Battery detection method, battery manufacturing process, and battery

Abstract

The application discloses a battery detection method, a battery manufacturing process and a battery. In the battery detection method, first, a preset process is used to form an electric core to obtain a formed electric core; then, an electric core parameter in the forming process is detected to obtain an electric core parameter detection data set; data processing is performed on the electric core parameter detection data set to obtain an electric core life characteristic value; finally, according to a size relationship between the electric core life characteristic value and a preset life characteristic value, the formed electric core is screened to screen out a normal electric core. Thus, the battery detection method provided by the application can ensure that the forming process can be normally performed, and can also detect the electric core parameter in the forming process in real time, without needing to perform a long-time charge-discharge cycle test, so that the electric core detection efficiency is improved, and the production efficiency of the battery is improved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a battery testing method, a battery manufacturing process, and a battery. Background Technology

[0002] With the rapid development of the new energy industry, the lifespan of existing lithium-ion batteries is getting longer and longer, and some can even achieve thousands of charge-discharge cycles. Currently, there are relatively mature prediction models for the lifespan of lithium-ion batteries, including thermodynamic life prediction models or remaining life prediction models. However, these models all rely on certain charge-discharge cycle test data, which means they are mainly limited to the research and development stage. For mass-produced battery cells with a large daily output, traditional life assessment methods based on long-term cycle testing cannot meet the high efficiency requirements of actual production. Summary of the Invention

[0003] In view of the above problems, this application provides a battery testing method, a battery manufacturing process, and a battery, which can improve the efficiency of cell testing and thus improve battery production efficiency.

[0004] Firstly, this application provides a method for detecting a battery, comprising:

[0005] Detect cell parameters during the cell formation process to obtain a set of cell parameter detection data;

[0006] Data processing is performed on the battery cell parameter detection data set to obtain the characteristic value of battery cell life;

[0007] Based on the relationship between the cell life characteristic value and the preset life characteristic value, the cells after formation are screened to select normal cells.

[0008] In the technical solution of this application embodiment, cell parameters are detected during the formation process, and the obtained cell parameter detection data set is processed to obtain cell life characteristic values. Then, by comparing the cell life characteristic values ​​with preset life characteristic values, the formed cells are screened to select normal cells. In this way, while ensuring that the formation process can proceed normally, cell parameters during the formation process can be detected in real time without the need for long-term charge-discharge cycle testing, which improves the efficiency of cell detection and thus improves the production efficiency of batteries.

[0009] In some embodiments, the detection of cell parameters during the cell formation process to obtain a cell parameter detection data set specifically includes:

[0010] The cell parameters are detected between the first formation step and the second formation step to obtain a cell parameter detection data set; the first formation step is to charge the initial cell with a first formation current for a first preset time to obtain a first formed cell; the second formation step is to charge the first formed cell with a second formation current for a second preset time to obtain a second formed cell.

[0011] Wherein, the first formation current is less than or equal to the second formation current.

[0012] In this embodiment, the SEI layer formation process can be carried out in stages, namely, two low-rate film formation processes through a first formation step and a second formation step, to promote the formation of the SEI layer and ensure its integrity and stability. Since the cell's dynamic characteristics change most significantly in the early stage of SEI layer formation, cell parameters can be detected between the first and second formation steps to obtain a set of cell parameter detection data. The data acquired at this time provides the highest resolution, helping to more accurately screen out normal cells.

[0013] In some embodiments, the first formation step and the second formation step are further included by placing the first formed cell for a first resting time.

[0014] The step of detecting cell parameters between the first and second formation steps to obtain a set of cell parameter detection data specifically includes:

[0015] During the process of letting the first formed cell stand for a first standing time, the cell parameters are detected between the first formation step and the second formation step to obtain a set of cell parameter detection data.

[0016] In the battery detection method of this application embodiment, after charging the initial cell with a first formation current for a first preset time, the cell is allowed to settle for a first settling time to achieve a relatively stable electrochemical state, providing a stable basis for subsequent cell parameter acquisition, thereby improving the efficiency and accuracy of cell detection. During this process, the cell parameters of the first formed cell are detected to obtain a cell parameter detection data set. Since the kinetic characteristics of the cell change most significantly in the early stage of SEI layer formation, detecting the cell parameters of the first formed cell provides the highest resolution data. This allows for data processing of the cell parameter detection data set to obtain cell lifetime characteristic values. Based on the relationship between the cell lifetime characteristic values ​​and preset lifetime characteristic values, normal cells can be more accurately screened.

[0017] In some embodiments, detecting cell parameters between the first formation step and the second formation step to obtain a cell parameter detection data set specifically includes:

[0018] Obtain the initial voltage before charging the initial cell with the first formation current and the termination voltage after charging the initial cell with the first formation current for a first preset time.

[0019] The cell parameter detection data set includes any one or more combinations of initial voltage, termination voltage, first formation current, and first preset time.

[0020] By comprehensively analyzing parameters such as initial voltage, termination voltage, first formation current, and first preset time, the characteristics of the battery cell during the formation process can be described more fully, providing a basis for obtaining the characteristic value of the battery cell's lifespan and improving the efficiency and accuracy of abnormal battery cell detection.

[0021] In some embodiments, the step of processing the cell parameter detection data set to obtain cell life characteristic values ​​specifically includes:

[0022] Calculate the difference between the initial voltage and the final voltage to obtain the voltage change.

[0023] The first preset time and voltage change are linearly fitted according to the preset linear fitting formula to obtain the first fitting parameters and the second fitting parameters after fitting.

[0024] The dynamic internal resistance is obtained based on the second fitting parameters, the termination voltage, and the first formation current, and the dynamic internal resistance is used as a characteristic value of the cell life.

[0025] The actual diffusion coefficient is obtained based on the first fitting parameters, and the actual diffusion coefficient is used as a characteristic value of cell life.

[0026] By obtaining first and second fitting parameters through linear fitting, data reflecting the internal electrochemical behavior of the battery cell are extracted. This yields the true resistance of the cell at a specific state of charge (SOC), i.e., the kinetic internal resistance, which is used to assess the cell's quality. Furthermore, the diffusion coefficient, reflecting the migration rate of lithium ions in the electrode material, is obtained through the first fitting parameters and serves as a crucial indicator of the cell's kinetic performance. Thus, through linear fitting and parameter calculation, the cell's lifespan characteristic values ​​are accurately obtained, allowing for the selection of cells with normal lifespan and improving battery lifespan.

[0027] In some embodiments, obtaining the dynamic internal resistance based on the second fitting parameters, the termination voltage, and the first formation current specifically includes:

[0028] Calculate the difference between the termination voltage and the second fitted parameter;

[0029] Calculate the ratio of the difference to the first formation current;

[0030] The ratio is used as the internal resistance of the dynamics.

[0031] By calculating the difference, static components independent of time (represented by the second fitting parameter) are eliminated, thus capturing the dynamic changes more accurately. At the same time, the ratio of the difference to the first formation current is used as the dynamic internal resistance, reflecting the internal resistance characteristics of the cell, thereby identifying abnormal cells and screening out normal cells.

[0032] In some embodiments, the step of linearly fitting the first preset time and voltage change according to a preset linear fitting formula to obtain the fitted first fitting parameters and second fitting parameters specifically includes:

[0033] Take the square root of the first preset time and use the square root as the first variable in the preset linear fitting formula;

[0034] The voltage change is used as the second variable in the pre-defined linear fitting formula;

[0035] The slope of the preset linear fitting formula is used as the first fitting parameter, and the intercept of the preset linear fitting formula is used as the second fitting parameter.

[0036] Using the square root of time as a variable reflects the diffusion behavior of lithium ions in the electrode material, and makes the relationship between voltage change and time closer to linear, thereby improving the accuracy of linear fitting results. The slope can accurately represent the diffusion rate of lithium ions in the electrode material, and the intercept can reflect the state of the cell in the early stage of formation, thus accurately evaluating the quality and state of the cell.

[0037] In some embodiments, the lifetime characteristic value includes the actual diffusion coefficient and / or the actual kinetic internal resistance. The step of screening the formed cells based on the relationship between the cell lifetime characteristic value and a preset lifetime characteristic value to select normal cells specifically includes:

[0038] When the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the formed cells are screened to select normal cells.

[0039] And / or, when the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the formed cells are screened to select normal cells.

[0040] By setting standard diffusion coefficients and standard kinetic internal resistances and comparing them with cell life characteristic values, the ability to distinguish abnormal cells is improved, thereby increasing the detection accuracy and production efficiency of cells.

[0041] In some embodiments, the first formation current is between 0.05 charge / discharge rate and 0.1 charge / discharge rate;

[0042] And / or the second formation current is between 0.05 charge / discharge rate and 0.1 charge / discharge rate.

[0043] A low current rate of 0.05C to 0.1C provides relatively mild conditions for the formation of the SEI layer. At this current intensity of the first formation current and / or the second formation current, lithium ions can be more uniformly embedded in the anode material, thereby promoting the stable formation of the SEI layer. The lower charging rate helps prevent lithium ions from depositing too quickly on the anode surface, thus reducing the risk of lithium plating. This not only improves battery safety but also extends its lifespan.

