Lithium ion battery fast charging strategy selection method and device, electronic equipment and storage medium

Abstract

The invention relates to a lithium ion battery fast charging strategy selection method and device, electronic equipment and a storage medium. The method comprises the steps that multiple candidate fast charging strategies meeting the fast charging requirement of a lithium ion battery are acquired; simulating the charging process of the lithium ion battery under each candidate fast charging strategy in the multiple candidate fast charging strategies by using an electrochemical-thermal-mechanical coupling simulation model of the lithium ion battery to obtain charging simulation data corresponding to each candidate fast charging strategy in the multiple candidate fast charging strategies; according to the charging simulation data corresponding to each candidate fast charging strategy, determining Mises stress corresponding to each candidate fast charging strategy; and selecting a target fast charging strategy of the lithium ion battery from the multiple candidate fast charging strategies according to the Mises stress corresponding to each candidate fast charging strategy in the multiple candidate fast charging strategies. In this way, the optimal target fast charging strategy is effectively screened out from the multiple candidate fast charging strategies meeting the fast charging requirement.

Description

Technical Field

[0001] The present disclosure relates to the field of lithium-ion batteries, and in particular to a method, device, electronic device and storage medium for selecting a fast charging strategy for a lithium-ion battery. Background Art

[0002] Lithium-ion batteries are widely used in the field of new energy vehicles due to their excellent electrochemical performance, long cycle life, no memory effect during use, and strong adaptability to high and low temperatures. Currently, one of the limiting factors affecting the popularity of lithium-ion batteries is the charging speed of lithium-ion batteries. In order to increase the charging speed of lithium-ion batteries while ensuring safety, and to quickly meet the requirements of charging lithium batteries within a specified time and save testing resources, the electrochemical-thermal coupling simulation modeling method of lithium-ion batteries can be used by computers to obtain a fast charging strategy for lithium-ion batteries.

[0003] At present, most of the fast charging strategies obtained by computer simulation modeling use the solid-liquid potential difference at the negative electrode-diaphragm ≤ 0V (or this value can be set to a value between 0-5mV for safety) as the simulation method of the switching rate node to obtain the fast charging strategy of the lithium battery. The setting of the charging rate during the simulation fast charging process depends on the experience of the engineer. Therefore, this simulation method can simultaneously output multiple fast charging strategies that meet the fast charging requirements (that is, meet the specified duration and capacity). How to select the optimal fast charging strategy from the multiple fast charging strategies that meet the fast charging requirements needs to be solved. Summary of the invention

[0004] In view of this, the present disclosure proposes a lithium-ion battery fast charging strategy selection method, device, electronic device and storage medium, which can effectively screen out the optimal target fast charging strategy from a variety of candidate fast charging strategies that meet the fast charging requirements.

[0005] According to one aspect of the present disclosure, a method for selecting a fast charging strategy for a lithium-ion battery is provided, comprising: obtaining a plurality of candidate fast charging strategies that meet the fast charging requirements of the lithium-ion battery; simulating the charging process of the lithium-ion battery under each of the plurality of candidate fast charging strategies using an electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, and obtaining charging simulation data corresponding to each of the plurality of candidate fast charging strategies, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple moments during the charging process; determining the Mises stress corresponding to each of the plurality of candidate fast charging strategies according to the charging simulation data corresponding to each of the plurality of candidate fast charging strategies, wherein the Mises stress corresponding to each of the plurality of candidate fast charging strategies includes the Mises stress exerted on the surface of negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each of the candidate fast charging strategies; and selecting a target fast charging strategy for the lithium-ion battery from the plurality of candidate fast charging strategies according to the Mises stress corresponding to each of the plurality of candidate fast charging strategies.

[0006] In a possible implementation, the method of determining the Mises stress corresponding to each of the multiple candidate fast charging strategies according to the charging simulation data corresponding to each of the multiple candidate fast charging strategies includes: for any candidate fast charging strategy, obtaining the negative electrode lithium ion concentration deviation at each moment according to the difference between the solid-phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to the candidate fast charging strategy and the specified initial lithium ion concentration; determining the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment according to the negative electrode parameters of the lithium-ion battery and the negative electrode lithium ion concentration deviation at each moment; wherein the negative electrode parameters include the radius, Young's modulus, Poisson's ratio and partial molar volume of the negative electrode active material particles; determining the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment according to the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment.

[0007] In one possible implementation, the Mises stress corresponding to each candidate fast-charging strategy includes the maximum Mises stress borne on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each candidate fast-charging strategy; wherein, selecting the target fast-charging strategy of the lithium-ion battery from the multiple candidate fast-charging strategies according to the Mises stress corresponding to each candidate fast-charging strategy among the multiple candidate fast-charging strategies includes: selecting, according to the maximum Mises stress corresponding to each candidate fast-charging strategy among the multiple candidate fast-charging strategies, the candidate fast-charging strategy with the smallest maximum Mises stress as the target fast-charging strategy of the lithium-ion battery.

[0008] In a possible implementation, the electrochemical-thermal-mechanical coupling simulation model characterizes the electrochemical reaction process, heat conduction process, and positive and negative electrode stress change process of the lithium-ion battery during charging according to any fast charging strategy, wherein the process of establishing the electrochemical-thermal-mechanical coupling simulation model includes: constructing a battery geometric model of the lithium-ion battery based on the battery design parameters of the lithium-ion battery, and the battery design parameters include: positive electrode thickness, negative electrode thickness, diaphragm thickness, radius and specific surface area of ​​positive electrode active material particles, radius and specific surface area of ​​negative electrode active material particles, diaphragm porosity, solid phase volume fraction of the positive electrode, solid phase volume fraction of the negative electrode, liquid phase volume fraction; based on the battery physical parameters of the lithium ion battery, an electrochemical-thermal coupling simulation model is constructed on the battery geometric model, and the battery physical parameters include: reaction rate constant, solid phase diffusion coefficient, diffusion activation energy, Brugmann coefficient, solid phase effective conductivity, liquid phase effective conductivity, charge transfer coefficient, thermal conductivity, convection heat transfer coefficient and radiation heat transfer coefficient; based on the battery mechanical parameters of the lithium ion battery, an electrochemical-thermal-mechanical coupling simulation model is constructed on the electrochemical-thermal coupling simulation model, and the battery mechanical parameters include: Young's modulus, Poisson's ratio, partial molar volume, radial stress and tangential stress of positive and negative electrode active material particles.

[0009] In a possible implementation, the electrochemical model in the electrochemical-thermal-mechanical coupling simulation model includes: an intercalation reaction model constructed based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium ion solid-phase diffusion model and a lithium ion liquid-phase diffusion model constructed based on Fick's second diffusion law; wherein the intercalation reaction model characterizes the intercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or embedded in the positive and negative electrodes; the solid-phase charge conservation model characterizes the solid-phase current density in the active material particles in the positive and negative electrodes of the lithium ion battery; the liquid-phase charge conservation model characterizes the liquid-phase current density in the electrolyte of the lithium ion battery; the lithium ion solid-phase diffusion model characterizes the solid-phase lithium ion concentration in the active material particles in the positive and negative electrodes; and the lithium ion liquid-phase diffusion model characterizes the liquid-phase lithium ion concentration in the electrolyte.

[0010] In a possible implementation, the deintercalation reaction model is expressed as:

[0011]

[0012] j 0 =FK(c s,max -c s ) 0.5 (c s ) 0.5 (cl ) 0.5

[0013]

[0014] Where j is the deintercalation reaction current density of the positive or negative electrode, j 0 is the exchange current density of the deintercalation reaction of the positive or negative electrode, exp represents an exponential function with a natural constant as the base, α a is the charge transfer coefficient of the positive electrode, α c is the charge transfer coefficient of the negative electrode, F is the Faraday constant, R is the gas constant, η is the overpotential of the positive or negative electrode, T is the battery temperature, Φ s is the solid phase potential of the positive or negative electrode, Φ l is the liquid phase potential of the electrolyte, E Eq is the equilibrium potential of the positive or negative electrode, K is the reaction rate constant of the positive or negative electrode, c s is the solid phase lithium ion concentration of the positive or negative electrode, c s,max is the maximum solid phase lithium ion concentration of the positive or negative electrode, c l is the liquid phase lithium ion concentration, Ω is the partial molar volume of the active material particles of the positive or negative electrode, σ h,rp is the hydrostatic stress on the surface of the active material particles of the positive or negative electrode, σ r,rp is the radial stress on the surface of the active material particles of the positive or negative electrode, σ θ,rp The tangential stress on the surface of the active material particles of the positive or negative electrode;

[0015] The solid phase charge conservation model is expressed as:

[0016]

[0017] Among them, i s is the solid phase current density of the positive or negative electrode, σ s is the solid phase effective conductivity of the positive or negative electrode, is the gradient of the solid phase potential of the positive or negative electrode;

[0018] The liquid phase charge conservation model is expressed as:

[0019]

[0020] Among them, i l is the liquid phase current density, σ l is the liquid effective conductivity, is the gradient of liquid potential, f(c l ) is the concentration of lithium ions in the liquid phase c l The associated activity coefficient, is the lithium ion transport number, Represents lncl The gradient of

[0021] The lithium ion solid phase diffusion model is expressed as:

[0022]

[0023] Among them, c s is the solid phase lithium ion concentration of the positive or negative electrode, t is the time, D s is the solid phase diffusion coefficient of the positive or negative electrode, r p is the radius of the active material particle of the positive or negative electrode, J Li is the lithium ion flux, E is the Young's modulus of the active material particles of the positive or negative electrode, ν is the Poisson's ratio of the active material particles of the positive or negative electrode, K Li is the thermodynamic factor, E Eq is the equilibrium potential of the positive or negative electrode, x Li is the negative electrode lithiation fraction, represents partial derivative;

[0024] The lithium ion liquid phase diffusion model is expressed as:

[0025]

[0026] Among them, c l is the liquid phase lithium ion concentration, ε l is the liquid volume fraction, x is any position in the lithium-ion battery, a s is the specific surface area of ​​the active material particles of the positive or negative electrode, D l is the effective diffusion coefficient of the liquid phase, Brugg is the Bruggmann coefficient, D l,0 is the initial liquid phase diffusion coefficient, E a is the diffusion activation energy, T ref is the reference temperature.

