An extensible electric vehicle chassis structure topology optimization method

Abstract

The application discloses a kind of scalable electric vehicle chassis structure topology optimization method, it is related to electric vehicle chassis structure design field, comprising the following steps: step 1, determine design space, boundary condition and design variable initialization;Step 2, the mapping relationship between design variable and physical structure is established;Step 3, topology optimization modeling;Step 4, design response solution;Step 5, sensitivity analysis;Step 6, optimization model solution;Step 7, convergence judgment;Step 8, optimization result post-processing.The application solves the problem that the traditional "facing different application requirements repeated development" cost is high, cycle is long and performance robustness is difficult to guarantee, and the scalable electric vehicle chassis configuration suitable for different wheelbase, different load, different endurance demand can be obtained.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle chassis structure design, and in particular to a scalable electric vehicle chassis structure topology optimization method. Background Technology

[0002] With the increasing prominence of global warming and the energy crisis, electric vehicles, with their advantages of low pollution and low noise, have become a new direction for the automotive industry. Currently, electric vehicle manufacturers typically adopt a "pure electric development" model, which involves rationally designing the body and chassis structure based on the external characteristics of the three core electric systems (motor, electronic control, and battery). However, if requirements such as vehicle model, size, or range change, the same development process must be repeated. This results in a long development cycle and high production line investment costs for the "pure electric development" model, making it difficult to meet the demands of today's electric vehicles for personalized customization, medium-volume production, and rapid market response. The recently emerging skateboard-style electric vehicle chassis promises to overcome these bottlenecks. Based on an integrated design concept of battery pack and chassis structure, this integrated design can further explore the potential for structural lightweighting to improve the range of electric vehicles. Furthermore, the main load-bearing structure of the vehicle shifts to the chassis structure, thus reducing the requirements for load-bearing safety performance of the bodywork. This supports a new business model of providing the same chassis for bodywork with different needs, significantly reducing the R&D cycle and manufacturing costs for companies.

[0003] Despite the aforementioned advantages of skateboard-style electric vehicle chassis, a standardized approach to scientifically designing wheelbase, range, and load requirements for scalable electric vehicle chassis structures has yet to be established. Currently, manufacturers typically rely on readily available commercial products, extending the chassis's application to electric vehicles with different loads, wheelbases, and ranges by simply increasing the beam length, adding battery cells within the extra space, or increasing the cross-sectional thickness of the beams or crossbeams, all while maintaining the same topology. However, this design method cannot guarantee the robustness of the electric vehicle chassis performance in all scenarios. In some situations, certain mechanical properties of the chassis structure may fail to meet design requirements, or the weight of the structure may prevent achieving the expected range. This often necessitates repeated trial and error adjustments by engineers, resulting in a lengthy development cycle.

[0004] Therefore, those skilled in the art are dedicated to developing a scalable electric vehicle chassis topology optimization method to obtain scalable electric vehicle chassis configurations that are adaptable to different wheelbases, loads, and range requirements. Summary of the Invention

[0005] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is that the traditional "repeated development for different application needs" is costly, has a long cycle, and is difficult to guarantee performance robustness.

[0006] To achieve the above objectives, this invention provides a scalable electric vehicle chassis structure topology optimization method, comprising the following steps:

[0007] Step 1: Use finite element discrete design space for the chassis structure, including setting the front and rear frames, the middle beam, and the battery cell area; determine the corresponding loads and boundary conditions based on various working conditions, and establish a finite element model of the electric vehicle chassis structure; define and initialize design variables;

[0008] Step 2: Establish the mapping relationship between the design variables and the physical structure;

[0009] Step 3: Based on the objective function and constraints, establish a scalable electric vehicle chassis topology optimization model;

[0010] Step 4: Design the response solution;

[0011] Step 5: Based on the chain rule, calculate the sensitivity value of the objective function to the design variables;

[0012] Step 6: Solve the topology optimization model using the moving asymptote algorithm and update the design variables;

[0013] Step 7: Optimize convergence judgment;

[0014] Step 8: Post-processing of optimization results.

[0015] Furthermore, the front and rear frames are configured as three-dimensional design domains Ω1 and Ω2; the cross-section of the central beam is configured as a two-dimensional design domain Ω′2; and the battery cell area is configured as a non-design domain Ω. * The design variables are defined as μ and υ. The design variable μ indirectly controls the presence or absence of materials in the design domains Ω1 and Ω2, and the design variable υ indirectly controls the presence or absence of materials in the design domain Ω′2.

