Cyclic fracture prediction method, device and equipment, medium, battery and electric equipment

Abstract

The invention discloses a cyclic fracture prediction method, device and equipment, a medium, a battery and electric equipment, and the method comprises the steps: substituting the foil thickness of a candidate pole piece and the volume density change value of an active material into a relation model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material; the corrected predicted elongation at break of the candidate pole piece can be directly calculated; the size parameter of the battery to be manufactured is substituted into the relation model between the size parameter of the battery and the analogue simulation elongation of the battery pole piece, and the analogue simulation elongation of the candidate pole piece can be calculated; according to the analogue simulation elongation rate of the candidate pole piece and the corrected predicted fracture elongation rate, whether the candidate pole piece has the pole piece fracture risk in the battery cell circulation period or not can be judged, and whether the battery pole piece without the pole piece fracture risk can be manufactured or not according to the volume density of the candidate foil and the active material or not can be determined; and the battery prepared from the candidate pole piece does not have the problem of cyclic fracture.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery, in particular to a cycle fracture prediction method, device, equipment, medium, battery and electric equipment. BACKGROUND

[0002] In the prior art, during the continuous charging and discharging cycle process of the battery cell, when lithium ions are embedded in the negative electrode material, a certain degree of volume expansion phenomenon will inevitably occur. When this expansion phenomenon is within a normal range, the battery can still maintain a stable working state, but once the expansion degree exceeds the limit that the pole piece can withstand, the foil material used to carry the electrode active material at the outermost circle is most likely to be broken due to the huge stress caused by expansion, which is the cycle fracture problem of the battery pole piece. Once the foil material is broken, the electronic conduction path and ion diffusion channel in the battery will be damaged, which will directly lead to the failure of the cell and the cycle diving problem, so that the battery cannot be normally charged and discharged and provide sufficient power output. SUMMARY

[0003] The present application provides a cycle fracture prediction method, device, equipment, medium, battery and electric equipment for the battery pole piece to solve the problem that the prepared battery pole piece is prone to cycle fracture.

[0004] When facing the problem of cycle failure of the cell pole piece, in the conventional operation process and detection system, experimenters can only think of using the two relatively single indicators of pole piece folding light transmittance and tensile strength to roughly estimate and judge whether the pole piece will appear in the future. However, the cycle fracture problem of the battery pole piece cannot be solved.

[0005] Based on the above consideration, in an embodiment, a cycle fracture prediction method for a battery pole piece is provided, comprising: Obtaining the foil thickness of the to-be-made pole piece, the volume density change value of the active material of the foil before and after the rolling treatment, and the size parameter of the to-be-made battery; According to the foil thickness and the volume density change value of the active material, and combining the relationship model between the fracture elongation rate of the pole piece and the foil thickness, the volume density change value of the active material, the predicted fracture elongation rate of the pole piece is obtained; According to the size parameter of the to-be-made battery, and combining the relationship model between the size parameter of the battery and the simulation elongation rate of the battery pole piece, the simulation elongation rate of the pole piece is obtained; According to the simulation elongation rate of the pole piece and the predicted fracture elongation rate after being corrected by a preset proportion coefficient, it is determined whether the pole piece made of the foil thickness and the active material with the volume density change value will appear the pole piece fracture risk during the cycle of the cell.

[0006] In an embodiment, a cycle fracture prediction device for a battery electrode sheet is provided, comprising: a data acquisition module configured to acquire a thickness of a foil to be used to make the electrode sheet, a volume density change value of an active material of the foil before and after a rolling process, and a size parameter of a battery to be made; a predicted fracture elongation calculation module configured to obtain a predicted fracture elongation of the electrode sheet according to the thickness of the foil and the volume density change value of the active material, and in combination with a relationship model between the fracture elongation of the electrode sheet and the thickness of the foil and the volume density change value of the active material; a simulation elongation calculation module configured to obtain a simulation elongation of the electrode sheet according to the size parameter of the battery to be made, and in combination with a relationship model between the size parameter of the battery and the simulation elongation of the electrode sheet of the battery; a risk prediction module configured to determine that the electrode sheet made of the foil with the thickness and the active material with the volume density change value has a risk of fracture during a cycle of the battery cell when the simulation elongation of the electrode sheet is greater than the predicted fracture elongation corrected by a preset proportion coefficient.

[0007] In an embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cycle fracture prediction method.

[0008] In an embodiment, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is executed by a processor to implement the cycle fracture prediction method.

