Battery detection method, battery manufacturing process and battery

Abstract

The invention discloses a battery detection method, a battery manufacturing process and a battery. The battery detection method comprises the following steps: firstly, forming a battery cell by adopting a preset process to obtain a formed battery cell; then detecting cell parameters in the formation process to obtain a cell parameter detection data set; carrying out data processing on the battery cell parameter detection data set to obtain a battery cell life characteristic value; and finally, screening the formed battery cells according to a size relationship between the battery cell life characteristic value and a preset life characteristic value so as to screen out normal battery cells. Therefore, according to the battery detection method provided by the invention, the normal operation of the formation process can be ensured, the battery cell parameters in the formation process can be detected in real time, a long-time charge-discharge cycle test is not needed, the high efficiency of battery cell detection is improved, and the production efficiency of the battery is further improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery detection method, a battery manufacturing process and a battery. Background Art

[0002] With the rapid development of the new energy industry, the service life of existing lithium-ion batteries is getting higher and higher, and can even achieve thousands of charge and discharge cycles. At present, there are relatively mature prediction models for the service life of lithium-ion batteries, including thermodynamic life prediction models, or remaining service life prediction models, but these models must rely on certain charge and discharge cycle test data to be realized, that is, they are mainly limited to the research and development stage; and for the large number of mass-produced cells produced every day, the traditional life assessment method 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, the present application provides a battery detection method, a battery manufacturing process and a battery, which can improve the efficiency of battery cell detection and thus improve the production efficiency of the battery.

[0004] In a first aspect, the present application provides a battery detection method, comprising: Detecting the battery cell parameters during the battery cell formation process to obtain a battery cell parameter detection data set; Processing the battery cell parameter detection data group to obtain the battery cell life characteristic value; According to the size relationship between the battery cell life characteristic value and the preset life characteristic value, the formed battery cells are screened to screen out normal battery cells.

[0005] In the technical solution of the embodiment of the present application, the battery cell parameters in the formation process are detected, and the battery cell parameter detection data group obtained after the detection is processed to obtain the battery cell life characteristic value, and then the battery cell life characteristic value is compared with the preset life characteristic value to screen the formed battery cells to screen out normal battery cells. In this way, while ensuring that the formation process can proceed normally, the battery cell parameters in the formation process can also be detected in real time, without the need for long-term charge and discharge cycle testing, which improves the efficiency of battery cell detection and thus improves the production efficiency of the battery.

[0006] In some embodiments, the detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: Detecting the cell parameters of the cell between the first formation step and the second formation step to obtain a cell parameter detection data group; the first formation step is to use a first formation current to charge the initial cell for a first preset time to obtain a first formed cell; the second formation step is to use a second formation current to charge the first formed cell for a second preset time to obtain a second formed cell; The first formation current is less than or equal to the second formation current.

[0007] In the embodiment of the present application, the formation process of the SEI layer can be carried out in stages, that is, two small-rate film formations are carried out through the first formation step and the second formation step to promote the formation of the SEI layer and ensure its integrity and stability. Since the dynamic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the battery cell parameters between the first formation step and the second formation step can be detected to obtain a battery cell parameter detection data group. The data obtained at this time can provide the highest resolution, which helps to more accurately screen out normal batteries.

[0008] In some embodiments, between the first formation step and the second formation step, the first formation cell is left to rest for a first resting time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

[0009] In the detection method of the battery of the embodiment of the present application, after the initial battery cell is charged with the first formation current for the first preset time, the battery cell is allowed to reach a relatively stable electrochemical state by standing for the first resting time, and a stable basis is provided for the subsequent collection of battery cell parameters, thereby improving the efficiency and accuracy of battery cell detection. In this process, the battery cell parameters of the first formed battery cell during the formation process are detected to obtain a battery cell parameter detection data group. Since the dynamic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the battery cell parameters of the first formed battery cell are detected, and the acquired data can provide the highest resolution, so as to obtain the battery cell life characteristic value by data processing the battery cell parameter detection data group, and according to the size relationship between the battery cell life characteristic value and the preset life characteristic value, the normal battery cell can be screened out more accurately.

[0010] In some embodiments, the detecting of the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: Obtaining an initial voltage corresponding to before the initial battery cell is charged with the first formation current and a termination voltage corresponding to when the initial battery cell is charged with the first formation current for a first preset time; The battery cell parameter detection data group includes any one or more combinations of an initial voltage, a termination voltage, a first formation current, and a first preset time.

[0011] Through comprehensive analysis of 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 more comprehensively described, providing a basis for subsequently obtaining the characteristic values ​​of the battery cell life, thereby improving the efficiency and accuracy of abnormal battery cell detection.

[0012] In some embodiments, the step of processing the battery cell parameter detection data group to obtain the battery cell life characteristic value specifically includes: Calculate the difference between the initial voltage and the final voltage to obtain the voltage change; Performing a linear fit on 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; The kinetic internal resistance is obtained according to the second fitting parameter, the termination voltage and the first formation current, and the kinetic internal resistance is used as a characteristic value of the battery cell life; The actual diffusion coefficient is obtained according to the first fitting parameter, and the actual diffusion coefficient is used as the characteristic value of the battery cell life.

[0013] The first fitting parameter and the second fitting parameter are obtained through linear fitting, and the data that can reflect the electrochemical behavior inside the battery cell are extracted, and then the real resistance of the battery cell at a specific SOC, that is, the kinetic internal resistance, is obtained to detect the quality of the battery cell through the kinetic internal resistance. And the diffusion coefficient reflecting the migration rate of lithium ions in the electrode material is obtained through the first fitting parameter, and is used as one of the important indicators for measuring the kinetic performance of the battery cell. In this way, through linear fitting and parameter calculation, the battery cell life characteristic value of the battery cell is accurately obtained to screen the battery cell with normal life and improve the service life of the battery.

[0014] In some embodiments, obtaining the kinetic internal resistance according to the second fitting parameter, the termination voltage and the first formation current specifically includes: Calculating the difference between the termination voltage and the second fitting parameter; Calculating the ratio of the difference to the first formation current; The ratio is taken as the kinetic internal resistance.

[0015] The static components that are not related to time (represented by the second fitting parameter) are excluded through difference calculation, so as to capture the dynamic change part more accurately. At the same time, the ratio of the difference to the first formation current is used as the dynamic internal resistance to reflect the internal resistance characteristics of the battery cell, thereby identifying abnormal batteries and screening out normal batteries.

[0016] In some embodiments, performing linear fitting on the first preset time and the voltage change amount according to a preset linear fitting formula to obtain the fitted first fitting parameter and the second fitting parameter specifically includes: Taking the square root of the first preset time, and using the square root as the first variable of the preset linear fitting formula; The voltage change is used as the second variable of the preset linear fitting formula; 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.

[0017] Using the square root of time as a variable reflects the diffusion behavior of lithium ions in the electrode material, and can make the relationship between the voltage change and time closer to linear, thereby improving the accuracy of the 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 battery cell at the initial stage of formation, thereby accurately evaluating the quality and state of the battery cell.

[0018] In some embodiments, the life characteristic value includes an actual diffusion coefficient and / or an actual kinetic internal resistance, and the step of screening the formed battery cells according to the size relationship between the battery cell life characteristic value and the preset life characteristic value to screen out normal battery cells specifically includes: When the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the formed cells are screened to select normal cells; And / or, when the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the formed battery cells are screened to select normal battery cells.

[0019] By setting the standard diffusion coefficient and standard kinetic internal resistance and comparing them with the characteristic values ​​of battery cell life, the ability to distinguish abnormal batteries is improved, and the detection accuracy and production efficiency of batteries are improved.

[0020] In some embodiments, the first formation current is between 0.05 charge and discharge rates and 0.1 charge and discharge rates; And / or the second formation current is between 0.05 charge and discharge rate and 0.1 charge and discharge rate.

[0021] The low rate current of 0.05C to 0.1C provides relatively mild conditions for the formation of the SEI layer. Under the current intensity of the first formation current and / or the second formation current, lithium ions can be more evenly embedded in the negative electrode material, thereby promoting the stable formation of the SEI layer. The lower charging rate helps prevent lithium ions from depositing too quickly on the negative electrode surface, thereby reducing the risk of lithium plating. This not only improves the safety of the battery, but also extends its service life.

[0022] In a second aspect, the present application provides a manufacturing process for a battery, wherein the manufacturing process for the battery comprises forming a battery cell using a preset process to obtain a formed battery cell; The battery detection method described in any one of the above items is performed during the formation process of the battery cell.

[0023] In the embodiment of the present application, the formation process of the SEI layer can be carried out in stages, that is, two small-rate film formations are carried out through the first formation step and the second formation step to promote the formation of the SEI layer. Since the dynamic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the detection method of the battery described in any of the above items can be performed during the formation process of the battery cell. That is, the battery cell parameters between the first formation step and the second formation step are detected to obtain a battery cell parameter detection data group. In this way, normal batteries can be screened out more accurately.

