Method for predicting gas generation capacity of lithium-ion battery, computer-readable storage medium

Abstract

The application discloses a method for predicting the gas production capacity of a lithium ion battery, and a computer readable storage medium. The lithium ion battery comprises an electrolyte, and the electrolyte comprises a first type of cyclic carbonate and a second type of cyclic carbonate. The method comprises the following steps: (1) obtaining a first mapping relationship between the mass concentration of the first type of cyclic carbonate and the corresponding first gas production, and a second mapping relationship between the mass concentration of the second type of cyclic carbonate and the corresponding gas production; (2) calculating a third mapping relationship between the mass concentration of the first type of cyclic carbonate, the mass concentration of the second type of cyclic carbonate and the corresponding total gas production; and (3) obtaining the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, and calling the third mapping relationship to process the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, so as to predict the corresponding gas production capacity of the electrolyte to be predicted.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, specifically relating to a method for predicting the gas production capacity of a lithium-ion battery and a computer-readable storage medium. Background Technology

[0002] Lithium-ion batteries consist of a positive electrode containing positive active materials, a negative electrode containing negative active materials, an electrolyte, and a casing. The composition of the electrolyte has a certain impact on film formation, cell rate capability, lifespan, and gas production during the cell formation stage. To assess the impact of electrolyte composition on cell gas production capacity, current techniques involve testing gas production after mass electrolyte injection into cells, which is resource-intensive and time-consuming.

[0003] In addition, the characteristics of the negative electrode material also have a certain impact on the gas production of the battery cell. However, there is currently no effective means to assess the impact of the characteristics of the negative electrode material on the gas production capacity of the battery cell. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, one objective of this invention is to propose a method for predicting the gas production capacity of lithium-ion batteries. This method is a prediction method for the influence of electrolyte composition on gas production under a standardized main material system. This prediction method only requires obtaining the mass concentrations of the first and second types of cyclic carbonates in the electrolyte to be predicted, and then processing these mass concentrations using a third mapping relationship to predict the gas production capacity of the electrolyte. This eliminates the need for long-term verification, allowing for preliminary screening of whether the electrolyte composition meets requirements, significantly shortening the prediction cycle and saving costs.

[0005] The second objective of this invention is to provide a computer-readable storage medium.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the gas production capacity of a lithium-ion battery. According to an embodiment of the present invention, the lithium-ion battery includes an electrolyte, the electrolyte comprising a first type of cyclic carbonate and a second type of cyclic carbonate, and the method includes:

[0007] (1) Assuming that all other conditions except the mass concentration of the first type of cyclic carbonate in the electrolyte remain unchanged, obtain a mass concentration sample of the first type of cyclic carbonate in the electrolyte and a corresponding first gas production sample, and fit the first gas production of the electrolyte with the change of the mass concentration of the first type of cyclic carbonate in the electrolyte to obtain a first mapping relationship between the mass concentration of the first type of cyclic carbonate and the corresponding first gas production.

[0008] Assuming that all other conditions remain unchanged except for the mass concentration of the second type of cyclic carbonate in the electrolyte, obtain a mass concentration sample of the second type of cyclic carbonate in the electrolyte and a corresponding second gas production sample. Fit the second gas production of the electrolyte to the change of the mass concentration of the second type of cyclic carbonate in the electrolyte to obtain a second mapping relationship between the mass concentration of the second type of cyclic carbonate and the corresponding gas production.

[0009] (2) Based on the first mapping relationship and the second mapping relationship, calculate the third mapping relationship between the mass concentration of the first type of cyclic carbonate, the mass concentration of the second type of cyclic carbonate and the corresponding total gas production.

[0010] (3) Obtain the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, and call the third mapping relationship to process the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, so as to predict the gas production capacity corresponding to the electrolyte to be predicted.