[0009] In an embodiment, a battery is provided, wherein an electrode sheet arranged in the battery is made by screening according to the cycle fracture prediction method.

[0010] In an embodiment, a power consuming device is provided, which comprises the battery.

[0011] The cycle fracture prediction method, device, equipment, medium and battery of the battery pole piece can directly calculate the predicted fracture elongation of the candidate pole piece without actual fracture elongation test by bringing the foil thickness of the candidate pole piece, the volume density change value of the active material before and after the rolling treatment into the relationship model between the fracture elongation of the pole piece and the foil thickness, the volume density change value of the active material; then, the size parameters of the battery to be made of the customer customized product are brought into the relationship model between the size parameters of the battery and the simulation elongation of the battery pole piece, so that the simulation elongation of the candidate pole piece can be directly calculated without establishing a simulation model with the same size and structure appearance as the actual battery cell; finally, according to the simulation elongation of the candidate pole piece and the corrected predicted fracture elongation, whether the pole piece made of the candidate foil will have a pole piece fracture risk during the cycle of the battery cell can be judged. The pole piece fracture risk can be accurately judged in advance before the battery pole piece is prepared, so that it can be determined whether the candidate foil can be used to make a battery pole piece without pole piece fracture risk, so that the pole piece prepared by the candidate foil does not have the problem of cycle fracture. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0013] Figure 1 is a schematic diagram of an application environment of the cycle fracture prediction method of the battery pole piece in an embodiment of the present application; Figure 2 is a flowchart of the cycle fracture prediction method of the battery pole piece in an embodiment of the present application; Figure 3 is a schematic diagram of the tensile strength-fracture elongation curve in an embodiment of the present application; Figure 4 is a schematic diagram of the relationship model fitting data between the fracture elongation of the pole piece and the foil thickness, the volume density change value in an embodiment of the present application; Figure 5 is a schematic diagram of the relationship model between the size parameters and the simulation elongation of the battery pole piece in an embodiment of the present application; Figure 6 is a schematic diagram of the cycle fracture prediction device in an embodiment of the present application; Figure 7 is a schematic diagram of the computer equipment in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of the present application.

[0015] The cycle fracture prediction method of the battery pole piece provided in the embodiments of the present application can be applied in the application environment as shown in Figure 1 . Specifically, the cycle fracture prediction method is applied in a cycle fracture prediction system, and the cycle fracture prediction system includes a client and a server as shown in Figure 1 . The client and the server communicate through a network to realize cycle fracture prediction of the battery pole piece. The client, also known as the user end, is a program that provides local services for clients corresponding to the server. The client can be installed on, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0016] In an embodiment, as shown in Figure 2 , a cycle fracture prediction method of a battery pole piece is provided. Taking the server or the battery management system in Figure 1 as an example, the method includes the following steps: S201, obtaining the foil thickness of a to-be-made pole piece, the volume density change value of an active material before and after rolling treatment, and the size parameter of a to-be-made battery; In this embodiment, the cycle fracture prediction method is a method for predicting whether the pole piece will have a risk of fracture in the subsequent cycle process, and is used for selecting the design and manufacturing process of the battery with the pole piece. Therefore, the data required in this step is the foil thickness of a certain candidate pole piece to be made, and the volume density change value of the active material of the material before and after rolling treatment. The foil thickness can be obtained by actual measurement or determined according to the parameters provided by the manufacturer.

[0017] In this step, the rolling treatment is a process in the manufacturing process of the battery pole piece. The volume density change value of the active material of different materials before and after rolling treatment can be determined according to the parameters provided by the manufacturer, or the volume density before and after rolling treatment can be measured by actually rolling the pole piece, and the difference between the volume densities before and after rolling treatment is obtained to obtain the volume density change value of the active material before and after rolling treatment.

[0018] In this step, the size parameters of the battery to be made are known values, which are the size parameters required when the customer customizes the product.

[0019] S202, according to the foil thickness and the volume density change value of the active material, and combining the relationship model between the fracture elongation rate of the pole piece and the foil thickness, the volume density change value of the active material, the predicted fracture elongation rate of the pole piece is obtained; Wherein, the applicant found that in the relationship between the fracture elongation rate of the pole piece and the foil thickness, the volume density change value of the active material, the foil thickness and the volume density change value of the active material can directly affect the size of the fracture elongation rate of the pole piece, specifically, the foil thickness and the fracture elongation rate of the pole piece show a positive correlation, the volume density change value of the active material and the fracture elongation rate of the pole piece show a negative correlation, when the foil thickness increases, and the volume density change value of the active material decreases, the predicted fracture elongation rate of the pole piece is also larger, therefore, the relationship model between the fracture elongation rate of the pole piece and the foil thickness, the volume density change value is established by using the preset method.

