Battery pack upper cover optimization method and device

Abstract

The invention provides a battery pack upper cover optimization method and device, and relates to the field of structure optimization of battery pack upper covers. The method comprises the following steps: in response to optimization operation for the upper cover of the battery pack, constructing a CAE model corresponding to the upper cover of the battery pack; according to the CAE model, structural characteristics and constraint conditions of the upper cover of the battery pack are obtained, the structural characteristics comprise rigidity characteristics and mass distribution characteristics, and the constraint conditions comprise constraints applied to the upper cover by all mounting points in the upper cover of the battery strip and rotational degree-of-freedom constraints applied to a symmetric plane of the upper cover of the battery pack; carrying out optimization analysis on the battery pack upper cover through a target optimization function according to the rigidity characteristic, the mass distribution characteristic and a constraint condition, and obtaining an optimization result corresponding to each region of the battery pack upper cover; and obtaining an optimization scheme corresponding to the optimization result, and performing optimization operation on the battery pack upper cover according to the optimization scheme. The problems that when the design level of an engineer is low, the design of the upper cover of the battery pack is large in weight and low in strength, and in addition, the manual design process is often low in efficiency, and the requirements of rapid iteration and efficient production cannot be met are solved.

Description

Technical Field

[0001] The present application relates to the field of structural optimization of battery pack covers, and in particular to a method and device for optimizing battery pack covers. Background Art

[0002] The battery pack cover is an important part of the battery pack of electric vehicles. Its main function is to protect the battery cells and electrical components inside the battery to ensure their safety and stability. When designing, it is necessary to reduce the weight as much as possible to improve the vehicle's endurance while ensuring the structural strength and rigidity. In addition, functions such as heat dissipation, sealing, and protection must also be considered to cope with the temperature changes and external environmental impacts of the battery during operation.

[0003] At present, the design of the battery pack cover is mostly based on the experience of engineers. However, different engineers have different design levels. When the design level of the engineer is low, the design of the battery pack cover will have problems such as heavy weight and low strength. In addition, the manual design process is often inefficient and cannot meet the needs of rapid iteration and efficient production.

[0004] Therefore, there is an urgent need for a battery pack cover optimization method and device. Summary of the invention

[0005] The present application provides a battery pack cover optimization method and device, which solves the problem that when the engineer's design level is low, the design of the battery pack cover will be heavy and low in strength. In addition, the manual design process is often inefficient and cannot meet the needs of rapid iteration and efficient production.

[0006] In a first aspect of the present application, a battery pack cover optimization method is provided, the method comprising: in response to an optimization operation on the battery pack cover, constructing a CAE model corresponding to the battery pack cover; according to the CAE model, obtaining structural characteristics and constraints of the battery pack cover, the structural characteristics include stiffness characteristics and mass distribution characteristics, the constraints include constraints imposed on the cover by various mounting points in the battery bar cover, and rotational freedom constraints imposed on the symmetry plane of the battery pack cover; according to the stiffness characteristics, mass distribution characteristics and constraints, optimizing and analyzing the battery pack cover through an objective optimization function, and obtaining optimization results corresponding to various areas of the battery pack cover; obtaining an optimization scheme corresponding to the optimization results, and optimizing the battery pack cover according to the optimization scheme.

[0007] Optionally, constructing a CAE model corresponding to the battery pack cover specifically includes: obtaining material property parameters corresponding to the battery pack cover, the material property parameters including elastic modulus parameters, Poisson's ratio parameters, density parameters, thickness parameters and rib structure parameters; using shell unit meshing to divide the battery pack cover into multiple areas, the multiple areas including a design area and a flange surface area; obtaining a property assignment method corresponding to a target area in a CAE model construction database, the CAE model construction database is used to save the correspondence between the target area and the property assignment method, the target area is any one of the multiple areas; assigning material property parameters to the target area according to the property assignment method to construct a CAE model.