[0011] The method for predicting the gas production capacity of lithium-ion batteries according to embodiments of the present invention is a method for predicting the influence of electrolyte composition on gas production under a standardized main material system. This prediction method only needs to obtain the mass concentrations of the first type of cyclic carbonate and the second type of cyclic carbonate in the electrolyte to be predicted, and then process the mass concentrations of the first type of cyclic carbonate and the second type of cyclic carbonate in the electrolyte to be predicted by calling a third mapping relationship, so as to predict the gas production capacity corresponding to the electrolyte to be predicted. It does not require long-term verification and can preliminarily screen whether the composition of the electrolyte meets the requirements, which greatly shortens the prediction cycle and saves costs.

[0012] In addition, the method for predicting the gas production capacity of a lithium-ion battery according to the above embodiments of the present invention may also have the following additional technical features:

[0013] In some embodiments of the present invention, the first mapping relationship is X1 = 124.15a. 2-53.002a+8.691, where X1 is the first gas production rate of the first type of cyclic carbonate, a is the mass concentration of the first type of cyclic carbonate, and the derivative of it is x1=248a-53.002, where x1 represents the gas production rate corresponding to the first type of cyclic carbonate.

[0014] In some embodiments of the present invention, the second mapping relationship is X2 = 886.56b. 2 -54.274b+3.2944, where X2 is the second gas production rate of the second type of cyclic carbonate, b is the mass concentration of the second type of cyclic carbonate, and the derivative is x2 = 1.773.12b-54.274, where x2 represents the gas production rate corresponding to the second type of cyclic carbonate.

[0015] In some embodiments of the present invention, in step (2), the third mapping relationship is X = γX1 + βX2, where X represents the total gas production capacity of the electrolyte, γ represents the influence factor of the first type of cyclic carbonate on the gas production capacity, and γ = x1 / (x1 + x2) = (248a - 53.002) / {(248a - 53.002) + (1.773.12b - 54.274)};

[0016] β represents the influence factor of the second type of cyclic carbonate on gas production capacity, β=x2 / (x1+x2)=(1.773.12b-54.274) / {(248a-53.002)+(1.773.12b-54.274)}.

[0017] In some embodiments of the present invention, the first type of cyclic carbonate is selected from at least one of ethylene carbonate, propylene carbonate and butene carbonate; and / or, the second type of cyclic carbonate is selected from at least one of vinylene carbonate and fluorocyclic carbonate.

[0018] In some embodiments of the present invention, the lithium-ion battery further includes a negative electrode sheet containing a negative electrode active material, and the method further includes:

[0019] (a) Assuming that other conditions of the lithium-ion battery remain unchanged, obtain the specific surface area B sample, particle size D sample, graphitization degree C sample and corresponding gas production sample of the negative electrode active material, calculate the change of the negative electrode gas production with the numerical values ​​of the specific surface area B, particle size D and graphitization degree C of the negative electrode active material, and obtain the fourth mapping relationship between the specific surface area B sample, particle size D sample, graphitization degree C sample and corresponding gas production of the negative electrode active material.

[0020] (b) Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode active material to be predicted, and call the fourth mapping relationship to process the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted, so as to predict whether the gas production of the negative electrode to be predicted meets the requirements.

[0021] In some embodiments of the present invention, in step (a), the calculation of the changes in the negative electrode gas production with respect to the specific surface area B, particle size D, and graphitization degree C of the negative electrode active material, to obtain a fourth mapping relationship between the specific surface area B sample, particle size D sample, graphitization degree C sample of the negative electrode active material, and the corresponding gas production includes:

[0022] Let Y = {(D / B) + C}, where B ranges from 1 to 2m. 2 / g, D value ranges from 10-20nm, C value ranges from 90%-95%, and Y value is calculated for the first time based on the ranges of B, D and C;

[0023] The Y value obtained from the first calculation is correlated with the negative electrode gas production. By limiting the negative electrode gas production within a set range, the Y value is thus limited to a certain range.

