Lithium ion battery calendar life prediction method and device, electronic equipment and storage medium

Abstract

The invention relates to a lithium ion battery calendar life prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: simulating the natural aging process of a lithium ion battery at a specified environment temperature, and obtaining natural aging simulation data; according to the time-varying volume fraction of the transition metal ions dissolved in the positive electrode in the natural aging process and the natural aging simulation data, the time-varying SEI film growth reaction current density is determined; and determining a calendar life prediction result of the lithium ion battery according to the SEI film growth reaction current density changing along with time. Therefore, the calendar life of any lithium ion battery can be predicted more accurately and quickly, and the method has relatively high interpretability and generalization ability.

Description

Technical Field

[0001] The present disclosure relates to the field of lithium-ion batteries, and in particular, to a method and device for predicting the calendar life of a lithium-ion battery, an electronic device, and a storage medium. Background Art

[0002] Lithium-ion batteries have been widely used in many fields such as consumer electronics, electric vehicles, and energy storage. In various application scenarios, the main manifestation of battery aging is that during use and storage, due to side reactions at the electrode-electrolyte interface, the battery capacity gradually decreases and the internal resistance increases. Especially during battery aging, the growth of the solid electrolyte interface (SEI) film on the graphite negative electrode, which leads to a gradual decrease in battery capacity and an increase in internal resistance, is considered the most important aging mechanism. The test cycle of the calendar life (i.e., the natural aging life under non-cycling conditions, that is, from the production date of the battery, even if not used, it will gradually decline due to the aging of internal chemical materials until the performance drops to a certain specific threshold (usually the capacity decays to 80% of the initial capacity)) is relatively long (for example, experimental tests may take several years), so predicting the calendar life has important practical significance.

[0003] Among them, the calendar life is affected by the coupling of multiple factors such as time, ambient temperature, state of charge (SOC), and ambient humidity. In particular, high temperature and high SOC will accelerate the decomposition of the electrolyte and side reactions of the electrode material, resulting in capacity decay and an increase in internal resistance. At present, the prediction of the calendar life mainly adopts data-driven methods. For example, fitting based on existing measured data and machine learning algorithms can mine the laws of historical data, but they have poor interpretability, poor generalization ability, and particularly low accuracy. Summary of the Invention

[0004] In view of this, the present disclosure provides a method and device for predicting the calendar life of a lithium-ion battery, an electronic device, and a storage medium, which can more accurately and quickly predict the calendar life of any lithium-ion battery and have high interpretability and generalization ability.

[0005] According to one aspect of the present disclosure, a method for predicting the calendar life of a lithium-ion battery is provided, including: obtaining natural aging simulation data by simulating the natural aging process of the lithium-ion battery at a specified ambient temperature; the natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte that change over time during the natural aging process; determining the SEI film growth reaction current density that changes over time according to the volume fraction of the transition metal ions dissolved in the positive electrode that changes over time during the natural aging process, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential in the natural aging simulation data, where the SEI film growth reaction current density characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; determining the prediction result of the calendar life of the lithium-ion battery according to the SEI film growth reaction current density that changes over time.

[0006] In a possible implementation, the method further includes: determining the positive electrode dissolution reaction current density that changes over time according to the battery temperature, the positive electrode solid-phase potential and the liquid-phase potential that change over time in the natural aging simulation data, the preset exchange current density and equilibrium potential of the positive electrode dissolution reaction, and the charge transfer coefficient of the anodic reaction; where the positive electrode dissolution reaction current density characterizes the current density generated during the dissolution process of the transition metal ions in the positive electrode active material; determining the volume fraction of the transition metal ions dissolved in the positive electrode that changes over time according to the positive electrode dissolution reaction current density that changes over time, the preset maximum lithium intercalation concentration of the positive electrode active material, and the positive electrode thickness.

[0007] In a possible implementation, the positive electrode dissolution reaction current density is expressed as:

[0008]

[0009] where, i dis represents the positive electrode dissolution reaction current density, i 0,dis represents the exchange current density of the positive electrode dissolution reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E dis represents the equilibrium potential of the positive electrode dissolution reaction, F represents the Faraday constant, α a represents the charge transfer coefficient of the anodic reaction, R represents the gas constant, and exp represents the exponential function with the natural number as the base;

[0010] where, the volume fraction of the transition metal ions dissolved in the positive electrode is expressed as:

[0011]

[0012] where, n solrepresents the volume fraction of the transition metal ions dissolved from the positive electrode, c s,max represents the maximum lithium intercalation concentration of the positive electrode active material, L_pos represents the thickness of the positive electrode, F represents the Faraday constant, and t represents time.

[0013] In a possible implementation, determining the SEI film growth reaction current density varying with time according to the volume fraction of the transition metal ions dissolved from the positive electrode varying with time during the natural aging process, the battery temperature varying with time, the solid-phase potential of the negative electrode, and the liquid-phase potential in the natural aging simulation data includes: determining the SEI film growth reaction exchange current density varying with time according to the volume fraction of the transition metal ions dissolved from the positive electrode varying with time during the natural aging process, where the SEI film growth reaction exchange current density characterizes the exchange current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; determining the SEI film growth reaction current density varying with time according to the SEI film growth reaction exchange current density varying with time, the battery temperature varying with time, the solid-phase potential of the negative electrode, and the liquid-phase potential in the natural aging simulation data.

[0014] In a possible implementation, the SEI film growth reaction exchange current density is expressed as:

[0015]

[0016] where, i 0,SEI represents the SEI film growth reaction exchange current density, n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode varying with time, c EC represents the concentration of the solvent participating in the SEI film growth reaction in the electrolyte, k represents a positive fitting coefficient, β and b respectively represent empirical parameters, D EC represents the diffusion coefficient of lithium ions in the SEI film, r s represents the particle radius of the negative electrode active material, t represents time, and δ represents the film thickness of the SEI film;

[0017] The SEI film growth reaction current density is expressed as:

[0018]

[0019]

[0020] where, i SEI represents the SEI film growth reaction current density, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E SEI represents the equilibrium potential of the SEI film growth reaction, Rfilm represents the total resistance of the SEI film, i n represents the negative electrode current, α c represents the charge transfer coefficient of the cathode reaction, F represents the Faraday constant, R represents the gas constant, exp represents the exponential function with the natural number as the base; R0 represents the initial resistance of the SEI film, κ SEI represents the conductivity of the SEI film.

[0021] In a possible implementation, the film thickness δ of the SEI film is expressed as:

[0022]

[0023] where, δ0 represents the initial thickness of the SEI film, Δδ represents the increase in the thickness of the SEI film, r s represents the particle radius of the negative electrode active material, M SEI represents the molar mass of the SEI film, ρ SEI represents the density of the SEI film, c SEI represents the concentration of the SEI film, Δc SEI represents the increase in the concentration of the SEI film.

[0024] In a possible implementation, according to the SEI film growth reaction current density that changes with time, determining the calendar life prediction result of the lithium-ion battery includes: determining the capacity recovery rate of the lithium-ion battery that changes with time at the specified ambient temperature according to the SEI film growth reaction current density that changes with time and the initial capacity of the lithium-ion battery; determining the calendar life prediction result of the lithium-ion battery according to the capacity recovery rate of the lithium-ion battery that changes with time at the specified ambient temperature, where the calendar life prediction result of the lithium-ion battery includes the time length experienced by the lithium-ion battery to reach the specified capacity recovery rate at the specified ambient temperature.

[0025] In a possible implementation, the determining the capacity recovery rate of the lithium-ion battery that changes with time at the specified ambient temperature according to the SEI film growth reaction current density that changes with time and the initial capacity of the lithium-ion battery includes: determining the battery capacity loss that changes with time due to the growth of the SEI film according to the SEI film growth reaction current density that changes with time, the preset negative electrode solid phase volume fraction, the particle radius of the negative electrode active material, and the negative electrode thickness; determining the capacity recovery rate of the lithium-ion battery that changes with time at the specified ambient temperature according to the battery capacity loss that changes with time due to the growth of the SEI film and the initial capacity of the lithium-ion battery.

[0026] According to another aspect of the present disclosure, there is provided a device for predicting the calendar life of a lithium-ion battery, including: a simulation module configured to obtain natural aging simulation data by simulating the natural aging process of the lithium-ion battery at a specified ambient temperature; the natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte that vary with time during the natural aging process; a current density determination module configured to determine the SEI film growth reaction current density that varies with time according to the volume fraction of the transition metal ions dissolved in the positive electrode that varies with time during the natural aging process, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential that vary with time in the natural aging simulation data, where the SEI film growth reaction current density characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; a life prediction module configured to determine the calendar life prediction result of the lithium-ion battery according to the SEI film growth reaction current density that varies with time.

[0027] According to another aspect of the present disclosure, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps of the above method.

[0028] According to another aspect of the present disclosure, there is provided a non-volatile computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the above method.

[0029] According to another aspect of the present disclosure, there is provided a computer program product including a computer program or a non-volatile computer-readable storage medium carrying the computer program, where the computer program, when executed by a processor, implements the steps of the above method.

[0030] According to various aspects of the present disclosure, by using the natural aging simulation data obtained through simulating the natural aging process of the lithium-ion battery and combining the volume fraction of the transition metal ions dissolved in the positive electrode to determine the SEI film growth reaction current density, it is possible to consider the catalytic effect of the transition metal ions generated by the dissolution of the positive electrode transition metal on the formation of the SEI film during the formation process of the negative electrode SEI film, making the calculated SEI film growth reaction current density more accurate. Since the battery capacity attenuation is mainly due to the SEI film formed by the side reaction between the negative electrode and the electrolyte, therefore, using the more accurate SEI film growth reaction current density can quickly and accurately predict the calendar life of the lithium-ion battery, and at the same time has high interpretability and generalization ability.