[0016] Furthermore, step 2 also includes:

[0017] Step 2.1: Based on density filtering and the Heaviside projection function, convert the design variables μ and υ into physical variables. and

[0018] Step 2.2: Based on the penalized solid isotropic material method (SIMP) formula, construct an interpolation model for element physical variables and element stiffness.

[0019] Furthermore, the density filtering formula is as follows:

[0020]

[0021]

[0022] In the formula, M e ={i|||X i -X e ||≤r min} is the unit e with r min For the neighborhood of radius, X i With X e H represents the center coordinates of element i and element e, respectively. ie =max{r min -||X i -X e ||,0} represents the weighting coefficients;

[0023] The formula for the Heaviside projection function is as follows:

[0024]

[0025]

[0026] In the formula, β is the steepness of the projection, and η is the projection threshold; the projection steepness β doubles every 100 optimization iterations until it reaches a preset maximum value; where and These represent the unit physical variables of Ω1 and Ω2, and the unit physical variable of Ω′2, respectively.

[0027] Furthermore, the interpolation model for the element physical variables and element stiffness is as follows:

[0028]

[0029]

[0030] In the formula, E e E is the Young's modulus obtained by interpolation of element e; E0 is the Young's modulus of the material used; E min To avoid small values ​​introduced by the singularity of the matrix in the finite element solution; p is the penalty coefficient of the SIMP formula, which is usually taken as 3.

[0031] Furthermore, the topology optimization model for the scalable electric vehicle chassis structure is as follows:

[0032]

[0033] stV G ≤0

[0034] μ e ,υ e ∈[0,1],e∈Ω1,Ω2,Ω'2

[0035] K i U ij=F ij

[0036] In the formula, the objective function is to minimize the weighted structural flexibility c under multiple wheelbases and multiple working conditions, and the objective function is the chassis frame structural volume constraint V. G For constraints; c ij The wheelbase d represents the j-th working condition. i Chassis structural flexibility, K i The wheelbase is represented by d. i Chassis structure stiffness matrix, U ij The wheelbase d represents the j-th working condition. i The deformation vector of the chassis, F ij The wheelbase d represents the j-th working condition. i External loads on the chassis, M represents the total number of working conditions, and N represents the number of wheelbase types that the chassis can be expanded with.

[0037] Furthermore, step 4 also includes:

[0038] Based on the structure density field at the current optimization iteration step, by solving K i U ij =F ij The wheelbase d under the j-th working condition is obtained. i The deformation vector of the chassis is used to calculate the weighted structural flexibility and solve the volumetric constraint response of the chassis frame structure.

[0039] Furthermore, step 5 also includes:

[0040] Structural flexibility c versus design variable μ e The sensitivity can be obtained using the chain rule:

[0041]

[0042] in It can be obtained using the following formula:

[0043]

[0044] In the formula, m represents the total number of elements in the design domains Ω1 and Ω2. The wheelbase d represents the j-th working condition. i The deformation of the z-th unit of the chassis. The wheelbase is represented by d. i The element stiffness matrix of the chassis structure;

[0045] Structural flexibility c on design variable υ e The sensitivity can be obtained using the chain rule:

[0046]

[0047] in It can be obtained using the following formula:

[0048]

[0049] In the formula, n is the total number of elements in the design domain Ω'2.

[0050] Furthermore, step 7 also includes:

[0051] If the rate of change of the objective function is less than 0.2% within the current 5 iterations, the current optimization result is determined to have converged, the optimization is completed, and the optimization result is output; otherwise, steps 4 to 7 are repeated.

[0052] Furthermore, step 8 also includes:

[0053] By using the projection bisection method and setting a threshold of 0.5, grayscale units in the optimization results are eliminated, presenting clear optimization results, and the extended states of this type of chassis under different requirements are plotted.

[0054] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0055] The scalable electric vehicle chassis structure optimization design method of the present invention comprehensively considers the scalability of the electric vehicle chassis under different wheelbases and range requirements during the design stage, and obtains an electric vehicle chassis structure design with high performance robustness, thereby reducing development costs and R&D cycle.

