Electrode material screening method, apparatus, device, and storage medium

Abstract

The present disclosure provides an electrode material screening method, device, equipment and storage medium, and relates to the technical field of batteries. The method comprises: obtaining porosity of an electrode sheet and a plurality of particle size distribution electrode materials; generating a plurality of virtual electrodes according to the porosity of the electrode sheet and the plurality of particle size distribution electrode materials, calculating tortuosity of each virtual electrode, and one virtual electrode corresponding to one particle size distribution electrode material; calculating Bruggeman coefficients of each virtual electrode according to the porosity of the electrode sheet and the tortuosity of each virtual electrode; calculating charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficients of each virtual electrode; if the charge-discharge performance of a target virtual electrode meets a preset condition, the particle size distribution of the electrode material corresponding to the target virtual electrode is determined as an optimal particle size distribution, the target virtual electrode is any one of the plurality of virtual electrodes, the material particle size screening is realized, the research and development cost is reduced, and the research and development efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of batteries, and in particular to an electrode material screening method, an electrode material screening device, an electronic device, and a computer-readable storage medium. BACKGROUND

[0002] In the technical field of batteries, the charge-discharge capacity of an electrode is one of the core indicators for measuring the performance of a battery, and directly determines the endurance, service life, and application scenarios of the battery. The particle size distribution of electrode materials affects the charge-discharge capacity of the electrode.

[0003] In related technologies, the particle size ratio of positive and negative electrode materials of a battery is usually screened through a large number of experimental tests. However, the above-mentioned material particle size screening method has the problems of high cost and long cycle. SUMMARY

[0004] The present disclosure provides an electrode material screening method, device, equipment, and storage medium, which at least partially solves the problems of high cost and long cycle in the electrode material screening method of related technologies.

[0005] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a production electrode material screening method is provided, including: obtaining a porosity of an electrode sheet and a plurality of particle size distributions of electrode materials; generating a plurality of virtual electrodes according to the porosity of the electrode sheet and the plurality of particle size distributions of the electrode materials, calculating a tortuosity of each of the virtual electrodes, wherein one of the virtual electrodes corresponds to one of the particle size distributions of the electrode materials; calculating a Bruggeman coefficient of each of the virtual electrodes according to the porosity of the electrode sheet and the tortuosity of each of the virtual electrodes; calculating a charge-discharge performance of each of the virtual electrodes according to the particle size distribution of each of the electrode materials and the Bruggeman coefficient of each of the virtual electrodes; and determining a particle size distribution of an electrode material corresponding to a target virtual electrode as an optimal particle size distribution if the charge-discharge performance of the target virtual electrode meets a preset condition, the target virtual electrode being any one of the plurality of virtual electrodes.

[0007] In one embodiment of the present disclosure, the tortuosity of each of the virtual electrodes is calculated, including: simulating a motion path of an electrode material particle in a corresponding virtual electrode, and counting a mean square displacement of the electrode material particle in the corresponding virtual electrode; and calculating the tortuosity of each of the virtual electrodes according to the mean square displacement.

[0008] In one embodiment of the present disclosure, the motion path of the simulated electrode material particles in the corresponding virtual electrode, and the statistical mean square displacement of the electrode material particles in the corresponding virtual electrode, comprise: simulating the motion path of the electrode material particles in the corresponding virtual electrode by using a Monte Carlo algorithm or a random walk algorithm, and statistically calculating the mean square displacement of the electrode material particles in the corresponding virtual electrode.

[0009] In one embodiment of the present disclosure, the calculation of the tortuosity of each virtual electrode according to the mean square displacement comprises: determining the effective diffusion coefficient of the electrode material particles according to the mean square displacement of the electrode material particles in the corresponding virtual electrode; and calculating the tortuosity of each virtual electrode according to the free diffusion coefficient and the effective diffusion coefficient of the electrode material particles.

[0010] In one embodiment of the present disclosure, the calculation of the charge and discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode comprises: calculating the charge and discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode based on an electrochemical model; wherein the electrochemical model comprises at least one of a multi-particle DFN model, a deep learning model, and a three-dimensional model based on tomographic image reconstruction.

[0011] In one embodiment of the present disclosure, the charge and discharge capacity of the virtual electrode comprises at least one of a charge capacity, a discharge capacity, and an energy efficiency; wherein the charge and discharge performance of the target virtual electrode satisfies a preset condition, comprising: the charge capacity of the target virtual electrode is the minimum value of the charge capacities of the plurality of virtual electrodes; the discharge capacity of the target virtual electrode is the maximum value of the discharge capacities of the plurality of virtual electrodes; and / or the energy efficiency of the target virtual electrode is the maximum value of the energy efficiencies of the plurality of virtual electrodes, wherein the energy efficiency of the virtual electrode is determined according to the charge capacity and the discharge capacity of the virtual electrode.

[0012] In one embodiment of the present disclosure, the generation of a plurality of virtual electrodes according to the porosity of the pole piece and a plurality of particle size distributions of electrode materials comprises: generating a virtual electrode structure according to the porosity of the pole piece; initializing electrode material particles according to a plurality of particle size distributions of electrode materials; and randomly releasing the electrode material particles into the virtual electrode structure to generate a plurality of virtual electrodes.

[0013] In one embodiment of the present disclosure, the Bruggeman coefficient of each virtual electrode is determined by the following formula: ; wherein, is the tortuosity of the virtual electrode; is the porosity of the pole piece; is the Bruggeman coefficient of the virtual electrode.

[0014] According to another aspect of the present disclosure, there is provided an electrode material screening device, comprising: an acquisition module configured to acquire porosities of an electrode tab and electrode materials of a plurality of particle size distributions; a first calculation module configured to generate a plurality of virtual electrodes according to the porosities of the electrode tab and the electrode materials of the plurality of particle size distributions, and calculate tortuosity of each of the virtual electrodes, wherein one of the virtual electrodes corresponds to one of the electrode materials of the plurality of particle size distributions; a second calculation module configured to calculate Bruggeman coefficients of each of the virtual electrodes according to the porosities of the electrode tab and the tortuosity of each of the virtual electrodes; a third calculation module configured to calculate charge-discharge performance of each of the virtual electrodes according to the particle size distribution of each of the electrode materials and the Bruggeman coefficients of each of the virtual electrodes; and a material screening module configured to determine a particle size distribution of an electrode material corresponding to a target virtual electrode as an optimal particle size distribution if the charge-discharge performance of the target virtual electrode meets a preset condition, the target virtual electrode being any one of the plurality of virtual electrodes.

[0015] According to another aspect of the present disclosure, there is also provided an electronic device, comprising: a processor; a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the electrode material screening method of any one of the above aspects via execution of the executable instructions.

[0016] According to another aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the electrode material screening method of any one of the above aspects.

[0017] According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, the computer program being executed by a processor to implement the electrode material screening method of any one of the above aspects.