[0031] According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear. Description of the Drawings

[0032] The accompanying drawings that are included in and form a part of the specification illustrate exemplary embodiments, features, and aspects of the present disclosure and are used to explain the principles of the present disclosure together with the specification.

[0033] Figure 1 A flowchart showing a method for predicting the calendar life of a lithium-ion battery according to an embodiment of the present disclosure.

[0034] Figure 2 A schematic diagram of a curve showing the capacity recovery rate varying with time at an ambient temperature of 60 °C according to an embodiment of the present disclosure.

[0035] Figure 3 A schematic diagram showing the calendar life prediction result of Example 1 according to an embodiment of the present disclosure.

[0036] Figure 4 A schematic diagram showing the calendar life prediction result of Comparative Example 1 according to an embodiment of the present disclosure.

[0037] Figure 5 A block diagram showing a device for predicting the calendar life of a lithium-ion battery according to an embodiment of the present disclosure.

[0038] Figure 6 A block diagram showing an electronic device 1900 according to an embodiment of the present disclosure. Detailed Description of Specific Embodiments

[0039] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. Identical reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0040] As used herein, the terms "comprising," "including," "having," or variations thereof are open-ended and include one or more stated features, wholes, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, wholes, elements, steps, components, functions, or groups thereof.

[0041] When an element is referred to as being "connected," "coupled," "responsive," or variations thereof to another element, it can be directly connected, coupled, or responsive to the other element, or intervening elements may be present.

[0042] 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. Thus, 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.

[0043] As used herein, the term "exemplary" means "serving as an example, instance, or illustration". Any embodiment described as "exemplary" herein need not be construed as superior to or better than other embodiments.

[0044] In addition, for a better illustration of the present disclosure, numerous specific details are given in the following detailed description. Those skilled in the art should understand that the present disclosure can also be implemented without some of these specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0045] As described above, during the battery aging process, the growth of the solid electrolyte interface (SEI) film on the graphite negative electrode is considered the most important aging mechanism, while the existing calendar life methods have poor interpretability, poor generalization ability, and low accuracy. Therefore, the embodiments of the present disclosure propose a calendar life prediction method based on electrochemical mechanisms, which not only considers that the capacity decay during the natural aging process mainly results from the SEI film formed by the side reaction between the negative electrode and the electrolyte, but also considers the catalytic effect of transition metal ions generated by the dissolution of the positive electrode transition metal on the formation of the SEI film, realizing the incorporation of the capacity decay mechanism into the simulation model of the lithium-ion battery to predict the calendar life of the lithium-ion battery. In other words, the calendar life prediction method proposed in the embodiments of the present disclosure focuses on the side reaction between the electrolyte and the negative electrode (mainly the formation of the SEI film) and the dissolution of the positive electrode and its catalytic effect on the SEI film, improving the prediction efficiency and prediction accuracy of the calendar life for any lithium-ion battery, having high interpretability and generalization ability, and being beneficial to providing guidance for the development of battery cells and shortening the development cycle.

[0046] In practical applications, the lithium-ion battery calendar life prediction method of the embodiments of the present disclosure can be deployed on various terminal devices through software or hardware transformation. The terminal devices involved in the embodiments of the present disclosure may refer to devices with wireless connection functions and / or wired connection functions. The wireless connection function means that it can be connected to other devices through wireless connection methods such as Wi-Fi and Bluetooth. The terminal devices involved in the embodiments of the present disclosure can also communicate with other devices through the wired connection function. The terminal devices involved in the embodiments of the present disclosure can be touch-screen, non-touch-screen, or without a screen. Touch-screen devices can be controlled by clicking, swiping, etc. on the display screen with fingers, styluses, etc. Non-touch-screen devices can be connected to input devices such as mice, keyboards, and touch panels to control the terminal devices. Devices without a screen can be, for example, Bluetooth speakers without a screen. For example, the terminal devices of the present application may include, but are not limited to, user equipment (UE), mobile devices, user terminals, terminals, handheld devices, tablet computers, laptop computers, palmtop computers, computing devices, etc.

[0047] The lithium-ion battery fast charge calendar life prediction method of the embodiments of the present disclosure can also be deployed on a server. The 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 a container, with a wireless communication function. Among them, the wireless communication function can be set in the chip (system) or other components or assemblies of the server. It can refer to a device with a wireless connection function. The wireless connection function means that it can be connected to other servers or terminal devices through wireless connection methods such as Wi-Fi and Bluetooth. The server involved in the embodiments of the present disclosure can also have the function of communicating through a wired connection. For example, the server can receive the electrochemical-thermal coupling simulation model of the lithium-ion battery and the specified environmental temperature sent by the terminal device, execute the lithium-ion battery calendar life prediction method of the embodiments of the present disclosure by the server, obtain the calendar life prediction result of the lithium-ion battery, and return the calendar life prediction result to the terminal device to display the determined calendar life prediction result to the user in the terminal device. The embodiments of the present disclosure do not limit this.

[0048] Figure 1 The flowchart of a lithium-ion battery calendar life prediction method according to an embodiment of the present disclosure is shown. As Figure 1 shown, the method includes: step S11 to step S13.

[0049] In step S11, by simulating the natural aging process of the lithium-ion battery at the specified environmental temperature, natural aging simulation data is obtained; the natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte that change with time during the natural aging process.

[0050] In practical applications, the natural aging process of a lithium-ion battery can be simulated by constructing a simulation model of the lithium-ion battery and using the battery simulation model to simulate the natural aging process of the lithium-ion battery at a specified ambient temperature, so as to obtain natural aging simulation data. Among them, the simulation model of the lithium-ion battery can be used to describe the electrochemical reaction, heat transfer process and side reaction during the charge and discharge process of the lithium-ion battery. It can be known that the natural aging process of the lithium-ion battery is actually a process of discharging at an extremely low rate. Therefore, using the simulation model of the lithium-ion battery to simulate the natural aging process of the lithium-ion battery at a specified ambient temperature is actually using the simulation model to simulate the discharge process of the lithium-ion battery at a specified ambient temperature. Specifically, the simulation model can be used to simulate the process of the lithium-ion battery discharging at an extremely low rate (such as 1 / 10000C rate or a lower rate) from a specified initial capacity (such as 100% SOC) at a specified ambient temperature, so as to obtain the battery temperature, the solid-phase potential of the positive electrode, the solid-phase potential of the negative electrode and the liquid-phase potential of the electrolyte that change with time during this discharge process, that is, to obtain natural aging simulation data.

[0051] In practical applications, those skilled in the art can use open-source battery simulation modeling software in the art, such as pyBaMM software, to establish a simulation model of the lithium-ion battery. Among them, the established simulation model can adopt a known battery simulation model in the art. Of course, it can also be a battery simulation model independently designed by those skilled in the art. The embodiments of the present disclosure do not limit this.

[0052] The embodiments of the present disclosure provide a process for establishing a battery simulation model and the established battery simulation model. Specifically, the process for establishing the battery simulation model of a lithium-ion battery includes: constructing a battery simulation model that couples an electrochemical model, a heat generation model, and a side reaction model based on the battery design parameters and battery material parameters of the lithium-ion battery. Among them, the battery design parameters include: the thickness of the positive electrode, the thickness of the negative electrode, the particle radius and specific surface area of the positive electrode active material, the particle radius and specific surface area of the negative electrode active material, the areal density of the positive and negative electrodes, the tap density, the length of the electrode sheet, the width of the electrode sheet, the number of electrode sheet layers, the solid-phase volume fraction of the positive electrode, the solid-phase volume fraction of the negative electrode, the liquid-phase volume fraction, the porosity of the separator, the thickness of the separator, the material and thickness of the foil used in the battery, the initial lithium salt concentration in the electrolyte, the concentration of solvents (such as ethylene carbonate, dimethyl carbonate, etc.), the upper voltage limit of the battery, the lower voltage limit of the battery, the battery density, the isobaric heat capacity of the battery, the battery thermal conductivity, etc.; the battery material parameters include: the solid-phase diffusion coefficient of the positive and negative electrode active materials, the SOC~OCV (open circuit voltage) data of the active materials, the reaction rate constant, the solid-phase effective conductivity, the liquid-phase effective conductivity, the diffusion activation energy, the Bruggeman coefficient, the charge transfer coefficient, the thermal conductivity, the convective heat transfer coefficient, and the radiative heat transfer coefficient, the conductivity, diffusion coefficient, and lithium-ion transference number of the electrolyte, the density, conductivity, thermal conductivity, and isobaric heat capacity of the foil used in the battery.

[0053] For example, a battery simulation model can be first established in the pyBaMM software according to the battery design parameters and battery material parameters. The embodiments of the present disclosure do not limit the specific steps for constructing the simulation model using the pyBaMM software. It can be understood that, among them, the battery simulation model can include an electrochemical model, a heat generation model, and a side reaction model that are mutually coupled. That is, the output of the electrochemical model can be used as the input of the parameters in the heat generation model and the side reaction model, the output of the heat generation model can be used as the input of the parameters in the electrochemical model and the side reaction model, and the output of the side reaction model can be used as the input of the parameters in the electrochemical model. Among them, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte can be calculated using the electrochemical model, the battery temperature can be calculated using the heat generation model, and the volume fraction of the dissolved transition metal in the positive electrode and the SEI growth reaction current density can be calculated using the side reaction model. In the embodiments of the present disclosure, in step S11, the natural aging simulation data can be mainly obtained based on the electrochemical model and the heat generation model, and in the subsequent steps, the volume fraction of the dissolved transition metal in the positive electrode and the SEI growth reaction current density can be obtained based on the side reaction model, and then the calendar life prediction result of the lithium-ion battery can be obtained.

