Secondary battery manufacturing system

Abstract

According to exemplary embodiments, a secondary battery manufacturing system is provided. The system comprises: a secondary battery manufacturing facility; a particle sensing device installed in the secondary battery manufacturing facility and configured to collect particle data indicating the size and density of particles generated from the secondary battery manufacturing facility; a particle collecting device installed in the secondary battery manufacturing facility and configured to collect the particles; and a particle analyzer configured to collect composition data of the particles collected by the particle collecting device.

Description

Secondary battery manufacturing system

[0001] The present invention relates to a secondary battery manufacturing system. The present application claims the benefit of Korean application No. 10-2024-0130624, filed on September 26, 2024, which is incorporated herein by reference in its entirety.

[0002] Unlike primary batteries, secondary batteries can be charged and discharged multiple times. Secondary batteries are widely used as energy sources for various wireless devices such as handsets, laptops, and cordless vacuum cleaners. Recently, as the manufacturing cost per unit capacity of secondary batteries has decreased dramatically due to improved energy density and economies of scale, and as the driving range of BEVs (battery electric vehicles) has increased to a level equivalent to that of fuel vehicles, the primary use of secondary batteries is shifting from mobile devices to mobility.

[0003] Secondary batteries are manufactured through electrode, assembly, and activation processes. To improve the yield and reliability of the secondary battery manufacturing process, it is necessary to identify foreign substances generated inside the manufacturing equipment.

[0004] The problem that the technical concept of the present invention aims to solve is to provide a secondary battery manufacturing system capable of identifying foreign substances occurring within a secondary battery manufacturing facility.

[0005] According to exemplary embodiments of the present invention for solving the above-described problem, a secondary battery manufacturing system is provided. The system comprises: a secondary battery manufacturing facility; a particle sensing device installed in the secondary battery manufacturing facility and configured to collect particle data indicating the size and density of particles generated from the secondary battery manufacturing facility; a particle collecting device installed in the secondary battery manufacturing facility and configured to collect said particles; and a particle analyzer configured to collect composition data of said particles collected by said particle collecting device.

[0006] The above particle capture device includes a filter and a suction device.

[0007] The above filter is a HEPA filter.

[0008] The particle analyzer is configured to perform an XRF (X-ray Fluorescence) test on the filter to collect the composition data.

[0009] It further includes a filter transfer system configured to transfer the filter to the particle analyzer.

[0010] The above filter transfer system includes one of a humanoid, a mobile manipulator, and a quadruped robot.

[0011] The filter is detachably coupled to the particle collection device.

[0012] The particle collection device includes a first hose connected to the particle sensing device.

[0013] The particle collection device further includes a second hose spaced apart from the particle sensing device.

[0014] The above system further includes a server comprising a particle prediction model trained based on the particle data and the composition data.

[0015] The above particle prediction model is configured to determine the elements constituting the particles and the amount of each of the elements based on the particle data.

[0016] The above server is configured to monitor the density and composition of particles within the secondary battery manufacturing facility in real time.

[0017] According to exemplary embodiments, a secondary battery manufacturing system is provided. The system comprises: an air blow configured to blow an electrode sheet; a suction configured to suck in particles generated from the electrode sheet; a particle collecting device connected to the suction and configured to collect the particles; and a particle analyzer configured to collect composition data of the particles collected by the particle collecting device.

[0018] The particle collection device includes a hose connected to the suction and a filter connected to the hose.

[0019] A secondary battery manufacturing system according to exemplary embodiments of the present invention includes a particle sensing device installed within a secondary battery manufacturing facility and configured to collect particle data indicating the concentration and size of particles, and an analyzer configured to collect composition data indicating the composition of particles captured by the filter assembly. A server of the secondary battery manufacturing system can be trained based on the particle data and composition data, and accordingly, by monitoring the particle data within the secondary battery manufacturing facility in real time, the density, size, and composition of the particles within the secondary battery manufacturing facility can be known.

[0020] The effects obtainable from the exemplary embodiments of the present invention are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art to which the exemplary embodiments of the present disclosure belong from the following description. That is, unintended effects resulting from the implementation of the exemplary embodiments of the present disclosure can also be derived by those skilled in the art from the exemplary embodiments of the present disclosure.