[0027] In a possible implementation, the heat generation model in the electrochemical-thermal-mechanical coupling model is determined based on a heat dissipation power model, a liquid-phase ohmic heat generation power model, a solid-phase ohmic heat generation power model of the positive and negative electrodes, a polarization heat generation power model, and a reversible heat power model; wherein the heat dissipation power characterizes the power of heat dissipated by the lithium-ion battery; the liquid-phase ohmic heat generation power model characterizes the heat generation power when current flows through the electrolyte; the solid-phase ohmic heat generation power model characterizes the heat generation power when current flows through the active materials of the positive and negative electrodes; the polarization heat generation power model characterizes the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; and the reversible heat power model characterizes the power of heat generated due to entropy changes in the positive and negative electrodes during electrochemical reactions.

[0028] In a possible implementation, the heat generation model is expressed as:

[0029]

[0030] Where ρ is the density of lithium-ion battery materials, C p is the specific heat capacity of lithium-ion battery materials, λ is the thermal conductivity, a a is the specific surface area of ​​the positive electrode active material particles, a c is the specific surface area of ​​the negative electrode active material particles, j a is the deintercalation reaction current density of the positive electrode, j c is the deintercalation reaction current density of the negative electrode, η a is the overpotential of the positive electrode, η c is the negative pole crossing point, is the entropy thermal coefficient of the positive electrode, E eq,a is the open circuit potential of the positive electrode, is the entropy thermal coefficient of the negative electrode, E eq,c is the open circuit potential of the negative electrode, i s,a is the solid phase current density of the positive electrode, is the gradient of the solid phase potential of the positive electrode, i s,c is the solid phase current density of the negative electrode, is the gradient of the solid phase potential of the negative electrode, h is the convection heat transfer coefficient, T amb is the ambient temperature, ε is the radiation heat transfer coefficient, and σ is the Boltzmann constant;

[0031] in, is the heat dissipation power model, is the solid phase ohmic heat generation power model of the positive electrode, is the solid phase ohmic heat generation power of the negative electrode, is the liquid phase ohmic heat generation power model, a a j a η a is the polarization heat generation power model of the positive electrode, a c j c η c is the polarization heat generation power model of the negative electrode, is the reversible thermal power model of the positive electrode, Reversible thermal power model for the negative electrode.

[0032] According to another aspect of the present disclosure, a device for selecting a fast charging strategy for a lithium-ion battery is provided, comprising: a strategy acquisition module, for acquiring a plurality of candidate fast charging strategies that meet the fast charging requirements of the lithium-ion battery; a charging simulation module, for simulating the charging process of the lithium-ion battery under each of the plurality of candidate fast charging strategies using the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, and obtaining charging simulation data corresponding to each of the plurality of candidate fast charging strategies, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple moments during the charging process; a stress determination module, for determining the Mises stress corresponding to each of the plurality of candidate fast charging strategies according to the charging simulation data corresponding to each of the plurality of candidate fast charging strategies, wherein the Mises stress corresponding to each of the candidate fast charging strategies includes the Mises stress exerted on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each of the candidate fast charging strategies; and a strategy selection module, for selecting a target fast charging strategy for the lithium-ion battery from the plurality of candidate fast charging strategies according to the Mises stress corresponding to each of the plurality of candidate fast charging strategies.

[0033] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0034] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0035] According to another aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0036] According to various aspects of the present disclosure, by utilizing an electrochemical-thermal-mechanical coupling simulation model of a lithium-ion battery to simulate the charging process under different candidate fast-charging strategies, the solid-phase lithium ion concentration of the negative electrode at multiple moments in the charging process corresponding to each candidate fast-charging strategy is obtained, and the solid-phase lithium ion concentration of the negative electrode is used to determine the Mises stress to which the negative electrode active material particles are subjected under each candidate fast-charging strategy. Then, based on the Mises stress, a target fast-charging strategy is selected from a plurality of candidate fast-charging strategies. This can effectively screen out a candidate fast-charging strategy in which the negative electrode active material particles are safer (i.e., not prone to rupture and damage) from a plurality of candidate fast-charging strategies that meet the fast-charging requirements as the optimal target fast-charging strategy for the lithium-ion battery, which is beneficial for assisting R&D personnel in quickly screening out a better target fast-charging strategy from a plurality of candidate fast-charging strategies.

[0037] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0039] Figure 1 A flow chart of a method for determining a fast charging strategy for a lithium-ion battery according to an embodiment of the present disclosure is shown.

[0040] Figure 2 A schematic diagram showing the change in Mises stress on negative electrode active material particles during the charging process corresponding to two candidate fast charging strategies according to an embodiment of the present disclosure.

[0041] Figure 3 A schematic diagram showing changes in the negative electrode during the charging process corresponding to two candidate fast charging strategies according to an embodiment of the present disclosure is shown.

[0042] Figure 4 A block diagram of a lithium-ion battery fast charging strategy selection device according to an embodiment of the present disclosure is shown.

[0043] Figure 5 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0044] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0045] The word "exemplary" used exclusively herein means "used as an example, embodiment or illustrative". Any embodiment described herein as "exemplary" is not necessarily to be construed as being superior or better than other embodiments. The term "and / or" herein is merely a description of an association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of, for example, including at least one of A, B, and C, may represent including any one or more elements selected from the set consisting of A, B, and C. The term "multiple" means two or more, and "multiple" means two or more.

[0046] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0047] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0048] As mentioned above, the existing computer modeling simulation method can simultaneously output a variety of fast charging strategies that meet the fast charging requirements. Therefore, it is necessary to select a better fast charging strategy from the multiple fast charging strategies that meet the fast charging requirements as the target fast charging strategy for lithium-ion batteries. In order to evaluate the advantages and disadvantages of each fast charging strategy and select the best fast charging strategy, the embodiment of the present disclosure proposes a lithium-ion battery fast charging strategy selection method. By calculating the Mises stress on the negative electrode active material particles of the lithium-ion battery during the charging process corresponding to different candidate fast charging strategies, the advantages and disadvantages of each candidate fast charging strategy are evaluated to effectively assist R&D personnel in screening out the best target fast charging strategy.

[0049] It is known that during the charging process of lithium-ion batteries, the active material particles in the positive and negative electrodes will expand or shrink due to the influence of lithium insertion and extraction, thereby generating stress. Specifically, the positive electrode will shrink due to lithium extraction, and the negative electrode will expand due to lithium insertion. If the stress is too large and exceeds the maximum stress that the positive and negative electrode active material particles can bear, it will cause mechanical crushing of the active material particles, especially the negative electrode active material particles will be subjected to greater stress due to lithium insertion expansion, and will be more prone to mechanical crushing than the positive electrode active material particles. Mechanical crushing is an important factor that causes the performance of lithium-ion batteries to deteriorate during fast charging. Depending on the scale, mechanical crushing can be divided into the separation of electrode particles, conductive materials and adhesives, the rupture of electrode particles, the separation between active materials and current collectors, and the stratification between electrode sheets. The main reason for these phenomena is that the gradient distribution of lithium concentration during fast charging causes uneven stress between components. During fast charging, Li+ quickly escapes from the positive electrode and embeds into the negative electrode, resulting in severe strain mismatch between Li+ and different parts of the electrode particles. When the energy release rate or stress intensity factor exceeds a certain value, cracks will propagate in the particles, resulting in cracking of the solid electrolyte interface (SEI) of the negative electrode or the cathode electrolyte interface (CEI). The effects of mechanical crushing on battery performance can be divided into loss of active materials (LAM), loss of lithium inventory (LLI) and impedance growth. First, cracks lead to poor conductivity of the material or even complete detachment of the pole piece; secondly, more surfaces are exposed by cracks and react with the electrolyte, and the high temperature caused by fast charging further promotes this side reaction. This process leads to further growth of the SEI layer, thereby increasing resistance and causing LAM and LLI; finally, the consumption of the electrolyte will also reduce the wettability of the electrode and hinder Li+ transmission. Therefore, the lithium-ion battery fast charging strategy selection method proposed in the embodiment of the present disclosure calculates the Mises stress exerted on the negative electrode active material particles during the charging process of the lithium-ion battery under different candidate fast charging strategies, and evaluates the advantages and disadvantages of each candidate fast charging strategy, so as to effectively assist R&D personnel in screening out a target fast charging strategy with safer negative electrode active material particles (i.e., not prone to rupture and damage) from a variety of candidate fast charging strategies that meet the fast charging requirements.

[0050] In practical applications, the lithium-ion battery fast charging strategy selection method of the embodiment of the present disclosure can be deployed on various terminal devices through software or hardware modification. The terminal device involved in the embodiment of the present disclosure may refer to a device with a wireless connection function and / or a wired connection function. The wireless connection function refers to the ability to connect to other devices through wireless connection methods such as wifi and Bluetooth. The terminal device involved in the embodiment of the present disclosure can also communicate with other devices through a wired connection function. The terminal device involved in the embodiment of the present disclosure can be a touch screen, a non-touch screen, or a screen-free device. The touch screen can control the terminal device by clicking and sliding on the display screen with a finger or a stylus. The non-touch screen device can be connected to an input device such as a mouse, keyboard, touch panel, etc., and the terminal device can be controlled by the input device. For example, the device without a screen can be a Bluetooth speaker without a screen. For example, the terminal device of the present application may include but is not limited to user equipment (UE), mobile device, user terminal, terminal, handheld device, tablet computer, laptop computer, PDA, computing device, etc.

[0051] The lithium-ion battery fast charging strategy selection method of the embodiment of the present disclosure can also be deployed on a server, which can be located in the cloud or locally, and can be a physical device or a virtual device, such as a virtual machine, a container, etc., with a wireless communication function, wherein the wireless communication function can be set in the chip (system) or other parts or components of the server. It can refer to a device with a wireless connection function, and the wireless connection function refers to the ability to connect to other servers or terminal devices through wireless connection methods such as Wi-Fi and Bluetooth. The server involved in the embodiment of the present disclosure may also have the function of communicating through a wired connection. For example, the server can receive a variety of candidate fast charging strategies that meet the fast charging requirements of lithium-ion batteries sent by the terminal device, and the server executes the lithium-ion battery fast charging strategy selection method of the embodiment of the present disclosure to select the optimal target fast charging strategy from the multiple candidate fast charging strategies, and return the target fast charging strategy to the terminal device, so as to display the selected target fast charging strategy to the user in the terminal device.