[0054] Among them, an electrochemical model can be constructed based on the reaction kinetics equation, that is, an electrochemical model can be established based on the following five reactions inside the battery: 1) The electrochemical reaction occurring at the solid-liquid interface described by the Bulter-Volmer kinetic equation, that is, the insertion / extraction reaction (i.e., the lithium insertion / extraction reaction) occurring at the contact interface between the active particles and the electrolyte; 2) Solid-phase charge conservation; 3) Liquid-phase charge conservation; 4) The solid-phase diffusion of lithium ions in the active material particles described by Fick's second diffusion law; 5) The liquid-phase diffusion of lithium ions in the electrolyte described by the Nernst-Planck equation. Thus, the electrochemical model in the electrochemical-thermal coupling simulation model can include: the insertion / extraction reaction equation constructed based on the Bulter-Volmer kinetic equation, the solid-phase charge conservation equation, the liquid-phase charge conservation equation, the solid-phase diffusion equation of lithium ions constructed based on Fick's second diffusion law, and the liquid-phase diffusion equation of lithium ions constructed based on the Nernst-Planck equation.

[0055] Among them, the insertion / extraction reaction equation can characterize the insertion / extraction reaction current density on the surfaces of the positive and negative electrodes when lithium ions are extracted or inserted in the positive and negative electrodes. Thus, the insertion / extraction reaction current density can refer to the magnitude of the current of the electrode reaction per unit area when lithium ions are extracted (lithium deinsertion) or inserted (lithium insertion) on the surfaces of the positive and negative electrodes during the charge and discharge process of the lithium-ion battery; Exemplarily, the insertion / extraction reaction equation can be expressed as Formula (1-1), Formula (1-2), and Formula (1-3):

[0056]

[0057] η = Φ s -Φ l -E Eq -ΔΦ film (1-2)

[0058] i0 = FK(c s,max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5 (1-3)

[0059] Among them, i is the insertion / extraction reaction current density of the positive or negative electrode, i0 is the insertion / extraction reaction exchange current density of the positive or negative electrode (calculated using the reaction rate constant), exp represents the exponential function with the natural constant as the base, α a is the charge transfer coefficient of the anodic reaction, α cα is the charge transfer coefficient of the cathode reaction. Herein, the anode reaction and the cathode reaction respectively refer to the oxidation reaction and the reduction reaction in the redox reaction. F is the Faraday constant, R is the gas constant, η is the overpotential of the positive electrode or the negative electrode, T is the battery temperature (which can be obtained using the heat generation model), Φ s is the solid-phase potential of the positive electrode or the negative electrode, Φ l is the liquid-phase potential of the electrolyte, E Eq is the equilibrium potential of the positive electrode or the negative electrode, K is the reaction rate constant of the positive electrode or the negative electrode, c s is the solid-phase lithium-ion concentration of the positive electrode or the negative electrode (i.e., the lithium-ion concentration in the active material particles of the positive electrode or the negative electrode), c s,max is the maximum lithium intercalation concentration of the positive electrode or the negative electrode (for example, c s,max of the positive electrode can be 23000 moles per cubic meter (mol / m 3 ³), and c s,max of the negative electrode can be 30555 mol / m 3 ³), c l is the liquid-phase lithium-ion concentration (i.e., the lithium-ion concentration in the electrolyte), ΔΦ film is the membrane resistance pressure drop (which can be expressed as the product of the total resistance of the SEI film and the negative electrode current in the SEI film generation model described later).

[0060] Among them, α a , α c , F, R, K, c s,max are fixed values, c s and c l can be determined through the lithium-ion solid-phase diffusion model and the lithium-ion liquid-phase diffusion model respectively; Φ s and Φ l For example, the simulation calculation ability provided by the pyBaMM software can be used to perform simulation calculations on the battery simulation model. The present disclosure embodiment does not limit the calculation process of the solid-phase potential and the liquid-phase potential. It can be known that the equilibrium potential E Eq of the positive and negative electrodes changes with the SOC. Therefore, the relationship curve between the equilibrium potential of the positive and negative electrodes and the SOC can be obtained through the charge-discharge experiment test. Furthermore, based on the relationship curve between the equilibrium potential of the positive and negative electrodes and the SOC, the equilibrium potential E Eq of the positive and negative electrodes at different SOCs during the natural aging process can be obtained.

[0061] Among them, the solid-phase charge conservation equation characterizes the solid-phase current density within the active material particles of the positive and negative electrodes of the lithium-ion battery (i.e., the current density inside the positive and negative active material particles); Exemplarily, the solid-phase charge conservation equation can be expressed as formula (2):

[0062]

[0063] where \(i\) s is the solid-phase current density of the positive or negative electrode, \(\sigma\) s is the effective solid-phase conductivity of the positive or negative electrode (i.e., the effective conductivity inside the active material particles of the positive and negative electrodes), is the gradient of the solid-phase electric potential of the positive or negative electrode; where is a constant value, can also be expressed as Brugg is the Bruggeman constant, \(\varepsilon\) s is the solid-phase volume fraction of the positive or negative electrode, \(\sigma\) s,0 is the initial effective solid-phase conductivity of the positive or negative electrode, Brugg, \(\varepsilon\) s and \(\sigma\) s,0 are constant values. Where can be obtained by performing simulation calculations using the built-in simulation calculation capabilities of the pyBaMM software.

[0064] Among them, the liquid-phase charge conservation equation characterizes the liquid-phase current density in the electrolyte of the lithium-ion battery (i.e., the current density in the electrolyte); exemplarily, the liquid-phase charge conservation equation can be expressed as formula (3):

[0065]

[0066] where \(i\) l is the liquid-phase current density, \(\kappa\) eff is the effective liquid-phase conductivity (i.e., the effective conductivity of the electrolyte), is the gradient of the liquid-phase electric potential (i.e., the gradient of the electric potential in the electrolyte), \(f(c\) l ) is the activity coefficient related to the liquid-phase lithium-ion concentration \(c\) l , is the cation transference number (i.e., the lithium-ion transport number), represents the gradient of \(\ln c\) l ; where \(\kappa\) eff , \(R\), \(F\), are constant values, \(\kappa\) eff can also be expressed as Brugg is the Bruggeman constant, \(\varepsilon\) l is the liquid-phase volume fraction, \(\sigma\) l,0 is the initial effective liquid-phase conductivity,

[0067] Brugg, \(\varepsilon\) l and \(\sigma\) l,0 are constant values; the lithium-ion transport number refers to the ratio of the flow rate of lithium ions through a unit cross-section per unit time to the total charge flow rate in the electrolyte or electrode material, can be obtained through experimental tests; It can be obtained by using pyBaMM software for simulation calculations; It can be obtained through experimental tests, f A is the activity. In practical applications, the relationship curve between the liquid-phase lithium-ion concentration and the activity coefficient can be obtained through experimental tests. Based on this relationship curve between the liquid-phase lithium-ion concentration and the activity coefficient, the activity coefficient at different liquid-phase lithium-ion concentrations can be obtained.

[0068] Among them, the lithium-ion solid-phase diffusion equation characterizes the solid-phase lithium-ion concentration in the active material particles of the positive and negative electrodes (the lithium-ion concentration in the active material particles); Fick's second law of diffusion can be used to describe the diffusion of lithium ions inside the active material particles of the positive and negative electrodes due to the lithium concentration gradient to obtain the lithium-ion concentration in the active material particles of the positive and negative electrodes; Exemplarily, the lithium-ion solid-phase diffusion equation is expressed as formula (4):

[0069]

[0070] Among them, c s is the solid-phase lithium-ion concentration of the positive or negative electrode, t is time, is the solid-phase diffusion coefficient of the positive or negative electrode, r is the particle radius of the active material of the positive or negative electrode, represents the partial derivative. For example, represents the partial derivative of c s with respect to t, and so on, which will not be elaborated here; among them, and r are constant values. It should be understood that the solid-phase lithium-ion concentration c at any time can be obtained by integrating formula (4) s .

[0071] Among them, the lithium-ion liquid-phase diffusion equation characterizes the liquid-phase lithium-ion concentration in the electrolyte (that is, the lithium-ion concentration in the electrolyte). Fick's second law of diffusion can be used to describe the diffusion of lithium ions in the electrolyte to obtain the lithium-ion concentration in the electrolyte; Exemplarily, the lithium-ion liquid-phase diffusion equation is expressed as formula (5-1) and formula (5-1):

[0072]

[0073] Among them, c l is the liquid-phase lithium-ion concentration, ε l is the liquid-phase volume fraction, x is any position in the lithium-ion battery (this position can be custom-set), S * is the specific surface area of the active material particles of the positive or negative electrode, is the liquid-phase effective diffusion coefficient (calculated using the Bruggeman coefficient and the diffusion activation energy), Brugg is the Bruggeman coefficient, D l,0 is the initial liquid-phase diffusion coefficient, E ais the activation energy for diffusion, and T ref is the reference temperature (e.g., 25 °C), and T is the battery temperature (which can be obtained using the heat generation model). is the cation transference number; J is the lithium flux, equivalent to the deintercalation reaction current density i mentioned above; where ε l , a s , Brugg, D l,0 , E a , T ref are fixed values. It should be understood that the liquid-phase lithium-ion concentration c at any time can be obtained by integrating formula (5-1). l .

