Electronic device for predicting electrode structure changing according to soc, and method therefor

Abstract

Provided is an electronic device for predicting an electrode structure, the electronic device comprising: a memory for storing one or more instructions; and a processor configured to, by executing the one or more instructions, identify a first active electrode structure corresponding to a first state-of-charge (SoC) through a simulation to which at least one design parameter is applied, identify first structure information of the first active electrode structure, and map the first SoC and the first structure information.

Description

Electronic device and method for predicting electrode structure that varies according to SOC

[0001] The present disclosure relates to an electronic device and a method for predicting an electrode structure that varies according to the SoC.

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2025-0026096 dated February 27, 2025, and all contents disclosed in the document of said Korean Patent Application are incorporated herein as part of this specification.

[0003] During battery operation, carrier ions move between electrodes, causing changes in the electrode structure. For example, when the State of Charge (SoC) is high, a large number of carrier ions are concentrated on the negative electrode, leading to an increase in its thickness; conversely, when the SoC is low, a large number of carrier ions are concentrated on the positive electrode, causing the positive electrode's thickness to increase. Furthermore, changes in SoC alter the ion composition along with the electrode thickness, ultimately resulting in changes to the electrode's porosity and tortuosity.

[0004] One of the conventional techniques for simulating the electrode structure of a battery is the Pseudo 2-Dimension (P2D) model. However, the P2D model has limitations in that the simulation results of the electrode structure differ somewhat from reality because the change in electrode thickness according to the SoC is not reflected in the model.

[0005] The disclosed embodiments aim to provide an electronic device and a method for predicting an electrode structure that varies according to the SoC. Specifically, one objective is to provide a method for measuring the thickness, porosity, and curvature of an electrode according to the battery SoC from an electrode structure simulation, and for estimating what thickness, porosity, and curvature an actual battery will have according to the SoC.

[0006] The technical problems that this embodiment aims to solve are not limited to those described above, and other technical problems can be inferred from the following embodiments.

[0007] One aspect of the present disclosure may provide an electronic device for predicting an electrode structure, comprising: a processor; and a memory for storing one or more instructions, wherein the processor is configured to identify a first active electrode structure corresponding to a first State-of-Charge (SoC) through a simulation in which at least one design parameter is applied by executing the one or more instructions, identify first structural information of the first active electrode structure, and map the first SoC to the first structural information.

[0008] In one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, configured to measure the thickness, porosity, and tortuosity of the first active electrode structure in a virtual environment corresponding to the simulation, and to verify the first structure information including the thickness, porosity, and tortuosity.

[0009] Additionally, in one embodiment of the present disclosure, the electronic device for predicting an electrode structure may be included, wherein the processor is configured to generate an electrode structure table representing a mapping relationship between the SoC and the structure information for at least one design parameter, based on the mapped first SoC and the first structure information, after mapping the first SoC and the first structure information.

[0010] Additionally, in one embodiment of the present disclosure, the electronic device for predicting an electrode structure may be included, wherein the processor, after generating the electrode structure table, identifies second structure information corresponding to a second SoC based on the electrode structure table, and outputs the second structure information as structure information predicted to be possessed by the second SoC of an actual electrode manufactured by applying the at least one design parameter.

[0011] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, which is configured to identify second performance information corresponding to the second SoC based on the electrode structure table and output the second performance information together with the second structure information as performance information predicted to be possessed by the actual electrode in the second SoC.

[0012] In addition, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, which is configured to generate the electrode structure table as one of a plurality of electrode structure tables, each mapped according to a combination of design parameters.

[0013] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, wherein the processor identifies a deviation parameter obtained by applying a process deviation to the design parameter, identifies an electrode structure table corresponding to the deviation parameter among the plurality of electrode structure tables, identifies third structure information corresponding to a second SoC based on the electrode structure table corresponding to the deviation parameter, and outputs the third structure information as one of the structure information predicted that the actual electrode may have in the second SoC as the process deviation is reflected.

[0014] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, configured to identify a rolled electrode structure through a loading and rolling simulation to which the at least one design parameter is applied, perform an activation simulation to which the first SoC is applied to the rolled electrode structure, and identify the rolled electrode structure to which the activation simulation is applied as the first activated electrode structure.

[0015] Additionally, in one embodiment of the present disclosure, the electronic device for predicting an electrode structure may be included, wherein the processor is configured to identify a loading electrode structure by performing a loading simulation based on an electrode composition included in at least one design parameter, and to identify a rolled electrode structure by performing a rolling simulation for the loading electrode structure based on a rolling thickness included in at least one design parameter.

[0016] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, which is configured to adjust the stoichiometric ratio of carrier ions on the rolled electrode structure to correspond to the first SoC as the activation simulation.

[0017] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, which is configured to check first performance information for the first active electrode structure based on the first structure information after checking the first structure information.

[0018] Additionally, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, configured to identify a first effective factor for the first active electrode structure based on the first structure information, identify a first reaction current for the first active electrode structure based on the first effective factor, and identify the first performance information based on the first reaction current.

