Method and device for predicting infiltration effect of battery cell

Abstract

The invention discloses a cell infiltration effect prediction method and device, and belongs to the field of cell manufacturing. The method comprises the following steps: acquiring pole piece infiltration characterization parameters; based on the pole piece infiltration characterization parameters, simulating the pole piece infiltration effect by using a finite element calculation method to obtain optimal parameters of complete infiltration; and setting cell infiltration process parameters according to the optimal parameters of complete infiltration. On the basis of a finite element calculation method, the infiltration behavior of the electrolyte on the surface of the battery cell pole piece can be accurately simulated, damage to the battery cell by a traditional detection method is avoided, the infiltration yield of the battery cell is increased, and the detection cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of battery cell manufacturing, and in particular to a method and device for predicting battery cell wetting effect. Background Art

[0002] The wetting effect of the battery cell directly affects the performance and stability of the battery. In traditional methods, the wetting effect is usually observed directly by disassembling the battery cell. For example, a Chinese patent with authorization announcement number CN114993888 B discloses a battery electrolyte wettability test method. First, the battery cell is infiltrated with an electrolyte containing an organic luminescent material, and the infiltrated battery cell is dried; then, a resin is injected into the dried battery cell to seal the battery cell to obtain a battery cell specimen; finally, the battery cell specimen is cut, and the luminescence intensity at the cross section of the battery cell specimen is observed to obtain the electrolyte infiltration at the cross section of the battery cell specimen. This method requires cutting the battery cell at different cross sections to observe the wetting state inside the pole piece. However, cutting can cause damage to the battery cell, which in turn leads to an increase in production and testing costs. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and device for predicting the wetting effect of a battery cell, so as to solve the problem of damaging the battery cell when detecting the wetting effect in the traditional method, improve the wetting yield of the battery cell, and reduce the detection cost.

[0004] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for predicting the wetting effect of a battery cell, comprising: Collect the pole piece wetting characterization parameters; Based on the pole piece wetting characterization parameters, the finite element calculation method is used to simulate the pole piece wetting effect and obtain the optimal parameters for complete wetting; Set the cell wetting process parameters according to the optimal parameters for complete wetting.

[0005] By collecting the wetting characterization parameters of the electrode and combining them with finite element calculations, the wetting behavior of the electrolyte on the surface of the battery cell electrode can be accurately simulated, thereby predicting the wetting effect of the battery cell in actual production. At the same time, it can also avoid the damage to the battery cell when testing the wetting effect in traditional detection methods, thereby improving the battery cell wetting yield and reducing detection costs.

[0006] Optionally, the pole piece wetting characterization parameters include pole piece rolling process parameters, pole piece microstructure data and hot pressing process parameters.

[0007] Optionally, the pole piece rolling process parameters include one or more of rolling extrusion pressure, rolling speed, roller spacing and rolling line speed; the pole piece microstructure data includes one or more of grain size, grain boundary density and porosity; the hot pressing process parameters include hot pressing pressure, hot pressing temperature and hot pressing time.

[0008] By introducing the pole piece rolling and hot pressing process parameters, the influence of the pre-wetting process of the battery cell on the wetting effect can be fully considered. By introducing the microstructure data, the diffusion and penetration process of the electrolyte in the pole piece can be more realistically reflected. By accurately collecting these key parameters, the performance of the pole piece during the wetting process can be better predicted, and the accuracy of the finite element simulation can be improved.

[0009] Optionally, based on the pole piece wetting characterization parameters, using a finite element calculation method to simulate the pole piece wetting effect and obtain the optimal parameters for complete wetting, includes: Conduct geometric modeling of the pole piece, construct a 3D solid model of the single-layer pole piece surface, and assemble it into a battery cell solid model; Mesh the cell entity model to obtain a mesh model; Import the mesh model into the finite element calculation software to calculate the transient thermal flow field of the mesh model; The calculation time of the transient thermal flow field is continuously adjusted, and whether the battery cell has completed infiltration is determined based on the calculation results. If it is determined that the battery cell has completed infiltration, the optimal parameters for complete infiltration are obtained.

[0010] Finite element calculations can be used to accurately simulate the wetting behavior of the electrolyte on the surface of the battery cell electrode, thereby predicting the wetting effect of the battery cell, avoiding the destructive detection of the battery cell wetting effect by traditional methods, and improving the battery cell wetting yield.

