A Smart Screening Method for Cell Self-Discharge

Abstract

This invention relates to the field of power batteries and discloses an intelligent screening method for battery cell self-discharge. First, the self-discharge rate factor K value of the battery cell is calculated during the finished product storage stage. If the K value is below standard, the battery cell is rejected; if the K value is within standard, the battery cell enters the finished product storage stage. Then, the self-discharge rate factor K2 value of the battery cell is calculated during the coating stage. Based on the K2 values ​​of the battery cell at different production time periods and temperatures, a nonlinear binary regression equation is fitted to calculate the theoretical and actual values ​​of K2, thereby determining the self-discharge status of the battery cell. This intelligent screening method for battery cell self-discharge is applicable to the entire process from battery cell production in the finished product storage stage to its formation into modules or units, and finally to finished product packaging. It intercepts battery cells and modules with poor self-discharge, thereby preventing battery cells with voltage difference and undervoltage risks from entering the market, improving the accuracy of battery cell self-discharge performance detection and the quality of the finished product.

Description

Technical Field

[0001] This invention relates to the field of power batteries, and more specifically, to a method for intelligent screening of cell self-discharge. Background Technology

[0002] With the increasing number of electric vehicles on the road, the requirements for power batteries are becoming increasingly stringent. Statistics show that undervoltage is the most common quality problem with electric vehicle power batteries. Undervoltage can significantly reduce the vehicle's range, or even render it unusable. Solving the undervoltage problem requires precise and effective interception measures at the module and packaging stages, using control methods such as the K-value to screen out cells with abnormal self-discharge. Traditional K-value screening methods introduce a self-discharge quantification factor K-value. This K-value only considers the voltage change rate between upstream processes, but it cannot identify and control the self-discharge changes of cells at the module and packaging stages. Historical experience and data indicate that self-discharge problems in cells have a certain probability of originating at the module and packaging stages. Summary of the Invention

[0003] This invention provides a method for intelligent screening of battery cell self-discharge. This method is applicable to the entire process of battery cell K-value control from the time it is put into the finished product storage warehouse, to the time it is made into modules or modules, and then to the final product packaging. It aims to intercept battery cells and modules with poor self-discharge and achieve intelligent screening of undervoltage battery cells by means of big data algorithm program, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for intelligent screening of battery cell self-discharge includes the following steps: Step S1: After being subjected to capacity testing, the battery cell enters the OCV3 process located in the finished product settling warehouse. After being settling in the capacity testing frame for a certain period of time, the battery cell enters the OCV4 process. Based on the open circuit voltage value and test time of OCV3 measured in the OCV3 process, and the open circuit voltage value and test time of OCV4 measured in the OCV4 process... Calculate the cell self-discharge rate factor in the finished product static storage process. value;

[0006] Step S2: After the battery cells are placed in the settling chamber, they enter the OCV5 coating process. The barcode scanner at the coating station scans the QR code of the battery cells to obtain the self-discharge rate factor of the battery cells. Value, open circuit voltage value OCV3, test time The information was traced, and then the open-circuit voltage value OCV5 and the test time were measured using a voltage and internal resistance tester inside the coating equipment. The processing unit calculates the open-circuit voltage value OCV5 and the test time. as well as the open-circuit voltage value OCV3 and the test time. Data was used to calculate the cell self-discharge rate factor in the OCV5 overmolding process. value;

[0007] Step S3: Compare the self-discharge rate factors of battery cells at different online time periods. The value is obtained from mathematical statistics methods to determine the normal battery cell's value over a certain period of time. The standard value and upper and lower limits of the value, and according to different online time periods. and temperature of Values, fitted Value and online time period and temperature Nonlinear bivariate regression equation By importing this equation into the computer's processing unit, the actual online time and temperature can be calculated. Theoretical value;

[0008] Step S4: When the battery cell enters the OCV5 coating process, the nonlinear binary regression equation fitted in step S3 is used... Calculate the cell's The theoretical value and upper and lower limits are then used to calculate the cell's performance according to the method described in step S2. The actual value is calculated by the arithmetic unit. Theoretical value and Difference between actual values ;

[0009] Step S5: Determined by the logic processing unit and The relationship between the upper and lower limits of the theoretical value, if exist If the theoretical value is within the upper and lower limits, the cell is considered to be qualified for self-discharge. Exceeding If the theoretical value falls within the upper and lower limits, the cell is determined to have poor self-discharge, which will be intelligently identified and eliminated by the next process.