[0019] In addition, in one embodiment of the present disclosure, the processor may include an electronic device for predicting an electrode structure, which is configured to check the first performance information including the remaining electric charge and internal resistance according to the charge / discharge time of the first activation electrode structure based on the first reaction current.

[0020] Another aspect of the present disclosure provides a method for predicting an electrode structure of an electronic device, comprising: identifying a first active electrode structure corresponding to a first State-of-Charge (SoC) through a simulation in which at least one design parameter is applied; identifying first structural information of the first active electrode structure; and mapping the first SoC to the first structural information.

[0021] Another aspect of the present disclosure may provide a computer-readable non-transient recording medium that records a program for executing the electrode structure prediction method described above on a computer.

[0022] Specific details of other embodiments are included in the detailed description and drawings.

[0023] According to the proposed embodiment, one or more of the following effects can be expected.

[0024] According to the embodiments of the present specification, it is possible to estimate what thickness, porosity, and curvature the actual battery will have based on the SoC.

[0025] In addition, according to the embodiments of the present specification, battery performance can be predicted by taking into account that the thickness of the electrode changes during the actual use of the battery.

[0026] In addition, according to the embodiments of the present specification, the performance deviation of the battery due to process error can also be predicted.

[0027] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description in the claims.

[0028] FIG. 1 is an exemplary drawing showing an electronic device that predicts an electrode structure according to one embodiment.

[0029] FIG. 2 is a flowchart illustrating an electrode structure prediction method according to one embodiment.

[0030] FIG. 3 is a diagram showing an example of a visual representation of an activated electrode structure identified by the stoichiometric ratio of carrier ions according to one embodiment.

[0031] FIG. 4 is a flowchart illustrating an example of an electrode structure table generation process according to one embodiment.

[0032] FIG. 5 shows a block diagram of an electronic device according to one embodiment.

[0033] The terms used in the embodiments have been selected to be as widely used as possible, taking into account their functions in the present disclosure; however, these may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section. Therefore, terms used in the present disclosure should be defined not merely by their names, but based on their meanings and the overall content of the present disclosure.

[0034] When a part of a specification is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0035] The expression "at least one of a, b, and c" described throughout the specification may include 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'a, b, and c all'.

[0036] The "terminal" mentioned below may be implemented as a computer or portable terminal capable of connecting to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, or laptop equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals like IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), and LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0037] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein.

[0038] Embodiments of the present disclosure will be described in detail below with reference to the drawings. First, it should be noted that in the present disclosure, the term "electrode structure" may encompass both a positive electrode structure and a negative electrode structure that may be included in a battery.

[0039] FIG. 1 is an exemplary drawing showing an electronic device that predicts an electrode structure according to one embodiment.

[0040] Referring to FIG. 1, the electronic device (100) may include an electrode structure simulator (110) and an electrode structure table (120). First, the electrode structure simulator (110) may perform a simulation in which at least one design parameter related to a battery manufacturing process is applied. More specifically, the simulation may correspond to a simulation for simulating an electrode structure manufactured by performing each unit process of the battery manufacturing process. The simulation of the present disclosure is considered to encompass any simulation capable of simulating an electrode structure by virtually performing each unit process of the battery manufacturing process. As will be explained in detail later, the electrode structure simulator (110) may perform a loading simulation, a rolling simulation, and an activation simulation as simulations for each unit process. Specific details of each simulation will be explained later. In addition, the description below that the electronic device (100) performs a simulation can be understood as meaning that the simulation is performed through an electrode structure simulator (110) inside the electronic device (100) as shown in the drawing, or through an electrode structure simulator inside another electronic device that operates in conjunction with the electronic device (100).

[0041] Next, the electrode structure table (120), as will be explained in detail later, may correspond to a Look-Up Table (LUT) configured to output structural information of an actually manufactured electrode structure predicted for an input SoC based on a mapping relationship between the SoC and structural information. However, the electrode structure table (120) is not limited to this format, and any method that can be configured to output structural information predicted for an input SoC can be applied. For example, the electrode structure table (120) may be implemented in the form of a function, despite the name 'table'. Furthermore, it may be implemented in various ways other than the examples described above. Also, although the electronic device (100) is depicted in the drawing as including a single electrode structure table (120), this is for convenience of explanation, and in reality, it may include one or more electrode structure tables. Furthermore, the electrode structure table (120) may be stored in another electronic device or database that operates in conjunction with the electronic device (100).

[0042] FIG. 2 is a flowchart illustrating an electrode structure prediction method according to one embodiment.

[0043] In step S210, the electronic device (100) can identify a first active electrode structure corresponding to a first State-of-Charge (SoC) through a simulation in which at least one design parameter is applied. In step S220, the electronic device (100) can identify first structural information of the first active electrode structure. In step S230, the electronic device (100) can map the first SoC to the first structural information. A method for predicting an electrode structure according to an embodiment will be described in detail below.

[0044] According to one embodiment, the electronic device (100) may perform a loading simulation, a rolling simulation, and an activation simulation in sequence as simulations for each unit process. The simulation procedure and sequence of the loading-rolling-activation process described above are merely one example, and may be performed in a different order or configuration for a series of simulations including various other unit processes.