[0024] In some embodiments, the forming of the battery cell using a preset process specifically includes: A first formation step: charging the initial battery cell with a first formation current for a first preset time to obtain a first formed battery cell; A second formation step: charging the first formation cell for a second preset time using a second formation current to obtain a second formation cell; A third formation step: charging the second formation cell for a third preset time using a third formation current to obtain a third formation 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 detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: The cell parameters of the cell between the first formation step and the second formation step are detected to obtain a cell parameter detection data set.

[0025] In the embodiment of the present application, the initial battery cell is charged with the first formation current to promote the initial formation of the SEI layer, and then the formation of the SEI layer is completed with the second formation current to ensure the quality and integrity of the SEI layer. Finally, after the SEI layer is stably formed, the third formation step is performed, that is, the second formation battery cell is quickly charged with the third formation current for a third preset time, so that the power reaches the preset capacity. In this way, the formation of the SEI layer is promoted by two small rate film formations, and the integrity and stability are ensured. Since the dynamic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the battery cell parameters between the first formation step and the second formation step can be detected to obtain a battery cell parameter detection data group, and the obtained battery cell parameter detection data group can provide the highest resolution, which helps to more accurately screen out normal batteries.

[0026] In some embodiments, after charging the initial battery cell with the first formation current for a first preset time to obtain the first formed battery cell, the battery detection method further includes: Allowing the first formed battery cell to stand for a first standing time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

[0027] In the manufacturing process of the battery of the embodiment of the present application, after the initial battery cell is charged with the first formation current for the first preset time, the first formed battery cell is left to stand for the first resting time, and in this process, the battery cell parameters of the first formed battery cell during the formation process are detected to obtain a battery cell parameter detection data set. The battery cell parameter detection data set is further processed to obtain a battery cell life characteristic value, and based on the size relationship between the battery cell life characteristic value and the preset life characteristic value, abnormal batteries are identified and normal batteries are screened out. In this way, the data obtained is the data of the initial stage of SEI layer formation, the dynamic characteristics of the battery cell change most obviously, and the highest resolution can be provided, which improves the accuracy of the battery life detection results.

[0028] In some embodiments, the third formation current is between 0.33 charge / discharge rates and 0.5 charge / discharge rates. Compared with low rate currents such as the first formation current and the second formation current, a higher rate current of 0.33C to 0.5C can significantly speed up the charging speed, thereby shortening the time of the entire formation process and improving production efficiency.

[0029] In some embodiments, before the battery cell is formed by a preset process to obtain a formed battery cell, the manufacturing process of the battery further includes: Wetting the battery cells; The initial cell after soaking is left to rest for the second resting time, and the open circuit voltage after soaking is obtained. The open circuit voltage is determined as the initial voltage before the initial battery cell is formed.

[0030] By standing for the second standing time, it can be ensured that the electrolyte has enough time to fully penetrate between the positive and negative electrode materials to form a uniform electrochemical environment, which is conducive to the stable formation of the subsequent SEI layer and the smooth progress of the electrochemical reaction. At the same time, measuring the open circuit voltage (OCV) after infiltration after standing can effectively evaluate the effect of electrolyte infiltration. In this way, problems in the infiltration process can be discovered and corrected in a timely manner. In addition, the open circuit voltage can also be determined as the initial voltage before the initial battery cell is formed. In the battery detection method, there is no need to repeatedly measure the initial voltage corresponding to the initial battery cell before charging the initial battery cell with the first formation current, which simplifies the operation steps and speeds up the detection speed and efficiency.

[0031] In some embodiments, after the second formation step, the manufacturing process of the battery further includes: The second formation cell is left to stand for a third standing time, and the third formation step is performed after the third standing time ends.

[0032] After the second formation step, the formed SEI layer can be further stabilized by standing for a third resting time. During the third resting time, the electrochemical reaction tends to be stable, the quality and integrity of the SEI layer are enhanced, and the negative effects that may be caused by the subsequent high-rate charging in the third formation step, such as lithium plating, are reduced.

[0033] In a third aspect, the present application provides a battery, which is manufactured using any of the manufacturing processes for the batteries described above.

[0034] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings: Figure 1 A flowchart of a first embodiment of a battery detection method provided by the present application; Figure 2 A flowchart of a second embodiment of a battery detection method provided by the present application; Figure 3 A flowchart of a third embodiment of a battery detection method provided by the present application; Figure 4 A flowchart of a fourth embodiment of a battery detection method provided by the present application; Figure 5 A flowchart of a first embodiment of a manufacturing process of a battery provided in the present application; Figure 6 A flowchart of a second embodiment of a manufacturing process of a battery provided in the present application; Figure 7 A schematic diagram of a curve showing voltage variation over time in the first embodiment of the battery detection method provided by the present application; Figure 8 A schematic diagram of a curve showing voltage variation over time in the second embodiment of the battery detection method provided by the present application; Fig. 9 A schematic diagram of a curve showing voltage variation over time in the third embodiment of the battery detection method provided by the present application; Fig.10 A schematic diagram of a curve showing voltage variation over time in a fourth embodiment of a battery detection method provided by the present application; Fig.11 A schematic diagram of test results of the first embodiment of the battery detection method provided by the present application; Fig.12 A schematic diagram of test results of the second embodiment of the battery detection method provided by the present application; Fig.13 A schematic diagram of test results of the third embodiment of the battery detection method provided by the present application; Fig.14 This is a schematic diagram of test results of the fourth embodiment of the battery detection method provided in the present application.

[0036] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0037] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0039] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0040] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0041] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0042] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0043] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0044] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0045] At present, from the perspective of market development, the application of power batteries is becoming more and more extensive. Power batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power and solar power stations, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, electric cars, as well as military equipment and aerospace and other fields. With the continuous expansion of the application field of power batteries, the market demand is also constantly expanding.

[0046] The inventors have noticed that with the rapid development of the new energy industry, the service life of existing lithium-ion batteries is getting higher and higher, and even thousands of charge and discharge cycles can be achieved. At present, there are relatively mature prediction models for the service life of lithium-ion batteries, including thermodynamic life prediction models, remaining service life prediction models, etc. However, these models must rely on detailed charge and discharge cycle test data to be realized, that is, after the battery cell is formed, the battery cell is subjected to multiple charge and discharge cycles to simulate the working conditions in actual use to evaluate the performance and life of the battery under different working conditions. However, each charge and discharge cycle requires a certain amount of time, and it may take weeks or even months to complete thousands of cycle tests. This is not only time-consuming and labor-intensive, but also costly, and it is difficult to achieve real-time monitoring and rapid identification of abnormal batteries on the production line. It is not suitable for real-time monitoring needs in large-scale mass production environments, that is, it can only be applied to research and development perspectives.

[0047] For mass-produced cells that are produced in huge quantities every day, the traditional life assessment method based on long-term cycle testing obviously cannot meet the efficiency requirements of actual production, that is, there is no relevant model or test method to monitor whether the battery life will be abnormal. Specifically: long-term testing, due to the low detection rate, will affect the production rate, resulting in low production efficiency of the production line and failure to meet timeliness; and if load test equipment is introduced or the equipment of the existing production line is modified, the production cost will increase; traditional methods are difficult to accurately screen out cells with potential life abnormalities in mass production, which may cause defective products to enter the market, affecting the product quality of the battery and user satisfaction.

[0048] Based on the above problems, failure to identify and remove abnormal cells in a timely manner may lead to rework or product scrapping, increasing manufacturing costs. In addition, after the cells with potential problems enter the market, they may pose safety risks during use, such as overheating, short circuits, etc., affecting user experience and even causing safety accidents.

[0049] In order to alleviate the problem that the traditional life assessment method based on long-term cycle testing cannot meet the efficiency requirements of actual production, the inventor has designed a battery detection method after in-depth research, aiming to achieve rapid, low-cost and efficient life risk detection of mass-produced batteries by optimizing the formation process and real-time data collection and processing during the formation process. That is, the battery cell is formed by adopting a preset process to obtain a formed battery cell and form a high-quality SEI layer (Solid Electrolyte Interphase, solid electrolyte interface), thereby ensuring that the battery cell has a good service life. At the same time, the battery cell parameters in the formation process are detected to obtain a battery cell parameter detection data group that reflects the electrochemical behavior of the battery cell under specific conditions, and the battery cell parameter detection data group is processed to obtain the battery cell life characteristic value. That is, data is acquired during the formation process, and the battery cell life characteristic value is calculated based on the acquired data. For example, the real impedance (internal resistance) and diffusion coefficient of the battery cell are calculated so that abnormal cells in mass production can be identified based on any one or more of the real impedance and diffusion coefficient, thereby screening out normal cells that meet quality requirements. This method can complete the quality inspection of battery cells in a short time, improve production efficiency, eliminate the need to introduce new equipment and transform production lines, and reduce the cost of inspection and production processes. In addition, by calculating the life characteristic values, the ability to distinguish between normal and abnormal cells is enhanced, ensuring the service life of the battery, providing strong technical support for large-scale production and application.