[0008] Optionally, the battery pack cover is optimized and analyzed according to the stiffness characteristics, mass distribution characteristics and constraints through the objective optimization function, specifically including: constructing a stiffness matrix corresponding to the battery pack cover according to the stiffness characteristics; constructing a mass distribution matrix corresponding to the battery pack cover according to the mass distribution characteristics; constructing the constraint mode corresponding to the battery pack cover under each constraint condition according to the stiffness characteristics and mass distribution characteristics; solving the constraint mode, and constructing the objective optimization function according to the solved constraint mode; optimizing the battery pack cover through the objective optimization function.

[0009] Optionally, according to the stiffness characteristics, mass distribution characteristics and constraint conditions, a constraint mode corresponding to the battery pack cover is constructed, specifically including: constructing the constraint mode according to the following formula:

[0010] [K]{φ}=ω 2 [M]{φ};

[0011] Where [K] is the stiffness matrix, [M] is the mass distribution matrix, {φ} is the modal vector under different constraint conditions, and ω is the natural frequency corresponding to the constrained mode under each constraint condition.

[0012] Optionally, solving the constraint mode and constructing the target optimization function according to the solved constraint mode specifically includes: calculating and constructing the target optimization function according to the following formula:

[0013]

[0014] Wherein, f(X) is the target optimization function, wherein the independent variable X is the rib structure parameters of the design area, the rib structure parameters of the design area are parameter sets, the rib structure parameters of the design area include rib width parameters, rib thickness parameters, rib height parameters and rib angle parameters, and the target optimization function is used to obtain the optimal solution of the rib structure parameters of the design area, w i is the constraint mode correspondence under the i-th constraint condition, ω iis the natural frequency corresponding to the constrained mode under the i-th constraint condition, α is the weight coefficient, which is used to control the trade-off between stiffness characteristics and mass distribution characteristics, [K] i (X) is the stiffness matrix corresponding to the constraint mode under the i-th constraint condition, [M] i (X) is the distribution matrix of the corresponding quantity of the constraint mode under the i-th constraint condition.

[0015] Optionally, the optimization results corresponding to each area of ​​the battery pack cover are obtained, specifically including: calculating the target solution corresponding to each area of ​​the battery pack cover through the target optimization function; judging whether the target solution is less than or equal to a first optimization threshold; if the target solution is less than or equal to the first optimization threshold, confirming that the optimization result is the first optimization result; if the target solution is greater than the first optimization threshold, judging whether the target solution is less than or equal to the second optimization threshold; if the target solution is less than or equal to the second optimization threshold, confirming that the optimization result is the second optimization result; if the target solution is greater than the second optimization threshold, confirming that the optimization result is the third optimization result.

[0016] Optionally, an optimization scheme corresponding to the optimization result is obtained, specifically including: if the optimization result is confirmed to be the first optimization result, the area is a reservable area, and the optimization scheme corresponding to the reservable area is not to perform an optimization operation; if the optimization result is confirmed to be the second optimization result, the area is an optimizable area, and the optimization scheme corresponding to the reservable area is the first optimization operation; if the optimization result is confirmed to be the third optimization result, the area is a key optimization area, and the optimization scheme corresponding to the key optimization area is the second optimization operation, and the optimization degree of the second optimization operation is greater than the optimization degree of the first optimization operation.

[0017] In a second aspect of the present application, a battery pack cover optimization device is provided, the device comprising an acquisition module and a processing module, wherein:

[0018] The acquisition module is used to construct a CAE model corresponding to the battery pack cover in response to the optimization operation on the battery pack cover.

[0019] The processing module is used to obtain the structural characteristics and constraints of the battery pack cover according to the CAE model, wherein the structural characteristics include stiffness characteristics and mass distribution characteristics, and the constraints include the constraints imposed on the cover by various installation points in the battery bar cover and the rotational freedom constraints imposed on the symmetry plane of the battery pack cover; according to the stiffness characteristics, mass distribution characteristics and constraints, the battery pack cover is optimized and analyzed through the target optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained; the optimization plan corresponding to the optimization result is obtained, and the battery pack cover is optimized according to the optimization plan.

