Smart high temperature aging system

Abstract

The present invention relates to a high-temperature aging system that includes a battery cell tray stack having a structure in which battery cell trays, each accommodating a large number of battery cells, are stacked in multiple stages; at least one tray rack that is housed inside a high-temperature aging chamber and includes a lattice-shaped storage space in which the battery cell tray stack is housed; a stacker crane that transports the battery cell trays to the lattice-shaped storage space; a thermal imaging camera that is installed on the stacker crane and acquires thermal image temperature data of the battery cell tray stack loaded in the lattice-shaped storage space; and a control unit that adjusts the temperature inside the high-temperature aging chamber based on the thermal image temperature data.

Description

[Technical field]

[0001] The present invention relates to a smart high temperature aging system for battery cells.

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2021-0088257 dated July 6, 2021, and all contents disclosed in the documents of that Korean patent application are incorporated herein by reference. [Background technology]

[0003] In recent years, rechargeable secondary batteries have been widely used as energy sources for wireless mobile devices. Secondary batteries are also attracting attention as energy sources for electric vehicles and hybrid electric vehicles, which are proposed as a solution to address air pollution caused by existing gasoline and diesel vehicles that use fossil fuels. Therefore, the types of applications that use secondary batteries are becoming increasingly diverse due to the advantages of secondary batteries, and secondary batteries are expected to be applied to many more fields and products in the future.

[0004] Such secondary batteries are classified into lithium ion batteries, lithium ion polymer batteries, lithium polymer batteries, etc. according to the configuration of the electrodes and electrolyte, and the use of lithium ion polymer batteries, which are less likely to leak electrolyte and are easy to manufacture, is increasing. In general, secondary batteries are classified into cylindrical batteries and prismatic batteries in which an electrode assembly is housed in a cylindrical or prismatic metal can, and pouch-type batteries in which an electrode assembly is housed in a pouch-type case of an aluminum laminate sheet, according to the shape of the battery case. The electrode assembly housed in the battery case is a chargeable and dischargeable power generating element having a structure in which a positive electrode, a negative electrode, and a separator are interposed between the positive electrode and the negative electrode, and is classified into a jelly roll type in which a separator is interposed between a long sheet-like positive electrode and a negative electrode coated with an active material and wound up, and a stack type in which a number of positive electrodes and negative electrodes of a predetermined size are stacked in sequence with a separator interposed between them.

[0005] Such secondary batteries generally undergo an activation process after injecting an electrolyte. In the activation process, an SEI film is formed on the battery cell through initial charging, and then metal foreign matter is quickly dissolved through high-temperature aging to prevent low-voltage defects.

[0006] The high-temperature aging is generally performed at a temperature of 60°C or higher, and therefore, the work must be performed in a space where the temperature is maintained constant. Conventionally, however, after stacking trays on which a large number of battery cells are mounted, the temperature is adjusted using a general thermometer. In this case, however, among the battery cells in the high-temperature aging room, the battery cells in the center of the tray have difficulty in circulating heat compared to the battery cells in the outer shell, and therefore the temperature rises excessively, and in this case, a phenomenon occurs in which the capacity of the battery cells located in the center is reduced due to an irreversible reaction. In addition, when the temperature is adjusted manually, it is difficult to adjust the temperature uniformly, and the work must be performed manually one by one, which causes problems of a lot of time and cost. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Korean Patent Publication No. 10-2015-0026994 Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention has been devised to solve the above problems, and an object of the present invention is to provide a smart high-temperature aging system having an algorithm that can minimize temperature deviation within a battery cell tray during the high-temperature aging process. [Means for solving the problem]

[0009] The high-temperature aging system of the present invention includes a battery cell tray stack having a structure in which battery cell trays, each of which houses a number of battery cells, are stacked in multiple stages, at least one tray rack that is housed inside a high-temperature aging chamber and includes a lattice-shaped storage space in which the battery cell tray stack is housed, a stacker crane that transports the battery cell trays to the lattice-shaped storage space, a thermal imaging camera that is installed on the stacker crane and acquires thermal image temperature data of the battery cell tray stack loaded in the lattice-shaped storage space, and a control unit that adjusts the temperature inside the high-temperature aging chamber based on the thermal image temperature data.

