Lithium battery cycle life prediction method and device, and storage medium

Abstract

The present disclosure relates to a lithium battery cycle life prediction method and device, and a storage medium, the method comprising: simulating a lithium battery cycle charging and discharging process based on a lithium battery coupling simulation model, and obtaining a negative electrode SEI film growth capacity loss, a negative electrode lithium precipitation capacity loss, and a negative electrode particle crack capacity loss; the coupling simulation model comprising: a negative electrode SEI film growth model, a negative electrode lithium precipitation model, and a negative electrode particle crack model; the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film in the cycle charging and discharging process, the negative electrode lithium precipitation model is used to determine the lithium precipitation capacity loss of the negative electrode in the cycle charging and discharging process, and the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode crack in the cycle charging and discharging process; and the capacity retention rate is determined according to the growth capacity loss, the lithium precipitation capacity loss, and the crack capacity loss to predict the cycle life of the lithium battery. Thus, the prediction accuracy of the cycle life of the lithium battery can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of lithium batteries, and in particular to a method and apparatus for predicting the cycle life of lithium batteries, and a storage medium. Background Technology

[0002] With the increasing global demand for clean energy and efficient energy storage technologies, lithium-ion batteries (LIBs) have become the preferred energy storage device for electric vehicles, portable electronic devices, and large-scale energy storage systems due to their advantages such as high energy density, long cycle life, and low self-discharge rate. However, despite the many advantages of lithium-ion batteries, their cycle life (the number of charge-discharge cycles at which the battery's capacity decays to a specified threshold (e.g., 80%)) remains one of the key factors limiting their widespread application.

[0003] In practical use, the performance of lithium batteries gradually declines with the increase of charge-discharge cycles. This not only affects the lifespan of equipment but also increases maintenance and replacement costs. Therefore, in-depth research into the cycle life degradation mechanism of lithium batteries and accurate prediction of it through simulation models are of great significance for optimizing battery design, improving battery performance, and reducing costs.

[0004] While some progress has been made in predicting the cycle life of lithium batteries, the accuracy of current technologies in predicting the cycle life of lithium batteries still needs to be improved. Summary of the Invention

[0005] In view of this, this disclosure proposes a method, apparatus, and storage medium for predicting the cycle life of lithium batteries, which can improve the prediction accuracy of lithium battery cycle life and obtain more accurate prediction results.

[0006] According to one aspect of this disclosure, a method for predicting the cycle life of a lithium battery is provided, comprising: simulating the cyclic charge-discharge process of a lithium battery based on a coupled simulation model, and obtaining the negative electrode SEI film growth capacity loss, negative electrode lithium plating capacity loss, and negative electrode particle crack capacity loss; the coupled simulation model includes: a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model; the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during the cyclic charge-discharge process, and the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode cracks during the cyclic charge-discharge process; and determining the capacity retention rate based on the growth capacity loss, lithium plating capacity loss, and crack capacity loss to predict the cycle life of the lithium battery.

[0007] In one possible implementation, the coupled simulation model further includes an active material loss model; the active material loss model is used to determine the electrode solid volume fraction of the electrode active material during cyclic charge and discharge, so as to determine the growth capacity loss, lithium plating capacity loss and crack capacity loss based on the electrode solid volume fraction.

[0008] In one possible implementation, the active material loss model is used to determine the electrode solid volume fraction of the electrode active material during cyclic charge-discharge, including: determining the loss volume fraction of the electrode active material during cyclic charge-discharge, and determining the electrode solid volume fraction using the loss volume fraction and the initial solid volume fraction.

[0009] In one possible implementation, the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during cyclic charge-discharge, including: determining the SEI film growth reaction current density during cyclic charge-discharge, and determining the growth capacity loss through the SEI film growth current density and the negative electrode solid phase volume fraction.

[0010] In one possible implementation, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge-discharge processes, including: determining the lithium plating reaction current density of the negative electrode during cyclic charge-discharge processes, and determining the lithium plating capacity loss by means of the lithium plating reaction current density and the volume fraction of the negative electrode solid phase.

[0011] In one possible implementation, the negative electrode SEI film growth model is as follows:

[0012] Q SEI =c SEI *F*l neg *W cell *H cell

[0013]

[0014]

[0015] η SEI =Φ s -Φ l -E Eq,SEI -U de

[0016]

[0017] Among them, Q SEI For growth capacity loss, c SEI The lithium concentration lost due to the SEI film growth reaction is l neg Where W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness.cell H represents the battery width. cell j is the battery length. SEI j is the current density for the negative electrode SEI film growth reaction. 0,SEI α represents the exchange current density of the negative electrode SEI film growth reaction. c,SEI Let R be the transfer coefficient, T be the gas constant, and η be the cell temperature. SEI Φ is the overpotential for the negative electrode SEI film growth reaction. s For solid-state potential, Φ l E is the liquid phase potential. Eq,SEI U is the equilibrium potential for the negative electrode SEI film growth reaction. de For potential drop, j neg j is the current density for the negative electrode deintercalation reaction. lpl The current density for the lithium plating reaction at the negative electrode is δ. film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film. s,neg M represents the specific surface area of ​​the negative electrode active material particles. SEI ρ is the molar mass of the SEI film. SEI c is the density of the SEI film. lpl M represents the lithium concentration loss due to the lithium plating reaction at the negative electrode. Li ρ is the molar mass of lithium. Li ε represents the lithium concentration. s,neg r represents the volume fraction of the negative electrode solid phase. neg The radius of the negative electrode active material particles; Represents the partial derivative.

[0018] In one possible implementation, the lithium plating model for the negative electrode is:

[0019] Q lpl =c lpl *F*l neg *W cell *H cell

[0020]

[0021] η lpl =Φ s -Φ l -E Eq,Li -U de

[0022]

[0023] Among them, Q lpl For lithium plating capacity loss, c lpl The lithium concentration lost due to the lithium plating reaction at the negative electrode, l negWhere W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness. cell H represents the battery width. cell j is the battery length. lpl j is the current density for the lithium plating reaction at the negative electrode. 0,lpl α represents the exchange current density of the lithium plating reaction at the negative electrode. a,lpl and α c,lpl Here, R is the transfer coefficient, T is the gas constant, and η is the cell temperature. lpl Φ is the overpotential for the lithium plating reaction at the negative electrode. s For solid-state potential, Φ l Let j be the liquid phase potential. neg j is the current density for the negative electrode deintercalation reaction. SEI E represents the current density during the growth reaction of the negative electrode SEI film. Eq,Li U is the equilibrium potential for the lithium plating reaction at the negative electrode. de For voltage drop, δ film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film. s,neg c represents the specific surface area of ​​the negative electrode active material particles. SEI M represents the lithium concentration lost due to the SEI film growth reaction. SEI ρ is the molar mass of the SEI film. SEI M is the density of the SEI film. Li ρ is the molar mass of lithium. Li Let a be the concentration of lithium. s,neg ε represents the specific surface area of ​​the negative electrode active material particles. s,neg r represents the volume fraction of the negative electrode solid phase. neg The radius of the negative electrode active material particles; Represents the partial derivative.

[0024] In one possible implementation, the negative electrode particle crack model is as follows:

[0025]

[0026] Among them, Q m For crack capacity loss, r neg l is the particle radius of the negative electrode active material. cr ρ represents the length of the existing crack. cr σ is the number of cracks per unit area of ​​the particle, b, m, and k are correction factor constants, and σ is the number of cracks per unit area of ​​the particle. θ，max Let a0 be the maximum tangential stress on the particle surface and a0 be the depth of the existing crack. This represents the initial thickness of the SEI film. ρ SEI M is the density of the SEI film. SEI σ is the molar mass of the SEI membrane, F is the Faraday constant, N is the number of charge-discharge cycles, and σ is the molar mass of the SEI membrane.θ,max Let be the maximum tangential stress on the surface, v be the Poisson's ratio of the negative electrode active material, E be the Young's modulus of the negative electrode active material, Ω be the partial molar volume of the negative electrode active material, and D be the maximum tangential stress on the surface. s I is the solid-phase diffusion coefficient of lithium ions. cell ε is the battery current. s,neg A represents the volume fraction of the negative electrode solid phase. neg For the negative electrode area, l neg The thickness is the negative electrode thickness.

[0027] In one possible implementation, the loss model of the active material is as follows:

[0028]

[0029] η dis =Φ s -Φ l -E Eq,dis

[0030] Where, ε s ε represents the volume fraction of the solid phase at the positive or negative electrode. s,0 j is the initial solid volume fraction. dis C is the current density for the dissolution reaction of the active material. s,max The maximum lithium intercalation concentration of the electrode, l s For electrode thickness, j 0,dis The exchange current density for the dissolution reaction of the active material is given by F, F is the Faraday constant, R is the gas constant, T is the cell temperature, and η is the voltammetric ... dis Φ is the overpotential for the dissolution reaction of active materials. s For solid-state potential, Φ l E is the liquid phase potential. Eq,dis ε represents the equilibrium potential of the dissolution reaction of the active material; exp is an exponential function with a base of natural numbers; t is time.

[0031] In one possible implementation, the coupled simulation model further includes one or more of the following: an insertion / extraction reaction model, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium-ion solid-phase diffusion model, and a lithium-ion liquid-phase diffusion model;

[0032] The deintercalation / intercalation reaction model is as follows:

[0033]

[0034] η=Φ s -Φ l -E Eq -U de

[0035] j0=FK(c s,max -c s )0.5 (c s ) 0.5 (c l ) 0.5

[0036] Where j is the deintercalation current density at the positive or negative electrode, j0 is the exchange current density at the positive or negative electrode, and α a and α c η is the transfer coefficient, η is the overpotential of the positive or negative electrode, T is the battery temperature, and Φ is the transfer coefficient. s The solid-state potential, Φ, is the potential of the positive or negative electrode. l E is the liquid phase potential of the electrolyte. Eq Where K is the equilibrium potential of the positive or negative electrode, and C is the reaction rate constant of the positive or negative electrode. s c represents the solid-phase lithium-ion concentration at the positive or negative electrode. s,max c represents the maximum solid-phase lithium-ion concentration at the positive or negative electrode. l This refers to the concentration of lithium ions in the liquid phase; U de The potential drop is due to a side reaction;

[0037] The solid-phase charge conservation model is as follows:

[0038]

[0039] Among them, i s Let σ be the solid-state current density. s For the effective conductivity of the solid phase, The gradient of the solid-state potential;

[0040] The liquid phase charge conservation model is as follows:

[0041]

[0042] Among them, i l Let κ be the liquid phase current density and κ be the effective liquid phase conductivity. The gradient of the liquid phase potential, f(c) l () is related to the concentration of lithium ions in the liquid phase, c l The relevant activity coefficient, This refers to the lithium-ion transport number. Representing lnc l The gradient of R is the gas constant, T is the battery temperature, and F is the Faraday constant;

[0043] The lithium-ion solid-phase diffusion model is as follows:

[0044]

[0045] Among them, c s Let D be the solid-phase lithium-ion concentration, t be time, and D be the solid-phase lithium-ion concentration.s Where is the solid-phase diffusion coefficient, and r is the particle radius of the positive or negative electrode active material. Represents partial derivatives;

[0046] The lithium-ion liquid-phase diffusion model is as follows:

[0047]

[0048] ε l =ε0-ε g

[0049]

[0050] Among them, c l ε represents the concentration of lithium ions in the liquid phase. l Let be the liquid volume fraction, and x be any position within the lithium battery. Let a be the lithium-ion transport number. s D represents the specific surface area of ​​the active material particles for the positive or negative electrode. l ε is the effective diffusion coefficient of the liquid phase, ε0 is the initial liquid phase volume fraction, and ε g δ is the change in liquid volume fraction. film ε represents the change in SEI film thickness. s denoted as the solid volume fraction of the positive or negative electrode, and r is the particle radius of the positive or negative electrode active material.