[0020] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0021] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. When optimizing the battery pack cover, a CAE model corresponding to the battery pack cover is constructed; and based on the CAE model, the structural characteristics and constraints of the battery pack cover are obtained, so that according to the stiffness characteristics, the mass distribution characteristics and the constraints, the battery pack cover is optimized and analyzed through the objective optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained, and then the optimization scheme corresponding to the optimization result is obtained, and the battery pack cover is optimized according to the optimization scheme, thereby optimizing the overall structure of the battery pack cover. According to the optimization results, different optimization schemes are executed accordingly, so that the weak positions in the battery pack cover can be determined more accurately. Compared with the design based on the experience of engineers, it is not only more accurate and efficient, but also can significantly improve the structural reliability and safety of the battery pack cover, reduce material waste, and optimize production costs.

[0024] 2. By setting multiple constraints and constructing a target optimization function under the constraints of multiple constraints, the design area of ​​the battery pack cover can be analyzed and optimized from multiple dimensions, so that the battery pack cover has better structural performance and vibration resistance in actual use.

[0025] 3. By formulating different optimization plans for different optimization results, it is possible to perform optimization operations more targeted according to the different performance requirements of different areas in the design area, thereby achieving more accurate and efficient optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a battery pack cover optimization method provided in an embodiment of the present application;

[0027] Figure 2 It is a module schematic diagram of a battery pack cover optimization device provided in an embodiment of the present application;

[0028] Figure 3It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0029] Explanation of the reference numerals: 21, model building module; 22, optimization analysis module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0030] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0033] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0034] The battery pack cover is an important part of the battery pack of electric vehicles. Its main function is to protect the battery cells and electrical components inside the battery to ensure their safety and stability. When designing, it is necessary to reduce the weight as much as possible to improve the vehicle's endurance while ensuring the structural strength and rigidity. In addition, functions such as heat dissipation, sealing, and protection must also be considered to cope with the temperature changes and external environmental impacts of the battery during operation.

[0035] At present, the design of the battery pack cover is mostly based on the experience of engineers. However, different engineers have different design levels. When the design level of the engineer is low, the design of the battery pack cover will have problems such as heavy weight and low strength. In addition, the manual design process is often inefficient and cannot meet the needs of rapid iteration and efficient production.

[0036] Therefore, there is an urgent need for a battery pack cover optimization method and device.

[0037] Please refer to Figure 1 , which shows a flow chart of a battery pack cover optimization method provided in an embodiment of the present application. The method is applied to a server. The flow chart mainly includes the following steps: S101 to S104.

[0038] Step S101, in response to the optimization operation on the battery pack cover, construct a CAE model corresponding to the battery pack cover.

[0039] Specifically, users can improve the stiffness and strength of the overall structure of the battery pack cover through the solution in this application. When the user performs optimization operations on the battery pack cover, a CAE (Computer Aided Engineering) model corresponding to the battery pack cover is constructed. The CAE model is a three-dimensional digital model based on finite element analysis. The CAE model can describe the structural characteristics of the cover in detail, including data such as nodes, units, boundary conditions, material properties, and load conditions, so that in the subsequent modal analysis and optimization process, the dynamic and static properties of the battery pack cover can be simulated with high precision.

[0040] In this possible implementation, step S101 also includes: obtaining material property parameters corresponding to the battery pack cover, the material property parameters including elastic modulus parameters, Poisson's ratio parameters, density parameters and thickness parameters; using shell unit meshing to divide the battery pack cover into multiple areas, the multiple areas including a design area and a flange surface area; obtaining a property assignment method corresponding to the target area in a CAE model construction database, the CAE model construction database is used to save the correspondence between the target area and the property assignment method, the target area is any one of the multiple areas; assigning material property parameters to the target area according to the property assignment method to construct a CAE model.