[0044] Secondly, this application provides a battery manufacturing process, which includes a battery cell formation process using preset steps to obtain the formed battery cell.

[0045] The battery testing method described above is performed during the cell formation process.

[0046] In this embodiment, the SEI layer formation process can be carried out in stages, namely, two low-rate film formation processes through a first formation step and a second formation step to promote SEI layer formation. Since the kinetic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the battery detection method described above can be executed during the cell formation process. Specifically, the cell parameters between the first and second formation steps are detected to obtain a set of cell parameter detection data. This allows for more accurate screening of normal battery cells.

[0047] In some embodiments, the formation of the battery cell using a preset process specifically includes:

[0048] First formation step: Charge the initial cell with a first formation current for a first preset time to obtain the first formed cell;

[0049] Second formation step: The first formed cell is charged with a second formation current for a second preset time to obtain a second formed cell;

[0050] The third formation step: The second formed cell is charged with a third formation current for a third preset time to obtain the third formed cell;

[0051] Among them, the first formation current is less than or equal to the second formation current, and the second formation current is less than the third formation current;

[0052] The process of detecting cell parameters during cell formation to obtain a cell parameter detection data set specifically includes:

[0053] The cell parameters are detected between the first and second formation steps to obtain a set of cell parameter detection data.

[0054] In this embodiment, a first formation current is used to charge the initial cell to promote the initial formation of the SEI layer. Then, a second formation current is used to complete the SEI layer formation, ensuring its quality and integrity. Finally, after the SEI layer has stabilized, a third formation step is performed, where the second-formed cell is rapidly charged with a third preset current for a third preset time until the charge reaches a preset capacity. Thus, through two low-rate film formation processes, the formation of the SEI layer is promoted, ensuring its integrity and stability. Since the cell's dynamic characteristics change most significantly in the early stages of SEI layer formation, cell parameters can be detected between the first and second formation steps to obtain a cell parameter detection data set. This data set provides the highest resolution, helping to more accurately screen out normal cells.

[0055] In some embodiments, after charging the initial cell with a first formation current for a first preset time to obtain a first formed cell, the battery detection method further includes:

[0056] The first formed battery cell is left to stand for a first settling time.

[0057] The step of detecting cell parameters between the first and second formation steps to obtain a set of cell parameter detection data specifically includes:

[0058] During the process of letting the first formed cell stand for a first standing time, the cell parameters are detected between the first formation step and the second formation step to obtain a set of cell parameter detection data.

[0059] In the battery manufacturing process of this application embodiment, after charging the initial cell with a first formation current for a first preset time, the obtained first formed cell is left to stand for a first settling time. During this process, the cell parameters of the first formed cell are detected to obtain a cell parameter detection data set. Further, the cell parameter detection data set is processed to obtain cell lifespan characteristic values. Based on the relationship between the cell lifespan characteristic values ​​and preset lifespan characteristic values, abnormal cells are identified, and normal cells are selected. Thus, the acquired data is from the initial stage of SEI layer formation, where the changes in cell dynamic characteristics are most pronounced, providing the highest resolution and improving the accuracy of battery lifespan detection results.

[0060] In some embodiments, the third formation current is located between a charge / discharge rate of 0.33C and a charge / discharge rate of 0.5C. Compared to lower rate currents such as the first and second formation currents, the higher rate current of 0.33C to 0.5C can significantly accelerate the charging speed, thereby shortening the overall formation process time and improving production efficiency.

[0061] In some embodiments, before the cell is formed using a preset process to obtain the formed cell, the battery manufacturing process further includes:

[0062] The battery cells are impregnated;

[0063] The initially immersed cells were left to stand for a second settling time, and the open-circuit voltage after immersion was obtained.

[0064] The open-circuit voltage is determined as the initial voltage before the initial cell is formed.

[0065] By allowing a second settling period, sufficient time can be ensured that the electrolyte fully penetrates between the positive and negative electrode materials, forming a uniform electrochemical environment. This facilitates the stable formation of the subsequent SEI layer and the smooth progress of the electrochemical reaction. Simultaneously, measuring the open-circuit voltage (OCV) after settling effectively assesses the electrolyte wetting effect. This method allows for timely detection and correction of problems during the wetting process. Furthermore, the open-circuit voltage can be determined as the initial voltage before formation of the initial cell. In battery testing methods, it is unnecessary to repeatedly measure the initial voltage corresponding to the initial cell before charging with the first formation current, simplifying the operation and accelerating the testing speed and efficiency.

[0066] In some embodiments, after the second formation step, the battery manufacturing process further includes:

[0067] The second formed cell is left to stand for a third standing time, and after the third standing time is completed, the third formation step is performed.

[0068] After performing the second formation step, allowing the SEI layer to stand for a third time further stabilizes it. During this third standing period, the electrochemical reaction tends to stabilize, the quality and integrity of the SEI layer are enhanced, and the potential negative impacts, such as lithium plating, that may occur during subsequent high-rate charging in the third formation step are reduced.

[0069] Thirdly, this application provides a battery manufactured using the battery manufacturing process described in any of the above claims.

[0070] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0071] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0072] Figure 1 A flowchart of the first embodiment of the battery detection method provided in this application;

[0073] Figure 2 A flowchart of the second embodiment of the battery detection method provided in this application;

[0074] Figure 3 A flowchart of the third embodiment of the battery detection method provided in this application;

[0075] Figure 4 A flowchart of the fourth embodiment of the battery detection method provided in this application;

[0076] Figure 5 A flowchart of the first embodiment of the battery manufacturing process provided in this application;

[0077] Figure 6 A flowchart of the second embodiment of the battery manufacturing process provided in this application;

[0078] Figure 7 A schematic diagram of the voltage change over time in the first embodiment of the battery detection method provided in this application;

[0079] Figure 8 A schematic diagram of the voltage change over time in the second embodiment of the battery detection method provided in this application;

[0080] Figure 9 A schematic diagram of the voltage change over time in the third embodiment of the battery detection method provided in this application;

[0081] Figure 10 A schematic diagram of the voltage change over time in the fourth embodiment of the battery detection method provided in this application;

[0082] Figure 11 A schematic diagram of the test results for the first embodiment of the battery testing method provided in this application;

[0083] Figure 12 A schematic diagram of the test results for the second embodiment of the battery testing method provided in this application;

[0084] Figure 13 A schematic diagram of the test results for the third embodiment of the battery testing method provided in this application;

[0085] Figure 14 A schematic diagram of the test results for the fourth embodiment of the battery testing method provided in this application.

[0086] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0087] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0088] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0089] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0090] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0091] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0092] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0093] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0094] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0095] Currently, judging from market trends, the application of power batteries is becoming increasingly widespread. Power batteries are not only used in energy storage systems such as hydropower, thermal power, wind power, and solar power plants, but also extensively used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. With the continuous expansion of power battery applications, market demand is also constantly increasing.

[0096] The inventors have noted that with the rapid development of the new energy industry, the lifespan of existing lithium-ion batteries is increasing, even reaching thousands of charge-discharge cycles. Currently, there are relatively mature prediction models for the lifespan of lithium-ion batteries, including thermodynamic lifespan prediction models and remaining lifespan prediction models. However, these models all rely on detailed charge-discharge cycle test data. This means that after cell formation, the cells are subjected to multiple charge-discharge cycles to simulate actual usage conditions and evaluate the battery's performance and lifespan under different conditions. However, each charge-discharge cycle takes time, and completing thousands of cycles may take weeks or even months. This is not only time-consuming and labor-intensive but also costly, making it difficult to implement real-time monitoring and rapid identification of abnormal cells on the production line. It is unsuitable for the real-time monitoring needs of large-scale mass production environments and can only be applied from a research and development perspective.

[0097] For mass-produced battery cells with a large daily output, traditional lifespan assessment methods based on long-term cycle testing are clearly insufficient to meet the high-efficiency requirements of actual production. In other words, there are no relevant models or testing methods to monitor whether battery lifespan will become abnormal. Specifically: long-term testing, due to its low detection rate, will affect production speed, leading to low production line efficiency and failing to meet timeliness requirements; furthermore, introducing load testing equipment or modifying existing production line equipment will increase production costs; traditional methods are difficult to accurately screen out cells with potential lifespan abnormalities in mass production, potentially leading to defective products entering the market and affecting battery product quality and user satisfaction.

[0098] The inability to identify and remove defective battery cells in a timely manner due to the aforementioned issues may lead to rework or product scrapping, increasing manufacturing costs. Furthermore, if potentially problematic battery cells enter the market, they may pose safety risks during use, such as overheating or short circuits, affecting user experience and potentially causing safety accidents.

