Lithium ion battery attenuation prediction method and system based on multi-physics field coupling model

Abstract

The invention discloses a lithium ion battery attenuation prediction method and system based on a multi-physics field coupling model, and the method comprises the steps: determining a multi-physics field coupling relation and a model scale from a particle level to a module level; defining each sub-model and acquiring parameters of the multi-field coupling model according to actual simulation requirements, wherein the sub-models are used for simulating mass transfer, electrochemical reaction, temperature rise and thermal expansion, expansion force change and capacity attenuation of the battery; constructing a multi-field coupling model and boundary conditions, and determining the coupling mechanism and boundary conditions of the model, including electrochemical, heat conduction and mechanical constraint conditions; determining a numerical solution method based on COMSOL Multiphysics to perform simulation calculation of an electrochemical, thermal, force and aging coupling model; and the optimal pre-tightening force value is determined by simulating and analyzing the influence of different pre-tightening forces on the expansion force and the capacity attenuation of the battery. The method can effectively predict the capacity fading process of the battery, optimizes the battery design, and improves the reliability and service life of the battery in different application scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a lithium-ion battery attenuation prediction method and system based on an electrochemical-thermal-mechanical-aging coupling model. Background Art

[0002] Lithium-ion batteries have been widely used in new energy vehicles, energy storage systems and other fields due to their advantages such as high energy density, long life and high safety. However, the performance and service life of lithium batteries are largely affected by their working conditions. In particular, studies in recent years have shown that external forces have an important influence on battery performance and life. Studies have found that mechanical factors have a significant effect on the capacity decay of batteries during long-term use. Specifically, too low a preload may cause the components inside the battery to be in loose contact, while too high a preload may reduce the porosity of the porous components, thereby causing lithium precipitation, thereby affecting the normal operation and life of the battery. However, in battery design, determining the optimal preload through long-term cycle experiments is both time-consuming and costly, and this problem needs to be solved urgently.

[0003] In order to effectively predict the capacity decay of batteries under different preloads, and then guide the structural design of battery packs and improve their reliability throughout their life cycle, researchers proposed an electrochemical-thermal-mechanical-aging coupling model. However, the various existing models still have some limitations and fail to fully solve the complex interactions between mechanics, thermal forces and aging during the long cycle of batteries.

[0004] At present, the existing battery performance models mainly include electrochemical-thermal-mechanical coupling models and particle-level electrochemical-thermal-mechanical-aging coupling models. The former mainly focuses on the evolution of forces during a single charge and discharge process. Although it takes into account the coupling effects of electrochemistry, heat and mechanics, it fails to involve the aging process of the battery, nor can it predict the capacity decay of the battery during a long cycle and its impact on the mechanical state. The latter can predict the capacity decay of the battery to a certain extent by considering the influence of intercalation stress at the microscopic particle level on SEI film growth and active material fragmentation, but there are still some shortcomings: its calculation is limited to the particle level, and the influence of macroscopic preload on the long-cycle behavior of the battery is not considered, and the difference in battery capacity decay rate under different preloads cannot be effectively predicted.

[0005] Therefore, there is an urgent need for a new method that can combine the mechanical failure mechanism with the traditional electrochemical-thermal-mechanical coupling model to predict the capacity decay of the battery during long cycling under different preload forces and determine the optimal preload force, thereby effectively improving the battery life and performance. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for predicting the degradation of lithium-ion batteries based on a multi-physics field coupling model.

[0007] In order to achieve the above invention objectives, the technical solutions adopted by the present invention are as follows:

[0008] A method for predicting the degradation of lithium-ion batteries based on a multi-physics field coupling model, comprising the following steps:

[0009] S1: Determine the multi-physics field coupling relationship and model scale; the relationship includes the interaction mechanisms between electrochemistry-thermal, thermal-mechanical, mechanical-aging, and electrochemistry-mechanical, and the model scale includes particle level, electrode level, cell level, and module level;

[0010] S2: Define the dimensions of each sub-model according to the actual simulation requirements and obtain the multi-field coupling model parameters; the sub-models include the P2D electrochemical model, lumped thermal model, phenomenological mechanical model, and aging model. The P2D electrochemical model is used to simulate the solid-liquid mass transfer, conduction, and electrochemical reactions inside the battery. The lumped thermal model calculates the temperature rise and thermal expansion of the battery. The phenomenological mechanical model calculates the expansion of the battery and simulates the change of the expansion force under the constant-gap constraint condition of the battery in the actual module. The aging model calculates the capacity degradation of the battery under the action of three aging mechanisms: SEI growth, lithium plating, and mechanical failure;

[0011] S3: Construct a multi-field coupling model and boundary conditions, and determine the model coupling mechanism, including the P2D electrochemical model, lumped thermal model, phenomenological mechanical model, and aging model; the boundary conditions include the boundary conditions of electrochemical reactions, heat conduction, and mechanical constraints,

[0012] S4: Determine the model numerical solution method; the method is based on COMSOL Multiphysics for numerical simulation, and the electrochemical, thermal, mechanical, and aging coupling models are calculated through the lithium-ion battery nodes.

[0013] S5: Model verification and determination of the optimal pre-tightening force. By simulating the influence of different pre-tightening forces on the battery expansion force and capacity degradation, the optimal pre-tightening force value is obtained.

