A cell terminal voltage prediction method and system based on lattice boltzmann simulation

Abstract

The application discloses a kind of based on lattice Boltzmann simulation cell end voltage prediction method and system, it is related to lithium ion battery technical field, the method includes: based on the microstructure parameter of the positive and negative electrode sheet of to-be-measured cell, constructs the three-dimensional porous medium model including active particle, pore phase and binder phase.Lattice Boltzmann method is used to simulate the infiltration process of electrolyte in model, and key parameters are extracted.The parameters are input into the macroscopic electrochemical model to replace the original empirical constant of the model with the tortuosity and effective conductivity obtained by simulation, and the discretization process is completed along the thickness direction of the sheet.Finally, the zero-current boundary condition is set to solve the spatial distribution of solid-phase potential, and the end voltage before cell formation is calculated by the difference between the solid-phase potential of the positive and negative electrode current collectors.The application solves the problem of long production cycle and high cost caused by the need for physical cell sample testing to predict the end voltage before cell formation in the prior art.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery technology, and in particular to a method and system for predicting cell terminal voltage based on lattice Boltzmann simulation. Background Technology

[0002] In the production of lithium-ion batteries, electrolyte injection is one of the key processes. The wetting effect of the electrolyte in the porous media such as positive and negative electrodes and separators directly determines the efficiency of subsequent formation processes, as well as the final capacity, internal resistance, and cycle life of the battery. After electrolyte injection and before formation, a tiny terminal voltage (typically in the tens to two hundred millivolts range) can be measured between the positive and negative terminals of the cell. This voltage originates from the electrochemical potential difference spontaneously formed after the electrolyte comes into contact with the electrode materials, and its magnitude is closely related to the degree of wetting of the porous structure of the electrode by the electrolyte. If the wetting is poor (such as the presence of dry areas or bubbles inside the electrode), this tiny voltage will deviate from the normal range, leading to uneven local current density, lithium plating, or even short circuits during formation. Therefore, how to accurately predict this tiny terminal voltage (OCV) before formation has become an important technical problem in the field of lithium-ion battery manufacturing.

[0003] The detection and evaluation of electrolyte wettability has long been a technical concern in the lithium-ion battery manufacturing field. Scholars and companies both domestically and internationally have conducted extensive research on this topic, which can be broadly categorized into several technical approaches based on technological development: electrical parameter testing, ultrasonic testing, voltage monitoring, electrochemical impedance spectroscopy, and image analysis.

[0004] In terms of electrical parameter testing methods, one approach estimates electrolyte wettability by measuring the capacitance between the positive and negative electrodes. This involves measuring the capacitance at predetermined time points after electrolyte injection and inferring the degree of wetting from capacitance changes. While improved methods have emerged, these methods provide quantitative electrical parameter responses after injection, but the results are discrete time-point data, making it difficult to obtain continuous evolution information of the wetting process. Furthermore, the quantitative relationship between capacitance and wetting degree lacks a clear physical model, essentially remaining an empirical measurement method. Ultrasonic testing offers another physical perspective for wettability assessment. It assesses the uniformity of electrolyte distribution and wetting effect by observing the reflection and transmission characteristics of ultrasound within the battery. This method can obtain spatial distribution information of wetting, but it relies on dedicated ultrasonic scanning equipment, is costly, and has limited adaptability to cell structures other than pouch cells (such as prismatic and cylindrical cells). It is more suitable for qualitatively assessing wetting uniformity than quantitatively predicting electrochemical performance parameters. Voltage monitoring methods have received widespread attention in recent years.

[0005] Subsequently, another method emerged: testing the battery voltage at fixed time intervals during the static immersion process after electrolyte injection, plotting voltage-time curves, and determining wettability by the change in voltage difference. A voltage difference not exceeding 40mV was considered good wettability. This method is simple, fast, requires no additional equipment, and allows for non-destructive testing. It should be noted that the voltage measured by this method is precisely the minute terminal voltage (typically in the tens to two hundred millivolt range) between the positive and negative terminals of the cell after electrolyte injection, before formation, and in the uncharged / undischarged state. Its physical source is the electrochemical potential difference spontaneously formed after the electrolyte and electrode materials come into contact, rather than the open-circuit voltage after traditional charge / discharge. Other methods involve real-time monitoring of this minute voltage change during electrolyte injection, establishing an empirical correlation between it and the degree of wettability. However, these voltage monitoring methods are essentially experimental induction methods, requiring testing based on already fabricated physical cells and summarizing empirical thresholds (such as 40mV) from a large amount of experimental data. They cannot predict new systems during the design phase, and the applicability of empirical thresholds to different material systems lacks systematic verification.

[0006] While the aforementioned methods each contribute to electrolyte wettability testing, they all share a fundamental limitation: they all require testing based on physical cell samples, making it impossible to predict the wettability and OCV of systems that have not yet been produced during the cell design stage. This results in a long "design-sample preparation-testing" cycle and high costs.

[0007] Therefore, existing technologies still lack a method to quantitatively predict the terminal voltage of a battery cell before formation without the need for physical samples. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a method and system for predicting the terminal voltage of a battery cell based on lattice Boltzmann simulation, which aims to solve the problem that the terminal voltage prediction before battery cell formation in the prior art requires testing based on physical battery cell samples, resulting in long production cycles and high costs.