[0045] According to one embodiment, a loading simulation is a simulation of a 'loading' process during a battery manufacturing process, and can be performed by an electronic device (100) to predict and analyze coating results according to process variable settings by reproducing physical or chemical phenomena that occur when a material in the form of a slurry or powder is applied to a current collector in a virtual environment. For example, through a loading simulation simulating a roll coating method of a slurry material, the electronic device (100) can simulate at least some of the characteristics of a loading electrode structure, such as electrode thickness, uniformity, and degree of internal particle aggregation, as a result of a loading process in which at least some of various process variables are applied, such as the composition of the slurry material, the application speed, the gap of the slit die, the injection pressure, the injection angle, and the binder ratio. Alternatively, through a loading simulation simulating a dry coating method of a powder material, the electronic device (100) can simulate at least some of the characteristics of a loading electrode structure as a result of a loading process in which at least some of process variables related to the composition of the powder, the particle size distribution of the powder, the injection speed, and electrostatic interaction with the current collector are applied. The results of such a loading simulation can be output as various numerical and visual results related to the results of the aforementioned loading process. According to one embodiment, the visual results may include an image that visually represents, within a virtual environment corresponding to the simulation, how each particle is distributed on the loading electrode structure identified through the loading simulation. Furthermore, according to one embodiment, at least some of the various numerical values ​​related to the aforementioned loading results may be verified by calculating the interactions between each particle on the loading electrode structure within the virtual environment. Additionally, the various numerical values ​​related to the aforementioned loading results are not limited to those listed above and may include values ​​that do not include the values ​​of the examples mentioned above, or may include various other types of values ​​generally used for loading results.

[0046] According to one embodiment, a rolling simulation is a simulation of the ‘rolling’ process during the battery manufacturing process, and can be performed by an electronic device (100) to predict and analyze rolling results according to process variable settings by reproducing in a virtual environment the process in which a coated electrode structure passes through rollers and is compressed to a set thickness. For example, through the rolling simulation, the electronic device (100) can simulate at least some of the characteristics of the rolled electrode structure, such as thickness, porosity, curvature, and internal stress distribution, as a result of a rolling process in which at least some of the various process variables, such as the gap between rollers, rolling speed, initial physical properties of the electrode material, and target rolling thickness, are applied. The results of such a rolling simulation can be output as various numerical and visual results related to the results of the aforementioned rolling process. According to one embodiment, the visual results may include an image that visually represents, within a virtual environment corresponding to the simulation, how each particle is distributed on the rolled electrode structure confirmed through the rolling simulation. In addition, according to one embodiment, various numerical values ​​related to the aforementioned rolling results can be verified by calculating the interactions between each particle on the rolled electrode structure within a virtual environment. Furthermore, the various numerical values ​​related to the aforementioned rolling results are not limited to those listed above, and may include values ​​that do not include the values ​​of the aforementioned examples, or may include various other types of values ​​related to rolling results that are generally used.

[0047] According to one embodiment, the activation simulation simulates the 'activation' process during the battery manufacturing process and can be performed by an electronic device (100) to predict and analyze activation results based on process variable settings by reproducing the process of activating an electrode structure that has been rolled by initial charging and discharging in a virtual environment. For example, through the activation simulation, the electronic device (100) can simulate at least some of the characteristics of the activated electrode structure, such as volume, internal stress, interfacial resistance, porosity, and curvature, as a result of the activation process in which at least some of the various process variables, such as voltage and current profiles, are applied. The results of such an activation simulation can be output as various numerical and visual results related to the results of the aforementioned activation process. According to one embodiment, the visual results may include an image that visually represents, within a virtual environment corresponding to the simulation, how each particle is distributed on the activated electrode structure confirmed through the activation simulation. Additionally, according to one embodiment, the various numerical values ​​related to the aforementioned activation results can be confirmed by calculating the interactions between each particle on the activated electrode structure within the virtual environment. In addition, the various figures related to the aforementioned activation results are not limited to those listed above, and may include figures that do not include the examples mentioned above, or may include various other types of figures commonly used for activation results.

[0048] According to one embodiment, as a specific detailed operation of the activation simulation, if the SoC during the initial charge / discharge described above is input, the activation electrode structure at that point in time can be simulated. For example, if an SoC corresponding to 10% during charging or 70% during discharging is input, the electronic device (100) can simulate numerical and visual results related to the activation electrode structure at 10% during charging or 70% during discharging as a detailed operation during the activation simulation. To this end, as a specific detailed operation of the activation simulation, the electronic device (100) can verify the stoichiometric ratio of carrier ions, e.g., lithium ions, on the electrode structure corresponding to the input SoC, verify the activation electrode structure by adjusting the stoichiometric ratio of carrier ions on the rolled electrode structure to correspond to the verified ratio, and simulate the aforementioned numerical and visual results by calculating the physical or chemical reaction of the activation electrode structure according to the adjusted ratio. Refer to Fig. 3 to examine an example of a visual representation image of an activated electrode structure output through the activation simulation described above.