[0010] In a specific example, the stacker crane includes a mast that moves left and right and a loading platform that is mounted on the mast and moves up and down, and the thermal imaging camera can move together with the mast and the loading platform when they move to obtain thermal image temperature data of the battery cell trays.

[0011] In a specific example, the high-temperature aging chamber may be equipped with at least one heater and one blower fan for controlling the temperature inside.

[0012] In a specific example, the controller may calculate temperatures at the center and outer periphery of the battery cell tray stack from the thermal image temperature data, and adjust the temperature inside the high-temperature aging chamber based on the calculated temperatures.

[0013] Specifically, the control unit may adjust the temperature inside the high-temperature aging chamber when the temperature of the center of the battery cell tray stack or the temperature difference between the center and the outer shell of the battery cell tray stack falls outside a reference range.

[0014] In one specific example, the control unit calculates the temperature of the center of the entire battery cell tray stack stored in the tray rack or the average temperature difference between the center and the outer shell, and can adjust the temperature of the entire high-temperature aging chamber if the calculation result is outside a reference range.

[0015] In another specific example, the control unit may calculate the temperature at the center of some of the battery cell tray stacks stored in the tray rack, or the average temperature difference between the center and the outer shell, and locally adjust the temperature of the area in which the battery cell tray stack is located if the calculation result is outside a reference range.

[0016] At this time, the temperature in the high-temperature aging chamber may be adjusted by stopping or restarting at least one of at least one heater and at least one blower fan, or a combination thereof.

[0017] Specifically, when the temperature inside the high-temperature aging chamber is to be reduced, the control unit can stop the operation of the heater and operate the blower fan.

[0018] In addition, when the temperature inside the high-temperature aging chamber is to be increased, the control unit can operate the heater and stop the operation of the blower fan.

[0019] In another example, the control unit may learn the thermal image temperature data and derive a temperature control algorithm that minimizes a temperature difference between a center portion and an outer shell portion of the battery cell tray stack.

[0020] Specifically, the control unit may include artificial intelligence that collects thermal image temperature data to construct learning data and derives a temperature control algorithm from the learning data to minimize the temperature difference between the center and outer shell of the battery cell tray stack.

[0021] The artificial intelligence may compare a predicted temperature according to the derived temperature control algorithm with thermal image temperature data of an actual battery cell tray stack to verify the validity of the algorithm and update the verification results as learning data.

[0022] The temperature adjustment algorithm may relate to adjusting the location and number of heaters or blower fans to be turned on or off, and the operating time of the heaters or blower fans.

[0023] The artificial intelligence may be comprised of a deep neural network for deep learning. Effect of the Invention

[0024] The present invention can automatically minimize the temperature deviation in the battery cell tray during the high-temperature aging process by optimally adjusting the temperature in the high-temperature aging chamber based on an artificial intelligence algorithm, thereby saving energy and improving the performance of the battery cells. [Brief description of the drawings]

[0025] [Figure 1] FIG. 1 is a schematic diagram showing a high-temperature aging system according to the present invention. [Diagram 2] FIG. 1 is a schematic diagram showing a high-temperature aging system according to the present invention. [Diagram 3] FIG. 2 is a schematic diagram showing the structure of a battery cell tray and a battery cell tray stack; [Figure 4] FIG. 2 is a schematic diagram showing the structure of a battery cell tray and a battery cell tray stack; [Diagram 5] 11A to 11C are schematic diagrams showing the process of storing a battery cell tray stack in a tray rack. [Figure 6] 1 is a graph showing temperature change over time of a battery cell tray within a battery cell tray stack. [Figure 7] 4 is a flow chart illustrating a temperature adjustment algorithm according to the present invention. [Figure 8] This is a schematic diagram showing the structure of a deep neural network for deep learning. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] The present invention will be described in detail below. Before that, the terms and words used in the specification and claims should not be interpreted limitedly to their ordinary or dictionary meanings, but should be interpreted as meanings and concepts that are consistent with the technical idea of ​​the present invention, based on the principle that the inventor can appropriately define the concept of the term to best describe his own invention.