[0020] In this step, by substituting the foil thickness and the volume density change value of the active material of the candidate pole piece obtained in the previous step into the relationship model between the fracture elongation rate of the pole piece and the foil thickness, the volume density change value of the active material, the predicted fracture elongation rate of the pole piece can be predicted.

[0021] S203, according to the size parameters of the battery to be made, and combining the relationship model between the size parameters of the battery and the simulation elongation rate of the battery pole piece, the simulation elongation rate of the pole piece is obtained; Wherein, the applicant found that in the relationship between the size parameters of the battery and the simulation elongation rate of the battery pole piece, the size parameters of the battery can directly affect the simulation elongation rate of the battery pole piece, for example, the size parameters of the battery can be the width-thickness ratio of the battery, when the width-thickness ratio of the battery is smaller, the simulation elongation rate of the battery pole piece is larger, therefore, the relationship model between the size parameters of the battery and the simulation elongation rate of the battery pole piece is established by using the preset method.

[0022] In this step, by substituting the size parameters of the battery required when the customer customizes the product in step S201 into the above relationship model between the size parameters and the simulation elongation rate of the battery pole piece, the simulation elongation rate of the candidate pole piece can be estimated.

[0023] S204, according to the simulation elongation rate of the pole piece and the predicted fracture elongation rate after the preset proportion coefficient correction, it is determined whether the pole piece made of the active material with the foil thickness and the volume density change value has a risk of pole piece fracture during the battery cycle.

[0024] The reason for modifying the predicted breaking elongation in step S202 by the preset proportionality coefficient is that the predicted breaking elongation δ1 calculated by the battery tab manufacturing process test is usually smaller than the simulation elongation δ2 when the tab breaks in the battery cycle process. It is considered that the tab has a slower elongation rate in the cycle test, which is much smaller than the rate in the battery tab tensile curve test, so that the tab can resist a larger tensile displacement in the cycle process. When the tab is in the cycle test environment, the slow elongation process gives the internal microstructure of the tab more time to adjust and adapt to the external stress. Compared with the fast tensile test of the battery tab, the crystal structure and chemical bonding of the tab in the cycle process have more time to redistribute the stress, so that it can resist a larger tensile displacement in the macroscopic view, thereby causing the simulation elongation δ2 when the tab breaks to be relatively high.

[0025] The applicant found that there is a preset proportionality conversion relationship between the predicted breaking elongation δ1 and the simulation elongation δ2, for example, the conversion relationship between the predicted breaking elongation δ1 and the simulation elongation δ2 is as follows: δ2=k2*δ1, and the conversion proportionality coefficient k2 ranges from 1 to 3, and the preferred range is 1.5 to 2.5. That is, after modifying the predicted breaking elongation in step S202 by the preset proportionality coefficient, the simulation elongation in step S203 is equal to the simulation elongation, which can determine that the tab made of the candidate foil thickness and the active material volume density change value will not have a tab breaking risk during the battery cycle.

[0026] The cycle breaking prediction method of the present embodiment can directly calculate the predicted breaking elongation of the candidate tab by bringing the foil thickness of the candidate tab and the volume density change value of the active material before and after the rolling treatment into the relationship model between the breaking elongation of the tab and the foil thickness and the volume density change value of the active material without actually testing the breaking elongation. Then, the size parameters of the battery to be made for the customer's customized product are brought into the relationship model between the size parameters of the battery and the simulation elongation of the battery tab, so that the simulation elongation of the candidate tab can be directly calculated without establishing a simulation model with the same size and structure as the actual battery. Finally, according to the simulation elongation of the candidate tab and the modified predicted breaking elongation, it can be determined whether the tab made of the candidate foil will have a tab breaking risk during the battery cycle. By accurately judging the tab breaking risk in advance before preparing the battery tab, it can be determined whether the candidate foil can be made into a battery tab without tab breaking risk, so that the tab prepared by the candidate foil does not have the problem of cycle breaking.