[0052] Figure 1 A flow chart of a method for determining a fast charging strategy for a lithium-ion battery according to an embodiment of the present disclosure is shown. The method can be executed by an electronic device such as the above-mentioned terminal device or server. Figure 1 As shown, the method includes: step S11 to step S14.

[0053] In step S11, a plurality of candidate fast charging strategies that meet the fast charging requirements of the lithium-ion battery are obtained.

[0054] Among them, the fast charging requirement can indicate a specified charging capacity within a specified time. In actual applications, the user can set the fast charging requirement according to actual needs. For example, it can be set to charge from 8% state of charge (SOC) to 80% SOC within 21 minutes, wherein 21 minutes is the specified time, and the charging capacity from 8% SOC to 80% SOC is the specified charging capacity. The embodiments of the present disclosure do not limit the specific content of the fast charging requirement.

[0055] Each candidate fast-charging strategy may include the charging rate of the lithium-ion battery at different SOCs and different battery temperatures. In practical applications, the multiple candidate fast-charging strategies that meet the fast-charging requirements of lithium-ion batteries may be artificially formulated fast-charging strategies, or they may be fast-charging strategies obtained by computer electrochemical-thermal coupling simulation modeling. As long as they meet the fast-charging requirements of lithium-ion batteries, the disclosed embodiments do not limit the generation method of the candidate fast-charging strategies.

[0056] In step S12, the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery is used to simulate the charging process of the lithium-ion battery under each of the multiple candidate fast charging strategies, and the charging simulation data corresponding to each of the multiple candidate fast charging strategies are obtained, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple times during the charging process.

[0057] Among them, the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery can characterize the electrochemical reaction process, heat conduction process and positive and negative electrode stress change process of the lithium-ion battery during charging according to any fast charging strategy. In practical applications, those skilled in the art can use open source battery simulation modeling software in the field, such as pyBaMM software, to establish an electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, wherein the established electrochemical-thermal-mechanical coupling simulation model can adopt a thermoelectric coupling simulation model known in the art, and of course, an electrochemical-thermal-mechanical coupling simulation model can also be independently designed, which is not limited to the embodiments of the present disclosure.

[0058] The embodiment of the present disclosure provides a process for establishing an electrochemical-thermal-mechanical coupling simulation model and the established electrochemical-thermal-mechanical coupling simulation model. Specifically, the process for establishing the electrochemical-thermal-mechanical coupling simulation model includes:

[0059] Based on the battery design parameters of lithium-ion batteries, the battery geometry model of lithium-ion batteries is constructed. The battery design parameters include: positive electrode thickness, negative electrode thickness, separator thickness, radius and specific surface area of ​​positive electrode active material particles, radius and specific surface area of ​​negative electrode active material particles, separator porosity, solid phase volume fraction of positive electrode, solid phase volume fraction of negative electrode, and liquid phase volume fraction;

[0060] Based on the physical properties of lithium-ion batteries, an electrochemical-thermal coupling simulation model is constructed on the battery geometry model. The physical properties of the battery include: reaction rate constant, solid phase diffusion coefficient, diffusion activation energy, Bruggmann coefficient, solid phase effective conductivity, liquid phase effective conductivity, charge transfer coefficient, thermal conductivity, convection heat transfer coefficient and radiation heat transfer coefficient.

[0061] Based on the battery mechanical parameters of the lithium-ion battery, an electrochemical-thermal-mechanical coupling simulation model is constructed on the electrochemical-thermal coupling simulation model. The battery mechanical parameters include: Young's modulus, Poisson's ratio, partial molar volume, radial stress, and tangential stress of active material particles of the positive or negative electrode.

[0062] For example, a battery geometric model can be first established in the pyBaMM software according to the battery design parameters, and then an electrochemical-thermal-mechanical coupling simulation model can be established in the battery geometric model according to the battery physical parameters and the battery mechanical parameters. The embodiments of the present disclosure do not limit the specific steps of using the pyBaMM software to construct the electrochemical-thermal-mechanical coupling simulation model. It can be understood that the electrochemical-thermal-mechanical coupling simulation model can include a bidirectionally coupled electrochemical model, a heat generation model, and a mechanical model, that is, the output of the electrochemical model can be used as an input of the parameters in the heat generation model, and the output of the heat generation model can be used as an input of the parameters in the electrochemical model, thereby realizing the bidirectional coupling of the electrochemical model and the thermal model; the output of the electrochemical model can be used as an input of the parameters in the mechanical model, and the output of the mechanical model can be input into the electrochemical model to affect the calculation of the electrochemical model parameters, thereby realizing the bidirectional coupling of the electrochemical model and the mechanical model.

[0063] In a possible implementation, an electrochemical-thermal-mechanical coupling computer simulation model can be established based on the physical and chemical process mechanism inside the lithium-ion battery, wherein the electrochemical model can be established specifically based on the following five reactions inside the battery: 1) deintercalation reaction occurring at the contact interface between active material particles and electrolyte (i.e., deintercalation reaction of lithium); 2) solid-phase charge conservation; 3) liquid-phase charge conservation; 4) solid-phase diffusion of lithium ions; 5) liquid-phase diffusion of lithium ions. Thus, the electrochemical model in the electrochemical-thermal coupling simulation model can include: a deintercalation reaction model constructed based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium ion solid-phase diffusion model and a lithium ion liquid-phase diffusion model constructed based on Fick's second diffusion law.

[0064] Among them, the deintercalation reaction model can characterize the deintercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or embedded in the positive and negative electrodes. Therefore, the deintercalation reaction current density can refer to the current size of the electrode reaction per unit area when lithium ions are extracted from the positive electrode surface (delithiation) or embedded in the negative electrode surface (lithium insertion) during the charge and discharge process of the lithium ion battery; illustratively, the deintercalation reaction model considering the influence of stress can be expressed as formula (1-1), formula (1-1), formula (1-3) and formula (1-4):

[0065]

[0066] j 0 =FK(c s,max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5 (1-3)

[0067]

[0068] Where j is the deintercalation reaction current density of the positive or negative electrode, j 0 is the exchange current density of the deintercalation reaction of the positive or negative electrode (calculated using the reaction rate constant), exp represents an exponential function with a natural constant as the base, α a is the charge transfer coefficient of the positive electrode, α c is the charge transfer coefficient of the negative electrode, F is the Faraday constant, R is the gas constant, η is the overpotential of the positive or 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 or negative electrode, Φ l is the liquid phase potential of the electrolyte, E Eq is the equilibrium potential of the positive or negative electrode, K is the reaction rate constant of the positive or negative electrode, c s is the solid phase lithium ion concentration of the positive or negative electrode (i.e., the lithium ion concentration in the active material particles of the positive or negative electrode), c s It changes at any time during the charging process, c s,max is the maximum solid phase lithium ion concentration of the positive or negative electrode (e.g., c s,max It can be 23000 mol / m 3 ), the negative electrode c s,max It can be 30555mol / m 3 ), c l is the liquid phase lithium ion concentration (i.e., the lithium ion concentration in the electrolyte), Ω is the partial molar volume of the active material particles of the positive or negative electrode, σ h,rp is the hydrostatic stress on the surface of the active material particles of the positive or negative electrode, σr,rp is the radial stress on the surface of the active material particles of the positive or negative electrode, σ θ,rp It is the tangential stress on the surface of the active material particles of the positive or negative electrode.

[0069] Among them, α a , α c ,F,R,K,c s,max , Ω is a constant, c s and c l It can be determined by the lithium ion solid phase diffusion model and the lithium ion liquid phase diffusion model respectively. When simulating the charging process of each candidate fast charging strategy using the electrochemical-thermal-mechanical coupling simulation model, the solid phase lithium ion concentration c of the negative electrode at each moment can be determined specifically by the lithium ion solid phase diffusion model. s ; Φ s and Φ l For example, the thermoelectric coupling simulation calculation capability of the pyBaMM software can be used to perform thermoelectric coupling simulation calculations on the electrochemical-thermal-mechanical coupling simulation model to obtain the equilibrium potential E of the positive and negative electrodes. Eq As the SOC changes, the relationship curve between the equilibrium potential of the positive and negative electrodes and SOC can be obtained through the buckling test, so the equilibrium potential E of the positive and negative electrodes under different SOCs can be obtained based on the relationship curve between the equilibrium potential of the positive and negative electrodes and SOC. Eq σ r,rp and σ θ,rp It can be calculated by referring to the mechanical model shown in formula (8) and formula (9) below. Those skilled in the art can use any related technology known in the art to obtain the partial molar volume of the positive and negative active material particles in the above lithium ion battery, which is not limited by the embodiments of the present disclosure.

[0070] The solid phase charge conservation model characterizes the solid phase current density in the active material particles in the positive and negative electrodes of the lithium ion battery (that is, the current density inside the positive and negative electrode active material particles); illustratively, the solid phase charge conservation model can be expressed as formula (2):

[0071]

[0072] Among them, i s is the solid phase current density of the positive or negative electrode, σ s is the solid phase effective conductivity of the positive or negative electrode (that is, the effective conductivity inside the positive or negative electrode active material particles), is the gradient of the solid phase potential of the positive or negative electrode; where σ s is a constant value, σ s It can also be expressed as Brugg is the Brugg constant, ε sis the solid phase volume fraction of the positive or negative electrode, σ s,0 is the initial solid phase effective conductivity of the positive or negative electrode, Brugg, ε s and σ s,0 is a fixed value. The pyBaMM software can be used to perform thermoelectric coupling simulation calculations on the electrochemical-thermal-mechanical coupling simulation model to obtain the result.

[0073] The liquid phase charge conservation model characterizes the liquid phase current density in the electrolyte of the lithium ion battery (i.e., the current density in the electrolyte); illustratively, the liquid phase charge conservation model can be expressed as formula (3):

[0074]

[0075] Among them, i l is the liquid phase current density, σ l is the effective conductivity of the liquid phase (i.e. the effective conductivity of the electrolyte), is the gradient of the liquid phase potential (i.e. the gradient of the potential in the electrolyte), f(c l ) is the concentration of lithium ions in the liquid phase c l The associated activity coefficient, is the lithium ion transport number, Represents lnc l The gradient of l , R, F, is a constant value, σ l It can also be expressed as Brugg is the Bruggman constant, ε l is the liquid volume fraction, σ s,0 is the initial liquid phase effective conductivity, Brugg, ε l and σ l,0 is a fixed value; lithium ion transport number It refers to the ratio of the flow rate of lithium ions passing through a unit cross section in a unit time to the total charge flow rate in an electrolyte or electrode material. It can be obtained through experimental testing; The pyBaMM software can be used to perform thermoelectric coupling simulation calculations on the electrochemical-thermal-mechanical coupling simulation model to obtain the value of f(c l ) can be obtained through experimental testing. For example, a relationship curve between the liquid phase lithium ion concentration and the activity coefficient can be obtained through experimental testing, so as to obtain the activity coefficient under different liquid phase lithium ion concentrations based on the relationship curve between the liquid phase lithium ion concentration and the activity coefficient.