[0018] In the embodiments of the present disclosure, the porosities of an electrode tab and electrode materials of a plurality of particle size distributions are acquired; a plurality of virtual electrodes are generated according to the porosities of the electrode tab and the electrode materials of the plurality of particle size distributions, and tortuosity of each of the virtual electrodes is calculated, one of the virtual electrodes corresponding to one of the electrode materials of the plurality of particle size distributions; Bruggeman coefficients of each of the virtual electrodes are calculated according to the porosities of the electrode tab and the tortuosity of each of the virtual electrodes; charge-discharge performance of each of the virtual electrodes is calculated according to the particle size distribution of each of the electrode materials and the Bruggeman coefficients of each of the virtual electrodes; and a particle size distribution of an electrode material corresponding to a target virtual electrode is determined as an optimal particle size distribution if the charge-discharge performance of the target virtual electrode meets a preset condition, the target virtual electrode being any one of the plurality of virtual electrodes. The present disclosure can quickly realize material particle size screening, reduce research and development costs, and improve research and development efficiency by using virtual electrodes in combination with simulation technology.

[0019] It should be understood that the general description and detailed description below are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings hereof incorporated in and constituting a part of this specification illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those of ordinary skill in the art without creative labor on the basis of these drawings.

[0021] Figure 1 A structural schematic diagram of an energy storage system according to an embodiment of the present disclosure is shown.

[0022] Figure 2 A flowchart of an electrode material screening method according to an embodiment of the present disclosure is shown.

[0023] Figure 3 A flowchart of a virtual electrode generation method according to an embodiment of the present disclosure is shown.

[0024] Figure 4 A flowchart of a tortuosity calculation method of a virtual electrode according to an embodiment of the present disclosure is shown.

[0025] Figure 5 A flowchart of another tortuosity calculation method of a virtual electrode according to an embodiment of the present disclosure is shown.

[0026] Figure 6 A flowchart of another electrode material screening method according to an embodiment of the present disclosure is shown.

[0027] Figure 7 A structural schematic diagram of a virtual electrode according to an embodiment of the present disclosure is shown.

[0028] Figure 8 An electrode material particle size distribution diagram according to an embodiment of the present disclosure is shown.

[0029] Figure 9 A structural schematic diagram of an electrode material screening device according to an embodiment of the present disclosure is shown.

[0030] Figure 10 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0031] Example implementations will now be described with reference to the drawings. Example implementations can, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive concept to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations in any suitable manner.

[0032] Moreover, the drawings are not necessarily to scale. Like reference numerals can be used to denote like parts throughout the various illustrations. Some of the blocks in the drawings represent functional entities that can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0033] Since the energy required by people has strong time and space, in order to reasonably use energy and improve the utilization rate of energy, it is necessary to store one form of energy into the same or another form of energy through a medium or device, and release it in a specific energy form based on future application needs. At present, the main way to generate green electricity is to develop photovoltaic, wind power and other green energy to replace fossil energy.

[0034] At present, the generation of green electricity generally depends on photovoltaic, wind power, water potential, etc., while wind energy and solar energy have strong intermittency and large fluctuation, which will cause the instability of power grid, insufficient electricity at peak electricity demand, and too much electricity at low electricity demand. Unstable voltage will also cause damage to electricity, so it may cause "abandoning wind and light" due to insufficient electricity demand or insufficient grid acceptance capacity. To solve these problems, it is necessary to rely on energy storage. That is, the electricity is stored in other forms of energy through physical or chemical means, and the energy is converted into electricity and released when needed. In simple terms, energy storage is similar to a large "power bank", which stores electricity when photovoltaic and wind energy is sufficient, and releases the stored electricity when needed.

[0035] Taking electrochemical energy storage as an example, the present scheme provides an energy storage device 110 applied to an energy storage system 100, which is provided with a group of chemical batteries inside. The main function is to use the chemical elements in the battery as energy storage medium. The charging and discharging process is accompanied by chemical reaction or change of the energy storage medium. In simple terms, the electricity generated by wind and solar energy is stored in the chemical battery, and the stored electricity is released for use when the external electricity use reaches the peak, or transferred to places where electricity is in short supply for use.

[0036] Current energy storage applications are widely used, including energy storage on the power generation side, the power grid side, and the power consumption side. The corresponding energy storage devices 110 include:

[0037] (1) Large-scale energy storage power stations (including multiple energy storage prefabricated cabins) applied on the side of wind power and photovoltaic power stations can assist renewable energy power generation to meet grid connection requirements and improve renewable energy utilization. As high-quality active / reactive power regulation power sources on the power supply side, energy storage power stations can realize load matching in time and space, enhance renewable energy consumption capacity, reduce instantaneous power changes, reduce the impact on the power grid, and improve new energy power generation consumption. It is of great significance in terms of power grid system backup, relieving peak load power supply pressure, and peak regulation.

[0038] (2) Energy storage prefabricated cabins applied on the power grid side mainly function as peak regulation, frequency regulation, and congestion relief. They can realize peak clipping and valley filling of power consumption, i.e., charging energy storage batteries during low power consumption load valleys and releasing stored power during high power consumption load peaks, thereby balancing power production and consumption.

[0039] (3) Small-scale energy storage cabinets applied on the power consumption side mainly function as power self-generation and self-use, peak-valley price difference arbitrage, capacity cost management, and improvement of power supply reliability. According to different application scenarios, power consumption side energy storage can be divided into industrial and commercial energy storage cabinets, household energy storage devices, and energy storage charging piles, which are generally used with distributed photovoltaic power. Industrial and commercial users can use energy storage for peak-valley price difference arbitrage and capacity cost management. In the electricity market implementing peak-valley electricity prices, the energy storage system is charged during low electricity prices and discharged during high electricity prices to realize peak-valley price difference arbitrage and reduce electricity costs. In addition, industrial enterprises using two-part electricity prices can use the energy storage system to store energy during low power consumption and discharge during peak load, thereby reducing peak power and the maximum demand amount reported, achieving the purpose of reducing capacity electricity costs. Household photovoltaic power with storage can improve the level of power self-generation and self-use. Due to high electricity prices and poor power supply stability, household photovoltaic power demand is driven. Considering that photovoltaic power is generated during the day and users generally have high load at night, configuring energy storage can better utilize photovoltaic power and improve the level of self-generation and self-use while reducing electricity costs. In addition, communication base stations and data centers need to configure energy storage for backup power.

[0040] In some embodiments, please refer to Figure 1 , Figure 1 the structure of the energy storage system 100 of an embodiment of the present application Figure 1 , and the present application Figure 1 embodiments are described by taking the shared energy storage scenario on the power generation and distribution side as an example. The energy storage device of the present application is limited to an energy storage prefabricated cabin in the power generation and distribution point energy storage scenario.