[0074] It can be understood that the heat generation during the charge and discharge process of a lithium-ion battery follows the energy conservation equation, and a heat generation model can be constructed based on the energy conservation equation to calculate the battery temperature during the natural aging process by calculating the heat generation in the heat generation model; specifically, the heat generation model in the battery simulation model can be determined based on the heat dissipation power, the liquid-phase ohmic heat generation power, the solid-phase ohmic heat generation power of the positive and negative electrodes, the polarization heat generation power, and the reversible heat power; where the heat dissipation power represents the power of the lithium-ion battery to dissipate heat, specifically including the heat dissipation power generated by the three heat dissipation processes of heat conduction, convection, and thermal radiation; the liquid-phase ohmic heat generation power represents the heat generation power when the current flows through the electrolyte; the solid-phase ohmic heat generation power represents the heat generation power when the current flows through the active materials of the positive and negative electrodes; the polarization heat generation power represents the heat generation power due to the polarization phenomenon of the positive and negative electrodes; the reversible heat power represents the heat generation power due to the entropy change in the electrochemical reaction of the positive and negative electrodes.

[0075] Exemplarily, the heat generation model can be expressed as formula (6-1) and formula (6-1):

[0076]

[0077] where ρ is the battery density, C p is the isobaric heat capacity of the battery, λ is the thermal conductivity of the battery, is the specific surface area of the positive electrode active material particles, is the specific surface area of the negative electrode active material particles, i a is the deintercalation reaction current density of the positive electrode, i c is the deintercalation reaction current density of the negative electrode, η a is the overpotential of the positive electrode, η c is the overpotential of the negative electrode, is the entropy heat coefficient of the positive electrode (i.e., the partial derivative of the open-circuit potential of the positive electrode with respect to temperature), E eq,a is the open-circuit potential of the positive electrode, is the entropy heat coefficient of the negative electrode (i.e., the partial derivative of the open-circuit potential of the negative electrode with respect to temperature), E eq,cis the open-circuit potential of the negative electrode, i s is the solid-phase current density, is the gradient of the solid-phase electric potential, h is the convective heat transfer coefficient on the battery surface, T amb is the ambient temperature, ε is the Boltzmann constant, σ is the radiation heat transfer coefficient, that is, the emissivity of the battery surface; among them, ρ, C p , a a , a c , h, T amb , ε, σ are constant values, and can be obtained through experimental tests respectively;

[0078] Among them, is the power of the heat conduction part in the heat dissipation power, h(T amb -T) is the power of the convective heat transfer part in the heat dissipation power, is the power of the thermal radiation part in the heat dissipation power; is the solid-phase ohmic heat generation power, is the liquid-phase ohmic heat generation power, is the polarization heat generation power of the positive electrode, is the polarization heat generation power of the negative electrode, is the reversible heat power of the positive electrode, is the reversible heat power of the negative electrode; it should be understood that the battery temperature T can be obtained by integrating formulas (6-1) and (6-2).

[0079] Among them, in the open-source pyBaMM software, the above formulas (1-1) to (6-2) can be used to establish various electrochemical models and heat generation models. It can be understood that the solid-phase current density and liquid-phase current density of the positive and negative electrodes can be obtained from the electrochemical models of the above formulas (2) and (3), and then the solid-phase ohmic heat generation power and liquid-phase ohmic heat generation power can be calculated; the solid-phase lithium-ion concentration and liquid-phase lithium-ion concentration of the positive and negative electrodes can be obtained from the electrochemical models of the above formulas (4), (5-1) and (5-2) and input into formula (1-1) to obtain the intercalation / deintercalation reaction current density, and then the polarization heat generation power and reversible heat power of the positive and negative electrodes can be calculated using the intercalation / deintercalation reaction current density; the heat dissipation power can be obtained by inputting the thermal conductivity, convective heat transfer coefficient (measured experimentally) and radiation heat transfer coefficient (tested experimentally) into formula (6-2). Integrating all the heat generation powers and heat dissipation powers into the heat generation model (6-1) can calculate the battery temperature T, and the battery temperature T can be input into the electrochemical model again to realize the two-way coupling of the electrochemical model and the thermal model.

[0080] Among them, through the membrane resistance voltage drop ΔΦ in formula (1-2) filmThe side reaction models (including the positive electrode dissolution reaction model and the SEI film growth reaction model) can be coupled into the above electrochemical model, that is, the SEI film growth reaction and the positive electrode dissolution reaction are integrated into the electrochemical reactions described by the electrochemical model. Thus, the open-source pyBaMM software can be used to numerically solve the battery simulation model coupled with the electrochemical model - heat generation model - side reaction model, and the current density of the SEI film growth reaction changing with time can be obtained. Furthermore, the capacity recovery rate of the lithium-ion battery changing with time at a specified ambient temperature can be calculated.

[0081] Optionally, the above formula (1-2) can also be η = Φ s - Φ l - E Eq , in this case, the side reaction model in the following text is not coupled with the electrochemical model. This is equivalent to directly using the electrochemical model and the heat generation model to calculate the natural aging simulation data. The embodiments of the present disclosure do not limit this.

[0082] It should be noted that the various electrochemical models and heat generation models shown in the above formula (1-1) to formula (6-2) are some possible implementation manners provided by the embodiments of the present disclosure. In fact, those skilled in the art can customize and design the electrochemical model and the heat generation model according to actual needs. The embodiments of the present disclosure do not limit this. Among them, the positive electrode dissolution reaction model and the SEI growth reaction model in the side reaction model will be elaborated in detail later.

[0083] In practical applications, the specific values of the battery material parameters used in the above battery simulation model can be obtained through pre-calibration or experimental measurement. Alternatively, the initial values can also be estimated based on historical experience and then corrected. The embodiments of the present disclosure do not limit this. As described above, a battery simulation model of a lithium-ion battery can be established in battery simulation modeling software such as pyBaMM software. Therefore, the constraint simulation boundary (that is, the initial state of charge (i.e., the initial capacity), the final state of charge, the discharge rate, the ambient temperature, the spatial boundary of the battery, etc.) can also be set in battery simulation modeling software such as pyBaMM software, so that the battery simulation model simulates the natural aging process of the lithium-ion battery discharging from the initial capacity at a very small discharge rate at a specified ambient temperature, to obtain the natural aging simulation data, and further obtain the current density of the SEI film growth reaction to calculate the calendar life of the lithium-ion battery.

[0084] It can be known that for the positive electrode active material containing transition metal layered oxides, the positive electrode dissolution reaction will occur at a relatively high temperature and a relatively high SOC. The positive electrode dissolution reaction refers to the dissolution of transition metal oxides (such as LiCoO2, LiMn2O4, etc.) in the positive electrode active material during the battery discharge process, and the dissolved transition metal ions (such as Co 2+ 、Mn2+ ) have strong catalytic activity, mainly because their electronic structure and chemical properties enable them to effectively participate in and promote chemical reactions. In an electrolyte environment, these transition metal ions can induce non-uniform decomposition of solvents (such as ethylene carbonate, EC) in the electrolyte. This non-uniform decomposition generates more organic by-products, such as lithium alkyl carbonate. This is because the catalytic action of transition metal ions changes the decomposition path of solvent molecules, making some originally unstable intermediates easier to form and further react to generate organic by-products. These organic by-products participate in the formation process of the SEI film, resulting in an increase in the proportion of organic components in the SEI film, and thus leading to an increase in internal resistance and capacity attenuation. Therefore, in the embodiments of the present disclosure, when determining the growth reaction current density of the negative electrode SEI film, the catalytic effect of transition metal ions generated by the positive electrode dissolution reaction on the growth of the SEI film is considered, so that a more accurate SEI film growth reaction current density can be determined. Therefore, in a possible implementation manner, the above method may further include:

[0085] Determine the positive electrode dissolution reaction current density varying with time according to the battery temperature varying with time, the solid-phase potential and liquid-phase potential of the positive electrode, the preset exchange current density and equilibrium potential of the positive electrode dissolution reaction, and the charge transfer coefficient of the anodic reaction in the natural aging simulation data; wherein, the positive electrode dissolution reaction current density characterizes the current density generated during the dissolution process of transition metal ions in the positive electrode active material. Here, the current density is also the magnitude of the current per unit area generated;

[0086] Determine the volume fraction of the transition metal ions dissolved from the positive electrode varying with time according to the positive electrode dissolution reaction current density varying with time, the preset maximum lithium intercalation concentration of the positive electrode active material, and the positive electrode thickness.

[0087] Among them, the positive electrode dissolution reaction current density can be described by the anodic Tafel equation, and the positive electrode dissolution reaction current density can be expressed as formula (7), that is, formula (7) can be used to determine the positive electrode dissolution reaction current density i dis :

[0088]

[0089] Among them, i dis represents the positive electrode dissolution reaction current density, i 0,dis represents the exchange current density of the positive electrode dissolution reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E dis represents the equilibrium potential of the positive electrode dissolution reaction, F represents the Faraday constant, α aα represents the charge transfer coefficient of the anodic reaction, R represents the gas constant, and exp represents the exponential function with the natural number as the base.