[0050] The battery disclosed in the embodiment of the present application can be used in, but not limited to, electrical devices such as vehicles, ships or aircraft. The battery disclosed in the present application can be used to form a power supply system for the electrical device, which is conducive to alleviating and automatically adjusting the deterioration of the expansion force of the battery cell, replenishing the consumption of the electrolyte, and improving the stability of the battery performance and the battery life.

[0051] The present application embodiment provides a battery detection method for efficient and accurate quality control of mass-produced battery cells. During the manufacturing process, the battery cells are packaged into more complete battery cells after processes such as injection, infiltration, formation, and screening. Therefore, the battery cell is an important stage in battery production, and its quality and performance directly affect the performance of the final product.

[0052] In some embodiments of the present application, the battery can be used not only as an operating power source for the vehicle, but also as a driving power source for the vehicle, replacing or partially replacing fuel or natural gas to provide driving power for the vehicle.

[0053] In a battery, there can be multiple battery cells, which can be connected in series, in parallel, or in a mixed connection. A mixed connection means that multiple battery cells are connected in series and in parallel. Multiple battery cells can be directly connected in series, in parallel, or in a mixed connection, and then the whole formed by multiple battery cells is accommodated in a box; of course, the battery can also be a battery module formed by connecting multiple battery cells in series, in parallel, or in a mixed connection, and then multiple battery modules are connected in series, in parallel, or in a mixed connection to form a whole, and accommodated in a box. The battery can also include other structures. For example, the battery can also include a busbar component for realizing electrical connection between multiple battery cells.

[0054] On the battery production line, a huge number of battery cells need to be tested every day. Traditional methods based on long-term cycle testing cannot meet the needs of efficient production, while the battery detection method of the present application can complete the evaluation of each battery cell in a short time through optimized formation process and real-time battery cell life characteristic value calculation, ensuring that only high-quality battery cells can enter the subsequent assembly process. That is, the battery detection method of the present application can effectively identify battery cells with potential problems during the battery production and manufacturing stage, ensure that each battery has stable performance, and reduce the risk of safety hazards caused by battery failure.

[0055] It should be noted that the production of lithium-ion batteries is a complex and delicate process involving multiple key processes to ensure the safety and reliability of the final product. For example, the battery cell needs to be encapsulated in a shell to protect the internal components. For example, first, the wound or laminated battery cell is placed in a metal shell or a soft-pack shell. And the positive and negative pole ears are welded together with the external connection terminals to ensure good electrical connection. Finally, the battery shell is sealed to prevent foreign substances from entering. Then the battery cell is injected with an appropriate amount of electrolyte to ensure that the electrochemical reaction can proceed smoothly. Then the infiltration process is carried out to allow the electrolyte to fully penetrate between the electrode materials, and it is left to stand for a period of time so that the electrolyte is evenly distributed throughout the battery cell to form a preliminary electrochemical environment. Then the formation operation can be carried out. By charging the battery cell, a stable SEI layer is formed on the surface of the negative electrode to ensure that the battery cell has a good service life. The detection method of the battery in the present application improves the formation process, so that the battery cell generates a high-quality SEI layer, ensuring that the battery cell has a good cycle life; at the same time, the battery cell life characteristic value can be calculated to evaluate the battery cell life risk, and the battery cells with abnormal life can be identified in time to avoid flowing into subsequent processes and clients, thereby reducing the user experience.

[0056] In one embodiment, referring to Figure 1 , Figure 1 The present invention is a flow chart of an embodiment of a battery detection method according to the present invention. The battery detection method includes: Step S100, detecting the battery cell parameters during the battery cell formation process to obtain a battery cell parameter detection data set; Step S200, processing the battery cell parameter detection data group to obtain a battery cell life characteristic value; Step S300: Screening the formed cells according to the size relationship between the cell life characteristic value and the preset life characteristic value to screen out normal cells.

[0057] In the detection method of the battery of the present application, it is necessary to first use a preset process to form the battery cell to obtain the formed battery cell; for example, first use a small formation current to form a small rate film to preliminarily form the SEI layer to ensure the stability and integrity of the SEI layer. Then continue to use a smaller formation current to complete the final formation of the SEI layer. Finally, after the SEI layer has been fully formed and stabilized, use a larger formation current to quickly charge to a predetermined SOC level (such as 30%~70%) to prepare for other subsequent processes such as subsequent high-temperature aging. Through two small rate film formations, the formation of the SEI layer can be more fully promoted, which not only helps to improve the performance of the battery, but also reduces the occurrence of adverse phenomena such as lithium precipitation. Then detect the battery cell parameters during the formation process to obtain a battery cell parameter detection data group; then perform data processing on the battery cell parameter detection data group to obtain a battery cell life characteristic value; finally, according to the size relationship between the battery cell life characteristic value and the preset life characteristic value, the formed battery cell is screened to screen out normal batteries. The inventors noticed that the data after the first low-rate film formation reflects the state of the SEI layer when it is initially formed, while the data after the second low-rate film formation reflects the state after the SEI layer is fully formed. In the early stage of SEI layer formation (such as 0.03~0.08CnAh) and when it is close to completion (such as 0.07~0.09CnAh), the dynamic characteristics of the battery cell change most significantly. Therefore, the data obtained in these two time periods can provide a strong resolution ability, which helps to more accurately identify abnormal batteries with potential problems. Therefore, the battery cell parameters after the first low-rate film formation can be obtained, and the obtained battery cell parameter detection data group can be processed, such as using Python algorithm to write calculation software, batch processing of mass-produced batteries, and obtaining the corresponding life characteristic value of each battery cell, and comparing them with the preset life characteristic values ​​respectively, so as to screen out normal batteries that meet the quality requirements, and perform subsequent process operations, such as aging test, capacity division, etc., to ensure its long-term performance and consistency. For abnormal batteries, rework treatment can be carried out, and the abnormal batteries can be re-formed to improve the quality and electrochemical performance of the SEI layer. And according to the cause of the abnormality, adjust the formation parameters or process conditions to optimize the performance of the battery cell. Abnormal batteries that have been confirmed to be unrepairable or have serious quality problems should be clearly marked to avoid misuse, and in accordance with environmental protection regulations, the abnormal batteries should be safely destroyed to prevent them from entering the market. Optionally, batteries that do not meet high-demand application scenarios can be used in application scenarios with lower requirements to maximize resource utilization, such as evaluating the scope of application of abnormal batteries based on their specific problems. Abnormal batteries are classified into different levels and used in suitable application scenarios, such as energy storage systems, power tools, etc.

[0058] The battery detection method of the present application detects the battery cell parameters during the formation process, calculates the life characteristic value of the life risk of the mass-produced battery cells, identifies the battery cells with abnormal life, and screens out the normal battery cells to prevent the abnormal battery cells from flowing into the subsequent processes and the client. In this way, efficient and accurate quality control of mass-produced battery cells is achieved, providing strong support for large-scale production. This solution can significantly improve product quality and user experience without affecting production efficiency.

[0059] In one embodiment, optionally, the detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: Detecting the cell parameters of the cell between the first formation step and the second formation step to obtain a cell parameter detection data group; the first formation step is to use a first formation current to charge the initial cell for a first preset time to obtain a first formed cell; the second formation step is to use a second formation current to charge the first formed cell for a second preset time to obtain a second formed cell; The first formation current is less than or equal to the second formation current.

[0060] In this embodiment, the formation process of the SEI layer can be carried out in stages, that is, two small-rate film formations are performed through the first formation step and the second formation step to promote the formation of the SEI layer and ensure its integrity and stability. Since the dynamic characteristics of the battery cell change most significantly in the early stage of SEI layer formation, the battery cell parameters between the first formation step and the second formation step can be detected to obtain a battery cell parameter detection data group. The data obtained at this time can provide the highest resolution, which helps to more accurately screen out normal batteries. The data after the first small-rate film formation using the first formation step reflects the state of the SEI layer when it is initially formed, while the data after the second small-rate film formation using the second formation step reflects the state after the SEI layer is fully formed. In the early stage of SEI layer formation (such as 0.03~0.08CnAh) and when it is close to completion (such as 0.07~0.09CnAh), the dynamic characteristics of the battery cell change most significantly, so the data obtained in these two time periods can provide a strong resolution. Optionally, the cell parameters after the first low-rate film formation can be obtained, and the obtained cell parameter detection data group can be processed to obtain the cell life characteristic value. It should be noted that after the cell is subjected to two low-rate film formation steps, the second formed cell can be charged at a high rate to prepare for subsequent high-temperature aging tests.

[0061] In practical applications, obtaining the cell parameters between the first formation step and the second formation step can provide a stronger resolution capability, thereby improving the accuracy and reliability of the detection results.