[0041] The application provides a kind of energy storage system 100, the energy storage system 100 includes: high voltage cable 120, first electric energy conversion device 130, second electric energy conversion device 140 and the energy storage device 110 provided by the application, in some embodiments of power generation side scene, second electric energy conversion device 140 can be wind power electric energy conversion device, since the fluctuation, randomness and intermittence of wind power electric energy conversion generated electric energy, can be stored to energy storage device by grid connection first, the unstable electric energy output by wind power electric energy conversion device, energy storage device is connected with high voltage cable 120 and exports smooth electric energy to supply distribution network for use, realize peak shaving and frequency modulation, grid stable operation;Or, wind power electric energy conversion device is always connected with high voltage cable 120, and the electric energy output by wind power electric energy conversion device is supplied to distribution network for use by high voltage cable in ordinary power generation, and when current electricity load is low, wind power electric energy conversion device generates excess, first store the electric quantity of overproduction to energy storage device 110, reduce the rate of wind curtailment, improve new energy power generation consumption problem;And when electricity load is high, grid issues instructions, and the electric quantity stored in energy storage device 110 is transmitted to power side for use by grid connection mode with high voltage cable 120, provides peak shaving, frequency modulation, backup and other services for grid operation, fully plays the role of grid peak shaving, promotes grid peak clipping, and relieves grid power supply pressure.

[0042] In some embodiments of distribution network side, first electric energy conversion device 130 can be photovoltaic panel, energy storage device 110 is connected with high voltage cable 120 and is installed between downstream of high voltage cable 120 and user load, and the electric energy output by photovoltaic panel is stored in energy storage device 110, which can be used as backup power source in time when grid / distribution network fails;Or, when high voltage cable 120 transmission line appears line congestion, provide power support to delay economic pressure generated by grid / distribution expansion.

[0043] Optionally, first electric energy conversion device 130 can include but is not limited to photovoltaic panel, and second electric energy conversion device 140 can include but is not limited to wind power electric energy conversion device, and the first electric energy conversion device 130 and the second electric energy conversion device 140 can convert at least one of solar energy, light energy, wind energy, heat energy, tidal energy, biomass energy and mechanical energy into electric energy.

[0044] Optionally, energy storage device 110 can include but is not limited to energy storage application scenarios such as energy storage power station, hydroelectric power / wind power generation system, solar power generation system, mobile power system, smart home system or temporary power supply system, and is also applied to data center, military equipment, aerospace, charging pile, electric vehicle and other fields.

[0045] Optionally, the energy storage device 110 can include a battery module, a battery pack, a battery cluster, a mobile power supply, a battery storage cabinet / battery storage prefabricated cabin, and the like battery integrated system composed of single batteries. The actual application form of the energy storage device 110 provided in the embodiments of the present application can be, but is not limited to, the listed products, and can also be other application forms. The embodiments of the present application do not strictly limit the application form of the energy storage device 110.

[0046] Optionally, the single battery is not limited to at least one of a cylindrical battery, a square battery, a prismatic battery, or other shaped batteries.

[0047] Optionally, the single battery can be a secondary battery, which refers to a single battery that can be activated by charging after discharging to continue to use. The single battery can be a lithium ion battery, a sodium ion battery, a sodium lithium ion battery, a lithium metal battery, a sodium metal battery, a lithium sulfur battery, a magnesium ion battery, a nickel-hydrogen battery, a nickel-cadmium battery, a lead-acid battery, and the like, which is not limited in the present application.

[0048] The charge and discharge capacity of the battery refers to the amount of electricity that the electrode can store or release under certain conditions (such as current density, temperature, cycle number, etc.). The charge and discharge capacity of the electrode is not a fixed value, which is affected by many factors such as electrode material, battery structure, process, and use conditions. For example, in terms of electrode material, active material is the core of electrode storage and discharge of electricity, and theoretical capacity and utilization rate directly determine the upper limit of electrode capacity.

[0049] The particle size distribution of the active material has a crucial influence on the charge and discharge capacity of the electrode. A too wide particle size distribution or a mismatched particle size leads to uneven electrode structure. Large particles cause a smaller specific surface area, which limits the lithium ion migration rate, making it difficult to fully develop the capacity, especially at high-speed charging and discharging; while a large number of small particles can provide more active sites, shorten the ion diffusion path, and improve the rate performance, but their high specific surface area will exacerbate side reactions, consume more electrolyte and form a thicker unstable solid electrolyte interface film, resulting in increased irreversible capacity loss and reduced cycle life. Therefore, optimizing the particle size distribution of the active material to achieve a suitable combination of a narrow and appropriate size of particles is the key to achieving high capacity, high rate, and long cycle life.

[0050] In the related art, the particle size ratio of the positive and negative electrode materials of the battery is usually selected through a large number of experimental tests. However, the above-mentioned particle size selection method of the positive and negative electrode materials has the problems of high research and development cost and long cycle.

[0051] To solve at least part of the above technical problems, the electrode material screening method provided by the embodiments of the present disclosure includes the following steps S200-S208.

[0052] The electrode material screening method provided by the embodiments of the present disclosure can be executed by any electronic device with computing processing capability, such as a controller. The controller can include a programmable logic controller (PLC) or a single-chip microcomputer, as long as it can realize the corresponding functions.

[0053] Figure 2 A flowchart of the electrode material screening method is shown in the embodiments of the present disclosure, as shown in FIG. 1, the electrode material screening method provided by the embodiments of the present disclosure includes the following steps S200-S208. Figure 2

[0054] S200, obtaining the porosity of the electrode sheet and electrode materials with multiple particle size distributions.

[0055] In one embodiment, the electrode sheet is an important component of a battery, used to bear the conduction of current and the catalytic action of chemical reaction. The electrode sheet of the battery includes a positive electrode sheet and a negative electrode sheet.

[0056] The positive electrode sheet is the energy release part in the battery, which can include active material, conductive agent and binder. The active material of the positive electrode sheet can include metal oxide, polymer or other compounds, which can chemically react with the active material of the negative electrode to release electrons. The conductive agent can play a role in conducting electrons, ensuring that electrons flow from the positive electrode to the negative electrode. The binder is used to fix the active material and the conductive agent, and to keep the structure of the electrode stable.

[0057] ​The negative electrode sheet is an energy storage part in the battery, and is usually composed of carbon materials. The active material of the negative electrode sheet can include, but is not limited to, graphite, silicon-based composite materials, etc. When the battery is discharged, ions will undergo a chemical reaction on the negative electrode sheet, release electrons, and form a new compound; when charged, electrons will flow from the external power source to the negative electrode sheet, causing the stored ions to be reduced to their original form.

[0058] The porosity of the electrode sheet refers to the proportion of the voids in the electrode sheet, which can be expressed by the percentage of the volume of the pores in the coating of the electrode sheet to the total volume. The porosity of the electrode sheet affects the electrolyte infiltration and lithium ion conduction efficiency. For example, the porosity of the electrode sheet of a lithium ion battery is usually between 3.5%-5.5%.

[0059] The porosity of the electrode sheet can be determined by a pressure pump method, a gas adsorption method, etc. It should be noted that the porosity of the electrode sheet can be obtained by testing the electrode sheet prepared in the actual production process, or can be pre-stored in the server. The present disclosure does not make any limitation, and the particle size distribution of the electrode material can be matched based on the porosity of the electrode sheet to make the charge and discharge performance of the electrode optimal.

[0060] The particle size distribution of the electrode material refers to the probability distribution of the particle size of the active material in the electrode sheet.