[0027] In an embodiment, in step S202, the step of determining the relationship model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material includes: S301, elongation testing is performed on N pole pieces made of the same foil material to obtain the fracture elongation of the N pole pieces, N > 2; S302, obtaining the foil thickness of the N pole pieces, the volume density change value of the active material before and after the rolling process, and performing fitting regression calculation according to the foil thickness, the volume density change value of the active material, and the fracture elongation to obtain the relationship model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material.

[0028] In an example, the fracture elongation can be measured by performing elongation testing on a certain fixed material foil under different foil thicknesses and different volume density change values of the active material under different tensile forces. In another example, the fracture elongation of the pole piece can also be read from the tensile strength-fracture elongation curve of the different pole piece foils, for example, as shown in the tensile strength-fracture elongation curve, the fracture elongation is about 2.8%. The foil thickness and the volume density change value of the active material before and after the rolling process are known values. Then, the functional relationship between the foil thickness, the different volume density change values of the active material, and the corresponding fracture elongation is fitted to determine the relationship model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material. Figure 3

[0029] The cycle fracture prediction method of the embodiment constructs the relationship model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material, so that when the foil of the candidate pole piece is screened, the predicted fracture elongation of the candidate pole piece can be directly calculated without actual fracture elongation testing, and the prediction efficiency of the predicted fracture elongation of the candidate pole piece is improved.

[0030] In an embodiment, in step S302, the expression of the relationship model between the fracture elongation of the pole piece and the foil thickness and the volume density change value of the active material is as follows: δ1=a+b*Τ+c*ρ-d*ρ 2 Where δ1 is the fracture elongation of the pole piece, T is the foil thickness, ρ is the volume density change value of the active material before and after the rolling process, a, b, c, and d are constant coefficients, b is related to the strength, hardness, and elastic modulus of the foil material, and c / d is related to the rolling process, rolling pressure / gap / rolling speed, and pole piece material particle size / hardness / viscosity. The above constant coefficients can be determined by fitting regression calculation, for example, Figure 4 ​As shown, after the fitting regression calculation, the expression of the specific relationship model can be determined as: δ1=0.00708+0.002532*T+0.0092*ρ-0.01*ρ 2 .

[0031] In this step, the relationship model between the fracture elongation of the pole piece and the thickness of the foil and the change value of the volume density of the active material is suitable for other types of current collector foils such as nickel foil, stainless steel foil, foamed copper, foamed nickel, and composite foil.

[0032] The cycle fracture prediction method of this embodiment gives an expression of a specific relationship model between the fracture elongation of the pole piece and the thickness of the foil and the change value of the volume density of the active material, which can accurately represent the relationship between the fracture elongation of the pole piece and the thickness of the foil and the change value of the volume density of the active material, and improve the reliability of the predicted fracture elongation of the candidate pole piece.

[0033] In an embodiment, in step S203, the step of determining the relationship model between the size parameter of the battery and the simulation elongation of the battery pole piece includes: S401, establishing an electric core simulation model with different size parameters, simulating the elongation change of the pole piece during the expansion process by using the electric core simulation model, and simulating the simulation elongation of the pole piece in the battery when expanded to a preset battery expansion ratio; S402, fitting according to the different size parameters and the simulation elongation to obtain the relationship model between the size parameter of the battery and the simulation elongation of the battery pole piece.

[0034] The cycle fracture prediction method of this embodiment can predict the simulation elongation of the candidate foil without establishing an electric core simulation model by constructing a relationship model between the size parameter of the battery and the simulation elongation of the battery pole piece, and directly obtaining the simulation elongation through the above relationship model, thereby improving the prediction efficiency of the simulation elongation.

[0035] In an embodiment, in step S402, the expression of the relationship model between the size parameter of the battery and the simulation elongation of the battery pole piece is as follows: δ2=k1*e τθ Wherein, δ2 is the simulation elongation of the battery pole piece, θ is the size parameter of the battery, and k1 and τ are constant coefficients.

[0036] In this embodiment, when simulating the extension rate of the electrode tab under different expansion changes of the battery cell, it is found that when the expansion ratio of the battery is constant, the smaller the width / thickness ratio of the battery size, the greater the corresponding electrode tab extension rate. For a battery with a relatively "flat" size (small width / thickness ratio), the relative deformation degree in the thickness direction will be greater under the same expansion ratio. In an example, as shown in FIG. 8, the expression of the relationship model between the established size parameter and the simulated extension rate of the battery electrode tab is y = 0.0681x + 0.0001, where y represents the simulated extension rate of the battery electrode tab, and x represents the size parameter (cell width / thickness ratio) of the battery. Figure 5

