification, includes the following steps: S1: Obtain the suspension multibody model.

[0027] This specification describes the process of rapid sensitivity analysis of multiple influencing factors of suspension K&C performance. In the embodiments described herein, this rapid sensitivity analysis of multiple influencing factors of suspension K&C performance can be performed by a server. Of course, this specification does not limit the type of device or platform used to perform this rapid sensitivity analysis of multiple influencing factors of suspension K&C performance; for example, a personal computer, mobile terminal, or other devices or platforms can also be used. For ease of description, the following description uses a server as the execution entity.

[0028] In one or more embodiments of this specification, the server can obtain a suspension multibody model. This is equivalent to an engineer or testing personnel inputting a suspension multibody model in a format such as ASY, so that the server can automatically analyze and identify the suspension multibody model. The suspension multibody model may include a suspension subsystem, a steering subsystem, and a lateral stabilizer bar subsystem. Users can selectively input each subsystem of the suspension multibody model to perform rapid sensitivity analysis of multiple K&C performance influencing factors for single or multiple subsystems.

[0029] S2: Identify the hard points and bushings in the suspension multibody model, name the coordinates of each hard point and store them in the hard point set, and name the stiffness of each bushing and store it in the bushing set.

[0030] In one or more embodiments of this specification, the server is able to identify hard points and bushings in the suspension multibody model, and store the coordinates (X, Y, Z) of each hard point in the hard point set, and store the stiffness (TX, TY, TZ, RX, RY, RZ) of each bushing in the bushing set.

[0031] Furthermore, since hardpoints are the key hinge points connecting various components in the suspension multibody model, the coordinates of a hardpoint can be represented as (X, Y, Z). Therefore, the server can determine the coordinates of each hardpoint in the X, Y, and Z directions, and store them in the hardpoint set (hardpoint_inf) after naming them sequentially in the form of hardpoint_name-X, hardpoint_name-Y, and hardpoint_name-Z. In this specification, each hardpoint_name-X (or Y or Z) coordinate point can be considered as an influencing factor.

[0032] Furthermore, since the bushing has stiffness (degree of softness) in the suspension multibody model, and the stiffness is different in different directions, it has 6 degrees of freedom, namely translational stiffness and oscillating stiffness.

[0033] Movement stiffness: X-axis push-pull, Y-axis push-pull, and Z-axis push-pull can be represented as TX, TY, and TZ, respectively.

[0034] Oscillating stiffness: Torsion about the X-axis, torsion about the Y-axis, and torsion about the Z-axis can be represented as RX, RY, and RZ, respectively.

[0035] Therefore, the server can determine the translational stiffness and oscillation stiffness of each bushing in the X, Y, and Z directions, and store them in the bushing set (bushing_inf) after naming them sequentially in the form of bushing name-TX, bushing name-TY, bushing name-TZ, bushing name-RX, bushing name-RY, and bushing name-RZ. Of course, in this specification, the total stiffness of each bushing can also be considered as an influencing factor and stored in the bushing set in the form of its bushing name. The stiffness represented by the bushing name stored in the bushing set is its total stiffness (i.e., including all 6 dimensions of stiffness of the bushing name-TX (or TY or TZ or RX or RY or RZ)).

[0036] S3: Display the hard point coordinates in the hard point set and the bushing stiffness in the bushing set to the user, and determine the influencing factors of the suspension multibody model in response to the user's selection operation.

[0037] In one or more embodiments of this specification, the server displays the coordinates of each hardpoint in the hardpoint set and the stiffness of each bushing in the bushing set in a named format to the user on the user interface. The user can view and freely select which factors to use as influencing factors. The server can then determine the influencing factors of the suspension multibody model in response to the user's selection. For example, a factor representing the coordinates of a hardpoint in the user-selected hardpoint set can be used as an influencing factor and included in the hardpoint input set (input_hardpoint_inf). Similarly, the server can also use a factor representing the stiffness of a bushing in the user-selected bushing set as an influencing factor and include it in the bushing input set (input_bushing_inf). Of course, the user can select multiple influencing factors for both the hardpoint input set and the bushing input set.

[0038] S4: For each influencing factor, change the influencing factor according to the preset change method, determine the first change data and the second change data of the influencing factor, and use the influencing factor as the basic data.

[0039] In one or more embodiments of this specification, the server can change each influencing factor selected by the user (i.e., the coordinates of each hard point in input_hardpoint_inf or the stiffness of each bushing in input_bushing_inf) according to a preset change method, determine the first change data and the second change data of the influencing factor, and use the influencing factor as the basic data.