[0099] To address the issue that traditional lifespan assessment methods based on long-term cycle testing cannot meet the high-efficiency requirements of actual production, the inventors, after in-depth research, designed a battery testing method. This method aims to achieve rapid, low-cost, and efficient lifespan risk detection for mass-produced battery cells by optimizing the formation process and real-time data acquisition and processing during formation. Specifically, the battery cells are formed using a predetermined process to obtain a high-quality SEI layer (Solid Electrolyte Interphase), ensuring a good lifespan. Simultaneously, battery parameters are detected during the formation process to obtain a set of battery parameter detection data reflecting the electrochemical behavior of the battery cell under specific conditions. This data is then processed to obtain the battery cell lifespan characteristic value. In short, data is acquired during the formation process, and the battery cell lifespan characteristic value is calculated based on the acquired data. For example, calculating the true impedance (internal resistance) and diffusion coefficient of the battery cell allows for the identification of abnormal cells in mass production based on any one or more of these parameters. This enables the selection of normal cells that meet quality requirements. This method can complete the quality inspection of the battery cells in a short time, improving production efficiency without the need to introduce new equipment or modify production lines, reducing the cost of inspection and production processes. Furthermore, by calculating lifespan characteristic values, it enhances the ability to distinguish between normal and abnormal cells, ensuring the battery's lifespan and providing strong technical support for large-scale production and application.

[0100] The batteries disclosed in this application can be used, but are not limited to, in electrical devices such as vehicles, ships, or aircraft. A power system for such an electrical device can be constructed using batteries disclosed in this application. This helps to mitigate and automatically regulate the deterioration of cell expansion forces, replenish electrolyte consumption, and improve the stability of battery performance and battery life.

[0101] This application provides a battery testing method for efficient and precise quality control of mass-produced battery cells. During the manufacturing process, after undergoing processes such as electrolyte injection, impregnation, formation, and screening, the battery cells are packaged into more complete battery cells. Therefore, the battery cell is a crucial stage in battery production, and its quality and performance directly affect the performance of the final product.

[0102] In some embodiments of this application, the battery can not only serve as the operating power source for the vehicle, but also as the driving power source for the vehicle, replacing or partially replacing fuel or natural gas to provide driving power for the vehicle.

[0103] In a battery, there can be multiple battery cells, which can be connected in series, parallel, or a combination thereof. A combination thereof means that multiple battery cells are connected in both series and parallel configurations. Multiple battery cells can be directly connected in series, parallel, or a combination thereof, and then the entire assembly of these battery cells is housed within a casing. Alternatively, a battery can consist of multiple battery cells first connected in series, parallel, or a combination thereof to form a battery module, and then these modules are connected in series, parallel, or a combination thereof to form a whole, which is also housed within a casing. The battery can also include other structures; for example, it can include a busbar component for electrical connection between the multiple battery cells.

[0104] On battery production lines, a massive number of battery cells need to be inspected daily. Traditional methods based on long-term cycle testing cannot meet the demands of efficient production. However, the battery inspection method proposed in this application can evaluate each cell in a short time through an optimized formation process and real-time cell lifespan characteristic value calculation, ensuring that only high-quality cells can proceed to subsequent assembly processes. In other words, the battery inspection method of this application can effectively identify cells with potential problems during the battery manufacturing stage, ensuring that each battery has stable performance and reducing the safety risks caused by battery failures.

[0105] It's important to note that lithium-ion battery production is a complex and meticulous process involving multiple key steps to ensure the safety and reliability of the final product. For example, the battery cell needs to be encapsulated in a casing to protect the internal components. First, the wound or stacked battery cell is placed in a metal or pouch casing. The positive and negative tabs are then soldered to external terminals to ensure a good electrical connection. Finally, the battery casing is sealed to prevent the ingress of external substances. Next, the battery cell undergoes an electrolyte injection process, injecting an appropriate amount of electrolyte to ensure the electrochemical reaction can proceed smoothly. Then, an impregnation process is performed, allowing the electrolyte to fully penetrate between the electrode materials. After a period of settling, the electrolyte is evenly distributed throughout the battery cell, forming a preliminary electrochemical environment. Following this, a formation process can be performed, charging the battery cell to form a stable SEI layer on the negative electrode surface, ensuring a long battery life. The battery testing method in this application improves the formation process, enabling the cell to generate a high-quality SEI layer and ensuring a good cycle life. At the same time, it can calculate the cell life characteristic value to assess the cell life risk, identify cells with abnormal life in a timely manner, and prevent them from flowing into subsequent processes and to customers, thus reducing the user experience.

[0106] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart of an embodiment of a battery testing method according to this application. The battery testing method includes:

[0107] Step S100: Detect cell parameters during the cell formation process to obtain a cell parameter detection data set;

[0108] Step S200: Process the cell parameter detection data set to obtain the cell life characteristic value;

[0109] Step S300: Based on the relationship between the cell life characteristic value and the preset life characteristic value, the formed cells are screened to select normal cells.

[0110] In the battery testing method of this application, the battery cell needs to be formed using a preset process to obtain the formed cell. For example, a low-rate film formation is first performed with a small formation current to initially form the SEI layer, ensuring its stability and integrity. Then, a small formation current is used to complete the final formation of the SEI layer. Finally, after the SEI layer has been fully formed and stabilized, a larger formation current is used to quickly charge the battery to a predetermined SOC level (e.g., 30%~70%) to prepare for subsequent high-temperature aging and other follow-up processes. By performing two low-rate film formations, the formation of the SEI layer can be more fully promoted, which not only helps improve battery performance but also reduces the occurrence of defects such as lithium plating. Then, the cell parameters during the formation process are detected to obtain a cell parameter detection data set. The cell parameter detection data set is then processed to obtain cell lifetime characteristic values. Finally, based on the relationship between the cell lifetime characteristic values ​​and the preset lifetime characteristic values, the formed cells are screened to select normal cells. The inventors noted that data from the first low-rate film deposition reflects the initial state of the SEI layer, while data from the second low-rate film deposition reflects the state after the SEI layer is fully formed. The kinetic characteristics of the battery cell change most significantly during the early stages of SEI layer formation (e.g., 0.03~0.08 CnAh) and near completion (e.g., 0.07~0.09 CnAh). Therefore, data acquired during these two time periods provides strong resolution, helping to more accurately identify abnormal cells with potential problems. Thus, battery cell parameters after the first low-rate film deposition can be obtained, and the obtained cell parameter detection data can be processed. For example, calculation software written in Python can be used to batch process mass-produced cells, obtaining the lifetime characteristic value corresponding to each cell. This value is then compared with preset lifetime characteristic values ​​to screen out normal cells that meet quality requirements for subsequent processes such as aging tests and capacity testing, ensuring long-term performance and consistency. For abnormal cells, rework can be performed, re-forming the abnormal cells to improve SEI layer quality and electrochemical performance. Based on the cause of the anomaly, formation parameters or process conditions are adjusted to optimize cell performance. For cells confirmed to be irreparable or with serious quality issues, they are clearly marked to prevent misuse and safely disposed of in accordance with environmental regulations to prevent them from entering the market. Optionally, cells that do not meet high-requirement application scenarios can be used in lower-requirement scenarios to maximize resource utilization, such as assessing the applicability of the anomaly cell based on its specific problems. Anomaly cells are classified into different levels and used in suitable application scenarios, such as energy storage systems and power tools.

[0111] The battery testing method described in this application calculates the lifespan characteristic values ​​of mass-produced cells based on the cell parameters detected during the formation process. This identifies cells with abnormal lifespans, allowing for the selection of normal cells and preventing them from entering subsequent processes or reaching the customer. This achieves efficient and precise quality control of mass-produced cells, providing strong support for large-scale production. This solution can significantly improve product quality and user experience without affecting production efficiency.

[0112] In one embodiment, optionally, the detection of cell parameters during the cell formation process to obtain a cell parameter detection data set specifically includes:

[0113] The cell parameters are detected between the first formation step and the second formation step to obtain a cell parameter detection data set; the first formation step is to charge the initial cell with a first formation current for a first preset time to obtain a first formed cell; the second formation step is to charge the first formed cell with a second formation current for a second preset time to obtain a second formed cell.

[0114] Wherein, the first formation current is less than or equal to the second formation current.

[0115] In this embodiment, the SEI layer formation process can be carried out in stages, namely, two low-rate film deposition processes through a first formation step and a second formation step, to promote the formation of the SEI layer and ensure its integrity and stability. Since the cell's kinetic characteristics change most significantly in the early stage of SEI layer formation, cell parameters can be detected between the first and second formation steps to obtain a set of cell parameter detection data. The data acquired at this time provides the highest resolution, helping to more accurately screen out normal cells. The data after the first low-rate film deposition using the first formation step reflects the initial state of the SEI layer, while the data after the second low-rate film deposition using the second formation step reflects the state after the SEI layer is fully formed. The cell's kinetic characteristics change most significantly in the early stage of SEI layer formation (e.g., 0.03~0.08 CnAh) and near completion (e.g., 0.07~0.09 CnAh), therefore, the data acquired during these two time periods provides strong resolution. Optionally, the cell parameters after the first low-rate film formation can be obtained, and the obtained cell parameter detection data set can be processed to obtain the cell lifetime characteristic value. It should be noted that after performing two low-rate film formation steps on the cell, the second-formed cell can be charged at a high rate to prepare for subsequent high-temperature aging and other tests.

[0116] In practical applications, obtaining cell parameters between the first and second formation steps can provide strong resolution, thus improving the accuracy and reliability of the test results.

[0117] In one embodiment, the first formation step and the second formation step are further included by placing the first formed cell for a first settling time between the two steps.