[0051] In one possible implementation, the coupled simulation model further includes: a heat generation model; the heat generation model is:

[0052]

[0053] Where ρ is the density of the lithium battery material, C p λ represents the specific heat capacity of the lithium battery material, where the subscript i indicates the negative or positive electrode, T is the battery temperature, and λ is the thermal conductivity. For heat dissipation power, i l The liquid phase current density, The gradient of the liquid phase potential. For liquid phase ohmic heat generation power, i s For solid-state current density, The gradient of the solid-state potential. For solid-state ohmic heat generation power, a i j represents the specific surface area of ​​the positive or negative electrode active material particles. i η represents the reaction current density at the positive or negative electrode. i For the overpotential of the positive or negative electrode, ∑ i a i j i η i For polarization heat generation power, Eeq,i This represents the open-circuit potential of the positive or negative terminal. The entropy coefficient of the positive or negative electrode. It is a reversible heat generation power.

[0054] According to another aspect of this disclosure, a lithium battery cycle life prediction device is provided, comprising: a simulation module for simulating the cyclic charge-discharge process of a lithium battery based on a lithium battery coupled simulation model, and obtaining the negative electrode SEI film growth capacity loss, negative electrode lithium plating capacity loss, and negative electrode particle crack capacity loss; the coupled simulation model includes: a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model; the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during the cyclic charge-discharge process, and the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode crack during the cyclic charge-discharge process; and a prediction module for determining the capacity retention rate based on the growth capacity loss, lithium plating capacity loss, and crack capacity loss, so as to predict the cycle life of the lithium battery.

[0055] According to another aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0056] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0057] According to another aspect of this disclosure, a computer program product is provided, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0058] According to various aspects of this disclosure, the cyclic charge-discharge process of a lithium battery is simulated by a coupled simulation model based on a lithium battery. This allows for the determination of the capacity loss due to the growth of the negative electrode SEI film, the lithium plating of the negative electrode, and the cracking of negative electrode particles during the cyclic charge-discharge process. In other words, it comprehensively determines the capacity loss caused by multiple decay mechanisms such as the growth of the negative electrode SEI film, the lithium plating of the negative electrode, and the cracking of negative electrode particles during the cyclic charge-discharge process. This makes the capacity retention rate determined based on the capacity loss of the negative electrode SEI film, the lithium plating of the negative electrode, and the cracking of negative electrode particles more accurate, thereby obtaining a more accurate cycle life prediction result and improving the prediction accuracy of the cycle life of lithium batteries.

[0059] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0060] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0061] Figure 1 A flowchart is shown for a method for predicting the cycle life of a lithium battery according to an embodiment of the present disclosure.

[0062] Figure 2 A schematic diagram showing the loss rate variation curve of a positive electrode active material according to an embodiment of the present disclosure is provided.

[0063] Figure 3 This diagram illustrates a capacity loss curve caused by surface cracks in negative electrode active particles according to an embodiment of the present disclosure.

[0064] Figure 4 A comparative schematic diagram showing the capacity retention rate change results obtained by using the prediction method of the present disclosure, existing prediction methods, and actual measurements is presented.

[0065] Figure 5 A block diagram of a lithium battery cycle life prediction device according to an embodiment of the present disclosure is shown.

[0066] Figure 6 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. Detailed Implementation

[0067] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0068] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0069] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0070] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0071] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0072] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0073] As mentioned above, the accuracy of current technologies in predicting the cycle life of lithium-ion batteries still needs improvement. This is because the degradation of lithium-ion batteries during use is the result of multiple side reactions. Most existing simulation models only consider a limited number of degradation mechanisms, such as the formation of the solid electrolyte interface (SEI) film on the negative electrode and lithium plating at the negative electrode. This fails to fully reflect the complex degradation process of lithium-ion batteries in actual use, leading to significant deviations between predicted results and actual conditions. The capacity degradation process of lithium-ion batteries involves the interaction of multiple physical fields, including electrochemistry, thermodynamics, and mechanics. However, existing simulation models often focus only on electrochemical processes, neglecting the influence of thermal and mechanical effects. This decoupled multiphysics analysis cannot accurately describe the actual behavior of lithium-ion batteries during charging and discharging, limiting the prediction accuracy of simulation models. To improve the accuracy of simulation models, some studies have attempted to introduce complex multiphysics coupling models, but these models are usually computationally intensive and time-consuming, making it difficult to meet the rapid prediction requirements of practical engineering applications. Conversely, some simplified models, while computationally efficient, lack sufficient accuracy and cannot provide reliable prediction results.

[0074] In view of this, the present disclosure proposes a method for predicting the cycle life of lithium batteries. By comprehensively considering multiple decay mechanisms such as the growth of the negative electrode SEI film, lithium plating on the negative electrode, surface cracks of negative electrode particles, and dissolution of active materials, the method predicts the cycle life of lithium batteries. It realizes multi-physics field coupling analysis, which can more comprehensively and accurately describe the capacity decay behavior of lithium batteries during the charging and discharging process, and improve the prediction accuracy of lithium battery cycle life.

[0075] In practical applications, the lithium battery cycle life prediction method of this disclosure can be deployed on various terminal devices through software or hardware modifications. The terminal devices involved in this disclosure can refer to devices with wireless and / or wired connection functions. Wireless connection means that they can connect to other devices via Wi-Fi, Bluetooth, or other wireless connection methods. The terminal devices involved in this disclosure can also communicate with other devices via wired connection functions. The terminal devices involved in this disclosure can be touchscreen, non-touchscreen, or screenless. Touchscreen devices can be controlled by clicking or swiping on the display screen using fingers, styluses, etc. Non-touchscreen devices can connect to input devices such as mice, keyboards, and touch panels to control the terminal device. Screenless devices can be, for example, screenless Bluetooth speakers. For example, the terminal devices in this application can include, but are not limited to, user equipment (UE), mobile devices, user terminals, terminals, handheld devices, tablet computers, laptops, PDAs, computing devices, etc.

[0076] The lithium battery cycle life prediction method of this disclosure can also be deployed on a server. This server can be located in the cloud or locally, and can be a physical device or a virtual device, such as a virtual machine or container. It has wireless communication capabilities, which can be configured in the server's chip (system) or other components. It can refer to a device with wireless connectivity, meaning it can connect to other servers or terminal devices via Wi-Fi, Bluetooth, or other wireless connection methods. The server involved in this disclosure can also have wired communication capabilities. For example, the server can receive the electrochemical-thermal-side reaction coupling simulation model of the lithium battery to be predicted sent by the terminal device, execute the lithium battery cycle life prediction method of this disclosure, obtain the cycle life prediction result of the lithium battery, and return the cycle life prediction result to the terminal device so that the determined cycle life prediction result can be displayed to the user on the terminal device.

[0077] Figure 1 A flowchart illustrating a method for predicting the cycle life of a lithium battery according to an embodiment of this disclosure is shown. Figure 1 As shown, the method includes:

[0078] Step S11: Simulate the cyclic charge-discharge process of a lithium battery based on a lithium battery coupled simulation model, and obtain the capacity loss of the negative electrode SEI film growth, the capacity loss of the negative electrode lithium plating, and the capacity loss of the negative electrode particle cracks; the coupled simulation model includes: a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model; the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during the cyclic charge-discharge process, and the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode cracks during the cyclic charge-discharge process;

[0079] Step S12: Determine the capacity retention rate based on growth capacity loss, lithium plating capacity loss, and crack capacity loss to predict the cycle life of the lithium battery.

[0080] In step S11, the coupled simulation model of the lithium battery can be obtained by coupling the electrochemical model, the heat generation model, the negative electrode SEI film growth model, the negative electrode lithium plating model, and the negative electrode particle crack model. In practical applications, those skilled in the art can use open-source battery simulation modeling software, such as pyBaMM software, to establish the coupled simulation model of the lithium battery. This disclosure does not limit the specific steps of constructing the coupled simulation model using pyBaMM software. For example, the electrochemical-thermal-side reaction coupled simulation model of the lithium battery can be established by obtaining and based on the battery design parameters and battery physical property parameters. The battery design parameters include at least: positive electrode thickness, negative electrode thickness, positive electrode area, negative electrode area, battery width, battery length, separator thickness, separator porosity, particle radius of positive electrode active material, particle radius of negative electrode active material, initial solid volume fraction of positive electrode, initial solid volume fraction of negative electrode, and initial liquid volume fraction. The battery physical property parameters include at least: reaction rate constant, solid-phase diffusion coefficient, diffusion activation energy, Brugmann coefficient, effective solid-phase conductivity, effective liquid-phase conductivity, charge transfer coefficient, partial molar volume, Poisson's ratio, Young's modulus, maximum lithium intercalation concentration, thermal conductivity, convective heat transfer coefficient, and radiative heat transfer coefficient. In practical applications, the battery physical property parameters used in the above models can be obtained through pre-calibration or experimental measurement, or the initial values ​​can be estimated based on historical experience and then corrected. This disclosure does not limit this.

[0081] In practical applications, after establishing a coupled simulation model of a lithium battery in battery simulation modeling software, the simulation calculation capability of the battery simulation modeling software can be used to simulate the cyclic charging and discharging process of the lithium battery based on the established coupled simulation model, and obtain the capacity loss of negative electrode SEI film growth, negative electrode lithium plating capacity loss, and negative electrode particle cracking capacity loss during the cyclic charging and discharging process. That is, a coupled simulation model is established in the battery simulation modeling software, and the simulation calculation capability of the modeling software is used to calculate the capacity loss of negative electrode SEI film growth, negative electrode lithium plating capacity loss, and negative electrode particle cracking capacity loss generated during the cyclic charging and discharging process. The embodiments of this disclosure do not limit the software simulation calculation process.

[0082] In practical applications, simulating the cyclic charge-discharge process of a lithium battery based on a coupled lithium battery simulation model can include simulating the cyclic charge-discharge process of a lithium battery under specified charge-discharge conditions. Specifically, the specified charge-discharge conditions can be based on the charge-discharge conditions used when actually measuring the cycle life of the lithium battery. The electrochemical-thermal-side reaction coupled simulation model can then be used to simulate the cyclic charge-discharge process of the lithium battery. These specified charge-discharge conditions can be understood as a single charge-discharge cycle. For example, the specified charge-discharge conditions may include:

[0083] 1. Charge the battery at a current of 1C until the voltage reaches 4.3V. After that, switch to constant voltage charging mode and continue charging until the current drops below 0.05C.

[0084] 2. After charging is complete, the battery needs to be left to stand for 1800 seconds to ensure that its internal state is stable;

[0085] 3. After the resting period, discharge the battery at a current of 1C until the voltage drops to 2.75V;

[0086] 4. After the discharge is complete, let the battery rest for another 1800 seconds to complete a full charge and discharge cycle.

[0087] As mentioned above, a coupled simulation model of a lithium battery can be established in battery simulation modeling software such as pyBaMM. Therefore, it is also possible to set specified charge and discharge conditions and constrain simulation boundaries (such as ambient temperature, negative electrode lithium plating judgment conditions, total number of charge and discharge cycles, etc.) in pyBaMM software so that the coupled simulation model can simulate the cyclic charge and discharge process of a lithium battery based on the specified charge and discharge conditions. It can also monitor in real time the negative electrode SEI film growth capacity loss, negative electrode lithium plating capacity loss, and negative electrode particle crack capacity loss during the cyclic charge and discharge process simulated by the coupled simulation model, so as to obtain the negative electrode SEI film growth capacity loss, negative electrode lithium plating capacity loss, and negative electrode particle crack capacity loss corresponding to each charge and discharge process during the cyclic charge and discharge process. It should be understood that there are several sets of negative electrode SEI film growth capacity loss, negative electrode lithium plating capacity loss, and negative electrode particle crack capacity loss for several charge and discharge processes during the cyclic charge and discharge process.