[0040] Specifically, for each influencing factor, if the influencing factor is selected by the user from the hard point set, the server uses this influencing factor as the base data. Then, a preset change amount is subtracted from the coordinates of the influencing factor to determine the first change data for that influencing factor. Finally, a preset change amount is added to the coordinates of the influencing factor to determine the second change data for that influencing factor.

[0041] For example, the coordinates of each hardpoint in the input_hardpoint_inf set are changed by +Δmm and -Δmm in a single direction, such as ±Δmm being ±5mm or ±15mm. Therefore, if the base data is set to t0 (the coordinates of the base data), the lower limit data (i.e., the first changed data) is set to t1 (the coordinates of the base data - the change Δmm), and the upper limit data (i.e., the second changed data) is set to t2 (the coordinates of the base data + the change Δmm).

[0042] Of course, in this specification, for each influencing factor, if the influencing factor is selected by the user from the bushing set, that influencing factor is used as the basic data. The server then divides the stiffness of the influencing factor by a preset scaling factor to determine the first change data of the influencing factor. And multiplies the stiffness of the influencing factor by the preset scaling factor to determine the second change data of the influencing factor.

[0043] For example, for the input_bushing_inf data containing overall bushing influence factors or bushing stiffness influence factors in a single direction, the scaling factor for bushing stiffness variation is modified, for example, Δ is set to 2.0. This scaling factor is the same in all six directions of bushing stiffness data. Therefore, if the base data is set to t0 (stiffness of the base data), then the lower limit data (i.e., the first changed data) is set to t1 (stiffness of the base data ÷ scaling factor Δ) and the upper limit data (i.e., the second changed data) is set to t2 (stiffness of the base data × scaling factor Δ).

[0044] S5: Based on the first changed data, the second changed data, and the basic data, perform preset working condition simulations on the suspension multibody model respectively.

[0045] In one or more embodiments of this specification, the server can automatically compile the three influencing factors—first changed data, second changed data, and basic data—into a cmd command stream format file, and then input the cmd command stream format file into the suspension multibody model to start the ADAMS / Car software for preset working condition simulation. This is equivalent to writing an "operation list" for the software, telling it: "First move this point to t1, run a simulation; then move it back to t0, run it again; then move it to t2, run it again..."

[0046] When the influencing factors during simulation originate from hard points (mainly for suspension K-characteristic condition simulation), only a total of 2n+1 suspension K-characteristic condition simulations are needed, where n is the number of selected hard point coordinates and influencing factors.

[0047] Similarly, when the influencing factors in the simulation originate from the bushing (mainly performing C-characteristic simulation), only a total of 2m+1 suspension C-characteristic simulations are needed, where m is the number of bushing stiffness influencing factors selected.

[0048] S6: Determine the simulation results corresponding to the first changed data, the second changed data, and the basic data, and determine the K&C index values ​​corresponding to each simulation result.

[0049] In one or more embodiments of this specification, the server determines the simulation results corresponding to the first changed data, the second changed data, and the basic data, and determines the K&C index values ​​corresponding to each simulation result.

[0050] Specifically, this can be done by reading the simulation results in req format, then extracting a series of physical quantities for each K&C index from the input dataset, such as toe (toe angle) and camber (camber angle); then truncating the extracted data within a pre-set interval [-q, +q] for each K&C index, for example, q=25mm for parallel runout, q=1000N for longitudinal or lateral force, and q=50Nm for aligning torque; finally, performing linear fitting of the data trend within the pre-set interval [-q, +q] for each index to solve for the K&C index values ​​f1 (matching the simulation result corresponding to the first changed data), f0 (matching the simulation result corresponding to the basic data), and f2 (matching the simulation result corresponding to the second changed data).

[0051] S7: For each K&C index, based on the first changed data, the second changed data, the basic data, and the corresponding values ​​of the K&C index under each simulation result, perform polynomial fitting to determine the mathematical model.

[0052] In one or more embodiments of this specification, the server can perform polynomial fitting for each K&C index based on the first changed data, the second changed data, the basic data, and the corresponding values ​​of the K&C index under each simulation result to determine the mathematical model.

[0053] Specifically, the server can determine the first change amount (e.g., t1-t0) based on the first changed data (t1) and the basic data (t0), determine the second change amount (e.g., t2-t0) based on the second changed data (t2) and the basic data (t0), and determine the third change amount (e.g., t0-t0) based on the basic data (t0) and the basic data (t0).

[0054] For each K&C index, the K&C index value corresponding to the simulation result corresponding to the first changed data is taken as the first index value (i.e., f1), the K&C index value corresponding to the simulation result corresponding to the second changed data is taken as the second index value (i.e., f2), and the K&C index value corresponding to the simulation result corresponding to the basic data is taken as the third index value (i.e., f0).