[0021] FIG. 1 is a drawing showing a secondary battery manufacturing system according to exemplary embodiments.

[0022] FIG. 2 is a drawing showing a particle collection device according to exemplary embodiments.

[0023] FIG. 3 is a drawing showing the installation of a particle collection device according to exemplary embodiments.

[0024] Figure 4 is a graph showing the change in particle concentration over time.

[0025] Figure 5 shows the types and amounts of elements detected by XRF (X-ray Fluorescence) inspection.

[0026] FIG. 6 is a flowchart illustrating a method for providing a particle prediction model according to exemplary embodiments.

[0027] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. Prior to this, terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings. Instead, based on the principle that the inventor can appropriately define the concepts of terms to best describe his invention, they should be interpreted in a meaning and concept consistent with the technical spirit of the present invention.

[0028] Therefore, the embodiments described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention; thus, it should be understood that various equivalents and modifications that can replace them may exist at the time of filing this application.

[0029] In addition, in describing the present invention, if it is determined that a detailed description of related known components or functions may obscure the essence of the invention, such detailed description is omitted.

[0030] Since embodiments of the present invention are provided to more fully explain the invention to those skilled in the art, the shapes and sizes of the components in the drawings may be exaggerated, omitted, or schematically depicted for clearer explanation. Accordingly, the size or proportion of each component does not entirely reflect the actual size or proportion.

[0031]

[0032] (1st and 2nd embodiments)

[0033] FIG. 1 is a drawing showing a secondary battery manufacturing system (1000) according to exemplary embodiments.

[0034] FIG. 2 is a drawing showing a particle collection device (130) according to exemplary embodiments.

[0035] FIG. 3 is a drawing showing the installation of a particle collection device (130) according to exemplary embodiments.

[0036] Referring to FIGS. 1 to 3, a secondary battery manufacturing system (1000) may be configured to perform a secondary battery manufacturing process. The secondary battery manufacturing system (1000) may be configured to perform at least some of an electrode process, an assembly process, and an activation process.

[0037] The secondary battery manufacturing system (1000) may include secondary battery manufacturing facilities (111, 112, 113, 114), particle sensing devices (120), particle collecting devices (130), a filter transfer system (150), a particle analyzer (200), and a server (300).

[0038] Each of the secondary battery manufacturing facilities (111, 112, 113, 114) may be configured to perform at least one of some steps of the electrode process and some steps of the assembly process. Each of the secondary battery manufacturing facilities (111, 112, 113, 114) may perform different processes. Some of the secondary battery manufacturing facilities (111, 112, 113, 114) may perform the same process.

[0039] The electrode process may include forming an electrode assembly. The formation of the electrode assembly may include a mixing process, a coating process, a roll pressing process, and an optional slitting process.

[0040] The mixing process includes dissolving an electrode active material, a conductive material, a binder, etc., in a solvent medium to provide an electrode slurry. The solvent may be an aqueous solvent or a non-aqueous solvent. The solvent may include any one of DMSO, isopropyl alcohol, NMP, acetone, water, and mixtures thereof. The amount of solvent used may be determined based on the target viscosity of the slurry. Parameters determining the amount of solvent include the coating thickness of the slurry, manufacturing yield, and workability.

[0041] The electrode active material may be a positive electrode active material, but is not limited thereto. A positive electrode active material is a material capable of causing an electrochemical reaction. The positive electrode active material may be a lithium transition metal oxide. The positive electrode active material is, chemical formula Li 1+x M 1-y M' y PO 4-z X zIt may include, but is not limited to, an olivine-based lithium metal phosphate represented by (wherein M is a transition metal, more specifically one of Fe, Mn, Co and Ni, M' is one of Al, Mg and Ti, X is one of F, S and N, -0.5≤x≤+0.5, 0≤y≤0.5, and 0≤z≤0.1).