[0049] FIG. 3 is a diagram showing an example of a visual representation of an activated electrode structure identified by the stoichiometric ratio of carrier ions according to one embodiment.

[0050] In FIG. 3, first to fourth electrode structures (310 to 340) that mimic electrode structures corresponding to the negative electrode can be seen. Among these, the first electrode structure (310) corresponds to the state where the SoC is lowest, that is, the state where the stoichiometric ratio of carrier ions is lowest, and the fourth electrode structure (340) corresponds to the state where the SoC is highest, that is, the state where the stoichiometric ratio of carrier ions is highest. Additionally, as one moves from the first electrode structure (310) to the fourth electrode structure (340), the SoC gradually increases, and accordingly, the stoichiometric ratio of carrier ions also gradually increases, corresponding to a state. As the battery is charged, carrier ions move to the negative electrode; therefore, as can be seen in the first electrode structure (310) to the fourth electrode structure (340), the thickness of the electrode structure corresponding to the negative electrode may increase as the SoC increases. In the case of the positive electrode, the opposite trend may be observed.

[0051] Meanwhile, as the thickness and ion composition change, the porosity and curvature change, but the trend of change may differ depending on various variables, such as whether the electrode is an anode or a cathode, what the composition is, and what design parameters were applied to create the electrode structure. Conventional P2D simulation methods cannot reflect such differences in the simulation, but according to the embodiments described in this disclosure, structural information reflecting such differences can be confirmed.

[0052] According to one embodiment, the electronic device (100) can identify at least some of the process variables for each simulation listed as examples above as design parameters. For example, in relation to a loading simulation, the electronic device (100) can identify at least some of the process variables related to the selection information regarding whether a roll coating method of a slurry material or a dry coating method of a powder-form material will be applied, the composition of the slurry material, the application speed, the gap of the slit die, the injection pressure, the injection angle, the binder ratio, etc. in the case of a roll coating method, and the composition of the powder, the particle size distribution of the powder, the injection speed, the electrostatic interaction with the current collector, etc. in the case of a dry coating method as design parameters. Alternatively, in relation to a rolling simulation, the electronic device (100) can identify at least some of the process variables related to the gap between rollers, the rolling speed, the initial physical properties of the electrode material, the target rolling thickness, etc. as design parameters. Furthermore, the electronic device (100) can identify, in relation to the activation simulation, at least some of the process variables related to the voltage and current profiles during charging and discharging, or the SoC to be simulated during the activation process, as design parameters. According to one embodiment, the electronic device (100) can determine at least some of the aforementioned design parameters based on the battery model to be simulated and the process variables applied to manufacture the same.

[0053] According to one embodiment, the electronic device (100) can identify a loading electrode structure by performing a loading simulation based on an electrode composition included in at least one design parameter. Additionally, the electronic device (100) can identify a rolled electrode structure by performing a rolling simulation on the loading electrode structure based on a rolling thickness included in at least one design parameter. Furthermore, the electronic device (100) can identify the rolled electrode structure to which the activation simulation is applied as a first activated electrode structure by performing an activation simulation with a first SoC applied to the rolled electrode structure. The aforementioned electrode composition, rolling thickness, and first SoC, etc., are examples of design parameters related to the loading simulation, rolling simulation, and activation simulation, respectively, and various other design parameters mentioned above may be applied to the simulation. As a result of such simulation, the electronic device (100) can identify an activated electrode structure in which the stoichiometric ratio of carrier ions on the rolled electrode structure is adjusted to correspond to the first SoC.

[0054] According to one embodiment, the electronic device (100) can measure the thickness, porosity, and curvature of an activated electrode structure in a virtual environment corresponding to an activation simulation, and verify first structural information including the above. For example, the electronic device (100) can measure the thickness of the activated electrode structure by measuring the distance between the electrode surface coordinates and the corresponding coordinates on the current collector in the virtual environment. For example, the electronic device (100) can measure the porosity of the activated electrode structure by measuring the ratio of material and the ratio of pores in the internal grid cells of the electrode structure. For example, the electronic device (100) can measure the curvature of the activated electrode structure by separating the interior of the electrode structure into a solid region and a porous region, calculating the distribution of material transport paths by applying methods such as electrochemical flow analysis or width / depth priority search, and comparing this with a straight distance. The measurement methods for thickness, porosity, and curvature described above are merely examples and are not limited to those described above, and embodiments applying other methods are also considered to be covered by this disclosure. Furthermore, the thickness, porosity, and curvature mentioned as numerical values ​​included in the first structural information are merely examples and are not limited to those described above; it is also considered that the present disclosure encompasses embodiments in which the first structural information includes other measured values ​​in a virtual environment.

[0055] According to one embodiment, the electronic device (100) can verify first performance information regarding the first active electrode structure based on first structural information. For example, the first performance information may include information regarding the remaining electric charge and internal resistance according to the rate-speed charging and discharging of a battery including the first active electrode structure. As a process for verifying such first performance information, the electronic device (100) may perform a process of verifying a first effective factor based on the first structural information, verifying a first reaction current based on the first effective factor, and verifying first performance information based on the first reaction current. Each detailed process will be described in detail below.

