Battery cell self-discharge intelligent screening method

Abstract

The invention relates to the field of power batteries, and discloses a battery cell self-discharge intelligent screening method, which comprises the following steps: firstly, calculating a self-discharge rate factor K value of a battery cell in a finished product standing warehouse process section, if the K value does not reach the standard, rejecting the battery cell, if the K value reaches the standard, putting the battery cell into a finished product warehouse for standing, and then calculating a self-discharge rate factor K2 value of the battery cell in a rubber coating process section, according to K2 values of the battery cells in different offline time periods and at different temperatures, a nonlinear binary regression equation is fitted, so that a theoretical value and an actual value of K2 are calculated to judge the self-discharge condition of the battery cells, and the battery cell self-discharge intelligent screening method is suitable for battery cell offline from a finished product standing warehouse to module making or module making. According to the invention, the battery cells and modules with poor self-discharge are intercepted in the whole process of self-discharge and packaging of finished products, so that the battery cells with voltage difference and undervoltage risks are prevented from flowing into the market, and the detection accuracy and delivery quality of the self-discharge performance of the battery cells are improved.

Description

Technical Field

[0001] The present invention relates to the field of power batteries, and in particular to a method for intelligently screening battery cell self-discharge. Background Art

[0002] As the number of electric vehicles increases, the requirements for power batteries are becoming more and more stringent. Statistics show that the most common quality problem in electric vehicle power batteries is battery undervoltage. Undervoltage problems can significantly reduce the endurance of the entire vehicle, or even make it unusable. To solve the undervoltage problem, precise and effective interception measures must be taken at the module and packaging ends, and cells with abnormal self-discharge must be screened out through control methods such as the K value. The traditional K value screening method introduces the self-discharge quantification factor K value, which only considers the voltage change rate between some front-end processes, but cannot identify and control the self-discharge changes of the cell at the module and packaging ends. Historical experience and data show that self-discharge problems of the cell occur at the module and packaging ends with a certain probability. Summary of the invention

[0003] The present invention provides a method for intelligent screening of battery cell self-discharge, which is applicable to the whole process K value control of battery cells from the time they come off the production line in a finished product static warehouse, to the time they are made into modules or modules, and then to the finished product packaging, so as to intercept battery cells and modules with poor self-discharge, and with the help of a big data algorithm program, realize the intelligent screening of undervoltage battery cells, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for intelligent screening of battery cell self-discharge, the method comprising the following steps:

[0006] Step S1: After the cells are divided into different capacities, they enter the OCV3 process in the finished product storage. After being kept in the storage frame for a certain period of time, the cells enter the OCV4 process. According to the OCV3 open circuit voltage value and the test time T measured in the OCV3 process section, v3 , and the OCV4 open circuit voltage value and test time T measured in the OCV4 process section v4 , calculate the self-discharge rate factor K value of the battery cells in the finished product static warehouse process section.

[0007] Step S2: After the battery cell is put online from the static warehouse, it enters the encapsulation OCV5 process. The barcode scanner of the encapsulation station scans the QR code of the battery cell to obtain the self-discharge rate factor K value, open circuit voltage value OCV3, and test time T of the battery cell. v3 Then, the open circuit voltage value OCV5 of the OCV5 process section of the encapsulation is measured by the voltage internal resistance tester in the encapsulation equipment, and the test time is T v5 The calculation processing unit calculates the open circuit voltage value OCV5 and the test time Tv5 , as well as the open circuit voltage value OCV3 and the test time T v3 Data, calculate the self-discharge rate factor K2 value of the battery cell in the OCV5 encapsulation process section.

[0008] Step S3: Compare the self-discharge rate factor K2 values ​​of the battery cells in different offline time periods, and obtain the standard value and upper and lower limits of the K2 value of the normal battery cells in a certain period of time according to the mathematical statistics method, and according to the K2 values ​​of different offline time periods ΔT and temperature t, fit the nonlinear binary regression equation K2=f(ΔT, t) of the K2 value and the offline time period ΔT and temperature t. Import the equation into the computer operation unit, and then calculate the theoretical value of K2 according to the actual offline time and temperature.

[0009] Step S4: When the battery cell enters the OCV5 encapsulation process section, the K2 theoretical value and the upper and lower limits of the battery cell are calculated according to the nonlinear binary regression equation fitted in step S3, and then the K2 actual value of the battery cell is calculated according to the method described in step S2. The calculation unit calculates the difference Δk2 between the K2 theoretical value and the K2 actual value, and then the logic processing unit determines the relationship between Δk2 and the upper and lower limits of the K2 theoretical value.

