Rapid diagnosis method for self-discharge abnormity of battery

Abstract

The invention belongs to the technical field of batteries, and particularly relates to a battery self-discharge abnormity rapid diagnosis method, which comprises the following steps: S1, executing a capacity grading step on a to-be-detected battery, and standing after charging and discharging are completed; s2, after the first standing time is ended, measuring open-circuit voltage of the standing battery at n different time nodes, and recording corresponding time; s3, performing linear regression analysis based on each measurement data, and calculating a voltage change slope W; s4, abnormal batteries are screened according to the statistical distribution range of the slope W, the batteries are normal batteries when the slope W is within the distribution range, and the batteries are abnormal batteries when the slope W is outside the distribution range. Linear regression analysis based on open-circuit voltage change is introduced, and a statistical method is combined to screen the battery, so that errors caused by external environment change can be reduced, and the stability and reliability of a test result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and particularly relates to a method for quickly diagnosing abnormal self-discharge of batteries. Background Art

[0002] Due to characteristics such as high energy density, high power output, and long cycle life, batteries have been widely used in fields such as electronic communication equipment, new energy vehicles, and energy storage systems. With the rapid development of technology, the application scope of batteries continues to expand and plays an important role in many industries. However, due to factors such as material microdifferences and process complexity during the production process of batteries, there may be differences in capacity, internal resistance, etc. after the batteries are manufactured. These differences not only affect the overall performance of the battery pack but may also pose potential threats to the safety of the batteries.

[0003] Generally, the performance requirements of a battery pack include: (1) the capacity difference does not exceed 3%; (2) the internal resistance difference does not exceed 5%; (3) the self-discharge rate difference does not exceed 5%.

[0004] In the performance detection of batteries, the self-discharge test is a time-consuming but crucial link. Self-discharge refers to the phenomenon that the battery capacity gradually decreases due to internal chemical reactions and other reasons when the battery is in a static state. This phenomenon directly affects the charge retention ability and service life of the battery, and may seriously lead to premature battery failure or safety hazards. Therefore, the self-discharge performance of the battery is one of its key performances.

[0005] In the self-discharge test of batteries, currently two main methods are adopted: the capacity measurement method and the voltage measurement method. According to GB / T 31486 "Performance Requirements and Test Methods for Power Batteries for Electric Vehicles", after the battery module is charged to the termination voltage, it needs to be stored at room temperature for 28 days, and then a discharge test is carried out to calculate the charge retention capacity and recovery capacity. Although this method can accurately reflect the self-discharge performance of the battery, due to the long test cycle and time-consuming, it seriously affects the battery production efficiency. Especially in large-scale production, the long test time brings production bottlenecks.

[0006] Compared with the capacity measurement method, the voltage measurement method is relatively simple and less time-consuming. The voltage measurement method evaluates the self-discharge level by measuring the change in the open circuit voltage (OCV) before and after the battery is static. The calculation formula is: The voltage change of the battery in the static state can reflect its self-discharge degree, thereby indirectly evaluating its self-discharge performance. The voltage measurement method not only saves a lot of time but also simplifies the operation process, and is suitable for the rapid detection requirements in large-scale production.

[0007] In the existing battery self-discharge detection methods, the voltage measurement method is used to evaluate the self-discharge performance of the battery. Among them, the key parameter K value is calculated by measuring the voltage drop amount (△V) per unit time at the initial stage of the battery test. If the test starts immediately after the battery is fully charged, since the battery temperature is relatively high and the internal chemical reaction is relatively intense, the voltage drop amount per unit time is relatively large, and thus the calculated K value is also relatively large. On the contrary, if the time interval between the end of charging and the start of the test is relatively long, the initial state of the battery is relatively stable, the voltage drop amount per unit time decreases, and the calculated K value is relatively low. Thus, it can be seen that the time interval between the end of charging and the start of the test directly affects the calculation result of the K value, and further has a significant impact on the evaluation of the battery self-discharge performance.

[0008] The fluctuations of these factors may result in different K values for the same battery under different test conditions, leading to errors and instability in the test results, and further affecting the accurate evaluation of the self-discharge level. Therefore, the existing K value measurement method is difficult to provide reliable and stable test results in practical applications, and may lead to missed detection of abnormal batteries. Summary of the Invention

[0009] The present invention aims to solve the problems of accuracy and efficiency in the abnormal diagnosis of battery self-discharge, and discloses a method for rapid abnormal diagnosis of battery self-discharge. By introducing linear regression analysis based on the change of open circuit voltage (OCV) and combining statistical methods to screen batteries, it can reduce the errors caused by external environmental changes and improve the stability and reliability of test results.