[0056] According to one embodiment, the electronic device (100) can determine a first effective factor based on the ratio of the porosity and the curvature of the first active electrode structure. For example, the electronic device (100) can determine a first effective factor based on the following mathematical formula 1.

[0057]

[0058] In mathematical formula 1, It corresponds to the porosity, can correspond to the curvature. Once the first effective factor is calculated in this way, the electrolytic phase electrical conductivity and diffusion coefficient and the solid phase electrical conductivity and diffusion coefficient as shown in Equation 2, when expressed by the equation, respectively The corresponding physical properties can be calculated. According to the Bruggeman Correlation, such electrolytic phase electrical conductivity and diffusion coefficient and solid phase electrical conductivity and diffusion coefficient This can be verified by multiplying the value confirmed using an effective factor or a function related to the effective factor in the basic physical properties. An example of applying the Bruggeman Correlation to verify the electrolyte phase diffusion coefficient can be seen through Equation 2.

[0059]

[0060] In mathematical formula 3 can refer to the basic diffusion coefficient of the material of the electrode structure, and or It can correspond to hyperparameters established empirically or theoretically. Once the electrolytic phase electrical conductivity and diffusion coefficients and the solid phase electrical conductivity and diffusion coefficients are confirmed, the electrolytic and solid phase carrier ion concentrations, respectively, as proposed in the Pseudo 2-Dimensional (P2D) model by Doyle, Fuller, and Newman or its variations, and It can be calculated as such, and subsequently, the first reaction current can be calculated according to the Butler-Volmer equation as shown in Equation 3 below.

[0061]

[0062] In mathematical formula 3, is the reaction rate constant, is the concentration of carrier ions in the electrolyte phase, and is the solid-phase carrier ion concentration and maximum concentration, Charge transfer coefficient, is overvoltage, is the Faraday constant, and can mean the gas constant and temperature, respectively.

[0063] Through the series of processes described above, the electronic device (100) can identify a first effective factor based on the first structural information and calculate a first reaction current based on the first effective factor. Once the first reaction current is calculated in this way, the electronic device (100) can calculate the remaining electric charge and internal resistance according to the rate limit over time when a virtual battery including the first activation electrode structure is charged and discharged. For example, the electronic device (100) can measure the voltage of the virtual battery described above in a simulated virtual space, and based on the voltage and the first reaction current, calculate the remaining electric charge according to time when each rate limit is applied, and also calculate the internal resistance. According to one embodiment, the voltage of the virtual battery can be calculated according to Equation 4.

[0064]

[0065] In mathematical formula 4, is the solid-phase anode potential of the electrode structure composition material, is the solid-phase cathodic potential of the electrode structure composition material, is the liquid phase anode potential of the electrode structure composition material, may refer to the liquid phase cathodic potential of the electrode structure composition material, and This may mean overvoltage. When the voltage is calculated in this way, the electronic device (100) can calculate the residual electric amount by integrating the current flowing through the virtual battery and the internal resistance by calculating the ratio of voltage to current.

[0066] According to one embodiment, the electronic device (100) can map the first SoC to the first structural information after verifying the first structural information. Subsequently, the electronic device (100) can generate an electrode structure table representing the mapping relationship between the SoC and the structural information. According to one embodiment, the electrode structure table can be generated based on the result of mapping the verified structural information and the corresponding SoCs, similar to the process of verifying the first structural information. For example, the electrode structure table can be generated based on the mapping between the verified structural information and the corresponding SoCs, by verifying the structural information corresponding to each at small SoC intervals, e.g., 1% intervals. As another example, the electrode structure table can be generated based on the mapping relationship between the overall SoC and structural information verified by verifying the structural information corresponding to each relatively large interval or a relatively small number of SoCs selected according to appropriate criteria, and applying interpolation or extrapolation operations to the thickness, porosity, and curvature included in the structural information. For example, the electronic device (100) can verify and map structural information for 10%, 30%, 50%, and 90% of the SoC, respectively, and then apply interpolation or extrapolation operations to the intermediate values ​​to verify the mapping relationship between the overall SoC and structural information, and then generate an electrode structure table based on this.

[0067] As an example regarding the use of the electrode structure table, the electronic device (100) can verify the input for the second SoC. The electronic device (100) can verify second structure information corresponding to the second SoC based on the electrode structure table. As described above, since the electrode structure table is generated based on the mapping relationship between the SoC and the structure information, the electronic device (100) can verify second structure information corresponding to the second SoC based on the mapping relationship of such electrode structure table. The electronic device (100) can output second structure information as structure information predicted to have an actual electrode in the second SoC. For example, an administrator who inputs the second SoC into the electronic device (100) can verify the thickness, porosity, and curvature predicted to have an actual electrode in the second SoC by referring to the output second structure information.

