Material proportion determination method for conductive material in battery conductive film, and electronic device and storage medium

Abstract

A material proportion determination method for a conductive material in a battery conductive film, and an electronic device and a storage medium. The material proportion determination method comprises: determining a percolation curve model of a preset conductive film, wherein the preset conductive film comprises at least one conductive material, and the percolation curve model is a relationship curve between a preset resistance value of the preset conductive film and the material proportion of each conductive material in the preset conductive film (S110); and determining the material proportion of each conductive material in a target conductive film on the basis of a target resistance value and percolation curve model of the target conductive film (S120). By means of the present application, the material proportion of each conductive material in a target conductive film can be obtained more quickly and accurately, thereby reducing time costs and the production costs of the target conductive film.

Description

Method for determining material ratio of conductive material in battery conductive membrane, electronic device and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on May 13, 2024, with application number 202410592927.2. The entire contents of the above application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of conductive material ratios, for example, to a method for determining the material ratio of conductive materials in a battery conductive membrane, an electronic device, and a storage medium. Background Art

[0003] As the market's requirements for the comprehensive performance of batteries continue to increase, traditional conductive carbon black (Super P, SP) positive electrode conductive agents can no longer meet the needs of high energy density and high-power discharge. The future conductive agent system will be a multi-component composite system.

[0004] In recent years, with the development of high-voltage systems, each battery manufacturer has been developing positive electrode conductive agent formulas of different types and proportions. Technical issues

[0005] The formulas of positive electrode conductive agents developed by different battery manufacturers are different. The industry does not have a clear solution route, and it is more complicated to confirm the proportion of conductive materials in the conductive agent. Solution

[0006] In one aspect of the present application, a method for determining the material ratio of a conductive material in a battery conductive membrane is provided, comprising:

[0007] Determining a percolation curve model of a preset conductive diaphragm; wherein the preset conductive diaphragm includes at least one conductive material, and the percolation curve model is a curve showing the relationship between a preset resistance value of the preset conductive diaphragm and a material proportion of each conductive material in the preset conductive diaphragm;

[0008] The material proportion of each conductive material in the target conductive film is determined according to the target resistance value of the target conductive film and the percolation curve model.

[0009] In some implementations, determining a percolation curve model of a predetermined conductive membrane includes:

[0010] Obtaining multiple conductive agent formulas, and determining a preset resistance value of a corresponding preset conductive film according to each conductive agent formula;

[0011] Setting an initial conductivity weight value for each conductive material, and determining an initial percolation curve model based on the initial conductivity weight value for each conductive material, the material proportion of each conductive material in each conductive agent formula, and a preset resistance value of a preset conductive film corresponding to each conductive agent formula;

[0012] The initial conductivity weight value of each conductive material in the initial percolation curve model is adjusted to determine the percolation curve model.

[0013] In some implementations, adjusting the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model includes:

[0014] Determining the goodness of fit of the preliminary seepage curve model based on the initial seepage curve model;

[0015] Adjusting the initial conductivity weight value according to the goodness of fit of the initial percolation curve model so that the goodness of fit is equal to the best goodness of fit;

[0016] The seepage curve model is determined according to the conductivity weight value corresponding to the best fitting goodness of fit.

[0017] In some implementations, the percolation curve model is:

[0018]

[0019]

[0020] In some implementations, the preset resistance value of the preset conductive film includes a first film resistance value after coating or a second film resistance value after cold pressing.

[0021] In some implementations, the conductive material includes at least one of a single-walled carbon nanotube conductive material, a multi-walled carbon nanotube conductive material, and a carbon black conductive material;

[0022] The material proportion of single-walled carbon nanotube conductive material is 0-0.12;

[0023] The material proportion of multi-walled carbon nanotube conductive material is 0-1.05;

[0024] The material proportion of carbon black conductive material is 0-2.

[0025] Another aspect of the present application provides a device for determining the material ratio of a conductive material in a battery conductive diaphragm, comprising:

[0026] a percolation curve determination module configured to determine a percolation curve model for a predetermined conductive diaphragm; wherein the predetermined conductive diaphragm includes at least two conductive materials, and the percolation curve model is a curve showing a relationship between a predetermined resistance value of the predetermined conductive diaphragm and a material proportion of each conductive material in the predetermined conductive diaphragm;

[0027] The material proportion determination module is configured to determine the material proportion of each conductive material in the target conductive diaphragm according to the target resistance value of the target conductive diaphragm and the percolation curve model.