[0088] In practical applications, battery simulation modeling software such as pyBaMM is used to simulate the cyclic charge-discharge process using coupled simulation models. The battery voltage and temperature output by the coupled simulation model can be monitored in real time, providing information such as battery voltage, battery temperature, and charge-discharge duration (i.e., the time required to complete one charge-discharge cycle). Battery voltage and temperature can be used to control the simulated cyclic charge-discharge process. For example, pyBaMM can be configured to obtain the solid-state potential of the positive electrode near the current collector and the negative electrode near the current collector at various moments during the simulated cyclic charge-discharge process. This allows for the calculation of the solid-state potential of the positive electrode near the current collector at any given time. Solid-state potential near the current collector end of the negative electrode The difference between the values ​​yields the battery voltage, which is the battery voltage at any given time. Furthermore, the simulation data obtained from the cyclic charging and discharging process, such as battery voltage, battery temperature, and charging and discharging time, can be compared and analyzed with the experimentally measured data of lithium batteries undergoing cyclic charging and discharging. This comparison allows for the verification of the accuracy of the coupled simulation model, resulting in a more accurate coupled simulation model. This, in turn, facilitates more accurate prediction of the cycle life of lithium batteries.

[0089] Among them, the growth capacity loss, lithium plating capacity loss, and crack capacity loss during the cyclic charge-discharge process include the growth capacity loss, lithium plating capacity loss, and crack capacity loss for each charge-discharge process during the cyclic charge-discharge process. For example, if a coupled simulation model is used to simulate 500 charge-discharge processes, the generation capacity loss, lithium plating capacity loss, and crack capacity loss for each charge-discharge process during the 500 charge-discharge processes can be obtained, that is, the generation capacity loss, lithium plating capacity loss, and crack capacity loss after 500 sets are obtained. Therefore, in step S12, after obtaining the growth capacity loss, lithium plating capacity loss, and crack capacity loss for each charge-discharge process during the cyclic charge-discharge process, the capacity retention rate corresponding to each charge-discharge process can be calculated based on the growth capacity loss, lithium plating capacity loss, and crack capacity loss for each charge-discharge process.

[0090] Among them, the capacity retention rate can characterize the ratio of the remaining capacity to the rated capacity after the capacity degradation of the lithium battery. The capacity retention rate P corresponding to each charge and discharge process can be determined by formula (1). cycle :

[0091]

[0092] Where Q0 is the rated capacity of the lithium battery, Q m Q represents the capacity loss due to cracks in the negative electrode particles, specifically the battery capacity loss caused by cracks on the surface of the active negative electrode particles. SEI This refers to the capacity loss due to the growth of the negative electrode SEI film, specifically the battery capacity loss caused by the negative electrode SEI film growth reaction. Q lpl This refers to the capacity loss due to lithium plating at the negative electrode, which is the battery capacity loss caused by the lithium plating reaction at the negative electrode.

[0093] It should be understood that after obtaining the capacity retention rate corresponding to each charge and discharge process during the cycle charge and discharge, the number of charge and discharge cycles when any capacity retention rate is reached can also be obtained. Therefore, the number of charge and discharge cycles (i.e., the number of cycles) when a specified capacity retention rate (e.g., 80%) is reached can be used as the cycle life prediction result of the lithium battery. In other words, the cycle life prediction result of the lithium battery can include the number of charge and discharge cycles when the capacity retention rate reaches the specified capacity retention rate. This disclosure does not limit this aspect.

[0094] In practical applications, the capacity retention rate corresponding to each charge-discharge cycle can be compared with the cycle life test results of actual batteries to verify the accuracy of the simulation model. The simulation model can also be optimized and adjusted based on the verification results to improve the prediction accuracy of cycle life. Specifically, experimentally obtained measured data on lithium battery voltage, current, kinetics, and thermodynamics can be used. The collected measured data can be cleaned and preprocessed to remove outliers and noise, ensuring the accuracy and reliability of the actual battery cycle life test results. Therefore, after optimizing the simulation model using the measured data, the prediction accuracy of the simulation model can be improved.

[0095] According to the method of this disclosure, the cyclic charging and discharging process of a lithium battery is simulated by a coupled simulation model based on a lithium battery. The capacity loss due to the growth of the negative electrode SEI film, the capacity loss due to lithium plating, and the capacity loss due to particle cracking during the cyclic charging and discharging process are obtained. In other words, the capacity loss caused by multiple decay mechanisms such as the growth of the negative electrode SEI film, lithium plating, and particle cracking during the cyclic charging and discharging process is comprehensively determined. This makes the capacity retention rate determined based on the capacity loss due to the growth of the negative electrode SEI film, the capacity loss due to lithium plating, and the capacity loss due to particle cracking more accurate, thereby obtaining a more accurate cycle life prediction result and improving the prediction accuracy of the cycle life of the lithium battery.

[0096] It is known that during the use of lithium batteries, the moisture contained in the lithium battery reacts with the electrolyte to generate highly corrosive hydrogen fluoride (HF). This HF gradually erodes the electrode active material, causing the active material to dissolve and resulting in a loss of active material, which in turn leads to a change in the volume fraction of the active material, thus affecting the performance and lifespan of the lithium battery. Therefore, this embodiment of the present disclosure also considers the attenuation mechanism of active material loss caused by the dissolution reaction of the active material to determine the capacity loss, which can improve the prediction accuracy of capacity loss and thus improve the prediction accuracy of cycle life. That is, in step S11, the above-mentioned coupled simulation model also includes an active material loss model; this active material loss model is used to determine the electrode solid phase volume fraction of the electrode active material during the cyclic charge and discharge process, specifically, to determine the electrode solid phase volume fraction that changes due to the dissolution reaction of the electrode active material during the cyclic charge and discharge process, so as to determine the growth capacity loss, lithium plating capacity loss, and crack capacity loss based on the electrode solid phase volume fraction.

[0097] In one possible implementation, the active material loss model is used to determine the electrode solid volume fraction of the electrode active material during cyclic charge-discharge. This can include: determining the loss volume fraction of the electrode active material during cyclic charge-discharge, and determining the electrode solid volume fraction using the loss volume fraction and the initial solid volume fraction. The loss volume fraction is the volume fraction of the electrode active material dissolved during cyclic charge-discharge (or the volume fraction lost), and the initial solid volume fraction is the original volume fraction of the electrode active material, or the volume fraction before the dissolution reaction. Therefore, the electrode solid volume fraction of the electrode active material during cyclic charge-discharge can be the difference between the initial solid volume fraction and the loss volume fraction.

[0098] The loss model of the active material is expressed by formulas (2-1) to (2-3):

[0099]

[0100] η dis =φ s -φ l -E Eq,dis (2-3)

[0101] Where, ε s ε represents the volume fraction of the solid phase at the positive or negative electrode. s,0 j represents the initial solid volume fraction (i.e., the initial solid volume fraction of the negative or positive electrode, which is a constant). dis The current density of the side reaction for the dissolution of the active material at the negative or positive electrode, C s,max The maximum lithium intercalation concentration of the electrode (i.e., the maximum lithium intercalation concentration of the negative or positive electrode, which is a constant), l s For the electrode thickness (i.e., the positive electrode thickness or the negative electrode thickness, which is a constant), j 0,dis Here, is the exchange current density for the dissolution reaction of the active material (which can be calibrated from measured cycle data and is a constant), F is the Faraday constant (a constant), R is the gas constant (a constant), T is the battery temperature (obtained from the heat generation model), and η is the estradiol. dis The overpotential of the dissolution side reaction of the active material at the negative or positive electrode, Φ s The solid-state potential (calculated from an electrochemical model), φ l E is the liquid phase potential (calculated from an electrochemical model). Eq,dis is the equilibrium potential of the dissolution reaction of the active material (an empirical value, for example, 4V); exp is an exponential function with a base of natural numbers; t is time.

[0102] It should be understood that the volume fraction of the positive or negative electrode solid phase at any point during the cyclic charge-discharge process can be obtained using formulas (2-1) to (2-3), which also allows us to obtain the electrode solid phase volume fraction that changes due to the dissolution reaction of the electrode active material. In this way, the precise electrode solid phase volume fraction can be determined by combining the effects of battery temperature and overpotential on the loss of electrode active material. For example, Figure 2 This diagram illustrates the predicted loss rate curve of the positive electrode active material using an active material loss model. Figure 2 As shown, the loss rate of positive electrode active material (the loss volume fraction of positive electrode active material relative to the initial solid phase volume fraction) increases with the increase of charge and discharge cycles. That is, the more charge and discharge cycles there are, the more electrode active material is lost, resulting in a greater loss of battery capacity.

[0103] In one possible implementation, in step S11, the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during cyclic charge-discharge processes. This includes: determining the SEI film growth reaction current density during cyclic charge-discharge processes, and determining the growth capacity loss using the SEI film growth current density and the negative electrode solid volume fraction. The SEI film growth reaction current density can refer to the current per unit area generated during the growth of the negative electrode SEI film during the charge-discharge process of the lithium-ion battery. The negative electrode solid volume fraction can be the actual volume fraction of the negative electrode active material after dissolution and loss during cyclic charge-discharge processes, as determined by the aforementioned active material loss model. By considering the actual negative electrode solid volume fraction after the dissolution reaction of the negative electrode active material, a more accurate negative electrode SEI film growth reaction current density and growth capacity loss can be determined, i.e., a more precise capacity loss due to the negative electrode SEI film growth reaction can be determined.

[0104] The growth model of the negative electrode SEI film is expressed by formulas (3-1) to (3-8):

[0105] Q SEI =c SEI *F*l neg *W cell *H cell (3-1)

[0106]

[0107] η SEI =Φ s -Φ l -E Eq,SEI -U de (3-4)

[0108]

[0109] Among them, Q SEI For growth capacity loss, c SEI The lithium concentration lost due to the SEI film growth reaction is l neg Where W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness. cell H represents the battery width. cell j is the battery length. SEI j is the current density for the negative electrode SEI film growth reaction. 0,SEI The exchange current density of the negative electrode SEI film growth reaction (which can be obtained from measured cycle data and is an empirical value), α c,SEI R is the transfer coefficient (a constant, for example, it can be 0.5), T is the cell temperature, and η is the transfer coefficient. SEI Φ is the overpotential for the negative electrode SEI film growth reaction. s For solid-state potential, φ l E is the liquid phase potential. Eq,SEI U is the equilibrium potential for the negative electrode SEI film growth reaction (an empirical value, for example, 0.4V). de For potential drop, j neg The current density for the negative electrode deintercalation reaction (calculated from the deintercalation reaction model), j lpl The current density for the lithium plating reaction at the negative electrode (calculated from the lithium plating model at the negative electrode), δ film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film (a constant). s,neg M represents the specific surface area of ​​the negative electrode active material particles (calculated from the volume fraction of the negative electrode solid phase). SEI ρ is the molar mass of the SEI film (a constant). SEI The density of the SEI film (a constant), c lpl M represents the lithium concentration lost due to the lithium plating reaction at the negative electrode (calculated using the negative electrode lithium plating model). Li ρ is the molar mass of lithium (a constant). Li Let ε be the lithium concentration (a constant). s,neg r represents the volume fraction of the negative electrode solid phase (calculated from the above active material loss model). neg The radius of the negative electrode active material particles (a constant); Represents the partial derivative.

[0110] It should be understood that the negative electrode SEI film growth model shown by formulas (3-1) to (3-8) can determine a more accurate negative electrode SEI film formation reaction current density by considering the change in the negative electrode solid phase volume fraction caused by the dissolution reaction of the negative electrode active material. It is understood that the loss of active material affects the specific surface area of ​​the particles, thus affecting the electrochemical reaction process of the lithium battery, and indirectly affecting the value of the capacity loss caused by the negative electrode SEI film growth reaction. Therefore, the above determination of growth capacity loss through SEI film growth current density and negative electrode solid phase volume fraction can include: determining the lithium concentration lost due to the negative electrode SEI film growth reaction in each charge-discharge process based on the negative electrode solid phase volume fraction, the particle radius of the negative electrode active material, and the negative electrode SEI film growth reaction current density; and determining the growth capacity loss caused by the negative electrode SEI film growth reaction in each charge-discharge process based on the lithium concentration lost due to the negative electrode SEI film growth reaction, the negative electrode thickness, and the battery size. The battery size includes the battery width and battery length.