[0037] In an embodiment, in step S204, whether the electrode tab made of the active material with the foil thickness and the volume density change value will have a risk of electrode tab fracture during the cell cycle is determined according to the simulated extension rate of the electrode tab and the predicted fracture extension rate after the preset proportion coefficient correction, including: S501, when the simulated extension rate of the electrode tab is greater than the predicted fracture extension rate after the preset proportion coefficient correction, it is determined that the electrode tab made of the active material with the foil thickness and the volume density change value will have a risk of electrode tab fracture during the cell cycle; S502, when the simulated extension rate of the electrode tab is not greater than the predicted fracture extension rate after the preset proportion coefficient correction, it is determined that the electrode tab made of the foil with the foil thickness and the volume density change value of the active material will not have a risk of electrode tab fracture during the cell cycle; After it is determined that the electrode tab made of the foil with the foil thickness and the volume density change value of the active material will have a risk of electrode tab fracture during the cell cycle, it further includes: S503, adjusting the foil thickness of the electrode tab to be made and / or the volume density change value of the active material before and after the rolling process, and calculating the predicted fracture extension rate of the electrode tab again by using the relationship model between the fracture extension rate of the electrode tab and the foil thickness, and the volume density change value of the active material; until the simulated extension rate of the electrode tab is not greater than the predicted fracture extension rate after the preset proportion coefficient correction, it is determined that the electrode tab made of the foil with the adjusted foil thickness and the volume density change value of the active material will not have a risk of electrode tab fracture during the cell cycle.

[0038] In step S501, the condition expression for determining whether there is a risk of electrode tab fracture is as follows: δ2 > k2 * δ1 ​​Wherein, δ2 is the simulation extension rate of the pole piece, k2 is a preset proportion coefficient for correction, the proportion coefficient ranges from 1 to 3, preferably from 1.5 to 2.5; δ1 is the predicted fracture extension rate of the pole piece.

[0039] Correspondingly, the condition expression for determining that there is no risk of pole piece fracture in step S502 is as follows: δ2≤k2*δ1 Wherein, δ2 is the simulation extension rate of the pole piece, k2 is a preset proportion coefficient for correction, and δ1 is the predicted fracture extension rate of the pole piece.

[0040] The cycle fracture prediction method of the embodiment can, after judging the risk of pole piece fracture of the candidate foil with designed parameters through steps S501 to S503, adjust the design parameters of the candidate foil, such as the foil thickness and the volume density change value of the active material, repeatedly perform the previous steps S202 and S203, S204, and again calculate the predicted fracture extension rate of the candidate pole piece and the simulation extension rate of the candidate pole piece after adjusting the design parameters, and then determine whether the pole piece made of the candidate foil will have a risk of pole piece fracture during the cycle of the battery cell according to the simulation extension rate of the candidate pole piece and the corrected predicted fracture extension rate. Therefore, the foil with adjusted design parameters that will not have a risk of pole piece fracture can be determined by judging the risk of pole piece fracture in advance, and used for the preparation of the pole piece in the subsequent process.

[0041] In an embodiment, in step S302, when the foil to be made into a pole piece is a copper foil, the thickness of the foil ranges from 3 to 12 um, and preferably ranges from 4 to 6 um; the volume density d of the active material before the rolling treatment ranges from 1.0 g / cm3 to 2.0 g / cm3, and preferably ranges from 1.4 to 1.9 g / cm3.

[0042] When the foil to be made into a pole piece is an aluminum foil, the thickness of the foil ranges from 5 to 18 um, and preferably ranges from 7 to 10 um. The volume density d of the active material before the rolling treatment ranges from 3.0 g / cm3 to 5.0 g / cm3, and preferably ranges from 4.0 to 4.4 g / cm3.

[0043] In an embodiment, in step S402, when the size parameter of the battery is the ratio of the width to the thickness of the battery, the ratio ranges from 1 to 20, and preferably ranges from 2 to 15; the preset battery expansion ratio ranges from 0 to 30%, and preferably ranges from 5% to 15%.

[0044] The cycle fracture prediction method of this embodiment provides a relationship model between the fracture elongation of the electrode and the foil thickness, the volume density change value of the active material, and the relationship model between the battery size parameters and the simulated elongation of the battery electrode. It can predict whether the battery cell will have the problem of fracture in the subsequent cycle test by testing and calculating the fracture elongation of the electrode in advance during the electrode manufacturing process, thereby ensuring that the battery cell will not fail in the long cycle test and effectively improving the sample submission pass rate.