[0061] The virtual electrode is a three-dimensional digital model constructed based on the porosity of the electrode sheet and the particle size distribution of the electrode material, to simulate the electrochemical reaction of the battery. The virtual electrode can reduce the multiphase composite structure composed of the electrode material and the pores at the microscale, so that the macroscopic properties of the electrode are matched with the porosity of the electrode sheet and the particle size distribution of the electrode material.

[0062] It should be noted that in addition to the porosity of the electrode sheet and the particle size distribution of the electrode material, the thickness of the electrode, the volume fraction of the conductive agent and the binder, etc. can also be combined to generate the virtual electrode.

[0063] In one embodiment, the particle size distribution and the number of particle size distributions can be determined according to the design needs of the battery. The particle size distribution of the electrode material can be determined by macroscopic testing or microscopic testing. The macroscopic testing can use the sedimentation observation method or the viscosity testing method. The microscopic testing can use the scanning electron microscope or the laser particle size method. The present disclosure does not make any limitation on the determination method of the particle size distribution of the electrode material.

[0064] For each particle size distribution of the electrode material and the porosity of the electrode sheet, a virtual electrode can be generated, i.e., the virtual electrode is one-to-one corresponding to each particle size distribution of the electrode material and the porosity of the electrode sheet.

[0065] It should be noted that the particle size distribution of the electrode material can be the distribution obtained by actually testing the electrode material, the different particle size distributions determined by simulating the electrode material, or the particle size distribution of the electrode material pre-stored in the server, which can evaluate the incoming electrode material in advance and guide the optimized production of the electrode material based on the prediction results.

[0066] S202, generating a plurality of virtual electrodes according to the porosity of the pole piece and the electrode material with a plurality of particle size distributions, and calculating the tortuosity of each virtual electrode, wherein one virtual electrode corresponds to one particle size distribution of the electrode material.

[0067] In one embodiment, the tortuosity of the virtual electrode is an electrode microstructure parameter used to describe the bending degree of the ion / electron transmission path, which affects the ion conductivity, diffusion rate, etc.

[0068] The tortuosity of the virtual electrode can be calculated in various ways, such as experimental testing method, prediction method based on porous structure theory, image analysis method, etc. Taking the image analysis method as an example, the three-dimensional microstructure image of the virtual electrode is obtained first, and then the tortuosity is directly calculated through image segmentation and path tracking algorithm (such as shortest path algorithm).

[0069] S204, calculating the Bruggeman coefficient of each virtual electrode according to the porosity of the pole piece and the tortuosity of each virtual electrode.

[0070] The Bruggeman coefficient of the virtual electrode is used to describe the relationship between the conductivity of the liquid phase conductive network in the virtual electrode and the porosity.

[0071] In one embodiment, the Bruggeman coefficient of each virtual electrode is determined by the following formula:

[0072] (Formula One);

[0073] Wherein, is the tortuosity of the virtual electrode; is the porosity of the pole piece; is the Bruggeman coefficient of the virtual electrode.

[0074] When the Bruggeman coefficient b is larger, the porosity , of the virtual electrode is smaller, and the tortuosity of the virtual electrode is smaller; when the porosity is fixed, the Bruggeman coefficient b is negatively correlated with ; when the Bruggeman coefficient is fixed, the porosity is positively correlated with .

[0075] It can be seen that the Bruggeman coefficient can be expressed as a function of tortuosity .

[0076] S206, calculate the charge and discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode.

[0077] In one embodiment, based on the electrochemical simulation, a multiple particle Doyle-Fuller-Newman (MP-DFN) model can be used in the simulation modeling of lithium ion batteries. The MP-DFN model is an extension and modification of the DFN model. The DFN model assumes that the electrode is composed of single-particle-size spherical particles, while the MP-DFN model assumes that the electrode is composed of multiple different sizes of electrode material particles following a certain particle size distribution.

[0078] When calculating the charge and discharge capacity of the virtual electrode using the MP-DFN model, the contribution of each particle size of the electrode material particles can be calculated respectively, and then weighted and summed to obtain the charge and discharge capacity of the virtual electrode.

[0079] In addition, the electrochemical model can also include a deep learning model (such as Transformer), a three-dimensional model based on tomographic image reconstruction, etc.

[0080] S208, if the charge and discharge performance of the target virtual electrode meets the preset condition, the particle size distribution of the electrode material corresponding to the target virtual electrode is determined as the optimal particle size distribution, and the target virtual electrode is any one of the plurality of virtual electrodes.

[0081] In one embodiment, the above target virtual electrode can be any one of a plurality of virtual electrodes constructed based on electrode materials with different particle size distributions, that is, as long as any one of the plurality of virtual electrodes meets the preset condition, the particle size distribution of the electrode material corresponding to the virtual electrode meeting the preset condition is determined as the optimal particle size distribution.

[0082] The optimal particle size distribution refers to the particle size distribution of the electrode material with the optimal charge and discharge performance among the plurality of virtual electrodes constructed, that is, at least one particle size distribution selected from a plurality of particle size distributions.

[0083] In one embodiment, the charge-discharge capacity of the virtual electrode can include at least one of a charge capacity, a discharge capacity and an energy efficiency, and the preset condition that the charge-discharge performance of the target virtual electrode meets can refer to that the charge capacity of the target virtual electrode meets the preset condition, the discharge capacity of the target virtual electrode meets the preset condition, or both the charge capacity and the discharge capacity of the target virtual electrode meet the preset condition, or the energy efficiency of the target virtual electrode calculated based on the charge capacity and the discharge capacity of the target virtual electrode meets the preset condition. In this way, the charge-discharge capacities of the virtual electrodes are compared to select the target virtual electrode whose charge-discharge performance meets the preset condition, and then the particle size distribution of the electrode material corresponding to the target virtual electrode is determined, so that the optimal particle size distribution can be quickly determined without experimental test, and the research and development efficiency is improved.

[0084] In one embodiment, the preset condition that the charge-discharge performance of the target virtual electrode meets can include at least one of the following: the charge capacity of the target virtual electrode is the minimum value of the charge capacities of the plurality of virtual electrodes; the discharge capacity of the target virtual electrode is the maximum value of the discharge capacities of the plurality of virtual electrodes; and the energy efficiency of the target virtual electrode is the maximum value of the energy efficiencies of the plurality of virtual electrodes, and the energy efficiency of the virtual electrode is determined based on the charge capacity and the discharge capacity of the virtual electrode.

[0085] The energy efficiency of the virtual electrode can be obtained by calculating the ratio of the discharge capacity to the charge capacity of the virtual electrode.

[0086] In the embodiments of the present disclosure, the porosity of the pole piece and a plurality of particle size distributions of electrode materials are obtained; a plurality of virtual electrodes are generated based on the porosity of the pole piece and the plurality of particle size distributions of electrode materials, the tortuosity of each virtual electrode is calculated, and one virtual electrode corresponds to one particle size distribution of electrode material; the Bruggeman coefficient of each virtual electrode is calculated based on the porosity of the pole piece and the tortuosity of each virtual electrode; the charge-discharge performance of each virtual electrode is calculated based on the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode; if the charge-discharge performance of the target virtual electrode meets the preset condition, the particle size distribution of the electrode material corresponding to the target virtual electrode is determined as the optimal particle size distribution, and the target virtual electrode is any one of the plurality of virtual electrodes. The present disclosure can quickly realize material particle size screening by using virtual electrodes combined with simulation technology, reduce research and development cost, and improve research and development efficiency.