[0009] The embodiments of the present invention are implemented as follows: A method for predicting cell terminal voltage based on lattice Boltzmann simulation, the method comprising: Based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test, a three-dimensional porous medium model including active particles, porous phase and binder phase is established. The wetting process of electrolyte in a three-dimensional porous medium model was simulated using the lattice Boltzmann method. Key parameters were extracted based on the simulation results, including saturation curve, tortuosity, and effective ionic conductivity. The extracted key parameters are input into a pre-constructed macroscopic electrochemical model, where tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction. In the macroscopic electrochemical model, a zero-current boundary condition is set, and the spatial distribution of the solid-phase potential is solved to obtain the solid-phase potential of the positive and negative current collectors. Based on the potential difference between the solid-phase potentials of the positive and negative current collectors, the terminal voltage of the cell before formation is obtained.

[0010] Furthermore, in the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, the formula for calculating the effective ionic conductivity is as follows: ; in, For effective ionic conductivity, The conductivity of the bulk electrolyte. For the degree of curvature, This is a saturation curve. This is the saturation index.

[0011] Furthermore, in the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, the step of setting zero current boundary conditions in the macroscopic electrochemical model and solving the spatial distribution of solid-phase potential to obtain the solid-phase potentials of the positive and negative current collectors includes: The positive and negative electrodes, after being discretized along the thickness direction of the electrodes, are divided into multi-layered series electrochemical micro-elements. The tortuosity and effective ionic conductivity parameters extracted by lattice Boltzmann simulation corresponding to different thickness positions of the electrodes are mapped and assigned to each layer of electrochemical micro-elements, forming an array of electrochemical parameters that are non-uniformly distributed along the thickness direction of the electrodes. Based on the law of charge conservation and the balance of electrode solid-phase potential, and combined with the non-uniform electrochemical parameter array of each electrochemical micro-element, a solid-phase potential control equation considering the non-uniformity of the electrolyte wetting space is constructed. Zero current boundary conditions are set on the outer end faces of the positive and negative electrode current collectors, and inner boundary conditions with continuous potential and current density are set on the interface of adjacent electrochemical micro-element. The solid-state potential control equation is solved numerically through iteration to obtain the full-field distribution of solid-state potential along the electrode thickness direction. The steady-state solid-state potential values ​​at the corresponding positions of the positive and negative current collectors are then extracted.

[0012] Furthermore, in the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, the step of numerically iteratively solving the solid-state potential control equation includes: The solid-state potential control equations are spatially discretized using the finite difference method, and iterative calculations are performed using a P2D solver written in Python; or An electrochemical-fluid coupling model at the macroscopic pore scale is constructed based on the extended lattice Boltzmann method. Within the unified numerical framework of this coupling model, the synchronous iterative solution of the solid-state potential governing equations is completed; or A macroscopic pore-scale electrochemical-fluid coupling model was constructed using commercial simulation software with an electrochemical numerical calculation module, and the numerical solution of the solid-phase potential control equation was completed by calling the software's built-in iterative solver.

[0013] Furthermore, in the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, the step of simulating the wetting process of the electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method includes: A multiphase flow lattice Boltzmann model was used to simulate the gas-liquid two-phase displacement during the electrolyte wetting process. The multiphase flow lattice Boltzmann model adopted either a pseudo-potential model or a color gradient model. The specific boundary conditions for the simulation of the multiphase flow lattice Boltzmann model are as follows: the inlet adopts a constant pressure or constant flow liquid injection mode, the outlet is set to atmospheric pressure to achieve gas discharge, and the wall is set as a non-wetting boundary or a partially wetting boundary.

[0014] Furthermore, the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, after the step of obtaining the cell terminal voltage before formation based on the solid-state potential of the positive and negative current collectors, further includes: Determine whether the terminal voltage of the battery cell before formation is within a preset threshold range; When it is determined that the terminal voltage of the cell before formation is within the preset terminal voltage threshold range, the cell is deemed to be qualified for wetting.

[0015] Furthermore, in the above-mentioned cell terminal voltage prediction method based on lattice Boltzmann simulation, the step of determining the preset terminal voltage threshold range includes: Obtain the stable terminal voltage value of the battery cell after it is fully immersed under different battery cell design parameters; Based on the production quality standards of the battery cell, the minimum electrolyte saturation of the battery cell is calculated, and combined with the evolution curve of the terminal voltage value, the corresponding lower limit threshold of the terminal voltage value is obtained. The range between the terminal voltage value and the lower limit threshold of the terminal voltage value is used as the preset terminal voltage threshold range for the corresponding battery cell. Multiple different battery cells and their corresponding preset terminal voltage threshold ranges are stored to build a terminal voltage threshold range database. When it is necessary to obtain the terminal voltage threshold range, the corresponding terminal voltage threshold range is found in the terminal voltage threshold range database based on different battery cells.

[0016] Another object of the present invention is to provide a cell terminal voltage prediction system based on lattice Boltzmann simulation, characterized in that the system comprises: A module is established to build a three-dimensional porous medium model containing active particles, porous phase and binder phase based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test. The simulation module is used to simulate the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method. Based on the simulation results, key parameters are extracted, including saturation curve, tortuosity, and effective ionic conductivity. The input module is used to input the extracted key parameters into the pre-constructed macroscopic electrochemical model. The tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction. The prediction module is used to set zero current boundary conditions in the macroscopic electrochemical model, solve the spatial distribution of solid phase potential to obtain the solid phase potential of the positive and negative current collectors, and obtain the terminal voltage of the cell before formation based on the solid phase potential of the positive and negative current collectors.