[0010] As a further aspect of the present invention: in step S1, the cell self-discharge rate factor of the finished product static storage process is calculated. The value is determined using the following formula: .

[0011] As a further aspect of the present invention: step S2 calculates the cell self-discharge rate factor of the OCV5 overmolding process. The value is determined using the following formula: .

[0012] As a further aspect of the present invention: the mathematical statistics method used in step S3 is to statistically analyze the number of battery cells launched within a certain time period. Value data, plotting The normal distribution plot of the values ​​is also known as the Gaussian plot, which represents the values ​​during that time period. The median of the Gaussian plot is set as the value of that time period. Standard value The upper and lower bounds of the range are the values ​​in the Gaussian plot. value.

[0013] As a further aspect of the present invention: determination Theoretical value and Difference between actual values ,and The relationship between the upper and lower limits of the theoretical value is the basis for judgment and comparison. and If the relationship, Then the cell is judged to be qualified for self-discharge. If the cell is found to have a self-discharge defect, it will be intelligently identified and eliminated by the next process.

[0014] As a further aspect of the present invention, the intelligent screening method for cell self-discharge further includes fitting different nonlinear regression equations according to different types of cells and cells with different SOC capacities, and the fitted equations have adaptive self-learning functions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: it realizes K-value control throughout the entire process of battery cells from the finished product storage warehouse to the production of modules or modules, and then to the final packaging, so as to intercept battery cells and modules with poor self-discharge, and realizes intelligent screening of undervoltage battery cells with the help of big data algorithm programs. Attached Figure Description

[0016] Figure 1 This is a flowchart of the intelligent screening method of the present invention;

[0017] Figure 2 This is a flowchart illustrating the calculation of the K and K2 values ​​in this invention.

[0018] Figure 3 This is a Gaussian distribution diagram of the K2 values ​​of battery cells that were put into operation at the same time in this invention;

[0019] Figure 4 This is a graph showing the change of OCV voltage over time during the period from OCV3 test to OCV5 test of the battery cell in this invention.

[0020] Figure 5 This is a graph showing the change of OCV voltage difference over time during the period from OCV3 test to OCV5 test of the battery cell in this invention.

[0021] Figure 6 This is a nonlinear curve showing the change of the K2 value with the online time ΔT at a constant temperature according to the present invention.

[0022] Figure 7 This is a process flow diagram of the battery cell packaging production line used in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention proposes an intelligent screening method for battery cell self-discharge, introducing battery cell self-discharge rate factors K and K2, such as... Figure 2 The diagram shows the calculation flowchart for collecting voltage and time parameters and calculating the K and K2 values ​​of the battery cell in the OCV3, OCV4, and OCV5 sections with rubber coating. The flowchart includes the following steps:

[0025] After the battery cells undergo capacity testing, they enter the OCV3 process, which is located in the finished product storage area. The battery cells are placed in a capacity testing tray. After all the battery cells in the tray enter the OCV3 process, the OCV3 testing equipment extends its probe to contact the positive and negative terminals of each battery cell within the contact frame, measures the OCV3 open-circuit voltage V3 at that moment, and records the time T using MES timing. V3 After being placed in the capacity testing frame for 7 days, the battery cells enter the OCV4 process. In the OCV4 process, the open-circuit voltage value V4 is collected by the testing equipment and the time T is recorded. V4 The processing module processes the data stored in the storage module, performs the K-value calculation, and uses the formula K=(V3-V4) / (T) V4 - T V3 The K value is obtained, and then the logic judgment unit determines whether the K value is within the standard range. If it exceeds the standard range, the cell is discharged as a defective product. If it is within the standard range, it continues to flow.