[0041] Specifically, the specific steps for constructing the CAE model corresponding to the battery pack cover are as follows: First, obtain the material property parameters corresponding to the battery pack cover. The cover material is SPCC steel. The material property parameters include but are not limited to: elastic modulus parameters, Poisson's ratio parameters, density parameters, thickness parameters and other property parameters. The material property parameters can be pre-stored through the cloud database for easy call at any time. The accurate acquisition of material property parameters ensures the accuracy of the CAE model in the physical performance simulation. After that, the battery pack cover is meshed with shell elements, that is, according to the geometric shape and structural requirements of the cover, the entire battery pack cover is divided into multiple areas, including the design area and the flange surface area. The design area is the area that can be optimized, and the flange surface area is the area where the flange is located. Through reasonable meshing, the calculation accuracy in the finite element analysis can be effectively improved, and the rib structure can be optimized in the design area. Next, the attribute assignment method corresponding to the target area is obtained in the CAE model construction database. The CAE model construction database is used to save the correspondence between each target area and its attribute assignment method. The target area can be a flange surface area or a design area, where the attribute assignment method corresponding to the flange surface area is as follows: the flange surface area is usually the key area where the battery pack cover is connected to other components, so its material assignment method needs to consider strength, stiffness and connection stability, thickness parameter assignment: the thickness of the flange surface area is generally larger to ensure its bearing capacity and stiffness, and the SPCC steel properties of a specific thickness are assigned to ensure that the flange surface can withstand stress concentration and high loads at the connection site; boundary condition parameter assignment: since the flange surface area is the location of the installation point, it is necessary to apply corresponding boundary condition parameters to it to simulate the actual stress conditions during assembly; elastic modulus parameters and Poisson's ratio Parameter assignment: The flange surface area usually requires higher stiffness, so the flange surface is assigned high-strength SPCC steel material properties, including higher elastic modulus parameters and Poisson's ratio parameters, to ensure its ability to resist deformation under large loads; The corresponding attribute assignment method for the design area is specifically as follows: The design area is the key area for optimization, which is usually used for the optimization design of the ribbed structure. The material assignment method for the design area considers more optimization goals such as lightweight and stiffness improvement: Ribbed structure parameter assignment: In the design area, the ribbed structure parameters are assigned as optimization variables. The ribbed structure parameters include: rib width parameters, rib thickness parameters, rib height parameters, and rib angle parameters. The above variables directly affect the strength and lightweight effect of the ribbed structure, and are also parameters that need to be optimized in this application. Thickness parameters: The design of the thickness parameters may be optimized, lower than the flange surface area, to reduce weight.The above-mentioned attribute assignment method is only a practical example listed in this application for the convenience of explanation, and does not mean that the attribute assignment method is limited to the above-mentioned method. The material attribute assignment method needs to consider the geometric characteristics, load distribution and optimization requirements of the target area to ensure that the material definition of each area is suitable for the corresponding physical and mechanical properties.

[0042] Step S102, based on the CAE model, obtain the structural characteristics and constraints of the battery pack cover, the structural characteristics include stiffness characteristics and mass distribution characteristics, the constraints include the constraints imposed on the cover by each installation point in the battery strip cover, and the rotational freedom constraints imposed on the symmetry plane of the battery pack cover.

[0043] Specifically, the stiffness characteristics and mass distribution characteristics are calculated through the CAE model. The stiffness characteristics reflect the deformation resistance of the battery pack cover under external forces. The CAE model will generate the stiffness matrix of the entire battery pack cover according to the previously assigned material properties and mesh divisions; it reflects how the mass of the battery pack cover is distributed in different areas. This feature is crucial to the impact on dynamic performance, modal analysis and structural vibration. The mass distribution of the battery pack cover mainly depends on the material density, thickness and structural design, especially the thickness change in the ribbed area has a great influence on the mass distribution. The constraints include the constraints imposed on the upper cover by each mounting point in the upper cover of the battery bar, and the rotational freedom constraints imposed on the symmetry plane of the upper cover of the battery pack. The constraints that can be determined include but are not limited to the constraints imposed on the upper cover by each mounting point in the upper cover of the battery bar, and the rotational freedom constraints imposed on the symmetry plane of the upper cover of the battery pack, etc., wherein, in the CAE model of the upper cover of the battery pack, the position of each mounting point is identified, and displacement constraints are imposed to limit its translation and rotational freedom in the three directions of X, Y, and Z to ensure that these points are fixed during the simulation process; for the upper cover structure with symmetry, the rotational freedom constraints can be imposed along the symmetry plane in the CAE model, that is, the nodes in the plane are restricted from rotating freely around the plane, so as to simulate the force behavior of the symmetrical structure, reduce the degrees of freedom calculated in the simulation, and improve efficiency; the rotational freedom constraints on the symmetry plane limit the rotational freedom on the symmetry plane of the upper cover, improve the efficiency of simulation calculation, and ensure the correct analysis of the symmetrical structure. This application does not limit the number of constraints, and the number of constraints needs to be set according to the actual optimization needs.