[0118] The step of detecting cell parameters between the first and second formation steps to obtain a set of cell parameter detection data specifically includes:

[0119] During the process of letting the first formed cell stand for a first standing time, the cell parameters are detected between the first formation step and the second formation step to obtain a set of cell parameter detection data.

[0120] In this embodiment, the initial cell is charged with a first formation current for a preset first time (pre-formation) to ensure the initial formation of the SEI layer. At the end of the pre-formation process, a first settling time is allowed to stabilize the electrochemical reaction and further solidify the SEI layer. The first settling time is set by the researchers. For example, any time within the range of 10 to 30 seconds can be selected as the first settling time to further stabilize the newly formed SEI layer. Furthermore, setting the first settling time between the first and second formation steps can reduce side reactions caused by rapid continuous charging. Since the cell is in a relatively stable electrochemical state during the settling period, it is suitable for data acquisition. Therefore, cell parameters can be detected during this settling stage to obtain a set of cell parameter detection data.

[0121] It should be noted that during the initial formation stage of the SEI layer, the electrochemical reactions inside the cell have not yet reached equilibrium, resulting in significant and substantial voltage changes during the resting period. These voltage changes directly reflect the quality and stability of the SEI layer, providing accurate fundamental data for calculating lifetime characteristics such as the diffusion coefficient and kinetic internal resistance. Simultaneously, the cell's internal resistance changes significantly after the initial formation, as the thickness and uniformity of the SEI layer directly affect the cell's internal resistance at this stage. By measuring the cell's internal resistance during the resting period, the quality of the SEI layer and the cell's health status can be accurately assessed. Furthermore, charge transport and ion diffusion are most active during the initial SEI layer formation stage, making the diffusion coefficient calculation most accurate at this time. Therefore, cell parameters can be detected during this resting stage, facilitating early identification and rejection of abnormal cells.

[0122] Utilizing data from the resting period for characteristic value calculations, such as diffusion coefficient and kinetic internal resistance, improves the accuracy of cell detection, helps identify potentially abnormal cells early, and ensures that only high-quality cells enter the market, thereby improving cell quality and user experience. Simultaneously, extending the resting time of the first-stage formation cell enhances the quality of the SEI layer and reduces the potential adverse effects of subsequent high-rate charging. The resting period also reduces the impact of lithium plating, improving battery safety and long-term performance.

[0123] In one embodiment, the process of detecting cell parameters between the first and second formation steps to obtain a cell parameter detection data set specifically includes:

[0124] Obtain the initial voltage before charging the initial cell with the first formation current and the termination voltage after charging the initial cell with the first formation current for a first preset time.

[0125] The cell parameter detection data set includes any one or more combinations of initial voltage, termination voltage, first formation current, and first preset time.

[0126] It should be noted that before formation, the battery cell usually needs to be impregnated. The main purpose of impregnation is to ensure that the electrolyte can fully penetrate into the electrode material, forming a uniform electrochemical environment, which provides a good foundation for subsequent formation and use. However, impregnated cells may have uneven electrolyte distribution, which can cause localized overheating in areas of uneven electrolyte distribution when current passes through the cell during subsequent formation. At the same time, uneven electrolyte distribution can also lead to uneven formation of the subsequent SEI layer, thus affecting the stability and safety of the battery.

[0127] Therefore, in this embodiment, before charging the initial cell with the first formation current, a high-precision voltmeter can be used to measure the cell's open-circuit voltage (OCV). To simplify the process, the measured voltage value can be directly used as the initial voltage before the first formation step. In this way, monitoring the open-circuit voltage can not only verify the immersion results, but also serve as the cell parameter in the cell parameter detection data set required for subsequent life characteristic value calculation. This simplifies the operation process, shortens the data acquisition time, and thus improves the detection efficiency.

[0128] Understandably, after sequentially injecting and impregnating the battery cells, monitoring the open-circuit voltage after impregnation and using it as the initial voltage allows for batch-to-batch consistency assessment when multiple cells are involved, ensuring each cell is in the same initial state. If the OCV does not meet expectations, it may indicate uneven electrolyte distribution, requiring re-impregnation or checking process parameters. If the OCV is within expectations, the initial cell can be charged with the first formation current for a first preset time. For example, for a 1Ah cell using a 0.1C current (0.1A), charging 0.05Ah would require 0.5 hours. In practical applications, after connecting the initial cell to the formation equipment, the first formation current is set to a charge / discharge rate of 0.1, and a timer is started to begin charging. After the first preset time (e.g., 0.5 hours), charging is immediately stopped, and the cell voltage is measured again using a high-precision voltmeter to obtain the termination voltage. In this way, the cell parameter detection data set, such as initial voltage, termination voltage, first formation current, and first preset time, can be obtained and recorded in a database or data table for subsequent data processing to obtain the cell life characteristic value. Based on the relationship between the cell life characteristic value and the preset life characteristic value, the formed cells are screened to select normal cells.

[0129] In this embodiment, computational software written in Python can be used to process the acquired data in batches. It is assumed that the cell parameter detection data sets are pre-stored in a CSV file, with each row containing a timestamp (including a first preset time) and voltage (including the initial voltage). and termination voltage Information such as voltage, current (including the first formation current), etc. is obtained. The raw voltage, current, and time data during the formation process are read using Python. Based on the current pattern (e.g., the current is 0 in the first step, not 0 in the second step, 0 in the third step, and not 0 in the fourth step), data preprocessing is performed to obtain the cell parameters detected during the first settling period of the first formed cell. Figure 7 As shown, Figure 7 This is a schematic diagram showing the voltage change over time during the initial resting period of the first-formed battery cell. The current pattern is defined by the R&D personnel and presented in code. After Python reads the cell parameters, it can perform further characteristic value calculations to screen out normal cells for subsequent manufacturing processes, and identify abnormal cells to prevent them from entering the market, causing battery quality problems, or even safety accidents.

[0130] By developing computational software using Python algorithms, this invention enables batch processing of formation data from multiple battery cells and extracts cell parameter data for each cell during the first resting period. This data is then used for further feature value calculations. Thus, the battery detection method of this application improves the accuracy of lifetime feature values ​​by acquiring data from a stage with high resolution during the formation process, thereby enhancing detection precision. This allows for accurate identification of abnormal cells and improved cell performance. Furthermore, the use of Python algorithms enables batch processing of mass-produced cells, increasing the detection rate without requiring additional equipment or modifications, thus reducing costs.

[0131] In one embodiment, reference Figure 2 The step of processing the cell parameter detection data set to obtain the cell life characteristic value specifically includes:

[0132] Step S310: Calculate the difference between the initial voltage and the termination voltage to obtain the voltage change.

[0133] Step S320: Perform linear fitting on the first preset time and voltage change according to the preset linear fitting formula to obtain the first fitting parameters and the second fitting parameters after fitting.

[0134] Step S330: Obtain the dynamic internal resistance based on the second fitting parameters, the termination voltage, and the first formation current, and use the dynamic internal resistance as a characteristic value of the cell life.

[0135] Step S340: Obtain the actual diffusion coefficient based on the first fitting parameters, and use the actual diffusion coefficient as a characteristic value of the cell life.

[0136] Based on the above embodiments, by employing two low-rate formation steps (first formation step and second formation step) and one high-rate formation step, staged formation of the battery cell is achieved. After charging the initial battery cell with the first formation current for a first preset time, it is left to rest for a first settling time to obtain the battery cell parameters during this resting stage, thus obtaining a battery cell parameter detection data set. The battery cell parameter detection data set includes, but is not limited to, initial voltage, termination voltage, first formation current, and first preset time. To facilitate the description of the calculation process, this embodiment uses the calculation of one battery cell for explanation. In this embodiment, the difference between the initial voltage and the termination voltage is first calculated to obtain the voltage change. ,in, The first preset time and voltage change are linearly fitted according to a preset linear fitting formula, which is: ,in, Let I be the time-varying quantity, I be the charging current, and k be the first fitting parameter. This represents the second fitting coefficient. It can be understood that the voltage change can be the difference between the voltages at any two different times. The voltage change is obtained by taking the difference between the initial voltage and the final voltage. hour, This corresponds to the duration of the first preset time. Then, based on the second fitting parameters, the termination voltage, and the first formation current, the kinetic internal resistance is obtained, i.e., the kinetic internal resistance is: ,in, For dynamic internal resistance, The termination voltage is the voltage at which the initial cell is charged using the first formation current for a first preset time.

[0137] It should be noted that kinetic internal resistance describes the internal resistance of the battery cell caused by electrochemical reactions during charging and discharging, reflecting the electrochemical reaction rate and interface characteristics of the cell. Kinetic internal resistance directly affects the charging and discharging efficiency of the battery cell. Lower kinetic internal resistance means less energy loss, thus improving charging and discharging efficiency. Lower kinetic internal resistance can also reduce the thermal effects during electrochemical reactions, lowering the internal temperature of the battery cell and thus extending its cycle life. Therefore, when the kinetic internal resistance is too high, local heat generation during charging and discharging increases, side reactions intensify, and lifespan decays faster. In this embodiment, kinetic internal resistance can be used as a characteristic value of battery cell lifespan to screen cells after formation, identifying abnormal cells and selecting normal cells for subsequent manufacturing processes. Furthermore, the first fitting parameter k is the actual diffusion coefficient, describing the diffusion rate of ions in the electrode material and reflecting the kinetic characteristics of the electrochemical reaction. When the diffusion coefficient is abnormal (too low), the risk of lithium plating during cell cycling increases, potentially leading to safety issues. Therefore, the actual diffusion coefficient can also be used as a lifetime characteristic value. By screening cells with normal diffusion coefficients, safety can be significantly improved, efficient and precise quality control can be achieved, ensuring that only high-quality cells enter the market, thereby improving production efficiency and safety.