[0111] Formula (3-3) allows us to determine the current density of the SEI film growth reaction based on the volume fraction of the negative electrode solid phase. The specific surface area of ​​the negative electrode active material particles can be calculated using formula (3-8) based on the volume fraction of the negative electrode solid phase and the particle radius of the negative electrode active material. Then, based on the specific surface area of ​​the negative electrode active material particles and the current density of the SEI film growth reaction, we can further refine formula (3-2). Integrate over the charge / discharge duration corresponding to each charge / discharge process. The lithium concentration c lost due to the SEI film growth reaction at the negative electrode was obtained for each charge-discharge process. SEI This means obtaining the lithium concentration loss due to the negative electrode SEI film growth reaction after one charge-discharge cycle. Furthermore, formula (3-1) can be used to calculate the growth capacity loss caused by the negative electrode SEI film growth reaction.

[0112] It should be understood that by considering the change in solid volume fraction caused by the loss of active materials due to the dissolution reaction of active materials (equivalent to combining the actual solid volume fraction during charge and discharge), a more accurate lithium concentration loss due to the growth reaction of the negative electrode SEI film can be determined. This is beneficial for determining the capacity loss due to the growth reaction of the negative electrode SEI film, and thus for determining a more accurate lithium battery cycle life.

[0113] It is known that lithium plating at the negative electrode also leads to capacity loss. In step S11, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge-discharge processes. This includes: determining the lithium plating reaction current density of the negative electrode during cyclic charge-discharge processes, and determining the lithium plating capacity loss through the lithium plating reaction current density and the negative electrode solid volume fraction. The negative electrode lithium plating reaction current density can refer to the current per unit area generated due to negative electrode lithium plating during the lithium battery charge-discharge process; the negative electrode solid volume fraction can be the actual volume fraction of the negative electrode active material after dissolution and loss during cyclic charge-discharge processes, as determined by the above-mentioned active material loss model. By considering the actual negative electrode solid volume fraction after the negative electrode active material dissolution reaction, a more accurate negative electrode lithium plating reaction current density and lithium plating capacity loss can be determined, that is, a more precise capacity loss caused by the negative electrode lithium plating reaction can be determined.

[0114] The lithium plating model for the negative electrode is expressed by formulas (4-1) to (4-8):

[0115] Q lpl =c lpl *F*l neg *W cell *H cell (4-1)

[0116]

[0117]

[0118] η lpl =Φ s -Φ l -E Eq,Li -U de (4-4)

[0119]

[0120] Among them, Q lpl For lithium plating capacity loss, c lpl The lithium concentration lost due to the lithium plating reaction at the negative electrode, l neg Where W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness. cell H represents the battery width. cell j is the battery length. lpl j is the current density for the lithium plating reaction at the negative electrode. 0,lpl The exchange current density for the lithium plating reaction at the negative electrode (which can be calibrated from measured cycle data and is an empirical value), α a,lpl and α c,lpl Here, R is the transfer coefficient for the cathode reaction and anodic reaction, respectively (constant values, e.g., 0.5 for both), T is the cell temperature, and η is the transfer coefficient for the anode reaction. lplΦ is the overpotential for the lithium plating reaction at the negative electrode. s The solid-state potential (calculated from an electrochemical model), φ l The liquid phase potential (calculated from an electrochemical model), j neg The current density for the negative electrode deintercalation reaction (calculated from the deintercalation reaction model), j SEI E represents the current density of the negative electrode SEI film growth reaction (calculated from the above negative electrode SEI film growth model). Eq,Li U represents the equilibrium potential of the lithium plating reaction at the negative electrode (an empirical value, for example, 0V); de For voltage drop, δ film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film. s,neg c represents the specific surface area of ​​the negative electrode active material particles. SEI M represents the lithium concentration lost due to the SEI film growth reaction. SEI ρ is the molar mass of the SEI film. SEI M is the density of the SEI film. Li ρ is the molar mass of lithium. Li Let a be the concentration of lithium. s,neg ε represents the specific surface area of ​​the negative electrode active material particles. s,neg r represents the volume fraction of the negative electrode solid phase (calculated from the above active material loss model). neg The radius of the negative electrode active material particles; Represents the partial derivative.

[0121] Where, when η lpl Lithium plating at the negative electrode only occurs when the voltage is less than 0V, that is, when η... lpl Capacity loss due to lithium plating at the negative electrode is only considered when the voltage is <0V. It should be understood that if lithium plating at the negative electrode does not occur during charging and discharging (i.e., η...), then capacity loss is considered. lpl If the voltage is ≥0V, then the capacity loss due to lithium plating at the negative electrode can be considered to be 0, and Q can be... lpl Substitute =0 into the above formula (1) to calculate the capacity retention rate when lithium plating does not occur at the negative electrode.

[0122] It should be understood that the negative electrode lithium plating model shown by formulas (4-1) to (4-8) can take into account the change in the volume fraction of the negative electrode solid phase caused by the loss of active material due to the dissolution reaction of the negative electrode active material when lithium plating occurs, and determine the negative electrode lithium plating capacity loss caused by the negative electrode lithium plating reaction more accurately, which is conducive to determining a more accurate lithium battery cycle life.

[0123] In practical applications, when lithium plating at the negative electrode does not occur during charging and discharging (i.e., η...), lpl When ≥0V), the voltage drop U de It can also be expressed as: SEI film thickness change δ caused by side reactions film It can also be expressed as: When lithium plating occurs at the negative electrode during charging and discharging (i.e., η), lpl When <0V), the voltage drop U caused by the side reaction de Formula (4-4) can be used to express the change in SEI film thickness caused by side reactions, which can be expressed as formula (4-6).

[0124] Therefore, in one possible implementation, where lithium plating does not occur at the negative electrode, the growth model of the negative electrode SEI film can be expressed as formulas (5-1) to (5-7):

[0125] Q SEI =c SEI *F*l neg *W cell *H cell (5-1)

[0126]

[0127] η SEI =Φ s -Φ l -E Eq,SEI -U de (5-3)

[0128]

[0129]

[0130] It should be understood that the negative electrode SEI film growth model shown by formulas (5-1) to (5-7) can take into account the change in the volume fraction of the negative electrode solid phase caused by the dissolution reaction of the negative electrode active material when lithium plating does not occur in the negative electrode. This allows for a more accurate determination of the capacity loss caused by the negative electrode SEI film growth reaction, which in turn helps to determine a more accurate lithium battery cycle life.

[0131] It is understandable that the loss of negative electrode active material affects the specific surface area of ​​the particles, thus affecting the electrochemical reaction process of the lithium battery, and indirectly affecting the capacity loss caused by the negative electrode lithium plating reaction. Therefore, the above-mentioned determination of lithium plating capacity loss through lithium plating reaction current density and negative electrode solid phase volume fraction can include: determining the lithium concentration lost due to negative electrode lithium plating reaction in each charge and discharge process based on the negative electrode solid phase volume fraction, negative electrode active material particle radius, and negative electrode lithium plating reaction current density; and determining the lithium plating capacity loss caused by negative electrode lithium plating reaction in each charge and discharge process based on the lithium concentration lost due to negative electrode lithium plating reaction, negative electrode thickness, and battery size.

[0132] The lithium plating reaction current density of the negative electrode can be calculated based on the volume fraction of the negative electrode solid phase using formula (4-3) or (5-2). Then, based on the volume fraction of the negative electrode solid phase and the particle radius of the negative electrode active material for each group, the specific surface area of ​​the negative electrode active material particles can be calculated (using formula (4-8) or (5-7) above). Finally, based on the specific surface area of ​​the negative electrode active material particles and the lithium plating reaction current density of the negative electrode, the current density can be calculated by applying formula (4-2) or (5-6) above. Integrate the charge / discharge duration corresponding to each charge / discharge process. The lithium concentration c lost due to the lithium plating reaction at the negative electrode is obtained for each charge-discharge process. lpl This means obtaining the lithium concentration loss due to the lithium plating reaction at the negative electrode after one charge-discharge cycle. Furthermore, the capacity loss due to the lithium plating reaction at the negative electrode can be calculated using formula (4-1) or (5-1):

[0133] It should be understood that by considering the change in the volume fraction of the negative electrode solid phase caused by the dissolution reaction of the active material (equivalent to combining the actual volume fraction of the solid phase during the charging and discharging process), a more accurate lithium concentration loss due to the lithium plating reaction of the negative electrode can be determined, thereby determining a more accurate lithium plating capacity loss due to the lithium plating reaction of the negative electrode, which is conducive to determining a more accurate lithium battery cycle life.

[0134] It is known that during the cyclic charging and discharging of lithium batteries, the active particles (such as graphite anode material) undergo expansion and contraction of their lattice structure due to the periodic insertion (charging) and extraction (discharging) of lithium ions. This repeated volume change leads to non-uniform mechanical stress within the particles. When the stress exceeds the material's fracture toughness, microcracks (crack initiation stage) first form on the particle surface / near-surface region. As the number of cycles increases, the cracks extend along grain boundaries or defects to form a network structure (crack propagation stage). After the newly exposed active surface comes into contact with the electrolyte, it triggers the heterogeneous growth of the solid electrolyte interphase (SEI) film at the crack, which leads to further capacity decay. The active particles are considered isolated. Since only crack growth at the surface / near-surface is considered, the embodiments of this disclosure do not consider the radial component of the stress at the particle surface, but only the tangential component of the surface stress, mainly considering the tangential stress when the tangential component at the surface reaches its maximum value.

[0135] Therefore, in one possible implementation, in step S11, the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode during cyclic charging and discharging, including: determining the maximum tangential stress on the surface of the negative electrode particle crack during cyclic charging and discharging through the negative electrode solid phase volume fraction, and determining the crack capacity loss through the maximum tangential stress on the particle surface and the negative electrode particle crack information. The negative electrode particle crack information includes the length, depth, and number of cracks per unit area of ​​existing cracks on the negative electrode particle surface. The negative electrode particle crack information can be empirical values ​​or calibrated experimentally; this embodiment of the present disclosure does not limit this. The negative electrode solid phase volume fraction can be the actual volume fraction of the negative electrode active material after dissolution and loss during cyclic charging and discharging, as determined by the above-mentioned active material loss model. By considering the actual negative electrode solid phase volume fraction after loss due to the dissolution reaction of the negative electrode active material, a more accurate maximum tangential stress on the particle surface can be determined, thereby facilitating a more precise determination of the capacity loss caused by cracks on the surface of the negative electrode active particles.

[0136] Specifically, the maximum tangential stress on the particle surface of the negative electrode active material particles corresponding to each charge-discharge process can be determined based on the negative electrode solid phase volume fraction, particle radius of the negative electrode active material, negative electrode physical properties, negative electrode size, and the current used during each charge-discharge process. Furthermore, based on the maximum tangential stress on the surface of the negative electrode active material particles corresponding to each charge-discharge process, the negative electrode particle crack information, and the number of charge-discharge cycles for each process, the crack capacity loss caused by surface cracks in the negative electrode active particles can be determined for each charge-discharge process. The negative electrode physical properties can include Poisson's ratio, Young's modulus, and partial molar volume of the negative electrode active material; the negative electrode size includes the negative electrode dimensions and area; and the current used during the charge-discharge process is the current used in the specified charge-discharge condition (e.g., 1C in the example of the specified charge-discharge condition mentioned above). The number of charge / discharge cycles corresponding to each charge / discharge process represents which cycle each charge / discharge process is. For example, the number of charge / discharge cycles corresponding to the first charge / discharge process is 1, and the number of charge / discharge cycles corresponding to the Nth charge / discharge process is N. The length, depth, and number of cracks per unit area of ​​existing cracks on the surface of the negative electrode particles included in the negative electrode particle crack information can be empirical values.