[0027] In this application, the terms "comprise" and "have" are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, and should be understood as not precluding the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof. In addition, when a layer, film, region, plate, or other part is described as being "on" another part, this includes not only the case where it is "directly on" the other part, but also the case where there is another part in between. Conversely, when a layer, film, region, plate, or other part is described as being "under" another part, this includes not only the case where it is "directly under" the other part, but also the case where there is another part in between. In addition, in this application, being "on" may include not only the case where it is located on the top, but also the case where it is located on the bottom.

[0028] The present invention will be described in detail below.

[0029] (First embodiment) 1 and 2 are schematic diagrams showing a high-temperature aging system according to the present invention, and Fig. 5 is a schematic diagram showing the process of housing a battery cell tray stack in a tray rack.

[0030] 1, 2 and 5, a high-temperature aging system 100 according to the present invention includes a battery cell tray stack 110 having a structure in which battery cell trays, each of which houses a number of battery cells, are stacked in multiple stages, at least one tray rack 130 which is housed inside a high-temperature aging chamber 120 and includes a lattice-shaped storage space in which the battery cell tray stack 110 is housed, a stacker crane 140 which transports battery cell trays to the lattice-shaped storage space, a thermal imaging camera 150 which is installed on the stacker crane 140 and obtains thermal image temperature data of the battery cell trays loaded in the lattice-shaped storage space, and a control unit 160 which adjusts the temperature inside the high-temperature aging chamber based on the thermal image temperature data.

[0031] In this way, the present invention adjusts the temperature of the high-temperature aging chamber based on the thermal image temperature data, and as described below, performs this temperature adjustment process based on an artificial intelligence algorithm, thereby saving energy and improving the performance of the battery cell.

[0032] Each component of the high-temperature aging system will be described in detail below.

[0033] 3 and 4 are schematic diagrams showing the structure of a battery cell tray and a battery cell tray stack.

[0034] In the present invention, the high-temperature aging process is carried out in a high-temperature aging chamber 120 whose interior is maintained at a constant high temperature, and a large number of battery cells 1 undergo the high-temperature aging process at once. To this end, the battery cells are mounted on a battery cell tray 10 in which a large number of battery cells 1 can be arranged at regular intervals.

[0035] The battery cell tray 10 may have partitions installed therein to form a lattice-shaped storage space, and the battery cells 1 may be arranged one by one in the spaces partitioned by the partitions. Although the battery cells 1 are illustrated as cylindrical battery cells in FIG. 3, the shape of the battery cells is not particularly limited, and various types of battery cells, such as rectangular battery cells or pouch-type battery cells, may be used. In this case, the shape of the partitions inside the battery cell tray 10 may be changed according to the shape of the battery cells.

[0036] In order to utilize space during the high-temperature aging process, a number of such battery cell trays 10 are stacked in multiple stages as one set to form a battery cell tray stack 110. In the case of Fig. 4, six battery cell trays 10 are stacked to form one battery cell tray stack 110, but the number of battery cell trays 10 that form the battery cell tray stack 110 is not particularly limited.

[0037] The battery cell tray 10 may be made of any material that will not deform in a high-temperature aging environment. For example, metal materials such as iron and aluminum, or polymer materials such as polycarbonate and acrylic may all be used.

