Battery state simulation method

Abstract

The present disclosure proposes a battery state simulation method, which decomposes the complex mathematical calculations when solving the solid phase concentration in the electrochemical simulation process into the calculation of the first sub-solid phase concentration and the second sub-solid phase concentration, wherein the first sub-solid phase concentration can be calculated based on the solid phase concentration of each discrete point in the previous time step and the matrix calculated based on the environmental parameters and the preset interval. The second sub-solid phase concentration can be calculated based on the discrete state parameters of the previous time step, the environmental parameters and the matrix of the previous time step, and finally the state parameters of the battery to be simulated are obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration and other state parameters. The calculation efficiency of the electrochemical model is improved and the calculation time is saved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a battery status simulation method. Background Art

[0002] Lithium-ion batteries are commonly used energy storage devices in energy storage systems. In actual applications, users focus on the battery status of lithium-ion batteries, such as charging speed, long life and high safety. Electrochemical models describe the reaction and transmission process of lithium-ion batteries, and can be used to estimate and optimize the status of lithium-ion batteries.

[0003] However, the electrochemical model in the related art is limited by the complex mathematical structure, and the corresponding numerical solution process is too complicated, resulting in the use of the model for related numerical solutions requiring a lot of computing resources and time, which limits the large-scale application of electrochemical models in battery design. Summary of the invention

[0004] The present disclosure provides a battery status simulation method.

[0005] The first aspect of the present disclosure provides a battery status simulation method, the method comprising:

[0006] Acquire environmental parameters and initial state parameters of the battery to be simulated, wherein the initial state parameters include initial solid phase concentration;

[0007] In the direction from the positive electrode to the negative electrode of the battery to be simulated, discretizing the initial state parameter into a plurality of discrete state parameters at preset intervals;

[0008] An initial matrix is ​​calculated based on the environmental parameters and the preset interval;

[0009] According to the solid phase concentration of the mth discrete point at the kth time step and the matrix of the kth time step, the first sub-solid phase concentration of the mth discrete point at the k+1th time step is calculated, when k=0, the matrix of the kth time step is the initial matrix, and the solid phase concentration of the mth discrete point at the kth time step is the initial solid phase concentration;

[0010] The second sub-solid phase concentration of the m-th discrete point in the k+1-th time step is calculated by using the discrete state parameter of the m-th discrete point in the k-th time step, the environmental parameter and the matrix of the k-th time step;

[0011] If the k+1th time step is the maximum time step, the state parameters of the battery to be simulated are obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration and the discrete state parameters of each discrete point.

[0012] In the embodiment of the present disclosure, the initial matrix is ​​calculated based on the environmental parameters and the preset interval, including:

[0013] Calculate the solid phase effective parameters of the battery to be simulated based on the environmental parameters;

[0014] Based on the solid phase effective parameters and the preset interval, calculating the corresponding elements at each discrete point;

[0015] An initial matrix is ​​constructed based on the elements at each discrete point.

[0016] In the embodiment of the present disclosure, the use of the discrete state parameter of the mth discrete point at the kth time step, the environmental parameter and the matrix of the kth time step includes:

[0017] Iterating the discrete state parameter of the mth discrete point at the kth time step to obtain the discrete state parameter of the mth discrete point at the k+1th time step;

[0018] The second sub-solid phase concentration is calculated using the discrete state parameters of the mth discrete point at the k+1th time step, the solid phase effective parameters at the k+1th time step and the matrix at the k+1th time step, and the matrix at the k+1th time step is obtained based on the solid phase effective parameters at the kth time step and the matrix at the kth time step.

[0019] In the embodiment of the present disclosure, before calculating the second sub-solid phase concentration using the discrete state parameter of the mth discrete point at the k+1th time step, the solid phase effective parameter at the k+1th time step and the matrix at the k+1th time step, the method further includes:

[0020] If the ratio of the solid phase effective parameter at the k-1th time step to the solid phase effective parameter at the kth time step is greater than a preset allowable error, the solid phase effective parameter at the kth time step is used as the solid phase effective parameter at the k+1th time step;

[0021] If the ratio of the solid phase effective parameters of the k-1th time step to the solid phase effective parameters of the kth time step is less than or equal to the preset allowable error, the solid phase effective parameters of the k-1th time step are used as the solid phase effective parameters of the k+1th time step.