[0024] In some embodiments of the present invention, step (b) includes:

[0025] Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted;

[0026] The Y value is calculated a second time based on Y = {(D / B) + C}. It is then determined whether the calculated Y value is within the specified range. If it is within the specified range, the negative electrode with specific surface area B1, particle size D1, and graphitization degree C1 meets the requirements; otherwise, it does not meet the requirements.

[0027] In some embodiments of the present invention, the negative electrode active material is selected from at least one of graphite and mesophase carbon microspheres.

[0028] In another aspect, the present invention provides a computer-readable storage medium, according to an embodiment of the invention, on which a program for predicting the gas production capacity of a lithium-ion battery is stored, wherein when the program for predicting the gas production capacity of a lithium-ion battery is executed by a processor, the program for predicting the gas production capacity of a lithium-ion battery implements the method for predicting the gas production capacity of a lithium-ion battery as described above.

[0029] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0030] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0031] Figure 1 This is a flowchart illustrating a method for predicting the gas production capacity of a lithium-ion battery according to an embodiment of the present invention. Detailed Implementation

[0032] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0033] In one aspect, the present invention provides a method for predicting the gas production capacity of a lithium-ion battery. According to an embodiment of the present invention, the lithium-ion battery includes an electrolyte comprising a first type of cyclic carbonate and a second type of cyclic carbonate, wherein the first type of cyclic carbonate is used as a solvent in the electrolyte, and the second type of cyclic carbonate is used as an additive in the electrolyte. (See attached diagram) Figure 1 The methods for predicting the gas production capacity of lithium-ion batteries include:

[0034] S100: Obtain the first mapping relationship between the mass concentration of the first type of cyclic carbonate and the corresponding first gas production, and the second mapping relationship between the mass concentration of the second type of cyclic carbonate and the corresponding gas production.

[0035] Specifically, S100 includes steps S110 and S120:

[0036] Wherein, S110: Assuming that all other conditions remain unchanged except for the mass concentration of the first type of cyclic carbonate in the electrolyte, obtain a mass concentration sample of the first type of cyclic carbonate in the electrolyte and a corresponding first gas production sample, fit the first gas production of the electrolyte with the change of the mass concentration of the first type of cyclic carbonate in the electrolyte, and obtain the first mapping relationship between the mass concentration of the first type of cyclic carbonate and the corresponding first gas production.

[0037] In this step, all conditions except the mass concentration of the first type of cyclic carbonate remained constant, making the mass concentration of the first type of cyclic carbonate the only variable. For example, without adding the second type of cyclic carbonate, only the content of the first type of cyclic carbonate was changed to verify the effect of the change in the content of the first type of cyclic carbonate on the first gas production of the electrolyte. The verification data are shown in Table 1, where a represents the mass concentration of the first type of cyclic carbonate (ethylene carbonate) in the electrolyte, and b represents the mass concentration of the second type of cyclic carbonate in the electrolyte.

[0038] Table 1

[0039]

[0040]

[0041] The variation of the first gas production rate X1 (mL) of the electrolyte in Table 1 with the mass concentration a (wt%) of the first type of cyclic carbonate in the electrolyte was fitted. The resulting curve function X1(a) showing the variation of the first gas production rate X1 (mL) with the mass concentration a of the first type of cyclic carbonate in the electrolyte is a linear function with two variables, X1 = 124.15a. 2 -53.002a+8.691, where a is the mass concentration of the first type of cyclic carbonate. Differentiating this, we get x1 = 248a - 53.002, where x1 represents the gas production rate corresponding to the first type of cyclic carbonate. It should be noted that the fitting process of the curve function X1(a) showing the change in the first gas production rate X1(a) with the mass concentration a of the first type of cyclic carbonate in the electrolyte is a conventional technique in this field and will not be elaborated upon here.

[0042] S120: Assuming that all other conditions remain unchanged except for the mass concentration of the second type of cyclic carbonate in the electrolyte, obtain a mass concentration sample of the second type of cyclic carbonate in the electrolyte and a corresponding second gas production sample. Fit the second gas production of the electrolyte with the change of the mass concentration of the second type of cyclic carbonate in the electrolyte to obtain a second mapping relationship between the mass concentration of the second type of cyclic carbonate and the corresponding gas production.