[0062] In one embodiment, between the first formation step and the second formation step, the first formation cell is left to rest for a first resting time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

[0063] In this embodiment, the first formation current is used to charge the initial battery cell for a preset first time (pre-formation) to ensure the initial formation of the SEI layer. At the end of the pre-formation process, the first resting time is left to stand, so that the electrochemical reaction can be stabilized and the SEI layer can be further consolidated. Among them, the first resting time is set by the R&D personnel. For example, any time is selected within the range of 10S to 30S and used as the first resting time to further stabilize the SEI layer just formed. And the first resting time is left to stand between the first formation step and the second formation step, which can reduce the side reactions caused by rapid and continuous charging. Since the battery cell is in a relatively stable electrochemical state during the resting period, it is suitable for data collection. Therefore, the battery cell parameters can be detected during the resting stage to obtain a battery cell parameter detection data set.

[0064] It should be noted that in the initial formation stage of the SEI layer, the electrochemical reaction inside the battery cell has not yet reached equilibrium, so the voltage change during the static period is large and obvious. These voltage changes directly reflect the quality and stability of the SEI layer, and provide accurate basic data for calculating life characteristic values ​​such as diffusion coefficient and kinetic internal resistance. At the same time, the internal resistance of the battery cell after the first formation changes significantly, because the thickness and uniformity of the SEI layer directly affect the internal resistance of the battery cell at this time. By measuring the internal resistance of the battery cell during the static stage, the quality of the SEI layer and the health of the battery cell can be accurately evaluated. In addition, in the initial formation stage of the SEI layer, charge transfer and ion diffusion behaviors are most active, so the diffusion coefficient calculation at this time is most accurate. Therefore, the battery cell parameters can be detected during this static stage to facilitate early identification and elimination of abnormal batteries.

[0065] The data during the rest period is used to calculate characteristic values, such as diffusion coefficient and kinetic internal resistance, which improves the accuracy of battery cell detection and helps to identify potential abnormal batteries at an early stage, ensuring that only high-quality batteries enter the market, thereby improving battery cell quality and user experience. At the same time, the first formed battery cell is left to rest for the first rest period to enhance the quality of the SEI layer and reduce the adverse effects that may be caused by subsequent high-rate charging. The rest period can also reduce the impact of lithium plating and improve the safety and long-term performance of the battery.

[0066] In one embodiment, the detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: Obtaining an initial voltage corresponding to before the initial battery cell is charged with the first formation current and a termination voltage corresponding to when the initial battery cell is charged with the first formation current for a first preset time; The battery cell parameter detection data group includes any one or more combinations of an initial voltage, a termination voltage, a first formation current, and a first preset time.

[0067] It should be noted that before the battery cell is formed, it is usually necessary to wet the battery cell. The main purpose of wetness is to ensure that the electrolyte can fully penetrate into the electrode material to form a uniform electrochemical environment, providing a good foundation for subsequent formation and use. However, the battery cell after wetness may have uneven electrolyte distribution, resulting in local overheating in the area with uneven electrolyte distribution when the current passes through the battery cell during the subsequent formation process. At the same time, uneven electrolyte distribution will also lead to uneven formation of the subsequent SEI layer, which in turn affects the stability and safety of the battery.

[0068] Therefore, in this embodiment, before charging the initial battery cell using the first formation current, a high-precision voltmeter can be used to measure the open circuit voltage (OCV) of the battery cell. In order to simplify the process, the measured voltage value can be directly used as the initial voltage corresponding to the initial voltage before the first formation step is used. In this way, monitoring the open circuit voltage can not only verify the infiltration results, but also serve as the cell parameters in the cell parameter detection data group required for subsequent life characteristic value calculation, which simplifies the operation process, shortens the data acquisition time, and thus improves the detection efficiency.

[0069] It is understandable that after the battery cells are injected and wetted in sequence, the open circuit voltage after wettability is monitored and used as the initial voltage. When there are multiple battery cells, the consistency between batches can be evaluated by measuring the OCV of multiple battery cells to ensure that each battery cell is in the same initial state. If the OCV does not meet the expected range, it may indicate that the electrolyte is unevenly distributed and needs to be re-wetted or the process parameters checked. If the OCV meets the expected range, the first formation current can be used to charge the initial battery cell for the first preset time. For example, for a battery cell with a capacity of 1Ah, a 0.1C current (ie, 0.1A) is used, and the charging capacity is 0.05Ah, then the required charging time is 0.5 hours. In actual applications, after the initial battery cell is connected to the formation equipment, the first formation current is set to a charge and discharge rate of 0.1, and the timer is started to start charging the initial battery cell. After the first preset time (for example, 0.5 hours) is over, the charging is stopped immediately, and the voltage of the battery cell is measured again using a high-precision voltmeter to obtain the termination voltage. In this way, a battery cell parameter detection data group such as the initial voltage, the termination voltage, the first formation current, the first preset time, etc. can be obtained and recorded in a database or a data table for subsequent data processing to obtain a battery cell life characteristic value, and based on the size relationship between the battery cell life characteristic value and the preset life characteristic value, the formed battery cells are screened to screen out normal battery cells.

[0070] In this embodiment, Python algorithm can be used to write computing software to perform batch processing on the acquired data. It is assumed that the battery cell parameter detection data group is stored in a CSV file in advance, and each line contains a timestamp (including the first preset time), a voltage (including the initial voltage and termination voltage ), current (including the first formation current), etc. The original voltage, current and time data in the formation process are read through Python, and data preprocessing is performed according to the current law (such as the first step current is 0, the second step current is not 0, the third step current is 0, and the fourth step current is not 0) to obtain the battery parameters detected during the first formation battery cell being left to rest for the first resting time, such as Figure 7 As shown, Figure 7 The following is a curve diagram of the voltage change over time during the first resting time of the first formed battery cell. The current law is set by the R&D personnel and presented in the form of code. After Python reads the battery cell parameters, further characteristic value calculations can be performed to screen out normal batteries for subsequent manufacturing processes and identify abnormal batteries to prevent them from entering the market, causing battery quality problems, and even leading to safety accidents.

[0071] Computing software is written using the Python algorithm to realize batch processing of formation data of multiple battery cells, and the battery cell parameter data of each battery cell in the first static resting time stage is extracted for further characteristic value calculation. In this way, the detection method of the battery of the present application obtains data from the stage with strong resolution during the formation process to improve the accuracy of the life characteristic value, thereby improving the detection accuracy, accurately identifying abnormal battery cells, and improving the performance of the battery cells. At the same time, the Python algorithm is used to write computing software, so that batch processing of mass-produced battery cells can be performed, which improves the detection rate, and at the same time, it does not require the addition of additional equipment or transformation costs, thereby reducing costs.

[0072] In one embodiment, reference Figure 2 The step of processing the battery cell parameter detection data group to obtain the battery cell life characteristic value specifically includes: Step S310, calculating the difference between the initial voltage and the final voltage to obtain the voltage variation; Step S320: performing linear fitting on 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; Step S330, obtaining a kinetic internal resistance according to the second fitting parameter, the termination voltage and the first formation current, and using the kinetic internal resistance as a characteristic value of the battery cell life; Step S340: obtaining an actual diffusion coefficient according to the first fitting parameter, and using the actual diffusion coefficient as a characteristic value of the battery cell life.

[0073] In combination with the above embodiments, by using the first formation step and the second formation step to perform two low-rate formations and one high-rate formation, the battery cell is formed in stages, and after the initial battery cell is charged with the first formation current for a first preset time, it is left to stand for a first resting time to obtain the battery cell parameters of the resting stage to obtain a battery cell parameter detection data set. The battery cell parameter detection data set includes but is not limited to the initial voltage, the termination voltage, the first formation current, and the first preset time. To facilitate the description of the calculation process, this embodiment uses the calculation of a battery cell for introduction and 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 the voltage change are linearly fitted according to a preset linear fitting formula, and the preset linear fitting formula is: ,in, is the time variation, I is the charging current, k is the first fitting parameter, is the second fitting coefficient. It can be understood that the voltage change can be the difference between the voltages corresponding to any two different moments. When the difference is the difference between the initial voltage and the final voltage, the voltage change is obtained. hour, Then, the duration corresponding to the first preset time is obtained. Then, the kinetic internal resistance is obtained according to the second fitting parameter, the termination voltage and the first formation current, that is, the kinetic internal resistance is: ,in, is the dynamic internal resistance, It is the termination voltage when the initial battery cell is charged for the first preset time using the first formation current.