[0017] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0018] Another object of the present invention is to provide an electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0019] This invention constructs a three-dimensional porous medium model containing active particles, a porous phase, and a binder phase based on the microstructural parameters of the positive and negative electrodes of the battery cell under test. This model can recreate the true microstructure of the electrodes using only design parameters, eliminating the need to prepare physical battery cell samples and fundamentally breaking away from the dependence of existing testing methods on physical batteries. Subsequently, the lattice Boltzmann method is used to simulate the wetting process of the electrolyte in the three-dimensional porous medium model, continuously tracking the complete evolution of the gas-liquid two-phase displacement. Three key parameters with clear physical meaning—saturation curve, tortuosity, and effective ionic conductivity—are extracted from the simulation results to replace traditional empirical test data at discrete time points, providing continuous and accurate microscopic input for electrochemical calculations. Next, the extracted key parameters are input into a pre-constructed macroscopic electrochemical model. The simulated tortuosity and effective ionic conductivity replace the original empirical constants in the model, and the electrodes are discretized along the thickness direction to establish a quantitative mapping between pore-scale fluid behavior and battery-scale electrochemical response, breaking down the scale barrier between existing wetting simulations and electrochemical calculations. Finally, a zero-current boundary condition matching the pre-formation operating conditions is set in the macroscopic electrochemical model to solve the spatial distribution of the solid-phase potential. The terminal voltage of the cell before formation is quantitatively calculated using the solid-phase potential difference between the positive and negative current collectors, achieving accurate prediction of the formation front-end voltage without physical samples. This solves the problems of existing technologies that rely on physical cell testing, cannot predict the formation front-end voltage during the cell design stage, and lack physical model support and quantitative accuracy in wetting assessment. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a cell terminal voltage prediction method based on lattice Boltzmann simulation provided in an embodiment of the present invention. Figure 2 This is a block diagram of the cell terminal voltage prediction system based on lattice Boltzmann simulation in the third embodiment of the present invention.

[0021] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0022] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0023] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] Example 1 Please see Figure 1 The figure shows a cell terminal voltage prediction method based on lattice Boltzmann simulation in the first embodiment of the present invention, the method including steps S10 to S13.

[0026] Step S10: Based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test, a three-dimensional porous medium model including active particles, porous phase and binder phase is established.

[0027] The microstructure parameters include porosity, pore size distribution, particle size, and binder distribution, which can be obtained through scanning electron microscopy (SEM) image recognition and electrode performance test reports. The modeling process involves independently constructing the positive and negative electrodes, using voxelized meshes to generate a three-dimensional computational domain. The active particles are set as the solid-phase main structure, the porous phase serves as the channel space for electrolyte flow, and the binder phase is the solid-phase component attached to the particle surface, ultimately forming a three-dimensional porous medium model containing a three-phase structure. The model size is set according to the representative volume unit of the electrode microstructure, with a computational region of 200μm*200μm*100μm selected. The mesh resolution is set to 1 micrometer per mesh to ensure accurate characterization of the pore morphology and particle arrangement features inside the electrode.

[0028] Step S11: Simulate the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method, and extract key parameters based on the simulation results. The key parameters include saturation curve, tortuosity, and effective ionic conductivity.

[0029] Specifically, the constructed three-dimensional porous medium model is imported into the lattice Boltzmann simulation framework. Fluid properties of the electrolyte and gas phases, including density, viscosity, and interfacial tension, are pre-set. The simulation measures the gradual penetration of the electrolyte from one side of the electrode into the internal pores, recording the electrolyte distribution within the pores at different times. After the simulation, three core parameters are extracted from the results: the first is the saturation curve, which shows the overall electrolyte saturation of the electrode as a function of wetting time; saturation is defined as the ratio of the volume occupied by the electrolyte in the pore space to the total pore volume. The second is tortuosity, which characterizes the degree of tortuosity of the pore channels and can be calculated using the tracer particle method or diffusion path statistics. The third is the effective ionic conductivity, reflecting the actual conductivity of the electrolyte in the porous medium under wetting conditions, calculated by combining the bulk electrolyte parameters with saturation and tortuosity.

[0030] For example, the formula for calculating effective ionic conductivity is as follows: ; in, Effective ionic conductivity directly reflects the actual ion transport capacity of the electrolyte within the pore structure. The conductivity of the bulk electrolyte can be obtained through experimental testing on an electrochemical workstation or by referring to parameters in the electrolyte manual. Its value is directly related to the electrolyte formulation, ambient temperature, and lithium salt concentration. The tortuosity is determined by the geometry of the three-dimensional porous structure. A higher tortuosity indicates a longer actual ion transport path and a lower effective conductivity. The saturation curve is directly output from the real-time results of the lattice Boltzmann wetting simulation. The saturation index reflects the rate of decay of conductivity under partially saturated conditions. Its value is related to the morphological characteristics of the electrode pore structure. The saturation index of conventional lithium-ion battery electrodes ranges from 1.0 to 1.5 and can be determined through experimental calibration or microscopic simulation fitting.

[0031] For example, the step of simulating the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method includes: A multiphase flow lattice Boltzmann model was used to simulate the gas-liquid two-phase displacement during the electrolyte wetting process. The multiphase flow lattice Boltzmann model adopted either a pseudo-potential model or a color gradient model. The specific boundary conditions for the simulation of the multiphase flow lattice Boltzmann model are as follows: the inlet adopts a constant pressure or constant flow liquid injection mode, the outlet is set to atmospheric pressure to achieve gas discharge, and the wall is set as a non-wetting boundary or a partially wetting boundary.

[0032] The multiphase flow lattice Boltzmann model employs either a pseudo-potential model or a color gradient model. The pseudo-potential model introduces intermolecular forces between different phases to achieve phase separation, naturally simulating the evolution of the interface and contact angle effects. Its simple parameter settings make it suitable for wetting simulations within complex porous structures. The color gradient model tracks interface positions by marking different phases with color functions, providing higher interface clarity and accurately simulating the propulsion morphology of the gas-liquid interface. It is suitable for simulation scenarios requiring high detail in the wetting front. Both models accurately describe the displacement and permeation behavior of the electrolyte within the porous electrode, allowing for flexible selection based on the simulation's focus.