[0076] Among them, the lithium ion solid phase diffusion model characterizes the solid phase lithium ion concentration in the active material particles in the positive and negative electrodes (that is, the concentration of lithium ions in the active material particles); Fick's second diffusion law can be used to describe the diffusion of lithium ions inside the positive and negative electrode active material particles due to the lithium concentration gradient to obtain the lithium ion concentration in the active material particles in the positive and negative electrodes; illustratively, the lithium ion solid phase diffusion model considering the influence of stress is expressed as formula (4-1), formula (4-2) and formula (4-3):

[0077]

[0078] Among them, c s is the solid phase lithium ion concentration of the positive or negative electrode, t is the time, D s is the solid phase diffusion coefficient of the positive or negative electrode, r p is the radius of the active material particle of the positive or negative electrode, J Li is the lithium ion flux, E is the Young's modulus of the active material particles of the positive or negative electrode, v is the Poisson's ratio of the active material particles of the positive or negative electrode, K Li is the thermodynamic factor, E Eq is the equilibrium potential of the positive or negative electrode, x Li is the negative electrode lithiation fraction, and θ represents the partial derivative, for example, Represents c s The partial derivative of t is similar and will not be described in detail. Among them, E, v, D s and r p is a constant; negative electrode lithiation fraction x Li It can be determined based on the ratio between the solid phase lithium ion concentration of the negative electrode during charging and the theoretical lithium ion concentration of the negative electrode when fully charged. As mentioned above, the equilibrium potential E of the positive and negative electrodes at different SOCs can be obtained based on the relationship curve between the equilibrium potential of the positive and negative electrodes and SOC. Eq Those skilled in the art can use any related technology known in the art to obtain the Young's modulus and Poisson's ratio of the positive and negative active material particles in the above lithium-ion battery, and the embodiments of the present disclosure are not limited to this. It should be understood that by integrating formula (4), the solid phase lithium ion concentration c of the positive or negative electrode at any time can be obtained. s .

[0079] The lithium ion liquid phase diffusion model characterizes the liquid phase lithium ion concentration in the electrolyte (i.e., the lithium ion concentration in the electrolyte). Fick's second diffusion law can be used to describe the diffusion of lithium ions in the electrolyte to obtain the lithium ion concentration in the electrolyte; illustratively, the lithium ion liquid phase diffusion model is expressed as formula (5-1) and formula (5-1):

[0080]

[0081] Among them, cl is the liquid phase lithium ion concentration, ε l is the liquid phase volume fraction, x is any position in the lithium-ion battery (the position can be customized, for example, it can be set to the junction of the negative electrode and the separator), a s is the specific surface area of ​​the active material particles of the positive or negative electrode, D l is the effective diffusion coefficient of the liquid phase (calculated using the Bruggmann coefficient and diffusion activation energy), Brugg is the Bruggmann coefficient, D l,0 is the initial liquid phase diffusion coefficient, E a is the diffusion activation energy, T ref is the reference temperature (for example, 298.15K (i.e., 25°C)), T is the battery temperature (which can be obtained using the heat generation model), and j is the above-mentioned deintercalation reaction current density; where ε l 、a s Brugg, D l,0 、E a , T ref It should be understood that by integrating formula (5-1), the liquid phase lithium ion concentration c at any time can be obtained. l .

[0082] Among them, a heat generation model can be constructed according to the energy conservation equation to calculate the battery temperature by calculating the heat generation in the heat generation model; specifically, the heat generation model in the electrochemical-thermal coupling model can be determined based on the heat dissipation power model, the liquid phase ohmic heat generation power model, the solid phase ohmic heat generation power model of the positive and negative electrodes, the polarization heat generation power model and the reversible heat power model; among them, the heat dissipation power represents the power of heat dissipated by the lithium-ion battery; the liquid phase ohmic heat generation power model represents the heat generation power when current flows through the electrolyte; the solid phase ohmic heat generation power model represents the heat generation power when current flows through the active materials of the positive and negative electrodes; the polarization heat generation power model represents the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; the reversible heat power model represents the power of heat generated due to the entropy change of the positive and negative electrodes in the electrochemical reaction.

[0083] Exemplarily, the heat generation model can be expressed as formula (6-1) and formula (6-1):

[0084]

[0085] Where ρ is the density of lithium-ion battery materials, C p is the specific heat capacity of lithium-ion battery materials, λ is the thermal conductivity, a a is the specific surface area of ​​the positive electrode active material particles, a c is the specific surface area of ​​the negative electrode active material particles, j a is the deintercalation reaction current density of the positive electrode, j c is the deintercalation reaction current density of the negative electrode, ηa is the overpotential of the positive electrode, η c is the negative pole crossing point, is the entropy thermal coefficient of the positive electrode (that is, 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 thermal coefficient of the negative electrode (i.e., E eq,c is the open circuit potential of the negative electrode, i s,a is the solid phase current density of the positive electrode, is the gradient of the solid phase potential of the positive electrode, i s,c is the solid phase current density of the negative electrode, is the gradient of the solid phase potential of the negative electrode, h is the convection heat transfer coefficient, T amb is the ambient temperature, ε is the radiation heat transfer coefficient, and σ is the Boltzmann constant; where ρ, C p 、a a 、a c ,h,T amb , ε, σ are fixed values, and Can be obtained through experimental tests respectively;

[0086] in, is the heat dissipation power model, is the solid phase ohmic heat generation power model of the positive electrode, is the solid phase ohmic heat generation power of the negative electrode, is the liquid phase ohmic heat generation power model, a a j a η a is the polarization heat generation power model of the positive electrode, a c j c η c is the polarization heat generation power model of the negative electrode, is the reversible thermal power model of the positive electrode, is the reversible thermal power model of the negative electrode; it should be understood that the battery temperature T can be obtained by integrating formula (6-1).

[0087] Among them, after using the open source pyBaMM software to establish the battery cell geometry model according to the battery cell design parameters, the various electrochemical models and heat generation models shown in the above formulas (1-1) to (6-2) can be used to describe different domains in the above battery geometry model to establish an electrochemical-thermal-mechanical coupling simulation model. The parameters in the heat generation model can be obtained and input from the electrochemical model. For example, the solid phase current density and liquid phase current density of the positive and negative electrodes can be obtained by using 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; using the above formulas (4), (5-1) and ( The electrochemical model of 5-2) can obtain the solid-phase lithium ion concentration and liquid-phase lithium ion concentration of the positive and negative electrodes and input them into formula (1-1) to obtain the deintercalation reaction current density, and then use the deintercalation reaction current density to calculate the polarization heat generation power and reversible heat power of the positive and negative electrodes; by inputting the thermal conductivity, convection heat transfer coefficient (experimentally measured) and radiation heat transfer coefficient (experimentally measured) into formula (6-2), the heat dissipation power can be obtained, and all the heat generation power and heat dissipation power are input into the heat generation model (6-1) for integration to calculate the battery temperature T, and the battery temperature T can be input into the electrochemical model, thereby realizing the two-way coupling of the electrochemical model and the thermal model.

[0088] In practical applications, the battery physical property parameters used in the above-mentioned electrochemical models and heat generation models can be pre-calibrated or experimentally measured to obtain specific values ​​of each parameter, or the initial values ​​can be estimated based on historical experience and then corrected, which is not limited to the embodiments of the present disclosure.

[0089] In step S13, the Mises stress corresponding to each of the multiple candidate fast charging strategies is determined based on the charging simulation data corresponding to each of the multiple candidate fast charging strategies, wherein the Mises stress corresponding to each of the multiple candidate fast charging strategies includes the Mises stress exerted on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each of the candidate fast charging strategies.

[0090] As mentioned above, during the charging process, the negative electrode expands due to the insertion of lithium, causing the negative electrode active material particles to be subjected to stress. The negative electrode active material particles at the junction of the negative electrode and the diaphragm are first inserted with lithium, which will cause the Mises stress on the negative electrode active material particles at the junction of the negative electrode and the diaphragm to be maximized, especially the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm to be maximized. Therefore, the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process can be calculated, that is, the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm of the electrochemical-thermal-mechanical coupling model of the lithium-ion battery is calculated during the operation of the candidate fast charging strategy, so as to select the optimal target fast charging strategy based on the Mises stress. For example, the candidate fast charging strategy with the smallest maximum Mises stress can be selected as the optimal target fast charging strategy.

[0091] In a possible implementation, determining the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies according to the charging simulation data corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies may include:

[0092] For any candidate fast charging strategy, the negative electrode lithium ion concentration deviation at each moment is obtained according to the difference between the solid phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to the candidate fast charging strategy and the specified initial lithium ion concentration;

[0093] According to the negative electrode parameters of the lithium-ion battery and the deviation of the negative electrode lithium ion concentration at each time, the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the separator at each time are determined; wherein the negative electrode parameters include the radius, Young's modulus, Poisson's ratio and partial molar volume of the negative electrode active material particles;

[0094] According to the radial stress and tangential stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment, the Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment is determined.

[0095] Among them, the negative electrode lithium ion concentration deviation can be expressed as: Δc = c s -c s,0 , where c s represents the solid lithium ion concentration of the negative electrode at any time during the charging process corresponding to any candidate fast charging strategy, c s,0Represents the specified initial lithium ion concentration, that is, the difference between the solid-phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to each candidate fast charging strategy and the specified initial lithium ion concentration can be calculated to obtain the negative electrode lithium ion concentration deviation at each moment in the charging process corresponding to each candidate fast charging strategy. Among them, the initial lithium ion concentration is also the solid-phase lithium ion concentration of the negative electrode initially. It should be understood that the initial lithium ion concentration of the negative electrode can be set according to the initial state of charge indicated by the fast charging requirement, and the embodiments of the present disclosure are not limited to this.