[0011] Optionally, judging whether the battery cell has completed the wetting according to the calculation result includes: If the calculated result shows that the electrolyte is completely immersed in the battery cell ≥ 95%, or the calculated result shows that the change rate of the battery cell wetting area within 1 hour is less than 5%, then the battery cell is judged to be completely wetted.

[0012] Optionally, in the mesh model, the quality of the drawn mesh is ≥ 0.9 to ensure the stability of the finite element calculation results.

[0013] Optionally, the battery cell wetting process parameters include wetting temperature and wetting time.

[0014] In a second aspect, the present invention provides a device for predicting the wetting effect of a battery cell, comprising: A collection module is used to collect the pole piece wetting characterization parameters; The optimal infiltration parameter calculation module is used to simulate the infiltration effect of the pole piece based on the pole piece infiltration characterization parameters and obtain the optimal parameters for complete infiltration using the finite element calculation method; The parameter setting module is used to set the cell wetting process parameters according to the optimal parameters for complete wetting.

[0015] The acquisition module collects the infiltration characterization parameters of the pole piece, and the infiltration optimal parameter calculation module uses the finite element calculation method to simulate the infiltration effect of the pole piece to obtain the optimal parameters for complete infiltration, thereby predicting the infiltration effect of the battery cell in actual production. The device can achieve accurate simulation of the battery cell infiltration process, avoid the damage to the battery cell when detecting the infiltration effect in the traditional method, improve the battery cell infiltration yield, and reduce the detection cost.

[0016] Optionally, the infiltration optimal parameter calculation module includes: The solid model building module is used to geometrically model the pole piece, build a 3D solid model of the single-layer pole piece surface, and assemble it into a battery cell solid model; A mesh model drawing module is used to draw a mesh on the cell entity model to obtain a mesh model; Finite element calculation module, used to import the mesh model into the finite element calculation software and calculate the transient thermal flow field of the mesh model; The judgment module is used to continuously adjust the calculation time of the transient thermal flow field, and judge whether the battery cell has completed the infiltration according to the calculation results. If it is judged that the battery cell has completed the infiltration, the optimal parameters for complete infiltration are obtained.

[0017] By modeling the battery cell and drawing the network, and then performing finite element calculations, the wetting process of the electrolyte in the battery cell electrode can be accurately simulated, thus predicting the wetting effect of the battery cell.

[0018] In a third aspect, the present invention provides an electronic device comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in the first aspect.

[0019] In a fourth aspect, the present invention provides a readable storage medium having one or more programs stored thereon, wherein the one or more programs include instructions which, when executed by a computing device, cause the computing device to execute instructions of any of the methods described in the first aspect.

[0020] Compared with the prior art, the beneficial technical effects achieved by the present invention are: The present invention collects the wetting characterization parameters of the pole piece, and then uses the finite element calculation method to simulate the wetting effect of the pole piece, obtains the optimal parameters for complete wetting, and thus predicts the wetting effect of the battery cell in actual production. This method can accurately simulate the wetting behavior of the electrolyte on the surface of the battery cell pole piece, and can avoid the damage to the battery cell when detecting the wetting effect in the traditional detection method, thereby improving the battery cell wetting yield and reducing the detection cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a schematic diagram of the structure of a battery cell according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the cell grid division according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the finite element calculation results (immersion for 1 hour) of the battery cell according to the embodiment of the present invention; Figure 5 It is a schematic diagram of the finite element calculation results (immersion for 2 hours) of the battery cell according to the embodiment of the present invention; Figure 6 It is a schematic diagram of the finite element calculation results (immersion for 3 hours) of the battery cell according to the embodiment of the present invention; Figure 7 It is a schematic diagram of the structure of the device of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with specific examples. The following examples are only used to more clearly illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting the wetting effect of a battery cell, comprising the following steps: Step S1, collecting pole piece wetting characterization parameters; Among them, the pole piece wetting characterization parameters include pole piece rolling process parameters, pole piece microstructure data and hot pressing process parameters.

[0024] Rolling and hot pressing are the pre-processes for cell infiltration, and their process parameter settings will affect the infiltration effect. The pole piece microstructure data mainly refers to the pole piece morphology data directly detected and read by scanning electron microscope and industrial CT equipment after the pole piece is rolled.

[0025] Among them, the pole piece rolling process parameters, including rolling extrusion pressure, rolling speed, roller spacing, rolling line speed and other parameters that affect the pole piece microstructure; Pole microstructure data, including grain size, grain boundary density, porosity, etc.; The hot pressing process parameters include hot pressing pressure, hot pressing temperature and hot pressing time.