[0010] In view of this, the present invention provides a method for rapid abnormal diagnosis of battery self-discharge, including:

[0011] S1: Perform a formation process on the battery to be tested, and perform static placement after charging and discharging are completed;

[0012] S2: After the end of the first static placement time, measure the open circuit voltages OCV1, OCV2... OCVn of the battery to be tested at n different time nodes, and record the corresponding times t1, t2... tn; where n≥2;

[0013] S3: Based on each measurement data (OCV1, 0), (OCV2, (t2 - t1) y )... (OCVn, (tn - t1) y ) perform linear regression analysis, calculate the voltage change slope W, and the y value is the power exponent parameter of the time difference;

[0014] S4: Screen abnormal batteries according to the statistical distribution range of the slope W. When the slope W is within the distribution range, it is a normal battery, and when the slope W is outside the distribution range, it is an abnormal battery.

[0015] Further, in step S1, the charging and discharging currents of the formation process are set to 0.05C to 2C. At the end of the formation process, a passivation treatment step is carried out: applying a reverse current of 0.05C - 1C to the battery, and the direction of the reverse current is opposite to the charging and discharging current direction at the end stage of formation.

[0016] Further, the current value of the passivation treatment is determined according to the verification of batteries of the same batch. By applying reverse currents with the same current value but different durations, or applying reverse currents with the same duration but different current values, to multiple groups of batteries of the same batch respectively, and monitoring the voltage changes after the verification batteries and the ambient temperature are the same, select the combination of current and time parameters that makes the linear regression correlation coefficient R 2 closest to 1 as the set current and time.

[0017] Further, in step S2, it is necessary to let the battery stand still every time the open-circuit voltage of the static battery is measured. After the open-circuit voltage detection results of the static battery measured at different time nodes meet the preset normal working range, the next standing still is carried out.

[0018] Further, in step S2, after the end of each standing still time, self-discharge detection is carried out on a batch of multiple batteries. By calculating the mean and standard deviation of the OCV of all the batteries, and using the formula μ OCV ±mσ OCV to judge whether the OCV of each battery meets the rule, and screen out unqualified batteries. Among them, μ OCV is the mean value of the battery OCV measured after multiple standing still times, and σ OCV is the standard deviation of the OCV.

[0019] Further, in step S3, the y value during the linear regression analysis based on the measured open-circuit voltage of the static battery is obtained by fitting according to the least squares method or by fitting according to the excel power exponential function, and the voltage change slope W is calculated based on the y value calculated by fitting.

[0020] Further, the range of the y value is set to 0.3 to 0.8.

[0021] Further, in step S3, W is obtained through calculation or linear regression analysis, and the mean value μ W of W is obtained through calculation; in step S4, batteries are screened according to μ W ±aσ W . σ W is the standard deviation of W, and a is a preset empirical parameter. When the slope W is within the range of μ W ±aσ W , it is a normal battery, and when the slope W is outside the range of μ W ±aσ W , it is an abnormal battery.

[0022] Further, in step S2, the standing temperature is controlled at 20-50°C, the first standing time is 0-24 hours, the last standing time does not exceed 360 hours, and the time interval between two adjacent standing times is 1-168 hours.

[0023] Further, the value range of n is 2-6, the value range of m is 2-6, and the value range of a is 2-6.

[0024] Compared with the prior art, the battery self-discharge abnormal rapid diagnosis method of the present invention has the following advantages:

[0025] (1) The battery self-discharge abnormal rapid diagnosis method described in this application measures the open-circuit voltage of the battery at multiple time nodes and uses linear regression analysis to calculate the slope W of the battery voltage change, which can effectively identify the self-discharge behavior of the battery. By combining the open-circuit voltage (OCV) measurement during the battery standing process with linear regression analysis, the accuracy and efficiency of battery self-discharge abnormal detection are significantly improved.

[0026] (2) The battery self-discharge abnormal rapid diagnosis method described in this application adopts the negative treatment of applying a reverse current after the formation process, effectively eliminating the influence of the concentration polarization phenomenon on the battery voltage, making the distribution of active substances inside the battery more uniform, improving the efficiency of self-discharge screening, avoiding misjudgment caused by voltage fluctuations, and improving the quality control level during the battery production process.