[0096] The radial stress can be calculated using formula (8), and the tangential stress can be calculated using formula (9):

[0097]

[0098] Among them, σ r,rp,neg represents the radial stress on the surface of the negative electrode active material particles, σ θ,rp,neg (r) represents the tangential stress on the surface of the negative electrode active material particles, E neg represents the Young's modulus of the negative electrode active material particles, v neg represents the Poisson's ratio of the negative electrode active material particles, r p This represents the radius of the negative electrode active material particle, Ω neg represents the partial molar volume of the negative electrode active material particles, Δc represents the negative electrode lithium ion concentration deviation at any time during the charging process corresponding to any candidate fast charging strategy, and r represents any position between the center of the negative electrode active material particle and the surface of the negative electrode active material particle. Among them, the Young's modulus E of the negative electrode active material particle neg , Poisson's ratio v neg and partial molar volume Ω neg It is known that those skilled in the art can use any related technology known in the art to obtain the Young's modulus E of the negative electrode active material particles in the above lithium ion battery. neg , Poisson's ratio v neg and partial molar volume Ω neg , and this embodiment of the present disclosure is not limited to this.

[0099] It should be understood that the radial stress and tangential stress on the surface of the positive electrode active material particles can be calculated by referring to the calculation method of the radial stress and tangential stress in the above formula (8) and formula (9), and will not be elaborated here. The above formulas (8) and (9) can also be understood as the mechanical models involved in the electrochemical-thermal-mechanical coupling simulation model. The mechanical model can characterize the stress changes of the positive and negative electrode active material particles during the charging process. Based on this, the above electrochemical model can be used to output the solid lithium ion concentration of the positive and negative electrodes and input them into formulas (8) and (9), and the radial stress and tangential stress on the surface of the positive and negative electrode active material particles can be calculated. At the same time, formulas (8) and (9) can be used to calculate the radial stress and tangential stress on the surface of the positive and negative electrode active material particles and input them into formulas (1-1), (1-2), (1-3) and (1-4), and the battery mechanical parameters can be input into the electrochemical model of formulas (4-1), (4-2) and (4-3), thereby realizing the two-way coupling of the electrochemical model and the mechanical model.

[0100] It should be noted that the various electrochemical models, heat generation models and mechanical models involved in the electrochemical-thermal-mechanical coupling simulation model shown in formulas (1-1) to (9) are some possible implementation methods provided by the embodiments of the present disclosure. In fact, those skilled in the art can customize the design of electrochemical models, heat generation models and mechanical models according to actual needs, as long as they can build an electrochemical-thermal-mechanical coupling simulation model of a lithium-ion battery.

[0101] As mentioned above, an electrochemical-thermal-mechanical coupling simulation model of a lithium-ion battery can be established in a battery simulation modeling software such as pyBaMM software. Therefore, a fast charging strategy can also be set in a battery simulation modeling software such as pyBaMM software, so that the electrochemical-thermal-mechanical coupling simulation model simulates the charging process of the lithium-ion battery under each candidate fast charging strategy; and the solid-phase lithium ion concentration of the negative electrode at different times during the charging process under each candidate fast charging strategy simulated by the electrochemical-thermal-mechanical coupling simulation model can be monitored in real time, that is, the various candidate fast charging strategies that meet the fast charging requirements are run using the established electrochemical-thermal-mechanical coupling simulation model to obtain charging simulation data for the various candidate fast charging strategies.

[0102] It should be understood that the radial stress and tangential stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the diaphragm at each moment in the charging process corresponding to each candidate fast charging strategy can be calculated using the above formulas (8) and (9), and then the Mises stress σ on the surface of the negative electrode active material particles at the interface between the negative electrode and the diaphragm at each moment in the charging process corresponding to each candidate fast charging strategy can be calculated using formula (10). mises :

[0103] σ mises =|σ r,rp,neg -σ θ,rp,neg | (10)

[0104] The above formula (10) can be understood as the Mises stress σ on the surface of the negative electrode active material particles at the interface between the negative electrode and the diaphragm at any time is obtained by calculating the absolute value of the difference between the radial stress and the tangential stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the diaphragm at any time. mises .

[0105] It should be understood that for each candidate fast charging strategy, the above formula (8) to formula (10) can be used to calculate the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment in the charging process, and then the maximum value can be selected from the Mises stress at each moment in the charging process corresponding to each candidate fast charging strategy as the maximum Mises stress corresponding to each candidate fast charging strategy. That is, for any candidate fast charging strategy, the maximum value of the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment in the charging process corresponding to the candidate fast charging strategy can be determined as the maximum Mises stress corresponding to the candidate fast charging strategy.

[0106] In step S14, a target fast charging strategy for the lithium-ion battery is selected from the multiple candidate fast charging strategies based on the Mises stress corresponding to each candidate fast charging strategy.

[0107] It should be understood that the smaller the mises stress during charging, the smaller the risk of the negative electrode active material particles breaking during charging. Therefore, by comparing the mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during charging of multiple candidate fast charging strategies, the candidate fast charging strategy with the smallest maximum mises stress during fast charging can be selected as the optimal target fast charging strategy. In other words, by comparing the mises stress of different fast charging strategies during charging, the advantages and disadvantages of each candidate fast charging strategy can be evaluated, and the optimal candidate fast charging strategy can be selected as the target fast charging strategy.

[0108] Specifically, the Mises stress corresponding to each candidate fast-charging strategy includes the maximum Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the diaphragm during the charging process corresponding to each candidate fast-charging strategy; wherein, the above-mentioned selection of the target fast-charging strategy for the lithium-ion battery from a plurality of candidate fast-charging strategies according to the Mises stress corresponding to each candidate fast-charging strategy in the plurality of candidate fast-charging strategies includes: selecting the candidate fast-charging strategy with the minimum maximum Mises stress from a plurality of candidate fast-charging strategies as the target fast-charging strategy for the lithium-ion battery according to the maximum Mises stress corresponding to each candidate fast-charging strategy in the plurality of candidate fast-charging strategies. In this way, the best candidate fast-charging strategy can be quickly screened out as the target fast-charging strategy.

[0109] Of course, those skilled in the art can also customize and adjust the target fast charging strategy selection strategy according to actual needs. For example, the second largest Mises stress corresponding to each candidate fast charging strategy can be used, and then the candidate fast charging strategy with the smallest second largest Mises stress can be selected from multiple candidate fast charging strategies as the target fast charging strategy for lithium-ion batteries. Alternatively, the candidate fast charging strategy with the second smallest maximum Mises stress can be selected from multiple candidate fast charging strategies as the target fast charging strategy for lithium-ion batteries. This is not limited to the embodiments of the present disclosure. It should be understood that any implementation method that uses Mises stress to select the target fast charging strategy should be within the protection scope of the embodiments of the present disclosure.

[0110] For example, taking the active material particles of the positive electrode lithium manganese iron phosphate and the negative electrode graphite system as an example, the particle radius of the lithium manganese iron phosphate material is 0.435um, and the particle radius of the graphite material is 7.675um. For the two candidate fast charging strategies shown in Table 1 and Table 2, the above steps S11 to S13 of the embodiment of the present disclosure can be used to obtain the following: Figure 2 The Mises stress on the negative electrode active material particles during the charging process corresponding to the two candidate fast charging strategies shown in Table 1 (i.e., the first fast charging strategy shown in Table 1 and the second fast charging strategy shown in Table 2) and the Figure 3 The negative electrode during the charging process corresponding to the two candidate fast charging strategies shown, where Figure 2 The normalized position 0 to 1 of the horizontal axis represents the position from the center of the negative electrode active material particle to the surface of the negative electrode active material particle, such as Figure 2 and Figure 3As shown, the negative electrode potential of the two candidate fast charging strategies is greater than 0 (that is, both can meet the safety requirement of no lithium precipitation at the negative electrode), and through comparison, it is found that the maximum Mises stress in the charging process of the two candidate fast charging strategies appears at the surface of the active material particles. The maximum Mises stress of the first fast charging strategy during the charging process is 101MPa, and the maximum Mises stress of the second fast charging strategy during the charging process is 89MPa. Since the maximum Mises stress on the negative electrode active material particles is smaller during the charging process of the second fast charging strategy, it means that the risk of breakage of the negative electrode active material particles is smaller when charging using the second fast charging strategy. Therefore, when both candidate fast charging strategies meet the fast charging requirements, the second fast charging strategy is better than the first fast charging strategy, and the second fast charging strategy can be selected as the target fast charging strategy for lithium-ion batteries.

[0111] Table 1 Working conditions of the first fast charging strategy

[0112] Start time End time Charging rate 0 611 2.4 611 801 2.1 801 1037 1.7 1037 1220 1.4

[0113] Table 2 Operating conditions of the second fast charging strategy.

[0114]

[0115]

[0116] According to the fast charging strategy selection method of the embodiment of the present disclosure, by using the electrochemical-thermal coupling simulation model of the lithium-ion battery to simulate the charging process under different candidate fast charging strategies, the solid-phase lithium ion concentration of the negative electrode at multiple moments in the charging process corresponding to each candidate fast charging strategy is obtained, and the solid-phase lithium ion concentration of the negative electrode is used to determine the Mises stress to which the negative electrode active material particles are subjected under each candidate fast charging strategy, and then the target fast charging strategy is selected from the multiple candidate fast charging strategies. The candidate fast charging strategy in which the negative electrode active material particles are safer (that is, not prone to rupture and damage) can be effectively screened out from the multiple candidate fast charging strategies that meet the fast charging requirements as the optimal target fast charging strategy for the lithium-ion battery, that is, the Mises stress to which the negative electrode active material particles are subjected during the fast charging process of the lithium-ion battery can be calculated, so as to evaluate the advantages and disadvantages of different candidate fast charging strategies based on the Mises stress (such as the maximum Mises stress) to which the negative electrode active material particles are subjected, which is conducive to assisting R&D personnel to quickly screen out a better target fast charging strategy from a variety of candidate fast charging strategies.

