Battery honeycomb structure optimization methods, devices, equipment, media and procedures.

Abstract

The present invention proposes a battery honeycomb structure optimization method, device, equipment, medium and program product, constructs a finite element analysis model of the honeycomb structure at different positions, and determines the design wall thickness of the honeycomb structure at different positions; takes the relative density of the honeycomb structure at different positions to be the minimum, and the total energy absorbed per unit volume to be the maximum as the optimization goal, adopts a multi-objective grey wolf optimization algorithm to perform optimization and solve, and obtains the Pareto solution set of the wall thickness of the corresponding honeycomb structure at different positions; determines the optimal wall thickness according to the design wall thickness and the Pareto solution set, ensures that the battery pack has excellent collision energy absorption capacity while maintaining lightweight, and can be widely used in fields such as electric vehicles that require high safety and lightweight.

Description

Technical Field

[0001] The present invention belongs to the technical field related to battery optimization, and in particular to a method, device, equipment, medium and program product for optimizing a battery honeycomb structure. Background Art

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

[0003] With the development of new energy vehicles, the safety, lightweight and impact resistance of battery packs have become core concerns. During the collision of electric vehicles, how to ensure the structural strength of the battery pack while absorbing the kinetic energy generated by the collision through effective deformation and minimizing the weight of the battery pack has become a key technical problem. Traditional battery pack structures usually use metal frames or fiber-reinforced composite materials. Although these materials perform well in strength and stiffness, their energy absorption capacity under dynamic collision loads is limited due to their relatively rigid structure, and it is difficult to achieve lightweight design requirements. Honeycomb structure has become a research hotspot for structural optimization in recent years due to its good energy absorption capacity and adjustability of geometric shape.

[0004] Conventional uniform honeycomb structures have obvious advantages in energy absorption, especially in thin-walled structures with small thickness, which can effectively reduce weight and improve collision safety. However, during a collision, the uniform distribution of traditional honeycomb structures leads to stress concentration in local areas, and the isotropic characteristics of the structure may lead to uneven energy distribution in certain specific collision directions, thereby affecting the overall energy absorption efficiency and stability of the structure. Therefore, further consideration needs to be given to the optimization design of the honeycomb structure to increase its collision safety. Summary of the invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a battery honeycomb structure optimization method, device, equipment, and medium program product to ensure that the battery pack has excellent collision energy absorption capacity while maintaining lightweight.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for optimizing a battery honeycomb structure, comprising:

[0008] According to the honeycomb structure parameters at different positions, a finite element analysis model is constructed, and according to the constructed finite element analysis model, the wall thickness at different positions of the honeycomb structure is designed according to a gradient to determine the design wall thickness of the honeycomb structure at different positions;

[0009] The relative density of the honeycomb structure at different positions is minimized, and the total energy absorbed per unit volume is maximized. The multi-objective grey wolf optimization algorithm is used to optimize and solve, and the Pareto solution set of the wall thickness of the honeycomb structure at the corresponding position is obtained.

[0010] According to the obtained Pareto solution set of the wall thickness of the honeycomb structure at different positions, combined with the designed wall thickness of the honeycomb structure at different positions, the optimal wall thickness of the honeycomb structure at different positions is determined, and the battery honeycomb structure is optimized based on the optimal wall thickness at different positions.

[0011] In a second aspect, the present invention provides a battery honeycomb structure optimization device, comprising:

[0012] A model building module is configured to: build a finite element analysis model according to the honeycomb structure parameters at different positions, and design the wall thickness at different positions of the honeycomb structure according to the gradient according to the built finite element analysis model to determine the design wall thickness of the honeycomb structure at different positions;

[0013] A solution module is configured to: minimize the relative density of the honeycomb structure at different positions and maximize the total energy absorbed per unit volume as the optimization objectives, use a multi-objective grey wolf optimization algorithm to perform optimization and solve, and obtain a Pareto solution set of the wall thickness of the honeycomb structure at the corresponding position;

[0014] The optimization module is configured to: determine the optimal wall thickness of the honeycomb structure at different positions according to the obtained Pareto solution set of the wall thickness of the honeycomb structure at different positions in combination with the designed wall thickness of the honeycomb structure at different positions, and optimize the battery honeycomb structure based on the optimal wall thickness at different positions.