[0028] In some implementations, the percolation curve determination module includes:

[0029] a diaphragm resistance determination unit configured to obtain a plurality of conductive agent formulas and determine a preset resistance value of a corresponding preset conductive diaphragm according to each conductive agent formula;

[0030] an initial curve determining unit configured to set an initial conductivity weight value for each conductive material, and determine an initial percolation curve model according to the initial conductivity weight value for each conductive material, a material proportion of each conductive material in each conductive agent formula, and a preset resistance value of a preset conductive film corresponding to each conductive agent formula;

[0031] The percolation curve determining unit is configured to adjust the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model.

[0032] Another aspect of the present application provides an electronic device, the electronic device comprising:

[0033] at least one processor; and

[0034] a memory communicatively connected to at least one processor; wherein,

[0035] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for determining the material proportion of the conductive material in the battery conductive membrane of any implementation of the present application.

[0036] In another aspect of the present application, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the material ratio of the conductive material in the battery conductive membrane of any implementation of the present application when executed. Beneficial effects

[0037] The method for determining the material proportion of the conductive material in the battery conductive diaphragm designed in the present application determines the seepage curve model of the preset conductive diaphragm, and can determine the material proportion of each conductive material in the target conductive diaphragm according to the conductive performance of the target conductive diaphragm required in practice, that is, the target resistance value of the target conductive diaphragm, and the seepage curve model, so that the material proportion of each conductive material in the target conductive diaphragm is obtained more quickly and accurately, saving time cost and the production cost of the conductive diaphragm. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1 is a flow chart of a method for determining the material ratio of a conductive material in a battery conductive membrane according to some implementations of the present application;

[0039] FIG2 is a flow chart of another method for determining the material ratio of a conductive material in a battery conductive membrane according to some implementations of the present application;

[0040] FIG3 is a schematic diagram of a conductive coating percolation curve model provided according to some implementations of the present application;

[0041] FIG4 is a schematic diagram of a cold-pressed conductive percolation curve model provided according to some implementations of the present application;

[0042] FIG5 is a schematic structural diagram of a device for determining the material ratio of a conductive material in a conductive membrane of a battery according to some implementations of the present application;

[0043] FIG6 shows a schematic structural diagram of an electronic device that can be used to implement some implementations of the present application. Modes for Carrying Out the Invention

[0044] It should be noted that the terms "first", "second" etc. in the specification, claims and drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, in addition to including the process, method, system, product or equipment of a series of steps or units shown in the embodiments of the present application, other processes, methods, systems, products and equipment of this series of steps or units not clearly listed may also be included, or other steps or units inherent to these processes, methods, systems, products or equipment.

[0045] The present application embodiment provides a method for determining the material ratio of a conductive material in a battery conductive diaphragm. FIG1 is a flow chart of a method for determining the material ratio of a conductive material in a battery conductive diaphragm according to an embodiment of the present application. Referring to FIG1 , the method for determining the material ratio of a conductive material in a battery conductive diaphragm includes:

[0046] S110, determining a percolation curve model of a preset conductive membrane; wherein the preset conductive membrane includes at least one conductive material, and the percolation curve model is a curve showing the relationship between a preset resistance value of the preset conductive membrane and a material proportion of each conductive material in the preset conductive membrane.

[0047] Here, at least one can be understood as one or more than one, and the embodiments of the present application are not limited to this. For example, the conductive diaphragm can include single-walled carbon nanotube (SWCNT) conductive materials, multi-walled carbon nanotube (MWCNT) conductive materials, array conductive materials, conductive graphite, graphene, carbon black conductive materials, etc., wherein carbon black conductive materials include superconducting carbon black materials, acetylene black materials, Ketjen black materials, etc. The method for determining the material proportion of conductive materials in a battery conductive diaphragm designed in the embodiments of the present application can be applied to lithium-ion batteries, solar cells, conductive ceramics and other fields.