[0117] Figure 4 A block diagram of a device for selecting a fast charging strategy for a lithium-ion battery according to an embodiment of the present disclosure is shown as follows: Figure 4 As shown, the device comprises:

[0118] A strategy acquisition module 401 is used to acquire multiple candidate fast charging strategies that meet the fast charging requirements of the lithium-ion battery;

[0119] A charging simulation module 402 is used to simulate the charging process of the lithium-ion battery under each of the multiple candidate fast-charging strategies using the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, and obtain charging simulation data corresponding to each of the multiple candidate fast-charging strategies, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple moments during the charging process;

[0120] A stress determination module 403 is used to determine the Mises stress corresponding to each of the multiple candidate fast charging strategies according to the charging simulation data corresponding to each of the multiple candidate fast charging strategies, wherein the Mises stress corresponding to each candidate fast charging strategy includes the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each candidate fast charging strategy;

[0121] The strategy selection module 404 is used to select a target fast charging strategy for the lithium-ion battery from the multiple candidate fast charging strategies according to the Mises stress corresponding to each candidate fast charging strategy in the multiple candidate fast charging strategies.

[0122] In a possible implementation, the method of determining the Mises stress corresponding to each of the multiple candidate fast charging strategies according to the charging simulation data corresponding to each of the multiple candidate fast charging strategies includes: for any candidate fast charging strategy, obtaining the negative electrode lithium ion concentration deviation at each moment according to the difference between the solid-phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to the candidate fast charging strategy and the specified initial lithium ion concentration; determining the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment according to the negative electrode parameters of the lithium-ion battery and the negative electrode lithium ion concentration deviation at each moment; wherein the negative electrode parameters include the radius, Young's modulus, Poisson's ratio and partial molar volume of the negative electrode active material particles; determining the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment according to the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm at each moment.

[0123] In one possible implementation, the Mises stress corresponding to each candidate fast-charging strategy includes the maximum Mises stress borne on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each candidate fast-charging strategy; wherein, selecting the target fast-charging strategy of the lithium-ion battery from the multiple candidate fast-charging strategies according to the Mises stress corresponding to each candidate fast-charging strategy among the multiple candidate fast-charging strategies includes: selecting, according to the maximum Mises stress corresponding to each candidate fast-charging strategy among the multiple candidate fast-charging strategies, the candidate fast-charging strategy with the smallest maximum Mises stress as the target fast-charging strategy of the lithium-ion battery.

[0124] In a possible implementation, the electrochemical-thermal-mechanical coupling simulation model characterizes the electrochemical reaction process, heat conduction process, and positive and negative electrode stress change process (or stress calculation process in positive and negative electrode active material particles) during the charging process of the lithium-ion battery according to any fast charging strategy, wherein the process of establishing the electrochemical-thermal-mechanical coupling simulation model includes: constructing a battery geometric model of the lithium-ion battery based on the battery design parameters of the lithium-ion battery, and the battery design parameters include: positive electrode thickness, negative electrode thickness, diaphragm thickness, radius and specific surface area of ​​positive electrode active material particles, radius and specific surface area of ​​negative electrode active material particles, diaphragm porosity, solid phase volume fraction of the positive electrode, The solid phase volume fraction and liquid phase volume fraction of the negative electrode; based on the battery physical parameters of the lithium ion battery, an electrochemical-thermal coupling simulation model is constructed on the battery geometric model, and the battery physical parameters include: reaction rate constant, solid phase diffusion coefficient, diffusion activation energy, Brugmann coefficient, solid phase effective conductivity, liquid phase effective conductivity, charge transfer coefficient, thermal conductivity, convection heat transfer coefficient and radiation heat transfer coefficient; based on the battery mechanical parameters of the lithium ion battery, an electrochemical-thermal-mechanical coupling simulation model is constructed on the electrochemical-thermal coupling simulation model, and the battery mechanical parameters include: Young's modulus, Poisson's ratio, partial molar volume, radial stress and tangential stress of positive and negative electrode active material particles.

[0125] In a possible implementation, the electrochemical model in the electrochemical-thermal-mechanical coupling simulation model includes: an intercalation reaction model constructed based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium ion solid-phase diffusion model and a lithium ion liquid-phase diffusion model constructed based on Fick's second diffusion law; wherein the intercalation reaction model characterizes the intercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or embedded in the positive and negative electrodes; the solid-phase charge conservation model characterizes the solid-phase current density in the active material particles in the positive and negative electrodes of the lithium ion battery; the liquid-phase charge conservation model characterizes the liquid-phase current density in the electrolyte of the lithium ion battery; the lithium ion solid-phase diffusion model characterizes the solid-phase lithium ion concentration in the active material particles in the positive and negative electrodes; and the lithium ion liquid-phase diffusion model characterizes the liquid-phase lithium ion concentration in the electrolyte.

[0126] In a possible implementation, the deintercalation reaction model is expressed as:

[0127]

[0128] j 0 =FK(c s,max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5

[0129]

[0130] Where j is the deintercalation reaction current density of the positive or negative electrode, j 0 is the exchange current density of the deintercalation reaction of the positive or negative electrode, exp represents an exponential function with a natural constant as the base, α a is the charge transfer coefficient of the positive electrode, α c is the charge transfer coefficient of the negative electrode, F is the Faraday constant, R is the gas constant, η is the overpotential of the positive or negative electrode, T is the battery temperature, Φ s is the solid phase potential of the positive or negative electrode, Φ l is the liquid phase potential of the electrolyte, E Eq is the equilibrium potential of the positive or negative electrode, K is the reaction rate constant of the positive or negative electrode, c s is the solid phase lithium ion concentration of the positive or negative electrode, c s,max is the maximum solid phase lithium ion concentration of the positive or negative electrode, c l is the liquid phase lithium ion concentration, Ω is the partial molar volume of the active material particles of the positive or negative electrode, σ h,rp is the hydrostatic stress on the surface of the active material particles of the positive or negative electrode, σ r,rpis the radial stress on the surface of the active material particles of the positive or negative electrode, σ θ,rp The tangential stress on the surface of the active material particles of the positive or negative electrode;

[0131] The solid phase charge conservation model is expressed as:

[0132]

[0133] Among them, i s is the solid phase current density of the positive or negative electrode, v s is the solid phase effective conductivity of the positive or negative electrode, is the gradient of the solid phase potential of the positive or negative electrode;

[0134] The liquid phase charge conservation model is expressed as:

[0135]

[0136] Among them, i l is the liquid phase current density, σ l is the liquid effective conductivity, is the gradient of liquid potential, f(c l ) is the concentration of lithium ions in the liquid phase c l The associated activity coefficient, is the lithium ion transport number, Represents lnc l The gradient of

[0137] The lithium ion solid phase diffusion model is expressed as:

[0138]

[0139] Among them, c s is the solid phase lithium ion concentration of the positive or negative electrode, t is the time, D s is the solid phase diffusion coefficient of the positive or negative electrode, r p is the radius of the active material particle of the positive or negative electrode, J Li is the lithium ion flux, E is the Young's modulus of the active material particles of the positive or negative electrode, v is the Poisson's ratio of the active material particles of the positive or negative electrode, K Li is the thermodynamic factor, E Eq is the equilibrium potential of the positive or negative electrode, x Li is the negative electrode lithiation fraction, represents partial derivative;

[0140] The lithium ion liquid phase diffusion model is expressed as:

[0141]

[0142] Among them, c lis the liquid phase lithium ion concentration, ε l is the liquid volume fraction, x is any position in the lithium-ion battery, a s is the specific surface area of ​​the active material particles of the positive or negative electrode, D l is the effective diffusion coefficient of the liquid phase, Brugg is the Bruggmann coefficient, D l,0 is the initial liquid phase diffusion coefficient, E a is the diffusion activation energy, T ref is the reference temperature.

[0143] In a possible implementation, the heat generation model in the electrochemical-thermal-mechanical coupling model is determined based on a heat dissipation power model, a liquid-phase ohmic heat generation power model, a solid-phase ohmic heat generation power model of the positive and negative electrodes, a polarization heat generation power model, and a reversible heat power model; wherein the heat dissipation power characterizes the power of heat dissipated by the lithium-ion battery; the liquid-phase ohmic heat generation power model characterizes the heat generation power when current flows through the electrolyte; the solid-phase ohmic heat generation power model characterizes the heat generation power when current flows through the active materials of the positive and negative electrodes; the polarization heat generation power model characterizes the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; and the reversible heat power model characterizes the power of heat generated due to entropy changes in the positive and negative electrodes during electrochemical reactions.

[0144] In a possible implementation, the heat generation model is expressed as:

[0145]

[0146] Where ρ is the density of lithium-ion battery materials, C p is the specific heat capacity of lithium-ion battery materials, λ is the thermal conductivity, a a is the specific surface area of ​​the positive electrode active material particles, a c is the specific surface area of ​​the negative electrode active material particles, j a is the deintercalation reaction current density of the positive electrode, j c is the deintercalation reaction current density of the negative electrode, η a is the overpotential of the positive electrode, η c is the negative pole crossing point, is the entropy thermal coefficient of the positive electrode, E eq,a is the open circuit potential of the positive electrode, is the entropy thermal coefficient of the negative electrode, E eq,c is the open circuit potential of the negative electrode, i s,a is the solid phase current density of the positive electrode, is the gradient of the solid phase potential of the positive electrode, i s,c is the solid phase current density of the negative electrode, is the gradient of the solid phase potential of the negative electrode, h is the convection heat transfer coefficient, T ambis the ambient temperature, ε is the radiation heat transfer coefficient, and σ is the Boltzmann constant;

[0147] in, is the heat dissipation power model, is the solid phase ohmic heat generation power model of the positive electrode, is the solid phase ohmic heat generation power of the negative electrode, is the liquid phase ohmic heat generation power model, a a j a η a is the polarization heat generation power model of the positive electrode, a c j c η c is the polarization heat generation power model of the negative electrode, is the reversible thermal power model of the positive electrode, Reversible thermal power model for the negative electrode.