[0033] The simulation boundary conditions for the multiphase flow lattice Boltzmann model are specifically set as follows: the inlet uses a constant pressure or constant flow rate electrolyte injection mode, the outlet is set to atmospheric pressure to allow gas discharge, and the wall surface is set as a non-wetting boundary or a partially wetting boundary. The inlet boundary has two modes: constant pressure and constant flow rate. In the constant pressure injection mode, the electrolyte pressure at the inlet is kept constant to simulate the constant pressure injection condition in the actual electrolyte injection process; in the constant flow rate injection mode, the electrolyte injection flow rate is kept fixed to match the quantitative electrolyte injection production scenario. The outlet boundary is set to atmospheric pressure to allow the gas displaced by the electrolyte in the pores to freely discharge, simulating the exhaust state of the cell's electrolyte injection port. The wall boundary is set as a non-wetting boundary or a partially wetting boundary. The contact angle is controlled by adjusting the interaction parameters between the wall and the electrolyte. The non-wetting boundary corresponds to a contact angle greater than 90 degrees, and the partially wetting boundary corresponds to a contact angle less than 90 degrees, which can match the wetting characteristics of different electrode materials and electrolytes.

[0034] Step S12: Input the extracted key parameters into the pre-constructed macroscopic electrochemical model, wherein tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction.

[0035] The process involves pre-constructing a macroscopic electrochemical model based on porous electrode theory, such as the P2D model. This model primarily focuses on the electrode thickness direction as the solution dimension, describing the fundamental laws governing solid-phase conductivity and liquid-phase ion transport within the electrode. The tortuosity and effective ionic conductivity extracted from lattice Boltzmann simulations are used as input parameters to replace the empirical constants previously employed in the model, ensuring that the model parameters correspond to the actual microscopic wetting state of the electrode. Simultaneously, the positive and negative electrodes are discretized along their thickness directions, dividing them into several computational nodes. This allows the parameters obtained from the microscopic simulation to be mapped point-by-point to the corresponding positions in the macroscopic model, achieving parameter transfer and scale integration from the pore scale to the cell scale.

[0036] Step S13: Set zero current boundary conditions in the macroscopic electrochemical model, solve the spatial distribution of solid phase potential to obtain the solid phase potential of the positive current collector and the negative current collector, and obtain the terminal voltage of the cell before formation based on the potential difference between the solid phase potentials of the positive current collector and the negative current collector.

[0037] In the macroscopic electrochemical model, a zero-current boundary condition is set, meaning no current flows through the external circuit of the cell, perfectly matching the actual operating condition of the cell being stored statically before formation. Based on the principle of charge conservation, the potential spatial distribution of the electrode solid phase and the potential spatial distribution of the electrolyte liquid phase are solved separately. The solid phase potential remains uniformly distributed at the current collector position, while the liquid phase potential changes with the wetting state and ion concentration distribution. After the solution is completed, the steady-state solid phase potential at the positive electrode current collector position and the steady-state solid phase potential at the negative electrode current collector position are extracted separately. The difference between the two is used to calculate the cell's terminal voltage. This terminal voltage is the open-circuit terminal voltage of the cell after electrolyte injection, before formation, and in the uncharged / undischarged state, typically on the order of tens to two hundred millivolts.

[0038] In addition, in some optional embodiments of the present invention, after the step of obtaining the terminal voltage of the cell before formation based on the solid-state potential of the positive current collector and the negative current collector, the method further includes: Determine whether the terminal voltage of the battery cell before formation is within a preset threshold range; When it is determined that the terminal voltage of the cell before formation is within the preset terminal voltage threshold range, the cell is deemed to be qualified for wetting.

[0039] Among them, after obtaining the terminal voltage before cell formation, the qualified determination of the cell immersion state can be completed based on the terminal voltage value.

[0040] The simulation-calculated front-end voltage value is compared with the pre-set terminal voltage threshold range of the corresponding design system to determine the range of the value. If the terminal voltage before cell formation is within the preset threshold range, the cell is considered to have passed the electrolyte wetting test. If the terminal voltage is within the preset threshold range, the electrolyte wetting state of the cell is considered acceptable, with no large dry areas or large bubbles inside the electrodes, and it can proceed to the subsequent formation process. If the terminal voltage is below the lower limit of the threshold range, the cell is considered to have poor wetting, with dry areas or residual bubbles inside the electrodes that have not been wetted by the electrolyte, requiring an extended settling time or investigation of electrolyte injection process defects. If the terminal voltage is above the upper limit of the threshold range, it is necessary to investigate whether the cell has micro-short circuits or material abnormalities. This judgment method can quickly screen out cells that fail to pass the electrolyte wetting test before the formation process, preventing defective products from flowing into subsequent processes and effectively reducing production costs.

[0041] Furthermore, the preset terminal voltage threshold range is determined through parametric scanning and quality back-calculation, and a threshold database can be further constructed to enable rapid retrieval.

[0042] First, the stable terminal voltage values ​​of the battery cell after full wetting were obtained under different cell design parameters. For cells with different design systems, the corresponding design parameters were adjusted, including the type of positive and negative electrode materials, electrode porosity, compaction density, cell structure, and electrolyte formulation. For each set of design parameters, the entire process of 3D modeling, lattice Boltzmann wetting simulation, and electrochemical calculations was repeatedly executed to calculate the stable terminal voltage value of the cell after full electrolyte wetting. This stable value represents the plateau voltage value after 100% saturation, corresponding to the theoretical upper limit of the terminal voltage after full wetting.