[0137] The negative electrode particle crack model can be expressed as formulas (6-1) to (6-2):

[0138]

[0139] Among them, Q m For crack capacity loss, r neg l is the particle radius of the negative electrode active material.cr ρ represents the length of the existing crack. cr σ is the number of cracks per unit area of ​​the particle, b, m, and k are correction factor constants, and σ is the number of cracks per unit area of ​​the particle. θ,max Let a0 be the maximum tangential stress on the particle surface and a0 be the depth of the existing crack. This represents the initial thickness of the SEI film. ρ SEI M is the density of the SEI film. SEI σ is the molar mass of the SEI membrane, F is the Faraday constant, N is the number of charge-discharge cycles, and σ is the molar mass of the SEI membrane. θ,max Let be the maximum tangential stress on the particle surface, v be the Poisson's ratio of the negative electrode active material, E be the Young's modulus of the negative electrode active material, Ω be the partial molar volume of the negative electrode active material, and D be the maximum tangential stress on the particle surface. s I is the solid-phase diffusion coefficient of lithium ions. cell ε is the battery current. s,neg A represents the volume fraction of the negative electrode solid phase (obtained from the above active material loss model). neg For the negative electrode area, l neg The thickness is the negative electrode thickness. Where v, E, Ω, and r are... neg F, I cell A neg and l neg It is a constant value.

[0140] For example, Figure 3 This diagram illustrates a predicted capacity loss curve due to surface cracks in the negative electrode active particles during a cyclic charge-discharge process according to an embodiment of the present disclosure. Figure 3 As shown, the capacity loss caused by surface cracks in the negative electrode active particles increases with the number of charge-discharge cycles.

[0141] It should be understood that by considering the change in the volume fraction of the negative electrode solid phase caused by the loss of the negative electrode active material due to the dissolution reaction of the negative electrode active material (equivalent to combining the actual volume fraction of the negative electrode solid phase during the charging and discharging process), a more accurate maximum tangential stress on the particle surface caused by surface cracks of the negative electrode active particles can be determined. This is beneficial for determining the crack capacity loss caused by surface cracks of the negative electrode active particles, and thus for determining a more accurate cycle life of the lithium battery.

[0142] According to the prediction method of this disclosure, a coupled simulation model of a lithium battery is established to simulate the cyclic charge-discharge process, obtaining the capacity loss of the negative electrode SEI film growth, the capacity loss of negative electrode lithium plating, and the capacity loss of negative electrode particle cracks corresponding to each charge-discharge process. In particular, the active material loss model is used to determine the change in the volume fraction of the negative electrode solid phase caused by the volume fraction loss due to the dissolution reaction of the electrode active material, and to determine the crack capacity loss caused by the surface cracks of the negative electrode active particles, the lithium plating capacity loss caused by the negative electrode lithium plating reaction, and the growth capacity loss caused by the negative electrode SEI film growth reaction. This method can comprehensively consider multiple attenuation mechanisms such as active material dissolution loss (i.e., considering the change in the volume fraction of the negative electrode solid phase due to the dissolution of active materials), surface cracks of negative electrode active particles, negative electrode lithium plating, and negative electrode SEI film formation to determine the battery capacity loss during cyclic charge-discharge, making the determined capacity retention rate more accurate, and thus obtaining a more accurate cycle life prediction result, improving the prediction accuracy of lithium battery cycle life.

[0143] As mentioned above, the lithium battery coupled simulation model is obtained by coupling the electrochemical model, the heat generation model, and various other side reaction models (active material loss model, negative electrode SEI film growth model, negative electrode lithium plating model, and negative electrode particle cracking model). The output of the electrochemical model can be used as the input of parameters in the heat generation model and the side reaction model, and the output of the side reaction model can be used as the input of parameters in the electrochemical model and the heat generation model, thereby realizing the mutual coupling between the electrochemical model, the heat generation model, and the side reaction model.

[0144] In one possible implementation, an electrochemical model and a thermal generation model can be established based on the internal physicochemical processes of a lithium battery. Specifically, the electrochemical model can be based on the following five reactions within the battery: 1) the intercalation / deintercalation reaction at the interface between the active particles and the electrolyte (i.e., lithium intercalation / deintercalation reaction); 2) solid-phase charge conservation; 3) liquid-phase charge conservation; 4) solid-phase diffusion of lithium ions; and 5) liquid-phase diffusion of lithium ions. Therefore, the electrochemical model in the coupled simulation model can include one or more of the following: an intercalation / deintercalation reaction model based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium-ion solid-phase diffusion model based on Fick's second diffusion law, and a lithium-ion liquid-phase diffusion model.

[0145] The insertion / extraction reaction model is used to determine the insertion / extraction reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or inserted into them. Therefore, the insertion / extraction reaction current density refers to the magnitude of the electrode reaction current per unit area during the charging and discharging process of a lithium battery, when lithium ions are extracted (delithiated) or inserted (lithi inserted) into the surface of the positive and negative electrodes. For example, the insertion / extraction reaction model can be expressed as formulas (7-1) to (7-3):

[0146]

[0147] η=Φ s -Φ l -E Eq -U de (7-2)

[0148] j0=FK(c s,max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5 (7-3)

[0149] Where j is the deintercalation / intercalation reaction current density at the positive or negative electrode, j0 is the deintercalation / intercalation reaction exchange current density at the positive or negative electrode (calculated using the reaction rate constant), and α a and α c These are the transfer coefficients for the anodic and cathodic reactions, respectively (e.g., both 0.5), η is the overpotential of the positive or negative electrode, T is the battery temperature (which can be obtained using a heat generation model), and Φ is the transfer coefficient for the anodic and cathodic reactions, respectively. s For solid-state potential, Φ l E is the liquid phase potential. Eq Where K is the equilibrium potential of the positive or negative electrode, and C is the reaction rate constant of the positive or negative electrode. s c represents the solid-phase lithium-ion concentration of the positive or negative electrode (i.e., the lithium-ion concentration in the active material particles of the positive or negative electrode). s,max The maximum solid-phase lithium-ion concentration at the positive or negative electrode (e.g., c at the positive electrode). s,max It can be 23,000 moles per square meter (mol / m²) 3 ), the negative electrode c s,max It can be 30555 mol / m 3 ), c l This refers to the concentration of lithium ions in the liquid phase (i.e., the concentration of lithium ions in the electrolyte); U de This is the potential drop mentioned above.

[0150] Where, α a α c F, R, K, c s,max For a constant value, c s and cl It can be determined using both the lithium-ion solid-phase diffusion model and the lithium-ion liquid-phase diffusion model; Φ s and Φ l For example, the simulation calculation capabilities of the pyBaMM software can be used to perform simulation calculations on the simulation model to obtain the potential. This embodiment of the disclosure does not limit the calculation process for the solid-phase potential and liquid-phase potential. It is known that the equilibrium potential E of the positive and negative electrodes... Eq The equilibrium potential changes with the state of charge (SOC). Therefore, the relationship curve between the equilibrium potential of the positive and negative electrodes and the SOC can be obtained through button cell experiments. Furthermore, based on the relationship curve between the equilibrium potential and the state of charge (SOC), the equilibrium potential E of the positive and negative electrodes at any SOC can be obtained. Eq .

[0151] The solid-phase charge conservation model is used to determine the solid-phase current density (i.e., the current density inside the active material particles of the positive and negative electrodes) in the lithium battery. For example, the solid-phase charge conservation model can be expressed as formula (8):

[0152]

[0153] Among them, i s σ is the solid-state current density at the positive or negative electrode. s This refers to the effective solid-phase conductivity of the positive or negative electrode (i.e., the effective conductivity within the active material particles of the positive or negative electrode). The gradient of the solid-state potential at the positive or negative electrode; where σ s For a constant value, σ s It can also be expressed as Brugg is the Bruggman constant, ε s σ represents the solid volume fraction of the positive or negative electrode. s,0 The initial effective solid-phase conductivity of the positive or negative electrode, Brugg, ε s and σ s,0 It is a constant. Wherein, The results can be obtained by using the pyBaMM software to perform simulation calculations on the battery simulation model.

[0154] The liquid phase charge conservation model is used to determine the liquid phase current density (i.e., the current density in the electrolyte) in the lithium battery electrolyte; for example, the liquid phase charge conservation model can be expressed as formula (9):

[0155]

[0156] Among them, i l Let be the liquid phase current density, and k be the effective conductivity of the liquid phase (i.e., the effective conductivity of the electrolyte). f(c) represents the gradient of the liquid phase potential (i.e., the potential gradient in the electrolyte). l () is related to the concentration of lithium ions in the liquid phase, c l The relevant activity coefficient, This refers to the lithium-ion transport number. Representing lnc l The gradient of; where k, R, F, For a constant value, κ can also be expressed as Brugg is the Bruggman constant, ε l σ is the liquid volume fraction. s,0 The initial effective conductivity of the liquid phase, Brugg, ε l and σ l,0 A constant value; lithium-ion transport number In electrolytes or electrode materials, the ratio of lithium ion flow rate through a unit cross-section per unit time to the total charge flow rate is considered. It can be obtained through experimental testing; The activity coefficient can be obtained by performing simulation calculations on a battery simulation model using the pyBaMM software. f can be obtained through experimental testing. A For activity coefficient, in practical applications, the relationship curve between liquid phase lithium ion concentration and activity coefficient can be obtained through experimental testing. Based on this relationship curve, the activity coefficient at different liquid phase lithium ion concentrations can be obtained.

[0157] The lithium-ion solid-phase diffusion model is used to determine the solid-phase lithium-ion concentration (lithium-ion concentration within the active material particles) in the positive and negative electrodes. The diffusion of lithium ions within the active material particles due to the lithium concentration gradient can be described using Fick's second diffusion law to obtain the lithium-ion concentration within the active material particles in the positive and negative electrodes. For example, the lithium-ion solid-phase diffusion model is expressed as formula (10):

[0158]

[0159] Among them, c s D represents the solid-phase lithium-ion concentration at the positive or negative electrode, t represents time, and D represents the solid-phase lithium-ion concentration at the positive or negative electrode. s denoted as the solid-phase diffusion coefficient of the positive or negative electrode, and r as the particle radius of the active material of the positive or negative electrode. Representing partial derivatives, for example, Represents c s The partial derivative with respect to t, and so on, will not be elaborated further; where, D s And r are constants. It should be understood that the solid-phase lithium ion concentration c at any time can be obtained by integrating formula (10). s .

[0160] The lithium-ion liquid-phase diffusion model characterizes the concentration of lithium ions in the liquid phase of the electrolyte (i.e., the lithium ion concentration in the electrolyte). Fick's second diffusion law can be used to describe the diffusion of lithium ions in the electrolyte to obtain the lithium ion concentration in the electrolyte; for example, the lithium-ion liquid-phase diffusion model is expressed by equations (11-1) to (11-4):

[0161]

[0162] ε l =ε0-ε g (11-2)

[0163]

[0164] Among them, c l ε represents the concentration of lithium ions in the liquid phase. l Let x be the liquid phase volume fraction, and x be any position within the lithium battery (this position can be customized). s D represents the specific surface area of ​​the active material particles for the positive or negative electrode. l The effective diffusion coefficient of the liquid phase (calculated using the Brügmann coefficient and diffusion activation energy) is specifically... Brugg is the Bruggman coefficient, D l,0 E is the initial liquid phase diffusion coefficient. a For diffusion activation energy, T ref The reference temperature is (e.g., 298.15 K (i.e., 25 °C)), T is the battery temperature (which can be obtained using a heat generation model), j is the current density of the above-mentioned insertion / extraction reaction; ε0 is the initial liquid phase volume fraction, ε g δ represents the change in liquid volume fraction caused by the side reaction. film ε represents the change in SEI film thickness mentioned above. s The solid volume fraction of the positive or negative electrode (ε) s (Originally obtained from the active material loss model), where r is the particle radius of the active material in the positive or negative electrode; where ε0, r, ε l a s Brugg, D l,0 E a T ref This is a constant. It should be understood that the liquid-phase lithium-ion concentration c at any given time can be obtained by integrating formula (11-1). lIn this embodiment, the liquid phase lithium-ion concentration calculated using formulas (11-1) to (11-4) takes into account the change in particle specific surface area due to the dissolution loss of the positive or negative electrode active materials, rather than using a fixed particle specific surface area. Therefore, the calculated liquid phase lithium-ion concentration is more accurate, which is beneficial for determining more accurate insertion / extraction reaction current density and liquid phase current density. This, in turn, is beneficial for determining more accurate battery voltage, solid phase potential, liquid phase potential, and other data, which is beneficial for subsequently determining more accurate battery capacity loss.