[0038] 5 together with FIGS. 1 and 2, at least one or more tray racks 130 may be provided in the high-temperature aging chamber 120 to accommodate the battery cell tray stacks 110. The tray rack 130 has a structure in which a lattice-shaped storage space is formed like a bookshelf so that the battery cell tray stacks 110 can be accommodated. The lattice-shaped storage space is made up of a certain number of rows and stages, and although FIGS. 1 and 5 show the lattice-shaped storage space formed in 9 rows x 5 stages, there is no particular limit to the size and number of the lattice-shaped storage spaces.

[0039] The battery cell tray stack 110 is transported to the lattice-shaped storage space of the tray rack 130 and then stored at high temperature for a predetermined time. A stacker crane 140 transports the battery cell trays 10 to the lattice-shaped storage space. At this time, the battery cell trays 10 may be transported in the form of individual battery cell trays, or may be stacked in multiple stages in the form of the battery cell tray stack 110 and then transported to the storage space at once.

[0040] In the present invention, the stacker crane 140 includes a mast 141 that moves left and right, and a loading platform 142 that is installed on the mast 141 and moves up and down. The stacker crane 140 includes a traveling cart 143 configured to move left and right along one side of the tray rack on the floor of the high-temperature aging chamber 120. The traveling cart 143 includes wheels that make rolling contact with the bottom surface. In this case, a guide rail (not shown) may be formed on a moving path of the traveling cart 143 to move while maintaining a constant gap between the tray rack 130 and the stacker crane 140.

[0041] The mast 141 is mounted on the traveling cart 143 in the form of an upright column, supports the loading platform, and provides a path along which the loading platform 142 can move up and down. In addition, a guide groove (not shown) for guiding the loading platform 142 to move up and down may be formed on the mast 141.

[0042] The loading platform 142 is plate-shaped and is the part on which the battery cell trays 10 are mounted during transportation, and a wheel (not shown) that can be inserted into a guide groove (not shown) is coupled to the surface coupled to the mast 141, so that the loading platform 142 can move up and down on the mast 141 along the guide groove. The loading platform 142 moves up and down on the mast 141, and the mast 141 moves left and right along the guide rails by the traveling cart 143, so that the battery cell trays 10 can be loaded into or unloaded from a desired space.

[0043] The high-temperature aging chamber 120 is provided with at least one heater 121 and one or more ventilation fans 122 for controlling the temperature inside. In order to uniformly increase and decrease the temperature of the high-temperature aging chamber 120, the heaters 121 or ventilation fans 122 may be arranged in a certain pattern along the walls and ceiling of the high-temperature aging chamber. Therefore, as described below, when the temperature of a portion of the battery cell tray stack arranged in the storage space increases or decreases excessively, the ventilation fans 122 or heaters 121 of the portion can be operated to control the temperature through thermal convection within the high-temperature aging chamber. Although the heaters 121 and ventilation fans 122 are alternately arranged in FIGS. 1 and 2, the present invention is not limited thereto, and the heaters can be arranged on one side of the high-temperature aging chamber to control the temperature through the ventilation fans.

[0044] Meanwhile, the thermal imaging camera 150 is installed on the stacker crane 140 to acquire thermal image temperature data of the battery cell tray stack 110 loaded in the lattice-shaped storage space. Specifically, the thermal imaging camera 150 acquires thermal image temperature data of the battery cell tray while moving together with the mast 141 and the loading platform 142 when they move for loading or unloading the battery cell trays 10. For example, the thermal imaging camera 150 may be in a form coupled to the loading platform 142. In this case, the battery cell tray stack 110 may be naturally photographed while the mast 141 moves left and right and the loading platform 142 moves up and down.

[0045] Specifically, the thermal imaging camera 150 captures an image of the surface of the battery cell tray stack 110 and displays the temperature distribution in each region with a color. This makes it possible to simultaneously measure the temperature at two or more points on the measurement object. Furthermore, by using the thermal imaging camera 150, it is possible to continuously check the temperature between each point, so that the temperature distribution in the entire region of the measurement object can be intuitively or qualitatively understood. For example, a relatively low temperature area may be darker than a high temperature area. Alternatively, each temperature distribution may be displayed in a different color, with high temperature areas being red and low temperature areas being blue.