[0044] Step S103, based on the stiffness characteristics, mass distribution characteristics and constraint conditions, the battery pack cover is optimized and analyzed through the target optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained.

[0045] Specifically, according to the stiffness characteristics, mass distribution characteristics and constraints, the battery pack cover is optimized and analyzed through the target optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained. The specific steps are as follows: According to the stiffness characteristics, the stiffness matrix corresponding to the battery pack cover is constructed; according to the mass distribution characteristics, the mass distribution matrix corresponding to the battery pack cover is constructed; according to the stiffness characteristics and the mass distribution characteristics, the constraint mode corresponding to the battery pack cover under each constraint condition is constructed, wherein the constraint mode can be constructed according to the following formula:

[0046] [K]{φ}=ω 2 [M]{φ};

[0047] Where [K] is the stiffness matrix, [M] is the mass distribution matrix, {φ} is the modal vector under different constraint conditions, and ω is the natural frequency corresponding to the constrained mode under each constraint condition; the constrained mode is solved, and the target optimization function is constructed based on the solved constrained mode. The target optimization function can be calculated according to the following formula:

[0048]

[0049] Wherein, f(X) is the target optimization function, wherein the independent variable X is the rib structure parameters of the design area, the rib structure parameters of the design area are parameter sets, the rib structure parameters of the design area include rib width parameters, rib thickness parameters, rib height parameters and rib angle parameters, and the target optimization function is used to obtain the optimal solution of the rib structure parameters of the design area, w i is the constraint mode correspondence under the i-th constraint condition, ω i is the natural frequency corresponding to the constrained mode under the i-th constraint condition, α is the weight coefficient, which is used to control the trade-off between stiffness characteristics and mass distribution characteristics, [K] i (X) is the stiffness matrix corresponding to the constraint mode under the i-th constraint condition, [M] i(X) is the distribution matrix of the corresponding quantity of the constraint mode under the i-th constraint condition; then, the target solution corresponding to each area of ​​the battery pack cover is calculated through the target optimization function; it is determined whether the target solution is less than or equal to the first optimization threshold; if the target solution is less than or equal to the first optimization threshold, the optimization result is confirmed to be the first optimization result; if the target solution is greater than the first optimization threshold, it is determined whether the target solution is less than or equal to the second optimization threshold; if the target solution is less than or equal to the second optimization threshold, the optimization result is confirmed to be the second optimization result; if the target solution is greater than the second optimization threshold, the optimization result is confirmed to be the third optimization result. For the above steps, it is necessary to determine the target solution of each unit grid through the optimization function, and determine in which optimization threshold interval the target solution corresponding to each unit grid is, and take the adjacent unit grids in the same optimization threshold interval as the overall optimization area. This process ensures that the optimization operation is continuous and coordinated in space, and avoids poor overall performance caused by local optimization.

[0050] Step S104, obtaining an optimization solution corresponding to the optimization result, and performing optimization operations on the battery pack cover according to the optimization solution.

[0051] Specifically, if the optimization result is confirmed to be the first optimization result, the region is a reservable region, and the optimization solution corresponding to the reservable region is not to perform optimization operations; if the optimization result is confirmed to be the second optimization result, the region is an optimizable region, and the optimization solution corresponding to the reservable region is the first optimization operation. This type of optimization is mainly aimed at the part in the "optimizable region", and appropriate parameter adjustments are used to improve the local performance of the battery pack cover. The optimization range is small, and the focus is on balancing the stiffness and mass distribution to ensure that the stiffness and strength of the structure are improved without significantly increasing the mass; if the optimization result is confirmed to be the third optimization result, the region is a key optimization region, and the optimization solution corresponding to the key optimization region is the second optimization operation. The optimization degree of the second optimization operation is greater than that of the first optimization operation. This type of optimization is mainly aimed at the "key optimization region", and the rib structure parameters need to be significantly adjusted to significantly enhance the performance of this region. The optimization range is large, and the height and thickness of the ribs may be increased, thereby significantly improving the stiffness and vibration performance of this region, ensuring that it can maintain good strength under harsh conditions.