[0165] As mentioned above, the coupled simulation model may also include: a heat generation model; wherein, a heat generation model can be constructed based on the energy conservation equation, so as to calculate the battery temperature by calculating the heat generation in the heat generation model; specifically, the heat generation model can be determined based on heat dissipation power, liquid phase ohmic heat generation power, solid phase ohmic heat generation power of the positive and negative electrodes, polarization heat generation power, and reversible heat power; wherein, heat dissipation power characterizes the power of heat dissipation of the lithium battery; liquid phase ohmic heat generation power characterizes the heat generation power when current flows through the electrolyte; solid phase ohmic heat generation power characterizes the heat generation power when current flows through the active materials of the positive and negative electrodes; polarization heat generation power characterizes the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; reversible heat power characterizes the power of heat generated due to the entropy change of the positive and negative electrodes in the electrochemical reaction. For example, the heat generation model can be expressed as formula (12):

[0166]

[0167] Where ρ is the density of the lithium battery material, C p λ represents the specific heat capacity of the lithium battery material, where the subscript i stands for neg or pos, representing the negative or positive electrode, T is the battery temperature, and λ is the thermal conductivity. For heat dissipation power, i l The liquid phase current density, The gradient of the liquid phase potential. For liquid phase ohmic heat generation power, i s For solid-state current density, The gradient of the solid-state potential. For solid-state ohmic heat generation power, a i j represents the specific surface area of ​​the positive or negative electrode active material particles. i η represents the reaction current density at the positive or negative electrode. i For the overpotential of the positive or negative electrode, ∑ i a i j i η i For polarization heat generation power, E eq,i This represents the open-circuit potential of the positive or negative terminal. The entropy coefficient of the positive or negative electrode. This represents the reversible heat generation power. It should be understood that the battery temperature T can be obtained by integrating equation (12).

[0168] Where, ∑ i a i j i η i This can include the polarization heat generation power ∑ generated by the insertion / extraction reaction. i a s,i j q,i η q,i ∑ Polarization heat generation power generated by the dissolution reaction of active materials i a s,i j dis,i η dis,i The polarization heat generation power a generated by the negative electrode SEI film growth reaction s,neg j SEI η SEI And the polarization heat generation power a generated by the lithium plating reaction at the negative electrode s,neg j lpl η lpl ; This can include reversible heat generation power generated by the insertion / extraction reaction. Reversible heat generation power ∑ from the dissolution reaction of active materials i a s,i j dis,i η dis,i Reversible heat generation power generated by the negative electrode SEI film growth reaction and the reversible heat generation power generated by the lithium plating reaction at the negative electrode.

[0169] Where i is neg, a s,neg j represents the specific surface area of ​​the negative electrode active material particles. q,neg The deintercalation reaction current density at the negative electrode (obtained from the above deintercalation reaction model), η q,neg This is the overpotential of the negative electrode in the insertion / extraction reaction. The entropic thermal coefficient of the negative electrode (obtained experimentally) is E, and the open-circuit potential of the negative electrode is E. eq,neg (partial derivative with respect to temperature), j dis,neg η is the current density of the dissolution reaction of the negative electrode active material. dis,neg This is the overpotential for the dissolution reaction of the negative electrode active material; when i is pos, a s,pos The specific surface area of ​​the positive electrode active material particles (i.e. ε s,pos r is the solid volume fraction of the cathode determined by the active material loss model. pos (where j is the radius of the positive electrode active material particle), q,pos The current density of the deintercalation reaction at the positive electrode (obtained from the above deintercalation reaction model), η q,pos This is the overpotential at the positive electrode. The entropic thermal coefficient of the positive electrode (obtained experimentally) is E, and the open-circuit potential of the positive electrode is E. eq,pos (partial derivative with respect to temperature), j dis,pos η is the current density of the dissolution reaction of the positive electrode active material. dis,pos The overpotential of the dissolution reaction of the positive electrode active material; i l The concentration of lithium ions in the liquid phase (obtained from the above liquid phase charge conservation model) is denoted as . The gradient of the liquid phase potential, i s The solid-state current density is obtained from the solid-state charge conservation model described above. The gradient of the solid-state potential.

[0170] It should be noted that the various electrochemical models, side reaction models, and heat generation models provided in the above embodiments of this disclosure are some possible implementation methods provided in the embodiments of this disclosure. In fact, under the guidance of the embodiments of this disclosure, those skilled in the art can customize and design electrochemical models, side reaction models, and heat generation models, as long as they can realize the construction of a coupled simulation model of lithium battery. The embodiments of this disclosure do not impose any restrictions on this.

[0171] In this embodiment, the electrochemical model is established based on the electrochemical reaction principle of lithium batteries, encompassing processes such as electrode reactions, electrolyte transport, and charge transfer. The model considers factors such as the concentration distribution of electrode materials, the concentration distribution of the electrolyte, and the electric field distribution within the battery. When establishing the negative electrode SEI film growth reaction model, the increase in internal resistance and capacity decay caused by the growth of the negative electrode SEI film is considered. By introducing the kinetic equation of SEI growth into the simulation model, considering factors such as the thickness, composition, and reaction rate with the electrolyte of the SEI film, and solving the SEI growth equation, the changes in the SEI film during cycling can be obtained. Regarding capacity loss caused by surface cracks in the negative electrode particles, considering the generation of cracks on the particle surface during charging and discharging, the impact of cracks on battery performance is evaluated by calculating the changes in cracks during cycling. When establishing the negative electrode lithium plating reaction model, it was considered that negative electrode lithium plating is one of the common failure modes in lithium battery cycling. By establishing the negative electrode lithium plating reaction model, the influence of factors such as battery potential, temperature, and electrolyte concentration on the negative electrode lithium plating behavior was predicted, the conditions and extent of lithium plating occurrence were predicted, and the impact on battery performance was quantified and integrated into the coupled simulation model. When establishing the active material loss model, the loss of active materials mainly includes the erosion of active materials by hydrofluoric acid (HF). A kinetic model of positive electrode active material loss was established, considering the influence of factors such as battery charge and discharge current density, temperature, and potential on active material loss. By solving the active material loss equation, the changes of active materials during cycling were obtained, and their impact on battery performance was reflected in the simulation model.

[0172] In this embodiment, the model considering four degradation mechanisms is coupled using a multiphysics method. The multiphysics coupled simulation model is discretized using the finite element method to solve for the potential distribution (solid phase potential distribution and liquid phase potential distribution), battery temperature distribution, and concentration distribution (solid phase concentration distribution and liquid phase concentration distribution) of the lithium battery during cycling. The solution results are used to analyze the performance changes of the lithium battery at different cycling stages, such as capacity decay curves and capacity-voltage curves at different cycle numbers, to achieve accurate prediction of the lithium battery's cycle life. For example, based on the simulated battery capacity retention rate change curve, combined with battery failure criteria (such as capacity retention rate below 80%), the battery's cycle life can be predicted.

[0173] In this embodiment, by comprehensively considering four degradation mechanisms—negative electrode SEI film growth, negative electrode lithium plating, negative electrode particle surface cracking, and active material loss—and taking into account the impact of overall battery temperature and overpotential on positive electrode active material loss from the perspective of particle surface reaction, this calculation method more closely reflects the actual working process of the battery cell and yields better results. Furthermore, the coupled simulation model provided in this embodiment can more comprehensively reflect the actual performance changes of lithium batteries during cycling, thereby improving the accuracy of cycle life prediction.

[0174] It is known that the side reaction mechanisms during the cyclic charging and discharging process of lithium batteries are quite complex. Existing simulation models in publicly available technologies generally describe the side reaction mechanisms of active material loss by directly defining a loss constant, rather than describing them from the perspective of actual particle surface side reactions. This differs from the actual operating conditions of lithium batteries. Furthermore, existing technologies typically consider only two decay mechanisms: negative electrode SEI film formation and negative electrode lithium plating, which is not comprehensive enough. This disclosure's embodiments predict the cycle life of lithium batteries by considering multiple decay mechanisms, including active material loss side reaction mechanisms, negative electrode SEI film growth reaction mechanisms, negative electrode lithium plating reaction mechanisms, and negative electrode particle surface cracking. Compared with other publicly available technologies, the prediction method proposed in this disclosure provides a higher accuracy in fitting the predicted results to the measured results.

[0175] For example, this disclosure uses a ternary lithium battery cell as an example. In this ternary lithium battery cell, the positive electrode uses a ternary material, the negative electrode uses a graphite material, and the electrolyte uses LiPF6 as the solvent material. For the aforementioned ternary lithium battery cell, the cycle life of the ternary lithium battery cell is predicted using the prediction method described in this disclosure (i.e., method A) and the existing prediction method (i.e., method B), respectively. Simultaneously, the cycle life of the ternary lithium battery cell is actually measured to obtain... Figure 4The diagram shows a comparison of the capacity retention rate changes; the charge / discharge conditions used for predicting and measuring cycle life can be the examples of the specified charge / discharge conditions proposed in the embodiments of this disclosure; existing prediction methods only consider two decay mechanisms, namely negative electrode SEI film formation and negative electrode lithium plating, to predict cycle life, while the prediction method of this disclosure comprehensively considers four decay mechanisms, namely negative electrode SEI film growth, negative electrode lithium plating, negative electrode particle surface cracks, and active material loss, to predict cycle life. The parameters used in constructing the electrochemical-thermal-side reaction coupled simulation model of the ternary battery cell using the prediction method of this embodiment may include: negative electrode thickness 77 μm, separator thickness 13 μm, positive electrode thickness 56 μm, negative electrode particle diameter 12 μm, positive electrode particle diameter 4.5 μm, negative electrode solid volume fraction 0.66, positive electrode solid volume fraction 0.74, negative electrode liquid volume fraction 0.27, positive electrode liquid volume fraction 0.21, separator porosity 0.42, and maximum lithium intercalation concentration of the negative electrode 30555 mol / m³. 3 The maximum lithium intercalation concentration at the positive electrode is 49211 mol / m³. 3 The negative electrode reaction rate constant is 2e⁻¹⁰ m / s, the positive electrode reaction rate constant is 3e⁻¹² m / s, and the negative electrode diffusion coefficient is 7.9e⁻¹⁴ m / s. 2 / s], positive electrode diffusion coefficient 8.8e-14[m 2 / s], Negative electrode diffusion activation energy 50000 [J / mol], Positive electrode diffusion activation energy 50000 [J / mol], Negative electrode Brugg coefficient 2, Positive electrode Brugg coefficient 1.75, Separator Brugg coefficient 1.75, Negative electrode conductivity 460 [S / m], Positive electrode conductivity 16 [S / m], Initial electrolyte concentration 1050 [mol / m] 3 The negative electrode density is 2240 kg / m³. 3 The positive electrode density is 4600 kg / m³. 3 Electrolyte density 1210 [kg / m³] 3 The specific heat capacity of the negative electrode is 881 J / (kg·K), the specific heat capacity of the positive electrode is 1000 J / (kg·K), the specific heat capacity of the separator is 1978 J / (kg·K), and the density of the SEI film is 2640 kg / m³. 3 The SEI film has a molar mass of 0.026 kg / mol and a lithium density of 534 kg / m³. 3 Lithium molar mass 0.00694 kg / mol, SEI formation current density 2.3E-7 A / m 2 Lithium plating current density 2E-3 [A / m] 2 The dissolution current density of the active material is 2E-4 [A / m]. 2SEI formation equilibrium potential: 0.4 V; lithium plating equilibrium potential: 0 V; active material dissolution equilibrium potential: 4 V; initial film thickness: 5 nm; SEI film conductivity: 6e⁻⁶ S / m; negative electrode partial molar volume: 3.497E⁻⁶ m³ / s. 3 / mol], correction factor m is 2.7, correction factor k is 6E-11, correction factor b is 1.12, existing crack depth is 2E-9[m], existing crack length is 2E-9[m], number of cracks per unit area of ​​particle is 2.54E13[1 / m 2 ].