[0068] According to one embodiment, the electronic device (100) can generate a plurality of electrode structure tables for each combination of design parameters. For example, the electronic device (100) can generate electrode structure tables according to each combination, such as a first electrode structure table for a first combination applying a first coating process variable, a first rolling process variable, and a first activation process variable, and a second electrode structure table for a second combination applying a second coating process variable, a second rolling process variable, and a second activation process variable, through the aforementioned process of generating electrode structure tables.

[0069] According to one embodiment, when a plurality of electrode structure tables are generated for each combination of design parameters, the electronic device (100) can perform the aforementioned operation after identifying the electrode structure table corresponding to the input design parameter combination among the plurality of electrode structure tables. For example, the electronic device (100) can identify the input combination of design parameters, first identify the one corresponding to the combination among the plurality of electrode structure tables, and then, when a second SoC is input, output second structure information as structure information predicted to be possessed by the second SoC, which is an actual electrode manufactured by applying the design parameters corresponding to the combination.

[0070] According to one embodiment, the electrode structure table may be generated based further on the result of mapping performance information with the SoC, along with structural information. For example, the electronic device (100) may map the first SoC and the first performance information after verifying the first performance information. Additionally, similar to the mapping relationship between the SoC and the structural information described above, the electrode structure table may be generated to further represent the mapping relationship between the SoC and the performance information. Accordingly, the electrode structure table may be configured to further output predicted performance information along with the structural information predicted for the input SoC, based further on the mapping relationship between the SoC and the performance information. According to one embodiment, the process of making the electrode structure table represent the mapping relationship between the SoC and the performance information may be similar to the process described above performed to make the electrode structure table represent the mapping relationship between the SoC and the structural information.

[0071] Refer to Figure 4, which allows you to see at a glance an example of the electrode structure table generation process described above as a flowchart.

[0072] FIG. 4 is a flowchart illustrating an example of an electrode structure table generation process according to one embodiment.

[0073] Referring to FIG. 4, the electronic device (100) can perform a simulation (420) by applying design parameters (410). That is, the electronic device (100) can perform a coating simulation (421), a rolling simulation (422), and an activation simulation (423), and output structural information (430) as a result. The electronic device (100) can output performance information (440) based on the structural information. Afterward, the electronic device (100) can check (450) whether the output of structural information and performance information for each set SoC is complete. If the output is not yet complete, the electronic device (100) can reset the SoC (460) and perform the activation simulation (423) again. At this time, although not shown in the drawing, the electronic device (100) can calculate the stoichiometric ratio of carrier ions on the electrode structure corresponding to the SoC to be reset and apply this to perform the activation simulation (423). When the output of structure information and performance information for each set SoC is completed by repeating this process, the electronic device (100) can check the mapping relationship, generate an electrode structure table (470), and terminate the process (480).

[0074] As an example of the use of an electrode structure table generated according to such an embodiment, an electronic device (100) can verify second performance information corresponding to an input second SoC based on the electrode structure table. Subsequently, the electronic device (100) can output the second performance information along with the second structure information as performance information predicted to be possessed by the actual electrode in the second SoC. Through this, an administrator who inputs the second SoC into the electronic device (100) can verify the remaining electric charge and internal resistance by rate speed according to the charge / discharge time predicted to be possessed by the actual electrode in the second SoC by referring to the output second performance information. In addition, when performance information is verified based on one of a plurality of electrode structure tables generated for each combination of design parameters that corresponds to the input combination, such second performance information can correspond to performance information predicted to be possessed by the actual electrode in the second SoC by applying design parameters corresponding to the input combination of design parameters.

[0075] Meanwhile, based on multiple electrode structure tables generated for each combination of design parameters, it is also possible to identify the characteristics of the actual electrode that have errors caused by process deviations. For example, as one of the design parameters, the loading amount of the electrode composition material Even if set to this, the actual loading amount is due to process equipment error. , It can be manufactured as such. Since structural and performance information may vary due to such differences, it is necessary to additionally identify the characteristics of the actual electrode with errors.

[0076] To this end, according to one embodiment, the electronic device (100) can determine a deviation parameter by applying a process deviation to a design parameter. Here, the deviation parameter may be a parameter that has a value similar to the existing design parameter but has a value that is slightly varied due to the process deviation. For example, the existing design parameter is as the loading amount of the electrode composition material. If included, the deviation parameter for the same item or It may include the like. According to one embodiment, the deviation parameter may be set according to an appropriate probability distribution, or may be set based on error values ​​measured after the electrode is actually manufactured.

[0077] According to one embodiment, the electronic device (100) can identify, based on such deviation parameters, an electrode structure table corresponding to a deviation parameter among a plurality of electrode structure tables. Based on the example described above, the electronic device (100) has a loading amount of an electrode composition material. An electrode structure table corresponding to the deviation parameter can be identified. In such an electrode structure table, the loading amount of the electrode composition material The electrode structure table corresponding to the design parameter may differ. According to one embodiment, the electronic device (100) can verify third structure information corresponding to the second SoC based on the electrode structure table corresponding to the deviation parameter. Subsequently, the electronic device (100) can output the third structure information corresponding to the second SoC as one of the structure information predicted to be possessed by the actual electrode, which has some error due to the reflection of process deviation, in the second SoC. Similarly, the electronic device (100) can verify third performance information corresponding to the second SoC based on the electrode structure table corresponding to the deviation parameter, and output the third performance information as one of the performance information predicted to be possessed by the actual electrode, which has some error due to the reflection of process deviation, in the second SoC. Based on the example described above, the second structure information and the second performance information are based on the loading amount of the electrode composition material actually It can represent structural information and performance information of a battery manufactured by, and the third structural information is due to an error in the loading amount of the electrode composition material. It can display structural information and performance information of a battery manufactured using this method.