[0042] The positive electrode active material comprises a layered compound such as lithium cobalt oxide (LiCoO2) and lithium nickel oxide (LiNiO2) substituted with one or more transition metals; lithium manganese oxide substituted with one or more transition metals; and the chemical formula LiNi 1-y M y Lithium nickel-based oxide represented by O2 (where M is any one of Co, Mn, Al, Cu, Fe, Mg, B, Cr, Zn, and Ga, and 0.01≤y≤0.7); Li 1+z Ni 1 / 3 Co 1 / 3 Mn 1 / 3 O2 and Li 1+zN i 0.4 Mn 0.4 Co 0.2 Li like O2 1+z Ni b Mn c Co 1-(b+c+d) M d O (2-e) A e It may also include a lithium nickel cobalt manganese composite oxide represented as (where -0.5≤z≤0.5, 0.1≤b≤0.8, 0.1≤c≤0.8, 0≤d≤0.2, 0≤e≤0.2, b+c+d<1, M is any one of Al, Mg, Cr, Ti, Si and Y, and A is any one of F, P and Cl).

[0043] In addition, the electrode active material may be a negative electrode active material. The negative electrode active material may include carbon, for example, non-graphitizable carbon, graphite-based carbon, etc. The negative electrode active material is, for example, Li xFe2O3(0≤x≤1), LixWO2(0≤x≤1), Sn x Me 1-x Me y O z (wherein Me is any one of Mn, Fe, Pb, and Ge, and Me' is any one of Al, B, P, Si, Group 1, Group 2, and Group 3 elements of the periodic table, and halogens; 0 <x≤1 이고; 1≤y≤3 이며; 1≤z≤8) 등의 금속 복합 산화물을 포함할 수 있다. 음극 활물질은, 예컨대, 리튬 금속; 리튬 합금; 규소계 합금; 및 주석계 합금 중 어느 하나를 포함할 수 있다. 음극 활물질은, 예컨대, SnO, SnO2, PbO, PbO2, Pb2O3, Pb3O4, Sb2O3, Sb2O4, Sb2O5, GeO, GeO2, Bi2O3, Bi2O4및 Bi2O5등의 금속 산화물을 포함할 수 있다. 음극 활물질은, 예컨대, 폴리아세틸렌 등의 도전성 고분자 및 Li-Co-Ni 계 재료 등을 포함할 수도 있다.

[0044] The conductive material can possess conductivity without causing chemical changes in the secondary battery ultimately manufactured. The conductive material may include, for example, graphite such as natural graphite or artificial graphite; carbon black such as acetylene black, ketjen black, channel black, furnace black, lamp black, and thermal black; conductive fibers such as carbon fibers or metal fibers; metal powders such as carbon fluoride, aluminum, and nickel powder; conductive whiskey such as zinc oxide and potassium titanate; conductive metal oxides such as titanium oxide and polyphenylene derivatives.

[0045] The binder can enhance the bonding between the active material and the conductive material and the bonding strength to the current collector sheet (CS). The binder may include PVDF (Polyvinylidene Fluoride), polyvinyl alcohol, CMC, starch, hydroxypropyl cellulose, regenerated cellulose, polyvinylpyrrolidone, tetrafluoroethylene, polyethylene, polypropylene, EPDM, sulfonated EPDM, styrene butylene rubber, fluororubber, and various copolymers.

[0046] In the coating process, an electrode slurry may be applied to the electrode current collector. The coating process may be a roll-to-roll process. The coating process may be performed by a die coater. In the coating process, an insulating slurry may be further applied to the electrode current collector.

[0047] In a roll pressing process, an electrode sheet containing an electrode current collector and an active material can be passed between a pair of opposing work rolls. The roll pressing process may be a roll-to-roll process. The electrode sheet can be pressed by a pair of work rolls. By the roll pressing process, the surface of the electrode sheet can be flattened, and the bonding strength between the electrode active material and the electrode current collector can be enhanced.

[0048] The electrode sheet can be separated into a plurality of electrode sheets by a slitting process. The slitting process can be performed selectively depending on the specifications of the battery cell to be manufactured based on the electrode sheet.

[0049] In the notching process, a plurality of electrode tabs may be formed on the electrode sheet. The notching process may be performed by a laser notcher or by a notching machine including a notching knife.

[0050] The formation of the electrode assembly may further include a lamination and stacking process or a winding process. A stacked electrode assembly may be provided comprising a plurality of anodes, a plurality of cathodes, and a plurality of separators separating them, which are stacked in one direction by a lamination and stacking process. A jellyroll type electrode assembly may be provided by a winding process, which includes a wound structure of anodes, cathodes, and separators.