[0176] like Figure 4 As shown, compared to the existing prediction method (i.e., method B), the capacity retention curve obtained by the prediction method (method A) proposed in this embodiment is closer to the actual measured capacity retention curve (measured data). In contrast, the difference between the existing prediction method and the measured data increases significantly with the increase of charge-discharge cycles. Therefore, the prediction method proposed in this embodiment can more accurately predict the cycle degradation of lithium batteries, obtaining more precise cycle life prediction results. Furthermore, it can simultaneously output the loss status of the positive and negative electrode active materials (e.g., ...). Figure 2 The curve showing the loss rate variation of the positive electrode active material) and the capacity loss caused by the formation of negative electrode cracks (such as...) Figure 3 The capacity loss curve shown provides a strong basis for researchers to optimize and improve battery design.

[0177] The prediction method proposed in this disclosure comprehensively considers multiple degradation mechanisms during lithium battery cycling, including negative electrode SEI film growth, negative electrode particle surface cracking, negative electrode lithium plating, and positive electrode active material loss. By establishing multi-physics field coupled simulations such as battery electrochemical model, heat generation model, and side reaction model, accurate prediction of battery cycle life is achieved. In particular, from the perspective of particle surface reaction, the influence of the entire battery temperature and overpotential on the loss of electrode active materials is comprehensively considered. This prediction method is closer to the actual working process of the battery cell and has better results.

[0178] Figure 5 A block diagram of a lithium battery cycle life prediction device according to an embodiment of the present disclosure is shown, such as Figure 5 As shown, the device includes:

[0179] The simulation module 501 is used to simulate the cyclic charge-discharge process of a lithium battery based on a lithium battery coupled simulation model, and to obtain the capacity loss of the negative electrode SEI film growth, the capacity loss of the negative electrode lithium plating, and the capacity loss of the negative electrode particle cracks. The coupled simulation model includes: a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model. The negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during the cyclic charge-discharge process, and the negative electrode particle crack model is used to determine the crack capacity loss of the negative electrode cracks during the cyclic charge-discharge process.

[0180] The prediction module 502 is used to determine the capacity retention rate based on the growth capacity loss, lithium plating capacity loss and crack capacity loss, so as to predict the cycle life of the lithium battery.

[0181] In one possible implementation, the coupled simulation model further includes an active material loss model; the active material loss model is used to determine the electrode solid volume fraction of the electrode active material during cyclic charge and discharge, so as to determine the growth capacity loss, lithium plating capacity loss and crack capacity loss based on the electrode solid volume fraction.

[0182] In one possible implementation, the active material loss model is used to determine the electrode solid volume fraction of the electrode active material during cyclic charge-discharge, including: determining the loss volume fraction of the electrode active material during cyclic charge-discharge, and determining the electrode solid volume fraction using the loss volume fraction and the initial solid volume fraction.

[0183] In one possible implementation, the negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during cyclic charge-discharge, including: determining the SEI film growth reaction current density during cyclic charge-discharge, and determining the growth capacity loss through the SEI film growth current density and the negative electrode solid phase volume fraction.

[0184] In one possible implementation, the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge-discharge processes, including: determining the lithium plating reaction current density of the negative electrode during cyclic charge-discharge processes, and determining the lithium plating capacity loss by means of the lithium plating reaction current density and the volume fraction of the negative electrode solid phase.

[0185] In one possible implementation, the negative electrode SEI film growth model is as follows:

[0186] Q SEI =c SEI *F*l neg *W cell *H cell

[0187]

[0188] η SEI =Φ s -Φ l -E Eq,SEI -U de

[0189]

[0190] Among them, Q SEI For growth capacity loss, c SEI The lithium concentration lost due to the SEI film growth reaction is l neg Where W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness. cell H represents the battery width. cell j is the battery length. SEI j is the current density for the negative electrode SEI film growth reaction. 0,SEI α represents the exchange current density of the negative electrode SEI film growth reaction. c,SEI Let R be the transfer coefficient, T be the gas constant, and η be the cell temperature. SEI Φ is the overpotential for the negative electrode SEI film growth reaction. s For solid-state potential, Φ l E is the liquid phase potential. Eq,SEI U is the equilibrium potential for the negative electrode SEI film growth reaction. de For potential drop, j neg j is the current density for the negative electrode deintercalation reaction. lpl The current density for the lithium plating reaction at the negative electrode is δ. film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film. s,neg M represents the specific surface area of ​​the negative electrode active material particles. SEI ρ is the molar mass of the SEI film. SEI c is the density of the SEI film. lpl M represents the lithium concentration loss due to the lithium plating reaction at the negative electrode. Li ρ is the molar mass of lithium. Li ε represents the lithium concentration. s,neg r represents the volume fraction of the negative electrode solid phase. neg The radius of the negative electrode active material particles; Represents the partial derivative.

[0191] In one possible implementation, the lithium plating model for the negative electrode is:

[0192] Q lpl =c lpl *F*l neg *W cell *H cell

[0193]

[0194] η lpl =Φ s -Φ l -E Eq,Li -U de

[0195]

[0196] Among them, Q lpl For lithium plating capacity loss, c lpl The lithium concentration lost due to the lithium plating reaction at the negative electrode, l neg Where W is the negative electrode thickness, F is the Faraday constant, and W is the negative electrode thickness. cell H represents the battery width. cell j is the battery length. lpl j is the current density for the lithium plating reaction at the negative electrode. 0,lpl α represents the exchange current density of the lithium plating reaction at the negative electrode. a,lpl and α c,lpl Here, R is the transfer coefficient, T is the gas constant, and η is the cell temperature. lpl Φ is the overpotential for the lithium plating reaction at the negative electrode. s For solid-state potential, Φ l Let j be the liquid phase potential. neg j is the current density for the negative electrode deintercalation reaction. SEI E represents the current density during the growth reaction of the negative electrode SEI film. Eq,Li U is the equilibrium potential for the lithium plating reaction at the negative electrode. de For voltage drop, δ film κ represents the change in SEI film thickness. SEI a is the conductivity of the SEI film. s,neg c represents the specific surface area of ​​the negative electrode active material particles. SEI M represents the lithium concentration lost due to the SEI film growth reaction. SEI ρ is the molar mass of the SEI film. SEI M is the density of the SEI film. Li ρ is the molar mass of lithium. Li Let a be the concentration of lithium. s,neg ε represents the specific surface area of ​​the negative electrode active material particles. s,neg r represents the volume fraction of the negative electrode solid phase. neg The radius of the negative electrode active material particles; Represents the partial derivative.

[0197] In one possible implementation, the negative electrode particle crack model is as follows:

[0198]

[0199] Among them, Q m For crack capacity loss, r neg l is the particle radius of the negative electrode active material. cr ρ represents the length of the existing crack. cr σ is the number of cracks per unit area of ​​the particle, b, m, and k are correction factor constants, and σ is the number of cracks per unit area of ​​the particle. θ,max Let a0 be the maximum tangential stress on the particle surface and a0 be the depth of the existing crack. This represents the initial thickness of the SEI film. ρ SEI M is the density of the SEI film. SEI σ is the molar mass of the SEI membrane, F is the Faraday constant, N is the number of charge-discharge cycles, and σ is the molar mass of the SEI membrane. θ,max Let be the maximum tangential stress on the surface, v be the Poisson's ratio of the negative electrode active material, E be the Young's modulus of the negative electrode active material, Ω be the partial molar volume of the negative electrode active material, and D be the maximum tangential stress on the surface. s I is the solid-phase diffusion coefficient of lithium ions. cell ε is the battery current. s,neg A represents the volume fraction of the negative electrode solid phase. neg For the negative electrode area, l neg The thickness is the negative electrode thickness.

[0200] In one possible implementation, the loss model of the active material is as follows:

[0201]

[0202] η dis =Φ s -Φ l -E Eq,dis

[0203] Where, ε s ε represents the volume fraction of the solid phase at the positive or negative electrode. s,0 j is the initial solid volume fraction. dis C is the current density for the dissolution reaction of the active material. s,max The maximum lithium intercalation concentration of the electrode, l s For electrode thickness, j 0,dis The exchange current density for the dissolution reaction of the active material is given by F, F is the Faraday constant, R is the gas constant, T is the cell temperature, and η is the voltammetric ... dis Φ is the overpotential for the dissolution reaction of active materials. s For solid-state potential, Φ l E is the liquid phase potential. Eq,dis ε represents the equilibrium potential of the dissolution reaction of the active material; exp is an exponential function with a base of natural numbers; t is time.

[0204] In one possible implementation, the coupled simulation model further includes one or more of the following: an insertion / extraction reaction model, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium-ion solid-phase diffusion model, and a lithium-ion liquid-phase diffusion model;

[0205] The deintercalation / intercalation reaction model is as follows:

[0206]

[0207] η=Φ s -Φ l -E Eq -U de

[0208] j0=FK(c s,max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5

[0209] Where j is the deintercalation current density at the positive or negative electrode, j0 is the exchange current density at the positive or negative electrode, and α a and α c η is the transfer coefficient, η is the overpotential of the positive or negative electrode, T is the battery temperature, and Φ is the transfer coefficient. s The solid-state potential, Φ, is the potential of the positive or negative electrode. l E is the liquid phase potential of the electrolyte. Eq Where K is the equilibrium potential of the positive or negative electrode, and C is the reaction rate constant of the positive or negative electrode. s c represents the solid-phase lithium-ion concentration at the positive or negative electrode. s,max c represents the maximum solid-phase lithium-ion concentration at the positive or negative electrode. l This refers to the concentration of lithium ions in the liquid phase; U de The potential drop is due to a side reaction;

[0210] The solid-phase charge conservation model is as follows:

[0211]

[0212] Among them, i s Let σ be the solid-state current density. s For the effective conductivity of the solid phase, The gradient of the solid-state potential;

[0213] The liquid phase charge conservation model is as follows:

[0214]

[0215] Among them, i lLet κ be the liquid phase current density and κ be the effective liquid phase conductivity. The gradient of the liquid phase potential, f(c) l () is related to the concentration of lithium ions in the liquid phase, c l The relevant activity coefficient, This refers to the lithium-ion transport number. Representing lnc l The gradient of R is the gas constant, T is the battery temperature, and F is the Faraday constant;

[0216] The lithium-ion solid-phase diffusion model is as follows:

[0217]

[0218] Among them, c s Let D be the solid-phase lithium-ion concentration, t be time, and D be the solid-phase lithium-ion concentration. s Where is the solid-phase diffusion coefficient, and r is the particle radius of the positive or negative electrode active material. Represents partial derivatives;

[0219] The lithium-ion liquid-phase diffusion model is as follows:

[0220]

[0221] ε l =ε0-ε g

[0222]

[0223] Among them, c l ε represents the concentration of lithium ions in the liquid phase. l Let be the liquid volume fraction, and x be any position within the lithium battery. Let a be the lithium-ion transport number. s D represents the specific surface area of ​​the active material particles for the positive or negative electrode. l ε is the effective diffusion coefficient of the liquid phase, ε0 is the initial liquid phase volume fraction, and ε g δ is the change in liquid volume fraction. film ε represents the change in SEI film thickness. s denoted as the solid volume fraction of the positive or negative electrode, and r is the particle radius of the positive or negative electrode active material.

[0224] In one possible implementation, the coupled simulation model further includes: a heat generation model; the heat generation model is:

[0225]

[0226] Where ρ is the density of the lithium battery material, C p λ represents the specific heat capacity of the lithium battery material, where the subscript i indicates the negative or positive electrode, T is the battery temperature, and λ is the thermal conductivity. For heat dissipation power, i l The liquid phase current density, The gradient of the liquid phase potential. For liquid phase ohmic heat generation power, i s For solid-state current density, The gradient of the solid-state potential. For solid-state ohmic heat generation power, a i j represents the specific surface area of ​​the positive or negative electrode active material particles. i η represents the reaction current density at the positive or negative electrode. i For the overpotential of the positive or negative electrode, ∑ i a i j i η i For polarization heat generation power, E eq,i This represents the open-circuit potential of the positive or negative terminal. The entropy coefficient of the positive or negative electrode. It is a reversible heat generation power.