[0043] Secondly, based on the cell's production quality standards, the minimum electrolyte saturation level of the cell is calculated. Combined with the evolution curve of the terminal voltage value, the corresponding lower limit threshold of the terminal voltage value is obtained. According to the cell's production quality standards, including capacity loss limits, internal resistance limits, and cycle life requirements, the minimum electrolyte saturation level required for the cell to meet quality requirements is calculated. Substituting this minimum saturation level into the evolution curve of terminal voltage versus saturation level, the corresponding lower limit threshold of the terminal voltage is obtained. This lower limit value is the lowest terminal voltage corresponding to the cell's qualified wetting state.

[0044] The range defined by the terminal voltage value and the lower threshold of the terminal voltage value is used as the preset terminal voltage threshold range for the corresponding battery cell. The stable terminal voltage value after full immersion is used as the upper limit of the threshold range, and the lower threshold of the terminal voltage obtained by reverse calculation is used as the lower limit of the threshold range. The numerical range defined by the two is the preset terminal voltage threshold range for the battery cell of the corresponding design system. If the terminal voltage measured after the battery cell has been filled with electrolyte and allowed to stand falls within this range, it can be determined that the immersion state meets the production quality requirements.

[0045] Finally, multiple different battery cells and their corresponding preset terminal voltage threshold ranges are stored to construct a terminal voltage threshold range database. When a terminal voltage threshold range is needed, the corresponding range is retrieved from the database based on the different battery cells. A one-to-one correspondence is established between battery cells of multiple design systems and their corresponding preset terminal voltage threshold ranges, constructing a terminal voltage threshold range database covering multiple materials and structures. The database is indexed according to dimensions such as positive electrode material, negative electrode material, porosity, and cell structure. When the terminal voltage threshold range of a specific system is needed in the production or design phase, the corresponding cell design parameters can be input to quickly find the matching threshold range in the database, eliminating the need for repeated simulation calculations and significantly improving process decision-making efficiency and design iteration speed.

[0046] In summary, the cell terminal voltage prediction method based on lattice Boltzmann simulation in the above embodiments of the present invention constructs a three-dimensional porous medium model containing active particles, porous phases, and binder phases based on the microstructural parameters of the positive and negative electrode sheets of the cell under test. It can reconstruct the true microstructure of the electrode sheets using only design parameters, eliminating the need to prepare physical cell samples and fundamentally freeing existing detection methods from dependence on physical cells. Subsequently, the lattice Boltzmann method is used to simulate the wetting process of the electrolyte in the three-dimensional porous medium model, continuously tracking the complete evolution of the gas-liquid two-phase displacement process. Three key parameters with clear physical significance—saturation curve, tortuosity, and effective ionic conductivity—are extracted from the simulation results to replace traditional discrete-time empirical test data, providing continuous and accurate microscopic input for electrochemical calculations. Next, the extracted key parameters are input into a pre-constructed macroscopic electrochemical model. The original empirical constants in the model are replaced with the tortuosity and effective ionic conductivity obtained from the simulation. The electrode is discretized along the thickness direction to establish a quantitative mapping between pore-scale fluid behavior and cell-scale electrochemical response, thus breaking down the scale barrier between existing wetting simulations and electrochemical calculations. Finally, zero-current boundary conditions matching the pre-formation operating conditions are set in the macroscopic electrochemical model to solve the spatial distribution of solid-phase potential. Through the solid-phase potential difference between the positive and negative electrode current collectors, the terminal voltage of the cell before formation is quantitatively calculated, achieving accurate prediction of the formation front-end voltage under conditions without physical samples. This solves the problems of existing technologies that rely on physical cell testing, cannot predict the formation front-end voltage during the cell design stage, and lack physical model support and quantitative accuracy in wetting assessment.

[0047] Example 2 This embodiment also proposes a cell terminal voltage prediction method based on lattice Boltzmann simulation. The difference between the cell terminal voltage prediction method based on lattice Boltzmann simulation in this embodiment and the cell terminal voltage prediction method based on lattice Boltzmann simulation in Embodiment 1 is as follows: The steps of setting zero-current boundary conditions in the macroscopic electrochemical model and solving for the spatial distribution of the solid-phase potential to obtain the solid-phase potentials of the positive and negative electrode current collectors include: The positive and negative electrodes, after being discretized along the thickness direction of the electrodes, are divided into multi-layered series electrochemical micro-elements. The tortuosity and effective ionic conductivity parameters extracted by lattice Boltzmann simulation corresponding to different thickness positions of the electrodes are mapped and assigned to each layer of electrochemical micro-elements, forming an array of electrochemical parameters that are non-uniformly distributed along the thickness direction of the electrodes. Based on the law of charge conservation and the balance of electrode solid-phase potential, and combined with the non-uniform electrochemical parameter array of each electrochemical micro-element, a solid-phase potential control equation considering the non-uniformity of the electrolyte wetting space is constructed. Zero current boundary conditions are set on the outer end faces of the positive and negative electrode current collectors, and inner boundary conditions with continuous potential and current density are set on the interface of adjacent electrochemical micro-element. The solid-state potential control equation is solved numerically through iteration to obtain the full-field distribution of solid-state potential along the electrode thickness direction. The steady-state solid-state potential values ​​at the corresponding positions of the positive and negative current collectors are then extracted.

[0048] Among them, the process of setting zero current boundary conditions and solving the spatial distribution of solid-phase potential in the macroscopic electrochemical model is the core link to realize the quantitative mapping from microscopic wetting state to macroscopic terminal voltage. This link fully considers the actual characteristics of uneven wetting of electrolyte along the thickness direction of electrode. Accurate calculation is completed through layered micro-element mapping, non-uniform parameter modeling and constrained solution. The specific implementation process is as follows.