[0045] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0046] In one embodiment, a battery having a pole piece is provided, wherein the pole piece provided in the battery is a pole piece screened and manufactured using the above-mentioned cycle fracture prediction method.

[0047] The battery with the electrode sheet can be an ion battery, such as a lithium-ion battery, a sodium-ion battery, an aluminum-ion battery, a zinc-ion battery, etc.; the battery with the electrode sheet can also be a metal fuel cell. Taking a lithium-ion battery as an example, the lithium-ion battery includes: a wound electrode assembly and an electrolyte. The dimensions of the wound battery (thickness * width * length) are preset parameter dimensions. The wound electrode assembly includes a positive electrode sheet, a negative electrode sheet, and a separator. The separator is disposed between the positive electrode sheet and the negative electrode sheet. The positive electrode sheet includes a positive electrode current collector and a positive electrode material layer located on the surface of the positive electrode current collector. The negative electrode sheet includes a negative electrode current collector and a negative electrode material layer located on the surface of the negative electrode current collector.

[0048] In one embodiment, an electric device is provided, wherein the electric device has the above-mentioned battery built therein, and the battery is used to supply power to the electric device.

[0049] In one embodiment, a battery electrode cycle fracture prediction device is provided, which corresponds to the cycle fracture prediction method in the above embodiment. Figure 6 As shown, the cyclic fracture prediction device includes a data acquisition module, a predicted fracture elongation calculation module, a simulation elongation calculation module, and a risk prediction module. The functional modules are described in detail as follows: The data acquisition module 61 is used to obtain the thickness of the foil of the electrode to be manufactured, the change in volume density of the active material before and after the rolling process, and the dimensional parameters of the battery to be manufactured; A predicted elongation at break calculation module 62 is configured to obtain a predicted elongation at break of a pole piece based on the foil thickness and the volume density change value, in combination with a relationship model between the elongation at break of a pole piece and the foil thickness and the volume density change value of the active material in the foil; The simulation extension rate calculation module 63 is configured to obtain the simulation extension rate of the electrode tab according to the size parameter of the battery to be manufactured and according to a relationship model between the size parameter of the battery and the simulation extension rate of the electrode tab of the battery. The risk prediction module 64 is configured to determine that the electrode tab manufactured by the foil material with the foil thickness and the volume density change value of the active material has a risk of tab fracture during the cycle of the battery cell when the simulation extension rate of the electrode tab is greater than the predicted fracture extension rate after the preset proportion coefficient correction.

[0050] In an embodiment, the cycle fracture prediction device further comprises a first model building module of a relationship model between the fracture extension rate of the electrode tab and the foil thickness and the volume density change value of the active material, and specifically comprises: The test submodule is configured to test the extension rate of N electrode tabs with the same foil material to obtain the fracture extension rate of the N electrode tabs, and N>2. The first fitting submodule is configured to obtain the foil thickness of the N electrode tabs and the volume density change value of the active material before and after the rolling process, and perform fitting regression calculation according to the foil thickness, the volume density change value of the active material, and the fracture extension rate to obtain the relationship model between the fracture extension rate of the electrode tab and the foil thickness and the volume density change value of the active material.

[0051] In an embodiment, the relationship model between the fracture extension rate of the electrode tab and the foil thickness and the volume density change value of the active material has the following expression: δ1=a+b*Τ+c*ρ-d*ρ 2 wherein δ1 is the fracture extension rate of the electrode tab, T is the foil thickness, ρ is the volume density change value of the active material before and after the rolling process, a, b, c, and d are constant coefficients, and the constant coefficients are determined by the fitting regression calculation.

[0052] In an embodiment, the cycle fracture prediction device further comprises a second model building module of a relationship model between the size parameter of the battery and the simulation extension rate of the electrode tab of the battery, and specifically comprises: The simulation submodule is configured to establish a simulation model of a battery cell with different size parameters, simulate the change of the extension rate of the electrode tab during the expansion process by using the simulation model of the battery cell, and simulate the simulation extension rate of the electrode tab in the battery when the expansion reaches a preset battery expansion proportion. The second fitting submodule is configured to perform fitting according to the different size parameters and the simulation extension rate to obtain the relationship model between the size parameter of the battery and the simulation extension rate of the electrode tab of the battery.

[0053] In an embodiment, the expression of the relationship model between the size parameter of the battery and the simulation extension rate of the battery pole piece is as follows: δ2=k1*e τθ Wherein, δ2 is the simulation extension rate of the battery pole piece, θ is the size parameter of the battery, k1 and τ are constant coefficients.