[0010] Step S5: The logic processing unit determines the relationship between Δk2 and the upper and lower limits of the theoretical value of K2. If Δk2 is within the upper and lower limits of the theoretical value of K2, the self-discharge of the battery cell is determined to be qualified. If Δk2 exceeds the upper and lower limits of the theoretical value of K2, the self-discharge of the battery cell is determined to be qualified. If the self-discharge of the battery cell is determined to be poor, it will be intelligently identified and discharged by the next process.

[0011] As a further solution of the present invention: the step S1 calculates the self-discharge rate factor K value of the battery cell in the finished product storage process section, and the formula used is as follows: K = (OCV3-OCV4) / (T v4 -T v3 ).

[0012] As a further solution of the present invention: the step S2 calculates the self-discharge rate factor K2 value of the battery cell in the process section of encapsulating OCV5, and the formula used is as follows: K2 = (OCV3-OCV5) / (T v5 -T v3 ).

[0013] As a further solution of the present invention: the mathematical statistics method adopted in the step S3 is to count the K2 value data of the offline battery cells in a certain same time period, draw a normal distribution diagram of the K2 value, i.e., a Gaussian diagram, and the median of the Gaussian diagram of the K2 value in the time period is set as the K2 standard value of the time period, and the upper and lower limits of the K2 range can be selected as ±3Σ values ​​of the median in the Gaussian diagram.

[0014] As a further solution of the present invention: determine the relationship between the difference Δk2 between the theoretical value of K2 and the actual value of K2 and the upper and lower limits of the theoretical value of K2, that is, to judge and compare the relationship between Δk2 and 3Σ. If Δk2≤3Σ, the self-discharge of the battery cell is determined to be qualified. If Δk2>3Σ, the self-discharge of the battery cell is determined to be poor, and the battery cell will be intelligently identified and discharged by the next process.

[0015] As a further solution of the present invention: the intelligent screening method for battery cell self-discharge not only introduces the K2 value control factor in the battery cell encapsulation stage, but also can achieve self-discharge control in the entire process of battery cell module manufacturing and packaging.

[0016] As a further solution of the present invention: the method for intelligent screening of battery cell self-discharge also includes fitting different types of battery cells and battery cells with different SOC capacities into different nonlinear regression equations, and the fitted equations have an adaptive self-learning function.

[0017] Compared with the prior art, the beneficial effects of the present invention are: it realizes the K value control of the whole process from the battery cell coming off the line in the finished product static warehouse, to being made into modules or modules, and then to the finished product packaging, so as to intercept the battery cells and modules with poor self-discharge, and with the help of big data algorithm program, it realizes the intelligent screening of undervoltage battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a processing flow chart of the intelligent screening method of the present invention;

[0019] Figure 2 It is a calculation flow chart of K value and K2 value of the present invention;

[0020] Figure 3 It is a Gaussian distribution diagram of K2 values ​​of the cells online in the same period of time in the present invention;

[0021] Figure 4 It is a curve diagram of the OCV voltage change over time after the battery cell is put online from the static storage in the present invention;

[0022] Figure 5 It is a curve chart showing the change of OCV voltage difference over time after the battery cell is put online from the static storage in the present invention;

[0023] Figure 6 It is a nonlinear curve diagram of the K2 value of the online cells in different time periods changing with time in the present invention;

[0024] Figure 7 This is a process flow chart of the battery cell packaging production line used in the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The embodiment of the present invention proposes an intelligent screening method for self-discharge of battery cells, which introduces the self-discharge rate factors K value and K2 value of the battery cells, such as Figure 2 As shown in the figure, it is a calculation flow chart for collecting voltage and time parameters and calculating K value and K2 value from the cell going online in the static storage to the encapsulation and module stage, which specifically includes the following steps:

[0027] After the cells are divided into different capacities, they enter the OCV3 process, which is located in the finished product storage warehouse. The cells are placed in the divided capacity tray. After the entire tray of cells enters the OCV3 process, the OCV3 test equipment extends the probe to contact the positive and negative poles of each cell in the frame, measures the OCV3 open circuit voltage value at that moment, and records the time T at that moment through the MES timing method. V3 After 7 days of standing in the capacity frame, the battery cell enters the OCV4 process. In the OCV4 process, the test equipment collects the OCV4 open circuit voltage value and records the time T V4 ; The above data stored in the storage module is processed by the operation processing module, and the K value is solved by the formula K = (OCV3-OCV4) / (T V4 -T V3 ) to obtain the K value, and then the logic judgment unit determines whether the K value is within the standard range. If it exceeds the standard range, the battery cell will be discarded as a defective product, and if it is within the standard range, it will continue to flow down.