[0051]

[0052] Particle sensing devices (120) may be installed within secondary battery manufacturing facilities (111, 112, 113, 114). Unlike in FIG. 1, two or more particle sensing devices (120) may be installed in some of the secondary battery manufacturing facilities (111, 112, 113, 114), or particle sensing devices (120) may be installed only in some of the secondary battery manufacturing facilities (111, 112, 113, 114).

[0053] Particle sensing devices (120) can be installed in locations within secondary battery manufacturing facilities (111, 112, 113, 114) where there is a high probability of particle generation. For example, particle sensing devices (120) can be installed near a blowing device configured to perform a blowing process on an electrode slurry on an electrode current collector, near a laser device configured to perform an ablation process on an electrode slurry, and near a notching device. Particle sensing devices (120) can also be installed at locations where major foreign matter is generated during line setup, and traceability of the particle generation history during line setup can be enhanced.

[0054] Each of the particle sensing devices (120) may be configured to detect the atmosphere within the secondary battery manufacturing facility. Each of the particle sensing devices (120) may include a sensing module and a pump module.

[0055] The sensing module may be configured to detect particles contained in the air within secondary battery manufacturing facilities (111, 112, 113, 114) introduced into the sensing module. Each sensing module may be a laser diffuse reflection-based sensor, but is not limited thereto. The sensing module may be configured to detect the concentration and size of the particles. The sensing module may be configured to continuously detect the concentration and size of the particles.

[0056] The pump module may be configured to provide suction. Air inside the secondary battery manufacturing facilities (111, 112, 113, 114) may be introduced into the particle sensing devices (120) by the suction of the pump module. The pump module may include one of a rotary vane vacuum pump, a turbo molecular pump, a dry scroll pump, a Roots blower pump, a diffusion pump, a side channel pump, a piston pump, a diaphragm pump, a cryopump, a sputter ion pump, or a gas ballast pump.

[0057] The secondary battery manufacturing facility (111) may include a controller (111C). The secondary battery manufacturing facility (112) may include a controller (112C). The secondary battery manufacturing facility (113) may include a controller (113C). The secondary battery manufacturing facility (114) may include a controller (114C). Each of the controllers (111C, 112C, 113C, 114C) may be a PLC (Programmable Logic Controller). A PLC is a special type of microprocessor-based controller that uses programmable memory to store commands and controls machines and processes by implementing functions such as logic, sequencing, timing, counting, and arithmetic. PLCs are easy to operate and program.

[0058] Particle data (PD) sensed by particle sensing devices (120) can be transmitted to a server (300) via controllers (111C, 112C, 113C, 114C). Each sensing module of the particle sensing devices (120) can be connected via wired or wireless connection to a corresponding controller (111C, 112C, 113C, 114C) to communicate with a corresponding controller (111C, 112C, 113C, 114C). The particle data (PD) may include time-series data of the size and concentration of particles.

[0059] Figure 4 is a graph showing the change in particle concentration over time. In other words, Figure 4 is an example of particle data (PD). In Figure 4, the horizontal axis represents time, and the vertical axis represents the density of the particles.

[0060]

[0061] Particle collection devices (130) may be installed within secondary battery manufacturing facilities (111, 112, 113, 114). Each of the particle collection devices (130) may include hoses (131, 132), a filter (133), and a suction device (135).

[0062] Air within secondary battery manufacturing facilities (111, 112, 113, 114) can be introduced into a filter (133) through hoses (131, 132), and particles in the air can be captured in the filter (133). The filter (133) may include a HEPA filter, but is not limited thereto.

[0063] The suction device (135) may be, for example, a suction fan. The suction device (135) may provide suction power for hoses (131, 132) to suck air into secondary battery manufacturing facilities (111, 112, 113, 114).

[0064] One hose (131) of the hoses (131, 132) of the particle collection devices (130) may be connected to a corresponding one of the particle sensing devices (120). Accordingly, composition data (CD) collected through the particle collection devices (130) may be synchronized with particle data (PD) and may be used to train a particle prediction model as follows. The other hose (132) of the hoses (131, 132) of the particle collection devices (130) may not be connected to the particle sensing devices (120).