[0046] By using a thermal imaging camera 150 to capture the temperature of the battery cell tray stack 110, the present invention can capture the temperature distribution of the entire object on a single screen, as compared to using a conventional thermometer, and measure the temperature of the entire area of ​​the object, not just one point on the object. As a result, as will be described later, the temperature difference between the center and outer periphery of the battery cell tray stack 110 can be determined with a single capture, making it easier to determine the temperature of the center than when using a thermometer.

[0047] The control unit 160 may be a computing device, and when the thermal image temperature data is acquired by the thermal imaging camera, adjusts the temperature in the high-temperature aging chamber 120 based on the thermal image temperature data.

[0048] The control unit 160 converts the thermal image into a specific temperature value to obtain thermal image temperature data of the outer shell. The method in which the control unit converts the image captured by the thermal imaging camera into a specific temperature value can be performed by a general computing device or program.

[0049] Next, the control unit 160 calculates the temperatures of the center and outer periphery of the battery cell tray stack from the thermal image temperature data.

[0050] 5, a battery cell tray stack 110 in which battery cell trays are stacked in multiple stages is mounted in a lattice-shaped storage space in the tray rack 130. During the high-temperature aging process, the center A of the battery cell tray stack 110 has a higher temperature than the outer shell because heat circulation is difficult due to surrounding structures (other battery cell trays). The control unit 160 adjusts the temperature of the high-temperature aging chamber based on temperature data of the center and outer shell of the battery cell tray stack 110.

[0051] The control unit 160 adjusts the temperature of the high-temperature aging chamber 120 based on the temperature at the center of the battery cell tray stack 110 or the temperature difference between the center and the outer shell of the battery cell tray stack 110. Specifically, the control unit 160 adjusts the temperature in the high-temperature aging chamber 120 when the temperature at the center of the battery cell tray stack 110 or the temperature difference between the center and the outer shell is outside a reference range. Here, the reference range is a temperature range that is determined to be an appropriate value and can be suitably adjusted depending on the battery cell specifications, the size of the battery cell tray, the size of the storage space, and the temperature of the high-temperature aging chamber. For example, when the temperature at the center of the battery cell tray stack 110 is higher than the reference range or the temperature difference between the center and the outer shell is higher than the reference range, the control unit may stop heating the high-temperature aging chamber 120 to lower the internal temperature.

[0052] In one example, the control unit 160 may calculate the temperature at the center of all the battery cell tray stacks 110 housed in the tray rack 130 or the average temperature difference between the center and the outer periphery, and if the calculation result is outside a predetermined range, adjust the temperature of the entire high-temperature aging chamber 120. In this case, the control unit 160 may determine whether or not to adjust the temperature based on all the battery cells in the tray rack 130.

[0053] In another example, the control unit 160 calculates the temperature of the center of some battery cell tray stacks 110 housed in the tray rack 130 or the average value of the temperature difference between the center and the outer periphery, and locally adjusts the temperature of the region where the battery cell tray stacks 110 are located if the calculation result is outside of a reference range. For example, the control unit 160 calculates the average value of the temperature of the center of the battery cell tray stacks 110 located in any one row or column of the tray rack 130 or the temperature difference between the center and the outer periphery, and locally adjusts the temperature of the region where the battery cell tray stacks 110 are located if the calculation result is outside of a reference range. Alternatively, if some regions in the tray rack 130 have a higher temperature than other regions, the temperature of the corresponding region may be locally adjusted.

[0054] The temperature in the high-temperature aging chamber 120 may be adjusted by stopping or restarting the operation of at least one of the heaters 121 and the blower fans 122, or a combination thereof. The heaters 121 and the blower fans 122 are arranged in a certain pattern along the walls and ceiling of the high-temperature aging chamber 120, so that the temperature of a desired area may be adjusted by operating or not operating all or a part of the heaters 121 and the blower fans 122 installed in the high-temperature aging chamber 120.

