Method for determining optimal vacuum pumping and breaking times in lithium ion battery formation process

Abstract

The invention belongs to the technical field of lithium batteries, and particularly relates to a method for determining the optimal vacuum pumping and breaking times in the formation process of a lithium ion battery, which comprises the following steps of: acquiring each capacity of a historical battery cell, the height size of the battery cell and the optimal vacuum pumping and breaking times in each formation stage under the same system; removing battery cell data without poor interfaces in the data; respectively establishing a regression formula 1 for the n1, the n2, the n3 and the n4 by using one-time linear regression through historical data; establishing a linear regression formula 2 according to the n1, the capacity Cn of the battery cell and the height dimension H through historical data; substituting the new-type capacity C of the same system and the height size H of the battery cell into a regression formula 2; and n1 is substituted into the regression formula 1. According to the method, the quantitative regression model between the physical parameters (capacity and height dimension) of the battery cell and the optimal vacuum pumping times of each stage is established, so that the optimal process parameters required by the new type of battery cell can be quickly and accurately predicted.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium batteries, and particularly relates to a method for determining the optimal number of times of vacuum breaking in the formation process of a lithium ion battery. BACKGROUND

[0002] In the production process of a lithium ion battery, formation is a crucial procedure. In this process, electrochemical reactions occur inside the battery and generate gas. If these gases cannot be promptly and sufficiently discharged, black spots and other defects will be formed at the electrode interface, which will seriously affect the final performance of the battery cell, such as cycle life and safety.

[0003] With the increasing demand for high-capacity batteries in the market, the capacity and size of battery cells are also showing a diversification trend. This leads to an increase in the amount of gas generated during the formation process, and the internal space, winding or lamination method of different specifications of battery cells differ, making it difficult for a unified exhaust process to adapt to all models, and the exhaust effect is uneven.

[0004] Currently, the industry generally uses the "vacuum breaking" process to solve the problem of formation gas exhaust, that is, periodically performing vacuum and vacuum breaking operations during the formation process to forcibly discharge the accumulated gas inside the battery. However, how to determine the optimal number of times of vacuum breaking for different battery cells at each formation stage is a key problem faced by current production processes. Existing solutions mainly rely on repeated "trial and error" experimental verification: by setting different gradients of the number of times of vacuum breaking, multiple formation experiments are performed, and then the battery is disassembled to check its interface condition, thereby selecting the optimal number of times. This method has significant drawbacks: multiple experiments and disassembly processes consume a lot of time and cannot quickly respond to the process development needs of new products. The experimental process consumes a large number of battery cell samples, and the disassembly behavior itself is destructive, causing waste of resources and costs. Too few times of vacuum breaking will result in insufficient exhaust and high risk of interface defects, while too many times will unnecessarily prolong the formation cycle and reduce the overall production efficiency of the production line. SUMMARY

[0005] The purpose of the present application is to provide a method for determining the optimal number of times of vacuum breaking in the formation process of a lithium ion battery that can be quickly and accurately determined without the need for a large number of destructive experiments, in order to overcome the above-mentioned deficiencies in the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] The present application provides a method for determining the optimal number of times of vacuum breaking in the formation process of a lithium ion battery, comprising the following steps:

[0008] The first step is to collect the capacity (Cn) of the same system, the height size H of the battery cell, and the best vacuum breaking times n1, n2, n3 and n4 of each stage of the formation;

[0009] The second step is to exclude the battery cell data that does not appear in the interface. After the exclusion, there are at least 5 groups of available data.

[0010] The third step is to use the historical data to establish a linear regression formula 1: n2=K1n1+b1; n3=K2n1+b2; n4=K3n1+b3, respectively, by using n1 and n2, n3, n4 once. 1， K 2， K3 is a coefficient, and b1, b2 and b3 are constants.