[0027] (3) The battery self-discharge abnormal rapid diagnosis method described in this application significantly improves the accuracy and efficiency of the screening process by optimizing steps such as standing time, temperature control, and reverse current negative treatment, and is particularly suitable for rapid diagnosis in large-scale production, thereby ensuring the safety and performance consistency of the battery. Description of the Drawings

[0028] Figure 1 is the standing voltage-time characteristic curve of the battery described in the embodiment of the present invention;

[0029] Figure 2 is the voltage-time relationship curve of the battery with incomplete negative treatment during the standing process described in the embodiment of the present invention;

[0030] Figure 3 is the voltage-time relationship curve of the normal battery during the standing process described in the embodiment of the present invention;

[0031] Figure 4 is the fitting relationship curve of the average voltage within 90 days during the negative treatment verification process of 1000 groups of lithium-ion batteries described in the embodiment of the present invention with t 0.5 fitting relationship curve;

[0032] Figure 5It is the distribution diagram of the W value of the normal battery and the abnormal battery in the embodiment of the present invention. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0034] In the description of the present application, it should be noted that the terms used here are only for describing specific implementation manners, and are not intended to limit the exemplary embodiments according to the present application. For the convenience of description, the dimensions of the various parts shown in the drawings are not drawn according to the actual proportional relationship. Technologies, methods, and devices known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technologies, methods, and devices should be regarded as part of the authorization specification. In all the examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0035] It should be noted that the terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the description and claims means at least one of the connected objects. The character " / " generally means that the related objects before and after are in an "or" relationship.

[0036] It should be noted that in the description of the present application, the orientation or positional relationships indicated by the orientation words such as "front, back, up, down, left, right", "horizontal, vertical, perpendicular, horizontal", and "top, bottom" are usually based on the orientation or positional relationships shown in the drawings. It is only for the convenience of describing the present application and simplifying the description. Without contrary instructions, these orientation words do not indicate and imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the protection scope of the present application; the orientation words "inside, outside" refer to the inside and outside relative to the contour of each component itself.

[0037] It should be noted that in this application, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising such element. In addition, it should be noted that the scope of the methods and devices in the embodiments of this application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0038] As Figures 1 to 5 shown, this application discloses a method for rapid diagnosis of abnormal self-discharge of a battery, including:

[0039] S1: Perform a formation step on the battery under test, and perform a static placement after charging and discharging are completed;

[0040] S2: After the end of the first static placement time, measure the open-circuit voltages OCV1, OCV2... OCVn of the battery under test at n different time nodes during static placement, and record the corresponding times t1, t2... tn; where n ≥ 2;

[0041] Specifically, after the end of the first static placement time, measure the open-circuit voltage OCV1 of the battery under test at time t1, then perform a static placement on the battery under test, and measure the open-circuit voltage OCV2 of the battery under test at time t2, and so on, until the open-circuit voltage OCVn of the battery under test is measured at time tn.

[0042] S3: Perform a linear regression analysis based on each measurement data (OCV1, 0), (OCV2, (t2 - t1) y )... (OCVn, (tn - t1) y ) to calculate the voltage change slope W. The y-value is the power exponent parameter of the time difference, which is used to convert the static placement time to better fit the actual non-linear trend of the battery voltage decreasing over time, and its value is obtained by least squares fitting or Excel power exponent function fitting;

[0043] S4: Screen for abnormal batteries according to the statistical distribution range of the slope W. When the slope W is within the distribution range, it is a normal battery, and when the slope W is outside the distribution range, it is an abnormal battery.

[0044] The present application discloses a method for rapidly diagnosing abnormal self-discharge of a battery. By monitoring the change in the open-circuit voltage (OCV) of the battery during the static state and combining linear regression analysis, the self-discharge condition of the battery is rapidly diagnosed. First, the battery to be tested undergoes a formation process, i.e., the charging and discharging processes, and after completion, it is left static. During the static state, the open-circuit voltage of the battery is measured at different time nodes (such as t1, t2, tn), and the OCV values at different time points are obtained. Using these OCV data, linear regression analysis is used to calculate the voltage change slope W of the battery. The value of W reflects the rate of voltage change of the battery during the static state. According to the statistical distribution of the slope W value, by setting a reasonable distribution range, it is determined whether the battery is in a normal state. If the slope W value of the battery falls within this range, the battery is determined to be a normal battery; if the slope W exceeds this range, the battery is determined to be an abnormal battery. Through this method, batteries with abnormal self-discharge performance can be efficiently screened, thereby improving the screening efficiency and reliability of the batteries.