[0065] Additionally, unlike in FIG. 1, the particle collection devices (130) may be installed at a separate location from the particle sensing devices (120). FIG. 3 shows a particle collection device (130) installed separately from the particle sensing devices (120). More specifically, the example in FIG. 3 relates to a particle collection device (130) installed in a location where there is a high probability of particle generation.

[0066] FIG. 3 illustrates blowing devices (111B) and suctions (111S) of a secondary battery manufacturing facility (111). The blowing devices (111B) can blow air at a set pressure onto an electrode sheet (ES) as indicated by the arrow, and the suctions (111S) can suck up particles separated from the electrode sheet (ES) by the blowing air. The operation of the blowing devices (111B) and suctions (111S) can be performed to remove foreign matter on the electrode sheet (ES), but is not limited thereto.

[0067] The hoses (131, 132) can be connected to the suctions (111S). Accordingly, air containing particles sucked from the suctions (111S) can be introduced into the particle collection device (130), and accordingly, the particle collection device (130) can collect the particles.

[0068]

[0069] The filter (133) of the particle collection device (130) is detachable and replaceable. After the filter (133) has collected a sufficient amount of particles, it can be transported by a filter transport system (150) for analysis. The filter (133) can be delivered to a particle analyzer (200) by the filter transport system (150). The filter transport system (150) may include any one of a humanoid, a mobile manipulator, and a quadruped robot. The filter transport system (150) may be configured to detach the filter (133) from the particle collection devices (130) and load the filter (133) into the particle analyzer (200). The filter transport system (150) may be configured to reinstall a new filter (133) in the particle collection devices (130).

[0070]

[0071] The particle analyzer (200) may be configured to analyze particles captured by the filter (133). The particle analyzer (200) may be configured to perform X-ray Fluorescence (XRF) testing on the particles captured by the filter (133). XRF testing is an X-ray fluorescence analysis technique in which the type and amount of elements constituting the particles can be determined by measuring the amount of fluorescent radiation generated by irradiating a sample with X-rays. XRF testing is a non-destructive testing method that enables highly accurate elemental analysis without damaging the sample.

[0072] Figure 5 is a graph showing the types and amounts of elements detected by XRF inspection.

[0073] The composition data (CD) is similar to that shown in FIG. 5. The particle analyzer (200) may be configured to transmit the composition data (CD), which is the result of the XRF inspection, to a server (300). In FIG. 5, the area indicated by gray shading represents reference data collected from a filter (133) that does not capture particles, and the graph indicated by dashed lines represents composition data (CD) collected from a filter (133) that captures particles.

[0074] The server (300) may be, for example, a Manufacturing Execution System (MES). The server (300) may be configured to perform the input, processing, output, and communication of various data for the execution of the secondary battery process, such as the transmission and updating of recipes, management of material input and completion quantities, and process tracking.

[0075] According to other exemplary embodiments, the server (300) may be configured to store and process raw measurement data. The server (300) can manage the quality of processing of the electrode sheet (ES) by continuously monitoring the processing of the electrode sheet (ES) based on the measurement data. According to exemplary embodiments, the server (300) may be a Statistical Process Controller (SPC). By collecting and analyzing manufacturing data in near real-time, the server (300) can identify problematic conditions in a timely manner and provide an alarm to the operator before potential problems occur.

[0076] The server (300) may be configured to store particle data (PD) and composition data (CD). The server (300) may include a particle prediction model provided based on the particle data (PD) and composition data (CD). The particle prediction model may be, for example, a machine learning model learned based on the particle data (PD) and composition data (CD). The particle prediction model may be, for example, a deep learning model learned based on the particle data (PD) and composition data (CD). The particle prediction model may be configured to determine the composition of particles based on time series data of the size and concentration of particles detected by a sensing module. The particle prediction model may be learned by taking particle data (PD) as input and composition data (CD) matched to particle data (PD) as output, but is not limited thereto.

[0077] To provide a particle prediction model, particle data (PD) and composition data (CD) may be preprocessed. Preprocessing may include handling missing values, normalization and standardization, and data partitioning.

[0078] Particle prediction models can be regression models. Particle prediction models can be based on algorithms such as linear regression, multilayer perceptron, decision tree regression, random forest regression, and XGBoost.

