Method for manufacturing battery cell, manufacturing apparatus, and battery cell

Abstract

The application relates to a battery cell preparation method and preparation equipment and a battery cell. The battery cell preparation method connects the tab of the battery cell and the battery cell through ultrasonic welding; after the ultrasonic welding is completed, temperature field distribution information of a welding area is obtained; at least one temperature characteristic information representing welding thermal effects of the welding area is obtained based on the temperature field distribution information; the at least one temperature characteristic information is compared with preset determination criteria, and the welding quality is determined based on the comparison result; the energy input and thermal effects determining the welding quality are reflected through the temperature field distribution information of the welding area, the temperature characteristic information representing the welding thermal effects of the welding area is obtained based on the temperature field distribution information, the welding effect is judged according to the temperature characteristic information representing the welding thermal effects of the welding area, the detection of the thermal effects is more accurate, internal defects such as virtual welding and overwelding can be effectively identified, and the limitation that visual detection is only for surface topography is overcome.

Description

Technical Field

[0001] This application relates to the field of energy storage battery technology, and in particular to a method and equipment for preparing a battery cell, and the battery cell itself. Background Technology

[0002] In the manufacturing process of energy storage batteries, ultrasonic welding is used to connect the tabs to the cells. The quality of the welding directly affects the battery's internal resistance, overcurrent capacity, safety, and cycle life. Therefore, post-welding quality inspection is an indispensable part of battery production.

[0003] Currently, machine vision-based inspection methods are widely used. For example, 2D cameras acquire two-dimensional images of the welded surface and analyze its contour, area, and position. However, 2D cameras cannot identify the welding effect, posing a risk of defective products being shipped out. Another approach is to use 3D cameras to acquire the three-dimensional morphology of the welded surface and measure parameters such as tooth depth. However, the welding effect is affected by various factors such as welding energy, material thickness, surface condition, and weld head wear. Judging the welded surface morphology alone has a high misjudgment rate and is also difficult to directly determine the internal welding quality. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and equipment for preparing a battery cell, as well as the battery cell itself, to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for preparing a battery cell, comprising:

[0006] The tabs of the battery cells are connected to the cells using ultrasonic welding.

[0007] After ultrasonic welding is completed, the temperature field distribution information of the welding area is obtained;

[0008] Based on the temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained;

[0009] At least one of the temperature characteristic information is compared with a preset judgment criterion, and the welding quality is determined based on the comparison result.

[0010] In one embodiment, after the ultrasonic welding is completed, temperature field distribution information of the welding area is acquired, including:

[0011] In response to the welding completion command, at least one frame of thermal imaging image of the welding area is acquired within a preset time after welding;

[0012] Based on at least one frame of the thermal imaging image, the temperature field distribution information after welding is obtained.

[0013] In one embodiment, obtaining at least one temperature characteristic information characterizing the welding thermal effect of the welding region based on the temperature field distribution information includes:

[0014] The temperature field distribution information of the welding area is input into the feature analysis model to obtain at least one of the low-temperature area ratio of the welding area with a temperature lower than a first threshold and the high-temperature area ratio of the welding area with a temperature higher than a second threshold.

[0015] In one embodiment, comparing at least one of the temperature characteristic information with a preset judgment criterion and determining the welding quality based on the comparison result includes:

[0016] The low-temperature area ratio is compared with a first area threshold. If the low-temperature area ratio is greater than the first area threshold, a cold solder joint defect is determined to exist. Alternatively, the high-temperature area ratio is compared with a second area threshold. If the high-temperature area ratio is higher than the second area threshold, an over-soldering defect is determined to exist.

[0017] If the comparison results show no defects such as incomplete welding or over-welding, the welding quality is deemed acceptable; if the comparison results show at least one of defects such as incomplete welding or over-welding, the welding quality is deemed unacceptable.

[0018] In one embodiment, if the determination result is unqualified and the defect type is a cold solder joint defect, the battery cell is intercepted, the battery cell is returned to the welding station, and a welding parameter adjustment command is triggered to update the welding parameters. The battery cell is then re-welded according to the updated welding parameters.

[0019] If the judgment result is unqualified and the defect type is over-welding defect, the battery cell is released to the subsequent production line, and a welding parameter adjustment command is triggered to update the welding parameters. The subsequent battery cells are then welded according to the updated welding parameters.

[0020] If the determination result is qualified, the battery cell is released to the subsequent production line.

[0021] In one embodiment, it further includes:

[0022] Establish a mapping relationship between welding energy parameters and the welding temperature characteristic values ​​of individual battery cells, and set target characteristics;

[0023] Based on the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics, the parameter adjustment amount of the welding energy parameter is calculated through a preset feedback control algorithm.

[0024] The parameter adjustment amount is then compensated for in the welding parameters of subsequent battery cells.

[0025] In one embodiment, the target feature includes at least one of the target low-temperature area ratio, the target high-temperature area ratio, and the target temperature value;

[0026] The temperature field distribution characteristics include at least one of the measured low temperature area ratio, the measured high temperature area ratio, and the measured temperature value.

[0027] In one embodiment, the feedback control algorithm is as follows:

[0028]

[0029] Where e(t) is the deviation between the temperature field distribution characteristics and the target characteristics; K represents the rate of change of the deviation between the temperature field distribution characteristics and the target characteristics. p K i K d These are control parameters.

[0030] Secondly, this application provides an apparatus for preparing a single battery cell, comprising:

[0031] The welding station is used to connect the tabs of the battery cells to the battery cells using ultrasonic welding.

[0032] The inspection station is equipped with a thermal imaging acquisition module, an extraction module, and a judgment module.

[0033] The thermal imaging acquisition module is used to acquire temperature field distribution information of the welding area after ultrasonic welding is completed.