[0014] Furthermore, the multi-physical field coupling relationships include: electrochemistry-thermal: heat generation from electrochemical reactions increases the battery temperature, and the temperature affects the electrochemical reactions and mass transfer rate; electrochemistry-mechanical: lithium intercalation in the active material causes intercalation swelling, leading to fluctuations in force, and the force affects the component contact and material microstructure of the battery, and influences mass transfer; electrochemistry-aging: the capacity decay of the battery is affected by the internal mass transfer and the rate of electrochemical reactions; thermal-mechanical: thermal expansion causes fluctuations in force; thermal-aging: temperature affects the rate of battery capacity decay; force-aging: excessive force increases the risk of lithium plating in the battery, and too little force results in loose contact of the porous components.

[0015] Furthermore, the multi-physical field coupling model includes four parts: the P2D electrochemical model, the lumped thermal model, the phenomenological mechanics model, and the aging model; the P2D electrochemical model simulates the diffusion, electromigration mass transfer, and electrochemical behavior inside the battery; the lumped thermal model calculates the temperature rise and thermal expansion of the battery; the phenomenological mechanics model calculates the total expansion of the battery and simulates the change in expansion force under the constant gap constraint condition of the battery in the actual module; the aging model includes three aging mechanisms: SEI growth, lithium plating, and mechanical failure, which are described by different mathematical models respectively to control the battery capacity decay, electrode deactivation, and lithium plating behavior.

[0016] Furthermore, the P2D electrochemical model includes the following governing equations:

[0017] Solid-phase charge conservation equation:

[0018]

[0019] Liquid-phase charge conservation equation:

[0020]

[0021] Liquid-phase material conservation equation:

[0022]

[0023] Solid-phase material conservation equation:

[0024]

[0025] Electrochemical reaction current:

[0026]

[0027] Overpotential:

[0028]

[0029] In the equations, represents the effective solid-phase conductivity, φ srepresents the solid-phase potential, j represents the reaction current density, represents the effective liquid-phase conductivity, φ e represents the liquid-phase potential, represents the liquid-phase diffusion coefficient, c e represents the liquid-phase concentration, ε represents the porosity, represents the effective liquid-phase diffusion coefficient, t + represents the cation mobility, F represents the Faraday constant, c s represents the solid-phase concentration, D s represents the solid-phase diffusion coefficient, r represents the radius of the particle, a represents the specific surface area, i 0 represents the reference current density, α a represents the reaction coefficient of the anode, α c represents the reaction coefficient of the cathode, R represents the gas constant, T represents the battery temperature, η represents the overpotential, R film represents the resistance between the electrolyte and the electrode, U eq represents the equilibrium potential of the electrochemical reaction.

[0030] Furthermore, the lumped thermal model includes the following governing equations:

[0031] Heat source equation:

[0032]

[0033] Battery temperature change equation:

[0034]

[0035] Temperature dependence of the electrochemical model parameters:

[0036]

[0037] In the formula, represents the entropy heat coefficient of the electrochemical reaction of the battery, represents the effective liquid-phase conductivity, φ e represents the liquid-phase potential, C p : specific heat capacity, M cell : mass of the battery, Rate of change of the battery temperature with time, Q: heat generated by the heat source, A: heat dissipation area of the battery, h cell represents the convective heat transfer coefficient of the battery, T amb represents the ambient temperature, ψ represents the temperature-dependent parameters of physical quantities including diffusion coefficient, conductivity, and current density, ψ ref represents at the reference temperature T ref the parameter value at, represents the activation energy.

[0038] Furthermore, the phenomenological mechanics model includes the following governing equations:

[0039] Radial stress equation at the particle level:

[0040]

[0041] Tangential stress equation at the particle level:

[0042]

[0043] Strain equation at the particle level:

[0044]

[0045] Battery thickness change equation:

[0046] ΔL int =(ΔL p,corr +ΔL n,corr )·n p ·2

[0047]

[0048] ΔL total =ΔL int +ΔL th

[0049] Wherein, σ r,i (r) represents the radial stress at the particle level, Ω represents the partial molar volume of the particle, E represents the elastic modulus, v represents the Poisson's ratio, R i represents the radius of the particle, c s (r) represents the concentration distribution at a certain point in the particle, r represents the radial position, σ t,i (r) represents the tangential stress at the particle level, u i (r) represents the strain of the particle in the radial direction, represents the intercalation expansion of the battery, n p represents the number of positive electrode layers, ΔL p,corr and L n,corr and respectively represent the intercalation expansions of the positive and negative electrodes, ΔL th represents the thermal expansion of the battery, α cell represents the thermal expansion coefficient of the battery, L total represents the total thickness of the battery, T 0 represents the ambient temperature, x represents the spatial position, the distance from one end of the battery to the other end, ΔL total represents the total thickness change of the battery.

[0050] Furthermore, the aging model includes the following governing equations:

[0051] SEI growth control equation:

[0052]

[0053] Diffusion equation of EC in the electrolyte:

[0054]

[0055] SEI concentration change equation:

[0056]

[0057] Lithium plating control equation:

[0058]

[0059] Lithium plating-induced core swelling equation:

[0060]

[0061] Mechanical failure equation:

[0062]

[0063]

[0064] In the formula, j SEI represents the growth current density of the SEI film (solid electrolyte interface), k 0,SEI represents the reaction rate constant of the SEI reaction, α c,SEI represents the cathodic charge transfer coefficient of the SEI film reaction, j tot represents the total current density, R film represents the resistance of the film, U SEI represents the equilibrium potential of the SEI film, D EC represents the diffusion coefficient of EC, represents the concentration of EC near the solid electrolyte interface, represents the concentration of EC in the electrolyte, δ film represents the thickness of the SEI film, c SEI represents the concentration of the SEI reaction product, represents the concentration of ethylene molecules, j Li represents the lithium plating current density, i 0,Li represents the exchange current density of the lithium plating reaction, c Li represents the lithium ion concentration, represents the equilibrium value of the lithium ion concentration, α a,Li represents the anodic charge transfer coefficient of the lithium precipitation reaction, represents the equilibrium value of the lithium ion concentration in the electrolyte, α c,LiRepresents the cathodic charge transfer coefficient of the lithium precipitation reaction, ΔL Li Represents the amount of swelling of the battery core due to lithium precipitation, L n Represents the negative electrode thickness, R n Represents the particle size of the negative electrode particles, V n (θ n ) Represents the volume change coefficient of the battery core, ε s,n Represents the porosity of the battery core material.