[0022] In the embodiment of the present disclosure, the discrete state parameter of the mth discrete point at the kth time step is iterated to obtain the discrete state parameter of the mth discrete point at the k+1th time step, including:

[0023] According to the discrete state parameter corresponding to the mth discrete point at the pth iteration in the kth time step, calculate the solid phase concentration corresponding to the mth discrete point at the pth iteration;

[0024] Determine the increment based on the discrete state parameter corresponding to the m-th discrete point at the p-th iteration and the solid phase concentration corresponding to the m-th discrete point at the p-th iteration;

[0025] Based on the discrete state parameter corresponding to the mth discrete point at the pth iteration and the pth iteration increment, and the relationship between the preset threshold and the maximum number of iterations, the discrete state parameter of the mth discrete point at the k+1th time step is determined.

[0026] In the disclosed embodiment, the step of determining the increment based on the discrete state parameter corresponding to the mth discrete point at the pth iteration and the solid phase concentration corresponding to the mth discrete point at the pth iteration includes:

[0027] Based on the discrete state parameter corresponding to the m-th discrete point at the p-th iteration and the solid phase concentration corresponding to the m-th discrete point at the p-th iteration, calculate the residual vector and Jacobian matrix of the m-th discrete point at the p-th iteration;

[0028] The ratio of the Jacobian matrix and the residual vector is used as the increment of the p-th iteration.

[0029] In the embodiment of the present disclosure, the discrete state parameter of the mth discrete point at the k+1th time step is determined based on the relationship between the discrete state parameter corresponding to the mth discrete point at the pth iteration and the pth iteration increment, a preset threshold and a maximum number of iterations, including:

[0030] The discrete state parameter corresponding to the m-th discrete point at the p-th iteration is subtracted from the increment to obtain the discrete state parameter of the m-th discrete point at the p+1-th iteration;

[0031] If the ratio of the p-th iteration increment to the discrete state parameter of the m-th discrete point in the k-th time step at the p-th iteration is less than a preset threshold, and the p-th iteration has not reached the maximum number of iterations, the corresponding discrete state parameter of the m-th discrete point at the p-th iteration is used as the discrete state parameter of the m-th discrete point in the k+1-th time step.

[0032] In the embodiment of the present disclosure, the state parameter includes at least one of a solid phase concentration, a terminal voltage, and a temperature, and the discrete state parameter also includes an electric potential. The state parameter of the battery to be simulated is obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration, and the discrete state parameters of each discrete point, and includes at least one of the following:

[0033] The first sub-solid phase concentration is subtracted from the second sub-solid phase concentration to obtain the solid phase concentration of the mth discrete point in the k+1th time step;

[0034] The terminal voltage of the battery to be simulated is obtained by subtracting the potential of the discrete point corresponding to the negative electrode of the battery to be simulated from the potential of the discrete point corresponding to the positive electrode of the battery to be simulated;

[0035] Based on the discrete state parameters of the k+1th time step, the heat of the battery to be simulated is calculated; and according to the heat and the battery material of the battery to be simulated, the temperature of the battery to be simulated is determined.

[0036] The technical solution provided in the embodiments of the present disclosure has at least the following technical effects or advantages:

[0037] Acquire environmental parameters and initial state parameters of the battery to be simulated, wherein the initial state parameters include an initial solid phase concentration, and discretize the initial state parameters into a plurality of discrete state parameters at preset intervals in the direction from the positive electrode to the negative electrode of the battery to be simulated; calculate an initial matrix based on the environmental parameters and the preset intervals, and calculate the first sub-solid phase concentration of the m-th discrete point at the i+1-th time step according to the solid phase concentration of the m-th discrete point at the i-th time step and the matrix of the i-th time step, when i=1, the matrix of the i-th time step is the initial matrix, and the solid phase concentration of the m-th discrete point at the i-th time step is the initial solid phase concentration; calculate the second sub-solid phase concentration of the m-th discrete point at the i+1-th time step using the discrete state parameters of the m-th discrete point at the i-th time step, the environmental parameters, and the matrix of the i-th time step; if the i+1-th time step is the maximum time step, obtain the state parameters of the battery to be simulated based on the first sub-solid phase concentration, the second sub-solid phase concentration, and the discrete state parameters of each discrete point.

[0038] The complex mathematical calculations for solving the solid phase concentration in the electrochemical simulation process are decomposed into the calculation of the first sub-solid phase concentration and the second sub-solid phase concentration, wherein the first sub-solid phase concentration can be calculated based on the solid phase concentration of each discrete point in the previous time step and the matrix calculated based on the environmental parameters and the preset interval. The second sub-solid phase concentration can be calculated based on the discrete state parameters of the previous time step, the environmental parameters and the matrix of the previous time step, and finally the state parameters of the battery to be simulated are obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration and other state parameters. The calculation efficiency of the electrochemical model is improved and the calculation time is saved.