[0087] Figure 3 A flow chart of a virtual electrode generation method provided by an embodiment of the present disclosure is shown. As shown in Figure 3 In one embodiment, the generating a plurality of virtual electrodes based on the porosity of the pole piece and the plurality of particle size distributions of electrode materials in S202 can include the following steps.

[0088] S302, generating a virtual electrode structure based on the porosity of the pole piece;

[0089] S304, initializing the electrode material particles according to the electrode material with multiple particle size distributions;

[0090] S306, randomly releasing the electrode material particles into the virtual electrode structure to generate a plurality of virtual electrodes.

[0091] In S302, the porosity of the electrode tab is a value between 0 and 1, indicating the volume proportion of pores in the electrode.

[0092] In addition, the geometric size of the virtual electrode, such as length, width, etc., needs to be defined, and based on the porosity of the electrode tab and the geometric size, the virtual electrode structure can be generated.

[0093] In S304, the electrode material particles are initialized based on the electrode material with multiple particle size distributions. The initialization process is to configure the particle size of the electrode material particles. The particle size distribution can represent the proportion of different particle size battery material particles in the form of a list or array.

[0094] In S306, the discrete element method and the cohesive particle model can be used to simulate the microstructure of the electrode to generate the virtual electrode. By dividing the virtual electrode layer area into polygonal units, the electrode material particles are arranged to ensure that the electrode material particles do not overlap with each other.

[0095] In specific implementation, the electrode microstructure construction method can be used to automatically generate the virtual electrode based on the porosity of the electrode tab, the particle size distribution of the electrode material, and the geometric size of the electrode tab. The generative adversarial network (GAN) network can also be used to simulate the electrode microstructure to generate the virtual electrode.

[0096] In the embodiments of the present disclosure, the electrode microstructure is constructed based on the porosity of the electrode tab and the electrode material with multiple particle size distributions to automatically generate the virtual electrode, which provides a research basis for realizing the electrode material screening.

[0097] Figure 4 A flowchart of a method for calculating the tortuosity of a virtual electrode is shown. As shown in Figure 4 In one embodiment, the calculation of the tortuosity of each virtual electrode in S202 includes:

[0098] S402, simulating the motion path of the electrode material particles in the corresponding virtual electrode, and counting the mean square displacement of the electrode material particles in the corresponding virtual electrode;

[0099] S404, calculating the tortuosity of each virtual electrode according to the mean square displacement.

[0100] In one embodiment, in S402, a Monte Carlo algorithm or a random walk algorithm can be used to simulate the motion path of the electrode material particles in the corresponding virtual electrode, which refers to the mean square displacement of the electrode material particles at different times in the virtual electrode. The mean square displacement is the average of the square of the displacement of the electrode material particles in the virtual electrode.

[0101] The Monte Carlo algorithm is a numerical simulation method based on random sampling and probability statistics. By simulating a large number of random transmission trajectories of electrode material particles in the virtual electrode pores, the effective transmission path length is indirectly calculated, and then the tortuosity of the virtual electrode is calculated. The Monte Carlo algorithm combines the three-dimensional microstructure data of the virtual electrode with numerical simulation tools (such as MATLAB, Python's NumPy library, etc.) to calculate the tortuosity.

[0102] The random walk algorithm described above is a numerical simulation method based on probability statistics. The diffusion path of the electrode material particles can be simulated by simulating the random motion of the electrode material particles in the porous medium, and the structure parameters and transmission performance are indirectly obtained.

[0103] In S404, the tortuosity of the virtual electrode is calculated based on the mean square displacement. The calculation method can be determined according to the type of algorithm, which will be described later.

[0104] In the embodiments of the present disclosure, by simulating the motion path of the electrode material particles in the virtual electrode and calculating the mean square displacement of the electrode material particles, the tortuosity of the virtual electrode is calculated based on the mean square displacement. The real migration trajectory of the electrode material particles in the virtual electrode can be accurately captured from the micro level, which provides direct data support for quantitative analysis of the micro morphology and tortuosity of the virtual electrode. On the other hand, the tortuosity is calculated based on the mean square displacement of the electrode material particles, which is more close to the transmission characteristics under actual working conditions, and the calculation accuracy of the tortuosity is higher, which improves the accuracy and reliability of the tortuosity value, provides accurate quantitative basis for the particle size selection of the electrode material, and accelerates the research and development process of the electrode through virtual simulation technology to quickly iterate the tortuosity calculation results under different particle size distribution and porosity.

[0105] Figure 5 Another flowchart of a method for calculating the tortuosity of a virtual electrode is shown. As shown in Figure 5 In one embodiment, S404 calculates the tortuosity of each virtual electrode according to the mean square displacement, including:

[0106] S502, determining the effective diffusion coefficient of the electrode material particles according to the mean square displacement of the electrode material particles in the corresponding virtual electrode;

[0107] S504, calculating the tortuosity of each virtual electrode according to the free diffusion coefficient and the effective diffusion coefficient of the electrode material particles.

[0108] In S502, the mean square displacement (MSD) of the electrode material particles in the corresponding virtual electrode can be expressed as:

[0109] MSD(t) = < | r(t) - r(0) |2> (Formula Two); 2 MSD(t) = < | r(t) - r(0) |2> (Formula Two);

[0110] wherein MSD(t) is the mean square displacement of the electrode material particles at time t, r(t) is the position of the electrode material particles at time t, r(0) is the position of the electrode material particles at t=0, i.e., the position at the initial moment, · represents the ensemble average.

[0111] The motion of the electrode material particles in the virtual electrode, i.e., the diffusion behavior of the particles, follows the Einstein relationship, which describes the correlation between the mean square displacement of the electrode material particles and the diffusion coefficient , the diffusion time . For one-dimensional diffusion, the Einstein relationship is expressed as:

[0112] MSD(t) = D t (Formula Three);

[0113] wherein is the total diffusion time of the electrode material particles in the virtual electrode.

[0114] In the Monte Carlo algorithm, the total diffusion time t is the product of a single time step dt and the total motion step number N, i.e., , which can be substituted into the above Formula Three to determine the effective diffusion coefficient D eff as follows:

[0115] D = MSD(t) / t (Formula Four);

[0116] wherein is a single time step, and D is the effective diffusion coefficient.

[0117] The free diffusion coefficient refers to the diffusion ability of the electrode material particles in an ideal environment without any spatial restriction and interaction, and can measure the inherent property of the diffusion characteristics of the electrode material particles. The free diffusion coefficient is the mean square displacement of the electrode material particles in a free diffusion environment.

[0118] The tortuosity of the virtual electrode can be calculated by the following formula:

[0119] ​ (Formula 5).

[0120] It should be noted that, in addition to using the Monte Carlo algorithm or the random walk algorithm to calculate the tortuosity of the virtual electrode, other simulation techniques can also be used for calculation, and this disclosure does not make any specific limitations on them.