[0090] Among them, i 0,dis and E dis can be empirical values obtained through experimental measurements respectively. For example, the exchange current density i 0,dis of the positive electrode dissolution reaction of the lithium-ion battery can be deduced by measuring the accumulation amount of the negative electrode SEI film in the lithium-ion battery at a specified ambient temperature and for a certain period of time through experiments. In addition, the applied voltage value when the hydrofluoric acid in the electrolyte increases significantly can be measured by gradually increasing the applied voltage to the lithium-ion battery as the equilibrium potential of the positive electrode dissolution reaction (such as 4.1V, 4.0V, etc.). The embodiments of the present disclosure do not limit this. Among them, F, α a , and R are known fixed values. It should be understood that since Φ s , Φ l , and T are data that change with time (or Φ s , Φ l , and T at different moments), then i dis at different moments can be determined through formula (7), that is, the current density of the positive electrode dissolution reaction that changes with time is obtained.

[0091] Based on the above current density i dis of the positive electrode dissolution reaction, the volume fraction of the transition metal ions can be expressed by formula (8), that is, the volume fraction of the transition metal ions dissolved from the positive electrode that changes with time can be determined by formula (8):

[0092]

[0093] Among them, n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode that changes with time, c s,max represents the maximum lithium intercalation concentration of the positive electrode active material at this time, L_pos represents the positive electrode thickness, F represents the Faraday constant, and t represents time. Among them, c s,max , L_pos, and F are known fixed values. It should be understood that by substituting the current density i dis of the positive electrode dissolution reaction that changes with time into formula (8), the volume fraction of the transition metal ions dissolved from the positive electrode between the 0 moment and the t moment can be calculated, that is, the volume fraction n sol of the transition metal ions dissolved from the positive electrode at any time t is obtained, so that the volume fraction of the transition metal ions dissolved from the positive electrode that changes with time can be obtained, which is equivalent to obtaining the amount of the transition metal ions dissolved from the positive electrode at any time.

[0094] In practical applications, a positive electrode dissolution reaction model can be constructed based on the above formulas (7) and (8) to calculate the volume fraction of the transition metal dissolved in the positive electrode. That is, the constructed positive electrode dissolution reaction model can be expressed as formulas (8) to (7). Then, this positive electrode dissolution reaction model can characterize the volume fraction of the transition metal ions dissolved in the positive electrode. Furthermore, the positive electrode dissolution reaction model can be used to determine the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time according to the battery temperature, the solid-phase potential of the positive electrode, and the liquid-phase potential that change with time in the natural aging simulation data. Among them, the above positive electrode dissolution reaction model can be solved by simulation modeling software such as the pyBaMM software to obtain the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time during the natural aging process. The embodiments of the present disclosure do not limit this.

[0095] In step S12, according to the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time during the natural aging process, the battery temperature, the solid-phase potential of the negative electrode, and the liquid-phase potential that change with time in the natural aging simulation data, the growth reaction current density of the solid electrolyte interface SEI film that changes with time is determined. The growth reaction current density of the SEI film characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery.

[0096] As described above, the side reaction between the negative electrode and the electrolyte mainly generates the SEI film, and the capacity decay during the natural aging process mainly stems from the SEI film grown by the side reaction between the negative electrode and the electrolyte. Therefore, the embodiments of the present disclosure use the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time to calculate the growth reaction current density of the SEI film, which can determine a more accurate growth reaction current density of the SEI film by combining the catalytic effect of the transition metal ions dissolved in the positive electrode on the growth of the SEI film, and further helps to determine a more accurate capacity decay rate. It should be noted that during the natural aging process of the battery, it includes both the reaction of the growth of the negative electrode SEI and the further promotion of the growth reaction of the negative electrode SEI by the transition metal dissolved in the active material of the positive electrode, which is the sum of the two contributions. In other words, there is no sequence for the reaction of the formation of the negative electrode SEI film and the reaction of the dissolution of the active material of the positive electrode, and there is no execution sequence in the embodiments of the present disclosure either.

[0097] In a possible implementation manner, the above determining the growth reaction current density of the SEI film that changes with time according to the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time during the natural aging process, the battery temperature, the solid-phase potential of the negative electrode, and the liquid-phase potential that change with time in the natural aging simulation data includes:

[0098] Determine the exchange current density of the SEI film growth reaction varying with time according to the volume fraction of the transition metal ions dissolved in the positive electrode varying with time during the natural aging process, where the exchange current density of the SEI film growth reaction characterizes the exchange current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery;

[0099] Determine the current density of the SEI film growth reaction varying with time according to the exchange current density of the SEI film growth reaction varying with time, the battery temperature varying with time, the solid-phase potential, and the liquid-phase potential in the natural aging simulation data.

[0100] Among them, the exchange current density of the SEI film growth reaction can be expressed by formula (9-1), that is, the exchange current density of the SEI film growth reaction can be determined using formula (9-1); the current density of the SEI film growth reaction can be expressed by formulas (9-2) and (9-3), that is, the current density of the SEI film growth reaction can be determined using formulas (9-2) and (9-3):

[0101]

[0102] Among them, i SEI represents the current density of the SEI film growth reaction, i 0,SEI represents the exchange current density of the SEI film growth reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E SEI represents the equilibrium potential of the SEI film growth reaction (this equilibrium potential is a known empirical value, such as 0.2V, 0.3V, etc.), R film represents the total resistance of the SEI film, i n represents the negative electrode current, α c represents the charge transfer coefficient of the cathode reaction, F represents the Faraday constant, R represents the gas constant, exp represents the exponential function with the natural number as the base; n sol represents the volume fraction of the transition metal ions dissolved in the positive electrode varying with time, c EC represents the concentration of the solvent (such as ethylene carbonate, dimethyl carbonate, etc.) participating in the SEI film growth reaction in the electrolyte; k represents a positive fitting coefficient used to associate the multiplier with the volume fraction n sol of the dissolved transition metal ions; β and b respectively represent empirical parameters (specifically, parameters related to the battery materials); D EC represents the diffusion coefficient of lithium ions in the SEI film; r s represents the particle radius of the negative electrode active material, t represents time, δ represents the film thickness of the SEI film; R0 represents the initial resistance of the SEI film, κ SEIRepresents the conductivity of the SEI film.

[0103] It should be understood that in the SEI film growth reaction, the film thickness of the SEI film increases with time. In one possible implementation, the film thickness δ of the SEI film changing with time can be expressed by formula (10):

[0104]

[0105] where δ0 represents the initial thickness of the SEI film, Δδ represents the increase in the SEI film thickness, r s represents the particle radius of the negative electrode active material, M SEI represents the molar mass of the SEI film, ρ SEI represents the density of the SEI film, c SEI represents the SEI film concentration, Δc SEI represents the increase in the SEI film concentration; among them, δ0, r s , M SEI , ρ SEI are known fixed values, and the SEI film concentration c changing with time SEI can be expressed by formula (11):

[0106]

[0107] where i SEI represents the SEI film growth reaction current density, represents the specific surface area of the negative electrode active material, z represents the number of transferred electrons participating in the SEI film growth reaction, F represents the Faraday constant, and t represents time. Among them, z and F are known fixed values; it should be understood that by integrating formula (11), the SEI film concentration at any time t can be obtained Subtracting the SEI film concentration at the initial time (such as time 0) of the SEI film, the increase in the SEI film concentration from the initial time to any time t can be obtained, that is, the increase in the SEI film concentration Δc changing with time is obtained SEI .

[0108] Among them, the negative electrode current i n can specifically be the product of the negative electrode current density and the negative electrode surface area. It can be known that the negative electrode current actually includes the current on the negative electrode SEI film and the current on the negative electrode active material. Therefore, the negative electrode current density is the sum of the SEI film growth reaction current density i SEI and the above-mentioned negative electrode solid-phase current density i s The negative electrode surface area can be calculated from the length and width of the negative electrode plate. It can be known that the current density is the current magnitude per unit area. For example, the dimension of the current density is A / m 2, therefore, multiplying the negative electrode current density by the negative electrode surface area can obtain the current magnitude i of the entire negative electrode n , and then, multiplying i n by the total resistance R of the SEI film film can obtain the film resistance voltage drop generated due to the growth of the SEI film, that is, the film resistance voltage drop ΔΦ in the above formula (1-2) film = R film i n .

[0109] In practical applications, an SEI film growth reaction model can be constructed based on formulas (9-1) to (9-3), that is, the SEI film growth reaction model can be expressed as formulas (9-1) to (9-3). Then, the SEI film growth reaction model can characterize the SEI film growth reaction current density that changes with time. Thus, the constructed SEI film growth reaction model can be used to determine the SEI film growth reaction current density that changes with time according to the volume fraction of the dissolved transition metal ions in the positive electrode that changes with time, the battery temperature that changes with time in the natural aging simulation data, the negative electrode solid phase potential, and the liquid phase potential. Among them, R0, α c , κ SEI , F, R, c EC , k, β, b, r s , D EC , z are all known fixed values. By substituting Φ s,c , Φ l , T, and n sol into the SEI film growth reaction model shown in formulas (9-1) to (9-3) and performing numerical solution on the SEI film growth reaction model, the SEI film growth reaction exchange current density at any time can be obtained, and thus the SEI film growth reaction current density that changes with time can be obtained. As described above, a side reaction model (including the SEI film growth reaction model and the above positive electrode dissolution reaction model) can be constructed in simulation modeling software such as pyBaMM software. Then, the method of numerically solving the above SEI film growth reaction model by using simulation modeling software such as pyBaMM software can be used to solve the SEI film growth reaction current density that changes with time. The embodiments of the present disclosure do not limit this

[0110] It should be understood that using the above film resistance voltage drop ΔΦ film = R film i nThe SEI film growth reaction model and the above-mentioned positive electrode dissolution reaction model can be coupled with the above-mentioned electrochemical model and heat generation model. This is equivalent to integrating the SEI film growth reaction and the positive electrode dissolution reaction into the electrochemical reaction and heat generation process. Thus, a battery simulation model can be constructed by comprehensively considering equations such as the mass conservation equation, charge conservation equation, reaction kinetics equation, heat transfer equation, and side reaction equation. That is to say, a battery simulation model coupled with an electrochemical model, a heat generation model, and a side reaction model (including a positive electrode dissolution reaction model and an SEI film growth reaction model) is constructed.