[0065] Furthermore, the model is solved through numerical simulation, including the following sub-steps:

[0066] Select appropriate physical field modules, including electrochemical, thermal, mechanical, and aging modules, and define the corresponding physical properties and material parameters;

[0067] Create a battery geometric model and set boundary conditions, including electrochemical reaction, heat conduction, and mechanical constraint conditions;

[0068] Set the multi-physical field coupling relationship to ensure that the electrochemical, thermal, mechanical, and aging processes affect each other;

[0069] Select numerical solution methods, including time step, non-linear solver, and accuracy setting, to ensure the accuracy of the simulation results;

[0070] Divide the grid and perform simulation to solve, and evaluate the battery performance, including swelling force, temperature, and capacity decay, etc.;

[0071] Perform post-processing and analysis of the results, determine the optimal pre-tightening force, and verify the model.

[0072] The present invention also discloses a lithium-ion battery attenuation prediction system based on a multi-physical field coupling model. This system can be used to implement the above-mentioned lithium-ion battery attenuation prediction method based on a multi-physical field coupling model. Specifically, it includes:

[0073] Electrochemical model module: Used to simulate the mass transfer, electro-migration, and electrochemical reaction processes between the solid and liquid phases inside the battery. This module includes electrochemical reaction equations, charge conservation equations, material conservation equations, etc.;

[0074] Thermal model module: Used to calculate the temperature rise and thermal expansion of the battery. This module includes heat conduction equations, heat source calculations, and coupling of the temperature field;

[0075] Mechanical model module: Used to calculate the expansion and expansion force of the battery, and simulate the mechanical behavior of the battery under the constant gap constraint condition in the actual module. This module includes mechanical equations, stress-strain relationships, etc.;

[0076] Aging model module: used to describe the aging processes such as SEI growth, lithium plating, and mechanical failure of the battery. The module includes aging equations and corresponding physical mechanisms;

[0077] Multi-physics coupling module: used to couple electrochemistry, heat, mechanics, and aging models to ensure the mutual influence between physical fields;

[0078] Numerical solution module: used to calculate the mutual coupling and physical processes between modules through numerical simulation to ensure the solution and optimization of the model;

[0079] Result analysis module: used to analyze the simulation results and determine the optimal pre-tightening force, providing visualization and verification of battery performance.

[0080] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned lithium-ion battery attenuation prediction method based on the multi-physics coupling model is implemented.

[0081] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned lithium-ion battery attenuation prediction method based on the multi-physics coupling model is implemented.

[0082] Compared with the prior art, the advantages of the present invention are as follows

[0083] 1. By introducing the electrochemistry-thermal-mechanical-aging coupling model, the present invention can comprehensively predict the capacity attenuation of the battery under different pre-tightening force conditions, especially considering the combined effects of multiple aging mechanisms such as SEI growth, lithium plating, and mechanical failure, and solves the problem that traditional methods can only predict the influence of short cycles or single aging mechanisms.

[0084] 2. The present invention can simulate the evolution of the expansion force and capacity attenuation of the battery under different pre-tightening force conditions, determine the optimal pre-tightening force value, avoid the negative impacts caused by too low or too high pre-tightening force, and thus effectively improve the cycle life and safety of the battery.

[0085] 3. The present invention not only considers the electrochemical reactions inside the battery, but also comprehensively considers thermal effects, mechanical stresses, and aging mechanisms, and can more accurately simulate the complex behaviors of the battery in the actual working environment, overcoming the limitations of single-physics field models in the prior art.

[0086] 4. Through this model, the structural design of battery modules and battery packs can be optimized, the battery pre-tightening force can be reasonably adjusted, the battery damage caused by too large or too small mechanical stress can be prevented, and the high reliability of the battery pack throughout its life cycle can be ensured.

[0087] 5. Compared with the traditional experiment-based battery performance optimization method, the present invention provides a method based on numerical simulation, which can simulate the battery behavior under different working conditions through simulation, thereby reducing a large number of experiments and tests and saving the R & D time and cost.

[0088] 6. This model is not only applicable to power batteries in the field of new energy vehicles, but also can be widely applied to fields such as energy storage systems and consumer electronics, with strong generality and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 is the coupling framework diagram of the electrochemical-thermal-mechanical-aging model in the embodiment of the present invention;

[0090] Figure 2 is the conversion diagram of the force from particles to electrodes, battery expansion to module expansion force in the embodiment of the present invention;

[0091] Figure 3 is the schematic diagram of the phenomenological mechanics model in the embodiment of the present invention;

[0092] Figure 4 is the simulation result diagram of the typical different pre-tightening force conditions of the electrochemical-thermal-mechanical-aging model in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0093] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the following further describes the present invention in detail with reference to the drawings and by way of examples.