[0049] First, the positive and negative electrodes, discretized along the thickness direction, are divided into multi-layered, cascaded electrochemical micro-elements. The tortuosity and effective ionic conductivity parameters at different thickness positions of the corresponding electrodes, extracted from lattice Boltzmann simulations, are mapped and assigned to each layer of electrochemical micro-elements, forming a non-uniformly distributed array of electrochemical parameters along the electrode thickness direction. An electrochemical micro-element refers to a thin-layer computational unit obtained by dividing the electrode along its thickness direction. The thickness of each micro-element is consistent with the discrete step size of the macroscopic electrochemical model. The pore structure and wetting state within a single layer can be approximated as uniformly distributed; cascading multiple layers can reconstruct the complete structure along the electrode thickness direction. The wetting of the electrolyte within the porous electrode is a gradual process of penetration from the injection side inwards. The saturation and effective conductivity along the electrode thickness direction exhibit a gradual distribution characteristic. If the overall average parameters are directly substituted into the macroscopic model, the influence of the non-uniformity of the wetting space on the potential distribution will be ignored, leading to deviations in the calculated terminal voltage.

[0050] Before parameter mapping, the lattice Boltzmann 3D simulation results are first processed by surface averaging in the thickness direction. The average saturation, average tortuosity, and average effective ionic conductivity corresponding to each thickness position are extracted according to the discrete intervals of the macroscopic model. Then, following a one-to-one spatial correspondence rule, the parameters are sequentially assigned to the corresponding electrochemical micro-elements. The resulting non-uniform electrochemical parameter array is a sequence of parameters arranged along the electrode thickness direction. Each element in the sequence corresponds to the liquid-phase conductivity parameter of a layer of electrochemical micro-elements. The parameter values ​​of micro-elements closer to the injection end face are higher, and the parameter values ​​of micro-elements farther from the injection end face are lower, completely restoring the spatial gradient characteristics of the wetting process.

[0051] Secondly, based on the law of charge conservation and the balance relationship of the electrode solid-phase potential, and combined with the non-uniform electrochemical parameter array of each electrochemical element, a solid-phase potential control equation considering the non-uniformity of the electrolyte wetting space is constructed. Zero-current boundary conditions are set at the outer end faces of the positive and negative electrode current collectors, and continuous inner boundary conditions of potential and current density are set at the interface of adjacent electrochemical elements. The law of charge conservation is the fundamental physical principle for electrode electrochemical calculations, specifically manifested as the change in current density at any position within the electrode solid phase being equal to the exchange current density generated by the electrochemical reaction at the solid-liquid interface at that position. The solid-phase potential balance relationship means that under steady-state open-circuit conditions, the charge distribution at all points within the electrode remains stable, and the solid-phase potential no longer changes with time. The solid-phase potential control equation uses the solid-phase potential as the solution variable, and the liquid-phase conductivity coefficient in the equation directly adopts the non-uniform parameters of the corresponding electrochemical elements, which differs from the traditional model's use of constant empirical parameters, and can truly reflect the differences in ion transport capacity in the non-uniform wetting region. The zero-current boundary condition is set at the outer end face where the positive and negative electrode current collectors contact the active material layer. Specifically, the normal solid-phase current density at this end face is zero, perfectly matching the open-circuit condition after the cell has been filled with electrolyte and has not been charged or discharged, with the external circuit disconnected, ensuring that the calculated operating conditions are consistent with the actual state before formation. The inner boundary conditions of adjacent electrochemical micro-element interfaces include two constraints. The first is a potential continuity constraint, that is, the solid-phase potential values ​​on both sides of the interface are equal, corresponding to the physical reality of no potential abrupt change inside the electrode. The second is a current density continuity constraint, that is, the normal current density values ​​on both sides of the interface are equal, corresponding to the physical reality of no charge accumulation at the interface. The two constraints together ensure the continuity of physical quantities in the solution domain and the physical consistency of the calculation results.

[0052] Finally, the solid-state potential control equation is solved numerically through iteration to obtain the full-field distribution of solid-state potential along the electrode thickness direction, and the steady-state solid-state potential values ​​at corresponding positions of the positive and negative current collectors are extracted. The numerical iteration first transforms the continuous solid-state potential control equation into a set of algebraic equations corresponding to discrete nodes, each node corresponding to the center position of a layer of electrochemical micro-element. During the iteration process, the calculated solid-state potential value of each node is updated sequentially, and the degree of satisfaction of the charge conservation constraint is checked simultaneously. When the maximum deviation between the calculated potential values ​​of all nodes and the previous iteration value is less than the preset convergence threshold, the calculation is considered to have reached steady-state convergence. The preset convergence threshold can be set according to accuracy requirements; for conventional engineering calculations, it can be set to the order of 10⁻⁶ volts, while for high-precision scientific research calculations, the threshold can be further reduced. The output solid-state potential full-field distribution results after the solution is completed include the solid-state potential values ​​at all discrete positions along the electrode thickness direction, which can intuitively present the potential change law from the current collector side to the separator side. Finally, the corresponding end face positions of the positive and negative current collectors in the computational domain were located, and the solid-state potential values ​​after the steady state was reached at these positions were extracted as the basis data for subsequent calculation of the front-end voltage of the cell formation.

[0053] Furthermore, the step of numerically iteratively solving the solid-state potential control equation includes: The solid-state potential control equations are spatially discretized using the finite difference method, and iterative calculations are performed using a P2D solver written in Python; or An electrochemical-fluid coupling model at the macroscopic pore scale is constructed based on the extended lattice Boltzmann method. Within the unified numerical framework of this coupling model, the synchronous iterative solution of the solid-state potential governing equations is completed; or A macroscopic pore-scale electrochemical-fluid coupling model was constructed using commercial simulation software with an electrochemical numerical calculation module, and the numerical solution of the solid-phase potential control equation was completed by calling the software's built-in iterative solver.