[0111] In practical applications, the above-mentioned various battery simulation models such as electrochemical models, heat generation models, and side reaction models (including positive electrode dissolution reaction models and SEI film growth reaction models) and boundary conditions can be converted into corresponding Python codes, and the open-source battery simulation program PyBaMM can be used to numerically solve the entire battery simulation model to obtain simulation calculation results, that is, to obtain the SEI film growth reaction current density varying with time. Specifically, the electrochemical model, heat generation model, and side reaction model can be converted into corresponding codes and added to PyBaMM, and the solution can be carried out according to the model equations and model parameters.

[0112] In step S13, based on the SEI film growth reaction current density varying with time, the calendar life prediction result of the lithium-ion battery is determined.

[0113] As described above, the calendar life of a lithium-ion battery can include the time length experienced when the performance degrades to a certain specific threshold (usually the capacity decays to 80% of the initial capacity). Then, in one possible implementation, the above-mentioned determination of the calendar life prediction result of the lithium-ion battery based on the SEI film growth reaction current density varying with time may include:

[0114] Step S131, based on the SEI film growth reaction current density varying with time and the initial capacity of the lithium-ion battery, determine the capacity recovery rate of the lithium-ion battery varying with time at a specified ambient temperature;

[0115] Step S132, based on the capacity recovery rate of the lithium-ion battery varying with time at a specified ambient temperature, determine the calendar life prediction result of the lithium-ion battery, where the calendar life prediction result of the lithium-ion battery includes the time length experienced by the lithium-ion battery to reach a specified capacity recovery rate at a specified ambient temperature.

[0116] In step S131, first, based on the SEI film growth reaction current density that changes over time, the overall capacity loss of the battery caused by SEI growth can be calculated by integration, and then the capacity recovery rate can be calculated based on the overall capacity loss of the battery and the initial capacity. Specifically, determining the capacity recovery rate of the lithium-ion battery that changes over time at a specified ambient temperature according to the SEI film growth reaction current density that changes over time and the initial capacity of the lithium-ion battery includes:

[0117] Determine the battery capacity loss that changes over time due to SEI film growth according to the SEI film growth reaction current density that changes over time, the preset solid-phase volume fraction of the negative electrode, the particle radius of the negative electrode active material, and the thickness of the negative electrode;

[0118] Determine the capacity recovery rate of the lithium-ion battery that changes over time at a specified ambient temperature according to the battery capacity loss that changes over time due to SEI film growth and the initial capacity of the lithium-ion battery.

[0119] Among them, the battery capacity loss that changes over time due to SEI film growth, that is, the battery capacity loss during the natural aging process, can be calculated using formula (12):

[0120]

[0121] where, i SEI represents the SEI film growth reaction current density, ε s represents the solid-phase volume fraction of the negative electrode, r s represents the particle radius of the negative electrode active material, L a represents the thickness of the negative electrode, and t represents time. It should be understood that the battery capacity loss from the initial time to any time t can be calculated through formula (12).

[0122] Furthermore, the capacity recovery rate, that is, the ratio of the remaining battery capacity to the initial capacity, can be calculated using formula (13):

[0123]

[0124] where, Q0 represents the initial capacity, and Q0 - Q loss represents the remaining battery capacity. It should be understood that the battery capacity loss at any time can be calculated through formula (12), and then the capacity recovery rate at any time can be calculated through formula (13), that is, the capacity recovery rate of the lithium-ion battery that changes over time at a specified ambient temperature is obtained.

[0125] In step S132, the capacity recovery rate of the lithium-ion battery that changes over time at a specified ambient temperature is known. For example Figure 2A schematic curve showing the capacity recovery rate varying with time at an ambient temperature of 60°C is presented. From this, the time length experienced to reach any specified capacity recovery rate can be obtained. For example, Figure 2 when the time length experienced to reach a specified capacity recovery rate of 0.85 in Figure 2 is 150 days, the calendar life prediction result of the lithium-ion battery includes the time length of 150 days experienced when the capacity recovery rate is 0.85. Of course, the specified capacity recovery rate can also be a range of capacity recovery rates. The calendar life prediction result of the lithium-ion battery can include the time lengths corresponding to any capacity recovery rate within the range of capacity recovery rates (such as 0.8 - 0.9); or, the calendar life prediction result of the lithium-ion battery can also directly include the capacity recovery rate varying with time of the lithium-ion battery predicted through steps S131 to S132 at a specified ambient temperature. The embodiments of the present disclosure do not limit this.

[0126] In practical applications, multiple specified ambient temperatures can be set, and the capacity recovery rate of the lithium-ion battery varying with time at each specified ambient temperature can be predicted through the above steps S11 to S13 and steps S131 to S132. Then, the calendar life prediction result of the lithium-ion battery can include the time lengths experienced by the lithium-ion battery to reach the specified capacity recovery rate at each specified ambient temperature. The embodiments of the present disclosure do not limit this.

[0127] According to the calendar life prediction method of the embodiments of the present disclosure, by using the natural aging simulation data obtained by simulating the natural aging process of the lithium-ion battery and combining the volume fraction of the transition metal ions dissolved in the positive electrode to determine the SEI film growth reaction current density, it is possible to consider the catalytic effect of the transition metal ions generated by the dissolution of the positive electrode transition metal on the formation of the SEI film during the formation process of the negative electrode SEI film, making the calculated SEI film growth reaction current density more accurate. Since the battery capacity attenuation is mainly due to the SEI film formed by the side reaction between the negative electrode and the electrolyte, therefore, using the more accurate SEI film growth reaction current density can quickly and accurately predict the calendar life of the lithium-ion battery, and at the same time has high interpretability and generalization ability.

[0128] Exemplarily, the embodiments of the present disclosure take a NCM / graphite system battery cell as an example to predict the calendar life at ambient temperatures such as 25°C, 45°C, and 60°C with an initial capacity of 100% SOC. The prediction results of Example 1 and Comparative Example 1 are presented in terms of the capacity recovery rate. The prediction results can be plotted using Python and include the comparative verification of part of the experimental data (i.e., the results measured through actual experiments). In Example 1, the prediction method of the embodiments of the present disclosure is used for calendar life prediction, which mainly considers the film-forming reaction between the electrolyte and the negative electrode and the dissolution of the positive electrode transition metal and its catalytic effect on the SEI film, and can obtain Figure 3The calendar life prediction results of Example 1 shown. The difference between Comparative Example 1 and Example 1 is that in Comparative Example 1, the calendar life attenuation mechanism only considers the side reaction between the negative electrode and the electrolyte, and does not consider the catalytic effect of the dissolution of transition metals from the positive electrode on the SEI film growth reaction, that is, the exchange current density i of the negative electrode SEI film growth reaction is described 0SEI The equation becomes Equation (14):

[0129]

[0130] Among them, Comparative Example 1 uses the same battery cell as Example 1. Except for the above differences in the calendar life attenuation mechanism, other parameters are the same. According to the same steps, the calendar life prediction can be carried out at ambient temperatures such as 25 °C, 45 °C, and 60 °C, and the following can be obtained Figure 4 The calendar life prediction results of Comparative Example 1 shown

[0131] As Figure 3 and Figure 4 shown, the difference between the prediction results (i.e., simulation results) and experimental data of the two methods in Example 1 and Comparative Example 1 when predicting the calendar life at 25 °C is not obvious. However, at high temperatures such as 45 °C and 60 °C, the difference between the simulation results and experimental data is relatively large. This is because at 25 °C, the dissolution of transition metal ions is extremely small, while at high temperature environments such as 45 °C and 60 °C, the dissolution of transition metal ions is relatively obvious. Therefore, if the catalytic effect of the dissolved transition metal ions on the SEI film growth is not considered, the error of the predicted calendar life at a higher ambient temperature is relatively large. It can be seen that the prediction method of the embodiments of the present disclosure can more accurately predict the calendar life at any ambient temperature, especially can accurately predict the calendar life at a higher ambient temperature

[0132] A lithium-ion battery calendar life prediction method proposed by the embodiments of the present disclosure, by establishing a model from the principle, not only considers the side reaction between the negative electrode and the electrolyte, but also considers the main factors such as the catalytic effect of the dissolution of transition metals from the positive electrode on the SEI film growth reaction that cause capacity loss during the natural aging of the battery. Therefore, using the prediction method of the embodiments of the present disclosure can quickly evaluate the calendar life of lithium-ion batteries under different battery designs (components such as positive electrode, negative electrode, and electrolyte), thereby providing a guiding role for the cell design in lithium-ion batteries, shortening the R & D cycle, and saving test costs

[0133] In the existing publicly available technologies, when predicting the calendar life of lithium-ion battery systems such as ternary lithium NCM, lithium manganese iron phosphate LMFP, and lithium manganate LMO, due to the inability to consider the dissolution mechanism of the transition metal (such as Mn) element in the positive electrode, there is a large error in predicting the calendar life at different ambient temperatures. By using the prediction method proposed in the embodiments of the present disclosure, the error caused by the lack of the positive electrode transition metal dissolution mechanism can be reduced. In particular, it is applicable to situations where there is no experimental data or very little experimental data, and can quickly evaluate the accurate calendar life of lithium-ion batteries under different cell design schemes at any ambient temperature.