[0094] The present invention provides a method for predicting the attenuation of lithium-ion batteries based on a multi-physical field coupling model, which can predict the evolution of the expansion force during the long cycle of the battery and the difference in the capacity attenuation rate under different pre-tightening forces, and includes the following steps: ① determining the multi-physical field coupling relationship and model scale; ② defining the dimensions of each sub-model according to the actual simulation requirements and obtaining the multi-field coupling model parameters; ③ constructing the multi-field coupling model and boundary conditions, and determining the model coupling mechanism. This model includes four parts: the P2D electrochemical model, the lumped thermal model, the phenomenological mechanics model, and the aging model. The P2D electrochemical model simulates the mass transfer and electrochemical behaviors such as diffusion and electromigration inside the battery. The lumped thermal model calculates the temperature rise and thermal expansion of the battery. The phenomenological mechanics model calculates the total expansion of the battery and simulates the change of the expansion force under the constant gap constraint condition in the actual module. The aging model calculates the capacity attenuation of the battery under the action of three aging mechanisms: SEI growth, lithium plating, and mechanical failure. The models of each part are calculated in parallel and coupled with each other. The framework of the model coupling is as Figure 1 shown; ④ determining the model numerical solution method; ⑤ model verification and determination of the optimal pre-tightening force.

[0095] The following will introduce each step in detail:

[0096] ① Determine the multi-physical field coupling relationship and model scale

[0097] Clarify the interaction mechanisms among the electrochemical field, thermal field, mechanical field, and aging field, including:

[0098] Electrochemical-thermal: The heat generated by the electrochemical reaction increases the battery temperature, and the temperature affects the electrochemical reaction and mass transfer rate;

[0099] Electrochemical-mechanical: The intercalation of lithium into the active material causes intercalation swelling, resulting in force fluctuations. The force affects the component contact and material microstructure of the battery and mass transfer;

[0100] Electrochemical-aging: The capacity decay of the battery is affected by the internal mass transfer and the rate of the electrochemical reaction; the capacity decay will in turn cause the loss of active material, the loss of lithium storage, and the increase in impedance of the battery;

[0101] Thermal-mechanical: Thermal expansion causes force fluctuations;

[0102] Thermal-aging: Temperature affects the capacity decay rate of the battery;

[0103] Mechanical-aging: Excessive force increases the risk of lithium plating in the battery, and too little force causes loose contact of the porous components;

[0104] Determine the scale of each model, including:

[0105] Particle scale (the electrochemical model calculates the lithium concentration gradient, intercalation strain, and electrochemical reaction of the active material particles)

[0106] Electrode scale (the electrochemical model calculates the mass transfer in the battery thickness direction and the intercalation swelling of the electrode)

[0107] Cell scale (calculate the total expansion of the cell, and the thermal model calculates the cell temperature and thermal expansion)

[0108] Module scale (the phenomenological mechanics model calculates the battery expansion force)

[0109] Time scale (the mechanical aging model calculates the capacity decay rate of the battery)

[0110] ② Define the dimensions of each sub-model according to the actual simulation requirements and obtain the multi-field coupling model parameters

[0111] Define the dimensions of each sub-model:

[0112] Considering that the model needs to simulate the long cycle process of the battery, with high complexity and long calculation time, both the electrochemical and aging models adopt the P2D model

[0113] Similarly, in order to further improve the calculation efficiency of the model, the thermal model and the mechanical model adopt the zero-dimensional lumped thermal model and the phenomenological mechanics model

[0114] Obtain multi-field coupling parameters:

[0115] The model contains multiple physical fields and parameters at multiple levels. It can be freely selected according to actual needs through various methods such as experimental testing, literature research, and adjusting parameters by combining experimental data. The specific parameters and their physical meanings are shown in Table 1 below (all parameters that appear later are summarized in Table 1).

[0116] ③Construct a multi-field coupling model and boundary conditions, and determine the model coupling mechanism

[0117] P2D electrochemical model

[0118] The P2D electrochemical model solves the solid and liquid mass transfer, conduction, and electrochemical reactions inside the battery. The object described by the model is a pair of positive and negative electrode pairs facing each other. Among them, P2D, that is, "quasi-two-dimensional", includes: macroscopic one-dimensional (electrode thickness direction) and microscopic one-dimensional (radius direction of the active material particles). The main governing equations include:

[0119] Solid-phase charge conservation

[0120]

[0121] Liquid-phase charge conservation

[0122]

[0123] Liquid-phase material conservation

[0124]

[0125] Solid-phase material conservation

[0126]

[0127] Electrochemical reaction current

[0128]

[0129] Overpotential

[0130]

[0131] Lumped thermal model

[0132] The lumped thermal model is used to simulate the change of the average temperature of the battery. The heat source of the model comes from the electrochemical model, and the heat generation rate is:

[0133]

[0134] In the above formula, the three terms on the right side of the equal sign represent the entropy heat (reversible heat) in the electrochemical reaction process, the polarization heat caused by the deviation of the equilibrium potential, and the ohmic heat in the solid-liquid phase from left to right in sequence. Then, the average temperature change of the battery is calculated by the following formula:

[0135]

[0136] The feedback of the temperature field to the electrochemical field is reflected in the temperature dependence of the electrochemical model parameters, which will show an exponential increase or decrease with the increase of temperature. Specifically, the change of these parameters with temperature can be described by the Arrhenius equation:

[0137]

[0138] where ψ is any electrochemical model parameter affected by temperature, which realizes the two-way coupling of the electrochemical field and the temperature field.