[0054] In an embodiment, the risk prediction module specifically comprises: The comparison and determination submodule is configured to determine that the pole piece made of the foil with the thickness and the volume density change value of the active material will have a risk of pole piece fracture during the cycle of the battery cell when the simulation extension rate of the pole piece is greater than the predicted fracture extension rate after the preset proportional coefficient correction.

[0055] The cycle fracture prediction device further comprises: The correction calculation module is configured to adjust the thickness of the foil to be used to make the pole piece and / or the volume density change value of the active material before and after the rolling process, and then calculate the predicted fracture extension rate of the pole piece by using the relationship model between the fracture extension rate of the pole piece and the thickness of the foil and the volume density change value of the active material again, until the simulation extension rate of the pole piece is not greater than the predicted fracture extension rate after the preset proportional coefficient correction, and it is determined that the pole piece made of the foil with the adjusted thickness of the foil and the volume density change value of the active material will not have a risk of pole piece fracture during the cycle of the battery cell.

[0056] In an embodiment, when the foil to be used to make the pole piece is a copper foil, the thickness of the foil ranges from 3 to 12 um, and when the foil to be used to make the pole piece is an aluminum foil, the thickness of the foil ranges from 5 to 18 um.

[0057] In an embodiment, when the size parameter of the battery is the ratio of the width to the thickness of the battery, the ratio ranges from 1 to 20, and the preset battery expansion ratio ranges from 0 to 30%.

[0058] The specific limitations of the cycle fracture prediction device can be referred to the limitations of the cycle fracture prediction method described above, and will not be repeated here. Each module in the above cycle fracture prediction device can be realized by software, hardware and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0059] In one embodiment, a computer device is provided, which can be a server, and the internal structure diagram thereof can be as shown in Figure 7As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the foil thickness of the electrode, the volume density change value of the active material before and after the rolling process, and the size parameters of the battery to be manufactured, as well as the relationship model between the fracture elongation of the electrode and the foil thickness and the volume density change value of the active material, and the relationship model between the size parameters of the battery and the simulated elongation of the battery electrode. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cyclic fracture prediction method is implemented.

[0060] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the cycle break prediction method in the above embodiment is implemented, for example Figure 2 Steps S201-S204 shown are not described here in detail to avoid repetition. Alternatively, when the processor executes the computer program, the functions of each module / unit in the embodiment of the cycle fracture prediction device are realized, for example Figure 6 The cycle fracture prediction function shown is not described here in detail to avoid repetition.

[0061] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the cycle break prediction method in the above embodiment is implemented, for example Figure 2 Steps S201-S204 are not described here in detail to avoid repetition. Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the embodiment of the cycle fracture prediction device are realized, for example Figure 6 The cycle fracture prediction function shown is not described here in detail to avoid repetition.

[0062] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0064] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for predicting cycle fracture of a battery electrode, characterized in that: The cyclic fracture prediction method comprises: Obtaining the thickness of the foil material for the electrode to be manufactured, the change in volume density of the active material of the foil material before and after the rolling process, and the dimensional parameters of the battery to be manufactured; According to the foil thickness and the volume density change value, combined with the relationship model between the fracture elongation of the electrode piece and the foil thickness and the volume density change value of the active material, the predicted fracture elongation of the electrode piece is obtained; According to the size parameters of the battery to be manufactured, the simulated elongation of the electrode piece is obtained by combining the relationship model between the size parameters of the battery and the simulated elongation of the electrode piece of the battery; Based on the simulated elongation of the electrode and the predicted fracture elongation corrected by a preset proportional coefficient, it is determined whether there is a risk of electrode fracture during the battery cell cycle when the electrode is made of a foil with the foil thickness and the volume density change value of the active material.

2. The cycle break prediction method according to claim 1, characterized in that: The step of determining the relationship model between the elongation at break of the electrode piece and the thickness of the foil and the change in volume density of the active material comprises: Performing elongation tests on N pole pieces made of the same foil material to obtain the elongation at break of the N pole pieces, where N>2; Obtain the foil thickness of N of the pole pieces and the volume density change values ​​of the active material before and after the rolling process, perform fitting regression calculation based on the foil thickness of the N pole pieces, the volume density change values ​​of the active material and the elongation at break, and obtain a relationship model between the elongation at break of the pole piece and the foil thickness and volume density change values.