[0048] Exemplarily, a plurality of conductive agent formulas can be obtained, and the preset resistance value of the corresponding preset conductive membrane can be determined according to each conductive agent formula; and the initial conductive weight value of each conductive material can be set, and the initial seepage curve model can be determined according to the initial conductive weight value of each conductive material, the material proportion of each conductive material in each conductive agent formula and the preset resistance value of the preset conductive membrane corresponding to each conductive agent formula; the initial conductive weight value of each conductive material in the initial seepage curve model can be adjusted to determine the seepage curve model.

[0049] S120 , determining the material proportion of each conductive material in the target conductive film according to the target resistance value of the target conductive film and the percolation curve model.

[0050] For example, the percolation curve model can be used to determine the material ratio of each conductive material in the target conductive diaphragm based on the target resistance value of the target conductive diaphragm and the actual application scenario, namely, processing performance requirements, electrical performance requirements, cost requirements, etc. For example, the inflection point of the percolation curve model, namely the percolation threshold, can be used to assist in determining the optimal addition amount of each conductive material, thereby achieving cost savings and achieving better conductive performance. When production costs need to be considered, it is also possible to combine the percolation curve model with the selection of conductive materials with lower costs while ensuring the conductive performance of the target conductive diaphragm.

[0051] The method for determining the material proportion of the conductive material in the battery conductive diaphragm designed in the embodiment of the present application determines the seepage curve model of the preset conductive diaphragm, and can determine the material proportion of each conductive material in the target conductive diaphragm according to the conductive performance of the target conductive diaphragm required in practice, that is, the target resistance value of the target conductive diaphragm, and the seepage curve model. The embodiment of the present application can determine the material proportion of each conductive material in the target conductive diaphragm according to the seepage curve model of the preset conductive diaphragm and the target resistance value of the target conductive diaphragm, so that the material proportion of each conductive material in the target conductive diaphragm is obtained more quickly and accurately, saving time cost and production cost of the target conductive diaphragm.

[0052] FIG2 is a flow chart of another method for determining the material ratio of a conductive material in a battery conductive membrane according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0053] S210 , obtaining multiple conductive agent formulas, and determining a preset resistance value of a corresponding preset conductive film according to each conductive agent formula.

[0054] For example, a plurality of different conductive agent formulas can be designed, and the preset resistance value of the corresponding preset conductive film can be measured according to each conductive agent formula, or a plurality of different conductive agent formulas can be extracted based on existing historical data, and the preset resistance value of the preset conductive film corresponding to each conductive agent formula can be determined.

[0055] S220, setting an initial conductivity weight value for each conductive material, and determining an initial percolation curve model according to the initial conductivity weight value for each conductive material, the material proportion of each conductive material in each conductive agent formula, and the preset resistance value of the preset conductive membrane corresponding to each conductive agent formula.

[0056] For example, the seepage curve model may be:

[0057]

[0058]

[0059]

[0060] S230: Adjust the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model.

[0061] For example, the goodness of fit of the initial percolation curve model can be determined based on the initial percolation curve model, and the initial conductivity weight value can be adjusted based on the goodness of fit of the initial percolation curve model so that the goodness of fit is equal to the best fit goodness of fit. The percolation curve model is then determined based on the conductivity weight value corresponding to the best fit goodness of fit. The best fit goodness of fit can be set based on actual conditions and is not limited in this embodiment of the present application.

[0062] S240 : Determine the material proportion of each conductive material in the target conductive film according to the target resistance value of the target conductive film and the percolation curve model.

[0063] The execution steps and effects of S240 can be found in S120 and will not be described in detail here.

[0064] The method for determining the material proportion of the conductive material in the conductive diaphragm of a battery designed in an embodiment of the present application obtains a plurality of conductive agent formulas, and determines the preset resistance value of the corresponding preset conductive diaphragm according to each conductive agent formula, sets the initial conductive weight value of each conductive material, determines the initial percolation curve model according to the initial conductive weight value of each conductive material, the material proportion of each conductive material in each conductive agent formula, and the preset resistance value of the preset conductive diaphragm corresponding to each conductive agent formula, adjusts the initial conductive weight value of each conductive material in the initial percolation curve model, determines the percolation curve model, and determines the material proportion of each conductive material in the target conductive diaphragm according to the target resistance value of the target conductive diaphragm and the percolation curve model. The embodiment of the present application can determine the material proportion of each conductive material in the target conductive diaphragm according to the percolation curve model of the preset conductive diaphragm and the target resistance value of the target conductive diaphragm, so that the material proportion of each conductive material in the target conductive diaphragm is obtained more quickly and accurately, saving time cost and production cost of the target conductive diaphragm.