[0026] The K2 value in the intelligent screening method for battery cell self-discharge in this embodiment of the invention relates to the coating section. The calculation of the K2 value includes the following steps: after the battery cell is brought online from the settling warehouse, it enters the coating station; after the battery cell enters the coating station, the QR code of the battery cell is scanned by the barcode scanner of the coating station to obtain the K value, V3, and T of the battery cell. V3Traceability information; by using the probes of a voltage resistance tester connected inside the coating equipment to contact the positive and negative terminals of each online cell, the open-circuit voltage value V5 of OCV5 and the test time T of OCV5 at that moment are measured. V5 The processing unit processes the V5 and T data already stored in the memory unit. V5 Data, as well as V3 and T v3 The data is used for calculation, employing the formula K2 = (V3-V5) / (T) V5 -T v3 ) Calculate the value of K2.

[0027] The cell resting time during K-value measurement is a preset time, such as 7 days. However, since the timing of cell production is uncertain, (T) V5 - T V3 () can be a value greater than 7 days, such as Figure 3 The image shows a Gaussian distribution of the K2 values ​​of a batch of lithium iron phosphate cells in the same state that were left to rest for the same period (up to 10 days) before being put into production. Figure 3 It can be seen that the distribution of K2 values ​​of battery cells launched in the same period conforms to a normal distribution. Furthermore, the median (sample mean) and ±Σ (standard deviation) of K2 values ​​of battery cells launched in that period can be obtained from the figure. The median of K2 values ​​is equivalent to the theoretical value of K2 in that period, and ±3Σ or nΣ are the upper and lower limits of the range in which the actual value of K2 of a normal battery cell should be.

[0028] Figure 1 This is a flowchart of a cell self-discharge intelligent screening method according to this embodiment. During the period from the OCV3 test to the OCV5 test (with overmolding), the cell voltage decreases as the time difference ΔT increases, as shown below. Figure 4 The figure shows the nonlinear negative correlation curve of the OCV voltage of a single cell from the OCV3 test to the OCV5 test (with time difference ΔT). The voltage difference ΔV is the difference between V3 and V5, and its relationship with the resting time ΔT is... V5 - T V3 It is not a linear relationship, but a high-order nonlinear relationship, such as... Figure 5 As shown, according to Figure 4 The nonlinear curve is fitted to derive a functional regression equation for the voltage difference ΔV and time difference ΔT: F(ΔV, ΔT). This functional equation is a high-order equation with ΔT as the variable. Dividing this equation by ΔT yields ΔV / ΔT, which is the nonlinear equation of K2 value with respect to ΔT. Considering the influence of temperature, a binary regression equation for K2 value with ΔT and temperature t as factors is obtained: K2 = f(ΔT, t). Figure 6The figure shows the curve of K2 value at constant temperature versus online time ΔT, derived from the binary regression equation. By importing this equation into a computer processing unit, the theoretical value of K2 can be calculated based on the actual online time ΔT and temperature. Then, the actual value of K2 calculated from the actual online cell is compared with the theoretical value. If |K2| < ΔT, the theoretical value is obtained. 实际 -K2 理论 If |K2 ≤ 3Σ, then the cell's self-discharge is normal and it can continue to operate; if |K2 实际 -K2 理论 If the self-discharge of the cell is abnormal (>3Σ), it is marked as a defective product and discharged in the next process to prevent cells with abnormal self-discharge from being installed in modules or even packaging sections.