[0015] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0017] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0018] One or more of the above technical solutions have the following beneficial effects:

[0019] In the present invention, finite element analysis models of honeycomb structures at different positions are constructed to determine the design wall thickness of the honeycomb structures at different positions; the optimization objectives are to minimize the relative density of the honeycomb structures at different positions and to maximize the total energy absorbed per unit volume, and a multi-objective grey wolf optimization algorithm is used for optimization and solution, and the Pareto solution set of the wall thickness of the corresponding honeycomb structures at different positions is obtained; the optimal wall thickness is determined based on the design wall thickness and the Pareto solution set, ensuring that the battery pack has excellent collision energy absorption capacity while maintaining lightweight, and can be widely used in fields such as electric vehicles that require high safety and lightweight.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 A schematic diagram of an implementation environment of a battery honeycomb structure optimization method provided by an embodiment of the present invention;

[0023] Figure 2 A flow chart of a method for optimizing a battery honeycomb structure provided by an embodiment of the present invention;

[0024] Figure 3 A flowchart of a method for optimizing a battery honeycomb structure provided by an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of a gradient honeycomb structure and a side view of a shell provided by an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of a gradient honeycomb structure and a battery assembly provided by an embodiment of the present invention;

[0027] Figure 6 A schematic diagram of thickness regions provided by an embodiment of the present invention;

[0028] Figure 7 A schematic diagram of the structure of a battery honeycomb structure optimization device provided by an embodiment of the present invention;

[0029] Figure 8 A structural block diagram of a terminal provided by an embodiment of the present invention;

[0030] In the figure, 1. side shell of honeycomb structure battery pack, 2. gradient honeycomb unit, 3. battery cell, 4. honeycomb assembly. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0033] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0034] Figure 1 is a schematic diagram of an implementation environment of a battery honeycomb structure optimization method provided in an embodiment of the present application, see Figure 1 The implementation environment includes: an electronic device, which can be provided as a terminal 101, or can be provided as a terminal 101 and a server 102. If the electronic device is provided as a terminal 101 and a server 102, the terminal 101 and the server 102 can be connected via a wireless or wired network. In the embodiments of the present application, the electronic device is not specifically limited.

[0035] If the electronic device is provided as a terminal 101 and a server 102, and an application for topological optimization of the motor end cover is installed on the terminal 101, the terminal 101 can use the application to perform topological optimization on the structure of the motor end cover, so that the technician can design the structure of the motor end cover according to the topological optimization result. Among them, the server 102 is the server corresponding to the application, and provides background services for the application.

[0036] The terminal 101 is at least one of a mobile phone, a tablet computer, a laptop computer, a desktop computer, a PC (Personal Computer), an intelligent voice interaction device, and a vehicle-mounted terminal. The server 102 can be at least one of a server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0037] Conventional uniform honeycomb structures have obvious advantages in energy absorption, especially in thin-walled structures with small thickness, which can effectively reduce weight and improve collision safety. However, the uniform distribution of traditional honeycomb structures during collisions leads to stress concentration in local areas, and the isotropic characteristics of the structure may lead to uneven energy distribution in certain specific collision directions, thereby affecting the overall energy absorption efficiency and stability of the structure. Therefore, this embodiment proposes a battery honeycomb structure optimization method, designs the honeycomb structure, establishes its parameterized model according to the shape characteristics of the gradient honeycomb structure, and then uses the optimal hypercube Latin experimental design to select sample points for finite element analysis, uses the multi-objective gray wolf optimization algorithm to search for optimization, and obtains the optimal reliability design solution of the gradient honeycomb structure through solution set screening and convergence judgment to significantly improve its collision safety.