[0055] Specifically, when the control unit 160 is to reduce the temperature inside the high-temperature aging chamber 120, it stops the operation of the heater 121 and operates the blower fan 122. When the control unit 160 is to increase the temperature inside the high-temperature aging chamber 120, it operates the heater 121 and stops the operation of the blower fan 122. For example, when the temperature in the center portion has excessively increased and the temperature inside the high-temperature aging chamber 120 is to be reduced, if the operation of the heater 121 is stopped, heat is dissipated from the center portion of the battery cell tray stack 110 to the outer portion, so that the temperature of the outer portion can be maintained at a high temperature.

[0056] FIG. 6 shows the temperature change over time of a battery cell tray within a battery cell tray stack.

[0057] In Fig. 6, each graph shows the temperature change measured at various points of the battery cell tray stack constituting the battery cell tray stack 110. Referring to Fig. 6, it can be seen that after about 200 minutes have passed since the start of heating, the battery cell tray showing the highest temperature (battery cell tray located in the center) has a temperature of about 53°C, and the battery cell tray showing the lowest temperature (battery cell tray located in the outer periphery) has a temperature of about 35°C, resulting in a temperature difference of 18°C. In this case, it can be seen that the temperature difference gradually decreases by adjusting the temperature inside the high-temperature aging chamber using a blower fan and a heater as in the present invention.

[0058] Second embodiment FIG. 7 illustrates a temperature control algorithm according to the present invention.

[0059] In another example, the control unit 160 may learn the thermal image temperature data and derive a temperature control algorithm for minimizing a temperature difference between the center and the outer shell of the battery cell tray stack 110. As a result, the control unit 160 may automatically control the temperature in the operation manner as described in the first embodiment so as to minimize the temperature difference between the center and the outer shell through the algorithm, thereby precisely controlling the temperature difference with less time and cost than manually controlling the temperature directly.

[0060] Specifically, the control unit 160 may further include an artificial intelligence for temperature control. The artificial intelligence collects thermal image temperature data to construct learning data, and derives a temperature control algorithm for minimizing a temperature difference between a center portion and an outer portion of the battery cell tray stack 110 from the learning data. The artificial intelligence then controls the temperature inside the high-temperature aging room 120 in the same manner as in the first embodiment according to the temperature control algorithm. That is, the artificial intelligence learns the temperature control algorithm through machine learning or deep learning.

[0061] For this purpose, the high temperature aging system 100 according to the present invention stores thermal image temperature data obtained while performing multiple temperature measurements. This data may be stored in a classified state according to measurement time, initial temperature conditions, and specifications of battery cells, battery cell trays, tray racks, etc., and a DB for storing and managing the data may be provided in a separate storage device. The DB may be used as basic material for constructing the learning data.

[0062] Specifically, the control unit 160 may include an artificial intelligence that collects stored thermal image temperature data to configure learning data and derives a temperature control algorithm from the learning data to minimize a temperature difference between the center and the outer periphery of the battery cell tray stack 110. The artificial intelligence may configure learning data by updating a newly measured result in an already stored DB.

[0063] Next, the AI ​​derives a temperature control algorithm from the learning data to minimize the temperature difference between the center and outer shell of the battery cell tray stack 110. The temperature control algorithm may relate to adjusting the positions and number of heaters or blower fans to be operated or stopped, and the operating time of the heaters or blower fans. The AI ​​derives the temperature control algorithm based on the positions, number, and temperature data of the battery cell tray stack whose temperature is to be controlled.