[0121] In this embodiment, on the one hand, the Monte Carlo algorithm or random walk algorithm is used to simulate the motion path of electrode material particles in the corresponding virtual electrode, and the mean square displacement of the electrode material particles is statistically analyzed to calculate the tortuosity of the virtual electrode. This reduces dependence on R&D equipment, shortens the development cycle, reduces R&D costs, and achieves a balance between the accuracy and efficiency of tortuosity calculation. On the other hand, the Monte Carlo algorithm is used to determine the effective diffusion coefficient of electrode material particles in the virtual electrode, thereby calculating the tortuosity of the virtual electrode based on the free diffusion coefficient and the effective diffusion coefficient, ensuring the accuracy of tortuosity calculation and providing data support for electrode material screening.

[0122] Figure 6 A flowchart illustrating another electrode material particle size screening method provided in an embodiment of this disclosure is shown. Figure 6 As shown, in one embodiment, S206 above calculates the charge-discharge performance of each virtual electrode based on the particle size distribution of each electrode material and the Brügmann coefficient of each virtual electrode, including:

[0123] S2062. Based on the electrochemical model, calculate the charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Brügmann coefficient of each virtual electrode.

[0124] In one embodiment, the electrochemical model can be a multi-particle-size DFN model.

[0125] The probability density function of the particle radius of battery material particles is defined as f(r), where r is the particle radius, which can be obtained through experimental data, such as laser particle size analysis. In processes such as solid-phase mass transfer and interfacial reaction kinetics, the diffusion and current density of different particle sizes can be calculated, which can more accurately describe the electrochemical performance of the battery.

[0126] Constraints on the multi-particle-size MP-DFN model may include:

[0127] (Formula 6);

[0128] in, The active specific surface area; For the particle size distribution of the electrode material, Particle size; For particle size The particle exchange current density of the electrode material; for ionic conductivity; for porosity of the electrode sheet; for tortuosity of the virtual electrode; for particle phase potential.

[0129] In a specific implementation, the particle size distribution of the electrode material and the Bruggeman coefficient of the virtual electrode can be input into the multi-particle MP-DFN model at the same time, and the charge and discharge performance under different material particle size distributions can be calculated.

[0130] In addition, the electrochemical model can also be a deep learning model (such as a Transformer), a three-dimensional model based on tomographic image reconstruction, etc.

[0131] In one embodiment, the characteristic parameters input into the MP-DFN model further include: thickness of the virtual electrode, proportion of the conductive agent, and electrochemical parameters, etc. The electrochemical parameters further include active materials, electrolyte, reaction kinetics, etc. The active materials can include initial lithium concentration, solid-phase diffusion coefficient, maximum lithium concentration, open-circuit voltage curve, etc. The electrochemical parameters of the electrolyte can include diffusion coefficient, conductivity, transport number, etc. The electrochemical parameters of the reaction kinetics can include exchange current density, reaction rate constant, etc.

[0132] The virtual electrode is spatially discretized along the thickness direction to obtain a plurality of spatial nodes; at each spatial node, there is a complete particle group, and the particle group is discretized into a plurality of particle groups according to the particle size distribution, and each particle group is equivalent to a representative particle, and the weight of the representative particle is a fractional number.

[0133] At each time step and each spatial node, the following operations are performed:

[0134] For each representative particle of each particle size group, a solid-phase diffusion equation is solved to calculate the lithium ion concentration distribution inside each particle; according to the lithium ion concentration distribution inside the particle and the lithium concentration of the electrolyte, a Butler-Volmer equation is used to calculate the local reaction current density of the representative particle; and the total reaction current of the particle group is the local reaction current density multiplied by the corresponding total surface area.

[0135] The total reaction current of all particles in all particle groups at a spatial node is added to obtain the total reaction current of the spatial node. The ion conservation and charge conservation equations in the electrolyte phase are solved to couple the spatial nodes. After integration over the entire thickness of the virtual electrode, the macroscopic voltage of the virtual electrode is calculated.

[0136] The discharge capacity can be obtained by multiplying the time from the start of discharging to the voltage dropping to the cut-off voltage by the current, and the charge capacity can be obtained by multiplying the time from the start of charging to the voltage rising to the target charging voltage by the current.

[0137] It should be noted that the solving process of the MP-DFN model can adopt a digital simulation tool such as Python, and the specific implementation mode is not limited.

[0138] In the embodiments of the present disclosure, the electrochemical simulation is performed based on the MP-DFN model, the charge-discharge performance of the virtual electrode is calculated according to the particle size distribution of the electrode material and the Bruggeman coefficient of the virtual electrode, and the electrochemical performance difference of different particle size distributions is considered, so as to improve the accuracy of the charge-discharge performance.

[0139] The electrode material screening method of the present disclosure will be described below in combination with specific examples.

[0140] Figure 8 Three kinds of electrode materials with different particle size distributions are provided, which are Y1, Y2 and Y3 respectively. Three virtual electrodes are generated according to the porosity of the electrode sheet and the particle size distribution of the electrode material, and the structure of the virtual electrode is as shown in FIG. 1. Figure 7

[0141] The tortuosity of the virtual electrode is calculated by using the random walk algorithm, and the Bruggeman coefficient of the electrode is calculated according to Formula I, which is used for subsequent electrochemical simulation. The Bruggeman coefficients of Y1, Y2 and Y3 calculated are shown in Table 1.

[0142] Table 1: Calculation results of Bruggeman coefficients under three kinds of particle size distributions

[0143]

[0144] The electrochemical simulation adopts the MP-DFN model, which can consider the electrochemical performance difference of different particle size distributions. The calculated Bruggeman coefficients and the three kinds of particle size distributions are input into the MP-DFN model, the charge-discharge capacity of the electrode material under different particle size distributions is calculated, and the energy efficiency of the virtual electrode is calculated according to the charge-discharge capacity, as shown in Table 2.

[0145] Table 2: Calculation results of MP-DFN model

[0146]

[0147] The particle size distribution with the highest energy efficiency is selected as the optimal particle size distribution. It can be found from Table 2 that the electrode material with the particle size distribution Y1 has the highest energy efficiency of the corresponding virtual electrode, and therefore Y1 is the optimal particle size distribution.

[0148] ​Based on the same inventive concept, the embodiment of the present disclosure also provides an electrode material screening device, as follows. Since the principle of solving problems of the device embodiment is similar to the above-mentioned method embodiment, the implementation of the device embodiment can be referred to the implementation of the above-mentioned method embodiment, and the repeated parts will not be described here.

[0149] Figure 9 A schematic diagram of an electrode material screening device in an embodiment of the present disclosure is shown as follows. Figure 9 As shown in one embodiment, the device includes an acquisition module 900, a first calculation module 910, a second calculation module 920, a third calculation module 930, and a material screening module 940.

[0150] The acquisition module 900 is configured to acquire the porosity of the pole piece and the electrode material with a plurality of particle size distributions.

[0151] The first calculation module 910 is configured to generate a plurality of virtual electrodes according to the porosity of the pole piece and the electrode material with a plurality of particle size distributions, and calculate the tortuosity of each virtual electrode, wherein one virtual electrode corresponds to the electrode material with one particle size distribution.