[0043] In this step, all conditions except the mass concentration of the second type of cyclic carbonate remain unchanged. The mass concentration of the second type of cyclic carbonate is the only variable. For example, under the condition that the content of the first type of cyclic carbonate is constant, only the content of the second type of cyclic carbonate is changed to verify the effect of the change in the content of the second type of cyclic carbonate on the second gas production of the electrolyte. The verification data are shown in Table 2, where a represents the mass concentration of the first type of cyclic carbonate (ethylene carbonate) in the electrolyte, and b represents the mass concentration of the second type of cyclic carbonate (ethylene carbonate) in the electrolyte.

[0044] Table 2

[0045] a(wt%) b(wt%) <![CDATA[Gas production X2 (mL)]]> 20％ 0％ 3.312 20％ 1％ 2.838 20％ 2％ 2.475 20％ 3％ 2.523 20％ 4％ 2.634 20％ 5％ 2.712 20％ 8％ 4.634

[0046] The variation of the second gas production rate X2 (mL) of the electrolyte in Table 2 with the mass concentration b (wt%) of the second type of cyclic carbonate in the electrolyte was fitted. The resulting curve function X2(b) showing the variation of the second gas production rate X2 (mL) with the mass concentration b of the second type of cyclic carbonate in the electrolyte is a linear function with two variables: X2 = 886.56b. 2-54.274b+3.2944, where b is the mass concentration of the second type of cyclic carbonate. Differentiating this, we get x2 = 1.773.12b - 54.274, where x2 represents the gas production rate corresponding to the second type of cyclic carbonate. It should be noted that the fitting process of the curve function X2(b) showing the change in the second gas production rate X2(b) with the mass concentration b of the second type of cyclic carbonate in the electrolyte is a conventional technique in this field and will not be elaborated upon here.

[0047] It should be noted that the specific order of S110 and S120 is not particularly limited, and those skilled in the art can choose flexibly according to actual needs. Furthermore, in addition to the first type of cyclic carbonates, other types of solvents may be included in the electrolyte; in addition to the second type of cyclic carbonates, other types of additives may also be included.

[0048] In embodiments of the present invention, the specific type of the first type of cyclic carbonate is not particularly limited. As some preferred embodiments, the first type of cyclic carbonate is selected from at least one of ethylene carbonate, propylene carbonate, and butene carbonate. Similarly, the specific type of the second type of cyclic carbonate is not particularly limited. As some preferred embodiments, the second type of cyclic carbonate is selected from at least one of vinylene carbonate and fluorocyclic carbonate.

[0049] S200: Based on the first and second mapping relationships, the third mapping relationship between the mass concentration of the first type of cyclic carbonate, the mass concentration of the second type of cyclic carbonate, and the corresponding total gas production is calculated.

[0050] Specifically, the third mapping relationship is X = γX1 + βX2, where X represents the total gas production capacity of the electrolyte, γ represents the influence factor of the first type of cyclic carbonate on the gas production capacity, and γ = x1 / (x1 + x2) = (248a - 53.002) / {(248a - 53.002) + (1.773.12b - 54.274)};

[0051] β represents the influence factor of the second type of cyclic carbonate on gas production capacity, β=x2 / (x1+x2)=(1.773.12b-54.274) / {(248a-53.002)+(1.773.12b-54.274)}.

[0052] Then, X1 = 124.15a 2 -53.002a+8.691, X2=886.56b 2Substituting -54.274b+3.2944, γ=(248a-53.002) / {(248a-53.002)+(1.773.12b-54.274)}, and β=(1.773.12b-54.274) / {(248a-53.002)+(1.773.12b-54.274)} into the formula X=γX1+βX2, we obtain the total gas production capacity X of the electrolyte as a function of the mass concentration a of the first type of cyclic carbonate and the mass concentration b of the second type of cyclic carbonate.