[0034] The extraction module is used to obtain at least one temperature feature information characterizing the welding thermal effect of the welding area based on the temperature field distribution information.

[0035] The determination module is used to compare at least one of the temperature characteristic information with a preset determination standard, and determine the welding quality based on the comparison result.

[0036] In one embodiment, it further includes:

[0037] The processing module is configured to: if the judgment result is unqualified and the defect type is a cold solder joint defect, intercept the battery cell, return the battery cell to the welding station, trigger a welding parameter adjustment command to update the welding parameters, and perform a re-welding operation on the battery cell according to the updated welding parameters; if the judgment result is unqualified and the defect type is an over-welding defect, release the battery cell to the subsequent production line, trigger a welding parameter adjustment command to update the welding parameters, and perform welding operations on the subsequent battery cells according to the updated welding parameters; if the judgment result is qualified, release the battery cell to the subsequent production line.

[0038] Thirdly, this application provides a battery cell prepared by the method for preparing a battery cell as described in the first aspect, or prepared by the equipment for preparing a battery cell as described in the second aspect.

[0039] The aforementioned battery cell preparation method and equipment, and the battery cell itself, involve connecting the battery cell's tabs to the cell via ultrasonic welding. After ultrasonic welding, temperature field distribution information of the welding area is acquired. Based on the temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained. This application compares the at least one temperature feature information with a preset judgment standard and determines the welding quality based on the comparison result. By collecting temperature field distribution information of the welding area, which reflects the energy input and thermal effect that determine the welding quality, and obtaining at least one temperature feature information characterizing the welding thermal effect of the welding area based on the temperature field distribution information, the welding effect is judged based on the temperature feature information characterizing the welding thermal effect of the welding area. The detection based on the thermal effect is more accurate and can effectively identify internal defects such as incomplete welding and over-welding, overcoming the limitations of visual inspection which only targets surface morphology detection. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic flowchart of a method for preparing a single battery cell in one embodiment;

[0042] Figure 2 This is a schematic diagram of the process for obtaining temperature field distribution information of the welding area in one embodiment;

[0043] Figure 3 This is a block diagram of the equipment for preparing a single battery cell in one embodiment;

[0044] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] As described in the background section, while 2D cameras can acquire two-dimensional images of the welded surface and analyze its contour, area, and position, they cannot identify the welding effect, posing a risk of defective products being exported. Similarly, 3D cameras can acquire the three-dimensional morphology of the welded surface and measure parameters such as tooth depth. However, the welding effect is affected by various factors such as welding energy, material thickness, surface condition, and weld head wear. Judging solely from the surface morphology has a high misjudgment rate and is also difficult to directly assess the internal welding quality.

[0047] Furthermore, both 2D and 3D vision inspections are post-weld, offline, or off-line inspections. Once a defect is detected, the battery has often already entered subsequent workstations, resulting in extremely high rework costs, or even scrapping the entire unit due to the inability to rework it, thus reducing the overall equipment efficiency (OEE).

[0048] Secondly, ultrasonic welding heads and sockets wear down over long-term use, leading to a gradual decrease in vibration energy transmission efficiency and a shift in the welding process window. Currently, there is a lack of effective online, real-time monitoring methods to predict this slow process degradation, which can easily trigger batch quality incidents in the later stages of wear, causing significant losses.

[0049] Firstly, this application provides a method for preparing a single battery cell. The method for preparing a single battery cell provided in the embodiments of this application can be executed by a terminal. The terminal can be, but is not limited to, various industrial computers, laptops, servers, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0050] like Figure 1 As shown, the method for preparing a single battery cell in this application includes steps S101-S104.

[0051] Step S101: Connect the tabs of the battery cell to the cell using ultrasonic welding.

[0052] In this embodiment, the tabs of the battery cell are aligned with the corresponding positions of the battery cell, and then welded using ultrasonic welding equipment to form a strong electrical and mechanical connection between the tabs and the battery cell. The battery cell can be a storage battery such as a lithium-ion battery, sodium-ion battery, aluminum-ion battery, or magnesium-ion battery; this embodiment does not impose specific limitations on this.

[0053] Step S102: After ultrasonic welding is completed, obtain the temperature field distribution information of the welding area.

[0054] In this embodiment, within a preset time window after the completion of the ultrasonic welding process, such as within 0ms to 500ms after welding, an infrared thermal imaging device or other temperature sensing unit is used to collect temperature data of the welding area of ​​the battery cell, and obtain the temperature field distribution information of the welding area of ​​the battery cell; the temperature field distribution information is used to characterize the distribution law of Joule heat and frictional heat generated during the welding process in the welding area.

[0055] In this embodiment, the temperature field distribution information can be in the form of a thermal image, a temperature matrix, or a temperature curve. The temperature field distribution information includes spatial location information and temperature numerical information. Spatial location information refers to the coordinates of the welding area corresponding to each temperature value in the temperature field; temperature numerical information refers to temperature information related to the welding area, such as temperature readings at each coordinate, the highest temperature of the welding area, the lowest temperature of the welding area, the average temperature of the welding area, and the temperature distribution of the entire welding area.

[0056] Step S103: Based on the temperature field distribution information, obtain at least one temperature characteristic information that characterizes the welding thermal effect of the welding area.

[0057] In this embodiment, the acquired temperature field distribution information is processed and data is extracted to obtain temperature characteristic information characterizing the welding thermal effect. The temperature characteristic information may include, but is not limited to, at least one of the following: low-temperature area ratio, high-temperature area ratio, and temperature distribution standard deviation.

[0058] Among them, the low temperature area ratio refers to the proportion of the area in the welding region where the temperature is lower than the first preset temperature threshold to the total area of ​​the welding region; the high temperature area ratio refers to the proportion of the area in the welding region where the temperature is higher than the second preset temperature threshold to the total area of ​​the welding region; the temperature distribution standard deviation is used to characterize the dispersion of temperature in the welding region and reflect the uniformity of temperature distribution.