[0074] It should be noted that the kinetic internal resistance describes the internal resistance of the battery cell caused by the electrochemical reaction during the charging and discharging process, and reflects the electrochemical reaction rate and interface characteristics of the battery cell. The kinetic internal resistance directly affects the charging and discharging efficiency of the battery cell. Lower kinetic internal resistance means less energy loss, thereby improving the charging and discharging efficiency. Lower kinetic internal resistance can reduce the thermal effect during the electrochemical reaction, reduce the internal temperature of the battery cell, and thus extend the cycle life of the battery cell. Therefore, when the kinetic internal resistance is too large, the local heat generation increases during charging and discharging, the side reactions intensify, and the life decay becomes faster. In this embodiment, the kinetic internal resistance can be used as the characteristic value of the battery cell life, and the formed battery cell can be screened to identify abnormal batteries, screen out normal batteries, and flow into the subsequent manufacturing process. In addition, the first fitting parameter k is the actual diffusion coefficient, which describes the diffusion rate of ions in the electrode material and reflects the kinetic characteristics of the electrochemical reaction. When the diffusion coefficient is abnormal (low), the risk of lithium precipitation increases during the battery cell cycle, which may cause safety problems. Therefore, the actual diffusion coefficient can also be used as a characteristic value of life. By screening battery cells with normal diffusion coefficients, safety can be significantly improved, efficient and precise quality control can be achieved, ensuring that only high-quality battery cells enter the market, thereby improving production efficiency and safety.

[0075] By batch processing the battery cell formation data through the Python algorithm, the life characteristic value of each battery cell can be calculated quickly and accurately, improving the data processing efficiency. The diffusion coefficient and kinetic internal resistance are extracted as the life characteristic values ​​through linear fitting, ensuring that only high-quality batteries enter the subsequent manufacturing process, improving the overall production efficiency and safety. In addition, the actual measurement data usually contains noise and fluctuations, and directly using this data for analysis may lead to inaccurate results. Through linear fitting, the data can be smoothed and the impact of noise can be reduced, so as to obtain more stable and reliable life characteristic values. Since linear fitting fits the data through a mathematical model, it can better capture the trends and laws in the data and improve the accuracy of the test results.

[0076] In one embodiment, reference Figure 3 , the step of obtaining the kinetic internal resistance according to the second fitting parameter, the termination voltage and the first formation current specifically includes: Step S331, calculating the difference between the termination voltage and the second fitting parameter; Step S332, calculating the ratio of the difference to the first formation current; Step S333: Use the ratio as the dynamic internal resistance.

[0077] Combined with the above embodiments, the difference between the initial voltage and the final voltage is calculated to obtain the voltage change ,in, , the first preset time and the voltage change are linearly fitted according to a preset linear fitting formula, and the preset linear fitting formula is: ,in, is the time variation, I is the charging current, k is the first fitting parameter, is the second fitting coefficient. Then, based on the second fitting parameter (intercept R) obtained by linear fitting, the kinetic internal resistance is calculated. , that is, the dynamic internal resistance is: ,in, is the dynamic internal resistance, It is the termination voltage when the initial cell is charged for the first preset time using the first formation current. The kinetic internal resistance describes the internal resistance of the cell caused by the electrochemical reaction during the charge and discharge process, which directly affects the charge and discharge efficiency, power density, cycle life and safety of the cell.

[0078] It should be noted that the diffusion coefficient k describes the diffusion rate of ions in the electrode material, but in actual applications, the difference in diffusion coefficient between cells from different batches or production processes may be very small. This small difference makes it difficult to effectively distinguish the quality difference of cells based solely on the diffusion coefficient. It describes the internal resistance of the battery cell caused by electrochemical reactions during the charge and discharge process. It usually has a large numerical difference, making it easier to identify abnormal batteries. Of course, due to the diffusion coefficient k and kinetic internal resistance It reflects the different characteristics of the battery cells. 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 battery cell performance. Therefore, in this embodiment, the kinetic internal resistance and the actual diffusion coefficient are used as life characteristic values ​​to identify abnormal battery cells and screen out normal battery cells. By considering the diffusion coefficient and the kinetic internal resistance at the same time, abnormal battery cells can be identified more accurately and misjudgment caused by a single indicator can be avoided.

[0079] In large-scale production, the use of diffusion coefficient and kinetic internal resistance can realize automated screening and improve production efficiency. Batch processing and automated analysis can be realized through programming to eliminate unqualified cells at an early stage, reduce unnecessary waste costs in subsequent processes, and promptly discover and correct potential problems, thereby improving product quality and user satisfaction.

[0080] In one embodiment, reference Figure 4 , performing linear fitting on the first preset time and the voltage change according to a preset linear fitting formula to obtain the first fitting parameter and the second fitting parameter after fitting specifically includes: Step S321, taking the square root of the first preset time, and using the square root as the first variable of the preset linear fitting formula; Step S322, using the voltage change as the second variable of the preset linear fitting formula; Step S323: using the slope of the preset linear fitting formula as the first fitting parameter, and using the intercept of the preset linear fitting formula as the second fitting parameter.

[0081] In combination with the above embodiments, the cell parameters between the first formation step and the second formation step are obtained, that is, after completing the first formation step, the first formed cell can be left to rest for a first resting time, and the cell parameters during the resting stage can be detected to obtain a cell parameter detection data set. The cell parameter detection data set includes but is not limited to the initial voltage, the termination voltage, the first formation current, and the first preset time. In this embodiment, first, the difference between the initial voltage and the termination voltage is calculated to obtain the voltage change ,in, , the first preset time and the voltage change are linearly fitted according to a preset linear fitting formula, and the preset linear fitting formula is: ,in, is the time variation, I is the charging current, k is the first fitting parameter, is the second fitting coefficient.

[0082] In this embodiment, due to the electrochemical reaction, the voltage change With time The relationship is usually not linear. In order to linearize this relationship and facilitate the analysis using a linear fitting method, 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. Different groups of first variables (independent variables) and second variable values ​​(dependent variables) are brought in, and the slope and intercept are calculated through linear fitting, and used as the first fitting parameter and the second fitting parameter respectively, for further calculating 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 is a curve showing the change of voltage over time, and voltage is the original voltage data; in this embodiment, the linear part of the curve can be fitted, such as Fig. 9 As shown, the linear fitting prediction part after fitting is obtained, as Fig.10 As shown, the intercept and slope are obtained, so that the actual kinetic internal resistance and the actual diffusion coefficient can be obtained according to the intercept and the slope, and compared with the standard kinetic internal resistance and the standard diffusion coefficient respectively, abnormal cells can be identified, and normal cells can be screened out, thereby improving the performance and safety of the cells.

[0083] Taking the square root of time can transform nonlinear relationships into linear relationships and simplify the data analysis process. Linear relationships are easier to handle and interpret, reducing the complexity of data processing. At the same time, linear fitting methods have high accuracy and stability, and are especially suitable for processing large amounts of data. By taking the square root of time, trends and patterns in the data can be better captured, the accuracy of the fitting results can be improved, and the accuracy of abnormal battery cell detection can be improved. In addition, after taking the square root of time, the slope and intercept have clear physical meanings. For example, the slope k can directly reflect the diffusion coefficient, while the intercept b reflects the voltage offset in the initial state. This clear physical meaning helps to better understand the electrochemical behavior of the battery cell, thereby comprehensively evaluating the performance of the battery cell and ensuring its high quality and reliability. In one embodiment, the life characteristic value includes an actual diffusion coefficient and / or an actual kinetic internal resistance, and the step of screening the formed battery cells according to the magnitude relationship between the battery cell life characteristic value and the preset life characteristic value to screen out normal battery cells specifically includes: When the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the formed cells are screened to select normal cells; And / or, when the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the formed battery cells are screened to select normal battery cells.

[0084] In combination with the above-mentioned embodiments, the detection method of the battery of the present application ensures the formation of a high-quality SEI layer by optimizing the formation process in stages. That is, the first formation current is first used for pre-formation, and then the second formation current is used to continue the formation to form a complete SEI layer. Then, after the film formation (SEI layer) is completed, a larger rate (such as the third formation current) is used to quickly charge to a preset state (such as 30%~70% SOC). Between the two small-rate formation processes, the battery cell needs to be left to stand for the first standing time to form a stable chemical environment, and at the same time, it is convenient to obtain the battery cell parameters at the stage with strong resolution when the film is just formed, which is used to calculate the battery cell life characteristic value. Among them, the life characteristic value includes the actual diffusion coefficient and / or the actual kinetic internal resistance.