[0152] The second calculation module 920 is configured to calculate the Bruggeman coefficient of each virtual electrode according to the porosity of the pole piece and the tortuosity of each virtual electrode.

[0153] The third calculation module 930 is configured to calculate the charge and discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode.

[0154] The material screening module 940 is configured to determine the particle size distribution of the electrode material corresponding to the target virtual electrode as the optimal particle size distribution if the charge and discharge performance of the target virtual electrode meets the preset condition, and the target virtual electrode is any one of the plurality of virtual electrodes.

[0155] In one embodiment, the first calculation module 910 is configured to simulate the motion path of the electrode material particles in the corresponding virtual electrode, and count the mean square displacement of the electrode material particles in the corresponding virtual electrode; and calculate the tortuosity of each virtual electrode according to the mean square displacement.

[0156] In one embodiment, the first calculation module 910 is configured to simulate the motion path of the electrode material particles in the corresponding virtual electrode by using the Monte Carlo algorithm or the random walk algorithm, and count the mean square displacement of the electrode material particles in the corresponding virtual electrode.

[0157] In an embodiment, the first calculation module 910 is configured to determine an effective diffusion coefficient of the electrode material particles according to a mean square displacement of the electrode material particles in the corresponding virtual electrode; and calculate tortuosity of each virtual electrode according to the free diffusion coefficient and the effective diffusion coefficient of the electrode material particles.

[0158] In an embodiment, the second calculation module 920 is configured to calculate charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode based on an electrochemical model; wherein the electrochemical model comprises at least one of a multi-particle DFN model, a deep learning model, and a three-dimensional model based on tomographic image reconstruction.

[0159] In an embodiment, the charge-discharge capacity of the virtual electrode comprises at least one of a charge capacity, a discharge capacity, and an energy efficiency; and the material screening module 940 is configured to determine that the charge-discharge performance of the target virtual electrode satisfies a preset condition by at least one of: the charge capacity of the target virtual electrode is the minimum value of the charge capacities of the plurality of virtual electrodes; the discharge capacity of the target virtual electrode is the maximum value of the discharge capacities of the plurality of virtual electrodes; and / or the energy efficiency of the target virtual electrode is the maximum value of the energy efficiencies of the plurality of virtual electrodes, wherein the energy efficiency of the virtual electrode is determined according to the charge capacity and the discharge capacity of the virtual electrode.

[0160] In an embodiment, the Bruggeman coefficient of each virtual electrode is determined by the following formula: ; wherein, is the tortuosity of the virtual electrode; is the porosity of the electrode sheet; is the Bruggeman coefficient of the virtual electrode.

[0161] In an embodiment of the present disclosure, the porosity of the electrode sheet and the electrode material of a plurality of particle size distributions are obtained; a plurality of virtual electrodes are generated according to the porosity of the electrode sheet and the electrode material of a plurality of particle size distributions, the tortuosity of each virtual electrode is calculated, and one virtual electrode corresponds to one particle size distribution of the electrode material; the Bruggeman coefficient of each virtual electrode is calculated according to the porosity of the electrode sheet and the tortuosity of each virtual electrode; the charge-discharge performance of each virtual electrode is calculated according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode; if the charge-discharge performance of the target virtual electrode satisfies the preset condition, the particle size distribution of the electrode material corresponding to the target virtual electrode is determined as the optimal particle size distribution, and the target virtual electrode is any one of the plurality of virtual electrodes. The present disclosure can quickly realize material particle size screening by using virtual electrodes combined with simulation technology, reduce research and development costs, and improve research and development efficiency.

[0162] Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method or a program product. Therefore, various aspects of the present disclosure can be embodied as a whole hardware implementation, a whole software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.

[0163] The electronic device 1000 according to this embodiment of the present disclosure will be described below with reference to Figure 10 Figure 10 The electronic device 1000 shown is merely an example and should not limit the functions and usage range of the embodiments of the present disclosure.

[0164] In one embodiment, the electronic device 1000 includes a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to perform the electrode material screening method described above by executing the executable instructions.

[0165] As shown in Figure 10 The electronic device 1000 is in the form of a general computing device. The components of the electronic device 1000 can include, but are not limited to, the above-mentioned at least one processing unit 1010, the above-mentioned at least one storage unit 1020, and a bus 1030 connecting different system components, including the storage unit 1020 and the processing unit 1010.

[0166] The storage unit stores program code that can be executed by the processing unit 1010, so that the processing unit 1010 performs the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of the present specification. For example, the processing unit 1010 can perform the following steps of the above-mentioned method embodiments: obtaining the porosity of the pole piece and the electrode material of a plurality of particle size distributions; generating a plurality of virtual electrodes according to the porosity of the pole piece and the electrode material of a plurality of particle size distributions, and calculating the tortuosity of each virtual electrode, wherein one virtual electrode corresponds to the electrode material of one particle size distribution; calculating the Bruggeman coefficient of each virtual electrode according to the porosity of the pole piece and the tortuosity of each virtual electrode; calculating the charge and discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode; and if the charge and discharge performance of the target virtual electrode meets the preset condition, determining the particle size distribution of the electrode material corresponding to the target virtual electrode as the optimal particle size distribution, and the target virtual electrode is any one of the plurality of virtual electrodes.

[0167] The storage unit 1020 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 10201 and / or a cache memory 10202, and can further include a read-only memory (ROM) 10203.​

[0168] The storage unit 1020 also includes a program / utility 10204 having one or more program modules 10205, such as an operating system, one or more application programs, other program modules, and program data, each of which performs one or more tasks that facilitate implementations of various aspects. Note that the program / utility 10204 might be loaded from the storage unit 1020 into the memory 1012 for execution by the processing unit 1010.

[0169] The bus 1030 can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration bus, a processor or local bus using any of a variety of bus architectures, and so forth.

[0170] The electronic device 1000 can also communicate with one or more external devices 1040 such as a keyboard or pointing device, a Bluetooth device, etc.; one or more devices that enable a user to interact with the electronic device 1000; and / or one or more devices (e.g., a router, a modem, a server, etc.) that enable the electronic device 1000 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 1050. Still yet, the electronic device 1000 can communicate with one or more networks, such as one or more local area networks (LANs), one or more wide area networks (WANs), and / or one or more public networks, such as the Internet, via the network adapter 1060. As depicted, the network adapter 1060 communicates with the other components of the electronic device 1000 via the bus 1030. It should be appreciated that the network adapter 1060 and / or the bus 1030 might be implemented using one or more types of communication media, such as IO media, I2C, etc. Figure 10

[0171] Those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or firmware in addition to or instead of hardware. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in a software product including a number of instructions stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and the software product can be executed by one or more computers (which can be personal computers, servers, terminal devices, or network devices, etc.) to implement the methods according to the embodiments of the present disclosure.

[0172] ​In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer program product including a computer program that, when executed by a processor, implements the steps described in the above "Exemplary Method" section according to various exemplary embodiments of the present disclosure.