[0059] For example, the first preset temperature threshold can be 80℃ or lower, and further, the value range of the first preset temperature threshold is 50℃-80℃; the second preset temperature threshold can be 150℃ or higher, and further, the value range of the second preset temperature threshold is 150℃-250℃.

[0060] As can be understood, ultrasonic welding uses high-frequency vibrations to generate frictional heat at the connection points of the battery cells, thereby achieving a connection between the metal materials. During the welding process, if the welding quality is good, the heat will be distributed relatively evenly in the welding area; if there are defects such as incomplete welds or weak welds, the heat conduction at the weld will be uneven, leading to localized temperature anomalies. Therefore, by analyzing the above temperature characteristics, the quality of the weld can be effectively identified.

[0061] Step S104: Compare at least one temperature characteristic with a preset judgment standard, and determine the welding quality based on the comparison result.

[0062] In this embodiment, at least one temperature characteristic is compared with a preset judgment standard, which is either pre-set and stored on the terminal or defined by a third-party client. Based on the comparison result, it is determined whether the welding quality of the battery cell meets the judgment standard. If it does, the welding quality of the battery cell is qualified; otherwise, it is unqualified.

[0063] This embodiment acquires and performs welding quality inspection based on the temperature field distribution information of the welding area of ​​the battery cell after welding. This is an online real-time inspection that can immediately determine the quality after welding, preventing defective products from flowing into subsequent workstations, significantly reducing rework costs and the risk of scrapping entire units, and improving production efficiency and product yield. The temperature field distribution information directly reflects the thermal effect during the welding process, which can more accurately characterize the welding quality, overcoming the limitation of traditional visual inspection that can only detect surface morphology, and reducing the false judgment rate. By monitoring the changing trend of temperature characteristic information in real time, the wear of the ultrasonic welding head and welding seat can also be indirectly monitored. When the welding head wears down, causing a decrease in welding energy transfer efficiency, the temperature characteristic information will change accordingly, thereby achieving early warning of process degradation and avoiding the occurrence of batch quality accidents.

[0064] The above-mentioned method for preparing a battery cell involves connecting the tabs of the battery cell to the cell via ultrasonic welding. After ultrasonic welding, the temperature field distribution information of the welding area is acquired. Based on the temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained. The at least one temperature feature information is compared with a preset judgment standard, and the welding quality is determined based on the comparison result. This application collects the temperature field distribution information of the welding area, which reflects the energy input and thermal effect that determine the welding quality. Based on the temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained. The welding effect is judged based on the temperature feature information characterizing the welding thermal effect of the welding area. The detection based on the thermal effect is more accurate and can effectively identify internal defects such as incomplete welding and over-welding, overcoming the limitation of visual inspection which only targets the surface morphology.

[0065] In some embodiments, such as Figure 2 As shown, step S102: After ultrasonic welding is completed, the temperature field distribution information of the welding area is obtained, including steps S1021-S1022.

[0066] Step S1021: In response to the welding completion command, acquire at least one frame of thermal imaging image of the welding area within a preset time after welding.

[0067] In this embodiment, after the ultrasonic welding equipment completes the welding, it generates a welding completion command signal. After receiving the welding completion command, the terminal immediately triggers the infrared thermal imaging equipment to start collecting images and capture the temperature status of the welding area, thus avoiding the loss of key thermal feature information due to rapid heat diffusion.

[0068] In this embodiment, within a preset time window, the infrared thermal imaging device continuously and rapidly acquires data from the welding area to obtain at least one frame of thermal imaging image.

[0069] Step S1022: Obtain temperature field distribution information after welding based on at least one frame of thermal imaging image.

[0070] In this embodiment, the acquired thermal imaging image is preprocessed, including non-uniformity correction and bad pixel removal, to eliminate the inherent noise and imaging defects of the infrared detector itself. Then, the preprocessed image is calibrated and corrected, mapping the gray value of each pixel in the image to the absolute temperature value of the corresponding spatial location, generating a two-dimensional temperature matrix corresponding to the thermal imaging image. The rows and columns of the two-dimensional temperature matrix correspond to the horizontal and vertical coordinates of the welding area, respectively, and the matrix element values ​​are the instantaneous temperatures of the corresponding coordinate points, thus obtaining the temperature field distribution information after welding.

[0071] In some embodiments, based on temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained, including: inputting the temperature field distribution information of the welding area into a feature analysis model to obtain at least one of the proportion of low-temperature area within the welding area with a temperature below a first threshold and the proportion of high-temperature area within the welding area with a temperature above a second threshold. The feature analysis model can be a deep learning-based target segmentation model or a threshold segmentation algorithm, used to identify and statistically analyze the proportion of areas meeting preset temperature conditions from the temperature field distribution information. The feature analysis model can be trained with multiple samples to adapt to temperature distribution patterns under different battery models, different tab materials (such as aluminum tabs and copper tabs), and different welding process parameters, avoiding the problem of poor adaptability of fixed thresholds in different scenarios and reducing the false positive and false negative rates.

[0072] This embodiment introduces a feature analysis model to process temperature field distribution information, which not only realizes the automated and intelligent extraction of temperature feature information and improves the accuracy of the detection method, but also can adapt to the welding quality detection needs under different material and process conditions, reducing the cost of manual intervention and the risk of misjudgment.

[0073] In some embodiments, step S104 compares at least one temperature characteristic information with a preset judgment criterion and determines the welding quality based on the comparison result, including steps S1041-S1042.

[0074] Step S1041: Compare the low-temperature area ratio with the first area threshold. If the low-temperature area ratio is greater than the first area threshold, it is determined that there is a cold solder joint defect. And / or, compare the high-temperature area ratio with the second area threshold. If the high-temperature area ratio is higher than the second area threshold, it is determined that there is an over-soldering defect.