[0055] Then, based on the values ​​of the first and third indicators, determine the fourth change (e.g., f1 - f0), the fifth change (e.g., f2 - f0), and the sixth change (e.g., f0 - f0).

[0056] Finally, the server uses the first change (e.g., t1-t0), the second change (e.g., t2-t0), and the third change (e.g., t0-t0) as independent variables, and the fourth change (e.g., f1-f0), the fifth change (e.g., f2-f0), and the sixth change (e.g., f0-f0) as dependent variables, obtaining three sets of corresponding data: (t1-t0, f1-f0), (t2-t0, f2-f0), and (t0-t0, f0-f0). Polynomial fitting is then performed to determine the mathematical model. For example, a binomial model (y=ax) can be used. 2 Fit the data using the three sets of data (+bx+c), and then calculate the coefficients a, b, and c.

[0057] S8: Determine the sensitivity of the influencing factors of the K&C index based on the mathematical model.

[0058] In one or more embodiments of this specification, the server may determine the sensitivity of the factors influencing the K&C index based on a mathematical model.

[0059] Specifically, after differentiating the mathematical model, the slope of the tangent line is calculated when the independent variable is the third variable. This slope is then used as the sensitivity of the K&C index to the influencing factor. That is, taking the mathematical model y=ax... 2Taking +bx+c as an example, in the mathematical model, the derivative of the independent variable x is obtained as 2ax+b. Substituting this into the third change (t0-t0), i.e., x=0, the slope b of the tangent line at the point where the change in the basic data is 0 is then calculated. The coefficient b of the linear term directly represents the sensitivity of this influencing factor.

[0060] When a=0 and b=0, the quadratic equation becomes a horizontal line y=c, indicating that the change in influencing factors has no effect on the K&C index value. When a=0 and b≠0, the quadratic equation becomes a linear equation y=bx+c, indicating that the change in influencing factors has a linear effect on the index value. When a≠0, the quadratic equation becomes a non-linear equation, indicating that the change in influencing factors has a non-linear effect on the index value.

[0061] Furthermore, in this specification, the server can also determine the sensitivity of each influencing factor corresponding to the K&C index. Then, based on the sensitivity of each influencing factor corresponding to the K&C index, the overall sensitivity of the influencing factors of the K&C index is determined.

[0062] If the K&C indicator is the j-th K&C indicator, and the j-th K&C indicator has n influencing factors, then the total sensitivity of the influencing factors of the j-th K&C indicator can be:

[0063] In the formula, The overall sensitivity of the factors influencing the j-th K&C index is... The sensitivity of the influence factor for the i-th influencing factor corresponding to the j-th K&C index.

[0064] Furthermore, the server can also calculate the ratio of the sensitivity of the K&C index to the total sensitivity of the K&C index for each influencing factor, thereby determining the relative sensitivity of the K&C index for that influencing factor.

[0065] based on Figure 1This paper presents a method for rapid sensitivity analysis of multiple K&C performance influencing factors in suspension systems. By performing only three simulations (basic data, first modified data, and second modified data) on each selected influencing factor, a polynomial mathematical model between the influencing factor and each K&C index can be established, thus quickly obtaining the sensitivity. Compared with traditional sensitivity analysis methods that require a large number of simulations or experiments, this method significantly reduces computational resources and time costs, greatly reduces the number of times the suspension multibody model is run, and accelerates the sensitivity analysis of the influencing factors. The difference in computation time becomes more pronounced as the number of influencing factors increases. Users can interactively select the influencing factors to be analyzed from the hardpoint set and bushing set of the suspension multibody model, making the analysis process more targeted and enabling rapid focus on key design parameters based on actual engineering needs. This method supports the simultaneous analysis of multiple K&C indices (such as wheel bounce, lateral force, and self-centering torque corresponding to different operating conditions) and can provide the sensitivity of each influencing factor for each index, which helps to comprehensively understand the performance characteristics of the suspension system.

[0066] Furthermore, in this specification, when performing sensitivity analysis of the C-characteristic index, a holistic approach followed by a local approach can be adopted. First, the entire bushing is analyzed as a single influencing factor. After identifying bushings with significant impact, the selected bushings with significant impact are then input into the analysis process as six influencing factors, based on the six-directional stiffness of a single bushing (linear stiffness: TX, TY, TZ; yaw stiffness: RX, RY, RZ). This process allows for another round of sensitivity analysis to determine which directions of bushing stiffness have a more significant impact on the suspension C-characteristic index.

[0067] In one or more embodiments of this specification, the preset operating conditions for the simulation of the suspension multibody model can be referred to Table 1 below.