[0134] Figure 5 FIG. shows a block diagram of a device for predicting the calendar life of a lithium-ion battery according to an embodiment of the present disclosure, as Figure 5 shown, the device includes:

[0135] A simulation module 501, configured to obtain natural aging simulation data by simulating the natural aging process of the lithium-ion battery at a specified ambient temperature; the natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte that change with time during the natural aging process;

[0136] A current density determination module 502, configured to determine the time-varying SEI film growth reaction current density according to the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time during the natural aging process, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential that change with time in the natural aging simulation data, where the SEI film growth reaction current density characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery;

[0137] A life prediction module 503, configured to determine the calendar life prediction result of the lithium-ion battery according to the time-varying SEI film growth reaction current density.

[0138] In a possible implementation manner, the device further includes: a positive electrode dissolution current density determination module, configured to determine the time-varying positive electrode dissolution reaction current density according to the battery temperature, the positive electrode solid-phase potential and the liquid-phase potential that change with time in the natural aging simulation data, the preset exchange current density and equilibrium potential of the positive electrode dissolution reaction, and the charge transfer coefficient of the anodic reaction; wherein, the positive electrode dissolution reaction current density characterizes the current density generated during the dissolution process of the transition metal ions in the positive electrode active material; a volume fraction determination module, configured to determine the volume fraction of the transition metal ions dissolved in the positive electrode that changes with time according to the time-varying positive electrode dissolution reaction current density, the preset maximum lithium intercalation concentration of the positive electrode active material, and the positive electrode thickness.

[0139] In a possible implementation manner, the positive electrode dissolution reaction current density is expressed as:

[0140]

[0141] wherein, i dis represents the current density of the positive electrode dissolution reaction, and i 0,dis represents the exchange current density of the positive electrode dissolution reaction, Φ s represents the solid-phase potential, and Φ l represents the liquid-phase potential, T represents the battery temperature, and E dis represents the equilibrium potential of the positive electrode dissolution reaction, F represents the Faraday constant, and α a represents the charge transfer coefficient of the anodic reaction, R represents the gas constant, and exp represents the exponential function with the natural number as the base;

[0142] wherein, the volume fraction of the transition metal ions dissolved from the positive electrode is expressed as:

[0143]

[0144] wherein, n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode, c s,max represents the maximum lithium intercalation concentration of the positive electrode active material, L_pos represents the positive electrode thickness, F represents the Faraday constant, and t represents time.

[0145] In a possible implementation manner, determining the current density of the SEI film growth reaction changing with time according to the volume fraction of the transition metal ions dissolved from the positive electrode changing with time during the natural aging process, the battery temperature changing with time, the negative electrode solid-phase potential, and the liquid-phase potential in the natural aging simulation data includes: determining the exchange current density of the SEI film growth reaction changing with time according to the volume fraction of the transition metal ions dissolved from the positive electrode changing with time during the natural aging process, and the exchange current density of the SEI film growth reaction characterizes the exchange current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; determining the current density of the SEI film growth reaction changing with time according to the exchange current density of the SEI film growth reaction changing with time, the battery temperature changing with time, the negative electrode solid-phase potential, and the liquid-phase potential in the natural aging simulation data.

[0146] In a possible implementation manner, the exchange current density of the SEI film growth reaction is expressed as:

[0147]

[0148] wherein, i 0,SEI represents the exchange current density of the SEI film growth reaction, n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode changing with time, c ECrepresents the concentration of the solvent participating in the SEI film growth reaction in the electrolyte, k represents a positive fitting coefficient, β and b respectively represent empirical parameters, D EC represents the diffusion coefficient of lithium ions in the SEI film, r s represents the particle radius of the negative electrode active material, t represents time, δ represents the film thickness of the SEI film;

[0149] The current density of the SEI film growth reaction is expressed as:

[0150]

[0151] where, i SEI represents the current density of the SEI film growth reaction, Φ s represents the negative electrode solid phase potential, Φ l represents the liquid phase potential, T represents the battery temperature, E SEI represents the equilibrium potential of the SEI film growth reaction, R film represents the total resistance of the SEI film, i n represents the negative electrode current, α c represents the charge transfer coefficient of the cathode reaction, F represents the Faraday constant, R represents the gas constant, exp represents the exponential function with the natural number as the base; R0 represents the initial resistance of the SEI film, κ SEI represents the conductivity of the SEI film.

[0152] In a possible implementation, the film thickness δ of the SEI film is expressed as:

[0153]

[0154] where, δ0 represents the initial thickness of the SEI film, Δδ represents the increase in the SEI film thickness, r s represents the particle radius of the negative electrode active material, M SEI represents the molar mass of the SEI film, ρ SEI represents the density of the SEI film, c SEI represents the SEI film concentration, Δc SEI represents the increase in the SEI film concentration.

[0155] In a possible implementation manner, determining the calendar life prediction result of the lithium-ion battery according to the time-varying SEI film growth reaction current density includes: determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature according to the time-varying SEI film growth reaction current density and the initial capacity of the lithium-ion battery; determining the calendar life prediction result of the lithium-ion battery according to the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature, where the calendar life prediction result of the lithium-ion battery includes the time length experienced by the lithium-ion battery to reach the specified capacity recovery rate at the specified ambient temperature.

[0156] In a possible implementation manner, determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature according to the time-varying SEI film growth reaction current density and the initial capacity of the lithium-ion battery includes: determining the battery capacity loss varying with time caused by SEI film growth according to the time-varying SEI film growth reaction current density, the preset negative electrode solid-phase volume fraction, the particle radius of the negative electrode active material, and the negative electrode thickness; determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature according to the battery capacity loss varying with time caused by SEI film growth and the initial capacity of the lithium-ion battery.

[0157] The device according to an embodiment of the present disclosure can determine the volume fraction of the transition metal ions dissolved in the positive electrode through the natural aging simulation data obtained by simulating the natural aging process of the lithium-ion battery, and determine the SEI film growth reaction current density based on the volume fraction of the transition metal ions dissolved in the positive electrode, which can realize considering the catalytic effect of the transition metal ions generated by the dissolution of the positive electrode transition metal on the SEI film formation during the formation process of the negative electrode SEI film, making the calculated SEI film growth reaction current density more accurate. Since the battery capacity attenuation is mainly due to the SEI film formed by the side reaction between the negative electrode and the electrolyte, therefore, the capacity recovery rate calculated using the more accurate SEI film growth reaction current density is also more accurate, so that the calendar life when reaching any specified capacity recovery rate at any specified ambient temperature can be predicted quickly and accurately, and at the same time, it has high interpretability and generalization ability.

[0158] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0159] An embodiment of the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps of the above method.

[0160] An embodiment of the present disclosure also provides a non-volatile computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0161] An embodiment of the present disclosure also provides a computer program product, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0162] Figure 6 FIG. shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Referring to Figure 6 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0163] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or the like.

[0164] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, and the above computer program instructions can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0165] A computer-readable storage medium can be a tangible device that can hold and store programs / instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as being an instantaneous signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0166] The computer programs (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0167] A computer program (or computer program instructions) for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - 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 be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or, alternatively, may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0168] Aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0169] 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 the instructions are executed by the processor of the computer or other programmable data - processing apparatus, a device is created that implements the functions / acts 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, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0170] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, causing 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 such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0171] The flowcharts and block diagrams in the figures 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 the flowchart or block diagram may represent a module, a segment of code, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by special-purpose hardware-based systems that perform the specified functions or acts, or by combinations of special-purpose hardware and computer instructions.

[0172] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the calendar life of a lithium-ion battery, characterized in that, Including: Obtaining natural aging simulation data by simulating the natural aging process of a lithium-ion battery at a specified ambient temperature; The natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte that change over time during the natural aging process; Determining the SEI film growth reaction current density that changes over time according to the volume fraction of transition metal ions dissolved in the positive electrode that changes over time during the natural aging process, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential that change over time in the natural aging simulation data, where the SEI film growth reaction current density characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; Determining the calendar life prediction result of the lithium-ion battery according to the SEI film growth reaction current density that changes over time.

2. The method according to claim 1, wherein The method further includes: Determining the positive electrode dissolution reaction current density that changes over time according to the battery temperature, the positive electrode solid-phase potential and the liquid-phase potential that change over time in the natural aging simulation data, the preset exchange current density and equilibrium potential of the positive electrode dissolution reaction, and the charge transfer coefficient of the anodic reaction; where the positive electrode dissolution reaction current density characterizes the current density generated during the dissolution process of transition metal ions in the positive electrode active material; Determining the volume fraction of transition metal ions dissolved in the positive electrode that changes over time during the natural aging process according to the positive electrode dissolution reaction current density that changes over time, the preset maximum lithium intercalation concentration of the positive electrode active material, and the positive electrode thickness.

3. The method according to claim 2, wherein The positive electrode dissolution reaction current density is expressed as: where, i dis represents the current density of the positive electrode dissolution reaction, i 0,dis represents the exchange current density of the positive electrode dissolution reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E dis represents the equilibrium potential of the positive electrode dissolution reaction, F represents the Faraday constant, α a represents the charge transfer coefficient of the anodic reaction, R represents the gas constant, exp represents the exponential function with the natural number as the base; Where the volume fraction of transition metal ions dissolved in the positive electrode is expressed as: where n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode, c s,max represents the maximum lithium insertion concentration of the positive electrode active material, L_pos represents the thickness of the positive electrode, F represents the Faraday constant, and t represents time.