[0065] In one embodiment, adjusting the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model includes:

[0066] determining the goodness of fit of the initial seepage curve model according to the initial seepage curve model;

[0067] Adjusting the initial conductivity weight value of each conductive material according to the goodness of fit of the initial percolation curve model so that the goodness of fit is equal to the best goodness of fit;

[0068] The seepage curve model is determined according to the conductivity weight value corresponding to the best fitting goodness of fit.

[0069] Exemplarily, the goodness of fit of the initial percolation curve model can be determined based on the initial percolation curve model, and it is judged whether the current goodness of fit is equal to the best goodness of fit. If the current goodness of fit is equal to the best goodness of fit, the percolation curve model is determined based on the conductive weight value corresponding to the best goodness of fit. If the current goodness of fit is not equal to the best goodness of fit, the initial conductive weight value of each conductive material is adjusted based on the goodness of fit of the initial percolation curve model. After adjusting the initial conductive weight value, a new percolation curve model is determined based on the adjusted conductive weight value, and the new percolation curve model is fitted to determine the goodness of fit of the new percolation curve model, and it is judged whether the current goodness of fit is equal to the best goodness of fit. If the current goodness of fit is equal to the best goodness of fit, the percolation curve model is determined based on the conductive weight value corresponding to the best goodness of fit. If the current goodness of fit is not equal to the best goodness of fit, the conductive weight value of each conductive material is continued to be adjusted based on the goodness of fit of the current percolation curve model until the goodness of fit of the percolation curve model is equal to the best goodness of fit, and the percolation curve model is determined based on the conductive weight value corresponding to the best goodness of fit.

[0070] In one embodiment, the seepage curve model is:

[0071]

[0072]

[0073] After the coating treatment, the preset resistance value of the preset conductive diaphragm after the coating treatment can be measured, and the coating conductive seepage curve model can be determined according to the method for determining the seepage curve model. For example, Figure 3 is a schematic diagram of a coating conductive seepage curve model provided according to an embodiment of the present application. Referring to Figure 3, the coating conductive seepage curve model can be determined by the 17 data points shown in Figure 3; after the cold pressing treatment, the preset resistance value of the preset conductive diaphragm after the cold pressing treatment can be measured, and the cold pressed conductive seepage curve model can be determined according to the method for determining the seepage curve model. For example, Figure 4 is a schematic diagram of a cold pressed conductive seepage curve model provided according to an embodiment of the present application. Referring to Figure 4, the cold pressed conductive seepage curve model can be determined by the 17 data points shown in Figure 4.

[0074] In one embodiment, the predetermined resistance value of the predetermined conductive film includes a first film resistance value after coating or a second film resistance value after cold pressing.

[0075] In one embodiment, the conductive material includes at least one of a single-walled carbon nanotube (SWCNT) conductive material, a multi-walled carbon nanotube (MWCNT) conductive material, and a carbon black (SP) conductive material;

[0076] The material proportion of single-walled carbon nanotube conductive material is 0-0.12;

[0077] The material proportion of multi-walled carbon nanotube conductive material is 0-1.05;

[0078] The proportion of carbon black conductive material is material 0-2.

[0079] For example, the present embodiment provides a positive electrode conductive agent formulation based on at least one of a single-walled carbon nanotube (SWCNT) conductive material, a multi-walled carbon nanotube (MWCNT) conductive material, and a carbon black (SP) conductive material. Table 1 shows a formulation ratio table of a positive electrode plate after coating treatment, and Table 2 shows a formulation ratio table of a positive electrode plate after cold pressing treatment. The formulation composition of the positive electrode plate is shown in the following table:

[0080] Table 1 is a formula ratio table of a positive electrode after coating treatment

[0081]

[0082] Table 2 is a formula ratio table of a positive electrode after cold pressing treatment

[0083]

[0084]

[0085] After determining the resistance of the coated conductive film, the conductive coating seepage curve model can be determined by combining the values ​​in the formula ratio table of the positive electrode after coating through the percolation curve determination method. For example, the curve shown in FIG3 is:

[0086]