[0026] Step S2, based on the pole piece wetting characterization parameters, using the finite element calculation method to simulate the pole piece wetting effect and obtain the optimal parameters for complete wetting; The pole piece wetting characterization parameters collected in step S1 are used as input parameters for finite element calculation.

[0027] Specifically, based on the pole piece wetting characterization parameters, the finite element calculation method is used to simulate the pole piece wetting effect to obtain the optimal parameters for complete wetting, including: Step S201, geometric modeling of the pole piece, constructing a 3D solid model of the single-layer pole piece surface, and assembling it into a battery cell solid model; Step S202, meshing the cell physical model to obtain a mesh model; Among them, the drawn mesh quality should be ≥0.9 to ensure the stability of the finite element calculation results.

[0028] Step S203, importing the grid model into finite element calculation software to calculate the transient thermal flow field of the grid model; Import the mesh model into finite element calculation software such as FLUENT to perform finite element settings and calculate the transient thermal flow field of the mesh model.

[0029] Step S204, continuously adjusting the calculation time of the transient thermal flow field, and judging whether the battery cell has been fully wetted according to the calculation result, and if it is judged that the battery cell has been fully wetted, obtaining the optimal parameters for complete wettability; Among them, there are two indicators to refer to when judging whether the cell is completely wetted based on the calculation results, namely, when the electrolyte is completely immersed in the cell at a rate of more than 95% in the calculation results; or when the calculation results show that the cell wetted area change rate within 1 hour is less than 5%. As long as one of these two indicators is met, it can be considered that the cell wetted in the finite element calculation results has been completed.

[0030] Step S3, setting the cell wetting process parameters according to the optimal parameters for complete wetting; The infiltration process parameters include infiltration temperature and infiltration time.

[0031] A specific embodiment is given below, and the implementation steps are as follows: Step 100: Collecting pole piece wetting characterization parameters; Taking the cell manufacturing process of a battery manufacturer as an example, the collected pole piece characterization parameters include rolling extrusion pressure, rolling speed, porosity, hot pressing pressure, hot pressing temperature and hot pressing time, as shown in Table 1.

[0032] Table 1 Pole characterization parameters

[0033] Step 200, based on the pole piece wetting characterization parameters, using the finite element calculation method, simulate the pole piece wetting effect to obtain the optimal parameters for complete wetting; Based on the A-type battery cell produced by the factory, the electrode is geometrically modeled, a single-layer electrode surface 3D solid model is constructed, and then assembled into a battery cell solid model. The model structure is as follows: Figure 2 shown.

[0034] Will Figure 2 The model shown in the figure is divided into finite element calculation grids, and the obtained grid model is as follows Figure 3 shown.

[0035] The collected electrode wetting characterization parameters were input into the finite element calculation software, and the calculation was carried out. The wetting temperature was set to 60°C, and the distribution diagram of the electrolyte percentage in the battery cell was obtained when the wetting time was 1h, 2h, and 3h. The calculation results are as follows: Figures 4 to 6 shown.

[0036] In this embodiment, the total mass percentage of the electrolyte in the battery cell is 0.5, and the electrolyte is replaced by pure water for simulation. It can be seen from the calculation result that after 2 hours of immersion, the percentage of the electrolyte in the battery cell remains basically unchanged, which means that the optimal immersion parameter of the A-type battery cell produced by the factory is within 2 hours.

[0037] In a further embodiment, the time division of the finite element calculation can be shortened, and a closer optimal process parameter time for infiltration can be obtained by using the dichotomy method, which will not be described in detail in the present invention.

[0038] Step 300, setting the cell wetting process parameters according to the optimal parameters for complete wetting; According to the optimal parameters for complete wetting, the cell wetting process parameters are set to a wetting temperature of 60°C and a wetting time of 2h.

[0039] The method of the present invention realizes accurate simulation of the cell wetting process, avoids destructive detection of the cell wetting effect in traditional methods, and provides new methods and means for controlling and optimizing the cell production process.

[0040] like Figure 7 As shown, an embodiment of the present invention provides a device for predicting the wetting effect of a battery cell, comprising: The acquisition module 701 is used to acquire the electrode wetting characterization parameters; The optimal wetting parameter calculation module 702 is used to simulate the wetting effect of the pole piece based on the pole piece wetting characterization parameters and obtain the optimal parameters for complete wetting by using the finite element calculation method; The parameter setting module 703 is used to set the cell wetting process parameters according to the optimal parameters for complete wetting.