[0053] S300: Obtain the mass concentrations of the first type of cyclic carbonate and the second type of cyclic carbonate in the electrolyte to be predicted, and call the third mapping relationship to process the mass concentrations of the first type of cyclic carbonate and the second type of cyclic carbonate in the electrolyte to be predicted, so as to predict the gas production capacity corresponding to the electrolyte to be predicted.

[0054] Specifically, the mass concentrations a1 of the first type of cyclic carbonate (e.g., ethylene carbonate) and b1 of the second type of cyclic carbonate (e.g., vinylene carbonate) in the electrolyte to be predicted are obtained. Then, the mass concentrations a1 and b1 of the first type of cyclic carbonate in the electrolyte are substituted into the third mapping relationship X = γX1 + βX2, respectively. The gas generation capacity corresponding to the mass concentrations a1 and b1 of the first type of cyclic carbonate in the electrolyte is calculated. This determines whether the composition of the electrolyte to be predicted meets the requirements of lithium-ion batteries for electrolytes, as shown in Table 3.

[0055] Table 3

[0056]

[0057]

[0058] Based on the actual electrolyte formulation design, to meet the requirements of electrolyte conductivity within the range of 8-13 mS / cm and no loss of function at a low temperature of -20℃, the value of 'a' is set to 10% < a < 35%, and the value of 'b' is set to 2% < b < 8%. As can be seen from Table 3, when the value of 'a' is 10% < a < 35% and the value of 'b' is 2% < b < 8%, the value of 'X' is within the range of 1.25 < X < 4.5. Therefore, only four rows of data in Table 3 meet all the requirements for the electrolyte.

[0059] It should be noted that the data in Tables 1, 2, and 3 were obtained under the condition that all other factors were the same. Specifically, the carbon negative electrode was graphite, the degree of graphitization was 95%, and the specific surface area of ​​graphite was 1.2 m². 2 / g, the D50 particle size of graphite is 18μm; the mass ratio of negative electrode active material in the negative electrode active material layer is 95%, and the compaction density is 1.4g / cm³. 3 The negative electrode sheet has a width of 165 mm. The positive electrode active material is lithium iron phosphate, and the mass percentage of the positive electrode active material in the positive electrode active material layer is 97.5%, with a compaction density of 2.45 g / cm³. 3 The positive electrode width is 155mm. The lithium salt in the electrolyte is 1M LiPF6. Gas production in the battery cell is caused by multiple factors. Under the condition of keeping the lithium salt and additive system unchanged except for cyclic carbonate additives, the electrolyte is prepared according to the difference in cyclic carbonate content. The electrolyte is injected into a carbon negative electrode pouch with a certain system. After standing at 45℃ for 24 hours, the pouch battery cell is tested for gas production using Yuaneng in-situ gas production equipment to screen out electrolytes with low gas production.

[0060] According to further embodiments of the present invention, the lithium-ion battery further includes a negative electrode sheet containing a negative electrode active material. Under the same conditions, the step of determining whether the negative electrode gas production meets the requirements includes:

[0061] (a) Assuming other conditions of the lithium-ion battery remain unchanged, obtain samples of the specific surface area B, particle size D, graphitization degree C, and corresponding gas production of the negative electrode active material. Calculate the changes in the negative electrode gas production with the numerical values ​​of the specific surface area B, particle size D, and graphitization degree C of the negative electrode active material to obtain the fourth mapping relationship between the specific surface area B, particle size D, graphitization degree C, and corresponding gas production of the negative electrode active material.

[0062] In this step, the factors affecting the gas production of the negative electrode include the specific surface area B, particle size D, and degree of graphitization C of the negative electrode active material (usually a constant value). Under the same degree of graphitization, the particle size D and specific surface area B show opposite trends. Therefore, when the particle size D increases, the specific surface area B tends to decrease, and when the particle size D decreases, the specific surface area B tends to increase.