[0075] In this embodiment, all pixels in the temperature field distribution information are traversed, and the number of pixels with temperatures lower than a preset first temperature threshold is counted. Simultaneously, the total number of pixels in the welding area is counted, and the low-temperature area ratio of the welding area is calculated. This low-temperature area ratio is compared with a preset first area threshold to determine whether it is greater than, equal to, or less than the first area threshold. If it is greater than the first area threshold, a cold solder joint defect is determined to exist; if it is less than or equal to the first area threshold, a cold solder joint defect is determined not to exist.

[0076] The first area threshold can be set to a range of 3%-10%. In this embodiment, the first area threshold can be set to 5%, that is, the proportion of low temperature area in the welding area is higher than 5%, indicating a defective weld.

[0077] Similarly, all pixels in the temperature field distribution information are traversed, and the number of pixels with temperatures higher than a preset second temperature threshold is counted. Simultaneously, the total number of pixels in the welding area is counted, and the high-temperature area ratio of the welding area is calculated. This high-temperature area ratio is compared with a preset second area threshold to determine whether it is greater than, equal to, or less than the second area threshold. If it is greater than the second area threshold, a cold weld defect is determined to exist; if it is less than or equal to the second area threshold, a cold weld defect is determined not to exist.

[0078] The second area threshold can be set to a range of 3%-10%. In this embodiment, the second area threshold can be set to 5%, which means that the proportion of high-temperature area in the welding area is higher than 5%, indicating an over-welding defect.

[0079] It is understood that the first area threshold and the second area threshold in this embodiment can be flexibly set according to the cell type, battery size, etc.

[0080] Step S1042: If the comparison results show no cold solder joint defects or over-soldering defects, the welding quality is deemed acceptable; if the comparison results show cold solder joint defects and / or over-soldering defects, the welding quality is deemed unacceptable.

[0081] If there are no defects in the comparison results, that is, the following two conditions are satisfied simultaneously: the proportion of the low-temperature area is less than or equal to the first area threshold, and the proportion of the high-temperature area is less than or equal to the second area threshold, then it is determined that the welding quality of the current battery cell is qualified. The heat distribution in the welding area is uniform and within a reasonable range, without a low-temperature area caused by insufficient energy input or a high-temperature area caused by excessive energy input. A good metallurgical bond is formed at the welding interface, meeting the product quality requirements.

[0082] If there is at least one of the defects of virtual welding defect and over-welding defect in the comparison results, then it is determined that the welding quality of the current battery cell is unqualified.

[0083] The joule heat and frictional heat generated during the welding process are the direct energy sources for interface bonding. The temperature field distribution information can truly reflect the thermal effect state of the welding interface. In this embodiment, by monitoring the instantaneous temperature field distribution after welding, calculating the proportion of the low-temperature area and the proportion of the high-temperature area, and comparing them with the preset thresholds respectively, two typical welding defects of virtual welding and over-welding can be effectively identified, realizing accurate determination of welding quality.

[0084] In some embodiments, step S1041 further includes: comparing the standard deviation of the temperature distribution with the standard deviation threshold. If the standard deviation of the temperature distribution is greater than the standard deviation threshold, it is determined that there is a temperature distribution abnormal defect.

[0085] In step S1042, if none of the virtual welding defect, over-welding defect, and temperature distribution abnormal defect exists in the comparison results, it is determined that the welding quality is qualified; if at least one of the virtual welding defect, over-welding defect, and temperature distribution abnormal defect exists in the comparison results, it is determined that the welding quality is unqualified.

[0086] This embodiment can identify more types of welding abnormalities, effectively reducing the missed judgment rate and misjudgment rate.

[0087] It should be noted that the above first area threshold, second area threshold, and standard deviation threshold are all preset in advance, and can also be adjusted adaptively according to different battery models and different tab materials. This embodiment does not make specific restrictions on this.

[0088] It should be noted that the first preset temperature threshold, second preset temperature threshold, and standard deviation of the temperature distribution can be preset and adjusted according to different welding base material materials and welding process parameters. This embodiment does not make specific restrictions on this.

[0089] In this embodiment, after obtaining the temperature field distribution information of the welding area in step S102, the temperature field distribution information of the welding area is transmitted to the host computer. The host computer receives and stores the collected temperature field distribution information to form a temperature database. Based on the temperature database, the host computer uses a deep learning algorithm to train on a large amount of sample data, extracts key temperature features that can characterize welding quality, and then forms a standard temperature feature library. The standard temperature feature library includes at least one or more of the following: the minimum temperature standard value, the maximum temperature standard value, the average temperature standard value, and the temperature distribution standard deviation standard value of the welding area. Among them, the minimum temperature standard value is used as a benchmark for judging the cold weld defect. In this embodiment, the first preset temperature threshold, the second preset temperature threshold, the temperature distribution standard deviation, etc., are all issued by the host computer based on the standard temperature feature library, or can be set by a third-party client based on the standard temperature feature library.

[0090] In some embodiments, if the determination result is unqualified and the defect type is a cold weld defect, the battery cell is intercepted, the battery cell is returned to the welding station, and a welding parameter adjustment command is triggered to update the welding parameters. The battery cell is then re-welded according to the updated welding parameters. A cold weld defect indicates insufficient welding energy or inadequate interface bonding. By adjusting the welding parameters (such as increasing the welding energy, extending the welding time, or adjusting the welding head pressure) and then re-welding, the defect can be repaired, thereby avoiding the scrapping of the battery cell.