[0085] In this embodiment, the voltage, current, and time data of the formation process can be read in batches through Python, that is, the battery cell parameters in the static stage can be obtained to obtain a battery cell parameter detection data set. The battery cell parameter detection data set includes but is not limited to the initial voltage, the final voltage, the first formation current, and the first preset time. Then, the difference between the initial voltage and the final voltage is calculated to obtain the voltage change ,in, , the first preset time and the voltage change are linearly fitted according to a preset linear fitting formula, and the preset linear fitting formula is: ,in, is the time variation, I is the charging current, k is the first fitting parameter, is the second fitting coefficient. It can be understood that the voltage change can be the difference between the voltages corresponding to any two different moments. When the difference is taken as the difference between the initial voltage and the final voltage, the voltage change is obtained hour, Then, the duration corresponding to the first preset time is obtained. Then, the kinetic internal resistance is obtained according to the second fitting parameter, the termination voltage and the first formation current, that is, the kinetic internal resistance is: ,in, is the dynamic internal resistance, It is the termination voltage when the initial battery cell is charged for the first preset time using the first formation current. It can be seen from the preset linear fitting formula that the square root of time is taken and the square root is used as the first variable of the preset linear fitting formula, and the voltage change rate corresponding to time is used as the second variable of the preset linear fitting formula, and the first variable (independent variable) and the second variable values ​​(dependent variable) of different groups are respectively brought into the reference. Figure 7 , select the data of the 6th and 10th seconds, the voltage corresponding to the 6th is 2.6175V, and the voltage corresponding to the 10th is 2.6075V, then the voltage change is , time rate of change In this way, the values ​​of the first variable (independent variable) and the second variable of the first group can be obtained. According to the same method, another set of the values ​​of the first variable and the second variable are substituted, and the slope and intercept are calculated through linear fitting, and used as the first fitting parameter and the second fitting parameter respectively, which are used to further calculate the diffusion coefficient and the kinetic internal resistance, so as to evaluate the performance and quality of the battery cell.

[0086] In this embodiment, the technician can set a reasonable standard diffusion coefficient based on experimental data or industry standards. And set the standard kinetic internal resistance. It can be understood that the diffusion coefficient k directly affects the kinetic performance of the battery cell. When the diffusion coefficient is abnormal (low), the risk of lithium precipitation during the battery cell cycle increases; and when the actual resistance r of the battery cell at the SOC is large, the local heat generation during charging and discharging increases, the side reactions intensify, and the life decay becomes faster. Therefore, if the calculated actual diffusion coefficient is less than the standard diffusion coefficient, the battery cell is considered to be abnormal, marked as an abnormal battery cell, and further inspection or elimination is required. On the contrary, if the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the battery cell is considered to be normal and continues to enter the subsequent manufacturing process. If the actual kinetic internal resistance is greater than the standard kinetic internal resistance, the battery cell is considered to be abnormal and marked as an abnormal battery cell, which needs further inspection or elimination. If the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the battery cell is considered to be normal.

[0087] In order to improve the accuracy and reliability of the test results, the performance of the battery cell can be evaluated in combination with the diffusion coefficient and the kinetic internal resistance. If only 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 continue to enter the subsequent manufacturing process.

[0088] By combining the diffusion coefficient and kinetic internal resistance as characteristic values ​​of battery cell life, the performance of the battery cell can be comprehensively evaluated to ensure its high quality and reliability, providing strong technical support for large-scale production and application.

[0089] In one embodiment, the first formation current is between 0.05 charge and discharge rate and 0.1 charge and discharge rate; And / or the second formation current is between 0.05 charge and discharge rate and 0.1 charge and discharge rate.

[0090] In this embodiment, since a lower charging rate helps to form a uniform and stable SEI layer, a rate that is too low will result in a long pre-formation time, thereby affecting production efficiency; a rate that is too high may affect the SEI film quality and the accuracy of subsequent life characteristic values. Therefore, it is necessary to reasonably set the size of the first formation current. Therefore, the first formation current can be set between 0.05 charge and discharge rates and 0.1 charge and discharge rates. 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 the initial battery cell is charged with the first formation current for the first preset time, the second formation current is continued to be used to complete the SEI film formation. In this embodiment, the first formation current and the second formation current are both low-rate currents. The second formation current is used to charge to 10%~12% SOC. The charge amount within this range can ensure that the SEI layer is fully formed, and at the same time, too much electricity will not be overcharged, affecting subsequent operations. Taking the use of the second formation current to charge to 12% SOC as an example, the charging amount of the second small-rate formation is (0.12-x) CnAh, where x is the amount of electricity charged during the last pre-formation with the first formation current. If 0.05CnAh has been charged during pre-formation, it is necessary to continue to charge 0.07CnAh with the second formation current. Optionally, the second formation current is between 0.05 charge / discharge rate and 0.1 charge / discharge rate. Assuming that the second formation current is 0.1 charge / discharge rate (0.1C), the second preset time is .

[0091] The first formation current is set between 0.05 charge and discharge rates and 0.1 charge and discharge rates to ensure a lower charge rate. The second formation current is used to complete SEI film formation and charge to 10%~12% SOC. The charge amount within this range can ensure that the SEI layer is fully formed without overcharging, thus avoiding unnecessary energy waste and potential safety risks. In addition, the cell parameters are obtained during the first resting time after charging with the first formation current. At this time, the cell is in a relatively stable state with the strongest resolution. The cell parameters can be used for subsequent calculations of the diffusion coefficient and kinetic internal resistance to further evaluate the cell performance, thereby improving the detection accuracy.

[0092] The present application also provides a battery manufacturing process, the battery manufacturing process includes a formation process of forming a battery cell using a preset process to obtain a formed battery cell; The battery detection method described in any one of the above items is performed during the formation process of the battery cell.

[0093] It should be noted that the preset process can be at least two steps. For example, the initial battery cell is charged with a low rate current, and after the low rate charging is completed, it is quickly charged to a preset state with a high rate current. Alternatively, at least two formation steps are used to perform low rate charging with a smaller current, and then at least one high rate charging is performed.

[0094] Optionally, refer to Figure 5 The method of using a preset process to form the battery cell specifically includes: Step S10, charging the initial battery cell with a first formation current for a first preset time to obtain a first formed battery cell; Step S20, charging the first formed battery cell with a second formation current for a second preset time to obtain a second formed battery cell; Step S30, charging the second formation cell with a third formation current for a third preset time to obtain a third formation 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 detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: The cell parameters of the cell between the first formation step and the second formation step are detected to obtain a cell parameter detection data set.

[0095] In this embodiment, the manufacturing process of the battery of the present application ensures the formation of a high-quality SEI layer by optimizing the formation process in stages. For example, the first formation current is first used for the pre-formation process, and then the second formation current is used to continue the 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, a rate that is too low will result in a long pre-formation time, thereby affecting production efficiency; and a rate that is too high may affect the SEI film quality and the accuracy of subsequent life characteristic values. Therefore, it is necessary to reasonably set the size of the first formation current. Optionally, the first formation current is between 0.05 charge and discharge rates and 0.1 charge and discharge rates. For the first preset time, it is necessary to control it between 0.03 and 0.08CnAh. In this range, the resolution of the life characteristic value is the strongest, which helps to improve the accuracy of the subsequent calculation of the life characteristic value, thereby improving the accuracy of the battery cell detection. 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 the first formation current is used to charge the initial battery cell for the first preset time, the second formation current is continued to be used to complete the SEI film formation, that is, the second formation step is performed. In this embodiment, the first formation current and the second formation current are both low-rate currents, and the second formation current is used to charge to 10%~12% SOC. The charge amount within this range can ensure that the SEI layer is fully formed, and at the same time, too much electricity will not be overcharged, affecting subsequent operations. Taking the use of the second formation current to charge to 12% SOC as an example, the charging amount of the second small-rate formation is (0.12-x) CnAh, where x is the amount of electricity charged during the last pre-formation with the first formation current. If 0.05CnAh has been charged during pre-formation, it is necessary to continue to charge 0.07CnAh with the second formation current. Optionally, the second formation current is between 0.05 charge / discharge rate and 0.1 charge / discharge rate. Assuming that the second formation current is 0.1 charge / discharge rate (0.1C), the second preset time is . After the SEI film is formed, it is necessary to use a large rate formation current to quickly charge to a preset state to prepare for subsequent high-temperature aging or other processes. Since the SEI layer has been formed, at this time, the use of a higher rate can significantly shorten the charging time and improve production efficiency. Optionally, the third formation current is between 0.33 charge and discharge rates and 0.5 charge and discharge rates. According to actual needs and battery cell design, select a suitable SOC level for charging to ensure the safety and effectiveness of subsequent high-temperature aging and other processes. In this embodiment, choose to charge to 30%~70% SOC. If the current SOC is 12%, in order to reach 70% SOC, 0.58SOC is required. Taking the third formation current of 0.5C as an example, the third preset time is The first preset time, the second preset time and the third preset time are all calculated by taking a battery cell with a capacity of 1Ah as an example.

[0096] Combined with the above-mentioned 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 obviously, so the battery cell parameters between the first formation step and the second formation step can be detected to obtain a battery cell parameter detection data set. The battery cell parameter detection data set obtained at this time can provide the highest resolution, so that the battery cell parameter detection data set can be processed later to obtain the battery cell life characteristic value, and according to the size relationship between the battery cell life characteristic value and the preset life characteristic value, the normal battery cell can be screened out more accurately.