[0139] Phenomenological mechanics model

[0140] After the insertion and extraction of lithium ions in the active material of the battery during charge and discharge, the lattice parameters will change. The change of the equivalent particle size of the particles will cause the change of the thickness of the electrode sheet and the winding core, and then affect the thickness of the battery. The expansion of the battery in the module will be externally constrained and thus converted into the change of the expansion force. Specifically, see Figure 2 , and below, the conversion of the particle size of the particles into the expansion of the electrode sheet, the winding core and the expansion force of the battery in the model will be introduced in sequence at four levels: particles, electrodes, batteries, and modules

[0141] Particle level - Particle size change

[0142] During the charge and discharge process of the battery, the insertion and extraction of lithium ions inside the active material will cause stress inside the material, commonly known as stress caused by diffusion (DIS). Assuming that the active material particles are spherical and have a uniform particle size, the radial stress (σ r ), tangential stress (σ t ) and radial displacement (u) inside the particles can be calculated by the following formula:

[0143]

[0144]

[0145] Let r = R in the above formula (12) i , and the particle size change of the active material is:

[0146]

[0147] Electrode level - Electrode sheet thickness change

[0148] Since the expansion in the in-plane direction of the electrode is restricted by the interaction between the particles, the binder, and the current collector, assuming that only the active material particles contribute to the expansion of the electrode and the electrode expands only in the thickness direction, the following relationship exists between the volume change ΔV of the active material particles and the thickness change ΔL of the battery electrode:

[0149]

[0150] Based on Taylor's formula and combining the above equations (11&13), the intercalation expansion of a single electrode is:

[0151]

[0152] Battery level - Battery thickness change

[0153] The expansion of the battery core includes the expansion caused by the electrochemical intercalation reaction and the thermal expansion. Among them, the intercalation expansion is the sum of the intercalation expansions of all the electrode foils:

[0154] ΔL int =(ΔL p,corr +ΔL n,corr )·n p ·2 (16)

[0155] For the thermal expansion caused by the temperature rise of the battery during charge and discharge, it is also assumed that the thermal expansion occurs only in the thickness direction of the battery, and considering the difference between the local thermal expansions caused by the temperature gradient in the thickness direction, the thermal expansion is:

[0156]

[0157] When not considering aging, the total expansion of the battery core is (after considering the irreversible expansion caused by aging, Equation (18) is modified to Equation (33)):

[0158] ΔL total =ΔL int +ΔL th (18)

[0159] Module level - Battery expansion force change

[0160] In an actual battery module, different battery cells are usually stacked together by a pre-tightening force. The gap between two battery cells is usually fixed, and a gasket is added in the middle for buffering. The present invention is as follows Figure 3The phenomenological mechanics model shown predicts the change in cell thickness under this constant gap condition. The model considers three components at the module level: the core, the shell (aluminum-plastic film), and the gasket, each of which is regarded as a spring unit. Among them, the expansion of the core will be constrained by the shell, and the thickness changes of the two are always the same, so the two are considered to be in parallel, while the gasket mainly plays a buffering role and is connected in series with the two. The model divides the evolution of internal stress of the battery into four steps: (a) free expansion of the core; (b) free expansion under shell constraints; (c) gasket buffering and compression of external preload; (d) compression under constant gap constraints. The three processes (bd) are subjected to force analysis and balance respectively, and the following results are obtained:

[0161]

[0162] In the above formula, the input parameter is the expansion of the core That is, the total expansion of the core L obtained by formula (18) total , if all component stiffness parameters are known, the above equations can be combined to solve the gasket force, which is the battery expansion force F that can be measured in the experiment. s .

[0163] Mechanical aging model

[0164] The aging mechanism of the model of the present invention includes SEI growth, lithium deposition and mechanical failure. Among them, SEI growth and lithium deposition will deposit byproducts on the surface of negative electrode particles, thereby causing changes in particle equivalent particle size, electrode thickness and battery expansion force. SEI growth and mechanical failure will affect porosity and thus affect lithium deposition. The parameters of each aging mechanism include temperature dependence. In summary, the three aging mechanisms are coupled with the electrochemical-thermal-mechanical model. The specific control equation is as follows:

[0165] SEI Growth

[0166] The film-forming reaction of the most common typical EC electrolyte in lithium batteries is considered in the model, and the reaction chemical equation is:

[0167] 2C 2 H 4 CO 3 +2e - +2Li + →(CH 2 OCO 2 Li 2 )↓+C 2 H 4 (twenty two)

[0168] The current density of the reaction is calculated by the cathode Tafel equation:

[0169]

[0170] The diffusion of EC in the electrolyte follows Fick's law:

[0171]

[0172] The accumulation of SEI and gas products is calculated through the mass conservation equation:

[0173]

[0174] Meanwhile, the effects of SEI growth on the negative electrode film resistance, porosity, and core swelling are considered:

[0175]

[0176] Lithium plating

[0177] Under conditions such as low temperature and high-rate charging, the increase in negative electrode polarization loss may cause lithium ions to fail to embed normally into the negative electrode material and instead precipitate on the negative electrode surface in the form of metallic lithium, resulting in the so-called "lithium plating" phenomenon. Lithium plating is not completely irreversible. During the CV segment, relaxation segment, or discharge segment, due to the reduction of polarization, the negative electrode potential will gradually increase. When it is higher than the equilibrium potential of lithium plating, which is 0V, lithium stripping (oxidation reaction) occurs, and the precipitated lithium will be re-oxidized into lithium ions and re-embedded into graphite. At this time, a B-V equation can be used to uniformly describe this reversible lithium plating (lithium stripping) reaction:

[0178]

[0179] Lithium plating will sharply reduce the porosity of the negative electrode and cause core swelling:

[0180]

[0181] The core swelling caused by SEI growth and lithium plating is input as irreversible swelling into the core swelling in Equation (18). At this time, the total swelling of the core is:

[0182] ΔL total = ΔL int + ΔL th + ΔL SEI + ΔL Li (33)

[0183] After that, the evolution of force during the long cycle of the battery is calculated through the phenomenological mechanics model of Equations (19 - 21).