[0091] If the judgment result is unqualified and the defect type is over-welding defect, the battery cell is released to the subsequent production line, and a welding parameter adjustment instruction is triggered to update the welding parameters. The subsequent battery cells are then welded according to the updated welding parameters. Over-welding defects are accompanied by electrode tab damage, metal spatter, or material thinning. Re-welding is difficult to repair and may cause secondary damage. Therefore, the cells are released directly (they can be marked for subsequent sorting or downgrading). At the same time, the welding parameters are adjusted in a timely manner to prevent over-welding defects from continuing to occur in subsequent batches.

[0092] If the judgment result is qualified, the battery cell is released to the subsequent production line and enters the normal production process.

[0093] In this embodiment, battery cells with different defect types are treated differently based on the test results. Repairable cold solder joint defects are reflowed and re-soldered to reduce the scrap rate. For unrepairable over-soldering defects, process parameters are adjusted in a timely manner to avoid batch quality accidents. Qualified products are released normally, which effectively improves the yield rate of the production line and reduces production costs.

[0094] In some embodiments, if the determination result is unqualified and the defect type is abnormal temperature distribution defect, the handling method is the same as that for over-welding defects.

[0095] It should be noted that in the same welding area of ​​the same battery cell, defects such as incomplete welding, over-welding, and abnormal temperature distribution usually do not occur at the same time. If the test results show two or more of these defects simultaneously in the same welding area of ​​the same battery cell, it indicates a malfunction in the welding equipment, and the equipment should be stopped and repaired in a timely manner.

[0096] In some embodiments, the method for preparing a single battery cell further includes the following steps:

[0097] Step S100-1: Establish the mapping relationship between welding energy parameters and welding temperature characteristic values ​​of battery cells, and set target characteristics.

[0098] In this embodiment, step S100-1 is executed before step S101. During the process debugging stage before mass production, a mapping model T=f(μ) between temperature field distribution characteristics and welding energy is established. The mapping model is used to characterize the mapping relationship between welding energy input and temperature field distribution characteristics of the welding area. Temperature field distribution characteristics may include at least one of the following: measured temperature values ​​of the welding area (such as average temperature, peak temperature, or minimum temperature), the proportion of low-temperature area with temperature below a first threshold in the welding area, and the proportion of high-temperature area with temperature above a second threshold in the welding area.

[0099] In one example, a modeling method based on thermodynamic mechanisms is used to establish a dynamic model of the relationship between welding temperature and welding energy:

[0100] .

[0101] Where C is the heat capacity of the welding area, measured in joules per Kelvin (J / K), which characterizes the ability of the welding area to store heat; Let be the derivative of temperature T with respect to time t, representing the rate of temperature change over time during welding; α is the energy coupling coefficient, representing the efficiency of converting input energy into heat in the welding area; u(t) is the welding energy input function that varies with time, i.e., the power output of the ultrasonic welding machine; h is the heat transfer coefficient, measured in watts per Kelvin (W / K), reflecting the rate of heat exchange between the welding area and the environment; T is the current temperature of the welding area; T env This is the ambient temperature constant.

[0102] In this embodiment, the welding temperature field under different welding energies μ can be collected during the commissioning phase of the welding equipment, and features such as measured temperature values, low-temperature area ratio, and high-temperature area ratio can be obtained. For example, the temperature sampling range can be set to 100℃~150℃, and the mapping model T=f(μ) can be obtained through the collected (μ, feature value).

[0103] In this embodiment, during the process debugging stage, after the first piece is manufactured, the welding quality of the first piece is verified. When the welding tensile test result meets >75N and the residual area meets ≥80% (after passing the tensile test, the residual area on the fracture interface reaches more than 80%), the initial welding energy setting value μ1 of the current welding machine is recorded, and the temperature field distribution characteristics of the battery welding point are simultaneously acquired (such as average temperature T1, low temperature area percentage Alow1, high temperature area percentage A). high1 ), as the initial feature.

[0104] While keeping the welding pressure and welding amplitude of the welding machine constant, the welding parameters are continuously optimized until the welding tensile test result meets the requirement of >75N and the residual area reaches 100%. At this point, the welding energy setpoint μ0 of the welding machine is recorded, and the temperature field distribution characteristics at the battery welding point (such as average temperature, low-temperature area ratio, and high-temperature area ratio) are obtained as target features. In this embodiment, the target features include the target low-temperature area ratio A. low0 Target high-temperature area percentage A high0 At least one of the target temperature value T0.

[0105] Step S100-2: Based on the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics, the parameter adjustment amount of the welding energy parameters is calculated through a preset feedback control algorithm. The temperature field distribution characteristics of the battery cell after welding include at least one of the measured low-temperature area ratio, the measured high-temperature area ratio, and the measured temperature value.

[0106] In this embodiment, during the mass production process, after each battery cell completes ultrasonic welding, the measured temperature value is extracted from the temperature field distribution information obtained in step S102.

[0107] The target temperature value and the measured temperature value can be the overall average temperature of the welding area, the peak temperature, or the preset temperature of the weak point in the welding area. This embodiment does not limit this, but the target temperature value and the measured temperature value should be consistent. For example, the target temperature value is the overall average temperature of the welding area, and the measured temperature value is also the overall average temperature of the welding area.

[0108] In some embodiments, to improve sensitivity to insufficient welding energy, the target temperature value and the measured temperature value are selected as the lowest temperature value of the welding area. The lowest temperature value can most directly reflect the energy-weak areas that may exist in the welding interface. When a tendency for incomplete welding occurs, the lowest temperature often drops first. This setting can provide early warning and compensate for energy attenuation.

[0109] In this embodiment, the temperature field distribution characteristics of the battery cell after welding are compared with the target characteristics, and the deviation value is calculated. When the measured characteristics are lower than the target characteristics, there may be a risk of insufficient energy; when the temperature deviation value is higher than the target characteristics, there may be a trend of excessive energy. Based on the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics, the adjustment amount Δμ of the welding energy is calculated through a preset feedback control algorithm.

[0110] Step S100-3: Compensate the parameter adjustment amount to the welding parameters of the subsequent battery cells.