[0052] The present application adopts the above method. When performing the optimization operation of the battery pack cover, a CAE model corresponding to the battery pack cover is constructed; and according to the CAE model, the structural characteristics and constraints of the battery pack cover are obtained, so as to optimize and analyze the battery pack cover through the target optimization function according to the stiffness characteristics, mass distribution characteristics and constraints, and obtain the optimization results corresponding to each area of ​​the battery pack cover, and then obtain the optimization scheme corresponding to the optimization result, and optimize the battery pack cover according to the optimization scheme, thereby optimizing the overall structure of the battery pack cover, and executing different optimization schemes according to the optimization results, which can more accurately determine the weak positions in the battery pack cover. Compared with designing according to the experience of engineers, it is not only more accurate and efficient, but also can significantly improve the structural reliability and safety of the battery pack cover, reduce material waste, and optimize production costs.

[0053] Please refer to Figure 2 , which shows a module schematic diagram of a battery pack cover optimization device provided in an embodiment of the present application, the device is a server, and the server includes a model building module 21 and an optimization analysis module 22, wherein,

[0054] The model building module 21 is used to build a CAE model corresponding to the battery pack cover in response to the optimization operation on the battery pack cover.

[0055] The optimization analysis module 22 is used to obtain the structural characteristics and constraints of the battery pack cover according to the CAE model, wherein the structural characteristics include stiffness characteristics and mass distribution characteristics, and the constraints include the constraints imposed on the cover by each installation point in the battery strip cover, and the rotational freedom constraints imposed on the symmetry plane of the battery pack cover; according to the stiffness characteristics, mass distribution characteristics and constraints, the battery pack cover is optimized and analyzed through the target optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained; the optimization plan corresponding to the optimization result is obtained, and the battery pack cover is optimized according to the optimization plan.

[0056] In a possible implementation, the model building module 21 is used to build a CAE model corresponding to the battery pack cover, specifically including: obtaining material property parameters corresponding to the battery pack cover, the material property parameters including elastic modulus parameters, Poisson's ratio parameters, density parameters, thickness parameters and rib structure parameters; using shell unit meshing to divide the battery pack cover into multiple areas, the multiple areas including a design area and a flange surface area; obtaining a property assignment method corresponding to a target area in a CAE model building database, the CAE model building database is used to save the correspondence between the target area and the property assignment method, the target area is any one of the multiple areas; assigning material property parameters to the target area according to the property assignment method to build a CAE model.

[0057] In a possible implementation, the optimization analysis module 22 is used to optimize and analyze the battery pack cover through a target optimization function according to the stiffness characteristics, mass distribution characteristics and constraints, specifically including: constructing a stiffness matrix corresponding to the battery pack cover according to the stiffness characteristics; constructing a mass distribution matrix corresponding to the battery pack cover according to the mass distribution characteristics; constructing a constraint mode corresponding to the battery pack cover under each constraint condition according to the stiffness characteristics and mass distribution characteristics; solving the constraint mode, and constructing a target optimization function according to the solved constraint mode; and optimizing and analyzing the battery pack cover through the target optimization function.

[0058] In a possible implementation, the optimization analysis module 22 is used to construct the constraint mode corresponding to the battery pack cover according to the stiffness characteristics, mass distribution characteristics and constraint conditions, specifically including: constructing the constraint mode according to the following formula:

[0059] [K]{φ}=ω 2 [M]{φ};

[0060] Where [K] is the stiffness matrix, [M] is the mass distribution matrix, {φ} is the modal vector under different constraint conditions, and ω is the natural frequency corresponding to the constrained mode under each constraint condition.