[0184] Mechanical failure

[0185] The aging mechanism of mechanical failure is the key content of this invention. The model divides the impact of mechanical failure on the battery into two parts: too low force or too high force. On the one hand, when the force is too low, the gas generated by the SEI reaction (Equation (22)) occupies the pore volume, squeezing out the electrolyte. The active material cannot contact the electrolyte, blocking the mass transfer and conductive path, and the active material is deactivated. LAM causes the capacity attenuation of the battery. The specific governing equation is:

[0186] Integrating over all the negative electrode domains, the total volume of the gas can be obtained as:

[0187]

[0188] Equivalent all the gases in the battery to a cross-sectional area of A and a height of L gas , and the internal pressure of the battery is equal everywhere. Then the internal gas pressure P is equal to the external pressure F j / A exerted on the winding core. At this time, the total height L of the gas is calculated by the ideal gas state equation when the expansion force of different batteries changes: gas Change of:

[0189]

[0190] The gas volume affects the electrolyte saturation s:

[0191]

[0192] The electrolyte saturation affects the equivalent activity of the electrode:

[0193] a eq = f(s) (37)

[0194] Among them, the f(s) function describing the electrolyte saturation and the equivalent activity of the electrode can be adjusted according to the actual battery capacity attenuation curve. When the actual activity of the electrode is greater than the equivalent activity of the electrode, the electrode is deactivated:

[0195]

[0196] The reaction rate of deactivation is defined as:

[0197] r deact = k d ·(a - a eq ) (39)

[0198] On the other hand, when the force is too high, the porosity of the battery porous component decreases, the polarization increases, and the negative electrode potential is more likely to be lower than 0V, triggering lithium deposition. LLI causes the battery capacity to decrease rapidly:

[0199]

[0200] In the formula, the first term on the left side describes the decrease in porosity, and the second term describes the actual strain of the core The right end is the strain caused by particle intercalation, SEI, and lithium plating side reactions under free expansion conditions. The physical meaning of this formula is that when there is external mechanical constraint, a part of the particle-level particle size change is converted into the thickness change of the core, and the other part is converted into the porosity change.

[0201] The above are all the governing equations included in the electrochemistry-thermal-mechanical-aging coupling model of the present invention.

[0202] ④ Determine the numerical solution method of the model

[0203] Based on the above model governing equations, boundary conditions, and multi-physics coupling mechanism, the numerical calculation method based on COMSOL Multiphysics is as follows: In the model, both the electrochemistry model and the aging model are one-dimensional, and the lumped thermal model and the phenomenological mechanics model are zero-dimensional models. Therefore, only one component is required for simulation calculation, and the geometry is one-dimensional in the battery thickness direction. The electrochemistry and aging behaviors of the battery are calculated through the 'Lithium-ion battery' node. The two zero-dimensional models use a 'Global ordinary differential and differential algebraic equation' respectively to calculate the heat and force of the battery, and full coupling between multi-physics fields is achieved through the above governing equations.

[0204] ⑤ Model verification and determination of the optimal pre-tightening force

[0205] Figure 4 is the simulation output results under typical 1C / 1C long cycle conditions with different pre-tightening forces (0 - 2 MPa), and the comparison of electrode deactivation caused by too small force and lithium plating caused by too large force under different pre-tightening forces. It can be found that under too high pre-tightening force (2 MPa), the capacity loss caused by lithium plating is greater, and under too low pre-tightening force (0 MPa), the capacity loss caused by electrode deactivation is greater. At this time, the condition with the slowest capacity decay is 0.4 MPa, that is, the optimal pre-tightening force is about 0.4 MPa.

[0206] Table 1 Parameter symbols and meanings involved in the model

[0207]

[0208]

[0209]

[0210]

[0211] In another embodiment of the present invention, a lithium-ion battery degradation prediction system based on a multi-physics field coupling model is provided. This system can be used to implement the above-mentioned lithium-ion battery degradation prediction method based on the multi-physics field coupling model. Specifically, it includes:

[0212] Electrochemical model module: used to simulate the mass transfer, electro-migration, and electrochemical reaction processes in the solid and liquid phases inside the battery. This module includes electrochemical reaction equations, charge conservation equations, material conservation equations, etc.;

[0213] Thermal model module: used to calculate the temperature rise and thermal expansion of the battery. This module includes heat conduction equations, heat source calculations, and the coupling of the temperature field;

[0214] Mechanical model module: used to calculate the expansion and expansion force of the battery, and simulate the mechanical behavior of the battery under the constant-gap constraint condition in the actual module. This module includes mechanical equations, stress-strain relationships, etc.;

[0215] Aging model module: used to describe the aging processes such as SEI growth, lithium plating, and mechanical failure of the battery. This module includes aging equations and corresponding physical mechanisms;

[0216] Multi-physics field coupling module: used to couple the electrochemical, thermal, mechanical, and aging models to ensure the mutual influence between each physical field;

[0217] Numerical solution module: used to calculate the mutual coupling and physical processes between each module through numerical simulation to ensure the solution and optimization of the model;

[0218] Result analysis module: used to analyze the simulation results and determine the optimal pre-tightening force, and provide visualization and verification of the battery performance.