[0045] The method for rapidly diagnosing abnormal self-discharge of the battery described in the present application realizes rapid, accurate, and stable detection of abnormal self-discharge of the battery by combining the change in the open-circuit voltage of the static battery and linear regression analysis. Compared with traditional self-discharge detection methods, this method does not require long-term charge and discharge and complex capacity measurement, greatly shortening the detection cycle, and at the same time avoiding complex hardware requirements. The present application performs linear regression analysis on the voltage changes of the battery at multiple time nodes, effectively reducing the uncertainty caused by measurement errors at a single time point. Using the statistical distribution range of the slope W to screen abnormal batteries can accurately identify batteries that do not meet the performance standards, avoiding misjudgment caused by battery performance fluctuations or environmental factors. The method for rapidly diagnosing abnormal self-discharge of the battery described in the present application can rapidly process a large number of batteries, improving production efficiency, and at the same time reducing the risk of unqualified batteries entering the market, thereby improving the overall quality and safety of battery products. It is particularly applicable to other types of batteries such as lithium-ion batteries, nickel-metal hydride batteries, and lead-acid batteries that have charge and discharge characteristics and can generate measurable parameter changes during the self-discharge process.

[0046] As a preferred example of the present application, in step S1, the charging and discharging currents of the formation process are set to 0.05C to 2C, and a passivation treatment step is performed at the end of the formation process: a reverse current of 0.05C - 1C is applied to the battery, and the direction of the reverse current is opposite to the direction of the charging and discharging current at the end stage of the formation. In the example of the present application, the charging and discharging current range of the formation process is set to 0.05C to 2C, which helps to comprehensively evaluate the performance of the battery within the normal range. After the formation process is completed, batteries with different capacities are classified, and subsequent self-discharge screening is performed on the same type of batteries according to the classification results.

[0047] Under normal circumstances, the battery voltage shows a continuous downward trend as the standing time increases. However, if the grading capacity setting is unreasonable, it may lead to a relatively serious polarization phenomenon in the battery cell at the end of grading capacity, and then cause an abnormal increase or decrease in the battery cell voltage in the short term. This abnormal phenomenon will affect the screening accuracy of self-discharge. In order to eliminate the influence of these abnormal batteries on the calculation of the W value in the subsequent self-discharge screening and improve the screening accuracy, a depolarization treatment step is adopted at the end of grading capacity. Specifically: after the battery grading capacity process is completed, first exclude the battery under test identified as having process abnormalities or capacity abnormalities in the grading capacity process, and then perform self-discharge screening on the remaining batteries. In this screening process, batteries with a continuous downward trend in battery voltage as time increases are more conducive to self-discharge screening. During the charge and discharge process of the battery, the ion diffusion rate is less than the electrode reaction rate, resulting in a concentration gradient near the electrode, which in turn causes a potential change and forms a concentration polarization phenomenon. At this time, the battery voltage will fluctuate as the ion concentration near the electrode gradually recovers. After the discharge or charge is completed, the battery voltage may temporarily rise or fall until the concentration polarization gradually decreases and the battery returns to a stable state. Since these batteries require a certain amount of time to return to the steady state, the cycle of the screening process is prolonged, thus increasing the manufacturing cost. In order to effectively reduce the influence of concentration polarization, at the end of the grading capacity step, a reverse current opposite to the direction of the grading capacity current (current range: 0.05C to 1C) is applied to accelerate the recovery of the ion concentration inside the battery, enabling the battery to quickly reach a steady-state balance. This setting can effectively eliminate the interference of the concentration polarization phenomenon, shorten the time of self-discharge screening, improve the efficiency of the screening process, and at the same time reduce the screening error caused by the slow recovery of the battery to the steady state, ultimately optimizing the production cost and the accuracy of battery screening.

[0048] The battery self-discharge abnormal rapid diagnosis method described in this application effectively eliminates the influence of the concentration polarization phenomenon on the battery voltage by applying a reverse current for depolarization treatment at the end of the grading capacity process, making the distribution of active substances inside the battery more uniform, improving the efficiency of self-discharge screening, avoiding misjudgment caused by voltage fluctuations, and improving the quality control level in the battery production process.