[0148] According to the fast charging strategy selection device of the embodiment of the present disclosure, the charging process under different candidate fast charging strategies is simulated by an electrochemical-thermal-mechanical coupling simulation model based on a lithium-ion battery, and the solid-phase lithium ion concentration of the negative electrode at multiple moments in the charging process corresponding to each candidate fast charging strategy is obtained, and the solid-phase lithium ion concentration of the negative electrode is used to determine the Mises stress to which the negative electrode active material particles are subjected under each candidate fast charging strategy. Then, based on the Mises stress, a target fast charging strategy is selected from a plurality of candidate fast charging strategies, so that a candidate fast charging strategy that meets the fast charging demand and whose negative electrode active material particles are safer (i.e., not prone to rupture and damage) can be effectively screened out as the optimal target fast charging strategy for the lithium-ion battery. That is, the Mises stress to which the negative electrode active material particles are subjected during the fast charging process of the lithium-ion battery can be calculated, so as to evaluate the advantages and disadvantages of different candidate fast charging strategies based on the Mises stress, which is conducive to assisting R&D personnel to quickly screen out a better target fast charging strategy from a plurality of candidate fast charging strategies.

[0149] 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 method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0150] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0151] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0152] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0153] Figure 5 1 is 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. Figure 5 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application 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.

[0154] The electronic device 1900 may also 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 2003. TM , Mac OS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0155] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0156] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0157] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may 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 of computer-readable storage media (a non-exhaustive list) 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 disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0158] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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 copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The 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 the computer-readable storage medium in each computing / processing device.

[0159] The computer program instructions for performing the operation 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 "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0160] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0161] 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0162] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0163] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0164] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for selecting a fast charging strategy for a lithium-ion battery, characterized in that: include: Obtain multiple candidate fast charging strategies that meet the fast charging requirements of lithium-ion batteries; Using the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, simulating the charging process of the lithium-ion battery under each of the multiple candidate fast-charging strategies, and obtaining charging simulation data corresponding to each of the multiple candidate fast-charging strategies, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple moments during the charging process; Determine the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies according to the charging simulation data corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies, wherein the Mises stress corresponding to each candidate fast charging strategy includes the Mises stress exerted on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each candidate fast charging strategy; According to the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies, a target fast charging strategy for the lithium-ion battery is selected from the multiple candidate fast charging strategies.

2. The method according to claim 1, characterized in that The determining, according to the charging simulation data corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies, the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies comprises: For any candidate fast charging strategy, according to the difference between the solid phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to the candidate fast charging strategy and the specified initial lithium ion concentration, the negative electrode lithium ion concentration deviation at each moment is obtained; According to the negative electrode parameters of the lithium ion battery and the negative electrode lithium ion concentration deviation at each time, the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the separator at each time are determined; wherein the negative electrode parameters include the radius, Young's modulus, Poisson's ratio and partial molar volume of the negative electrode active material particles; The Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment is determined according to the radial stress and tangential stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment.

3. The method according to claim 1 or 2, characterized in that: The Mises stress corresponding to each candidate fast charging strategy includes the maximum Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator during the charging process corresponding to each candidate fast charging strategy; Wherein, selecting the target fast charging strategy of the lithium-ion battery from the multiple candidate fast charging strategies according to the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies includes: According to the maximum Mises stress corresponding to each of the multiple candidate fast charging strategies, the candidate fast charging strategy with the smallest maximum Mises stress is selected from the multiple candidate fast charging strategies as the target fast charging strategy for the lithium-ion battery.

4. The method according to claim 1, characterized in that: The electrochemical-thermal-mechanical coupling simulation model characterizes the electrochemical reaction process, heat conduction process, and positive and negative electrode stress change process of the lithium-ion battery during charging according to any fast charging strategy, wherein the establishment process of the electrochemical-thermal-mechanical coupling simulation model includes: Based on the battery design parameters of the lithium-ion battery, a battery geometric model of the lithium-ion battery is constructed, wherein the battery design parameters include: positive electrode thickness, negative electrode thickness, separator thickness, radius and specific surface area of ​​positive electrode active material particles, radius and specific surface area of ​​negative electrode active material particles, separator porosity, solid phase volume fraction of the positive electrode, solid phase volume fraction of the negative electrode, and liquid phase volume fraction; Based on the battery physical property parameters of the lithium-ion battery, an electrochemical-thermal coupling simulation model is constructed on the battery geometric model, wherein the battery physical property parameters include: reaction rate constant, solid phase diffusion coefficient, diffusion activation energy, Bruggmann coefficient, solid phase effective conductivity, liquid phase effective conductivity, charge transfer coefficient, thermal conductivity, convection heat transfer coefficient and radiation heat transfer coefficient; Based on the battery mechanical parameters of the lithium-ion battery, an electrochemical-thermal-mechanical coupling simulation model is constructed on the electrochemical-thermal coupling simulation model. The battery mechanical parameters include: Young's modulus, Poisson's ratio, partial molar volume, radial stress, and tangential stress of positive and negative electrode active material particles.

5. The method according to claim 1 or 4, characterized in that: The electrochemical model in the electrochemical-thermal-mechanical coupling simulation model includes: an intercalation and deintercalation reaction model constructed based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium ion solid-phase diffusion model and a lithium ion liquid-phase diffusion model constructed based on Fick's second diffusion law; Among them, the deintercalation reaction model characterizes the deintercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or embedded in the positive and negative electrodes; the solid-phase charge conservation model characterizes the solid-phase current density in the active material particles in the positive and negative electrodes of the lithium-ion battery; the liquid-phase charge conservation model characterizes the liquid-phase current density in the electrolyte of the lithium-ion battery; the lithium ion solid-phase diffusion model characterizes the solid-phase lithium ion concentration in the active material particles in the positive and negative electrodes; the lithium ion liquid-phase diffusion model characterizes the liquid-phase lithium ion concentration in the electrolyte.

6. The method according to claim 5, characterized in that The deintercalation reaction model is expressed as: j0=FK(c s，max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5 Where j is the deintercalation reaction current density of the positive or negative electrode, j0 is the deintercalation exchange current density of the positive or negative electrode, exp represents an exponential function with a natural constant as the base, α a is the charge transfer coefficient of the positive electrode, α c is the charge transfer coefficient of the negative electrode, F is the Faraday constant, R is the gas constant, η is the overpotential of the positive or negative electrode, T is the battery temperature, Φ s is the solid phase potential of the positive or negative electrode, Φ l is the liquid phase potential of the electrolyte, E Eq is the equilibrium potential of the positive or negative electrode, K is the reaction rate constant of the positive or negative electrode, c s is the solid phase lithium ion concentration of the positive or negative electrode, c s，max is the maximum solid phase lithium ion concentration of the positive or negative electrode, c l is the liquid phase lithium ion concentration, Ω is the partial molar volume of the active material particles of the positive or negative electrode, σ h,rp is the hydrostatic stress on the surface of the active material particles of the positive or negative electrode, σ r,rp is the radial stress on the surface of the active material particles of the positive or negative electrode, σ θ,rp The tangential stress on the surface of the active material particles of the positive or negative electrode; The solid phase charge conservation model is expressed as: Among them, i s is the solid phase current density of the positive or negative electrode, σ s is the solid phase effective conductivity of the positive or negative electrode, is the gradient of the solid phase potential of the positive or negative electrode; The liquid phase charge conservation model is expressed as: Among them, i l is the liquid phase current density, σ l is the liquid effective conductivity, is the gradient of liquid potential, f(c l ) is the concentration of lithium ions in the liquid phase c l The associated activity coefficient, is the lithium ion transport number, Represents lnc l The gradient of The lithium ion solid phase diffusion model is expressed as: Among them, c s is the solid phase lithium ion concentration of the positive or negative electrode, t is the time, D s is the solid phase diffusion coefficient of the positive or negative electrode, r p is the radius of the active material particle of the positive or negative electrode, J Li is the lithium ion flux, E is the Young's modulus of the active material particles of the positive or negative electrode, v is the Poisson's ratio of the active material particles of the positive or negative electrode, K Li is the thermodynamic factor, E Eq is the equilibrium potential of the positive or negative electrode, x Li is the negative electrode lithiation fraction, represents partial derivative; The lithium ion liquid phase diffusion model is expressed as: Among them, c l is the liquid phase lithium ion concentration, ε l is the liquid volume fraction, x is any position in the lithium-ion battery, a s is the specific surface area of ​​the active material particles of the positive or negative electrode, D l is the effective diffusion coefficient of the liquid phase, Brugg is the Bruggmann coefficient, D l，0 is the initial liquid phase diffusion coefficient, E a is the diffusion activation energy, T ref is the reference temperature.

7. The method according to claim 1 or 4, characterized in that: The heat generation model in the electrochemical-thermal-mechanical coupling model is determined based on the heat dissipation power model, the liquid phase ohmic heat generation power model, the solid phase ohmic heat generation power model of the positive and negative electrodes, the polarization heat generation power model and the reversible heat power model; Among them, the heat dissipation power represents the power of heat dissipated by the lithium-ion battery; the liquid-phase ohmic heat generation power model represents the heat generation power when current flows through the electrolyte; the solid-phase ohmic heat generation power model represents the heat generation power when current flows through the active materials of the positive and negative electrodes; the polarization heat generation power model represents the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; the reversible heat power model represents the power of heat generated due to the entropy change of the positive and negative electrodes in the electrochemical reaction.

8. The method according to claim 7, characterized in that The heat generation model is expressed as: Where ρ is the density of lithium-ion battery materials, C p is the specific heat capacity of lithium-ion battery materials, λ is the thermal conductivity, a a is the specific surface area of ​​the positive electrode active material particles, a c is the specific surface area of ​​the negative electrode active material particles, j a is the deintercalation reaction current density of the positive electrode, j c is the deintercalation reaction current density of the negative electrode, η a is the overpotential of the positive electrode, η c is the negative pole crossing point, is the entropy thermal coefficient of the positive electrode, E eq，a is the open circuit potential of the positive electrode, is the entropy thermal coefficient of the negative electrode, E eq，c is the open circuit potential of the negative electrode, i s，a is the solid phase current density of the positive electrode, is the gradient of the solid phase potential of the positive electrode, i s，c is the solid phase current density of the negative electrode, is the gradient of the solid phase potential of the negative electrode, h is the convection heat transfer coefficient, T amb is the ambient temperature, ε is the radiation heat transfer coefficient, and σ is the Boltzmann constant; in, is the heat dissipation power model, is the solid phase ohmic heat generation power model of the positive electrode, is the solid phase ohmic heat generation power of the negative electrode, is the liquid phase ohmic heat generation power model, a a j a η a is the polarization heat generation power model of the positive electrode, a c j c η c is the polarization heat generation power model of the negative electrode, is the reversible thermal power model of the positive electrode, Reversible thermal power model for the negative electrode.