[0078] FIG. 5 shows a block diagram of an electronic device according to one embodiment.

[0079] According to one embodiment, the electronic device (100) may include a memory (101) and a processor (102). The electronic device (100) illustrated in FIG. 5 is illustrated only with components related to the present embodiment. Therefore, it will be understood by those skilled in the art related to the present embodiment that other general components may be included in addition to the components illustrated in FIG. 5. In one embodiment, the processor (102) may be included in a controller.

[0080] The processor (102) can control the overall operation of the electronic device (100) and process data and signals. The processor (102) may be composed of at least one hardware unit. Additionally, the processor (102) may be operated by one or more software modules generated by executing program code stored in memory (101) or one or more instructions. The processor (102) may include memory, and the processor (102) can control the overall operation of the electronic device (100) and process data and signals by executing program code stored in memory.

[0081] The processor (102) can be configured to identify a first active electrode structure corresponding to a first State-of-Charge (SoC) through a simulation in which at least one design parameter is applied, identify first structural information of the first active electrode structure, and map the first SoC to the first structural information.

[0082] The processor (102) may be configured to measure the thickness, porosity, and tortuosity of the first active electrode structure in a virtual environment corresponding to the simulation, and to verify the first structure information including the thickness, porosity, and tortuosity.

[0083] After mapping the first SoC and the first structure information, the processor (102) may be configured to generate an electrode structure table representing the mapping relationship between the SoC and the structure information for at least one design parameter based on the mapped first SoC and the first structure information.

[0084] After generating an electrode structure table, the processor (102) can be configured to check second structure information corresponding to the second SoC based on the electrode structure table and output second structure information as structure information predicted to be in the second SoC by applying at least one design parameter.

[0085] The processor (102) can be configured to check second performance information corresponding to the second SoC based on the electrode structure table and output the second performance information together with the second structure information as performance information predicted that the actual electrode will have in the second SoC.

[0086] The processor (102) can be configured to generate an electrode structure table as one of a plurality of electrode structure tables mapped to each combination of design parameters.

[0087] The processor (102) can be configured to check a deviation parameter that applies a process deviation to a design parameter, check an electrode structure table corresponding to the deviation parameter among a plurality of electrode structure tables, check third structure information corresponding to a second SoC based on the electrode structure table corresponding to the deviation parameter, and output the third structure information as one of the structure information that the actual electrode is predicted to have in the second SoC as the process deviation is reflected.

[0088] The processor (102) can be configured to identify a rolled electrode structure through a loading and rolling simulation with at least one design parameter applied, perform an activation simulation with a first SoC applied to the rolled electrode structure, and identify the rolled electrode structure with the applied activation simulation as a first activation electrode structure.

[0089] The processor (102) can be configured to identify a loading electrode structure by performing a loading simulation based on an electrode composition included in at least one design parameter, and to identify a rolled electrode structure by performing a rolling simulation for the loading electrode structure based on a rolling thickness included in at least one design parameter.

[0090] The processor (102) can be configured to adjust the stoichiometric ratio of carrier ions on the rolled electrode structure to correspond to the first SoC as an activation simulation.

[0091] After verifying the first structure information, the processor (102) may be configured to verify first performance information for the first activation electrode structure based on the first structure information.

[0092] The processor (102) can be configured to identify a first effective factor for a first active electrode structure based on first structural information, identify a first reaction current for a first active electrode structure based on the first effective factor, and identify first performance information based on the first reaction current.

[0093] The processor (102) may be configured to check first performance information including the remaining electric amount and internal resistance according to the rate-speed of the first activation electrode structure based on the first reaction current.

[0094] According to an embodiment, the electronic device (100) may additionally include a transceiver for performing wired / wireless communication. The electronic device (100) may communicate with an external electronic device using the transceiver. The external electronic device may be a terminal or a server. In addition, communication technologies used by the transceiver may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc.

[0095] The electronic device according to the embodiments described above may include a processor, memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, and user interface devices such as a touch panel, a key, a button, etc. Methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable code or program instructions executable on the processor. Here, computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical reading media (e.g., CD-ROM, DVD: Digital Versatile Disc). The computer-readable recording medium may be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The medium may be readable by a computer, stored in memory, and executed by a processor.

[0096] The present embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the embodiment may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., which can execute various functions by the control of one or more microprocessors or other control devices. Similar to how components may be implemented as software programming or software elements, the present embodiment may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors. Additionally, the present embodiment may employ prior art for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations. The above terms may include the meaning of a series of software processes (routines) in conjunction with processors, etc.