[0173] In exemplary embodiments of the present disclosure, a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps described in the above "Exemplary Method" section according to various exemplary embodiments of the present disclosure is also provided. The computer-readable storage medium can be a readable signal medium or a readable storage medium.

[0174] In some possible implementations, various aspects of the present disclosure can also be implemented as a program product in the form of a computer program that, when executed by a processor, implements the electrode material screening method in any of the embodiments described above. In one possible implementation, the program product includes program code that, when executed on a terminal device, causes the terminal device to perform the steps described in the above "Exemplary Method" section according to various exemplary embodiments of the present disclosure.

[0175] More specific examples of the computer-readable storage medium in the present disclosure can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 of the foregoing.

[0176] In the present disclosure, the computer-readable storage medium can include a data signal carried in the baseband or as part of a carrier wave, in which readable program code is borne. Such a propagated data signal can take on many forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The readable signal medium can also be any readable medium that can send, propagate, or transfer program for use by or in connection with an instruction execution system, apparatus, or device.

[0177] Optionally, the program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0178] In particular embodiments, the program code utilized by the program code instructions can be written in any of a number of programming languages, including an object oriented programming language such as Java, C++, or the like; and conventional procedural programming languages; or combinations of the above. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider (ISP).

[0179] It should be noted that, although several modules or units of the devices for action execution are mentioned in the foregoing detailed description, such division is not mandatory. Indeed, features and functionalities of two or more modules or units described above can be embodied in one module or unit according to embodiments of the present disclosure. Conversely, features and functionalities of one module or unit described above can be further divided into multiple modules or units.

[0180] Furthermore, although various steps of the methods in the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all of the steps must be performed to achieve the desired results. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, one step can be split into multiple steps, and / or the like.

[0181] From the above description of the embodiments, those skilled in the art will readily perceive that the example embodiments described herein can be implemented by software and / or by hardware and / or by a combination of software and hardware. Accordingly, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium, such as a CD-ROM, a USB flash drive, a mobile hard disk, or the like, or on a network, and includes a number of instructions for causing a computing device (such as a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0182] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the disclosure be construed as including any patents, patent applications, publications, publications, or other disclosure of complementary subject matter that is within the scope of the disclosure. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method of electrode material screening, characterized by, The method comprises the following steps: obtaining the porosity of the pole piece and electrode materials with multiple particle size distributions; generating multiple virtual electrodes according to the porosity of the pole piece and the electrode materials with multiple particle size distributions, calculating the tortuosity of each virtual electrode, wherein one virtual electrode corresponds to electrode material with one particle size distribution; calculating the Bruggeman coefficient of each virtual electrode according to the porosity of the pole piece and the tortuosity of each virtual electrode; calculating the charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode; if the charge-discharge performance of the target virtual electrode meets the preset condition, the particle size distribution of the electrode material corresponding to the target virtual electrode is determined as the optimal particle size distribution, and the target virtual electrode is any one of the multiple virtual electrodes; wherein the calculation of the tortuosity of each virtual electrode comprises simulating the motion path of the electrode material particles in the corresponding virtual electrode and counting the mean square displacement of the electrode material particles in the corresponding virtual electrode; and the tortuosity of each virtual electrode is calculated according to the mean square displacement.

2. The method of claim 1, wherein, The simulation of the motion path of the electrode material particles in the corresponding virtual electrode and the counting of the mean square displacement of the electrode material particles in the corresponding virtual electrode comprise: simulating the motion path of the electrode material particles in the corresponding virtual electrode and counting the mean square displacement of the electrode material particles in the corresponding virtual electrode by using the Monte Carlo algorithm or the random walk algorithm.

3. The method of claim 1, wherein, The calculation of the tortuosity of each virtual electrode according to the mean square displacement comprises: determining the effective diffusion coefficient of the electrode material particles according to the mean square displacement of the electrode material particles in the corresponding virtual electrode; calculating the tortuosity of each virtual electrode according to the free diffusion coefficient and the effective diffusion coefficient of the electrode material particles.

4. The method of claim 1, wherein, The calculation of the charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode comprises: calculating the charge-discharge performance of each virtual electrode according to the particle size distribution of each electrode material and the Bruggeman coefficient of each virtual electrode based on an electrochemical model; wherein the electrochemical model comprises at least one of a multi-particle DFN model, a deep learning model, and a three-dimensional model based on a tomographic image reconstruction.

5. The method of claim 1, wherein, The charge-discharge performance of the virtual electrode includes at least one of the charging capacity, the discharging capacity, and the energy efficiency; wherein the charge-discharge performance of the target virtual electrode meets the preset condition comprises: the charging capacity of the target virtual electrode is the minimum value of the charging capacities of the multiple virtual electrodes; the discharging capacity of the target virtual electrode is the maximum value of the discharging capacities of the multiple virtual electrodes; and / or the energy efficiency of the target virtual electrode is the maximum value of the energy efficiencies of the multiple virtual electrodes, wherein the energy efficiency of the virtual electrode is determined according to the charging capacity and the discharging capacity of the virtual electrode.

6. The method of claim 1, wherein, The generation of multiple virtual electrodes according to the porosity of the pole piece and electrode materials with multiple particle size distributions comprises: generating a virtual electrode structure according to the porosity of the pole piece; According to the electrode materials of the plurality of particle size distributions, initializing particles of the electrode materials; Randomly releasing the particles of the electrode materials into the virtual electrode structure to generate a plurality of the virtual electrodes.

7. The method according to any one of claims 1 to 6, characterized in that, A Bruggeman coefficient of each of the virtual electrodes is determined by the following formula: ； wherein, tortuosity of the virtual electrode; porosity of the electrode tab; Bruggeman coefficient of the virtual electrode.

8. An electrode material screening device, characterized by The method comprises: An acquisition module is configured to acquire porosity of an electrode sheet and electrode materials of a plurality of particle size distributions; A first calculation module is configured to generate a plurality of virtual electrodes according to the porosity of the electrode sheet and the electrode materials of the plurality of particle size distributions, and calculate tortuosity of each of the virtual electrodes, wherein one of the virtual electrodes corresponds to electrode material of one of the particle size distributions; A second calculation module is configured to calculate the Bruggeman coefficient of each of the virtual electrodes according to the porosity of the electrode sheet and the tortuosity of each of the virtual electrodes; A third calculation module is configured to calculate charge-discharge performance of each of the virtual electrodes according to the particle size distribution of each of the electrode materials and the Bruggeman coefficient of each of the virtual electrodes; A material screening module is configured to determine a particle size distribution of electrode material corresponding to a target virtual electrode as an optimal particle size distribution if the charge-discharge performance of the target virtual electrode meets a preset condition, wherein the target virtual electrode is any one of the plurality of virtual electrodes. The first calculation module is configured to simulate a motion path of a particle of the electrode material in a corresponding virtual electrode, count mean square displacement of the particle of the electrode material in the corresponding virtual electrode, and calculate the tortuosity of each of the virtual electrodes according to the mean square displacement.

9. An electronic device, comprising: The method comprises: A processor; A memory configured to store executable instructions of the processor; The processor is configured to execute the executable instructions to perform the electrode material screening method according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the electrode material screening method according to any one of claims 1-7.

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