[0111] For example, if the welding energy used when welding the current k-th battery cell is μ(k), after calculating the parameter adjustment amount Δμ(k) in step S100-2, the welding energy of the (k+1)-th and subsequent battery cells is updated as follows: μ(k+1) = μ(k) + Δμ(k).

[0112] Wherein, Δμ(k) can be a positive value (requiring increased energy) or a negative value (requiring decreased energy), depending on the direction of deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics.

[0113] In some embodiments, to ensure process stability, upper and lower limits are set for the adjustment range of welding energy parameters: μmin ≤ μ(k+1) ≤ μmax. Here, μmin and μmax are the minimum and maximum energy values ​​preset according to equipment capacity and process requirements, respectively. If the calculated updated energy exceeds this range, its boundary value is taken as the actual welding energy.

[0114] After the parameters are updated, the new welding energy setting value μ(k+1) is written into the control system of the ultrasonic welding machine. Subsequent battery cells will be welded according to the updated welding energy, thus achieving closed-loop adaptive adjustment of the welding energy.

[0115] Through the above steps, this embodiment realizes online welding energy compensation based on temperature feedback. When the temperature field distribution characteristics of the battery cell after welding deviate from the target characteristics due to welding head wear, material batch fluctuations, or changes in ambient temperature, the system can automatically adjust the welding energy to keep the welding temperature of subsequent battery cells stable near the target temperature value, thereby preventing the generation of cold welding defects and improving the consistency and stability of welding quality of mass-produced battery products.

[0116] In some embodiments, in step S1042, if the welding quality is determined to be qualified, steps S100-2 to S100-3 do not need to be executed, that is, the welding parameters are not adjusted, the current battery cell is directly released to the subsequent production line, and the subsequent battery cells are welded with the current welding parameters; if the welding quality is determined to be unqualified, steps S100-2 to S100-3 are executed, the parameter adjustment amount is calculated based on the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics, and the adjustment amount is compensated to the welding parameters of the subsequent battery cells.

[0117] In this embodiment, the target feature is the target temperature value T0, which can be one of the target average temperature, target peak temperature, or target minimum temperature of the welding area.

[0118] In step S1042, if the welding quality is determined to be unqualified, the measured temperature value of the battery cell after welding is obtained based on the temperature field distribution information of the welding area of ​​the battery cell. The measured temperature value should be a comparable temperature value corresponding to the target temperature value T0. For example, if the target temperature value T0 is the target average temperature of the welding area, then the measured average temperature of the battery cell after welding is obtained based on the temperature field distribution information of the welding area of ​​the battery cell, and is used as the measured temperature value of the battery cell after welding. The embodiments for peak temperature or minimum temperature are the same and will not be described again.

[0119] Based on the deviation between the measured temperature value and the target temperature value of the battery cell after welding, the parameter adjustment amount of the welding energy parameter is calculated through a preset feedback control algorithm; the parameter adjustment amount is then compensated into the welding parameters of subsequent battery cells.

[0120] Furthermore, if the welding quality is deemed unqualified, and the battery cell exhibits a poor weld defect, insufficient welding energy, and incomplete fusion of the weld interface, the measured temperature value of the battery cell is extracted from the temperature field distribution information. The deviation between the measured temperature value and the target temperature value T0 is calculated, and the increase in welding energy is calculated based on the feedback control algorithm to compensate for the welding energy of subsequent battery cells.

[0121] If the welding quality is deemed unqualified, and the battery cell exhibits over-welding defects, indicating excessive welding energy, the measured temperature value of the battery cell is extracted from the temperature field distribution information. The deviation between the measured temperature value and the target temperature value T0 is calculated, and the reduction in welding energy is calculated based on the feedback control algorithm to compensate for the welding energy of subsequent battery cells.

[0122] In some embodiments, step S100-2 employs a proportional-integral-derivative (PID) control algorithm as the feedback control algorithm to calculate the adjustment parameter u(t) of the welding energy based on the temperature deviation e(t). The feedback control algorithm is as follows:

[0123] .

[0124] Where e(t) is the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics; K represents the rate of change of the deviation between the temperature field distribution characteristics of a single battery cell after welding and the target characteristics. p K i K d These are control parameters.

[0125] The proportional control section outputs a portion of the control quantity u(t) proportionally to the current error magnitude. When the error e(t) is large, the proportional term outputs a large value, enabling a rapid response to the error and guiding the system towards reducing the error.

[0126] It is the integral control section, K i It is the integral coefficient. The integral element accumulates the error over time. Its function is to eliminate the steady-state error of the system. Even after proportional control reduces the error, if a persistent small error exists in the system, the integral element will continuously accumulate this error and output a control quantity to further reduce the error until the steady-state error is zero.

[0127] This is the differential control part. d These are differential coefficients. It is the rate of change of the error. Derivative control can react in advance based on the trend of error change. For example, when the error tends to increase rapidly, the derivative term will output a larger value to suppress the rapid increase of the error, thereby increasing the stability of the system and reducing overshoot.

[0128] In this embodiment, K p K i K d The parameters can be tuned using one of the following methods: the Ziegler-Nichols method, which involves obtaining characteristic parameters such as the critical proportional gain and critical oscillation period of the system experimentally, and then calculating K using empirical formulas. p K i K d The initial values, determined based on the system's stability and oscillation characteristics, can achieve relatively good control performance to a certain extent. The frequency domain response experimental method involves inputting sinusoidal signals of different frequencies into the system, measuring the system's output response, and obtaining the system's frequency characteristics (e.g., Bode plot). Based on the desired performance indicators (e.g., phase margin, gain margin), K is adjusted in the frequency domain. p K i K d parameter.