[0061] In a possible implementation, the optimization analysis module 22 is used to solve the constraint mode and construct the target optimization function according to the solved constraint mode, specifically including: calculating and constructing the target optimization function according to the following formula:

[0062]

[0063] Wherein, f(X) is the target optimization function, wherein the independent variable X is the rib structure parameters of the design area, the rib structure parameters of the design area are parameter sets, the rib structure parameters of the design area include rib width parameters, rib thickness parameters, rib height parameters and rib angle parameters, and the target optimization function is used to obtain the optimal solution of the rib structure parameters of the design area, w i is the constraint mode correspondence under the i-th constraint condition, ω i is the natural frequency corresponding to the constrained mode under the i-th constraint condition, α is the weight coefficient, which is used to control the trade-off between stiffness characteristics and mass distribution characteristics, [K] i (X) is the stiffness matrix corresponding to the constraint mode under the i-th constraint condition, [M] i (X) is the distribution matrix of the corresponding quantity of the constraint mode under the i-th constraint condition.

[0064] In a possible implementation, the optimization analysis module 22 is used to obtain the optimization results corresponding to each area of ​​the battery pack cover, specifically including: calculating the target solution corresponding to each area of ​​the battery pack cover through the target optimization function; judging whether the target solution is less than or equal to the first optimization threshold; if the target solution is less than or equal to the first optimization threshold, confirming that the optimization result is the first optimization result; if the target solution is greater than the first optimization threshold, judging whether the target solution is less than or equal to the second optimization threshold; if the target solution is less than or equal to the second optimization threshold, confirming that the optimization result is the second optimization result; if the target solution is greater than the second optimization threshold, confirming that the optimization result is the third optimization result.

[0065] In a possible implementation, the optimization analysis module 22 is used to obtain an optimization solution corresponding to the optimization result, specifically including: if the optimization result is confirmed to be the first optimization result, the area is a reservable area, and the optimization solution corresponding to the reservable area is not to perform an optimization operation; if the optimization result is confirmed to be the second optimization result, the area is an optimizable area, and the optimization solution corresponding to the reservable area is the first optimization operation; if the optimization result is confirmed to be the third optimization result, the area is a key optimization area, and the optimization solution corresponding to the key optimization area is the second optimization operation, and the optimization degree of the second optimization operation is greater than the optimization degree of the first optimization operation.

[0066] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0067] The present application also provides an electronic device. Figure 3 , Figure 3 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0068] The communication bus 302 is used to realize the connection and communication between these components.

[0069] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0070] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0071] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0072] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a battery pack cover optimization application.

[0073] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the battery pack cover optimization application stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0074] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0080] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0081] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A battery pack cover optimization method, characterized in that: The method comprises: In response to the optimization operation on the battery pack upper cover, constructing a CAE model corresponding to the battery pack upper cover; According to the CAE model, the structural features and constraints of the battery pack cover are obtained, wherein the structural features include stiffness features and mass distribution features, and the constraints include constraints imposed on the cover by each mounting point in the battery strip cover and rotational freedom constraints imposed on the symmetry plane of the battery pack cover; According to the stiffness characteristics, the mass distribution characteristics and the constraint conditions, the battery pack upper cover is optimized and analyzed by using an objective optimization function, and an optimization result corresponding to each area of ​​the battery pack upper cover is obtained; Obtain an optimization scheme corresponding to the optimization result, and perform optimization operations on the battery pack cover according to the optimization scheme.

2. The method according to claim 1, characterized in that The step of constructing a CAE model corresponding to the battery pack cover specifically includes: Obtaining material property parameters corresponding to the battery pack cover, wherein the material property parameters include elastic modulus parameters, Poisson's ratio parameters, density parameters, thickness parameters, and rib structure parameters; Using shell unit meshing to divide the battery pack upper cover into a plurality of regions, wherein the plurality of regions include a design region and a flange surface region; Acquire a property assignment method corresponding to a target area in a CAE model construction database, wherein the CAE model construction database is used to store a corresponding relationship between the target area and the property assignment method, wherein the target area is any one of the multiple areas; The material property parameters are assigned to the target area according to the property assignment method to construct the CAE model.