[0038] like Figure 2 As shown, this embodiment discloses a battery honeycomb structure optimization method, including:

[0039] Step 1: construct a finite element analysis model according to the honeycomb structure parameters at different positions, and design the wall thickness at different positions of the honeycomb structure according to the constructed finite element analysis model according to a gradient to determine the design wall thickness of the honeycomb structure at different positions;

[0040] The specific method of using Latin optimal hypercube experimental design for gradient honeycomb structure is as follows: to consider the influence of uncertainty of design parameters, the parameter space is divided into multiple dimensions, and the value range and level are set for each parameter to generate uniformly distributed sample points. To select sample points for finite element analysis using the optimal hypercube Latin experimental design, the geometric model must first be meshed. The mesh contains nodes to form different regions. The initial honeycomb structure will have an equal wall thickness. The result obtained after analysis is the stress result. In simple terms, the region with more deformation and displacement is relatively weak, so variable thickness design should be carried out for this region in a targeted manner. A reasonable range is selected based on the initial wall thickness parameters. The optimal hypercube Latin experimental design method is used to select sample points for finite element analysis to obtain the upper and lower limits of the target optimization parameters. The upper and lower limits of the target optimization parameters are used as constraints of the multi-objective optimization algorithm to solve the multi-objective optimization. The result obtained is a Pareto solution set.

[0041] The specific method for establishing the parametric model of the gradient honeycomb structure is: selecting the number of basic cells, cell wall thickness and cell side length of the gradient honeycomb structure as design parameters, and generating the parametric model of the gradient honeycomb structure by setting the relationship between the parameters in CATIA.

[0042] First, the gradient honeycomb structure is composed of multiple honeycomb units. According to the weak areas obtained by finite element analysis, the wall thickness gradient design of the honeycomb structure is carried out; the thickness area is arranged according to the cross-sectional contour lines, and the gradient thickness distribution of different areas is according to the following formula:

[0043]

[0044] Among them, T min is the thickness of the minimum thickness area; T max is the thickness of the maximum thickness area; i is the i-th thickness area, and its value range is 1≤i≤n; n is the total number of thickness areas of the entire honeycomb structure; λ is the gradient index, which is used to control the nonlinear degree of thickness change; β is the adjustment coefficient, which controls the influence of the sine function on the thickness distribution; the introduction of the sine function To simulate periodic changes and further optimize the local adjustment of thickness.

[0045] Step 2: Taking the relative density of the honeycomb structure at different positions as the minimum and the total energy absorbed per unit volume as the maximum as the optimization objectives, the multi-objective grey wolf optimization algorithm is used to optimize and solve, and the optimal value of the wall thickness of the honeycomb structure at the corresponding position is obtained.

[0046] A new mathematical model of energy absorption rate per unit volume was established by utilizing the material properties of the honeycomb structure, which can calculate the energy absorption per unit volume of the honeycomb battery pack structure.

[0047] The formula for relative density is as follows:

[0048]

[0049]

[0050] in, is the relative density, ρ e is the equivalent density of the honeycomb structure; ρ m is the density of the honeycomb structure material; T is the thickness of the honeycomb structure; L is the side length of the cross section of the regular hexagonal honeycomb structure; α is the thickness-to-length ratio, T i ′ is the wall thickness of the honeycomb structure to be optimized in the i-th region.

[0051] The total deformation energy absorption per unit volume can be expressed as the sum of the elastic region and the plastic region under the stress-strain curve. The mathematical model is as follows:

[0052]

[0053] Among them, E total is the total energy absorbed; E e is the elastic energy per unit volume; E p is the plastic energy per unit volume; σp is the platform stress; e is the elastic strain; ε p is the plastic strain; E R is the equivalent elastic modulus of the honeycomb structure; E m is the elastic modulus of the material of the honeycomb structure.

[0054] The multi-objective optimization model is as follows:

[0055]

[0056] The multi-objective grey wolf optimization algorithm is used to search for the optimal solution, and the optimal reliability design solution of the gradient honeycomb structure is obtained through optimal solution screening and convergence judgment. Specifically:

[0057] Step 21: Initialize the population

[0058] Step 22: Update population position; Position update:

[0059]

[0060] in, represents the updated individual position; Indicates the current individual position; X best Represents the current optimal individual position; A and C are random vectors that control the update step and direction.