[0056] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0057] Figure 1 This is a flowchart of a preferred embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the design domain of a preferred embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram illustrating the optimized result of a preferred embodiment of the present invention. Detailed Implementation

[0060] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0061] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0062] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the present invention. The design work based on the flowchart and the technical solution includes the following steps:

[0063] Step 1, as follows Figure 2 As shown, the chassis structure dimensions are L1 = L2 = L3 = L4 = 2m, L5 = 1.2m; the front and rear frames and the central beam are made of aluminum alloy, with an elastic modulus of 69GPa and a Poisson's ratio of 0.30; using square elements with a side length of 1cm, the chassis area of ​​the electric vehicle to be designed is discretized and modeled to establish a finite element model, setting loads and boundary conditions corresponding to three types of working conditions: torsion, frontal collision, and lateral bending; two types of chassis design spaces are defined: the front and rear frames are set as three-dimensional design domains Ω1 and Ω2, the central beam is set as a two-dimensional design domain Ω′2 with its cross-section, and the battery cell area is set as a non-design domain Ω. * Where m is the total number of elements in design domains Ω1 and Ω2, and n is the total number of elements in design domain Ω′2; the design variables are defined as μ and υ, where the design variable μ indirectly controls the presence or absence of materials in design domains Ω1 and Ω2, and the design variable υ indirectly controls the presence or absence of materials in design domain Ω′2.

[0064] Step 2: In this embodiment, the density filtering formula used for the two design variables μ and υ is as follows:

[0065]

[0066]

[0067] In the formula, M e ={i|||X i -X e ||≤r min} is the unit e with r min For the neighborhood of radius, X i With X e H represents the center coordinates of element i and element e, respectively. ie =max{r min -||X i -X e ||,0} represents the weighting coefficients;

[0068] The Heaviside projection function formula used in this embodiment is as follows:

[0069]

[0070]

[0071] In the formula, β is the steepness of the projection, and η is the projection threshold; the projection steepness β doubles every 100 optimization iterations until it reaches a preset maximum value; where and These represent the unit physical variables of Ω1 and Ω2, and the unit physical variable of Ω′2, respectively.

[0072] After applying density filtering and the Heaviside projection function, the design variables μ and υ are converted into physical variables. and An interpolation model for element physical variables and element stiffness is constructed using the penalized Solid Isotropic Material Method (SIMP) formula, as follows:

[0073]

[0074]

[0075] In the formula, E e E is the Young's modulus obtained by interpolation of element e; E0 is the Young's modulus of the material used; E min To avoid small values ​​introduced by the singularity of the finite element solution matrix, this embodiment uses 1.0 × 10⁻⁶. -4 MPa; p is the penalty coefficient of the SIMP formula, usually taken as 3.

[0076] Step 3: Using minimizing the weighted structural flexibility c under multiple wheelbase and working conditions as the objective function, and using the chassis frame structural volume constraint V... G As a constraint, a topology optimization model for the scalable electric vehicle chassis structure is constructed as follows:

[0077]

[0078] stV G ≤0

[0079] μ e ,υ e ∈[0,1],e∈Ω1,Ω2,Ω'2

[0080] K i U ij =F ij

[0081] In the formula, c ij The wheelbase d represents the j-th working condition. i Chassis structural flexibility, K i The wheelbase is represented by d. iChassis structure stiffness matrix, U ij The wheelbase d represents the j-th working condition. i The deformation vector of the chassis, F ij The wheelbase d represents the j-th working condition. i The external load on the chassis is represented by M, which represents the total number of working conditions. In this embodiment, there are three: torsion, frontal collision, and side bending. N represents the number of wheelbase types that the chassis can expand to. In this embodiment, there are two types of wheelbase requirements, d1 and d2.

[0082] Step 4: Based on the structure density field under the current optimization iteration step, solve K... i U ij =F ij The wheelbase d under the j-th working condition is obtained. i The deformation vector of the chassis is used to calculate the weighted structural flexibility and solve the volumetric constraint response of the chassis frame structure.

[0083] Step 5: Based on the chain rule, calculate the sensitivity value of the objective function to the design variable, as shown in the following formula:

[0084] Structural flexibility c versus design variable μ e The sensitivity can be obtained using the chain rule:

[0085]

[0086] in It can be obtained using the following formula:

[0087]

[0088] In the formula, m represents the total number of elements in the design domains Ω1 and Ω2. The wheelbase d represents the j-th working condition. i The deformation of the z-th unit of the chassis. The wheelbase is represented by d. i The element stiffness matrix of the chassis structure;

[0089] Structural flexibility c on design variable υ e The sensitivity can be obtained using the chain rule:

[0090]

[0091] in It can be obtained using the following formula:

[0092]

[0093] In the formula, n is the total number of elements in the design domain Ω'2.

[0094] Step 6: Solve the topology optimization model using the Moving Asymptote Algorithm (MMA) and update the design variables μ and υ;

[0095] Step 7: Perform optimization convergence judgment; if the rate of change of the objective function is less than 0.2% in the current 5 iteration steps, it is determined that the current optimization result has converged, the optimization is completed, and the optimization result is output; otherwise, repeat steps 4 to 7.