[0063] Specifically, the changes in negative electrode gas production with the specific surface area B, particle size D, and graphitization degree C of the negative electrode active material are calculated, resulting in a fourth mapping relationship between the specific surface area B sample, particle size D sample, graphitization degree C sample of the negative electrode active material, and the corresponding gas production.

[0064] Let Y = {(D / B) + C}. In lithium-ion batteries, the value of B typically ranges from 1 to 2m. 2 / g, the D value range is generally 10-20nm, and the C value range is generally 90%-95%. According to the requirements of lithium-ion batteries for the range of B, D and C values, substitute them into the formula Y={(D / B)+C} to obtain the Y values ​​for the first calculation; Y represents a parameter value that describes the overall carbon anode material in terms of multiple factors.

[0065] The Y values ​​obtained from the first calculation are correlated with the negative electrode gas production. By limiting the negative electrode gas production within a set range, the Y values ​​are thus limited to a certain range.

[0066] For example, in Table 4, based on the requirements for the range of values ​​for B, D, and C in lithium-ion batteries, substituting these values ​​into the formula Y = {(D / B) + C}, the first calculation yields the values ​​of each Y. By setting a range for the negative electrode gas production that meets the gas production requirements, for example, limiting the gas production to the range of 2.2-2.55, the corresponding range of Y values ​​is 7.06-15.95. Thus, the various properties of the negative electrode material corresponding to Y values ​​within the range of 7.06-15.95 meet the requirements for negative electrode gas production, that is, under the premise that the degree of graphitization C is 95%, the specific surface area B is 1.2-1.8 m². 2 For a particle size of 11-18 nm (g / g), the anode material meets the requirements for anode gas production. It is understandable that when the degree of graphitization C is a different value, the prediction method is the same as in Table 4, and will not be repeated here.

[0067] Table 4

[0068] Graphitization degree <![CDATA[Specific surface area B (m 2 / g)]]> D50 particle size (μm) Y value Gas production (mL) 95％ 2.1 8 4.76 3.433 95％ 2 10 5.95 2.942 95％ 1.8 11 7.06 2.314 95％ 1.4 15 11.66 2.412 95％ 1.2 18 15.95 2.545 95％ 1 20 20.95 3.562 95％ 0.9 21 24.28 12.12

[0069] The values ​​in Table 4 were obtained under the following electrolyte conditions: the lithium salt in the electrolyte was 1M LiPF6; the solvent in the electrolyte included dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), and ethylene carbonate (EC); the mass ratio of DMC, EMC, and EC was 1:1:1; the additive in the electrolyte was 3wt% vinylene carbonate (VC); and all other conditions were the same as those used to obtain the data in Table 3.

[0070] (b) Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode active material to be predicted, and call the fourth mapping relationship to process the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted in order to predict whether the gas production of the negative electrode to be predicted meets the requirements.

[0071] Specifically, step (b) includes:

[0072] Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted;

[0073] The Y value is calculated a second time using the formula Y = {(D / B) + C}. It is then determined whether the calculated Y value falls within the specified range. If it does, the negative electrode with specific surface area B1, particle size D1, and graphitization degree C1 meets the requirements; otherwise, it does not. For example, as shown in Table 4, when the graphitization degree C1 is 95%, the specific surface area B1, particle size D1, and graphitization degree C1 are substituted into the formula Y = {(D / B) + C} to calculate the Y value. If the Y value is within the range of 7.06-15.95, the negative electrode with specific surface area B1, particle size D1, and graphitization degree C1 meets the requirements; otherwise, it does not.

[0074] In the embodiments of the present invention, the specific type of negative electrode active material is not particularly limited. As some preferred options, the negative electrode active material is selected from at least one of graphite and mesophase carbon microspheres.