[0087] According to the conductive weight values ​​of SWCNT, MWCNT and SP in the percolation curve, the replacement ratio of the conductive agents SWCNT, MWCNT and SP is determined to be 1:6.08:22.8. As shown in Table 1, the resistance value of the coated film calculated by the percolation curve model shown in FIG3 is slightly different from the measured resistance value of the coated film, that is, the quasi-optimality of the coating percolation curve model is good. Therefore, the material proportion of the conductive material in the conductive film can be determined by the coating conductive percolation curve model shown in FIG3. According to the inflection point of the coating percolation curve, it can be judged that schemes 4 / 6 / 7 / 9 / 14 / 15 / 16 reach the percolation threshold. The appropriate scheme can be selected according to the electrical performance requirements and production cost requirements. After determining the resistance of the cold-pressed conductive film, the cold-pressed conductive percolation curve model can be determined by the percolation curve determination method. For example, the curve shown in FIG4 is:

[0088]

[0089] Based on the conductivity weights of SWCNT, MWCNT, and SP in the percolation curves, the replacement ratio of the conductive agents SWCNT, MWCNT, and SP is determined to be 1:6.5:11.7. Table 2 shows that the cold-pressed diaphragm resistance calculated using the percolation curve model shown in Figure 4 is closely aligned with the measured cold-pressed diaphragm resistance, indicating a good fit to the cold-pressed percolation curve model. Therefore, the cold-pressed conductive percolation curve model shown in Figure 4 can be used to determine the proportion of conductive material in the conductive diaphragm. Based on the inflection points of the cold-pressed percolation curves, Schemes 1, 6, 8, 9, 12, 14, and 15 can be determined to have reached the percolation threshold. The appropriate scheme can be selected based on electrical performance and production cost requirements. Combining the coating percolation curves with the cold-pressed percolation curves reveals that Schemes 6, 9, 14, and 15 exhibit superior conductivity. Based on the electrical performance and production cost requirements, the scheme that meets practical needs can be selected.

[0090] The present application provides an apparatus for determining the material ratio of a conductive material in a battery conductive diaphragm. FIG5 is a schematic structural diagram of an apparatus for determining the material ratio of a conductive material in a battery conductive diaphragm according to an embodiment of the present application. Referring to FIG5 , the apparatus 300 for determining the material ratio of a conductive material in a conductive diaphragm includes:

[0091] A percolation curve determining module 310 is configured to determine a percolation curve model for a predetermined conductive diaphragm; wherein the predetermined conductive diaphragm includes at least one conductive material, and the percolation curve model is a curve showing a relationship between a predetermined resistance value of the predetermined conductive diaphragm and a material proportion of each conductive material in the predetermined conductive diaphragm;

[0092] The material proportion determination module 320 is configured to determine the material proportion of each conductive material in the target conductive film according to the target resistance value of the target conductive film and the percolation curve model.

[0093] In one embodiment, the percolation curve determination module 310 includes:

[0094] a diaphragm resistance determination unit configured to obtain a plurality of conductive agent formulas and determine a preset resistance value of a corresponding preset conductive diaphragm according to each conductive agent formula;

[0095] an initial curve determining unit configured to set an initial conductivity weight value for each conductive material, and determine an initial percolation curve model according to the initial conductivity weight value for each conductive material, a material proportion of each conductive material in each conductive agent formula, and a preset resistance value of a preset conductive film corresponding to each conductive agent formula;

[0096] The percolation curve determining unit is configured to adjust the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model.

[0097] In one embodiment, the permeation curve determining unit includes:

[0098] a goodness of fit determination subunit, configured to determine the goodness of fit of the preliminary seepage curve model according to the initial seepage curve model;

[0099] a best fit goodness of fit determination subunit, configured to adjust the initial conductivity weight value according to the goodness of fit of the initial percolation curve model so that the goodness of fit is equal to the best fit goodness of fit;

[0100] The percolation curve determination subunit is configured to determine the percolation curve model according to the conductive weight value corresponding to the best fit goodness of fit.

[0101] In one embodiment, the seepage curve model is:

[0102]

[0103]

[0104] In one embodiment, the predetermined resistance value of the predetermined conductive film includes a first film resistance value after coating or a second film resistance value after cold pressing.

[0105] In one embodiment, the conductive material includes at least one of a single-walled carbon nanotube conductive material, a multi-walled carbon nanotube conductive material, and a carbon black conductive material;

[0106] The material proportion of single-walled carbon nanotube conductive material is 0-0.12;

[0107] The material proportion of multi-walled carbon nanotube conductive material is 0-1.05;

[0108] The material proportion of carbon black conductive material is 0-2.