[0079] For training a particle prediction model, a loss function can be set and an optimization algorithm can be used. The loss function can generally be the mean squared error, which can minimize the difference between the predicted values ​​of the particle prediction model and the actual data. The optimization algorithm can be either stochastic gradient descent or ADAM (Adaptive Moment Estimation), but is not limited thereto.

[0080] To build the model, various hyperparameters such as the learning rate, the size and number of hidden layers, and the depth of the decision tree can be pre-set, and additional features can be used in addition to particle data (PD) and composition data (CD).

[0081] Particle prediction models can be provided based on Python, but are not limited thereto. Particle prediction models can be based on Python libraries such as Scikit-learn, TensorFlow / Keras, and XGBoost.

[0082] According to exemplary embodiments, the server (300) may be configured to determine the types of elements constituting the particles and the amounts of each element based on particle data (PD) occurring in real time. Accordingly, the server (300) may monitor the density of particles and the composition of particles within secondary battery manufacturing facilities (111, 112, 113, 114) in real time. The server (300) may be configured to generate a signal to generate an alarm if types of particles critical to the yield occur. The signal may be transmitted to a corresponding controller (111C, 112C, 113C, 114C).

[0083] Additional features may include low voltage failure test data. Low voltage failure test data may be collected through methods such as OCV measurement, charge / discharge test, load test, balancing test, low and high temperature environment test, and equivalent series resistance measurement.

[0084] A low voltage failure is when a secondary battery exhibits a voltage lower than the voltage expected within its normal operating range. A low voltage failure in a secondary battery can be caused by unwanted internal short circuits caused by particles. According to exemplary embodiments, the server (300) may be configured to predict the composition of particles based on particle data indicating the density and size of the particles, and to evaluate the risk of a low voltage failure in a battery cell based on particle data (PD) accumulated during the battery cell manufacturing process and the composition of particles predicted from the particle data (PD). Accordingly, selective high-intensity management can be performed on battery cells where the risk of a low voltage failure exceeds a threshold, and the throughput and reliability of secondary battery manufacturing can be improved.

[0085] The server (300) may include a physical server or a cloud server. The server (300) may be implemented as a virtual server, but is not limited thereto. The server (300) may provide data and analysis results to the operator through various frameworks. The framework may include a protocol that supports data transmission so that the client device can visualize the data through a user interface and provide an updated visualization when the data is calculated by the server (300). The protocol that supports data transmission may use HTML, JavaScript, and / or JSON.

[0086] The server (300) may include various APIs (Application Programming Interfaces) for storing data in databases and other data management tools. The APIs may also be used to retrieve data from databases of various data management systems. The data management system may provide access to the database, pull data from the database, retrieve data, and generate metrics. Here, metrics are tools for visualizing data. Metrics include time-series generated measurements and may be used for monitoring applications and generating status alerts.

[0087] The particle analyzer (200) and server (300) may be implemented using hardware, firmware, software, and combinations thereof. For example, the particle analyzer (200) and server (300) may include computing devices such as workstation computers, desktop computers, laptop computers, and tablet computers. The particle analyzer (200) and server (300) may include any one of a simple controller, a complex processor such as a microprocessor, CPU, GPU, etc., a processor configured by software, dedicated hardware, and firmware. The particle analyzer (200) and server (300) may be implemented using, for example, a general-purpose computer or application-specific hardware such as a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), and an Application Specific Integrated Circuit (ASIC).

[0088] The operation of the particle analyzer (200) and the server (300) may be implemented as instructions stored on a machine-readable medium that can be read and executed by one or more processors. Here, the machine-readable medium may include any mechanism for storing and / or transmitting information in a form readable by a machine (e.g., a computing device). For example, the machine-readable medium may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage medium, optical storage medium, flash memory, electrical, optical, acoustic, or other forms of radio signals (e.g., carrier waves, infrared signals, digital signals, etc.) and any other signals.

[0089] The particle analyzer (200) and the server (300) may be composed of firmware, software, routines, and instructions for performing the aforementioned operation or any process described below. For example, the particle analyzer (200) and the server (300) may be implemented in memory.

[0090] The secondary battery manufacturing system (1000) can implement a plug-and-play architecture with APIs for data acquisition to provide plug-and-play connectivity for sensors, measuring instruments, and inspectors. Accordingly, resources at specific process steps and specific sites can be easily transferred to other processes and other sites, or new resources can be easily introduced to each process step and site.