[0075] In embodiments of the present invention, other conditions for the method of predicting the gas production capacity of a lithium-ion battery are not particularly limited. As some specific examples, the positive electrode active material may include at least one of lithium iron phosphate, lithium iron manganese, and lithium manganese oxide. The mass percentage of the positive electrode active material in the positive electrode active material layer is 96-99%, and the compaction density is 2.2-2.7 g / cm³. 3 The width of the positive electrode sheet is 85mm-225mm. As specific examples, the mass percentage of the negative electrode active material in the negative electrode active material layer is 92-98%, and the compaction density is 1.2-1.6 g / cm³. 3 The width of the negative electrode sheet is 100mm-230mm. As some specific examples, the lithium salt in the electrolyte includes at least one of LiPF6, LiFSI, LiClO4, LIODFB, LIBOB, LIPF2O2, and LIBF4. As some specific examples, the electrolyte also includes at least one of other additives such as VC, FEC, PS, phosphate esters, phosphites, sulfate esters, sulfites, ethers, and acid anhydrides.

[0076] Therefore, firstly, this method is a prediction method for the influence of electrolyte composition on gas production under a standardized main material system. This prediction method only requires obtaining the mass concentrations of the first and second types of cyclic carbonates in the electrolyte to be predicted, and then processing the mass concentrations of the first and second types of cyclic carbonates in the electrolyte to be predicted using a third mapping relationship to predict the gas production capacity of the electrolyte to be predicted. It does not require long-term verification and can preliminarily screen whether the composition of the electrolyte meets the requirements, greatly shortening the prediction cycle and saving costs. Secondly, this invention can establish a pre-screening of low-gas-producing carbon anodes. It only requires obtaining the specific surface area B1, particle size D1, and graphitization degree C1 of the anode active material to be predicted, and then processing the specific surface area B1, particle size D1, and graphitization degree C1 of the anode to be predicted using a fourth mapping relationship to predict whether the gas production of the anode to be predicted meets the requirements, greatly shortening the prediction cycle and saving costs. In summary, this invention can make preliminary predictions about gas generation in battery cells from both the electrolyte and carbon anode directions.

[0077] In another aspect, the present invention provides a computer-readable storage medium, according to an embodiment of the invention, on which a program for predicting the gas production capacity of a lithium-ion battery is stored, wherein when the program for predicting the gas production capacity of a lithium-ion battery is executed by a processor, the program for predicting the gas production capacity of a lithium-ion battery implements the method for predicting the gas production capacity of a lithium-ion battery as described above.

[0078] It should be noted that the logic and / or steps described in the embodiments of the present invention, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-based system, or other system that can fetch and execute instructions from, or an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0079] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0080] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the gas production capacity of a lithium-ion battery, characterized in that, The lithium-ion battery includes an electrolyte, the electrolyte comprising a first type of cyclic carbonate and a second type of cyclic carbonate, and the method includes: (1) Assuming that all other conditions except the mass concentration of the first type of cyclic carbonate in the electrolyte remain unchanged, obtain a mass concentration sample of the first type of cyclic carbonate in the electrolyte and a corresponding first gas production sample, and fit the first gas production of the electrolyte with the change of the mass concentration of the first type of cyclic carbonate in the electrolyte to obtain a first mapping relationship between the mass concentration of the first type of cyclic carbonate and the corresponding first gas production. Assuming that all other conditions remain unchanged except for the mass concentration of the second type of cyclic carbonate in the electrolyte, obtain a mass concentration sample of the second type of cyclic carbonate in the electrolyte and a corresponding second gas production sample. Fit the second gas production of the electrolyte to the change of the mass concentration of the second type of cyclic carbonate in the electrolyte to obtain a second mapping relationship between the mass concentration of the second type of cyclic carbonate and the corresponding gas production. (2) Based on the first mapping relationship and the second mapping relationship, calculate the third mapping relationship between the mass concentration of the first type of cyclic carbonate, the mass concentration of the second type of cyclic carbonate and the corresponding total gas production; (3) Obtain the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, and call the third mapping relationship to process the mass concentration of the first type of cyclic carbonate and the mass concentration of the second type of cyclic carbonate in the electrolyte to be predicted, so as to predict the gas production capacity corresponding to the electrolyte to be predicted.