[0068] Table 1 Simulation conditions and their indicators

[0069] In one or more embodiments of this specification, after calculating the sensitivity of each K&C index influencing factor, the calculation results are summarized, and a Pareto chart of the sensitivity analysis of the influencing factors of each K&C index can be output.

[0070] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 This paper presents a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance.

[0071] This instruction manual also provides Figure 2 The diagram shows a schematic structural representation of the electronic device. Figure 2As shown, at the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above. Figure 1 This paper presents a method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance.

[0072] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0073] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0074] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0075] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0076] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0083] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic or disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0087] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0088] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance, characterized in that, Includes the following steps: S1. Obtain the suspension multibody model; S2. Identify the hard points and bushings in the suspension multibody model, and name and store the coordinates of each hard point in the hard point set, and name and store the stiffness of each bushing in the bushing set. S3. Display the hard point coordinates in the hard point set and the bushing stiffness in the bushing set to the user, and determine the influencing factors of the suspension multibody model in response to the user's selection operation. S4. For each influencing factor, change the influencing factor according to the preset change method, determine the first change data and the second change data of the influencing factor, and use the influencing factor as the basic data; S5. Based on the first changed data, the second changed data, and the basic data, perform preset working condition simulations on the suspension multibody model respectively; S6. Determine the simulation results corresponding to the first changed data, the second changed data, and the basic data, and determine the K&C index values ​​corresponding to each simulation result; S7. For each K&C index, based on the first changed data, the second changed data, the basic data, and the values ​​of the K&C index under each simulation result, perform polynomial fitting to determine the mathematical model; S8. Based on the mathematical model, determine the sensitivity of the factors affecting the K&C index.

2. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 1, characterized in that, S7 specifically includes: A first change amount is determined based on the first changed data and the basic data; a second change amount is determined based on the second changed data and the basic data; and a third change amount is determined based on the basic data and the basic data. For each K&C index, the K&C index value corresponding to the simulation result corresponding to the first changed data is taken as the first index value, the K&C index value corresponding to the simulation result corresponding to the second changed data is taken as the second index value, and the K&C index value corresponding to the simulation result corresponding to the basic data is taken as the third index value. A fourth change is determined based on the first indicator value and the third indicator value; a fifth change is determined based on the second indicator value and the third indicator value; and a sixth change is determined based on the third indicator value and the third indicator value. Using the first, second, and third changes as independent variables, and the fourth, fifth, and sixth changes as dependent variables, a polynomial fitting is performed to determine the mathematical model.

3. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 2, characterized in that, S8 specifically includes: After differentiating the mathematical model, the slope of the tangent line is calculated when the independent variable is the third variable. The slope of the tangent line is used as the sensitivity of the K&C index to the influencing factors.

4. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 1 or 3, characterized in that, It also includes step S9: Determine the sensitivity of each influencing factor corresponding to the K&C index; Based on the sensitivity of each influencing factor corresponding to the K&C index, the overall sensitivity of the influencing factors of the K&C index is determined.

5. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 4, characterized in that, The method further includes: For each influencing factor, calculate the ratio of the sensitivity of that influencing factor to the total sensitivity of the influencing factors of that K&C index, and determine the relative sensitivity of that influencing factor to that K&C index.

6. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 1, characterized in that, In S2, the coordinates of each hard point are named and stored in the hard point set, and the stiffness of each bushing is named and stored in the bushing set. Specifically, this includes: Determine the coordinates of each hard point in the X, Y, and Z directions, and store them in the hard point set after naming them in the form of hard point name-X, hard point name-Y, and hard point name-Z respectively. Determine the translational stiffness and oscillation stiffness of each bushing in the X, Y, and Z directions, and store them in the bushing set after naming them in the form of bushing name-TX, bushing name-TY, bushing name-TZ, bushing name-RX, bushing name-RY, and bushing name-RZ.

7. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 1, characterized in that, S4 specifically includes: For each influencing factor, if the influencing factor is selected by the user from the set of hard points, the influencing factor is used as the basic data; Subtract a preset amount of change from the coordinates of the influencing factor to determine the first change data of the influencing factor; and add the amount of change to the coordinates of the influencing factor to determine the second change data of the influencing factor.

8. The method for rapid sensitivity analysis of multiple influencing factors of suspension K&C performance as described in claim 1, characterized in that, S4 specifically includes: For each influencing factor, if the influencing factor is selected by the user from the bushing set, the influencing factor is used as the basic data; Divide the stiffness of the influencing factor by a preset scaling factor to determine the first change data of the influencing factor; and multiply the stiffness of the influencing factor by the scaling factor to determine the second change data of the influencing factor.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 8.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 8.

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