[0061] Step 23: Calculate each objective function value, find the non-dominated solutions to form the initial elite library, and select the three best solutions as the alpha wolf, beta wolf and delta wolf, and the rest of the wolf pack as the ω wolf; update the elite library according to the wolf pack hunting mechanism and the alpha wolf selection mechanism of the multi-objective gray wolf optimization algorithm;

[0062] Specifically, the crowding distance is calculated for the population after the position is updated; the updated population is screened according to the calculated crowding distance, and the elite library is updated; wherein the crowding distance is calculated as:

[0063]

[0064] in, represents the updated individual position; Indicates the current individual position; X best represents the current optimal individual position; i represents different types of objective functions; k represents the total number of objective functions; f represents the general term for individual objective functions in all wolf packs; f i (j+1), f i (j-1) represents the i-th objective function value of the two individuals adjacent to individual j; f imax 、f iminRepresents the maximum and minimum values ​​of each objective function.

[0065] Step 24: Update the Pareto optimal solution set;

[0066] Step 25: Iterate and update to determine whether it converges. If not, return to step 21. If it converges, end.

[0067] Step 3: According to the obtained optimal solutions of the wall thickness of the honeycomb structure at different positions, the optimal wall thickness of the honeycomb structure at different positions is determined in combination with the designed wall thickness of the honeycomb structure at different positions, and the battery honeycomb structure is optimized based on the optimal wall thickness at different positions.

[0068] Specifically, since the multi-objective optimization algorithm obtains a set of optimal Pareto solution sets, the optimal solution is selected from the Pareto solution set based on the design wall thickness obtained by formula (1). The solution whose wall thickness design value calculated by formula (1) is closest to a value in the optimal Pareto solution set is the optimal solution for wall thickness.

[0069] As 3- Figure 6 As shown, the battery includes a battery pack side shell 1, a honeycomb unit 2, and a battery cell 3. The honeycomb unit 2 presents a hollow regular hexagonal prism structure and contains a number of elastically connected honeycomb interlayers. The honeycomb unit 2 is set with different wall thicknesses according to finite element analysis and is arranged in an array. The battery cell 3 is cylindrical. The battery cell 3 is embedded in the gradient honeycomb unit to form a battery honeycomb structure integrated body 4, and the battery cell wall is exactly tangent to the wall of the honeycomb structure. The battery pack side shell 1 is connected to the edge of the honeycomb structure to form a packaging structure.

[0070] The honeycomb unit 2 is composed of a plurality of honeycomb units. The base materials of the battery pack side shell 1 and the honeycomb unit 2 are metal materials, glass fibers or composite materials. The honeycomb unit 2 is produced by a manufacturing process including welding, 3D printing and bonding. The mechanical properties of the honeycomb structure include factors such as relative density, elastic modulus and dynamic platform stress.

[0071] like Figure 7 As shown, the purpose of this embodiment is to provide a battery honeycomb structure optimization device, comprising:

[0072] A model building module is configured to: build a finite element analysis model according to the honeycomb structure parameters at different positions, and design the wall thickness at different positions of the honeycomb structure according to the gradient according to the built finite element analysis model to determine the design wall thickness of the honeycomb structure at different positions;

[0073] A solution module is configured to: minimize the relative density of the honeycomb structure at different positions and maximize the total energy absorbed per unit volume as the optimization objectives, use a multi-objective grey wolf optimization algorithm to perform optimization and solve, and obtain a Pareto solution set of the wall thickness of the honeycomb structure at the corresponding position;

[0074] The optimization module is configured to: determine the optimal wall thickness of the honeycomb structure at different positions according to the obtained Pareto solution set of the wall thickness of the honeycomb structure at different positions in combination with the designed wall thickness of the honeycomb structure at different positions, and optimize the battery honeycomb structure based on the optimal wall thickness at different positions.

[0075] refer to Figure 8 , Figure 8 The structure block diagram of a terminal 800 provided by an exemplary embodiment of the present application is shown. The terminal 800 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The terminal 800 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0076] Typically, the terminal 800 includes a processor 801 and a memory 802 .

[0077] The processor 801 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 801 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 801 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 801 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 801 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0078] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one program code, which is used to be executed by the processor 801 to implement the motor end cover optimization method provided in the method embodiment of the present application.