[0041] The infiltration optimal parameter calculation module 702 includes: The solid model building module is used to geometrically model the pole piece, build a 3D solid model of the single-layer pole piece surface, and assemble it into a battery cell solid model; A mesh model drawing module is used to draw a mesh on the cell entity model to obtain a mesh model; Finite element calculation module, used to import the mesh model into the finite element calculation software and calculate the transient thermal flow field of the mesh model; The judgment module is used to continuously adjust the calculation time of the transient thermal flow field, and judge whether the battery cell has completed the infiltration according to the calculation results. If it is judged that the battery cell has completed the infiltration, the optimal parameters for complete infiltration are obtained.

[0042] The present invention also provides an electronic device, comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the aforementioned methods.

[0043] The present invention also provides a readable storage medium having one or more programs stored thereon, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the aforementioned methods.

[0044] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0048] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for predicting the wetting effect of a battery cell, characterized in that: include: Collect the pole piece wetting characterization parameters; Based on the pole piece wetting characterization parameters, the finite element calculation method is used to simulate the pole piece wetting effect and obtain the optimal parameters for complete wetting; Set the cell wetting process parameters according to the optimal parameters for complete wetting.

2. The method for predicting the wetting effect of a battery cell according to claim 1, characterized in that: The pole piece wetting characterization parameters include pole piece rolling process parameters, pole piece microstructure data and hot pressing process parameters.

3. The method for predicting the wetting effect of a battery cell according to claim 2, characterized in that: The pole piece rolling process parameters include one or more of rolling extrusion pressure, rolling speed, roller spacing and rolling line speed; the pole piece microstructure data includes one or more of grain size, grain boundary density and porosity; the hot pressing process parameters include hot pressing pressure, hot pressing temperature and hot pressing time.

4. The method for predicting the wetting effect of a battery cell according to claim 1, characterized in that: The method of simulating the electrode wetting effect based on the electrode wetting characterization parameters and obtaining the optimal parameters for complete wetting by using the finite element calculation method includes: Conduct geometric modeling of the pole piece, construct a 3D solid model of the single-layer pole piece surface, and assemble it into a battery cell solid model; Mesh the cell entity model to obtain a mesh model; Import the mesh model into the finite element calculation software to calculate the transient thermal flow field of the mesh model; The calculation time of the transient thermal flow field is continuously adjusted, and whether the battery cell has completed infiltration is determined based on the calculation results. If it is determined that the battery cell has completed infiltration, the optimal parameters for complete infiltration are obtained.

5. The method for predicting the wetting effect of a battery cell according to claim 4, characterized in that: The step of judging whether the battery cell has been wetted according to the calculation result includes: If the calculated result shows that the electrolyte is completely immersed in the battery cell ≥ 95%, or the calculated result shows that the change rate of the battery cell wetting area within 1 hour is less than 5%, then the battery cell is judged to be completely wetted.

6. The method for predicting the wetting effect of a battery cell according to claim 4, characterized in that: In the mesh model, the drawn mesh quality is ≥ 0.

9.

7. The method for predicting the wetting effect of a battery cell according to claim 1, characterized in that: The battery cell wetting process parameters include wetting temperature and wetting time.

8. A device for predicting the wetting effect of a battery cell, characterized in that: include: A collection module is used to collect the pole piece wetting characterization parameters; The optimal infiltration parameter calculation module is used to simulate the infiltration effect of the pole piece based on the pole piece infiltration characterization parameters and obtain the optimal parameters for complete infiltration using the finite element calculation method; The parameter setting module is used to set the cell wetting process parameters according to the optimal parameters for complete wetting.

9. The battery cell wetting effect prediction device according to claim 8, characterized in that: The infiltration optimal parameter calculation module includes: The solid model building module is used to geometrically model the pole piece, build a 3D solid model of the single-layer pole piece surface, and assemble it into a battery cell solid model; A mesh model drawing module is used to draw a mesh on the cell entity model to obtain a mesh model; Finite element calculation module, used to import the mesh model into the finite element calculation software and calculate the transient thermal flow field of the mesh model; The judgment module is used to continuously adjust the calculation time of the transient thermal flow field, and judge whether the battery cell has completed the infiltration according to the calculation results. If it is judged that the battery cell has completed the infiltration, the optimal parameters for complete infiltration are obtained.

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