3. The method according to claim 1, characterized in that The optimizing analysis of the battery pack cover by using a target optimization function according to the stiffness characteristics, the mass distribution characteristics and the constraint conditions specifically includes: Constructing a stiffness matrix corresponding to the battery pack cover according to the stiffness characteristics; Constructing a mass distribution matrix corresponding to the battery pack cover according to the mass distribution characteristics; Constructing a constraint mode corresponding to the battery pack cover under each constraint condition according to the stiffness characteristics and the mass distribution characteristics; Solving the constraint mode, and constructing the target optimization function according to the solved constraint mode; The battery pack cover is optimized and analyzed using the objective optimization function.

4. The method according to claim 3, characterized in that According to the stiffness characteristics, the mass distribution characteristics and the constraint conditions, the constraint mode corresponding to the battery pack cover is constructed, specifically including: The constraint mode is constructed according to the following formula: [K]{φ}=ω 2 [M]{φ}; Wherein, [K] is the stiffness matrix, [M] is the mass distribution matrix, {φ} is the modal vector under different constraint conditions, and ω is the natural frequency corresponding to the constraint mode under each constraint condition.

5. The method according to claim 4, characterized in that Specifically, solving the constraint mode and constructing the target optimization function according to the solved constraint mode specifically includes: The objective optimization function is constructed according to the following formula: Wherein, f(X) is the objective optimization function, wherein the independent variable X is the rib structure parameter of the design area, the rib structure parameter of the design area is a parameter set, the rib structure parameter of the design area includes a rib width parameter, a rib thickness parameter, a rib height parameter and a rib angle parameter, and the objective optimization function is used to obtain the optimal solution of the rib structure parameter of the design area, w i is the constraint mode corresponding to the i-th constraint condition, ω i is the natural frequency corresponding to the constraint mode under the i-th constraint condition, α is the weight coefficient, which is used to control the trade-off between stiffness characteristics and mass distribution characteristics, [K] i (X) is the stiffness matrix corresponding to the constraint mode under the i-th constraint condition, [M] i (X) is the quantity distribution matrix corresponding to the constraint mode under the i-th constraint condition.

6. The method according to claim 5, characterized in that The obtaining of the optimization results corresponding to each area of ​​the battery pack cover specifically includes: Calculate the target solution corresponding to each area of ​​the battery pack cover through the target optimization function; Determining whether the target solution is less than or equal to a first optimization threshold; If the target solution is less than or equal to the first optimization threshold, confirming that the optimization result is the first optimization result; If the target solution is greater than the first optimization threshold, determining whether the target solution is less than or equal to a second optimization threshold; If the target solution is less than or equal to the second optimization threshold, confirming that the optimization result is the second optimization result; If the target solution is greater than the second optimization threshold, the optimization result is confirmed to be a third optimization result.

7. The method according to claim 6, characterized in that The obtaining of the optimization solution corresponding to the optimization result specifically includes: If it is confirmed that the optimization result is the first optimization result, the area is a reservable area, and the optimization scheme corresponding to the reservable area is not to perform the optimization operation; If it is confirmed that the optimization result is the second optimization result, the region is an optimizable region, and the optimization scheme corresponding to the retainable region is the first optimization operation; If it is confirmed that the optimization result is the third optimization result, the area is a key optimization area, the optimization scheme corresponding to the key optimization area is a second optimization operation, and the optimization degree of the second optimization operation is greater than the optimization degree of the first optimization operation.

8. A battery pack cover optimization device, characterized in that: The device includes a model building module and an optimization analysis module, wherein: The acquisition module is used to construct a CAE model corresponding to the battery pack cover in response to the optimization operation on the battery pack cover; The processing module is used to obtain the structural characteristics and constraints of the battery pack cover according to the CAE model, wherein the structural characteristics include stiffness characteristics and mass distribution characteristics, and the constraints include constraints imposed on the cover by various installation points in the battery strip cover and rotational freedom constraints imposed on the symmetry plane of the battery pack cover; based on the stiffness characteristics, the mass distribution characteristics and the constraints, the battery pack cover is optimized and analyzed through the objective optimization function, and the optimization results corresponding to each area of ​​the battery pack cover are obtained; the optimization scheme corresponding to the optimization result is obtained, and the battery pack cover is optimized according to the optimization scheme.

9. An electronic device, characterized in that: It includes a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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