[0049] As a preferred example of this application, the current value of the depolarization treatment is determined based on the verification of batteries of the same batch. The batteries of the same batch are other batteries of the same batch as the battery under test, which are used as verification batteries. By applying reverse currents with the same current value for different durations or reverse currents with the same duration but different current values to multiple groups of batteries of the same batch, and monitoring the voltage change of the verification batteries after they are at the same temperature as the environment, select the one that makes the linear regression correlation coefficient R 2The combination of current and time parameters closest to 1 is used as the set current and time. The passivation treatment method of this application is based on the verification experiment of the same batch of battery packs, and optimizes the internal balance state of the battery by applying reverse currents with different parameters. Specifically, experiments are carried out by applying different combinations of reverse currents to multiple groups of batteries of the same batch. First, reverse currents with the same current value but different application durations, or reverse currents with the same time but different current values are respectively used, and then the voltage changes of the batteries under the condition of the same temperature as the environment are monitored. Under each experimental combination, by analyzing the voltage changes of the batteries, the linear regression method is used to calculate the correlation between the battery voltage and time, and the correlation coefficient R is obtained. 2 . Finally, the combination of the reverse current value and time length with the correlation coefficient R 2 closest to 1 is selected as the optimal passivation treatment condition. The goal of this process is to quickly restore the balance of ion concentration inside the battery, thereby eliminating the influence of the concentration polarization phenomenon on the self-discharge screening accuracy of the battery. Through this method, targeted passivation treatment conditions can be provided for each batch of batteries, improving the treatment effect and ensuring the stability and accuracy of the batteries during the screening process. In the specific design process, first determine the reverse current values of different currents and times, and calculate the T y power of the voltage and time. According to the linear fitting relationship, a combination of current and time is selected so that the correlation coefficient R 2 is closest to 1. At this time, the combination of current and time obtained is the combination of current and time during the verified passivation treatment. When applying it in large quantities, by adopting the above optimized and verified current and time values, the performance and stability of the system can be ensured to reach the best state.

[0050] In the example of this application, by using different batteries of the same batch, according to the specific working steps, the end stage is selected as charging or discharging. At the end of the formation, a same current value is set and different times are applied (or a same time is set and different currents are applied), and the voltage changes of the batteries after they are the same as the environment temperature are monitored. According to (OCV1, 0), (OCV2, (t2 - t1) y , ……(OCVn, (tn - t1) y ) and other data, linear regression analysis is carried out to obtain the correlation R 2 closest to 1. At this time, the current and time are the set current and time. As Figure 3 , Figure 4 shown, during the passivation verification process, through the 0.1C reverse charge time, with the voltage at 12h after the end of the formation as the starting voltage, the t^0.5 is fitted, and the linear correlation is good within 90 days. Among them, when the reverse charge is 32 minutes, R 2 = 0.9976.

[0051] As a preferred example of the present application, in step S2, it is necessary to let the battery stand still each time when measuring the open-circuit voltage of the static battery. After the open-circuit voltage detection results of the static battery measured at different time nodes meet the preset normal working range, the next step of standing still is carried out. In the example of the present application, at the end of the first standing still time, the battery voltage OCV1 is measured, and the time t1 is recorded; if the OCV1 voltage detection result meets the preset normal working range, the next step of standing still is carried out; by judging OCV each time when standing still, unqualified batteries can be screened out, which can improve the accuracy of W obtained by linear regression analysis. In the example of the present application, the first standing still time can be determined according to the temperature of the battery after the formation process. The standing still here is used to eliminate the influence of temperature, that is, the formation process will cause the consistency of the battery temperature to decrease, and the consistency of the battery temperature is restored by standing still. Preferably, the standing still time of the battery after the formation process depends on the time required for the battery with the largest difference between the temperature at the end of formation and the standing still temperature to return to the standing still temperature.

[0052] During the measurement of the open-circuit voltage OCV of the battery in the present application, standing still is continuously carried out multiple times. After each standing still, it is necessary to measure the open-circuit voltage of the battery and make a judgment. Only when each measurement result meets the preset normal working range can the next step of linear regression analysis be continued. This step-by-step screening process can ensure the accuracy of the voltage data of each battery, reduce the influence caused by single measurement errors, and finally improve the accuracy of the voltage change slope W calculated by linear regression analysis through multiple measurement results, so as to effectively judge the self-discharge performance of the battery. The method for rapid diagnosis of abnormal self-discharge of the battery described in the present application improves the detection efficiency of abnormal batteries through multiple standing still measurements and data screening, effectively improves the quality control level in the battery production process, and thus ensures the high performance and high safety of the battery products. In the example of the present application, the preset normal working range refers to the voltage fluctuation range that can reflect the battery in the normal state, which is verified through experiments based on the chemical characteristics, design parameters, historical test data of the battery, as well as industry standards or internal quality control of the enterprise. This preset normal working range can also be set manually according to experience.