[0028] The K2 value in the intelligent screening method for self-discharge of battery cells in the embodiment of the present invention involves the encapsulation section. The calculation of the K2 value includes the following steps: the battery cell enters the encapsulation station after being put online from the static warehouse; after the battery cell enters the encapsulation station, the QR code of the battery cell is scanned by the barcode scanning gun of the encapsulation station to obtain the K value, OCV3, T V3 Traceability information; through the probe connected to the voltage internal resistance tester in the encapsulation equipment, contact the positive and negative poles of each online battery cell, and measure the OCV5 open circuit voltage value at that moment and the OCV5 test time T V5 ; The processing unit performs the calculation on the OCV5 and T stored in the storage unit V5 Data, as well as OCV3, T v3 The data is calculated using the formula K2 = (OCV3-OCV5) / (T V5 -T v3 ) to obtain the K2 value.

[0029] The cell rest time when measuring K value is a fixed value of 7 days. However, due to the uncertainty of when the cell will be put into production, (T V5 -T V3 ) can be a value greater than 7 days, such as Figure 3 As shown in FIG. 1 , it is a Gaussian distribution diagram of K2 values ​​of a batch of lithium iron phosphate batteries in the same state after being put on line for the same period of time (which can be 10 days). Figure 3 It can be seen that the distribution of K2 values ​​of the online cells in the same time period conforms to the normal distribution, and the median (sample mean) and ±Σ value (standard deviation) of the K2 values ​​of the online cells in this time period can be obtained from the figure. The median of the K2 value is equivalent to the theoretical value of K2 in this time period, and ±3Σ or nΣ are the upper and lower limits of the range in which the actual value of K2 of a normal cell should be.

[0030] Figure 1 Flow chart of a method for intelligent screening of self-discharge of battery cells in this embodiment. After the battery cell voltage is online from the static storage, it decreases as the static time increases. Figure 4 As shown in FIG. 1 , the OCV voltage of a single cell is nonlinearly negatively correlated with the online time after it is online from the static storage. The voltage difference ΔV is the difference between OCV3 and OCV5, which is related to the static time ΔT (T V5 -T V3 ) is not a linear relationship, but a high-order nonlinear relationship, such as Figure 5 As shown, according to Figure 4 The nonlinear curve graph is fitted with the function regression equation of the voltage difference ΔV and the time difference ΔT: F(ΔV,ΔT). This function equation is a high-order equation with ΔT as the variable. In this equation, dividing by ΔT can get ΔV / ΔT, that is, the nonlinear equation of K2 value and ΔT. Considering the influence of temperature, the binary regression equation K2=f(ΔT, t) of K2 value with ΔT and temperature t as factors can be obtained. Figure 6 As shown in the figure, it is a curve chart of the K2 value at constant temperature and the change of offline time ΔT according to the binary regression equation. By importing this equation into the computer calculation unit, the theoretical value K2 can be calculated according to the actual offline time and temperature. 理论 , and then compare the actual value of K2 calculated by the actual offline battery cell with the theoretical value. If | K2 实际 -K2 理论 |≤3Σ, the self-discharge of the battery cell is normal and can continue to flow; if |K2 实际 -K2 理论 |>3Σ, the battery cell is judged to have abnormal self-discharge, marked as defective, and discarded in the later process, thus preventing the abnormal self-discharge battery cell from being installed in the module or even the packaging stage.

[0031] In actual packaging production line applications, the parameter factor of K4 value can also be introduced to evaluate the self-discharge level at the module level after the battery cell is prepared into a module, such as Figure 7 As shown, in the module EOL test phase after the module preparation is completed, the module EOL equipment can automatically detect the voltage V6 of each string of cells and record the test time T6. In the electrical performance test phase after the battery pack is assembled and connected, the voltage V7 of each string of cells at the start of the electrical performance test is first measured and read and the time T7 is recorded. The calculation processing unit on the electrical measurement host computer automatically calculates K4 实际 , K4 实际 =(V6-V7) / (T7-T6). Similarly, the regression equation of K4 is fitted based on the collected data and substituted into the relevant time difference and temperature data to obtain K4 理论 ,according to Figure 1 The flowchart of the intelligent screening method for battery cell self-discharge is used for intelligent judgment and screening, so as to realize intelligent screening and interception at the module packaging level. The difference is that the self-discharge anomaly of the module and package may be caused by the wiring harness or BMS of the module and package. At this time, ABA verification can be used to find out the root cause of the problem and solve it by replacing the problematic parts.