[0029] In practical packaging production line applications, the K4 value can also be introduced as a parameter factor to evaluate the self-discharge level at the module level after the battery cell is fabricated into a module, such as... Figure 7 As shown, in the module EOL testing stage after module fabrication, the voltage V6 of each cell string can be automatically detected and the test time T6 can be recorded using the module EOL equipment. In the electrical performance testing stage after the battery pack assembly and electrical connection are completed, the voltage V7 of each cell string at the beginning of the electrical performance test is first measured and read, and the time T7 is recorded. The processing unit on the electrical testing host computer automatically calculates K4. 实际 K4 实际 = (V6-V7) / (T7-T6). Similarly, by fitting the regression equation for K4 based on the collected data and substituting the relevant time difference and temperature data, K4 is obtained. 理论 ,according to Figure 1 The flowchart of the intelligent screening method for cell self-discharge is shown. It performs intelligent judgment and screening, thereby realizing intelligent screening and interception at the module packaging level. The difference is that the self-discharge abnormality of the module and package may be caused by the wiring harness or BMS of the module and package. In this case, the root cause of the problem can be found through ABA verification and the problem can be solved by replacing the faulty parts.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0031] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for intelligent screening of battery cell self-discharge, characterized in that, The process includes the following steps: Step S1: After being sized and tested, the battery cells enter the OCV3 process located in the finished product settling warehouse. After being settling in the sized frame for a certain period of time, the battery cells enter the OCV4 process. Based on the OCV3 open circuit voltage value and test time measured in the OCV3 process, the process proceeds smoothly. And the open-circuit voltage value and test time of OCV4 measured in the OCV4 process section. Calculate the cell self-discharge rate factor in the finished product static storage process. value; Step S2: After the battery cells are placed in the settling chamber, they enter the OCV5 coating process. The barcode scanner at the coating station scans the QR code of the battery cells to obtain the self-discharge rate factor of the battery cells. Value, open circuit voltage value OCV3, test time The information was traced, and then the open-circuit voltage value OCV5 and the test time of the OCV5 process section of the coating equipment were measured using a voltage resistance tester inside the coating equipment. The processing unit calculates the open-circuit voltage value OCV5 and the test time. as well as the open-circuit voltage value OCV3 and the test time. Calculate the cell self-discharge rate factor in the OCV5 overmolding process. value; Step S3: Compare the self-discharge rate factors of battery cells at different online time periods. The value is obtained from mathematical statistics methods to determine the normal battery cell's value over a certain period of time. The standard value and upper and lower limits of the value, and according to different online time periods. and temperature of Values, fitted Value and online time period and temperature Nonlinear bivariate regression equation By importing this equation into the computer's processing unit, the actual online time and temperature can be calculated. Theoretical value; Step S4: When the battery cell enters the OCV5 coating process, the nonlinear binary regression equation fitted in step S3 is used... Calculate the cell's The theoretical value and upper and lower limits are then used to calculate the cell's performance according to the method described in step S2. The actual value is calculated by the arithmetic unit. Theoretical value and Difference between actual values ; Step S5: Determined by the logic processing unit and The relationship between the upper and lower limits of the theoretical value, if exist If the theoretical value is within the upper and lower limits, the cell is considered to be qualified for self-discharge. Exceeding If the theoretical value falls within the upper and lower limits, the cell is determined to have poor self-discharge, which will be intelligently identified and eliminated by the next process.

2. The intelligent screening method for cell self-discharge according to claim 1, characterized in that, In step S1, the cell self-discharge rate factor of the finished product storage process is calculated. The value is determined using the following formula: .

3. The intelligent screening method for cell self-discharge according to claim 1, characterized in that, Step S2 calculates the cell self-discharge rate factor in the OCV5 overmolding process. The value is determined using the following formula: .

4. The intelligent screening method for cell self-discharge according to claim 1, characterized in that, The mathematical statistics method used in step S3 is to statistically analyze the battery cells that were put into operation within a certain time period. Value, drawing Gaussian plot of the value, for that time period The median of the Gaussian plot is set as the value of that time period. Standard value The upper and lower bounds of the range are the values ​​in the Gaussian plot. value.

5. The intelligent screening method for cell self-discharge according to claim 4, characterized in that, like Then the cell is judged to be qualified for self-discharge. If the cell is found to have a self-discharge defect, it will be intelligently identified and eliminated by the next process.

6. The intelligent screening method for cell self-discharge according to claim 1, characterized in that, The intelligent screening method for cell self-discharge also includes fitting different nonlinear regression equations for different types of cells and cells with different SOC capacities, and the fitted equations have adaptive self-learning functions.

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