[0129] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0130] Secondly, this application provides an apparatus for preparing a single battery cell, such as... Figure 3 As shown, it includes a welding station 210 and an inspection station 220. The welding station 210 is used to connect the tabs of the battery cell to the cell using ultrasonic welding; the inspection station 220 is used to judge the welding quality, and the inspection station includes a thermal imaging acquisition module 221, an extraction module 222, and a judgment module 223.

[0131] The thermal imaging acquisition module 221 is used to acquire temperature field distribution information of the welding area after ultrasonic welding is completed.

[0132] Extraction module 222 is used to obtain at least one temperature feature information characterizing the welding thermal effect of the welding area based on temperature field distribution information.

[0133] The judgment module 223 is used to compare at least one temperature feature with a preset judgment standard and determine the welding quality based on the comparison result.

[0134] In some embodiments, the battery cell manufacturing equipment includes multiple stations arranged sequentially along the conveying direction. These multiple stations include a positive electrode welding station, a positive electrode testing station, a negative electrode welding station, a negative electrode testing station, and a sorting and processing station, arranged sequentially along the conveying direction.

[0135] The positive electrode welding station is equipped with a first ultrasonic welding machine, which is used to ultrasonically weld the positive electrode tab of the battery cell to the positive electrode tab of the battery cell.

[0136] The positive electrode inspection station, located downstream of the positive electrode welding station, is equipped with a first thermal imaging acquisition module, a first extraction module, and a first judgment module. These modules are used to acquire the positive electrode temperature field distribution information of the positive electrode welding area after the positive electrode welding is completed, and to judge the quality of the positive electrode welding based on the positive electrode temperature field distribution information.

[0137] The negative electrode welding station, located downstream of the positive electrode testing station, is equipped with a second ultrasonic welding machine, which is used to ultrasonically weld the negative electrode tabs of the battery cell to the negative electrode tabs of the battery cell.

[0138] The negative electrode detection station, located downstream of the negative electrode welding station, is equipped with a second thermal imaging acquisition module, a second extraction module, and a second judgment module. These modules are used to acquire the negative electrode temperature field distribution information of the negative electrode welding area after the negative electrode welding is completed, and to judge the quality of the negative electrode welding based on the negative electrode temperature field distribution information.

[0139] The classification and processing station, located downstream of the negative electrode testing station, is used to classify and process battery cells based on the welding quality judgment results of the positive electrode welding quality and / or the negative electrode welding quality.

[0140] In some embodiments, the thermal imaging acquisition module 221 (the first thermal imaging acquisition module and / or the second thermal imaging acquisition module) includes an acquisition unit and a temperature field distribution information acquisition unit. A response unit is used to acquire at least one frame of thermal imaging image within a preset time period after welding in response to a welding completion command; the temperature field distribution information acquisition unit is used to obtain temperature field distribution information after welding based on at least one frame of thermal imaging image.

[0141] In some embodiments, the extraction module stores a feature analysis model. The extraction module is used to input the temperature field distribution information of the welding area into the feature analysis model to obtain the proportion of low-temperature area with temperature below a first threshold and / or the proportion of high-temperature area with temperature above a second threshold in the welding area.

[0142] In some embodiments, the determination module includes a comparison unit and an output unit.

[0143] The comparison unit is used to compare the low-temperature area ratio with a first area threshold. If the low-temperature area ratio is greater than the first area threshold, it is determined that there is a cold solder joint defect. And / or, it compares the high-temperature area ratio with a second area threshold. If the high-temperature area ratio is higher than the second area threshold, it is determined that there is an over-soldering defect.

[0144] The output unit is used to output the judgment result. If the comparison result does not show any cold solder joint defects or over-soldering defects, the welding quality is judged to be qualified; if the comparison result shows any cold solder joint defects and / or over-soldering defects, the welding quality is judged to be unqualified.

[0145] In some embodiments, the battery cell manufacturing equipment further includes a processing module, configured to: if the determination result is unqualified and the defect type is a cold solder joint defect, intercept the battery cell, return the battery cell to the welding station, trigger a welding parameter adjustment command to update the welding parameters, and perform a re-welding operation on the battery cell according to the updated welding parameters; if the determination result is unqualified and the defect type is an over-welding defect, release the battery cell to the subsequent production line, trigger a welding parameter adjustment command to update the welding parameters, and perform a welding operation on the subsequent battery cells according to the updated welding parameters; if the determination result is qualified, release the battery cell to the subsequent production line.

[0146] In some embodiments, the battery cell manufacturing apparatus further includes a closed-loop processing module, which includes a mapping relationship establishment unit, an adjustment unit, and a compensation unit.

[0147] The mapping relationship establishment unit is used to establish the mapping relationship between welding energy parameters and welding temperature characteristic values ​​of battery cells, and to set target characteristics.

[0148] The adjustment unit is used to calculate the adjustment amount of the welding energy parameters based on the deviation between the temperature field distribution characteristics after the battery cell welding and the target characteristics, through a preset feedback control algorithm.

[0149] The compensation unit is used to compensate for the parameter adjustment amount in the welding parameters of subsequent battery cells.

[0150] In some embodiments, the target feature includes at least one of the target low-temperature area ratio, the target high-temperature area ratio, and the target temperature value; the temperature field distribution feature includes at least one of the measured low-temperature area ratio, the measured high-temperature area ratio, and the measured temperature value.

[0151] In some embodiments, the feedback control algorithm is as follows:

[0152]

[0153] Where e(t) is the deviation between the temperature field distribution characteristics and the target characteristics; K represents the rate of change of the deviation between the temperature field distribution characteristics and the target characteristics. p K i K d These are control parameters.

[0154] Each module in the aforementioned battery cell fabrication equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0155] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements the aforementioned method for preparing a single battery cell.

[0156] Those skilled in the art will understand that Figure 4 The structure shown is only a block diagram of a part of the structure related to the present application and does not constitute a limitation on the computer device on which the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0157] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for preparing a single battery cell.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for preparing a single battery cell.