2. The method according to claim 1, characterized in that, The first mapping relationship is X1=124.15a 2 -53.002a + 8.691, where X1 is the first gas production rate of the first type of cyclic carbonate, a is the mass concentration of the first type of cyclic carbonate, and the derivative of it is x1=248a-53.002, where x1 represents the gas production rate corresponding to the first type of cyclic carbonate.

3. The method according to claim 2, characterized in that, The second mapping relationship is X2 = 886.56b 2 -54.274b + 3.2944, where X2 is the second gas production rate of the second type of cyclic carbonate, b is the mass concentration of the second type of cyclic carbonate, and the derivative is x2 = 1.773.12b - 54.274, where x2 represents the gas production rate corresponding to the second type of cyclic carbonate.

4. The method according to claim 3, characterized in that, In step (2), the third mapping relationship is X = γX1 + βX2, where X represents the total gas production capacity of the electrolyte, γ represents the influence factor of the first type of cyclic carbonate on the gas production capacity, and γ = x1 / (x1 + x2) = (248a - 53.002) / {(248a - 53.002) + (1.773.12b - 54.274)}; β represents the influence factor of the second type of cyclic carbonate on gas production capacity, β=x2 / (x1+x2)=(1.773.12b-54.274) / {(248a-53.002)+(1.773.12b-54.274)}.

5. The method according to claim 1, characterized in that, The first type of cyclic carbonate is selected from at least one of ethylene carbonate, propylene carbonate, and butene carbonate; And / or, the second type of cyclic carbonate is selected from at least one of vinylene carbonate and fluorocyclic carbonate.

6. The method according to any one of claims 1-5, characterized in that, The lithium-ion battery further includes a negative electrode sheet, the negative electrode sheet containing a negative electrode active material, and the method further includes: (a) Assuming that other conditions of the lithium-ion battery remain unchanged, obtain the specific surface area B sample, particle size D sample, graphitization degree C sample and corresponding gas production sample of the negative electrode active material, calculate the change of the negative electrode gas production with the numerical values ​​of the specific surface area B, particle size D and graphitization degree C of the negative electrode active material, and obtain the fourth mapping relationship between the specific surface area B sample, particle size D sample, graphitization degree C sample and corresponding gas production of the negative electrode active material. (b) Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode active material to be predicted, and call the fourth mapping relationship to process the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted, so as to predict whether the gas production of the negative electrode to be predicted meets the requirements.

7. The method according to claim 6, characterized in that, In step (a), the calculation of the changes in the gas production of the negative electrode with respect to the specific surface area B, particle size D, and graphitization degree C of the negative electrode active material, to obtain the fourth mapping relationship between the specific surface area B sample, particle size D sample, graphitization degree C sample of the negative electrode active material and the corresponding gas production includes: Let Y = {(D / B) + C}, where B ranges from 1 to 2m. 2 / g, D value ranges from 10-20nm, C value ranges from 90%-95%, and Y value is calculated for the first time based on the ranges of B, D and C; The Y value obtained from the first calculation is correlated with the negative electrode gas production. By limiting the negative electrode gas production within a set range, the Y value is thus limited to a certain range.

8. The method according to claim 7, characterized in that, Step (b) includes: Obtain the specific surface area B1, particle size D1, and graphitization degree C1 of the negative electrode to be predicted; The Y value is calculated a second time based on Y = {(D / B) + C}. It is then determined whether the calculated Y value is within the specified range. If it is within the specified range, the negative electrode with specific surface area B1, particle size D1, and graphitization degree C1 meets the requirements; otherwise, it does not meet the requirements.

9. The method according to claim 6, characterized in that, The negative electrode active material is selected from at least one of graphite and mesophase carbon microspheres.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for predicting the gas production capacity of a lithium-ion battery, which, when executed by a processor, implements the method for predicting the gas production capacity of a lithium-ion battery according to any one of claims 1-9.

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