[0064] When the temperature control algorithm is derived, the AI ​​controls the temperature according to the algorithm and verifies the validity of the algorithm. This verification process may be performed by comparing a predicted temperature according to the derived temperature control algorithm with actual thermal image temperature data of the battery cell tray stack 110. For example, if the temperature difference between the center and outer periphery of the battery cell tray stack 110 deviates from the predicted result after controlling the temperature according to the derived temperature control algorithm, or if there is no significant difference in effectiveness compared to temperature data according to conventional conditions, the stored learning data is corrected and updated. In this case, the reason why the algorithm is not effective may be analyzed in consideration of the experimental conditions input together with the stored data. In this way, the present invention may precisely and efficiently control the temperature by machine learning the temperature control algorithm using the AI.

[0065] Third embodiment Figure 8 is a schematic diagram showing the structure of a deep neural network for deep learning.

[0066] 8, the control unit 160 may learn the thermal image temperature data to derive a temperature control algorithm for minimizing a temperature difference between a center portion and an outer portion of the battery cell tray stack 110, and the control unit 160 may further include an artificial intelligence for temperature control. The artificial intelligence collects thermal image temperature data to configure learning data, and derives a temperature control algorithm for minimizing a temperature difference between a center portion and an outer portion of the battery cell tray stack 110 from the learning data.

[0067] In this case, the artificial intelligence may be composed of a deep neural network for deep learning.

[0068] A deep neural network (DNN) is a machine learning model that classifies input data based on learned data. It refers to a system or network that constructs one or more layers in one or more computers and makes decisions based on multiple data.

[0069] Referring to FIG. 8, a deep neural network may be composed of an input layer (161), one or more hidden layers (162), and an output layer (163).

[0070] The learning data is input to the input layer 161, and the result values ​​calculated through the hidden layer and the output layer are compared with the actual values, and the weight values ​​are updated inversely. After all learning is completed, the information to be predicted can be input to obtain the result values.

[0071] The hidden layer 162 may include a convolution layer, a pooling layer, and a fully connected layer. Here, the convolution layer may extract a feature map and perform a convolution operation on the image input to the input layer. The pooling layer may be connected to the convolution layer and perform subsampling on the output of the convolution layer. The fully connected layer may be connected to the pooling layer and may learn the subsampled output of the pooling layer and learn according to the category output to the output layer 163.

[0072] Meanwhile, the connection structure of each layer constituting the deep neural network can be formed by suitably selecting a known algorithm, for example, a Convolutional Neural Network (CNN) structure or a Recurrent Neural Network (RNN) structure.

[0073] Such a deep neural network can be implemented within one computer, or can be implemented via a network in which multiple computers are connected together.

[0074] The learning unit inputs the updated learning data to an input layer 161 on a deep neural network. The input learning data is output as a final output from an output layer 163 via a hidden layer 162. The learning unit can learn the newly updated learning data by updating weight values ​​according to the verification result of the validity of the prediction result.

[0075] When the data learning is completed, the control unit derives a new temperature control algorithm from the learned data and applies the new algorithm to control the temperature in the high-temperature aging chamber. Then, the process of verifying the validity of the algorithm through the results of the temperature control and reflecting the results is automatically repeated, thereby enabling precise control of the temperature in the high-temperature aging chamber.

[0076] The above description is merely illustrative of the technical idea of ​​the present invention, and a person having ordinary knowledge in the technical field to which the present invention belongs may make various modifications and variations without departing from the essential characteristics of the present invention. Therefore, the drawings disclosed in the present invention are for the purpose of explanation, not for the purpose of limiting the technical idea of ​​the present invention, and the scope of the technical idea of ​​the present invention is not limited by such drawings. The scope of protection of the present invention should be interpreted by the claims, and all technical ideas within the equivalent range should be interpreted as being included in the scope of the present invention.