[0053] As a preferred example of the present application, in step S2, after the end of each standing still time, self-discharge detection is carried out on a batch of multiple batteries. By calculating the mean and standard deviation of the OCV of all the batteries, and using the formula μ OCV ±mσ OCV to judge whether the OCV of each battery is within

μ OCV -mσ OCV ,μ OCV +mσ OCV

[0054] The battery self-discharge abnormality rapid diagnosis method described in this application screens the qualified batteries through statistical analysis. This not only improves the accuracy of battery detection, but also makes the detection process more efficient and consistent, can handle the detection tasks of a large number of batteries, and improves the production efficiency. In addition, with the continuous accumulation of measurement data for each standing, the determination of the battery self-discharge characteristics is further optimized, making the detection more stable.

[0055] As a preferred example of the present application, in step S3, when performing linear regression analysis based on the measured open-circuit voltage of the stationary battery, the y value is obtained by fitting according to the least squares method or by fitting according to the excel power exponential function, and the voltage change slope W is calculated based on the y value obtained by fitting. As a preferred example of the present application, the value range of the y value is 0.3 to 0.8. For the method for rapid diagnosis of abnormal self-discharge of the battery described in the present application, first, the open-circuit voltage (OCV) of the battery at different time nodes during the stationary process is measured, and these voltage data are fitted by the least squares method or the power exponential function in excel to obtain a fitting curve, and the y value is solved according to the fitting curve. According to the actual situation of the voltage change of the stationary battery within a certain stationary time, the value range of the y value is usually set to 0.3 to 0.8. The y value represents the relationship between the battery voltage change and time. The y value obtained by fitting can reflect the voltage drop trend of the battery, and is further used to calculate the voltage change slope W of the battery. The calculated slope W value can effectively judge the self-discharge behavior of the battery, so as to screen out the batteries that may have abnormal self-discharge phenomena. By using the least squares method or the excel power exponential function to fit a large number of data points to calculate the y value, the present application reduces the error of individual measurement values, can achieve relatively accurate battery screening, reduces the error caused by manual measurement or single data deviation, and improves the efficiency and accuracy of screening.

[0056] As a preferred example of the present application, in step S3, W is obtained through calculation or linear regression analysis, and the mean value μ of W is obtained through calculation W ; in step S4, according to μ W ±aσ W batteries are screened, σW is the standard deviation of W, a is a preset empirical parameter, and when the slope W is within μ W ±aσ W the range, it is a normal battery, and when the slope W is outside the range of μ W ±aσ W it is an abnormal battery. In the present application, through the combination of the slope W value obtained by linear regression analysis and its mean value μ W and the standard deviation σ W , the slope W value reflects the rate of change of voltage with time during the self-discharge process of the battery. Combining the standard deviation σ W , abnormal batteries that do not conform to the self-discharge characteristics of most batteries can be effectively identified, thereby improving the efficiency and accuracy of the screening of the self-discharge performance of the battery. Combining the screening strategy within the range of μ W ±aσ W greatly improves the accuracy and stability of the screening process. As a preferred example of the present application, the value range of a is 2 to 6, thereby providing a flexible adjustment space to adapt to the performance fluctuations of different batches of batteries.

[0057] In this way, misjudgment caused by differences in battery characteristics can be reduced, the reliability of the screening results can be ensured, and safety risks caused by unqualified batteries can be avoided.

[0058] As a preferred example of the present application, the static temperature in step S2 is controlled at 20-50 °C, and the time interval between two adjacent static times is 1-168 hours; the first static time is 0-24 hours, the last static time does not exceed 360 hours, and the value range of n is 2-6.

[0059] As a specific example of the present application, the first static time is set to 0 to 24 hours, and OCV1 conforms to μ OCV1 ±mσ OCV1 ;

[0060] The second static time is set to 1 to 168 hours, and OCV2 conforms to μ OCV2 ±mσ OCV2 ;

[0061] The nth static time is set to 1 to 360 hours, and OCVn conforms to μ OCVn ±mσ ocvn ;

[0062] The temperature range during the static process is controlled between 20 and 50 degrees Celsius; and the temperature remains the same before and after during the static process;

[0063] By μ OCV1 ±mσ OCV Screen batteries with abnormal OCV, where the value range of n is 4±2.