[0159] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method for preparing a single battery cell.

[0160] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully permitted by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0162] In one exemplary embodiment, this embodiment provides a battery cell prepared by the battery cell preparation method as described in the above embodiments, or prepared by the battery cell preparation equipment as described in the above embodiments.

[0163] In one exemplary embodiment, this embodiment provides a battery device, including: any of the above-described battery cells, or including battery cells prepared using any of the above-described battery cell preparation methods, or including battery cells prepared using any of the above-described battery cell preparation equipment, wherein the battery device includes one or more of battery modules, battery packs, and energy storage batteries.

[0164] In one exemplary embodiment, this embodiment provides an electrical device that includes a battery device as described above, the battery device being used to provide electrical energy.

[0165] In one exemplary embodiment, this embodiment provides an energy storage device, which includes a battery device as described above, the battery device being used to provide electrical energy.

[0166] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for preparing a single battery cell, characterized in that, include: The tabs of the battery cells are connected to the cells using ultrasonic welding. After ultrasonic welding is completed, temperature data of the welding area is collected to obtain temperature field distribution information of the welding area; the temperature field distribution information is used to characterize the distribution pattern of heat generated during the welding process in the welding area. Based on the temperature field distribution information, at least one temperature feature information characterizing the welding thermal effect of the welding area is obtained; the temperature field distribution feature includes at least one of the low temperature area ratio and the high temperature area ratio. At least one of the temperature characteristic information is compared with a preset judgment criterion, and the welding quality is determined based on the comparison result.

2. The method for preparing a single battery cell according to claim 1, characterized in that, After the ultrasonic welding is completed, the temperature field distribution information of the welding area is obtained, including: In response to the welding completion command, at least one frame of thermal imaging image of the welding area is acquired within a preset time after welding; Based on at least one frame of the thermal imaging image, the temperature field distribution information after welding is obtained.

3. The method for preparing a battery cell according to claim 1 or 2, characterized in that, The process of obtaining at least one temperature characteristic information characterizing the welding thermal effect of the welding region based on the temperature field distribution information includes: The temperature field distribution information of the welding area is input into the feature analysis model to obtain at least one of the low-temperature area ratio of the welding area with a temperature lower than a first threshold and the high-temperature area ratio of the welding area with a temperature higher than a second threshold.

4. The method for preparing a single battery cell according to claim 3, characterized in that, The step of comparing at least one of the temperature characteristic information with a preset judgment criterion and determining the welding quality based on the comparison result includes: The low-temperature area ratio is compared with a first area threshold. If the low-temperature area ratio is greater than the first area threshold, a cold solder joint defect is determined to exist. And / or, the high-temperature area ratio is compared with a second area threshold. If the high-temperature area ratio is higher than the second area threshold, an over-soldering defect is determined to exist. If the comparison results show no defects such as incomplete welding or over-welding, the welding quality is deemed acceptable; if the comparison results show defects such as incomplete welding and / or over-welding, the welding quality is deemed unacceptable.

5. The method for preparing a single battery cell according to claim 4, characterized in that, If the judgment result is unqualified and the defect type is a cold weld defect, the battery cell is intercepted, the battery cell is returned to the welding station, and a welding parameter adjustment command is triggered to update the welding parameters. The battery cell is then re-welded according to the updated welding parameters. If the judgment result is unqualified and the defect type is over-welding defect, the battery cell is released to the subsequent production line, and a welding parameter adjustment command is triggered to update the welding parameters. The subsequent battery cells are then welded according to the updated welding parameters. If the determination result is qualified, the battery cell is released to the subsequent production line.

6. The method for preparing a single battery cell according to claim 1, characterized in that, Also includes: Establish a mapping relationship between welding energy parameters and the welding temperature characteristic values ​​of individual battery cells, and set target characteristics; Based on the deviation between the temperature field distribution characteristics of the battery cell after welding and the target characteristics, the parameter adjustment amount of the welding energy parameter is calculated through a preset feedback control algorithm. The parameter adjustment amount is then compensated for in the welding parameters of subsequent battery cells.

7. The method for preparing a single battery cell according to claim 6, characterized in that, The target features include at least one of the target low-temperature area ratio, the target high-temperature area ratio, and the target temperature value; the temperature field distribution features also include the measured temperature value.

8. A device for preparing a single battery cell, characterized in that, include: The welding station is used to connect the tabs of the battery cells to the battery cells using ultrasonic welding. The inspection station is equipped with a thermal imaging acquisition module, an extraction module, and a judgment module. The thermal imaging acquisition module is used to acquire temperature data of the welding area after ultrasonic welding is completed, and to obtain temperature field distribution information of the welding area; the temperature field distribution information is used to characterize the distribution pattern of heat generated during the welding process in the welding area. The extraction module is used to obtain at least one temperature feature information characterizing the welding thermal effect of the welding area based on the temperature field distribution information; the temperature field distribution feature includes at least one of the low temperature area ratio and the high temperature area ratio. The determination module is used to compare at least one of the temperature characteristic information with a preset determination standard, and determine the welding quality based on the comparison result.

9. The apparatus for preparing a single battery cell according to claim 8, characterized in that, Also includes: The processing module is configured to: if the judgment result is unqualified and the defect type is a cold solder joint defect, intercept the battery cell, return the battery cell to the welding station, trigger a welding parameter adjustment command to update the welding parameters, and perform a re-welding operation on the battery cell according to the updated welding parameters; if the judgment result is unqualified and the defect type is an over-welding defect, release the battery cell to the subsequent production line, trigger a welding parameter adjustment command to update the welding parameters, and perform welding operations on the subsequent battery cells according to the updated welding parameters; if the judgment result is qualified, release the battery cell to the subsequent production line.

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