[0097] The aforementioned embodiments can be implemented as artificial intelligence (AI) through the processor and memory of an electronic device. The processor may consist of one or more processors, and the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (digital signal processors), graphics-dedicated processors such as GPUs and VPUs (vision processing units), or AI-dedicated processors such as NPUs. The one or more processors may be controlled to process input data according to predefined operation rules or AI models stored in memory. Alternatively, if the one or more processors are AI-dedicated processors, the AI-dedicated processors may be designed with a hardware structure specialized for processing a specific AI model.

[0098] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform a desired characteristic (or objective) are created by a basic artificial intelligence model being trained using a number of learning data by a learning algorithm. Such learning may be performed on the electronic device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0099] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and can perform neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. The artificial neural network may include, but is not limited to, deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), bidirectional recurrent deep neural networks (BRDNN), or deep Q-networks.

[0100] The aforementioned embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. As an electronic device for predicting electrode structure, processor; and It includes memory that stores one or more instructions, and The above processor, by performing the above one or more instructions, By applying at least one design parameter to a simulation, a first active electrode structure corresponding to a first State-of-Charge (SoC) is identified, and Confirm the first structural information of the above first activation electrode structure, and An electronic device configured to map the first SoC and the first structural information.

2. In Paragraph 1, The above processor, In a virtual environment corresponding to the above simulation, the thickness, porosity, and tortuosity of the first activation electrode structure are measured, and An electronic device for predicting an electrode structure, configured to verify the first structural information including the thickness, the porosity, and the curvature.

3. In Paragraph 1, The above processor, After mapping the first SoC and the first structural information, An electronic device for predicting an electrode structure, configured to generate an electrode structure table representing a mapping relationship between the SoC and the structure information for at least one design parameter based on the mapped first SoC and the first structure information.

4. In Paragraph 3, The above processor, After generating the above electrode structure table, Based on the above electrode structure table, identify second structure information corresponding to the second SoC, and An electronic device for predicting an electrode structure, configured to output the second structural information as structural information predicted to be in the second SoC of an actual electrode manufactured by applying at least one design parameter.

5. In Paragraph 4, The above processor, Based on the above electrode structure table, second performance information corresponding to the second SoC is verified, and An electronic device for predicting an electrode structure, configured to output the second performance information together with the second structure information as performance information predicted to be present in the second SoC of the actual electrode.

6. In Paragraph 3, The above processor, An electronic device for predicting an electrode structure, configured to generate the electrode structure table as one of a plurality of electrode structure tables mapped to each combination of design parameters.

7. In Paragraph 6, The above processor, Check the deviation parameter by applying the process deviation to the above design parameter, and Among the plurality of electrode structure tables above, identify the electrode structure table corresponding to the deviation parameter, and Based on the electrode structure table corresponding to the above deviation parameter, confirm the third structure information corresponding to the second SoC, and An electronic device for predicting an electrode structure, configured to output the third structural information as one of the structural information that the actual electrode is predicted to have in the second SoC as a result of reflecting the above process deviation.

8. In Paragraph 1, The above processor, By verifying the rolled electrode structure through a loading and rolling simulation with at least one of the above design parameters applied, and An activation simulation applying the above-mentioned first SoC is performed on the above-mentioned rolled electrode structure, and An electronic device for predicting an electrode structure, configured to identify the rolled electrode structure to which the above activation simulation is applied as the first activation electrode structure.

9. In Paragraph 8, The above processor, By performing the loading simulation based on the electrode composition included in at least one design parameter, the loading electrode structure is identified, and An electronic device for predicting an electrode structure, configured to verify the rolled electrode structure by performing a rolling simulation for the loading electrode structure based on the rolling thickness included in at least one design parameter.

10. In Paragraph 8, The above processor, An electronic device for predicting an electrode structure, wherein, as the above activation simulation, the stoichiometric ratio of carrier ions on the rolled electrode structure is set to correspond to the first SoC.

11. In Paragraph 1, The above processor, After verifying the above first structural information, An electronic device for predicting an electrode structure, configured to verify first performance information for the first active electrode structure based on the first structural information.

12. In Paragraph 11, The above processor, Based on the above first structural information, a first effective factor for the first activation electrode structure is identified, and Based on the first effective factor above, confirm the first reaction current for the first activated electrode structure, and An electronic device for predicting an electrode structure, configured to verify the first performance information based on the first reaction current.

13. In Paragraph 12, The above processor, An electronic device for predicting an electrode structure, configured to verify first performance information including a residual electric quantity and internal resistance according to the rate limit based on the charge / discharge time of the first activation electrode structure, based on the first reaction current.

14. In a method for predicting the electrode structure of an electronic device, A step of identifying a first active electrode structure corresponding to a first State-of-Charge (SoC) through a simulation in which at least one design parameter is applied; A step of confirming the first structural information of the first activation electrode structure; and An electrode structure prediction method comprising the step of mapping the first SoC and the first structural information.

15. A computer-readable, non-transient recording medium having a program for executing the method of paragraph 14 on a computer.

Images