[0032] It will be apparent to those skilled in the art that the 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 the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0033] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes 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 following steps are involved: Step S1: After the cells are divided into different capacities, they enter the OCV3 process in the finished product storage. After being kept in the storage frame for a certain period of time, the cells enter the OCV4 process. According to the OCV3 open circuit voltage value and the test time T measured in the OCV3 process section, v3 , and the OCV4 open circuit voltage value and test time T measured in the OCV4 process section v4 , calculate the self-discharge rate factor K value of the battery cell in the finished product storage process section; Step S2: After the battery cell is put online from the static warehouse, it enters the encapsulation OCV5 process. The barcode scanner of the encapsulation station scans the QR code of the battery cell to obtain the self-discharge rate factor K value, open circuit voltage value OCV3, and test time T of the battery cell. v3 Then, the open circuit voltage value OCV5 of the OCV5 process section of the encapsulation is measured by the voltage internal resistance tester in the encapsulation equipment, and the test time is T v5 The calculation processing unit calculates the open circuit voltage value OCV5 and the test time T v5 , as well as the open circuit voltage value OCV3 and the test time T v3 Data, calculate the self-discharge rate factor K2 value of the battery cell in the OCV5 encapsulation process; Step S3: comparing the self-discharge rate factor K2 values ​​of the cells at different offline time periods, obtaining the standard value and upper and lower limits of the K2 value of the normal cells in a certain period of time according to the mathematical statistics method, and fitting the nonlinear binary regression equation K2=f(ΔT, t) of the K2 value and the offline time period ΔT and temperature t according to the K2 values ​​at different offline time periods ΔT and temperature t, and importing the equation into the computer operation unit, the theoretical value of K2 can be calculated according to the actual offline time and temperature; Step S4: When the battery cell enters the OCV5 encapsulation process section, the K2 theoretical value and the upper and lower limit ranges of the battery cell are calculated according to the nonlinear binary regression equation K2=f(ΔT, t) fitted in step S3, and then the K2 actual value of the battery cell is calculated according to the method described in step S2, and the calculation unit calculates the difference Δk2 between the K2 theoretical value and the K2 actual value; Step S5: The logic processing unit determines the relationship between Δk2 and the upper and lower limits of the theoretical value of K2. If Δk2 is within the upper and lower limits of the theoretical value of K2, the self-discharge of the battery cell is determined to be qualified. If Δk2 exceeds the upper and lower limits of the theoretical value of K2, the self-discharge of the battery cell is determined to be qualified. If the self-discharge of the battery cell is determined to be poor, it will be intelligently identified and discharged by the next process.

2. The method for intelligent screening of battery self-discharge according to claim 1, characterized in that: In step S1, the self-discharge rate factor K value of the battery cell in the finished product storage process section is calculated using the following formula: K=(OCV3-OCV4) / (T v4 -T v3 )。 3. The method for intelligent screening of battery self-discharge according to claim 1, characterized in that: The step S2 calculates the self-discharge rate factor K2 value of the battery cell in the OCV5 encapsulation process section, and the formula used is as follows: K2=(OCV3-OCV5) / (T V5 -T V3 )。 4. The method for intelligent screening of battery self-discharge according to claim 1, characterized in that: The mathematical statistics method adopted in step S3 is to count the K2 value data of the offline battery cells in a same time period, draw a normal distribution diagram of the K2 value, i.e., a Gaussian diagram, and the median of the Gaussian diagram of the K2 value in the time period is set as the K2 standard value in the time period. The upper and lower limits of the K2 range can be selected as ±3Σ values ​​of the median in the Gaussian diagram.

5. The method for intelligent screening of battery self-discharge according to claim 4, characterized in that: Determine the relationship between the difference Δk2 between the theoretical value of K2 and the actual value of K2 and the upper and lower limits of the theoretical value of K2, that is, to judge and compare the relationship between Δk2 and 3Σ. If Δk2≤3Σ, the self-discharge of the battery cell is qualified. If Δk2>3Σ, the self-discharge of the battery cell is poor and the next process will intelligently identify and discharge it.

6. The method for intelligent screening of battery self-discharge according to claim 1, characterized in that: The method for intelligent screening of battery cell self-discharge not only introduces the K2 value control factor in the battery cell encapsulation stage, but also can achieve self-discharge control in the entire process of battery cell module manufacturing and packaging.

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

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