[0064] Perform calculations or analyses on the batteries during the static process. Through data such as (OCV1, 0), (OCV2, (t2-t1) y , ……(OCVn, (tn-t1) y ) etc. for linear regression analysis, calculate the slope W, and the value range of y is 0.3 to 0.8. When n = 2, the calculation formula of W is:

[0065]

[0066] By μ W ±mσ W Screen batteries with abnormal self-discharge, where the value range of m is 4±2, and the distribution diagrams of W values of normal batteries and abnormal batteries are as Figure 5 shown.

[0067] This application places the battery at rest within a controlled temperature range (20 - 50 °C) to ensure that the temperature remains consistent during each resting process, thereby eliminating the impact of temperature fluctuations on voltage measurement. Then, through a series of resting processes and linear regression analysis, batteries with abnormal self-discharge are screened out. The entire process involves step-by-step screening and precise calculation, and finally, through the W ±mσ W screening criterion to determine whether the battery is qualified, which can effectively identify batteries with abnormal self-discharge characteristics, thereby improving the detection accuracy and efficiency during the battery production process.

[0068] The method for rapid diagnosis of abnormal self-discharge of batteries described in this application measures the change in the open-circuit voltage (OCV) of the battery during the resting process multiple times. By combining multiple measurements of the open-circuit voltage and linear regression analysis, measurements and data analysis are carried out at multiple time nodes, which can promptly detect batteries with abnormal self-discharge, ensure the consistency and stability of the screening process, and reduce misjudgments caused by battery performance fluctuations or test condition changes. The voltage change slope W of the battery is calculated using linear regression analysis, and qualified batteries are screened out in combination with statistical methods, avoiding the uncertainty brought by single measurement errors, making the screening results more accurate, and thus achieving the purpose of quickly, stably, and accurately detecting the self-discharge characteristics of batteries, greatly improving the efficiency and accuracy of abnormal self-discharge detection. Specific Embodiment

[0070] This embodiment provides a method for rapid diagnosis of abnormal self-discharge of batteries, and the specific steps are as follows:

[0071] 1. Charge and discharge the battery to be tested according to a predetermined grading process. After the charge and discharge are completed, the battery is placed at rest at a temperature of 25 °C.

[0072] 2. At the end of the first 4-hour rest, measure the open-circuit voltage OCV1 of the battery and record the time t1. OCV1 satisfies μ OCV1 ±3σ OCV1 .

[0073] 3. At the end of the second 4-hour rest, measure the open-circuit voltage OCV2 of the battery and record the time t2. OCV2 satisfies μ OCV2 ±3σ OCV2 .

[0074] 4. At the end of the third 4-hour rest, measure the open-circuit voltage OCV3 of the battery and record the time t3. OCV3 satisfies μ OCV3 ±3σ OCV3 .

[0075] 5. At the end of the fourth 4-hour rest, measure the open-circuit voltage OCV4 of the battery and record the time t4. OCV4 satisfies μ OCV4±3σ OCV4 。

[0076] 6. At the end of the fifth static state for 4 hours, measure the open circuit voltage OCV5 of the battery, record the time t5, and OCV5 satisfies μ OCV5 ±3σ OCV5 。

[0077] 7. By performing linear regression analysis on data such as (OCV1, 0), (OCV2, (t2 - t1) y , ……(OCVn, (tn - t1) y ), calculate the slope W, as Figure 1 shown, the correlation coefficient R 2 is 0.993.

[0078] 8. Screen the batteries according to the standard deviation range of μ W ±3σ W . The batteries with slope values within the range of μ W ±3σ W are normal batteries, and those outside this range are abnormal batteries.

[0079] For the method for rapid diagnosis of abnormal self-discharge of the battery described in the present invention, by performing linear regression analysis on data such as (OCV1, 0), (OCV2, (t2 - t1) y , ……(OCVn, (tn - t1) y ), calculate the slope W value to screen abnormal batteries. During long-term voltage monitoring, the W value remains stable. By performing linear regression analysis on the W value and the traditional K value, it is found that the correlation R 2 reaches 0.99, showing strong accuracy. Thus, without adding test devices, abnormal batteries can be quickly screened by testing the short-term W value, which is beneficial to reducing the time used for testing; and when the voltage deviation is caused by temperature change, the W value calculates the slope of the straight line through linear regression analysis, and has better anti-temperature interference ability than the K value.