[0227] According to the prediction device of this disclosure, the cyclic charging and discharging process of a lithium battery is simulated by a coupled simulation model based on a lithium battery. The capacity loss due to the growth of the negative electrode SEI film, the capacity loss due to lithium plating, and the capacity loss due to particle cracking during the cyclic charging and discharging process are obtained. In other words, the capacity loss caused by multiple decay mechanisms such as the growth of the negative electrode SEI film, lithium plating, and particle cracking during the cyclic charging and discharging process is comprehensively determined. This makes the capacity retention rate determined based on the capacity loss due to the growth of the negative electrode SEI film, the capacity loss due to lithium plating, and the capacity loss due to particle cracking more accurate, thereby obtaining a more accurate cycle life prediction result and improving the prediction accuracy of the lithium battery cycle life.

[0228] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0229] This disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0230] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0231] This disclosure also provides a computer program product, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0232] Figure 6 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 6 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0233] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0234] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0235] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by instruction execution devices. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0236] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0237] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing state information of computer-readable program instructions. These electronic circuits can execute computer-readable program instructions to implement various aspects of this disclosure.

[0238] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0239] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0240] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0241] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0242] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the cycle life of a lithium battery, characterized in that, include: The lithium battery cyclic charge and discharge process was simulated based on a lithium battery coupling simulation model, and the capacity loss of the negative electrode SEI film growth, the capacity loss of the negative electrode lithium plating, and the capacity loss of the negative electrode particle cracks were obtained. The coupled simulation model includes: an active material loss model, a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model. The negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during cyclic charge-discharge; the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge-discharge; and the negative electrode particle crack model is used to determine the crack capacity loss caused by the heterogeneous growth of the SEI film at the crack site triggered by the negative electrode crack during cyclic charge-discharge. The active material loss model is used to determine the change in electrode solid volume fraction due to the dissolution reaction of the electrode active material during cyclic charge-discharge, wherein the electrode solid volume fraction includes the negative electrode solid volume fraction. The negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, including: determining the SEI film growth reaction current density during the cyclic charge-discharge process, and determining the growth capacity loss through the SEI film growth current density and the negative electrode solid phase volume fraction. The negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge and discharge, including: determining the lithium plating reaction current density of the negative electrode during cyclic charge and discharge, and determining the lithium plating capacity loss by the lithium plating reaction current density and the volume fraction of the solid phase of the negative electrode. The negative electrode particle crack model is used to determine the crack capacity loss caused by the heterogeneous growth of the SEI film at the crack during the cycle charge and discharge process. This includes: determining the maximum tangential stress on the particle surface during the cycle charge and discharge process by the negative electrode solid phase volume fraction, and determining the crack capacity loss by the maximum tangential stress on the particle surface and the negative electrode particle crack information. The capacity retention rate is determined based on the growth capacity loss, lithium plating capacity loss, and crack capacity loss to predict the cycle life of lithium batteries.

2. The method according to claim 1, characterized in that, The active material loss model is used to determine the change in electrode solid volume fraction due to the dissolution reaction of the electrode active material during cyclic charge and discharge. It includes: determining the loss volume fraction of the electrode active material during cyclic charge and discharge, and determining the electrode solid volume fraction using the loss volume fraction and the initial solid volume fraction.

3. The method according to claim 1 or 2, characterized in that, The negative electrode SEI film growth model is as follows: in, For growth capacity loss, This represents the lithium concentration loss due to the SEI film growth reaction. Where is the negative electrode thickness, and F is the Faraday constant. For battery width, Battery length, This represents the current density during the growth reaction of the negative electrode SEI film. This represents the exchange current density during the growth reaction of the SEI film at the negative electrode. Let R be the transfer coefficient, R be the gas constant, and T be the cell temperature. This is the overpotential for the growth reaction of the SEI film at the negative electrode. For solid-state potential, The liquid phase potential, This is the equilibrium potential for the growth reaction of the SEI film at the negative electrode. For potential drop, The current density for the negative electrode deintercalation reaction. This represents the current density for the lithium plating reaction at the negative electrode. This represents the change in SEI film thickness. For the SEI film conductivity, This represents the specific surface area of ​​the negative electrode active material particles. The molar mass of the SEI membrane is... The density of the SEI film, This refers to the loss of lithium concentration due to the lithium plating reaction at the negative electrode. Where is the molar mass of lithium. The concentration of lithium. This represents the volume fraction of the negative electrode solid phase. The radius of the negative electrode active material particles; Represents the partial derivative.

4. The method according to claim 1 or 2, characterized in that, The lithium plating model for the negative electrode is as follows: in, Due to lithium plating capacity loss, This refers to the loss of lithium concentration due to the lithium plating reaction at the negative electrode. Where is the negative electrode thickness, and F is the Faraday constant. For battery width, Battery length, This represents the current density for the lithium plating reaction at the negative electrode. This represents the exchange current density for the lithium plating reaction at the negative electrode. and Here, R is the transfer coefficient, R is the gas constant, and T is the cell temperature. This is the overpotential for the lithium plating reaction at the negative electrode. For solid-state potential, The liquid phase potential, The current density for the negative electrode deintercalation reaction. This represents the current density during the growth reaction of the negative electrode SEI film. This is the equilibrium potential for the lithium plating reaction at the negative electrode; For voltage drop, This represents the change in SEI film thickness. For the SEI film conductivity, This represents the specific surface area of ​​the negative electrode active material particles. This represents the lithium concentration loss due to the SEI film growth reaction. The molar mass of the SEI membrane is... The density of the SEI film, Where is the molar mass of lithium. The concentration of lithium. This represents the specific surface area of ​​the negative electrode active material particles. This represents the volume fraction of the negative electrode solid phase. The radius of the negative electrode active material particles; Represents the partial derivative.

5. The method according to claim 1 or 2, characterized in that, The negative electrode particle crack model is as follows: in, For crack capacity loss, The radius of the negative electrode active material particles. The length of the existing crack. Here, b, m, and k represent the number of cracks per unit area of ​​the particle, and b, m, and k are correction factor constants. The maximum tangential stress on the particle surface. The depth of the existing crack. This represents the initial thickness of the SEI film. ; For the SEI film density, Where is the molar mass of the SEI film, F is the Faraday constant, and N is the number of charge-discharge cycles. The maximum tangential stress on the surface, Where is the Poisson's ratio of the negative electrode active material, and E is the Young's modulus of the negative electrode active material. This represents the partial molar volume of the negative electrode active material. is the solid-phase diffusion coefficient of lithium ions. Battery current, This represents the volume fraction of the negative electrode solid phase. For the negative electrode area, The thickness is the negative electrode thickness.

6. The method according to claim 1 or 2, characterized in that, The loss model of the active material is as follows: in, It refers to the volume fraction of the solid phase at the positive or negative electrode. This represents the initial solid volume fraction. The current density is the current density of the dissolution reaction of the active material. This represents the maximum lithium intercalation concentration of the electrode. For electrode thickness, The exchange current density of the dissolution reaction of the active material. It is Faraday's constant. The gas constant is... For battery temperature, This is the overpotential for the dissolution reaction of the active material. For solid-state potential, The liquid phase potential, This is the equilibrium potential for the dissolution reaction of the active material; It is an exponential function with the base of natural numbers; t is time.

7. The method according to claim 1, characterized in that, The coupled simulation model also includes one or more of the following: an insertion / extraction reaction model, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium-ion solid-phase diffusion model, and a lithium-ion liquid-phase diffusion model; The deintercalation / intercalation reaction model is as follows: in, The current density for the insertion / extraction reaction at the positive or negative electrode. The exchange current density for the intercalation / deintercalation reaction at the positive or negative electrode. and For the transmission coefficient, This is an overpotential at the positive or negative electrode. For battery temperature, The solid-state potential is either the positive or negative electrode. The liquid phase potential of the electrolyte. The equilibrium potential is the positive or negative electrode, and K is the reaction rate constant at the positive or negative electrode. This refers to the solid-phase lithium-ion concentration at the positive or negative electrode. This represents the maximum solid-phase lithium-ion concentration at the positive or negative electrode. This refers to the concentration of lithium ions in the liquid phase. The potential drop is due to a side reaction; The solid-phase charge conservation model is as follows: in, For solid-state current density, For the effective conductivity of the solid phase, The gradient of the solid-state potential; The liquid phase charge conservation model is as follows: in, The liquid phase current density, The effective conductivity of the liquid phase, The gradient of the liquid phase potential. To match the concentration of lithium ions in the liquid phase The relevant activity coefficient, This refers to the lithium-ion transport number. represent The gradient of R is the gas constant, T is the battery temperature, and F is the Faraday constant; The lithium-ion solid-phase diffusion model is as follows: in, This refers to the concentration of lithium ions in the solid phase. For a moment, Let be the solid-phase diffusion coefficient. The radius of the positive or negative electrode active material particles. Represents partial derivatives; The lithium-ion liquid-phase diffusion model is as follows: in, This refers to the concentration of lithium ions in the liquid phase. It is the liquid volume fraction. Any location within the lithium battery. This refers to the lithium-ion transport number. This refers to the specific surface area of ​​the active material particles used in the positive or negative electrode. The effective diffusion coefficient of the liquid phase is... This represents the initial liquid phase volume fraction. This represents the change in the volume fraction of the liquid phase. This represents the change in SEI film thickness. It refers to the volume fraction of the solid phase at the positive or negative electrode. The radius of the positive or negative electrode active material particles.

8. The method according to claim 1, characterized in that, The coupled simulation model further includes: a heat generation model; the heat generation model is: in, The density of lithium battery materials, Specific heat capacity of lithium battery materials, subscript This represents the negative or positive electrode, and T is the battery temperature. Thermal conductivity, For heat dissipation power, The liquid phase current density, The gradient of the liquid phase potential. For liquid phase ohmic heat generation power, For solid-state current density, The gradient of the solid-state potential. For solid-phase ohmic heat generation power, This refers to the specific surface area of ​​the positive or negative electrode active material particles. These represent the current densities of the various reactions at the positive or negative electrodes. This is an overpotential at the positive or negative electrode. To polarize the heat generation power, This represents the open-circuit potential of the positive or negative terminal. The entropy coefficient of the positive or negative electrode. It is the reversible heat generation power.

9. A lithium battery cycle life prediction device, characterized in that, include: The simulation module is used to simulate the cyclic charging and discharging process of a lithium battery based on a lithium battery coupled simulation model, and to obtain the capacity loss of the negative electrode SEI film growth, the capacity loss of the negative electrode lithium plating, and the capacity loss of the negative electrode particle cracks. The coupled simulation model includes: an active material loss model, a negative electrode SEI film growth model, a negative electrode lithium plating model, and a negative electrode particle crack model. The negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during cyclic charge-discharge; the negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge-discharge; and the negative electrode particle crack model is used to determine the crack capacity loss caused by the heterogeneous growth of the SEI film at the crack site triggered by the negative electrode crack during cyclic charge-discharge. The active material loss model is used to determine the change in electrode solid volume fraction due to the dissolution reaction of the electrode active material during cyclic charge-discharge, wherein the electrode solid volume fraction includes the negative electrode solid volume fraction. The negative electrode SEI film growth model is used to determine the growth capacity loss of the negative electrode SEI film during the cyclic charge-discharge process, including: determining the SEI film growth reaction current density during the cyclic charge-discharge process, and determining the growth capacity loss through the SEI film growth current density and the negative electrode solid phase volume fraction. The negative electrode lithium plating model is used to determine the lithium plating capacity loss of the negative electrode during cyclic charge and discharge, including: determining the lithium plating reaction current density of the negative electrode during cyclic charge and discharge, and determining the lithium plating capacity loss by the lithium plating reaction current density and the volume fraction of the solid phase of the negative electrode. The negative electrode particle crack model is used to determine the crack capacity loss caused by the heterogeneous growth of the SEI film at the crack during the cycle charge and discharge process. This includes: determining the maximum tangential stress on the particle surface during the cycle charge and discharge process by the negative electrode solid phase volume fraction, and determining the crack capacity loss by the maximum tangential stress on the particle surface and the negative electrode particle crack information. The prediction module is used to determine the capacity retention rate based on the growth capacity loss, lithium plating capacity loss and crack capacity loss, so as to predict the cycle life of the lithium battery.

10. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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