[0138] Batch processing of battery cell formation data using Python algorithms enables rapid and accurate calculation of the lifespan characteristic values ​​for each cell, improving data processing efficiency. Extracting the diffusion coefficient and kinetic internal resistance as lifespan characteristic values ​​through linear fitting ensures that only high-quality cells proceed to subsequent manufacturing processes, improving overall production efficiency and safety. Furthermore, actual measurement data often contains noise and fluctuations, and directly using this data for analysis may lead to inaccurate results. Linear fitting smooths the data, reducing the impact of noise and thus obtaining more stable and reliable lifespan characteristic values. Because linear fitting fits the data using a mathematical model, it can better capture trends and patterns in the data, improving the accuracy of the detection results.

[0139] In one embodiment, reference Figure 3The process of obtaining the kinetic internal resistance based on the second fitting parameters, the termination voltage, and the first formation current specifically includes:

[0140] Step S331: Calculate the difference between the termination voltage and the second fitted parameter;

[0141] Step S332: Calculate the ratio of the difference to the first formation current;

[0142] Step S333: Use the ratio as the internal resistance of the dynamics.

[0143] Based on the above embodiments, the difference between the initial voltage and the termination voltage is calculated to obtain the voltage change. ,in, The first preset time and voltage change are linearly fitted according to a preset linear fitting formula, which is: ,in, Let I be the time-varying quantity, I be the charging current, and k be the first fitting parameter. The second fitting coefficient is used. Then, based on the second fitting parameter (intercept R) obtained from the linear fitting, the dynamic internal resistance is calculated. That is, the internal resistance of the dynamics is: ,in, For dynamic internal resistance, This is the termination voltage when the initial cell is charged using the first formation current for a first preset time. Kinetic internal resistance describes the internal resistance of the cell caused by electrochemical reactions during charging and discharging, directly affecting the cell's charging and discharging efficiency, power density, cycle life, and safety.

[0144] It should be noted that the diffusion coefficient k describes the diffusion rate of ions in the electrode material. However, in practical applications, the difference in diffusion coefficient between cells from different batches or under different manufacturing processes can be very small. This small difference makes it difficult to effectively distinguish cell quality differences based solely on the diffusion coefficient. (Kinetic internal resistance) This describes the internal resistance of a battery cell caused by electrochemical reactions during charging and discharging. These resistances typically exhibit significant numerical differences, making it easier to identify abnormal cells. Of course, factors such as the diffusion coefficient k and kinetic internal resistance also play a role. This reflects the different characteristics of the battery cell. The diffusion coefficient mainly reflects the ion diffusion rate and the kinetic characteristics of the electrochemical reaction, while the kinetic internal resistance reflects the electrochemical reaction rate and interface characteristics inside the battery cell. Therefore, combining the two can provide a more comprehensive evaluation of the battery cell performance. Thus, in this embodiment, the kinetic internal resistance and the actual diffusion coefficient are used as lifetime characteristic values ​​to identify abnormal battery cells and screen out normal battery cells. By considering both the diffusion coefficient and the kinetic internal resistance simultaneously, abnormal battery cells can be identified more accurately, avoiding misjudgments caused by a single indicator.

[0145] In mass production, the use of diffusion coefficients and kinetic internal resistance enables automated screening, improving production efficiency. Through programming, batch processing and automated analysis can be implemented to eliminate substandard cells at an early stage, reducing unnecessary waste costs in subsequent processes, and promptly identifying and correcting potential problems, thereby improving product quality and customer satisfaction.

[0146] In one embodiment, reference Figure 4 The step of linearly fitting the first preset time and voltage change according to the preset linear fitting formula to obtain the first fitting parameters and the second fitting parameters specifically includes:

[0147] Step S321: Take the square root of the first preset time and use the square root as the first variable of the preset linear fitting formula;

[0148] Step S322: Use the voltage change as the second variable in the preset linear fitting formula;

[0149] Step S323: Use the slope of the preset linear fitting formula as the first fitting parameter, and use the intercept of the preset linear fitting formula as the second fitting parameter.

[0150] Based on the above embodiments, the cell parameters between the first formation step and the second formation step are obtained. Specifically, after completing the first formation step, the first-formed cell can be left to stand for a first standing time, and the cell parameters during this standing period are detected to obtain a cell parameter detection data set. The cell parameter detection data set includes, but is not limited to, initial voltage, termination voltage, first formation current, and a first preset time. In this embodiment, firstly, the difference between the initial voltage and the termination voltage is calculated to obtain the voltage change. ,in, The first preset time and voltage change are linearly fitted according to a preset linear fitting formula, which is: ,in, Let I be the time-varying quantity, I be the charging current, and k be the first fitting parameter. is the second fitting coefficient.

[0151] In this embodiment, due to the voltage change in the electrochemical reaction... With time The relationship is usually not linear. To linearize this relationship and facilitate analysis using linear fitting methods, in this embodiment, the square root of the first preset time is taken, and the square root is used as the first variable (independent variable) of the preset linear fitting formula, and the voltage change rate is used as the second variable (dependent variable) of the preset linear fitting formula. By substituting the values ​​of the first variable (independent variable) and the second variable (dependent variable) into different groups, linear fitting is performed to calculate the slope and intercept, which are then used as the first and second fitting parameters to further calculate the diffusion coefficient and kinetic internal resistance, thereby evaluating the performance and quality of the battery cell. (Reference) Figures 7 to 10 , Figure 7 and Figure 8 This is a curve showing the voltage change over time, with the voltage being the original voltage data. In this embodiment, the linear portion of the curve can be fitted, such as... Figure 9 As shown, the fitted linear prediction part is obtained, as follows. Figure 10 As shown, the intercept and slope are obtained, so that the actual kinetic internal resistance and actual diffusion coefficient can be obtained from the intercept and slope. They are then compared with the standard kinetic internal resistance and standard diffusion coefficient to identify abnormal cells and screen out normal cells, thereby improving cell performance and safety.

[0152] Taking the square root of time transforms nonlinear relationships into linear ones, simplifying the data analysis process. Linear relationships are easier to handle and interpret, reducing the complexity of data processing. Simultaneously, linear fitting methods offer high accuracy and stability, making them particularly suitable for processing large amounts of data. By taking the square root of time, trends and patterns in the data can be better captured, improving the accuracy of fitting results and thus enhancing the precision of abnormal cell detection. Furthermore, taking the square root of time gives the slope and intercept clear physical meanings. For example, the slope k directly reflects the diffusion coefficient, while the intercept b reflects the voltage deviation in the initial state. This clear physical meaning helps to better understand the electrochemical behavior of the cell, thereby comprehensively evaluating its performance and ensuring its high quality and reliability.

[0153] In one embodiment, the lifetime characteristic value includes the actual diffusion coefficient and / or the actual kinetic internal resistance. The step of screening the formed cells based on the relationship between the cell lifetime characteristic value and the preset lifetime characteristic value to select normal cells specifically includes:

[0154] When the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the formed cells are screened to select normal cells.

[0155] And / or, when the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the formed cells are screened to select normal cells.

[0156] Based on the above embodiments, the battery testing method of this application optimizes the formation process in stages to ensure the formation of a high-quality SEI layer. Specifically, a first formation current is used for pre-formation, followed by a second formation current to continue formation, resulting in a complete SEI layer. After the SEI layer is formed, it is rapidly charged to a preset state (e.g., 30%~70% SOC) using a higher rate (e.g., a third formation current). Between the two low-rate formation processes, the cell needs to be left to stand for a first settling time to create a stable chemical environment and facilitate the acquisition of cell parameters at the stage with higher resolution immediately after film formation, for use in calculating cell lifetime characteristic values. These lifetime characteristic values ​​include the actual diffusion coefficient and / or the actual kinetic internal resistance.

[0157] In this embodiment, Python can be used to read voltage, current, and time data from the formation process in batches, thereby obtaining the cell parameters during the resting stage and generating a cell parameter detection data set. This data set includes, but is not limited to, initial voltage, termination voltage, first formation current, and first preset time. The difference between the initial voltage and the termination voltage is then calculated to obtain the voltage change. ,in, The first preset time and voltage change are linearly fitted according to a preset linear fitting formula, which is: ,in, Let I be the time-varying quantity, I be the charging current, and k be the first fitting parameter. This represents the second fitting coefficient. It can be understood that the voltage change can be the difference between the voltages at any two different times. The voltage change is obtained by taking the difference between the initial voltage and the final voltage. hour, This corresponds to the duration of the first preset time. Then, based on the second fitting parameters, the termination voltage, and the first formation current, the kinetic internal resistance is obtained, i.e., the kinetic internal resistance is: ,in, For dynamic internal resistance, This refers to the termination voltage after charging the initial battery cell with the first formation current for a first preset time. According to the preset linear fitting formula, the square root of the time is used as the first variable in the formula, and the voltage change rate corresponding to the time is used as the second variable. Different sets of values ​​for the first variable (independent variable) and the second variable (dependent variable) are then substituted into the formula. (Reference) Figure 7 Selecting data from the 6th second and the 10th second, the voltage corresponding to the 6th second is 2.6175V, and the voltage corresponding to the 10th second is 2.6075V. Then, the voltage change... rate of change over time In this way, the values ​​of the first set of first variables (independent variables) and second variables can be obtained. Following the same method, the values ​​of another set of first variables and second variables are substituted into the first set. Through linear fitting, the slope and intercept are calculated and used as the first fitting parameter and the second fitting parameter, respectively, to further calculate the diffusion coefficient and kinetic internal resistance, thereby evaluating the performance and quality of the battery cell.