[0079] In some embodiments, the terminal 800 may further optionally include: a peripheral device interface 803 and at least one peripheral device. The processor 801, the memory 802 and the peripheral device interface 803 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 803 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807 and a power supply 808.

[0080] The peripheral device interface 803 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 801 and the memory 802. In some embodiments, the processor 801, the memory 802, and the peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 801, the memory 802, and the peripheral device interface 803 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0081] The radio frequency circuit 804 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 804 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 804 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 804 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 804 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 804 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0082] The display screen 805 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 805 is a touch display screen, the display screen 805 also has the ability to collect touch signals on the surface or above the surface of the display screen 805. The touch signal can be input to the processor 801 as a control signal for processing. At this time, the display screen 805 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 805 can be one, set on the front panel of the terminal 800; in other embodiments, the display screen 805 can be at least two, respectively set on different surfaces of the terminal 800 or in a folding design; in other embodiments, the display screen 805 can be a flexible display screen, set on the curved surface or folding surface of the terminal 800. Even, the display screen 805 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 805 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0083] The camera assembly 806 is used to capture images or videos. Optionally, the camera assembly 806 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 806 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0084] The audio circuit 807 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 801 for processing, or input them into the radio frequency circuit 804 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 800. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 801 or the radio frequency circuit 804 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 807 may also include a headphone jack.

[0085] The power supply 808 is used to power various components in the terminal 800. The power supply 808 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 808 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0086] In some embodiments, the terminal 800 further includes one or more sensors 809 , including but not limited to: an acceleration sensor 810 , a gyroscope sensor 811 , a pressure sensor 812 , an optical sensor 813 , and a proximity sensor 814 .

[0087] The acceleration sensor 810 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 800. For example, the acceleration sensor 810 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 801 can control the display screen 805 to display the user interface in a horizontal view or a vertical view based on the gravity acceleration signal collected by the acceleration sensor 810. The acceleration sensor 810 can also be used to collect game or user motion data.

[0088] The gyroscope sensor 811 can detect the body direction and rotation angle of the terminal 800, and the gyroscope sensor 811 can cooperate with the acceleration sensor 810 to collect the user's 3D actions on the terminal 800. Based on the data collected by the gyroscope sensor 811, the processor 801 can implement the following functions: motion sensing (such as changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0089] The pressure sensor 812 can be set on the side frame of the terminal 800 and / or the lower layer of the display screen 805. When the pressure sensor 812 is set on the side frame of the terminal 800, it can detect the user's holding signal of the terminal 800, and the processor 801 performs left and right hand recognition or shortcut operation based on the holding signal collected by the pressure sensor 812. When the pressure sensor 812 is set on the lower layer of the display screen 805, the processor 801 controls the operability controls on the UI interface based on the user's pressure operation on the display screen 805. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0090] The optical sensor 813 is used to collect the ambient light intensity. In one embodiment, the processor 801 can control the display brightness of the display screen 805 based on the ambient light intensity collected by the optical sensor 813. Specifically, when the ambient light intensity is high, the display brightness of the display screen 805 is increased; when the ambient light intensity is low, the display brightness of the display screen 805 is reduced. In another embodiment, the processor 801 can also dynamically adjust the shooting parameters of the camera component 806 based on the ambient light intensity collected by the optical sensor 813.

[0091] The proximity sensor 814, also called a distance sensor, is usually disposed on the front panel of the terminal 800. The proximity sensor 814 is used to collect the distance between the user and the front of the terminal 800. In one embodiment, when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually decreasing, the processor 801 controls the display screen 805 to switch from the screen-on state to the screen-off state; when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually increasing, the processor 801 controls the display screen 805 to switch from the screen-off state to the screen-on state.

[0092] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the terminal 800, and the terminal 800 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0093] In an exemplary embodiment, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the motor end cover optimization method in the above embodiment.

[0094] In an exemplary embodiment, a computer program product is further provided. The computer program product stores at least one program code. The at least one program code is loaded and executed by a processor to implement the motor end cover optimization method in the above embodiment.