[0219] In another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of the lithium-ion battery attenuation prediction method based on the multi-physical field coupling model.

[0220] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is the memory device in the terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0221] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the lithium-ion battery attenuation prediction method based on the multi-physical field coupling model in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0222] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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.) that contain computer-usable program code.

[0223] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0224] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0225] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0226] Those of ordinary skill in the art will realize that the embodiments described herein are for helping readers understand the implementation methods of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A lithium-ion battery attenuation prediction method based on a multi-physics field coupling model, characterized in that: The following steps are involved: S1: Determine the multi-physics coupling relationship and model scale; the relationship includes the interaction mechanisms between electrochemical-thermal, thermal-mechanical, mechanical-aging and electrochemical-mechanical, and the model scale includes particle level, electrode level, cell level and module level; S2: Define the dimensions of each sub-model and obtain the parameters of the multi-field coupling model according to the actual simulation requirements; the sub-models include a P2D electrochemical model, a lumped thermal model, a phenomenological mechanics model and an aging model. The P2D electrochemical model is used to simulate the solid-liquid mass transfer, conductivity and electrochemical reactions inside the battery. The lumped thermal model calculates the temperature rise and thermal expansion of the battery. The phenomenological mechanics model calculates the expansion of the battery and simulates the change in the expansion force of the battery under the constant gap constraint condition in the actual module. The aging model calculates the capacity decay of the battery under the three aging mechanisms of SEI growth, lithium precipitation and mechanical failure. S3: Construct a multi-field coupling model and boundary conditions, and determine the model coupling mechanism, including P2D electrochemical model, lumped thermal model, phenomenological mechanical model and aging model; the boundary conditions include boundary conditions of electrochemical reaction, boundary conditions of heat conduction and mechanical constraint conditions, S4: Determine a numerical solution method for the model; the method is based on COMSOL Multiphysics for numerical simulation, and calculates electrochemical, thermal, mechanical and aging coupling models through lithium-ion battery nodes; S5: Model verification and optimal preload force determination. By simulating the effects of different preload forces on battery expansion force and capacity attenuation, the optimal preload force value is obtained.

2. The lithium-ion battery attenuation prediction method according to claim 1, characterized in that: The multi-physical field coupling relationship includes: electrochemistry-heat: the heat generated by the electrochemical reaction increases the battery temperature, and the temperature affects the electrochemical reaction and mass transfer rate; electrochemistry-force: the lithium insertion of the active material causes intercalation expansion, which leads to force fluctuations. The force will affect the contact of the battery components and the microstructure of the material, affecting the mass transfer; electrochemistry-aging: the capacity decay of the battery is affected by the internal mass transfer and the electrochemical reaction rate; heat-force: thermal expansion will cause force fluctuations; heat-aging: temperature will affect the capacity decay rate of the battery; force-aging: excessive force will increase the risk of lithium deposition in the battery, and too small force will cause the porous components to be loosely contacted.

3. The lithium-ion battery attenuation prediction method according to claim 1, characterized in that: The multi-physics coupling model includes four parts: P2D electrochemical model, lumped thermal model, phenomenological mechanics model and aging model; the P2D electrochemical model simulates the diffusion, electromigration mass transfer and electrochemical behavior inside the battery; The lumped thermal model calculates the temperature rise and thermal expansion of the battery; the phenomenological mechanical model calculates the total expansion of the battery and simulates the change in the expansion force of the battery under the constant gap constraint conditions in the actual module; the aging model includes three aging mechanisms: SEI growth, lithium deposition and mechanical failure, which are described by different mathematical models to control the battery capacity decay, electrode deactivation and lithium deposition behavior.

4. The lithium-ion battery attenuation prediction method according to claim 1, characterized in that: The P2D electrochemical model includes the following governing equations: Solid phase charge conservation equation: Liquid phase charge conservation equation: Liquid phase material conservation equation: Solid phase material conservation equation: Electrochemical reaction current: Overpotential: In the formula, represents the solid phase effective conductivity, φ s represents the solid phase potential, j represents the reaction current density, represents the effective conductivity of the liquid phase, φ e represents the liquid phase potential, represents the liquid phase diffusion coefficient, c e represents the liquid concentration, ε represents the porosity, represents the effective diffusion coefficient of the liquid phase, t + represents the cation mobility, F represents the Faraday constant, c s represents the solid phase concentration, D s represents the solid phase diffusion coefficient, r represents the radius of the particle, a represents the specific surface area, i0 represents the reference current density, α a represents the anode reaction coefficient, α c represents the cathode reaction coefficient, R represents the gas constant, T represents the battery temperature, η represents the overpotential, and R film Indicates the resistance between the electrolyte and the electrode, U eq Represents the equilibrium potential of an electrochemical reaction.