[0011] The fourth step is to establish a linear regression formula 2: n1=K4Cn+K5H+b4, by using the historical data to establish a linear regression formula 2: n1=K4Cn+K5H+b4, by using n1 and the capacity Cn and the height size H of the battery cell.

[0012] The fifth step is to substitute the capacity C and the height size H of the same system into the regression formula 2 n1=K4Cn+K5H+b4 to obtain n1.

[0013] The sixth step is to substitute n1 into the regression formula 1 n2=K1n1+b1; n3=K2n1+b2; n4=K3n1+b3 to obtain n2, n3 and n4. The integral value of n1, n2, n3 and n4 is the best vacuum breaking time of each stage of the target new model battery cell.

[0014] Preferably, the number of screened historical battery cell data groups for establishing the regression relationship is not less than 5 groups.

[0015] Preferably, the regression analysis adopts a linear regression method.

[0016] Preferably, in the target number calculation step, the predicted numbers n1, n2, n3 and n4 obtained by calculation are all subjected to rounding integral processing.

[0017] Preferably, after the target number calculation step, a verification step is further included: the integral value of n1, n2, n3 and n4 obtained by calculation is applied to the formation process of the target new model battery cell for actual verification, and the constants of the first linear relationship and / or the second linear relationship are modified according to the verification result.

[0018] Preferably, the formation process includes:

[0019] The first stage is to charge at a current of 0.03C, and during the charging, a first vacuum breaking sub-process is performed for n1 times and a second vacuum breaking sub-process is performed for n2 times.

[0020] The second stage, charging at 0.1C, during which the third vacuum breaking sub-process is performed for n3 times and the fourth vacuum breaking sub-process is performed for n4 times.

[0021] Preferably, the first vacuum breaking sub-process is: after charging for 20 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, followed by vacuum extraction to -85 Kpa and maintained for 10 seconds.

[0022] Preferably, the second vacuum breaking sub-process is: after charging for 30 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, followed by vacuum extraction to -85 Kpa and maintained for 10 seconds.

[0023] Preferably, the third vacuum breaking sub-process is: after charging for 30 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, followed by vacuum extraction to -85 Kpa and maintained for 10 seconds.

[0024] Preferably, the fourth vacuum breaking sub-process is: after charging for 60 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, followed by vacuum extraction to -85 Kpa and maintained for 10 seconds.

[0025] The beneficial effects of the present application are: the method can quickly and accurately predict the best process parameters required for new models of battery cells by establishing a quantitative regression model between the physical parameters (capacity, height size) of the battery cell and the best number of vacuum breaking in each stage, fundamentally avoiding the large number of repetitive experiments and battery disassembly tests required by the traditional "trial and error method", significantly shortening the process development cycle, reducing the research and development cost and material waste; at the same time, by ensuring that each vacuum breaking is necessary and effective, the production efficiency of the production line is maximized on the premise of ensuring the quality of the formation interface (avoiding black spots due to insufficient exhaust), realizing the synergistic optimization of quality and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of the present application;

[0027] Figure 2 is a schematic diagram of the abnormal interface (black spots) of the battery cell after formation using a non-optimal number of vacuum breaking (according to the comparative example);

[0028] Figure 3 is a schematic diagram of the good interface state of the battery cell after formation using the optimal number of vacuum breaking determined by the method of the present application. DETAILED DESCRIPTION

[0029] The technical solutions of the present application will be described in detail below in conjunction with the drawings and examples. It should be noted that the following examples are only used to explain the present application and do not constitute a limitation on the scope of protection of the present application.

[0030] The application provides a method for determining the optimal number of times of vacuum breaking in the formation process of a lithium ion battery. The core of the method is that, based on the success data of historical models under the same battery system, a quantitative relationship model between the physical parameters (capacity, height) of the battery cell and the optimal number of times of vacuum breaking at each stage is established through regression analysis, so that the optimal process parameters of the new model battery cell are quickly and accurately predicted.