[0080] The embodiments of the present application have been described above in conjunction with the accompanying drawings. Without conflict, the embodiments and the features in the embodiments in the present application can be combined with each other. The present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

Claims

1. A method for quickly diagnosing abnormal self-discharge of a battery, characterized in that, Including: S1: Perform a formation process on the battery under test, and let it stand still after charging and discharging are completed. S2: After the first standing time ends, measure the open-circuit voltages OCV1, OCV2,..., OCVn of the battery under test at n different time nodes during standing, and record the corresponding times t1, t2,..., tn; where n ≥ 2. S3: Based on each measurement data (OCV1, 0), (OCV2, (t2 - t1) y ),... (OCVn, (tn - t1) y ), perform linear regression analysis to calculate the voltage change slope W, and the y value is the power exponent parameter of the time difference; S4: Screen for abnormal batteries according to the statistical distribution range of the slope W. When the slope W is within the distribution range, it is a normal battery; when the slope W is outside the distribution range, it is an abnormal battery.

2. The method for rapid diagnosis of abnormal self-discharge of a battery according to claim 1, characterized in that In step S1, the charging and discharging currents of the formation process are set to 0.05C to 2C, and a negative formation treatment step is performed at the end of the formation process: apply a reverse current of 0.05C - 1C to the battery, and the direction of the reverse current is opposite to the direction of the charging and discharging currents at the end of the formation stage.

3. The method for diagnosing the abnormal rapid self-discharge of a battery according to claim 2, wherein The current value of the passivation treatment is determined by verification based on batteries of the same batch. By applying reverse currents with the same current value but different durations, or reverse currents with the same duration but different current values, to multiple groups of batteries of the same batch, and monitoring the voltage change after the batteries and the ambient temperature are the same, select the combination of current and time parameters that makes the linear regression correlation coefficient R 2 closest to 1 as the set current and time.

4. The method for diagnosing abnormal rapid self-discharge of a battery according to claim 1, wherein, In step S2, it is necessary to let the battery stand still every time the open-circuit voltage of the standing battery is measured, and the next standing is carried out after the open-circuit voltage detection results of the standing battery measured at different time nodes meet the preset normal operating range.

5. The method for diagnosing abnormal rapid self-discharge of a battery according to claim 1, characterized in that, In step S2, after each standing time ends, self-discharge detection is performed on a batch of multiple batteries. By calculating the mean and standard deviation of the OCV of all the batteries, it is determined whether the OCV of each battery is within 【μ OCV -mσ OCV , μ OCV +mσ OCV 】. If so, it is screened as a qualified battery. Among them, μ OCV is the mean value of the OCV of the battery measured after multiple standing times, σ OCV is the standard deviation of the OCV, and m is a preset empirical parameter.

6. The method for rapid diagnosis of abnormal self-discharge of a battery according to claim 1, characterized in that In step S3, the y value during the linear regression analysis based on the measured open-circuit voltage of the standing battery is obtained by fitting according to the least squares method or by fitting according to the excel power exponential function, and the voltage change slope W is calculated based on the y value calculated by fitting.

7. The method for rapid diagnosis of abnormal self-discharge of a battery according to claim 6, wherein The range of the y value is set to 0.3 to 0.

8.

8. The method for rapid diagnosis of abnormal self-discharge of a battery according to claim 1, characterized in that In step S3, W is obtained through calculation or linear regression analysis, and the mean value μ of W is obtained through calculation. W ; In step S4, based on μ W ±aσ W batteries are screened. σ W is the standard deviation of W, a is a preset empirical parameter. When the slope W is within the range of μ W ±aσ W , it is a normal battery. When the slope W is outside the range of μ W ±aσ W , it is an abnormal battery.

9. The method for diagnosing abnormal rapid self-discharge of a battery according to claim 1, wherein In step S2, the standing temperature is controlled at 20 - 50 °C, the first standing time is 0 - 24 hours, the last standing time does not exceed 360 hours, and the time interval between adjacent two standing times is 1 - 168 hours.

10. The method for rapid diagnosis of abnormal self-discharge of a battery according to claim 3 or 8, characterized in that, The value range of n is 2 to 6, the value range of m is 2 to 6, and the value range of a is 2 to 6.

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