4. The method according to any one of claims 1 to 3, characterized in that The determining the SEI film growth reaction current density that changes over time according to the volume fraction of transition metal ions dissolved in the positive electrode that changes over time during the natural aging process, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential that change over time in the natural aging simulation data includes: Determining the SEI film growth reaction exchange current density that changes over time according to the volume fraction of transition metal ions dissolved in the positive electrode that changes over time during the natural aging process, where the SEI film growth reaction exchange current density characterizes the exchange current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; Determining the SEI film growth reaction current density that changes over time according to the SEI film growth reaction exchange current density that changes over time, the battery temperature, the negative electrode solid-phase potential, and the liquid-phase potential that change over time in the natural aging simulation data.

5. The method according to claim 4, characterized in that The SEI film growth reaction exchange current density is expressed as: Among them, i 0,SEI represents the exchange current density of the SEI film growth reaction, n sol represents the volume fraction of the transition metal ions dissolved in the positive electrode changing with time, c EC represents the concentration of the solvent participating in the SEI film growth reaction in the electrolyte, k represents a positive fitting coefficient, β and b respectively represent empirical parameters, D EC represents the diffusion coefficient of lithium ions in the SEI film, r s represents the particle radius of the negative electrode active material, t represents time, and δ represents the film thickness of the SEI film; The SEI film growth reaction current density is expressed as: Among them, i SEI represents the current density of the SEI film growth reaction, Φ s represents the solid-phase electric potential, Φ l represents the liquid-phase electric potential, T represents the battery temperature, E SEI represents the equilibrium potential of the SEI film growth reaction, R film represents the total resistance of the SEI film, i n represents the negative electrode current, α c represents the charge transfer coefficient of the cathode reaction, F represents the Faraday constant, R represents the gas constant, exp represents the exponential function with the natural number as the base; R0 represents the initial resistance of the SEI film, κ SEI represents the conductivity of the SEI film.

6. The method according to claim 5, characterized in that, The film thickness δ of the SEI film is expressed as: Among them, δ0 represents the initial thickness of the SEI film, Δδ represents the increase in the thickness of the SEI film, r s represents the particle radius of the negative electrode active material, M SEI represents the molar mass of the SEI film, ρ SEI represents the density of the SEI film, c SEI represents the concentration of the SEI film, Δc SEI represents the increase in the concentration of the SEI film.

7. The method according to claim 1, characterized in that The determining the calendar life prediction result of the lithium-ion battery according to the SEI film growth reaction current density that changes over time includes: Determining the capacity recovery rate of the lithium-ion battery that changes over time at the specified ambient temperature according to the SEI film growth reaction current density that changes over time and the initial capacity of the lithium-ion battery; Determine the calendar life prediction result of the lithium-ion battery according to the capacity recovery rate of the lithium-ion battery changing with time at the specified ambient temperature, wherein the calendar life prediction result of the lithium-ion battery includes the time length experienced by the lithium-ion battery to reach the specified capacity recovery rate at the specified ambient temperature.

8. The method according to claim 7, characterized in that, The determining the capacity recovery rate of the lithium-ion battery changing with time at the specified ambient temperature according to the SEI film growth reaction current density changing with time and the initial capacity of the lithium-ion battery includes: Determine the battery capacity loss changing with time due to SEI film growth according to the SEI film growth reaction current density changing with time, the preset negative electrode solid-phase volume fraction, the particle radius of the negative electrode active material, and the negative electrode thickness; Determine the capacity recovery rate of the lithium-ion battery changing with time at the specified ambient temperature according to the battery capacity loss changing with time due to SEI film growth and the initial capacity of the lithium-ion battery.

9. A calendar life prediction device for a lithium-ion battery, characterized in that Includes: A simulation module for obtaining natural aging simulation data by simulating the natural aging process of the lithium-ion battery at the specified ambient temperature; The natural aging simulation data includes: the battery temperature, the positive electrode solid-phase potential, the negative electrode solid-phase potential, and the liquid-phase potential of the electrolyte changing with time during the natural aging process; A current density determination module for determining the SEI film growth reaction current density changing with time according to the volume fraction of the transition metal ions dissolved in the positive electrode changing with time during the natural aging process, the battery temperature changing with time in the natural aging simulation data, the negative electrode solid-phase potential, and the liquid-phase potential, wherein the SEI film growth reaction current density characterizes the current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; A life prediction module for determining the calendar life prediction result of the lithium-ion battery according to the SEI film growth reaction current density changing with time.

10. The device according to claim 9, characterized in that, The device further includes: A positive electrode dissolution current density determination module for determining the positive electrode dissolution reaction current density changing with time according to the battery temperature changing with time, the positive electrode solid-phase potential and the liquid-phase potential in the natural aging simulation data, the preset exchange current density and equilibrium potential of the positive electrode dissolution reaction, and the charge transfer coefficient of the anodic reaction; wherein the positive electrode dissolution reaction current density characterizes the current density generated during the dissolution process of the transition metal ions in the positive electrode active material; A volume fraction determination module for determining the volume fraction of the transition metal ions dissolved in the positive electrode changing with time according to the positive electrode dissolution reaction current density changing with time, the preset maximum lithium intercalation concentration of the positive electrode active material, and the positive electrode thickness.

11. The device according to claim 10, characterized in that, The positive electrode dissolution reaction current density is expressed as: where, i dis represents the current density of the positive electrode dissolution reaction, i 0,dis represents the exchange current density of the positive electrode dissolution reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E dis represents the equilibrium potential of the positive electrode dissolution reaction, F represents the Faraday constant, α a represents the charge transfer coefficient of the anodic reaction, R represents the gas constant, exp represents the exponential function with the natural number as the base; Wherein, the volume fraction of the transition metal ions dissolved in the positive electrode is expressed as: where n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode, c s,max represents the maximum lithium insertion concentration of the positive electrode active material, L_pos represents the thickness of the positive electrode, F represents the Faraday constant, and t represents time.

12. The device according to any one of claims 9 to 11, characterized in that, Determining the SEI film growth reaction current density varying with time based on the volume fraction of transition metal ions dissolved from the positive electrode during the natural aging process varying with time, the battery temperature varying with time, the negative electrode solid-phase potential, and the liquid-phase potential in the natural aging simulation data, includes: Determining the SEI film growth reaction exchange current density varying with time based on the volume fraction of transition metal ions dissolved from the positive electrode during the natural aging process varying with time, where the SEI film growth reaction exchange current density characterizes the exchange current density generated by the growth of the SEI film on the surface of the negative electrode of the lithium-ion battery; Determining the SEI film growth reaction current density varying with time based on the SEI film growth reaction exchange current density varying with time, the battery temperature varying with time, the negative electrode solid-phase potential, and the liquid-phase potential in the natural aging simulation data.

13. The device according to claim 12, wherein, The SEI film growth reaction exchange current density is expressed as: where i 0,SEI represents the exchange current density of the SEI film growth reaction, n sol represents the volume fraction of the transition metal ions dissolved from the positive electrode changing with time, c EC represents the concentration of the solvent participating in the SEI film growth reaction in the electrolyte, k represents a positive fitting coefficient, β and b respectively represent empirical parameters, D EC represents the diffusion coefficient of lithium ions in the SEI film, r s represents the particle radius of the negative electrode active material, t represents time, and δ represents the film thickness of the SEI film; The SEI film growth reaction current density is expressed as: Among them, i SEI represents the current density of the SEI film growth reaction, Φ s represents the solid-phase potential, Φ l represents the liquid-phase potential, T represents the battery temperature, E SEI represents the equilibrium potential of the SEI film growth reaction, R film represents the total resistance of the SEI film, i n represents the negative electrode current, α c represents the charge transfer coefficient of the cathode reaction, F represents the Faraday constant, R represents the gas constant, exp represents the exponential function with the natural number as the base; R0 represents the initial resistance of the SEI film, κ SEI represents the conductivity of the SEI film.

14. The device according to claim 13, characterized in that, The film thickness δ of the SEI film is expressed as: Among them, δ0 represents the initial thickness of the SEI film, Δδ represents the increase in the thickness of the SEI film, r s represents the particle radius of the negative electrode active material, M SEI represents the molar mass of the SEI film, ρ SEI represents the density of the SEI film, c SEI represents the SEI film concentration, Δc SEI represents the increase in the SEI film concentration.

15. The device according to claim 9, characterized in that, Determining the calendar life prediction result of the lithium-ion battery based on the SEI film growth reaction current density varying with time, includes: Determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature based on the SEI film growth reaction current density varying with time and the initial capacity of the lithium-ion battery; Determining the calendar life prediction result of the lithium-ion battery based on the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature, where the calendar life prediction result of the lithium-ion battery includes the time length experienced by the lithium-ion battery at the specified ambient temperature to reach the specified capacity recovery rate.

16. The device according to claim 15, wherein Determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature based on the SEI film growth reaction current density varying with time and the initial capacity of the lithium-ion battery, includes: Determining the battery capacity loss varying with time caused by the growth of the SEI film based on the SEI film growth reaction current density varying with time, the preset negative electrode solid-phase volume fraction, the particle radius of the negative electrode active material, and the negative electrode thickness; Determining the capacity recovery rate of the lithium-ion battery varying with time at the specified ambient temperature based on the battery capacity loss varying with time caused by the growth of the SEI film and the initial capacity of the lithium-ion battery.

17. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

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

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