[0031] Embodiment

[0032] The application is described by taking a specific implementation process as an example:

[0033] Step 1: Data collection and screening

[0034] Firstly, the key data of 5 historical models of battery cells which have been successfully mass-produced under the same battery system (such as lithium iron phosphate-graphite system) are collected. These data include the capacity (Cn, unit: Ah) of the battery cell, the height (H, unit: mm) of the battery cell, and the optimal number of times of vacuum breaking at each stage (n1, n2, n3, n4) which are determined through a large number of experiments and can ensure good interface.

[0035] In order to ensure the quality of the data used for modeling, the data corresponding to the battery cells which have poor interface (such as black spots found after disassembly) in the historical verification process must be excluded. Finally, 5 sets of valid data are obtained, as shown in the following table:

[0036]

[0037] Step 2: Establishing the internal number relationship model

[0038] Taking n1 as the independent variable and n2, n3 and n4 as the dependent variables, a linear regression analysis is performed on the above 5 sets of data.

[0039] The relationship between n1 and n2 is established as follows: n2=K1n1+b1

[0040] The relationship between n1 and n3 is established as follows: n3=K2n1+b2

[0041] The relationship between n1 and n4 is established as follows: n4=K3n1+b3

[0042] Through calculation, the specific regression formula (coefficient and constant) is obtained as follows:

[0043] n2=0.5×n1 (i.e. K1=0.5, b1=0)

[0044] n3=0.7727×n1+0.4545 (i.e. K2=0.7727, b2=0.4545)

[0045] n4 = 0.3523 x n1 + 0.5455 (i.e. K3 = 0.3523, b3 = 0.5455)

[0046] This set of equations, namely Equation 1, reveals the internal proportion and correlation between the optimal number of times in each stage. Step 3: Establishing the relationship model between physical parameters and the basic number of times

[0047] Using the same 5 sets of data, we conduct multiple linear regression analysis with the cell capacity Cn and height dimension H as independent variables and the basic number of times n1 as the dependent variable.

[0048] Establish the relationship between Cn, H and n1: n1 = K4 x Cn + K5 x H + b4

[0049] Through calculation, we obtain the specific regression equation:

[0050] n1 = 0.014782 x Cn + 0.02032 x H - 0.467 (i.e. K4 = 0.014782, K5 = 0.02032, b4 = -0.467)

[0051] This equation, namely Equation 2, links the physical characteristics of the cell to the basic parameters of the process.

[0052] Step 4: Predicting the optimal number of times for a new model of cell

[0053] We now have a new model of cell with a capacity of C = 324 Ah and a height dimension of H = 207 mm. We need to determine the optimal number of times for each stage of formation.

[0054] Substitute C = 324 and H = 207 into Equation 2:

[0055] n1 = 0.014782 x 324 + 0.02032 x 207 - 0.467

[0056] Calculate n1 ≈ 7.78

[0057] Round the result (in this case, we use rounding) to obtain n1 = 8 times.

[0058] Substitute n1 = 8 into Equation 1:

[0059] N2 = 0.5 x 8 = 4 times

[0060] N3 = 0.7727 x 8 + 0.4545 ≈ 6.636, rounded to N3 = 7 times

[0061] N4 = 0.3523 x 8 + 0.5455 ≈ 3.364, rounded to N4 = 3 times

[0062] Therefore, the best combination of vacuum breaking times of the new type of battery cell is (8, 4, 7, 3).

[0063] Fifth step: verification and effect

[0064] The above prediction results are put into actual production for verification. Formation is carried out according to the following process:

[0065] First stage: charging at 0.03C current. After 20 minutes of charging, the cycle of "breaking vacuum to normal pressure for 10s, and then vacuum extraction to-85Kpa for 10s" is performed for a total of 8 times; then continue to charge for 30 minutes, and perform the same vacuum breaking cycle for a total of 4 times.