[0054] Among them, the numerical iterative solution of the solid-state potential control equation can be implemented in three different ways, which can be flexibly selected according to the computing resources and accuracy requirements.

[0055] The finite difference method (FD) is used to spatially discretize the solid-state potential control equations, and an iterative solution is performed using a Python-based P2D solver. The FD transforms continuous differential equations into a system of algebraic equations at each node, and spatial discretization is achieved by approximating derivatives with difference quotients. The P2D model solver, written in Python, includes a parameter input module, a mesh discretization module, an equation assembly module, and an iterative solution module. The iterative solution can employ either the Gauss-Seidel iteration method or the Newton-Raphson iteration method. This approach features flexible code and a high degree of customizability, allowing for adjustments to model parameters and solution logic as needed, making it suitable for scientific research and rapid validation of solutions.

[0056] A macroscopic pore-scale electrochemical-fluid coupled model is constructed based on the extended lattice Boltzmann method, enabling the synchronous iterative solution of the solid-phase potential governing equations within the unified numerical framework of this coupled model. The extended lattice Boltzmann method transforms the electrochemical governing equations into lattice Boltzmann form distribution function evolution equations, placing them within a unified numerical framework with the fluid wetting simulation. In this coupled model, the fluid wetting state and electrochemical parameters are transmitted bidirectionally in real time. Changes in saturation during the wetting process directly update the input parameters for the electrochemical calculations, and the results of the electrochemical calculations can also be fed back into the fluid simulation process. This approach enables the completion of the entire calculation process from microscopic wetting to macroscopic electrical performance within a single numerical platform, avoiding errors and efficiency losses caused by data interaction with multiple software programs.

[0057] A macroscopic pore-scale electrochemical-fluid coupling model is constructed using commercial simulation software with an electrochemical numerical calculation module. The software's built-in iterative solver is then used to numerically solve the solid-state potential governing equations. Optional commercial software includes electrochemical extension modules for multiphysics simulation software and computational fluid dynamics software. The electrode geometry model and parameter files output from the lattice Boltzmann simulation are imported into the software. Corresponding electrochemical boundary conditions and solution algorithms are set, and the software's built-in iterative solver is used to numerically solve the solid-state potential governing equations. This approach relies on mature commercial solvers, offering superior computational stability and convergence, making it suitable for large-scale engineering calculations and industrial applications.

[0058] In summary, the cell terminal voltage prediction method based on lattice Boltzmann simulation in the above embodiments of the present invention constructs a three-dimensional porous medium model containing active particles, porous phases, and binder phases based on the microstructural parameters of the positive and negative electrode sheets of the cell under test. It can reconstruct the true microstructure of the electrode sheets using only design parameters, eliminating the need to prepare physical cell samples and fundamentally freeing existing detection methods from dependence on physical cells. Subsequently, the lattice Boltzmann method is used to simulate the wetting process of the electrolyte in the three-dimensional porous medium model, continuously tracking the complete evolution of the gas-liquid two-phase displacement process. Three key parameters with clear physical significance—saturation curve, tortuosity, and effective ionic conductivity—are extracted from the simulation results to replace traditional discrete-time empirical test data, providing continuous and accurate microscopic input for electrochemical calculations. Next, the extracted key parameters are input into a pre-constructed macroscopic electrochemical model. The original empirical constants in the model are replaced with the tortuosity and effective ionic conductivity obtained from the simulation. The electrode is discretized along the thickness direction to establish a quantitative mapping between pore-scale fluid behavior and cell-scale electrochemical response, thus breaking down the scale barrier between existing wetting simulations and electrochemical calculations. Finally, zero-current boundary conditions matching the pre-formation operating conditions are set in the macroscopic electrochemical model to solve the spatial distribution of solid-phase potential. Through the solid-phase potential difference between the positive and negative electrode current collectors, the terminal voltage of the cell before formation is quantitatively calculated, achieving accurate prediction of the formation front-end voltage under conditions without physical samples. This solves the problems of existing technologies that rely on physical cell testing, cannot predict the formation front-end voltage during the cell design stage, and lack physical model support and quantitative accuracy in wetting assessment.

[0059] Example 3 Please see Figure 2 The figure shows a cell terminal voltage prediction system based on lattice Boltzmann simulation proposed in Embodiment 3 of the present invention. The system includes: Module 100 is established to build a three-dimensional porous medium model containing active particles, porous phase and binder phase based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test. The simulation module 200 is used to simulate the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method. Based on the simulation results, key parameters are extracted, including saturation curve, tortuosity, and effective ionic conductivity. The input module 300 is used to input the extracted key parameters into the pre-constructed macroscopic electrochemical model, wherein tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction. The prediction module 400 is used to set zero current boundary conditions in the macroscopic electrochemical model, solve the spatial distribution of solid phase potential to obtain the solid phase potential of the positive current collector and the negative current collector, and obtain the terminal voltage of the cell before formation based on the solid phase potential of the positive current collector and the negative current collector.

[0060] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0061] Example 4 In another aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in any one of Embodiments 1 to 2 above.

[0062] Example 5 In another aspect, the present invention provides an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of any one of the methods described in Embodiments 1 to 2 above.

[0063] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean 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.

[0065] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), 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 storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0066] 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.

[0067] In the description of this specification, 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 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.