[0091] In some embodiments, the secondary battery manufacturing system (1000) may further include a manual input system that allows an operator to input manufacturing data. The secondary battery manufacturing system (1000) may allow an operator to input data using an input tool and computer-based input of manufacturing data, such as scraping Excel files. The manual input system may be, for example, a Human-Machine Interface (HMI) of a Supervisory Control and Data Acquisition (SCADA). SCADA may generally include a combination of software and hardware, such as a PLC and Remote Terminal Units (RTU). The HMI is a screen that supports communication between the operator and the SCADA system and is a key element of the SCADA system. For example, manual input via the HMI may include the selection of defect types and the reflection of performance upon completion.

[0092]

[0093] (3rd Example)

[0094] FIG. 6 is a flowchart illustrating a method for providing a particle prediction model according to exemplary embodiments.

[0095] Referring to FIGS. 1 and FIGS. 6, particle data (PD) can be collected at P110. Particle data (PD) can be collected by particle sensing devices (120). Particle data (PD) can be transmitted to a server (300) via controllers (111C, 112C, 113C, 114C).

[0096] Subsequently, composition data (CD) can be collected in P120. The collection of composition data (CD) may include separating the filter (133) from the particle collection devices (130), transferring the filter (133) to a particle analyzer (200), and determining the type and amount of particles collected by the filter (133).

[0097] Next, in P130, a particle prediction model can be provided. According to exemplary embodiments, as described with reference to FIGS. 1 to 5, a particle prediction model can be trained based on particle data (PD) and composition data (CD).

[0098]

[0099] The present invention has been described in more detail above through drawings and embodiments. However, the configurations described in the drawings or embodiments described in this specification are merely one embodiment of the present invention and do not represent all technical concepts of the present invention; therefore, it should be understood that various equivalents and modifications that can replace them may exist at the time of filing this application.

Claims

1. Secondary battery manufacturing equipment; A particle sensing device installed in the above secondary battery manufacturing facility and configured to collect particle data indicating the size and density of particles generated from the secondary battery manufacturing facility; A particle collecting device installed in the above secondary battery manufacturing facility and configured to collect the particles; and A secondary battery manufacturing system comprising a particle analyzer configured to collect composition data of the particles captured by the particle capture device.

2. In Paragraph 1, A secondary battery manufacturing system characterized by the above particle capture device including a filter and a suction device.

3. In Paragraph 2, A secondary battery manufacturing system characterized in that the above filter is a HEPA filter.

4. In Paragraph 2, A secondary battery manufacturing system characterized by the particle analyzer being configured to perform an XRF (X-ray Fluorescence) inspection on the filter to collect the composition data.

5. In Paragraph 2, A secondary battery manufacturing system further comprising a filter transfer system configured to transfer the above filter to the particle analyzer.

6. In Paragraph 5, A secondary battery manufacturing system characterized by the filter transfer system comprising one of a humanoid, a mobile manipulator, and a quadruped robot.

7. In Paragraph 2, A secondary battery manufacturing system characterized in that the above filter is detachably coupled to the above particle capture device.

8. In Paragraph 1, A secondary battery manufacturing system characterized by the particle collection device including a first hose connected to the particle sensing device.

9. In Paragraph 8, A secondary battery manufacturing system characterized by the particle collection device further including a second hose spaced apart from the particle sensing device.

10. In Paragraph 1, A secondary battery manufacturing system further comprising a server including a particle prediction model trained based on the particle data and the composition data.

11. In Paragraph 10, A secondary battery manufacturing system characterized by the above particle prediction model being configured to determine the elements constituting the particles and the amount of each of the elements based on the particle data.

12. In Paragraph 10, A secondary battery manufacturing system characterized by the above-mentioned server being configured to monitor the density and composition of particles within the secondary battery manufacturing facility in real time.

13. An air blower configured to blow electrode sheets; A suction device configured to inhale particles generated from the electrode sheet; A particle collecting device connected to the suction and configured to collect the particles; and A secondary battery manufacturing system comprising a particle analyzer configured to collect composition data of the particles captured by the particle capture device.

14. In Paragraph 13, A secondary battery manufacturing system characterized by the particle capture device comprising a hose connected to the suction and a filter connected to the hose.

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