[0039] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present disclosure. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1 A discrete schematic diagram of a battery state simulation method provided by an embodiment of the present disclosure is shown;

[0042] Figure 2 A schematic diagram showing a flow chart of a battery state simulation method provided by an embodiment of the present disclosure;

[0043] Figure 3 A schematic diagram showing a flow chart of another battery state simulation method provided by an embodiment of the present disclosure;

[0044] Figure 4 A schematic diagram showing the effect comparison of a battery status simulation method provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0046] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.

[0047] Renewable energy, such as solar energy, wind energy, ocean energy and geothermal energy, is a renewable resource that is safe, clean, permanent and environmentally friendly. However, the intermittent and unstable nature of renewable energy limits its widespread application. In related technologies, the use of energy storage systems is a key solution to cache energy input and provide continuous supply. Currently, battery-based fixed energy storage devices are the most popular renewable energy storage systems.

[0048] As an efficient and rechargeable energy storage device, lithium-ion batteries are widely used in electric vehicles, portable electronic devices and energy storage systems due to their high energy density, long cycle life and low self-discharge rate. However, with the continuous expansion of application areas, the requirements for the performance of lithium-ion batteries are becoming higher and higher. Especially in electric vehicles and large-scale energy storage applications, fast charging, long life and high safety have become technical problems that need to be solved urgently. The electrochemical model describes the reaction and transmission process of lithium-ion batteries, and can be used to estimate the state and optimize the lithium-ion battery.

[0049] Lithium-ion batteries (LIBs) are usually composed of a positive electrode (cathode), a negative electrode (anode), a separator, and an electrolyte. The positive and negative electrodes are usually composed of a current collector, an active material, a conductive additive, and a polymer binder. The separator is a porous polymer membrane that is an electronic insulator sandwiched between the positive and negative electrodes. The electrolyte is an electronic insulator, but it is an ionic conductor that provides an ion channel between the positive and negative electrodes. Depending on the composition, electrolytes can be divided into liquid, solid polymer, and solid inorganic electrolytes. During the operation of lithium-ion batteries, electrochemical charge transfer reactions occur on the surface of the electrode active materials, accompanied by the movement of electrons and ions. The following formula shows the charge transfer reactions of the positive and negative electrodes of a LiMO2 / C6 battery.

[0050]

[0051] During the charging process, the electrons released from the positive electrode flow to the negative electrode through an external connection circuit. Electrochemical oxidation and reduction reactions occur simultaneously at the positive and negative electrodes, accompanied by the insertion and extraction of Li+. Subsequently, in order to maintain electrical neutrality, lithium ions move from the positive electrode to the negative electrode through the electrolyte by diffusion and migration. As a result, a potential difference is formed between the two electrodes. Therefore, lithium-ion batteries were originally called "rocking chair batteries."

[0052] Electrochemical models describe the thermodynamics, (electro)chemical reaction kinetics, and transport processes inside the battery. They provide sophisticated and advanced tools for reaction mechanism analysis, battery design optimization, battery state estimation, etc. One of the most famous electrochemical models is the pseudo-two-dimensional P2D porous electrode model developed by Newman and his colleagues. These models consist of a set of coupled ordinary differential equations (ODEs), partial differential equations (PDEs), and algebraic equations to describe various processes inside the battery.

[0053] Nowadays, porous electrodes have become the main components of commercial lithium-ion batteries due to their advantages over non-porous electrodes. Therefore, porous electrode models are widely used in battery modeling. Compared with empirical models and equivalent circuit models (ECMs), porous electrode models combine the physical description of battery performance with high accuracy, thanks to the detailed representation of electrode microstructure and electrolyte properties. However, due to its complex mathematical structure, the numerical solution of the P2D model usually requires a lot of computing resources and time, which limits its application in large-scale battery system design and optimization. Most of the simplified models derived from the P2D model have simplified the particle concentration of the lithium ion solid phase to a certain extent, such as the SPM single particle model (multiple electrode particles along the thickness direction are equivalent to one particle) and the PPM (the solid phase particle concentration is equivalent to a high-order polynomial.) Such simplifications can save computational costs and obtain higher computational accuracy when dealing with some small rate charging and discharging, but when facing large rate charging and discharging, the simplification of the solid phase particle concentration field will lead to large errors.