5. The lithium-ion battery attenuation prediction method according to claim 4, characterized in that: The lumped thermal model includes the following governing equations: Heat source equation: The temperature change equation of the battery is: Temperature dependence of electrochemical model parameters: In the formula, The entropy coefficient of the electrochemical reaction of the battery, represents the effective conductivity of the liquid phase, φ e represents the liquidus potential, C p : Specific heat capacity, M cell : The quality of the battery, The rate of change of battery temperature over time, Q: the heat generated by the heat source, A: the heat dissipation area of ​​the battery, h cell represents the convective heat transfer coefficient of the battery, T amb represents the ambient temperature, ψ represents the temperature-dependent parameters of physical quantities including diffusion coefficient, conductivity, and current density, ψ ref Indicates that at the reference temperature T ref The parameter values ​​below, represents the activation energy.

6. The lithium-ion battery attenuation prediction method according to claim 5, characterized in that: The phenomenological mechanics model includes the following governing equations: The radial stress equation at the particle level is: The tangential stress equation at the particle level is: The strain equation at the particle level is: Battery thickness change equation: ΔL int =(ΔL p,corr +ΔL n,corr )·n p ·2 ΔL total =ΔL int +ΔL th In the formula, σ r,i (r) represents the radial stress at the particle level, Ω represents the partial molar volume of the particle, E represents the elastic modulus, υ represents the Poisson's ratio, and R i represents the radius of the particle, c s (r) represents the concentration distribution at a point in the particle, r represents the radial position, σ t,i (r) represents the tangential stress at the particle level, u i (r) represents the strain of the particle in the radial direction, represents the intercalation expansion of the battery, n p Indicates the number of positive electrode layers, ΔL p,corr and L n,corr and represent the intercalation expansion of the positive and negative electrodes, respectively, ΔL th represents the thermal expansion of the battery, α cell Indicates the thermal expansion coefficient of the battery, L total represents the total thickness of the battery, T0 represents the ambient temperature, x represents the spatial position, the distance from one end of the battery to the other end, ΔL total Represents the total thickness change of the battery.

7. The lithium-ion battery attenuation prediction method according to claim 6, characterized in that: The aging model includes the following governing equations: SEI growth control equation: Diffusion equation of EC in electrolyte: SEI concentration change equation: Lithium deposition control equation: The core expansion equation caused by lithium deposition is: Mechanical failure equation: In the formula, j SEI represents the SEI film growth current density, k 0,SEI represents the exchange current density of the SEI film reaction, represents the concentration of ethylene carbonate in the electrolyte, α c,SEI represents the cathode charge transfer coefficient of the SEI film reaction, j tot represents the total current density, R film represents the resistance of the membrane, U SEI represents the intrinsic voltage of the SEI film, D EC represents the diffusion coefficient of EC, represents the concentration of EC near the solid electrolyte interface, represents the concentration of EC in the electrolyte, δ film represents the thickness of the SEI film, j SEI represents the growth current density of SEI film, c SEI represents the concentration of substances in the SEI film, represents the concentration of ethylene molecules, j Li represents the lithium deposition current density, i 0,Li represents the exchange current density of the lithium precipitation reaction, c Li represents the lithium ion concentration, represents the equilibrium value of lithium ion concentration, α α,Li represents the anodic charge transfer coefficient of the lithium precipitation reaction, Indicates the equilibrium value of ion concentration in the electrolyte, α c,Li represents the cathode charge transfer coefficient of the lithium precipitation reaction, ΔL Li Indicates the expansion of the battery core due to lithium plating, L n Indicates the core length, R n Represents the resistance of the battery core, V n (θ n ) represents the volume variation coefficient of the core, ε s,n Indicates the porosity of the core material.

8. The lithium-ion battery attenuation prediction method according to claim 1, characterized in that: The model is solved by numerical simulation, including the following sub-steps: Select appropriate physics modules, including electrochemical, thermal, mechanical, and aging modules, and define the corresponding physical properties and material parameters; Create a battery geometry model and set boundary conditions, including electrochemical reactions, heat conduction, and mechanical constraints; Set up multi-physics coupling relationships to ensure that electrochemical, thermal, mechanical and aging processes influence each other; Select the numerical solution method, including time step, nonlinear solver and accuracy settings to ensure the accuracy of simulation results; Divide the grid and perform simulation to evaluate battery performance, including expansion force, temperature, and capacity decay; Post-process and analyze the results to determine the optimal preload and verify the model.

9. A lithium-ion battery attenuation prediction system based on a multi-physics field coupling model, characterized in that: The system can be used to implement the lithium-ion battery attenuation prediction method according to any one of claims 1 to 8, specifically comprising: Electrochemical model module: used to simulate the mass transfer, electromigration and electrochemical reaction process between the solid phase and the liquid phase inside the battery. The module includes electrochemical reaction equations, charge conservation equations, material conservation equations, etc. Thermal model module: used to calculate the temperature rise and thermal expansion of the battery, the module includes heat conduction equations, heat source calculations and coupling of temperature fields; Mechanical model module: used to calculate the expansion and expansion force of the battery, and simulate the mechanical behavior of the battery under the constant gap constraint conditions in the actual module. The module includes mechanical equations, stress-strain relationships, etc. Aging model module: used to describe the aging process of the battery, such as SEI growth, lithium deposition, and mechanical failure. The module includes aging equations and corresponding physical mechanisms. Multi-physics coupling module: used to couple electrochemical, thermal, mechanical and aging models to ensure the mutual influence between the physical fields; Numerical solution module: used to calculate the mutual coupling and physical processes between modules through numerical simulation to ensure the solution and optimization of the model; Result analysis module: used to analyze simulation results and determine the optimal preload force, providing visualization and verification of battery performance.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for predicting lithium-ion battery attenuation based on a multi-physical field coupling model as described in one of claims 1 to 8 is implemented.

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