[0066] Second stage: charging at 0.1C current. After 30 minutes of charging, the vacuum breaking cycle is performed for a total of 7 times; then continue to charge for 60 minutes, and perform the vacuum breaking cycle for a total of 3 times.

[0067] After the formation is completed, the battery cell is disassembled to check the interface, and it is found that the interface is good, and there is no abnormality such as black spot (as shown in Figure 3 ). As a comparison, if the combination of times (7, 3, 7, 3) is used, interface abnormalities caused by insufficient exhaust (as shown in Figure 2 ) occur. The results show that the method provided by the present application can accurately and efficiently determine the best process parameters, while ensuring product quality, and avoiding resource waste and production efficiency loss caused by repeated trial and error.

[0068] The above description is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical range disclosed by the present application can be easily thought of by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1. A method for determining the optimal number of pump-down times in a lithium-ion battery formation process, the method comprising: The method comprises the following steps: ​ The first step is to collect the capacity Cn, the height size H of the battery cell, and the optimal vacuum breaking times n1, n2, n3 and n4 of each stage of the formation of the battery cell in the same system; The second step is to remove the battery cell data that does not appear in the interface of the data, and the minimum number of data after removal is 5 groups; Third step, through historical data to establish regression formula 1: n2=K1n1+b1; n3=K2n1+b2; n4=K3n1+b3, respectively, by using linear regression of n1 and n2, n3, n4, wherein K 1， K 2， K3 is the coefficient, b1, b2, b3 is a constant; The fourth step is to establish a linear regression formula 2: n1=K4Cn+K5H+b4 by the historical data, wherein K4, K5 and K6 are coefficients, and b4 is a constant; The fifth step is to substitute the capacity C and the height size H of the new model in the same system into the regression formula 2 n1=K4Cn+K5H+b4 to obtain n1; The sixth step is to substitute n1 into the regression formula 1 n2=K1n1+b1; n3=K2n1+b2; n4=K3n1+b3 to obtain n2, n3 and n4; wherein the integral value of n1, n2, n3 and n4 is the optimal vacuum breaking times of each stage of the target new model battery cell.

2. The method of claim 1, wherein, The number of screened historical battery cell data groups for establishing the regression relationship is not less than 5 groups.

3. The method of claim 1, wherein, The regression analysis adopts a linear regression method.

4. The method of claim 1, wherein, In the target number calculation step, the predicted numbers n1, n2, n3 and n4 obtained by calculation are all subjected to rounding integral processing.

5. The method of claim 1, wherein, After the target number calculation step, a verification step is further included: the integral value of n1, n2, n3 and n4 obtained by calculation is applied to the formation process of the target new model battery cell for actual verification, and the constant of the first linear relationship and / or the second linear relationship is modified according to the verification result.

6. The method of claim 1, wherein, The formation process comprises: In the first stage, the battery cell is charged at a current of 0.03C, and the first vacuum breaking sub-process is performed for n1 times and the second vacuum breaking sub-process is performed for n2 times; In the second stage, the battery cell is charged at a current of 0.1C, and the third vacuum breaking sub-process is performed for n3 times and the fourth vacuum breaking sub-process is performed for n4 times.

7. The method of claim 6, wherein, The first vacuum breaking sub-process is: after charging for 20 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, and then vacuum extraction to-85Kpa is performed and maintained for 10 seconds.

8. The method of claim 6, wherein, The second vacuum breaking sub-process is: after charging for 30 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, and then vacuum extraction to-85Kpa is performed and maintained for 10 seconds.

9. The method of claim 6, wherein, The third vacuum breaking sub-process is: after charging for 30 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, and then vacuum extraction to-85Kpa is performed and maintained for 10 seconds.

10. The method of claim 6, wherein, The fourth vacuum breaking sub-process is: after charging for 60 minutes, vacuum breaking to normal pressure is performed and maintained for 10 seconds, and then vacuum extraction to-85Kpa is performed and maintained for 10 seconds.

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