[0096] Step 8: Post-processing of optimization results. Based on the projection bisection method, a threshold of 0.5 is set to transform the above optimization results into a clear electric vehicle chassis structure without grayscale units. The extended states of this chassis structure under different wheelbases (d1 and d2) and different cell capacities (12 standard square cells and 15 standard square cells) are plotted, as follows. Figure 3 As shown.

[0097] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A scalable electric vehicle chassis topology optimization method, characterized in that, Includes the following steps: Step 1: Use finite element discrete design space for the chassis structure, including setting the front and rear frames, the middle beam, and the battery cell area; determine the loads and boundary conditions under various working conditions, and establish a finite element model of the electric vehicle chassis structure; define and initialize design variables; the front and rear frames are set as a three-dimensional design domain. and The cross-section of the intermediate beam is set as a two-dimensional design domain. The cell area is set as a non-design domain. The design variables are defined as follows: and Design variables Indirect control design domain and The presence or absence of materials in the design variables Indirect control design domain Does the Chinese material exist? Step 2: Establish the mapping relationship between the design variables and the physical structure; Step 2 further includes: Step 2.1: Based on density filtering and the Heaviside projection function, the design variables are... and Convert to physical variables and ; The density filtering formula is as follows: In the formula, For unit e by The neighborhood of the radius, and Units i With unit e The center coordinates, These are the weighting coefficients; The formula for the Heaviside projection function is as follows: In the formula, Let be the steepness of the projection. Projection threshold; projection steepness The value doubles every 100 optimization iterations until it reaches a preset maximum value; where and They represent and Unit physical variables, Unit physical variables; Step 2.2: Based on the SIMP formula, construct an interpolation model for element physical variables and element stiffness; Step 3: Based on the objective function and constraints, establish a scalable electric vehicle chassis topology optimization model; The topology optimization model for the scalable electric vehicle chassis structure is as follows: In the formula, the weighted structural flexibility under multiple wheelbases and multiple working conditions is minimized. c The objective function is defined by the volume constraint of the chassis frame structure. These are constraints; Representing the j The wheelbase under these working conditions is The structural flexibility of the chassis, Represents wheelbase as Chassis structure stiffness matrix Representing the j The wheelbase under these working conditions is The deformation vector of the chassis. Representing the j The wheelbase under these working conditions is External loads on the chassis M Represents the total number of operating conditions. N This represents the number of wheelbase types that the chassis can expand to. Step 4: Design the response solution; Step 5: Based on the chain rule, calculate the sensitivity value of the objective function to the design variables; Step 6: Solve the topology optimization model using the moving asymptote algorithm and update the design variables; Step 7: Convergence assessment; Step 8: Post-processing of optimization results.

2. The scalable electric vehicle chassis topology optimization method as described in claim 1, characterized in that, The interpolation model for the element physical variables and element stiffness is as follows: In the formula, For unit e Young's modulus obtained by interpolation; The Young's modulus of the material used; To avoid small values ​​introduced by the singularity of the matrix in the finite element solution; p This is the penalty coefficient for the SIMP formula, usually taken as 3.

3. The scalable electric vehicle chassis topology optimization method as described in claim 2, characterized in that, Step 4 also includes: Based on the structure density field at the current optimization iteration step, by solving... , obtained the j The wheelbase under these working conditions is The deformation vector of the chassis is used to calculate the weighted structural flexibility and solve the volumetric constraint response of the chassis frame structure.

4. The scalable electric vehicle chassis topology optimization method as described in claim 3, characterized in that, Step 5 further includes: Structural flexibility c For design variables The sensitivity can be obtained using the chain rule: in It can be obtained using the following formula: In the formula, m For design domain and The total number of units, Representing the j The wheelbase under these working conditions is The chassis z Deformation of each unit Represents wheelbase as The element stiffness matrix of the chassis structure; Structural flexibility c For design variables The sensitivity can be obtained using the chain rule: in It can be obtained using the following formula: In the formula, n For design domain The total number of units.

5. The scalable electric vehicle chassis topology optimization method as described in claim 4, characterized in that, Step 7 further includes: If the rate of change of the objective function is less than 0.2% within the current 5 iterations, the current optimization result is determined to have converged, the optimization is completed, and the optimization result is output; otherwise, steps 4 to 7 are repeated.

6. The scalable electric vehicle chassis topology optimization method as described in claim 5, characterized in that, Step 8 further includes: By using the projection bisection method and setting a threshold of 0.5, grayscale units in the optimization results are eliminated, presenting clear optimization results, and the extended states of this type of chassis under different requirements are plotted.

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