[0097] It is understandable that in order to ensure the stable formation of the SEI layer, the R&D personnel also need to reasonably set the formation temperature. Higher temperatures can accelerate the reaction between the electrolyte and the negative electrode material, and help to form a uniform and stable SEI layer. This not only improves the quality of the SEI layer, but also enhances its stability. However, too high a temperature may lead to side reactions or lithium precipitation, so it is necessary to find a balance point that not only promotes the formation of the SEI layer, but also avoids the adverse effects of overheating. In this embodiment, the temperature during the formation can be set to 45 ° C. In addition, during the formation process, especially during the formation of the SEI layer, some electrochemical reactions may occur, producing a small amount of gas (such as hydrogen, carbon dioxide, etc.). If these gases are not discharged in time, they may accumulate inside the battery cell, causing pressure to rise and even cause safety problems. In order to ensure that the gases generated during the formation process are effectively discharged and prevent gas accumulation from affecting the performance of the battery cell. In this embodiment, by applying a negative pressure of -80~-90kPa, these gases can be continuously extracted during the formation process to ensure that the pressure inside the battery cell remains stable. This measure not only prevents gas accumulation, but also reduces the risk of battery cell deformation or sealing failure due to gas expansion. In this way, negative pressure pumping 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, reduce the risk of explosion or leakage, and ensure the safety of the production process.

[0098] In practical applications, the formation process is optimized in stages by using the first formation current, the second formation current and the third formation current to ensure that the SEI layer is formed evenly and stably, thereby enhancing the stability and integrity of the SEI layer, shortening the charging time and improving production efficiency. In addition, it helps to calculate the subsequent life characteristic value and ensures the accuracy of the calculation of the life characteristic value, thereby improving the detection accuracy of the battery cell.

[0099] In one embodiment, after charging the initial battery cell with the first formation current for a first preset time to obtain the first formed battery cell, the battery detection method further includes: Allowing the first formed battery cell to stand for a first standing time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

[0100] In combination with the above-mentioned embodiments, after the initial battery cell is pre-formed by the first formation step, the first resting time is left to stand, so that the electrochemical reaction can be stabilized and the SEI layer can be further consolidated. And the first resting time is left to stand between the first formation step and the second formation step, so that the side reactions caused by rapid continuous charging can be reduced. Since the battery cell is in a relatively stable electrochemical state during the resting period, it is suitable for data collection. Therefore, the battery cell parameters can be detected in this resting stage to obtain the battery cell parameter detection data group.

[0101] The data during the rest period is used to calculate characteristic values, such as diffusion coefficient and kinetic internal resistance, which improves the accuracy of battery cell detection and helps to identify potential abnormal batteries at an early stage, ensuring that only high-quality batteries enter the market, thereby improving battery cell quality and user experience. At the same time, the first formed battery cell is left to rest for the first rest period to enhance the quality of the SEI layer and reduce the adverse effects that may be caused by subsequent high-rate charging. The rest period can also reduce the impact of lithium plating and improve the safety and long-term performance of the battery.

[0102] In one embodiment, the third formation current is between 0.33 charge and discharge rates and 0.5 charge and discharge rates.

[0103] In this embodiment, after the SEI film formation is completed by the first formation step and the second formation step, it is also necessary to use a large-rate formation current to quickly charge to a preset state in preparation for subsequent high-temperature aging or other processes. It can be understood that due to the higher rate, the charging time can be significantly shortened and the production efficiency can be improved. In this embodiment, the third formation current can be set between 0.33 charge and discharge rates and 0.5 charge and discharge rates. According to actual needs and battery cell design, select a suitable SOC level for charging to ensure the safety and effectiveness of subsequent high-temperature aging and other processes. For example, the preset state is 30%~70% SOC. If the current SOC is 12%, in order to reach 70% SOC, 0.58SOC is required. Taking the third formation current of 0.5C as an example, the third preset time is .

[0104] It should be noted that after executing the first formation step and the second formation step, 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 destroy this structure. Instead, it can make full use of its advantages and quickly reach the required SOC level.

[0105] The third formation current is set between 0.33 charge and discharge rates and 0.5 charge and discharge rates for fast charging to a preset state, which significantly shortens the charging time and improves production efficiency. The SOC level of 30% to 70% is selected to ensure the safety and effectiveness of subsequent high-temperature aging and other 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 the quality and performance of the battery cells.

[0106] In one embodiment, reference Figure 6 Before the battery cell is formed by the preset process to obtain the formed battery cell, the manufacturing process of the battery further includes: Step S01, soaking the battery cell; Step S02: leaving the soaked initial battery cell to rest for a second resting time, and obtaining an open circuit voltage after soaking. Step S03: determining the open circuit voltage as the initial voltage before forming the initial battery cell.

[0107] In the manufacturing process of lithium-ion batteries, the infiltration step is a key link to ensure that the electrolyte fully penetrates into the electrode material. The infiltration step is a conventional process in the battery manufacturing process and will not be described in detail here. Through the infiltration treatment, the electrolyte can be effectively injected into the battery cell, and the air or other gases between the electrode materials can be removed to ensure that the electrolyte is in full contact with the electrode material. At the same time, the full penetration of the electrolyte helps to improve the efficiency of the electrochemical reaction and ensure that the battery can work normally during the subsequent charging and discharging process. In addition, the infiltration treatment can provide the necessary conditions for the initial formation of the SEI layer, ensuring that the SEI layer can be formed more evenly and stably in the subsequent formation process.

[0108] In this embodiment, after the battery cell is infiltrated, the infiltration effect needs to be verified. For example, the battery cell needs to be left to stand after infiltration, and its open circuit voltage (OCV) needs to be measured. This step can ensure that the electrolyte is evenly distributed and provide a good foundation for the subsequent formation process. First, the initial battery cell is placed in the electrolyte for infiltration treatment to ensure that the electrolyte fully penetrates into the electrode material, and then the initial battery cell after infiltration is left to stand for a second standing time, and the second standing time can range from 10S to 60S. The standing process at this stage makes the electrolyte further evenly distributed and stabilizes the electrochemical state inside the battery cell. During the standing period, the battery cell is in a no-load state, and a high-precision voltmeter can be used to measure the open circuit voltage (OCV) of the battery cell, record the measured value and compare it with the preset voltage range. If the OCV is within the preset voltage range, it is considered that the infiltration effect is good; otherwise, it is necessary to re-infiltrate or check the process parameters. If the infiltration effect is good, the subsequent formation process can be continued.

[0109] In practical applications, OCV can not only be used to verify the wetting effect, but also as a data for subsequent life characteristic value calculation. In this embodiment, the operation process is simplified, thereby improving production efficiency. In addition, before the initial battery cell is formed with the first formation current, the initial battery cell after infiltration is first allowed to stand for a second standing time, and the open circuit voltage (OCV) after infiltration is obtained, which can ensure that the electrolyte is evenly distributed and verify the wetting effect, avoiding unnecessary operations on batteries with poor wetting effects in subsequent processes, significantly improving the efficiency of the entire production process and product quality.

[0110] In one embodiment, after the second formation step, the manufacturing process of the battery further includes: The second formation cell is left to stand for a third standing time, and the third formation step is performed after the third standing time ends.

[0111] In combination with the above-mentioned embodiments, after the initial battery cell after infiltration is left to stand for the second standing time, the first formation step is performed to obtain the first formed battery cell, and then the first formed battery cell is left to stand for the first standing time, and after the first standing time, the second formation step is performed to obtain the second formed battery cell, and then the second formed battery cell is continued to stand for the third standing time, so that the SEI layer is further stabilized, and the impact of subsequent high-rate charging on the performance of the battery cell is reduced. Among them, the third standing time is set in advance by the research and development personnel. In this embodiment, it is assumed that the third standing time is set to 30S. After 30S ends, the third formation step is performed, that is, the third formation current is used to charge the second formed battery cell for the third preset time. It can be understood that when the third formation current is used for high-rate charging to reach a preset state (such as 30%~70% SOC), it is still necessary to stand for a period of time (such as 30 seconds), and then turn off the negative pressure and stop the formation.

[0112] It should be noted that in the manufacturing process of lithium-ion batteries, high-rate charging can significantly shorten the charging time and improve production efficiency. However, high-rate charging also brings some potential problems, such as local overheating and uneven electrochemical reactions. Therefore, standing for a period of time after high-rate charging can further stabilize the electrochemical state inside the battery cell and reduce the effects of local overheating and uneven electrochemical reactions caused by high-rate charging. This helps to ensure the stability of the SEI layer, thereby improving the cycle life and safety of the battery cell. In addition, during high-rate charging, the negative pressure system is used to maintain the pressure inside the battery cell stable. Turning off the negative pressure system after standing for a period of time can ensure that the pressure inside the battery cell has been balanced, reducing the damage to the battery cell or safety hazards caused by suddenly shutting down the negative pressure system.

[0113] Third, the setting of the resting time makes the SEI layer more uniform and stable, and at the same time can alleviate possible side reactions, such as lithium precipitation, and improve the safety and cycle life of the battery. In addition, after the high-rate charging is completed, let it stand for a while and then turn off the negative pressure system to stop the formation. The appropriate amount of time of standing can further stabilize the electrochemical state inside the battery and reduce the impact of high-rate charging. Turning off the negative pressure system after standing for a period of time ensures that the pressure inside the battery has been balanced, reducing the damage to the battery or safety hazards caused by the sudden closure of the negative pressure system, thereby improving the safety and reliability of the product.