[0068] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for predicting cell terminal voltage based on lattice Boltzmann simulation, characterized in that, The method includes: Based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test, a three-dimensional porous medium model including active particles, porous phase and binder phase is established. The wetting process of electrolyte in a three-dimensional porous medium model was simulated using the lattice Boltzmann method. Key parameters were extracted based on the simulation results, including saturation curve, tortuosity, and effective ionic conductivity. The extracted key parameters are input into a pre-constructed macroscopic electrochemical model, where tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction. In the macroscopic electrochemical model, a zero-current boundary condition is set, and the spatial distribution of the solid-phase potential is solved to obtain the solid-phase potential of the positive and negative current collectors. Based on the potential difference between the solid-phase potentials of the positive and negative current collectors, the terminal voltage of the cell before formation is obtained.

2. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 1, characterized in that, The formula for calculating effective ionic conductivity is: ； in, For effective ionic conductivity, The conductivity of the bulk electrolyte. For the degree of curvature, This is a saturation curve. This is the saturation index.

3. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 1, characterized in that, The steps of setting zero-current boundary conditions in the macroscopic electrochemical model and solving for the spatial distribution of solid-phase potential to obtain the solid-phase potentials of the positive and negative electrode current collectors include: The positive and negative electrodes, after being discretized along the thickness direction of the electrodes, are divided into multi-layered series electrochemical micro-elements. The tortuosity and effective ionic conductivity parameters extracted by lattice Boltzmann simulation corresponding to different thickness positions of the electrodes are mapped and assigned to each layer of electrochemical micro-elements, forming an array of electrochemical parameters that are non-uniformly distributed along the thickness direction of the electrodes. Based on the law of charge conservation and the balance of electrode solid-phase potential, and combined with the non-uniform electrochemical parameter array of each electrochemical micro-element, a solid-phase potential control equation considering the non-uniformity of the electrolyte wetting space is constructed. Zero current boundary conditions are set on the outer end faces of the positive and negative electrode current collectors, and inner boundary conditions with continuous potential and current density are set on the interface of adjacent electrochemical micro-element. The solid-state potential control equation is solved numerically through iteration to obtain the full-field distribution of solid-state potential along the electrode thickness direction. The steady-state solid-state potential values ​​at the corresponding positions of the positive and negative current collectors are then extracted.

4. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 3, characterized in that, The steps for numerically iteratively solving the solid-state potential control equation include: The solid-state potential control equations are spatially discretized using the finite difference method, and iterative calculations are performed using a P2D solver written in Python; or An electrochemical-fluid coupling model at the macroscopic pore scale is constructed based on the extended lattice Boltzmann method. Within the unified numerical framework of this coupling model, the synchronous iterative solution of the solid-state potential governing equations is completed; or A macroscopic pore-scale electrochemical-fluid coupling model was constructed using commercial simulation software with an electrochemical numerical calculation module, and the numerical solution of the solid-phase potential control equation was completed by calling the software's built-in iterative solver.

5. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 1, characterized in that, The steps for simulating the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method include: A multiphase flow lattice Boltzmann model was used to simulate the gas-liquid two-phase displacement during the electrolyte wetting process. The multiphase flow lattice Boltzmann model adopted either a pseudo-potential model or a color gradient model. The specific boundary conditions for the simulation of the multiphase flow lattice Boltzmann model are as follows: the inlet adopts a constant pressure or constant flow liquid injection mode, the outlet is set to atmospheric pressure to achieve gas discharge, and the wall is set as a non-wetting boundary or a partially wetting boundary.

6. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 1, characterized in that, Following the step of obtaining the terminal voltage of the battery cell before formation based on the solid-phase potential of the positive and negative current collectors, the method further includes: Determine whether the terminal voltage of the battery cell before formation is within a preset threshold range; When it is determined that the terminal voltage of the cell before formation is within the preset terminal voltage threshold range, the cell is deemed to be qualified for wetting.

7. The cell terminal voltage prediction method based on lattice Boltzmann simulation according to claim 6, characterized in that, The steps for determining the preset terminal voltage threshold range include: Obtain the stable terminal voltage value of the battery cell after it is fully immersed under different battery cell design parameters; Based on the production quality standards of the battery cell, the minimum electrolyte saturation of the battery cell is calculated, and combined with the evolution curve of the terminal voltage value, the corresponding lower limit threshold of the terminal voltage value is obtained. The range between the terminal voltage value and the lower limit threshold of the terminal voltage value is used as the preset terminal voltage threshold range for the corresponding battery cell. Multiple different battery cells and their corresponding preset terminal voltage threshold ranges are stored to build a terminal voltage threshold range database. When it is necessary to obtain the terminal voltage threshold range, the corresponding terminal voltage threshold range is found in the terminal voltage threshold range database based on different battery cells.

8. A cell terminal voltage prediction system based on lattice Boltzmann simulation, characterized in that, The system includes: A module is established to build a three-dimensional porous medium model containing active particles, porous phase and binder phase based on the microstructure parameters of the positive and negative electrode sheets of the battery cell under test. The simulation module is used to simulate the wetting process of electrolyte in a three-dimensional porous medium model using the lattice Boltzmann method. Based on the simulation results, key parameters are extracted, including saturation curve, tortuosity, and effective ionic conductivity. The input module is used to input the extracted key parameters into the pre-constructed macroscopic electrochemical model. The tortuosity and effective ionic conductivity are used as input parameters to replace the empirical constants of the macroscopic electrochemical model, and the electrode is discretized along the thickness direction. The prediction module is used to set zero current boundary conditions in the macroscopic electrochemical model, solve the spatial distribution of solid phase potential to obtain the solid phase potential of the positive and negative current collectors, and obtain the terminal voltage of the cell before formation based on the solid phase potential of the positive and negative current collectors.

9. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the program, implements the steps of the method as described in any one of claims 1 to 7.

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