[0158] In this embodiment, technicians can set a reasonable standard diffusion coefficient and a standard kinetic internal resistance based on experimental data or industry standards. It is understood that the diffusion coefficient k directly affects the kinetic performance of the battery cell. When the diffusion coefficient is abnormal (too low), the risk of lithium plating increases during cell cycling; conversely, when the actual resistance r of the cell at the state of charge (SOC) is too high, local heat generation increases during charging and discharging, side reactions intensify, and lifespan decays faster. Therefore, if the calculated actual diffusion coefficient is less than the standard diffusion coefficient, the cell is considered abnormal and marked as such, requiring further inspection or rejection. Conversely, if the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the cell is considered normal and proceeds to subsequent manufacturing processes. If the actual kinetic internal resistance is greater than the standard kinetic internal resistance, the cell is considered abnormal and marked as such, requiring further inspection or rejection. If the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the cell is considered normal.

[0159] To improve the accuracy and reliability of test results, the performance of the battery cell can be evaluated by combining the diffusion coefficient and the kinetic internal resistance. If the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient and the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the battery cell is considered normal and can proceed to subsequent manufacturing processes.

[0160] By combining diffusion coefficient and kinetic internal resistance as characteristic values ​​of cell life, the performance of the cell can be comprehensively evaluated, ensuring its high quality and reliability, and providing strong technical support for large-scale production and application.

[0161] In one embodiment, the first formation current is located between 0.05 charge / discharge rate and 0.1 charge / discharge rate;

[0162] And / or the second formation current is between 0.05 charge / discharge rate and 0.1 charge / discharge rate.

[0163] In this embodiment, a lower charging rate helps form a uniform and stable SEI layer. However, too low a rate leads to excessively long pre-formation time, thus affecting production efficiency; too high a rate may affect the SEI film quality and the accuracy of subsequent lifetime characteristic values. Therefore, it is necessary to reasonably set the magnitude of the first formation current. Thus, the first formation current can be set between a charge / discharge rate of 0.05 and a charge / discharge rate of 0.1. For example, using a first formation current of 0.1C to charge 0.05CnAh, the charging time is... Therefore, the first preset time is 0.5 hours. After charging the initial cell with the first formation current for the first preset time, the second formation current is used to complete the SEI film formation. In this embodiment, both the first and second formation currents are low-rate currents. The second formation current is used to charge to 10%~12% SOC. The charging amount within this range can ensure that the SEI layer is fully formed, while avoiding overcharging and affecting subsequent operations. Taking charging to 12% SOC with the second formation current as an example, the charging amount for the second low-rate formation is (0.12-x) CnAh, where x is the amount of charge charged during the previous pre-formation with the first formation current. If 0.05CnAh has already been charged during pre-formation, then another 0.07CnAh needs to be charged using the second formation current. Optionally, the second formation current is located between 0.05 charge / discharge rate and 0.1 charge / discharge rate. Assuming the second formation current is 0.1 charge / discharge rate (0.1C), the second preset time is... .

[0164] Setting the first formation current between 0.05 and 0.1 charge / discharge rates ensures a low charging rate. The second formation current is used to complete SEI film formation, charging to 10%–12% SOC. This charging range ensures complete SEI layer formation without overcharging, avoiding unnecessary energy waste and potential safety risks. Furthermore, cell parameters are acquired during the initial resting period after the first formation current charging. At this time, the cell is in a relatively stable state with the highest resolution, allowing for subsequent calculations of diffusion coefficients and kinetic internal resistance to further evaluate cell performance and improve detection accuracy.

[0165] This application also provides a battery manufacturing process, which includes a battery cell formation process using preset steps to obtain the formed battery cell.

[0166] The battery testing method described above is performed during the cell formation process.

[0167] It should be noted that the preset process can have at least two steps. For example, charging the initial battery cell with a low-rate current, and then quickly charging it to the preset state with a high-rate current after the low-rate charging is completed. Alternatively, performing at least two formation steps with a low-rate charging using a smaller current, followed by at least one high-rate charging.

[0168] Optionally, refer to Figure 5 The process of forming the battery cell using a preset procedure specifically includes:

[0169] Step S10: Charge the initial cell with the first formation current for a first preset time to obtain the first formed cell;

[0170] Step S20: Charge the first formed battery cell with the second formation current for a second preset time to obtain the second formed battery cell;

[0171] Step S30: Charge the second formed cell with the third formation current for a third preset time to obtain the third formed cell;

[0172] Among them, the first formation current is less than or equal to the second formation current, and the second formation current is less than the third formation current;

[0173] The process of detecting cell parameters during cell formation to obtain a cell parameter detection data set specifically includes:

[0174] The cell parameters are detected between the first and second formation steps to obtain a set of cell parameter detection data.

[0175] In this embodiment, the battery manufacturing process of this application optimizes the formation process in stages to ensure the formation of a high-quality SEI layer. For example, a first formation current is used for the pre-formation process, and then a second formation current is used for further formation to form a complete SEI layer. It should be noted that although a lower rate helps to form a uniform and stable SEI layer, too low a rate will lead to an excessively long pre-formation time, thus affecting production efficiency; while too high a rate may affect the quality of the SEI film formation and the accuracy of subsequent lifetime characteristic values. Therefore, it is necessary to reasonably set the magnitude of the first formation current. Optionally, the first formation current is located between 0.05 and 0.1 charge / discharge rates. For the first preset time, it needs to be controlled between 0.03 and 0.08 CnAh. Within this range, the resolution of lifetime characteristic values ​​is strongest, which helps to improve the accuracy of subsequent lifetime characteristic value calculations, thereby improving the accuracy of cell detection. For example, if a first formation current of 0.1C is used to charge 0.05 CnAh, the charging time is... Therefore, the first preset time is 0.5 hours. After charging the initial cell with the first formation current for the first preset time, the second formation current is used to complete the SEI film formation, i.e., the second formation step is performed. In this embodiment, both the first and second formation currents are low-rate currents. The second formation current is used to charge to 10%~12% SOC. The charging amount within this range can ensure that the SEI layer is fully formed, while avoiding overcharging and affecting subsequent operations. Taking charging to 12% SOC with the second formation current as an example, the charging amount for the second low-rate formation is (0.12-x) CnAh, where x is the amount of charge charged during the previous pre-formation with the first formation current. If 0.05CnAh has already been charged during pre-formation, then another 0.07CnAh needs to be charged using the second formation current. Optionally, the second formation current is located between 0.05 charge / discharge rate and 0.1 charge / discharge rate. Assuming the second formation current is 0.1 charge / discharge rate (0.1C), the second preset time is... After SEI film formation is completed, a high-rate formation current is needed to quickly charge the battery to a preset state, preparing it for subsequent high-temperature aging or other processes. Since a complete SEI layer has already been formed, using a higher rate can significantly shorten charging time and improve production efficiency. Optionally, the third formation current is between a charge / discharge rate of 0.33 and 0.5. Based on actual needs and cell design, a suitable SOC level is selected for charging to ensure the safety and effectiveness of subsequent high-temperature aging and other processes. In this embodiment, charging to 30%~70% SOC is selected. If the current SOC is 12%, 0.58 SOC is needed to reach 70% SOC. Taking a third formation current of 0.5C as an example, the third preset time is... The first, second, and third preset times were all calculated using a 1Ah battery cell as an example.

[0176] Based on the above embodiments, since the end of the first formation step is the initial stage of SEI layer formation, the dynamic characteristics of the battery cell change most significantly. Therefore, the battery cell parameters between the first and second formation steps can be detected to obtain a battery cell parameter detection data set. This data set provides the highest resolution, facilitating subsequent data processing to obtain battery cell lifetime characteristic values. Furthermore, by comparing these lifespan characteristic values ​​with preset lifespan characteristic values, normal battery cells can be more accurately selected.