[0114] See also Figures 11 to 14 In the process of leaving the first formed battery cell to rest for a first resting time (such as 30 seconds), the battery cell parameters are obtained, such as batch reading the original process voltage, current, and time data between the first formation step and the second formation step through Python to obtain a battery cell parameter detection data group.

[0115] In order to facilitate the description of the calculation process, this embodiment uses the calculation of a battery cell for description. In this embodiment, the difference between the initial voltage and the final voltage is first calculated to obtain the voltage change ,in, , the time and voltage variation are linearly fitted according to the preset linear fitting formula, and the preset linear fitting formula is: ,in, is the time variation, I is the charging current, k is the first fitting parameter, is the second fitting coefficient. Then the kinetic internal resistance is obtained according to the second fitting parameter, the termination voltage and the first formation current, that is, the kinetic internal resistance is: ,in, is the dynamic internal resistance, It is the termination voltage when the initial battery cell is charged for the first preset time using the first formation current. In ordinary lithium-ion batteries (non-special systems such as lithium supplement, solid-state, sodium battery, etc.), the test results are as follows Figure 11 to Figure 12 As shown. Among them, Fig.11 For the screening results based on the diffusion coefficient k, Fig.12 The screening results are based on the dynamic internal resistance (real resistance). It can be found that whether from the k value or the r value, 6 abnormal cells can be effectively identified. Similarly, the battery detection method of this application is also applicable to lithium-ion batteries with chemical lithium supplementation. The test results are as follows Figure 13 to Figure 14 As shown. Among them, Fig.13 For the screening results based on the diffusion coefficient k, Fig.14 The screening results are based on the kinetic internal resistance (real resistance). It can be found that whether from the k value or the r value, 4 abnormal cells can be effectively identified. In addition to the two battery systems of lithium-ion batteries and chemically replenished lithium-ion batteries, this method can also be applied to special system batteries such as solid-state, solid-state, semi-solid-state, and sodium batteries to identify abnormal cells.

[0116] The abnormal cells identified by this method, through actual cycle observations, were found to have different degrees of lithium plating deterioration on their interfaces compared with normal cells, and their cycle life decay also deteriorated. Therefore, this application can achieve low-cost, high-efficiency, high-resolution and non-destructive risk monitoring of abnormal life cells from the perspective of electrochemical principles.

[0117] It should be noted that the manufacturing process of the battery performs the above-mentioned battery detection method during the formation process of the battery cell. Therefore, the manufacturing process of the battery adopts all the technical solutions of all the above-mentioned embodiments, and at least has all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here one by one.

[0118] The present application also proposes a battery, which is manufactured using any of the manufacturing processes for the batteries described above.

[0119] In this embodiment, the battery can be implemented by ordinary lithium-ion batteries, chemical lithium-supplemented lithium-ion batteries, solid-state batteries, solid-state batteries, semi-solid-state batteries, sodium-ion batteries, etc. And using the battery detection method proposed in this application for detection, potential problems can be discovered at an early stage to avoid abnormal batteries from flowing into subsequent processes and causing unnecessary operations. In this way, production efficiency and safety are improved.

[0120] It should be noted that the battery is manufactured using any of the manufacturing processes for the battery described above. Therefore, the battery adopts all the technical solutions of all the above embodiments, and thus at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A battery detection method, characterized in that: The battery detection method comprises: Detecting the battery cell parameters during the battery cell formation process to obtain a battery cell parameter detection data set; Processing the battery cell parameter detection data group to obtain the battery cell life characteristic value; According to the size relationship between the battery cell life characteristic value and the preset life characteristic value, the formed battery cells are screened to screen out normal battery cells.

2. The battery detection method according to claim 1, characterized in that: The detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: Detecting the cell parameters of the cell between the first formation step and the second formation step to obtain a cell parameter detection data group; the first formation step is to use a first formation current to charge the initial cell for a first preset time to obtain a first formed cell; the second formation step is to use a second formation current to charge the first formed cell for a second preset time to obtain a second formed cell; The first formation current is less than or equal to the second formation current.

3. The battery detection method according to claim 2, characterized in that: The first formation step and the second formation step further include placing the first formation cell at rest for a first resting time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

4. The battery detection method according to claim 3, characterized in that: The detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: Obtaining an initial voltage corresponding to before the initial battery cell is charged with the first formation current and a termination voltage corresponding to when the initial battery cell is charged with the first formation current for a first preset time; The battery cell parameter detection data group includes any one or more combinations of an initial voltage, a termination voltage, a first formation current, and a first preset time.

5. The battery detection method according to claim 4, characterized in that: The step of processing the battery cell parameter detection data group to obtain the battery cell life characteristic value specifically includes: Calculate the difference between the initial voltage and the final voltage to obtain the voltage change; Performing a linear fit on 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; The kinetic internal resistance is obtained according to the second fitting parameter, the termination voltage and the first formation current, and the kinetic internal resistance is used as a characteristic value of the battery cell life; The actual diffusion coefficient is obtained according to the first fitting parameter, and the actual diffusion coefficient is used as the characteristic value of the battery cell life.

6. The battery detection method according to claim 5, characterized in that: The obtaining of the kinetic internal resistance according to the second fitting parameter, the termination voltage and the first formation current specifically comprises: Calculating the difference between the termination voltage and the second fitting parameter; Calculating the ratio of the difference to the first formation current; The ratio is taken as the kinetic internal resistance.

7. The battery detection method according to claim 5, characterized in that: The linear fitting of the first preset time and the voltage variation according to the preset linear fitting formula to obtain the first fitting parameter and the second fitting parameter after fitting specifically includes: Taking the square root of the first preset time, and using the square root as the first variable of the preset linear fitting formula; The voltage change is used as the second variable of the preset linear fitting formula; 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.

8. The battery detection method according to claim 1, characterized in that: The life characteristic value includes an actual diffusion coefficient and / or an actual kinetic internal resistance. The step of screening the formed battery cells according to the size relationship between the battery cell life characteristic value and the preset life characteristic value to screen out normal battery cells specifically includes: When the actual diffusion coefficient is greater than or equal to the standard diffusion coefficient, the formed cells are screened to select normal cells; And / or, when the actual kinetic internal resistance is less than or equal to the standard kinetic internal resistance, the formed battery cells are screened to select normal battery cells.

9. The battery detection method according to claim 2, characterized in that: The first formation current is between 0.05 charge and discharge rate and 0.1 charge and discharge rate; And / or the second formation current is between 0.05 charge and discharge rate and 0.1 charge and discharge rate.

10. A battery manufacturing process, characterized in that: The manufacturing process of the battery includes a formation process of forming the battery cell using a preset process to obtain the formed battery cell; The battery detection method according to any one of claims 1 to 9 is performed during the formation process of the battery cell.

11. The manufacturing process of a battery according to claim 10, characterized in that: The use of a preset process to form the battery cell specifically includes: A first formation step: charging the initial battery cell with a first formation current for a first preset time to obtain a first formed battery cell; A second formation step: charging the first formation cell for a second preset time using a second formation current to obtain a second formation cell; A third formation step: charging the second formation cell for a third preset time using a third formation current to obtain a third formation 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 detecting of the cell parameters during the cell formation process to obtain the cell parameter detection data set specifically includes: The cell parameters of the cell between the first formation step and the second formation step are detected to obtain a cell parameter detection data set.

12. The manufacturing process of the battery according to claim 11, characterized in that: After the initial battery cell is charged with the first formation current for a first preset time to obtain a first formed battery cell, the battery detection method further includes: Allowing the first formed battery cell to stand for a first standing time; The step of detecting the cell parameters of the cell between the first formation step and the second formation step to obtain the cell parameter detection data set specifically includes: In the process of leaving the first formed battery cell to stand for a first standing time, the battery cell parameters between the first forming step and the second forming step are detected to obtain a battery cell parameter detection data set.

13. The manufacturing process of the battery according to claim 11, characterized in that: The third formation current is between 0.33 charge and discharge rates and 0.5 charge and discharge rates.

14. The process for manufacturing a battery according to any one of claims 10 to 13, characterized in that: Before the battery cell is formed by the preset process to obtain the formed battery cell, the manufacturing process of the battery further includes: Wetting the battery cells; The initial cell after soaking is left to rest for the second resting time, and the open circuit voltage after soaking is obtained. The open circuit voltage is determined as the initial voltage before the initial battery cell is formed.

15. The manufacturing process of the battery according to claim 11, characterized in that: After the second formation step, the manufacturing process of the battery further includes: The second formation cell is left to stand for a third standing time, so as to perform the third formation step after the third standing time has ended.

16. A battery, characterized in that: The battery is manufactured using the manufacturing process of the battery according to any one of claims 10 to 15.

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