[0109] The device for determining the material proportion of the conductive material in the battery conductive membrane provided in the embodiment of the present application can execute the method for determining the material proportion of the conductive material in the battery conductive membrane provided in any embodiment of the present application, and has the functional modules and effects corresponding to the execution method.

[0110] FIG6 shows a schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present application. The electronic device can be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also be a mobile device in various forms, such as a personal digital assistant, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0111] As shown in Figure 6, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0112] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0113] Processor 11 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for determining the material ratio of the conductive material in the conductive film.

[0114] In some embodiments, the method for determining the material ratio of the conductive material in the conductive film can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the material ratio of the conductive material in the conductive film described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining the material ratio of the conductive material in the conductive film by any other appropriate means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A machine-readable storage medium may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0118] To provide for user interaction, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) configured to display information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be configured to provide for user interaction; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0119] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the Internet.

[0120] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and virtual private server (VPS) services.

[0121] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the multiple steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

Claims

1. A method for determining the material ratio of a conductive material in a battery conductive membrane, comprising: Determine a percolation curve model of a preset conductive diaphragm; wherein the preset conductive diaphragm includes at least one conductive material, and the percolation curve model is a relationship curve between a preset resistance value of the preset conductive diaphragm and a material proportion of each conductive material in the preset conductive diaphragm; The material proportion of each conductive material in the target conductive film is determined according to the target resistance value of the target conductive film and the percolation curve model.

2. The method for determining the material ratio of the conductive material in the battery conductive membrane according to claim 1, wherein: The step of determining the seepage curve model of the preset conductive membrane comprises: Acquire multiple conductive agent formulas, and determine a corresponding preset resistance value of the preset conductive film according to each conductive agent formula; Setting an initial conductivity weight value for each conductive material, and determining an initial percolation curve model according to the initial conductivity weight value for each conductive material, the material proportion of each conductive material in each conductive agent formula, and a preset resistance value of a preset conductive film corresponding to each conductive agent formula; The initial conductivity weight value of each conductive material in the initial percolation curve model is adjusted to determine the percolation curve model.

3. The method for determining the material proportion of the conductive material in the battery conductive membrane according to claim 2, wherein: The step of adjusting the initial conductivity weight value of each conductive material in the initial percolation curve model to determine the percolation curve model includes: Determining the goodness of fit of the initial seepage curve model according to the initial seepage curve model; Adjusting the initial conductivity weight value according to the goodness of fit of the initial percolation curve model so that the goodness of fit is equal to the best goodness of fit; The percolation curve model is determined according to the conductivity weight value corresponding to the best fit goodness of fit.

4. The method for determining the material proportion of the conductive material in the battery conductive membrane according to claim 1, wherein: The seepage curve model is:

5. The method for determining the material proportion of the conductive material in the battery conductive membrane according to claim 1, wherein: The preset resistance value of the preset conductive film includes a first film resistance value after coating or a second film resistance value after cold pressing.

6. The method for determining the material proportion of the conductive material in the battery conductive membrane according to claim 2, wherein: The conductive material comprises at least one of a single-walled carbon nanotube conductive material, a multi-walled carbon nanotube conductive material and a carbon black conductive material; The material proportion of the single-walled carbon nanotube conductive material is 0-0.15; The material proportion of the multi-walled carbon nanotube conductive material is 0-1.5; The carbon black conductive material accounts for 0-2%.

7. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the material proportion of the conductive material in the battery conductive membrane according to any one of claims 1-6.

8. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the material ratio of the conductive material in the battery conductive membrane according to any one of claims 1 to 6 when executed.

9. A device for determining the material proportion of a conductive material in a battery conductive membrane, comprising: A percolation curve determination module, configured to determine a percolation curve model of a preset conductive diaphragm; wherein the preset conductive diaphragm includes at least one conductive material, and the percolation curve model is a relationship curve between a preset resistance value of the preset conductive diaphragm and a material proportion of each conductive material in the preset conductive diaphragm; The material proportion determination module is configured to determine the material proportion of each conductive material in the target conductive film according to the target resistance value of the target conductive film and the percolation curve model.

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