[0177] Understandably, to ensure the stable formation of the SEI layer, researchers need to carefully set the formation temperature. Higher temperatures can accelerate the reaction between the electrolyte and the negative electrode material, helping to form a uniform and stable SEI layer. This not only improves the quality of the SEI layer but also enhances its stability. However, excessively high temperatures may lead to side reactions or lithium plating, so a balance needs to be found to promote SEI layer formation while avoiding the adverse effects of overheating. In this embodiment, the formation temperature can be set to 45°C. Furthermore, during the formation process, especially in the SEI layer formation stage, some electrochemical reactions may occur, generating small amounts of gas (such as hydrogen and carbon dioxide). If these gases are not discharged in time, they may accumulate inside the cell, leading to increased pressure and even safety issues. To ensure that the gases generated during the formation process are effectively discharged and to prevent gas accumulation from affecting cell performance, in this embodiment, by applying a negative pressure of -80 to -90 kPa, these gases can be continuously extracted during the formation process, ensuring that the internal pressure of the cell remains stable. This measure not only prevents gas accumulation but also reduces the risk of cell deformation or sealing failure due to gas expansion. In this way, negative pressure suction reduces the possibility of diaphragm blockage, thereby ensuring good contact between electrodes and uniform distribution of electrolyte. It can also significantly reduce the internal pressure of the battery cell, reducing the risk of explosion or leakage and ensuring the safety of the production process.

[0178] In practical applications, by employing a phased optimization of the formation process using first, second, and third formation currents, the uniform and stable formation of the SEI layer is ensured. This enhances the stability and integrity of the SEI layer, shortens charging time, and improves production efficiency. Furthermore, it facilitates subsequent lifetime characteristic value calculations, ensuring their accuracy and thus improving the precision of cell testing.

[0179] In one embodiment, after charging the initial cell with a first formation current for a first preset time to obtain a first formed cell, the battery detection method further includes:

[0180] The first formed battery cell is left to stand for a first settling time.

[0181] The step of detecting cell parameters between the first and second formation steps to obtain a set of cell parameter detection data specifically includes:

[0182] During the process of letting the first formed cell stand for a first standing time, the cell parameters are detected between the first formation step and the second formation step to obtain a set of cell parameter detection data.

[0183] Based on the above embodiments, after pre-forming the initial cell using the first formation step, allowing it to stand for a first settling period can stabilize the electrochemical reaction and further solidify the SEI layer. Furthermore, allowing the cell to stand for a first settling period between the first and second formation steps can reduce side reactions caused by rapid continuous charging. Since the cell is in a relatively stable electrochemical state during the settling period, it is suitable for data acquisition. Therefore, cell parameters can be detected during this settling stage to obtain a set of cell parameter detection data.

[0184] Utilizing data from the resting period for characteristic value calculations, such as diffusion coefficient and kinetic internal resistance, improves the accuracy of cell detection, helps identify potentially abnormal cells early, and ensures that only high-quality cells enter the market, thereby improving cell quality and user experience. Simultaneously, extending the resting time of the first-stage formation cell enhances the quality of the SEI layer and reduces the potential adverse effects of subsequent high-rate charging. The resting period also reduces the impact of lithium plating, improving battery safety and long-term performance.

[0185] In one embodiment, the third formation current is located between a charge / discharge rate of 0.33 and a charge / discharge rate of 0.5.

[0186] In this embodiment, after completing the SEI film formation using the first and second formation steps, a high-rate formation current is required for rapid charging to a preset state to prepare for subsequent high-temperature aging or other processes. It is understood that a higher rate can significantly shorten charging time and improve production efficiency. In this embodiment, the third formation current can be set between a charge / discharge rate of 0.33 and 0.5. Based on actual needs and cell design, a suitable SOC level is selected for charging to ensure the safety and effectiveness of subsequent high-temperature aging and other processes. For example, if the preset state is 30%~70% SOC, and the current SOC is 12%, an additional 0.58 SOC is needed to reach 70%. Taking a third formation current of 0.5C as an example, the third preset time is... .

[0187] It should be noted that after performing the first and second formation steps, a uniform and stable SEI layer has been formed. Therefore, the high-rate charging in the third formation step (charging with the third formation current) will not damage this structure. On the contrary, it can make full use of its advantages and quickly reach the required SOC level.

[0188] Setting the third formation current between 0.33 and 0.5 charge / discharge rates enables rapid charging to a preset state, significantly shortening charging time and improving production efficiency. Selecting a SOC level of 30%–70% ensures the safety and effectiveness of subsequent high-temperature aging processes. By optimizing the formation process in stages and combining the advantages of low-rate and high-rate charging, production efficiency and product quality can be significantly improved while ensuring cell quality and performance.

[0189] In one embodiment, reference Figure 6 Before the cell is formed using a preset process to obtain the formed cell, the battery manufacturing process further includes:

[0190] Step S01: Immerse the battery cell;

[0191] Step S02: Allow the initially immersed battery cell to stand for a second standing time, and obtain the open-circuit voltage after immersion.

[0192] Step S03: Determine the open-circuit voltage as the initial voltage before forming the initial cell.

[0193] In the manufacturing process of lithium-ion batteries, the wetting step is a crucial step to ensure that the electrolyte fully penetrates the electrode materials. The wetting step is a standard procedure in battery manufacturing and will not be elaborated upon here. Through wetting, the electrolyte can be effectively injected into the cell, eliminating air or other gases between the electrode materials and ensuring full contact between the electrolyte and the electrode materials. Simultaneously, sufficient electrolyte penetration helps improve the efficiency of the electrochemical reaction, ensuring the battery can function normally during subsequent charge and discharge processes. Furthermore, the wetting process provides the necessary conditions for the initial formation of the SEI layer, ensuring a more uniform and stable SEI layer formation during subsequent formation processes.

[0194] In this embodiment, after immersion in the battery cell, the immersion effect needs to be verified. For example, the battery cell needs to be left to stand after immersion, and its open-circuit voltage (OCV) needs to be measured. This step ensures uniform distribution of the electrolyte, providing a good foundation for the subsequent formation process. First, the initial battery cell is immersed in the electrolyte to ensure that the electrolyte fully penetrates into the electrode material. Then, the immersed initial battery cell is left to stand for a second standing time, which can range from 10 seconds to 60 seconds. This standing process further uniformly distributes the electrolyte and stabilizes the electrochemical state inside the battery cell. During the standing period, the battery cell is in an unloaded state, and the open-circuit voltage (OCV) of the battery cell can be measured using a high-precision voltmeter. The measured value is recorded and compared with a preset voltage range. If the OCV is within the preset voltage range, the immersion effect is considered good; otherwise, re-immersion or checking of process parameters is required. If the immersion effect is good, the subsequent formation process can continue.

[0195] In practical applications, OCV can not only be used to verify the wetting effect, but also as a data point for subsequent lifetime characteristic value calculations. In this embodiment, the operation process is simplified, thereby improving production efficiency. Furthermore, before forming the initial cell using the first formation current, the initially wetted cell is allowed to stand for a second settling time, and the open-circuit voltage (OCV) after wetting is obtained. This ensures uniform electrolyte distribution and verifies the wetting effect, avoiding unnecessary operations on cells with poor wetting effects in subsequent processes, significantly improving the efficiency of the entire production process and product quality.

[0196] In one embodiment, after the second formation step, the battery manufacturing process further includes:

[0197] The second formed cell is left to stand for a third standing time, and after the third standing time is completed, the third formation step is performed.

[0198] Based on the above embodiments, after the initial immersed battery cell is left to stand for a second settling time, a first formation step is performed to obtain a first formed battery cell. Then, the first formed battery cell is left to stand for a first settling time. After the first settling time, a second formation step is performed to obtain a second formed battery cell. The second formed battery cell is then left to stand for a third settling time to further stabilize the SEI layer and reduce the impact of subsequent high-rate charging on battery cell performance. The third settling time is preset by the R&D personnel. In this embodiment, it is assumed that the third settling time is set to 30 seconds. After 30 seconds, the third formation step is performed, that is, the second formed battery cell is charged with a third formation current for a third preset time. It is understood that after high-rate charging with the third formation current to reach a preset state (e.g., 30%~70% SOC), a settling period (e.g., 30 seconds) is required before the negative voltage is turned off and formation is stopped.

[0199] It's important to note that high-rate charging can significantly shorten charging time and improve production efficiency during lithium-ion battery manufacturing. However, high-rate charging also introduces potential problems such as localized overheating and uneven electrochemical reactions. Therefore, allowing the battery to rest for a period after high-rate charging allows the internal electrochemical state of the cell to further stabilize, reducing the impact of localized overheating and uneven electrochemical reactions caused by high-rate charging. This helps ensure the stability of the SEI layer, thereby improving the cell's cycle life and safety. Furthermore, during high-rate charging, a negative pressure system is used to maintain stable internal pressure within the cell. Allowing the battery to rest for a period before shutting down the negative pressure system ensures that the internal pressure has balanced, reducing the risk of cell damage or safety hazards caused by abruptly shutting down the negative pressure system.

[0200] The third setting of a set resting time makes the SEI layer more uniform and stable, while mitigating potential side reactions such as lithium plating, thus improving cell safety and cycle life. Furthermore, after high-rate charging, a resting period is allowed before shutting off the negative pressure system to stop formation. This appropriate resting time further stabilizes the electrochemical state inside the cell, reducing the impact of high-rate charging. Allowing the cell to rest for a period before shutting off the negative pressure system ensures that the internal pressure of the cell has balanced, reducing cell damage or safety hazards caused by sudden shutdown of the negative pressure system, thereby improving product safety and reliability.