[0054] The most widely used electrochemical model is the quasi-two-dimensional (P2D) model of lithium-ion batteries established by M. Doyle and T. Fuller in 1993 based on the concentrated solution theory and porous electrode theory. This model describes the chemical variation law of the two phases (solid phase and liquid phase) and three regions (positive electrode, negative electrode and separator) inside the battery. The electrode region is a porous medium structure composed of active particles, fillers and electrolyte, and the average tortuosity and porosity are used to describe the influence of pores. The pseudo-two-dimensional model assumes that spherical particles of the same radius are distributed along the thickness direction of the electrode, and the surface of the particles is surrounded by liquid electrolyte. The P2D model uses Fick's law to describe the diffusion behavior of lithium ions in the solid phase.

[0055]

[0056] This equation describes the diffusion process of lithium ions in a spherical coordinate system. Indicates the lithium ion concentration in the solid phase of the positive or negative electrode, It means that the solid phase concentration is a function of the radial position and time; , represents the positive electrode or the negative electrode; Ri represents the radius of the solid particle, j represents the lithium ion pore wall flux, and j(x, t) represents the lithium ion pore wall flux as a function of the x-direction position and time. represents the effective solid phase diffusion coefficient. Need to be corrected by Arrhenius formula.

[0057]

[0058] Where Ed represents the activation energy (constant), represents the reference temperature (constant), R represents the ideal gas state constant, and T represents the actual temperature of the lithium-ion battery, which can be obtained by measurement. This equation describes that the lithium ion flux of the solid phase particle is 0 at the center, while the flux at the surface is equal to the interface reaction amount.

[0059] In the liquid phase diffusion process of lithium ions, it is also necessary to consider the diffusion in the thickness x direction (the direction from the positive electrode to the negative electrode). Compared with the solid phase process, the migration of lithium ions under the action of the electric field force is also required.

[0060]

[0061] in represents the lithium ion concentration in the liquid phase, represents the liquid phase lithium ion concentration as a function of x-direction position and time. Represents the liquid volume fraction of the positive and negative electrodes, represents the cation transference number, Represents the surface area and volume ratio of the positive and negative electrode particles, . The effective liquid phase diffusion coefficient of the positive or negative electrode is coupled with the lithium ion concentration in the liquid phase. Represent the two boundary points of the electrode in the direction.

[0062]

[0063] in, , brug represents the Bruegmann coefficient (tortuosity correction), represents the intrinsic diffusion coefficient of the material, Expresses the porosity raised to the power of brug.

[0064] At the boundary between the positive and negative electrodes, lithium ions cannot pass through, so the flux is 0. It should be noted that since there are no solid phase particles at the diaphragm, no charge transfer reaction occurs at the solid-liquid interface. The equation needs to be modified, and j on the right side of the equation is 0.

[0065] Electrons pass through the connection between solid phase particles and fillers (conductive agent and binder) to form an electron path. According to Ohm's law of the solid phase , the current density of the solid phase and the liquid phase is added to the externally applied current density . So finally we can conclude:

[0066]

[0067] in, represents the effective coefficient of the electrical conductivity in the solid phase region, The Faraday constant representing the lithium ion pore wall flux with respect to x-direction position and time is, The conductivity representing the effective coefficient of the negative electrode solid phase region, represents the value of the positive and negative electrode solid phase potential at the x position at time t, represents the value of the negative electrode solid phase potential at position x at time t, Indicates the battery operating current. The conductivity that represents the effective coefficient of the positive electrode solid phase region.

[0068] Since electrons cannot pass through the diaphragm to reach the other pole, the boundary condition at the diaphragm is that the current is zero. The boundary condition at the connection between the pole piece and the current collector is an external current.

[0069]

[0070] in, represents the volume fraction of the conductive agent and the binder, represents the volume fraction of liquid phase in the solid phase region.

[0071] Similarly, according to the law of charge conservation and Ohm's law in liquid phase, the potential equation of the liquid phase can be obtained.

[0072]

[0073] in .

[0074] Similar to the liquid phase effective diffusion coefficient, the liquid phase effective conductivity is also defined similarly.

[0075]

[0076] Note that the equation also needs to be corrected in the separator region, and the pore wall flux j of lithium ions is set to 0.

[0077] The last equation is to calculate the reaction rate at the solid-liquid interface on the surface of the active particles through the Butler-Volmer equation.