3. The cycle break prediction method according to claim 2, characterized in that: The relationship model between the elongation at break of the electrode and the thickness of the foil and the change in volume density of the active material is expressed as follows: δ1=a+b*T+c*ρ-d*ρ 2 Wherein, δ1 is the elongation at break of the electrode, T is the thickness of the foil, ρ is the change in volume density of the active material before and after the rolling process, a, b, c, and d are constant coefficients, and the constant coefficients are determined by the fitting regression calculation.

4. The cycle break prediction method according to claim 1, characterized in that: The step of determining a relationship model between the size parameters of the battery and the simulated elongation of the battery electrode includes: Establishing a battery cell simulation model with different size parameters, using the battery cell simulation model to simulate the change in the elongation of the electrode during the expansion process, and simulating the simulated elongation of the electrode in the battery when the battery expands to a preset battery expansion ratio; Fitting is performed based on the different dimensional parameters and the simulated elongation to obtain a relationship model between the dimensional parameters of the battery and the simulated elongation of the battery electrode.

5. The cycle break prediction method according to claim 4, characterized in that: The expression of the relationship model between the size parameters of the battery and the simulated elongation of the battery electrode is as follows: δ2=k1*e τθ Among them, δ2 is the simulated elongation of the battery electrode, θ is the size parameter of the battery, and k1 and τ are constant coefficients.

6. The cycle break prediction method according to claim 1, characterized in that: Determining whether a pole piece manufactured using a foil having the foil thickness and the volume density variation value will have a pole piece fracture risk during a battery cell cycle based on the simulated elongation of the pole piece and the predicted elongation at break corrected by a preset proportional coefficient includes: When the simulated elongation of the electrode piece is greater than the predicted fracture elongation after correction by a preset proportional coefficient, it is determined that the electrode piece made of the foil with the foil thickness and the volume density change value will have a risk of electrode fracture during the battery cell cycle; After determining that a pole piece made of a foil having the above-mentioned thickness and the above-mentioned volume density variation of the active material has a risk of pole piece fracture during battery cell cycling, the method further includes: Adjust the foil thickness of the electrode to be produced and / or the volume density change of the active material before and after the rolling process, and again use the relationship model between the fracture elongation of the electrode and the foil thickness and volume density change value to calculate the predicted fracture elongation of the electrode; until the simulated elongation of the electrode is no greater than the predicted fracture elongation after correction by a preset proportional coefficient, it is determined that the electrode produced by the active material with the adjusted foil thickness and volume density change value will not have the risk of electrode fracture during the battery cell cycle.

7. The cycle break prediction method according to claim 1, 2 or 3, characterized in that: When the foil material for the electrode to be manufactured is copper foil, the thickness of the foil material ranges from 3 to 12 μm. When the foil material for the electrode to be manufactured is aluminum foil, the thickness of the foil material ranges from 5 to 18 μm.

8. The cycle break prediction method according to claim 4 or 5, characterized in that: When the size parameter of the battery is the ratio of the width to the thickness of the battery, the range of the ratio is 1-20, and the range of the preset battery expansion ratio is 0-30%.

9. A battery electrode cycle fracture prediction device, characterized in that: The cycle fracture prediction device comprises: A data acquisition module is used to obtain the thickness of the foil material of the electrode to be manufactured, the change in volume density of the active material of the foil material before and after the rolling process, and the dimensional parameters of the battery to be manufactured; A predicted elongation at break calculation module is used to obtain the predicted elongation at break of the electrode piece based on the foil thickness and the volume density change value, combined with a relationship model between the elongation at break of the electrode piece and the foil thickness and the volume density change value of the active material; A simulation elongation calculation module is used to obtain the simulation elongation of the electrode piece according to the size parameters of the battery to be manufactured and in combination with a relationship model between the size parameters of the battery and the simulation elongation of the battery electrode piece; A risk prediction module is used to determine that a pole piece made of an active material having the foil thickness and the volume density change value has a risk of pole piece fracture during the battery cell cycle when the simulated elongation of the pole piece is greater than the predicted fracture elongation after correction by a preset proportional coefficient.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the cycle fracture prediction method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the cycle fracture prediction method according to any one of claims 1 to 8 is implemented.

12. A battery, characterized in that: The pole piece provided in the battery is a pole piece screened and manufactured using the cycle fracture prediction method according to any one of claims 1 to 8.

13. An electrical device, characterized in that: The electric device comprises the battery according to claim 12.

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