[0201] See Figures 11 to 14 During the process of letting the first formed cell rest for a first resting time (e.g., 30 seconds), cell parameters are obtained. For example, the original process voltage, current, and time data between the first and second formed steps can be read in batches using Python to obtain a cell parameter detection data set.

[0202] To facilitate the description of the calculation process, this embodiment uses the calculation of a single battery cell for explanation. In this embodiment, the difference between the initial voltage and the termination voltage is first calculated to obtain the voltage change. ,in, The time and voltage changes are linearly fitted according to a preset linear fitting formula, which is: ,in, Let I be the time-varying quantity, I be the charging current, and k be the first fitting parameter. The second fitting coefficient is given. Then, based on the second fitting parameters, the termination voltage, and the first formation current, the kinetic internal resistance is obtained, i.e., the kinetic internal resistance is: ,in, For dynamic internal resistance, This is the termination voltage when the initial cell is charged using the first formation current for the first preset time. In ordinary lithium-ion batteries (excluding special systems such as lithium-ion batteries, solid-state batteries, and sodium batteries), the test results are as follows: Figures 11 to 12 As shown. Among them, Figure 11 Based on the screening results of the diffusion coefficient k, Figure 12 Based on the screening results using kinetic internal resistance (actual resistance), it can be observed that both the k-value and r-value effectively identify six abnormal cells. Similarly, the detection method for batteries in this application is also applicable to chemically lithium-ion batteries, and the test results are as follows. Figures 13 to 14 As shown. Among them, Figure 13 Based on the screening results of the diffusion coefficient k, Figure 14Based on the screening results of kinetic internal resistance (actual resistance), it can be found that both the k-value and r-value can effectively identify four abnormal cells. Besides lithium-ion batteries and chemically replenished lithium-ion batteries, this method can also be applied to special cell systems such as solid-state, condensed-solid, semi-solid, and sodium-ion batteries to identify abnormal cells.

[0203] The abnormal cells identified by this method, after actual cycle observation, showed that compared with normal cells, their interfaces all exhibited varying degrees of lithium plating degradation, and their cycle life also deteriorated. Therefore, this application, from the perspective of electrochemical principles, can achieve low-cost, high-efficiency, high-resolution, and non-destructive risk monitoring of abnormal cells with poor lifespan.

[0204] It should be noted that the battery manufacturing process implements the battery testing method described above during the cell formation process. Therefore, the battery manufacturing process adopts all the technical solutions of all the above embodiments and has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be elaborated here.

[0205] This application also proposes a battery manufactured using the battery manufacturing process described in any of the above claims.

[0206] In this embodiment, the battery can be implemented using ordinary lithium-ion batteries, chemically replenished lithium-ion batteries, solid-state batteries, condensed-solid-state batteries, semi-solid-state batteries, sodium-ion batteries, etc. Using the battery testing method proposed in this application allows for early detection of potential problems, preventing abnormal cells from entering subsequent processes and causing unnecessary operations. This improves production efficiency and safety.

[0207] It should be noted that this battery is manufactured using the battery manufacturing process described in any of the above embodiments. Therefore, this battery adopts all the technical solutions of all the above embodiments, and thus has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated further here.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method of detecting a battery, characterized by, The detection method of the battery comprises: detecting the parameters of the battery cell during the formation process of the battery cell to obtain a parameter detection data set of the battery cell; performing data processing on the parameter detection data set of the battery cell to obtain a life characteristic value of the battery cell; screening the formed battery cell according to the size relationship between the life characteristic value of the battery cell and a preset life characteristic value to screen out a normal battery cell; the life characteristic value comprises an actual diffusion coefficient and / or an actual kinetic internal resistance, and the step of screening the formed battery cell according to the size relationship between the life characteristic value of the battery cell and the preset life characteristic value to screen out the normal battery cell specifically comprises: when the actual diffusion coefficient is greater than or equal to a standard diffusion coefficient, screening the formed battery cell to screen out the normal battery cell; and / or when the actual kinetic internal resistance is less than or equal to a standard kinetic internal resistance, screening the formed battery cell to screen out the normal battery cell; wherein the actual diffusion coefficient is the diffusion rate of lithium ions in the electrode material, and the kinetic internal resistance is the internal resistance of the battery cell caused by the electrochemical reaction during the charging and discharging process; the step of detecting the parameters of the battery cell during the formation process of the battery cell to obtain a parameter detection data set of the battery cell specifically comprises: detecting the parameters of the battery cell between a first formation step and a second formation step to obtain a parameter detection data set of the battery cell; the first formation step is to charge an initial battery cell with a first formation current for a first preset time to obtain a first formed battery cell; and the second formation step is to charge the first formed battery cell with a second formation current for a second preset time to obtain a second formed battery cell; wherein the first formation current is less than or equal to the second formation current; the first formation current is used for pre-formation, and the second formation current is used for formation to form a complete SEI layer; the step of performing data processing on the parameter detection data set of the battery cell to obtain a life characteristic value of the battery cell specifically comprises: calculating the difference between the initial voltage and the terminal voltage to obtain a voltage variation; linearly fitting the first preset time and the voltage variation according to a preset linear fitting formula to obtain a first fitting parameter and a second fitting parameter after fitting; obtaining the kinetic internal resistance according to the second fitting parameter, the terminal voltage and the first formation current, and taking the kinetic internal resistance as the life characteristic value of the battery cell; obtaining the actual diffusion coefficient according to the first fitting parameter, and taking the actual diffusion coefficient as the life characteristic value of the battery cell; wherein the first fitting parameter is the actual diffusion coefficient; the intercept of the preset linear fitting formula represents the voltage offset in the initial state; The preset linear fitting formula is: ; The formula of the kinetic internal resistance is: ; wherein, is a voltage change amount, is a first preset time, is a first formation current, is a first fitting parameter, is a second fitting coefficient, is a kinetic internal resistance, is a termination voltage when charging the initial battery cell with the first formation current for the first preset time.

2. The method of detecting a battery according to claim 1, wherein the first formation step and the second formation step further comprise resting the first formed battery cell for a first resting time; the step of detecting the parameters of the battery cell between the first formation step and the second formation step to obtain a parameter detection data set of the battery cell specifically comprises: detecting the parameters of the battery cell between the first formation step and the second formation step in the process of resting the first formed battery cell for the first resting time to obtain a parameter detection data set of the battery cell.

3. The method of detecting a battery according to claim 2, wherein the step of detecting the parameters of the battery cell between the first formation step and the second formation step to obtain a parameter detection data set of the battery cell specifically comprises: an initial voltage corresponding to before charging the initial battery cell with the first formation current, and a termination voltage corresponding to after charging the initial battery cell with the first formation current for a first preset time; wherein the battery cell parameter detection data set comprises any one or more combinations of the initial voltage, the termination voltage, the first formation current, and the first preset time.

4. The method of detecting a battery according to claim 1, wherein the first formation current is between 0.05C and 0.1C; and / or the second formation current is between 0.05C and 0.1C.

5. A method of manufacturing a battery, characterized by, The manufacturing method of the battery comprises a formation process of forming the battery cell with a preset procedure to obtain a formed battery cell. The detection method of the battery of any one of claims 1 to 4 is performed during the formation process of the battery cell.

6. The method of manufacturing a battery according to claim 5, wherein The formation of the battery cell with the preset procedure specifically comprises: a first formation step of charging the initial battery cell with the first formation current for a first preset time to obtain a first formed battery cell; a second formation step of charging the first formed battery cell with the second formation current for a second preset time to obtain a second formed battery cell; a third formation step of charging the second formed battery cell with the third formation current for a third preset time to obtain a third formed battery cell; wherein the first formation current is less than or equal to the second formation current, and the second formation current is less than the third formation current; The detection of the battery cell parameters during the formation process of the battery cell to obtain the battery cell parameter detection data set specifically comprises: detecting the battery cell parameters of the battery cell between the first formation step and the second formation step to obtain the battery cell parameter detection data set.

7. The method of manufacturing a battery according to claim 6, wherein After the charging of the initial battery cell with the first formation current for the first preset time to obtain the first formed battery cell, the manufacturing method of the battery further comprises: resting the first formed battery cell for a first resting time period; The detection of the battery cell parameters of the battery cell between the first formation step and the second formation step to obtain the battery cell parameter detection data set specifically comprises: detecting the battery cell parameters of the battery cell between the first formation step and the second formation step to obtain the battery cell parameter detection data set during the resting of the first formed battery cell for the first resting time period.

8. The method of manufacturing a battery according to claim 6, wherein the third formation current is between 0.33C and 0.5C.

9. The method of manufacturing a battery according to any one of claims 5 to 8, wherein Before the formation of the battery cell with the preset procedure to obtain the formed battery cell, the manufacturing method of the battery further comprises: immersing the battery cell; resting the immersed initial battery cell for a second resting time period to obtain an open circuit voltage of the immersed initial battery cell, determining the open circuit voltage as the initial voltage before the formation of the initial battery cell.

10. The method of manufacturing a battery according to claim 6, wherein After the second formation step, the manufacturing method of the battery further comprises: resting the second formed battery cell for a third resting time period, and performing the third formation step after the end of the third resting time period.

11. A battery, characterized by The battery is manufactured by the manufacturing method of the battery of any one of claims 5 to 10.

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