[0078]

[0079] in represents the exchange current density; represents the transfer coefficient a; R represents the particle radius; T represents the actual temperature of the lithium-ion battery; represents overpotential, which reflects the difference of electrode potential from equilibrium potential during electrode reaction; represents the transfer coefficient c.

[0080] Represents the effective reaction rate constant, which, like the diffusion coefficient of solid particles, needs to be corrected by the Arrhenius formula. Represents the maximum concentration of lithium ions that can be embedded in positive and negative electrode particles. and is the transfer coefficient. R represents the ideal gas state constant, and F represents the Faraday constant. U represents the open circuit voltage of the positive and negative electrode particles, which is a thermodynamic variable determined by the electrode material and is related to the temperature and lithium insertion state. SOC represents the lithium insertion state of the electrode active particles and can be defined as

[0081]

[0082] The above is the P2D model, an electrochemical model commonly used in batteries.

[0083] The realization of this model mainly uses the finite difference method. Figure 1 As shown in Figure 1, we first need to discretize the continuous region of the electrode. To represent the number of grids in the x direction of the positive electrode, the number of grids in the x direction of the separator, the number of grids in the x direction of the negative electrode, the number of grids in the R direction of the positive electrode particles, and the number of grids in the R direction of the negative electrode particles. The half-step central difference method is used, that is,

[0084]

[0085] The backward Euler implicit format (same as above) is used for time discretization, that is,

[0086]

[0087] Through spatial discretization and time discretization, the above equation can be obtained in discrete form. For example, the solid phase diffusion equation and the corresponding boundary conditions can be rewritten as

[0088]

[0089] Among them, n represents the negative pole; m represents the grid index in the x direction; k represents the kth time step after time discretization; Represents the solid lithium ion concentration (solid phase concentration) of the mth grid in the x direction of the negative electrode at the kth time step; represents the solid lithium ion concentration (solid phase concentration) of the mth grid in the x direction of the negative electrode at the k-1th time step; Ds represents the solid phase diffusion coefficient; Represents the coordinates of the nth position in the r direction, represents the middle between the nth and n+1th positions, Represents discrete grid intervals (preset intervals).

[0090] The lithium ion diffusion equation in the liquid phase can be written as:

[0091]

[0092] in, represents the liquid lithium ion concentration (liquid phase concentration) of the mth grid at the kth time step, represents the solid phase effective coefficient of the mth grid on the right side of the grid; x represents the grid spacing; Table m-th grid on the left side of the grid solid effective coefficient; Represents the lithium ion pore wall flux of the mth grid at the kth time step.

[0093] Represents the liquid lithium ion concentration on the right side of the electrode area close to the separator; Represents the liquid lithium ion concentration at the kth time at the i+1th grid in the x-axis direction of the positive or negative electrode. The lithium ion concentration at the grid index 1 representing the separator region; The grid spacing representing the diaphragm area; The grid spacing represents the positive or negative regions.

[0094] For the potential equation without the time derivative term, the same difference format can be used for the potential equations for the solid and liquid phases.

[0095]

[0096] in, represents the effective coefficient of the liquid conductivity to the right of the index position; Represents the effective coefficient of the liquid conductivity to the left of the index position; represents the liquid potential of the mth grid at the kth moment; is a constant; Represents the overall temperature of the battery cell at the Kth moment; represents the liquid potential of the first grid at the positive electrode; Represents the second last liquid phase potential of the positive electrode; Represents the liquid phase conductivity of the positive electrode; Represents the liquid potential of the first grid in the membrane region at the kth step.

[0097] The solid phase potential discrete equation is:

[0098]

[0099] in, Solid phase potential of the m+1th grid at the kth time step; Represents the effective conductivity of the positive and negative electrodes; represents the potential of the first grid at the kth step of the positive or negative electrode; Represents the current density applied to the electrode; Represents the solid phase potential of the last grid k-th step of the positive or negative electrode.

[0100] For the calculation of the Butler-Volmer algebraic equation and overpotential at the solid-liquid interface, the discretization method is as follows

[0101]

[0102] in, Represents the lithium ion pore wall flux of the mth grid of the negative electrode at the kth time step; represents the exchange current density; represents the overpotential of the ith grid at the positive or negative electrode; Represents the liquid phase lithium ion concentration of the positive or negative electrode; Represents the maximum embedding concentration of positive electrode particles or negative electrode particles; Represents the surface concentration of solid particles at the mth grid of the positive or negative electrode; Represents the solid phase potential at the mth grid of the positive or negative electrode; Represents the lithium ion concentration of the liquid phase at the mth grid of the positive or negative electrode.