[0077] In this specification, terms indicating directions such as up, down, left, right, front, and back are used for convenience of explanation, but it is self-evident that these terms may change depending on the position of the object in question, the position of the observer, etc. [Explanation of symbols]

[0078] 1: Battery cell 10: Battery cell tray 100: High temperature aging system 110: Battery cell tray stack 120: High temperature aging chamber 121: Heater 122: Blower fan 130: Tray rack 140: Stacker crane 141: Mast 142: Loading platform 143: Traveling cart 150: Thermal imaging camera 160: Control unit 161: Input layer 162: Hidden layer 163: Output layer

Claims

1. a battery cell tray stack having a structure in which battery cell trays, each of which houses a number of battery cells, are stacked in multiple stages; at least one tray rack that is accommodated within a high-temperature aging chamber and includes a lattice-shaped storage space in which the battery cell tray stack is accommodated; a stacker crane that transports the battery cell trays into the lattice-shaped storage space; a thermal imaging camera that is installed on the stacker crane and acquires thermal image temperature data of the battery cell tray stack loaded in the lattice-shaped storage space; a control unit that adjusts the temperature inside the high-temperature aging chamber based on the thermal image temperature data, the control unit calculates temperatures of a center portion and an outer portion of the battery cell tray stack from the thermal image temperature data, and adjusts a temperature inside the high-temperature aging chamber based on the calculated temperatures. High temperature aging system.

2. The stacker crane includes a mast that moves left and right and a loading platform that is installed on the mast and moves up and down, The high temperature aging system of claim 1 , wherein the thermal imaging camera obtains thermal image temperature data of the battery cell tray while moving together when the mast and the loading platform are moved.

3. The high-temperature aging system according to claim 1 , wherein the high-temperature aging chamber is provided with at least one heater and one blower fan for controlling an internal temperature.

4. The high-temperature aging system of claim 3 , wherein the control unit adjusts the temperature inside the high-temperature aging chamber when a temperature of a center portion of the battery cell tray stack or a temperature difference between the center portion and the outer shell portion falls outside a reference range.

5. 5. The high-temperature aging system of claim 4, wherein the control unit calculates a temperature of a center portion of the entire battery cell tray stack stored in the tray rack or an average value of a temperature difference between the center portion and the outer shell portion, and adjusts a temperature of the entire high-temperature aging chamber when the calculation result is outside a reference range.

6. 5. The high-temperature aging system of claim 4, wherein the control unit calculates temperatures of central portions of some of the battery cell tray stacks stored in the tray rack or an average value of a temperature difference between the central portion and the outer shell portion, and locally adjusts a temperature of an area where the battery cell tray stack is located when the calculation result is outside a reference range.

7. The high-temperature aging system according to claim 3 , wherein the temperature in the high-temperature aging chamber is adjusted by shutting down, restarting, or a combination of at least one of at least one heater and at least one blower fan.

8. The high-temperature aging system according to claim 7 , wherein the control unit stops operation of the heater and operates a blower fan when decreasing the temperature inside the high-temperature aging chamber.

9. The high-temperature aging system according to claim 7 , wherein the control unit operates the heater and stops operation of the blower fan when increasing the temperature inside the high-temperature aging chamber.

10. The control unit is The high temperature aging system according to claim 1 , further comprising: a temperature control algorithm for minimizing a temperature difference between a center portion and an outer shell portion of the battery cell tray stack by learning the thermal image temperature data.

11. 11. The high temperature aging system of claim 10, wherein the control unit comprises artificial intelligence that collects the thermal image temperature data to configure learning data, and derives a temperature control algorithm from the learning data to minimize a temperature difference between a center portion and an outer portion of the battery cell tray stack.

12. The high temperature aging system of claim 11, wherein the artificial intelligence verifies the validity of the algorithm by comparing a predicted temperature by the derived temperature control algorithm with thermal image temperature data of an actual battery cell tray stack, and updates the learning data with the verification result.

13. The high temperature aging system according to claim 10, wherein the temperature adjustment algorithm is related to adjusting the positions and numbers of heaters or blower fans to be operated or stopped, and the operating times of the heaters or blower fans.

14. The high-temperature aging system of claim 11 , wherein the artificial intelligence is composed of a deep neural network for deep learning.

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