[0095] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0096] The above description is only for the purpose of facilitating the technical solution of the present application to be understood by those skilled in the art, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing a battery honeycomb structure, characterized in that: include: According to the honeycomb structure parameters at different positions, a finite element analysis model is constructed, and according to the constructed finite element analysis model, the wall thickness at different positions of the honeycomb structure is designed according to a gradient to determine the design wall thickness of the honeycomb structure at different positions; The relative density of the honeycomb structure at different positions is minimized, and the total energy absorbed per unit volume is maximized. The multi-objective grey wolf optimization algorithm is used to optimize and solve, and the Pareto solution set of the wall thickness of the honeycomb structure at the corresponding position is obtained. According to the obtained Pareto solution set of the wall thickness of the honeycomb structure at different positions, combined with the designed wall thickness of the honeycomb structure at different positions, the optimal wall thickness of the honeycomb structure at different positions is determined, and the battery honeycomb structure is optimized based on the optimal wall thickness at different positions.

2. A battery honeycomb structure optimization method as claimed in claim 1, characterized in that: The optimization goal is to minimize the relative density of the honeycomb structure at different positions and maximize the total energy absorbed per unit volume. The specific multi-objective optimization function is: Where ρ is the relative density, T is the thickness of the honeycomb structure; L is the side length of the honeycomb structure cross section, E total is the total energy absorbed, E m is the material elastic modulus of the honeycomb structure, σ p is the dynamic platform stress, T′ i is the wall thickness of the honeycomb structure to be optimized in the i-th region.

3. A battery honeycomb structure optimization method as claimed in claim 1, characterized in that: The multi-objective grey wolf optimization algorithm is used to optimize and solve the problem, and the optimal value of the wall thickness of the honeycomb structure at the corresponding position is obtained, which is: Initialize the population; Update the population position, calculate each objective function value, find the non-dominated solutions to form the initial elite pool, and select the three best solutions as the alpha wolf, beta wolf and delta wolf, and the rest of the wolf pack as ω wolf; Update the elite database based on the wolf pack hunting mechanism and alpha wolf selection mechanism of the multi-objective gray wolf optimization algorithm; Determine whether it converges. If it converges, end the calculation and output the Pareto solution set. Otherwise, return to the step of initializing the population of the multi-objective grey wolf optimization algorithm.

4. A battery honeycomb structure optimization method as claimed in claim 3, characterized in that: The crowding distance is calculated for the population after the position is updated; the updated population is screened and the elite library is updated according to the calculated crowding distance.

5. A battery honeycomb structure optimization method as claimed in claim 1, characterized in that: According to the constructed finite element analysis model, the wall thickness of the honeycomb structure at different positions is designed according to the gradient, and the design wall thickness of the honeycomb structure at different positions is determined, specifically: Among them, T min is the thickness of the minimum thickness area; T max is the thickness of the maximum thickness area; i is the i-th thickness area, and its value range is 1≤i≤n; n is the total number of thickness areas of the entire honeycomb structure; λ is the gradient index, which is used to control the nonlinear degree of thickness change; β is the adjustment coefficient.

6. A battery honeycomb structure optimization method according to claim 1, characterized in that: According to the finite element analysis model, the upper and lower limits of the wall thickness of the honeycomb structure at different positions are determined, and the upper and lower limits of the wall thickness of the honeycomb structure at different positions are used as constraint conditions for multi-objective optimization solution.

7. A battery honeycomb structure optimization device, characterized in that: include: A model building module is configured to: build a finite element analysis model according to the honeycomb structure parameters at different positions, and design the wall thickness at different positions of the honeycomb structure according to the gradient according to the built finite element analysis model to determine the design wall thickness of the honeycomb structure at different positions; A solution module is configured to: minimize the relative density of the honeycomb structure at different positions and maximize the total energy absorbed per unit volume as the optimization objectives, use a multi-objective grey wolf optimization algorithm to perform optimization and solve, and obtain a Pareto solution set of the wall thickness of the honeycomb structure at the corresponding position; The optimization module is configured to: determine the optimal wall thickness of the honeycomb structure at different positions according to the obtained Pareto solution set of the wall thickness of the honeycomb structure at different positions in combination with the designed wall thickness of the honeycomb structure at different positions, and optimize the battery honeycomb structure based on the optimal wall thickness at different positions.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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