9. A lithium-ion battery fast charging strategy selection device, characterized in that: include: A strategy acquisition module, used to acquire multiple candidate fast charging strategies that meet the fast charging requirements of the lithium-ion battery; a charging simulation module, for simulating the charging process of the lithium-ion battery under each of the multiple candidate fast-charging strategies using the electrochemical-thermal-mechanical coupling simulation model of the lithium-ion battery, and obtaining charging simulation data corresponding to each of the multiple candidate fast-charging strategies, wherein the charging simulation data includes the solid-phase lithium ion concentration of the negative electrode at multiple moments during the charging process; A stress determination module, for determining the Mises stress corresponding to each of the multiple candidate fast charging strategies according to the charging simulation data corresponding to each of the multiple candidate fast charging strategies, wherein the Mises stress corresponding to each candidate fast charging strategy includes the Mises stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the diaphragm during the charging process corresponding to each candidate fast charging strategy; A strategy selection module is used to select a target fast charging strategy for the lithium-ion battery from the multiple candidate fast charging strategies according to the Mises stress corresponding to each candidate fast charging strategy in the multiple candidate fast charging strategies.

10. The device according to claim 9, characterized in that The determining, according to the charging simulation data corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies, the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies comprises: For any candidate fast charging strategy, according to the difference between the solid phase lithium ion concentration of the negative electrode at each moment in the charging process corresponding to the candidate fast charging strategy and the specified initial lithium ion concentration, the negative electrode lithium ion concentration deviation at each moment is obtained; According to the negative electrode parameters of the lithium ion battery and the negative electrode lithium ion concentration deviation at each time, the radial stress and tangential stress on the surface of the negative electrode active material particles at the junction of the negative electrode and the separator at each time are determined; wherein the negative electrode parameters include the radius, Young's modulus, Poisson's ratio and partial molar volume of the negative electrode active material particles; The Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment is determined according to the radial stress and tangential stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator at each moment.

11. The device according to claim 9 or 10, characterized in that The Mises stress corresponding to each candidate fast charging strategy includes the maximum Mises stress on the surface of the negative electrode active material particles at the interface between the negative electrode and the separator during the charging process corresponding to each candidate fast charging strategy; Wherein, selecting the target fast charging strategy of the lithium-ion battery from the multiple candidate fast charging strategies according to the Mises stress corresponding to each candidate fast charging strategy among the multiple candidate fast charging strategies includes: According to the maximum Mises stress corresponding to each of the multiple candidate fast charging strategies, the candidate fast charging strategy with the smallest maximum Mises stress is selected from the multiple candidate fast charging strategies as the target fast charging strategy for the lithium-ion battery.

12. The device according to claim 10, characterized in that The electrochemical-thermal-mechanical coupling simulation model characterizes the electrochemical reaction process, heat conduction process, and positive and negative electrode stress change process of the lithium-ion battery during charging according to any fast charging strategy, wherein the establishment process of the electrochemical-thermal-mechanical coupling simulation model includes: Based on the battery design parameters of the lithium-ion battery, a battery geometric model of the lithium-ion battery is constructed, wherein the battery design parameters include: positive electrode thickness, negative electrode thickness, separator thickness, radius and specific surface area of ​​positive electrode active material particles, radius and specific surface area of ​​negative electrode active material particles, separator porosity, solid phase volume fraction of the positive electrode, solid phase volume fraction of the negative electrode, and liquid phase volume fraction; Based on the battery physical property parameters of the lithium-ion battery, an electrochemical-thermal coupling simulation model is constructed on the battery geometric model, wherein the battery physical property parameters include: reaction rate constant, solid phase diffusion coefficient, diffusion activation energy, Bruggmann coefficient, solid phase effective conductivity, liquid phase effective conductivity, charge transfer coefficient, thermal conductivity, convection heat transfer coefficient and radiation heat transfer coefficient; Based on the battery mechanical parameters of the lithium-ion battery, an electrochemical-thermal-mechanical coupling simulation model is constructed on the electrochemical-thermal coupling simulation model. The battery mechanical parameters include: Young's modulus, Poisson's ratio, partial molar volume, radial stress, and tangential stress of active material particles of the positive and negative electrodes.

13. The device according to claim 9 or 12, characterized in that The electrochemical model in the electrochemical-thermal-mechanical coupling simulation model includes: an intercalation and deintercalation reaction model constructed based on the Butler-Volmer kinetic equation, a solid-phase charge conservation model, a liquid-phase charge conservation model, a lithium ion solid-phase diffusion model and a lithium ion liquid-phase diffusion model constructed based on Fick's second diffusion law; Among them, the deintercalation reaction model characterizes the deintercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are extracted or embedded in the positive and negative electrodes; the solid-phase charge conservation model characterizes the solid-phase current density in the active material particles in the positive and negative electrodes of the lithium-ion battery; the liquid-phase charge conservation model characterizes the liquid-phase current density in the electrolyte of the lithium-ion battery; the lithium ion solid-phase diffusion model characterizes the solid-phase lithium ion concentration in the active material particles in the positive and negative electrodes; the lithium ion liquid-phase diffusion model characterizes the liquid-phase lithium ion concentration in the electrolyte.

14. The device according to claim 13, characterized in that The deintercalation reaction model is expressed as: j0=FK(c s，max -c s ) 0.5 (c s ) 0.5 (c l ) 0.5 Where j is the deintercalation reaction current density of the positive or negative electrode, j0 is the deintercalation exchange current density of the positive or negative electrode, exp represents an exponential function with a natural constant as the base, α a is the charge transfer coefficient of the positive electrode, α c is the charge transfer coefficient of the negative electrode, F is the Faraday constant, R is the gas constant, η is the overpotential of the positive or negative electrode, T is the battery temperature, Φ s is the solid phase potential of the positive or negative electrode, Φ l is the liquid phase potential of the electrolyte, E Eq is the equilibrium potential of the positive or negative electrode, K is the reaction rate constant of the positive or negative electrode, c s is the solid phase lithium ion concentration of the positive or negative electrode, c s，max is the maximum solid phase lithium ion concentration of the positive or negative electrode, c l is the liquid phase lithium ion concentration, Ω is the partial molar volume of the active material particles of the positive or negative electrode, σ h，rp is the hydrostatic stress on the surface of the active material particles of the positive or negative electrode, σ r，rp is the radial stress on the surface of the active material particles of the positive or negative electrode, σ θ，rp The tangential stress on the surface of the active material particles of the positive or negative electrode; The solid phase charge conservation model is expressed as: Among them, i s is the solid phase current density of the positive or negative electrode, σ s is the solid phase effective conductivity of the positive or negative electrode, is the gradient of the solid phase potential of the positive or negative electrode; The liquid phase charge conservation model is expressed as: Among them, i l is the liquid phase current density, σ l is the liquid effective conductivity, is the gradient of liquid potential, f(c l ) is the concentration of lithium ions in the liquid phase c l The associated activity coefficient, is the lithium ion transport number, Represents lnc l The gradient of The lithium ion solid phase diffusion model is expressed as: Among them, c s is the solid phase lithium ion concentration of the positive or negative electrode, t is the time, D s is the solid phase diffusion coefficient of the positive or negative electrode, r p is the radius of the active material particle of the positive or negative electrode, J Li is the lithium ion flux, E is the Young's modulus of the active material particles of the positive or negative electrode, v is the Poisson's ratio of the active material particles of the positive or negative electrode, K is the thermodynamic factor, E Eq is the equilibrium potential of the positive or negative electrode, x Li is the negative electrode lithiation fraction, represents partial derivative; The lithium ion liquid phase diffusion model is expressed as: Among them, c l is the liquid phase lithium ion concentration, ε l is the liquid volume fraction, x is any position in the lithium-ion battery, a s is the specific surface area of ​​the active material particles of the positive or negative electrode, D l is the effective diffusion coefficient of the liquid phase, Brugg is the Bruggmann coefficient, D l，0 is the initial liquid phase diffusion coefficient, E a is the diffusion activation energy, T ref is the reference temperature.

15. The device according to claim 9 or 12, characterized in that The heat generation model in the electrochemical-thermal-mechanical coupling model is determined based on the heat dissipation power model, the liquid phase ohmic heat generation power model, the solid phase ohmic heat generation power model of the positive and negative electrodes, the polarization heat generation power model and the reversible heat power model; Among them, the heat dissipation power represents the power of heat dissipated by the lithium-ion battery; the liquid-phase ohmic heat generation power model represents the heat generation power when current flows through the electrolyte; the solid-phase ohmic heat generation power model represents the heat generation power when current flows through the active materials of the positive and negative electrodes; the polarization heat generation power model represents the power of heat generated due to the polarization phenomenon of the positive and negative electrodes; the reversible heat power model represents the power of heat generated due to the entropy change of the positive and negative electrodes in the electrochemical reaction.

16. The device according to claim 15, characterized in that The heat generation model is expressed as: Where ρ is the density of lithium-ion battery materials, C p is the specific heat capacity of lithium-ion battery materials, λ is the thermal conductivity, a a is the specific surface area of ​​the positive electrode active material particles, a c is the specific surface area of ​​the negative electrode active material particles, j a is the deintercalation reaction current density of the positive electrode, j c is the deintercalation reaction current density of the negative electrode, η a is the overpotential of the positive electrode, η c is the negative pole crossing point, is the entropy thermal coefficient of the positive electrode, E eq，a is the open circuit potential of the positive electrode, is the entropy thermal coefficient of the negative electrode, E eq，c is the open circuit potential of the negative electrode, i s，a is the solid phase current density of the positive electrode, is the gradient of the solid phase potential of the positive electrode, i s，c is the solid phase current density of the negative electrode, is the gradient of the solid phase potential of the negative electrode, h is the convection heat transfer coefficient, T amb is the ambient temperature, ε is the radiation heat transfer coefficient, and σ is the Boltzmann constant; in, is the heat dissipation power model, is the solid phase ohmic heat generation power model of the positive electrode, is the solid phase ohmic heat generation power of the negative electrode, is the liquid phase ohmic heat generation power model, a a j a η a is the polarization heat generation power model of the positive electrode, a c j c η c is the polarization heat generation power model of the negative electrode, is the reversible thermal power model of the positive electrode, Reversible thermal power model for the negative electrode.

17. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 8 when executing the instructions stored in the memory.

18. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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