[0103] The above process is a calculation method for calculating the solid phase concentration and the liquid phase concentration in the electrochemical model. The mathematical calculation of the above calculation method is too complicated, which leads to the problem that the electrochemical model cannot be widely used.

[0104] In view of this, the present disclosure provides a battery status simulation method, such as Figure 2 The figure shows a battery state simulation method provided by an embodiment of the present disclosure. The battery state simulation method can be applied to the simulation of the electrochemical reaction process of an existing battery, and then predict the subsequent operating conditions of the existing battery, and can also simulate the charging, discharging and usage of the battery to be manufactured. The battery state simulation method of the present disclosure focuses on calculating the solid phase concentration in the electrochemical reaction of the battery to be simulated, and then obtaining various state parameters of the battery to be simulated. Specifically, the battery state simulation method of the present disclosure may include the following steps:

[0105] In step S21, the environmental parameters and initial state parameters of the battery to be simulated are obtained.

[0106] Among them, the initial state parameters include the initial solid phase concentration.

[0107] Exemplarily, the environmental parameter may be data of the environment in which the battery to be simulated needs to be used, specifically, the temperature of the environment in which the battery to be simulated is used. If the battery to be simulated is an existing battery, the initial solid phase concentration may be measured; if the battery to be simulated is a battery to be manufactured, the initial solid phase concentration may be obtained according to the parameters set for the battery to be simulated.

[0108] In step S22, the initial state parameter is discretized into a plurality of discrete state parameters at preset intervals in the direction from the positive electrode to the negative electrode of the battery to be simulated.

[0109] For example, in order to determine the solid phase concentration of the battery to be simulated, the battery to be simulated is discretized in the direction from the positive electrode to the negative electrode, that is, the battery is spatially divided into multiple grids at preset intervals in this direction, and each divided point is a corresponding discrete point. Determining the solid phase concentration of the corresponding discrete point can also obtain the solid phase concentration of the battery to be simulated.

[0110] It should be noted that when calculating the solid phase concentration of the battery to be simulated, it is necessary to know the various discrete state parameters before the simulation. The discrete state parameters are discretized from the initial state parameters. Specifically, in the embodiment of the present disclosure, the initial state parameters may also include the liquid phase lithium ion concentration at each position of the battery to be simulated, the solid phase potential at each position, the lithium ion pore wall flux and overpotential, etc., where each position may include the positive and negative electrodes and the diaphragm.

[0111] Specifically, the state parameter U can be:

[0112]

[0113] in, represents the lithium ion concentration in the positive electrode liquid phase, Lithium ion concentration in the diaphragm liquid phase, Lithium ion concentration in the negative electrode liquid phase, is the positive electrode liquid electrolyte potential, is the electrolyte potential of the diaphragm liquid phase, is the negative electrode liquid electrolyte potential, is the negative electrode solid phase potential, is the solid phase potential of the positive electrode, are the lithium ion pore wall fluxes of the positive and negative electrodes, They are the overpotentials of the positive and negative electrodes respectively, n is the positive electrode and p is the negative electrode.

[0114] In step S23, an initial matrix is ​​calculated based on environmental parameters and preset intervals.

[0115] For example, in order to simplify the calculation method of solid phase concentration in the existing electrochemical model, the solid phase concentration solution process is divided into two parts of sub-solid phase concentration solution. Specifically, the equation of the discretized solid phase concentration is:

[0116]

[0117] Wherein, L is the electrode thickness, and Cm is the solid phase concentration corresponding to the mth discrete point.

[0118]

[0119] Among them, l represents the left side of the mth discrete point, L represents the thickness of the pole piece, and m represents the mth discrete point in the x direction. represents the solid phase concentration corresponding to the discrete point m at the kth time step, represents the solid phase concentration corresponding to the discrete point m at the k+1th time step. .

[0120] That is to say,

[0121]

[0122] A is the initial matrix.

[0123]

[0124] in, , ,

[0125] Therefore, the solid phase concentration at the k+1th time step is:

[0126]

[0127] in, represents the first sub-solid phase concentration, Represents the concentration of the second sub-solid phase.

[0128] That is to say, the initial matrix can be based on Solid phase effective coefficient, The preset interval and r are calculated on the right side of the corresponding discrete points, and The solid phase effective coefficient can be calculated based on the temperature of the battery to be simulated, that is, the initial matrix can be calculated based on environmental parameters and preset intervals.

[0129] In some embodiments, the above step S23 can be implemented by: calculating the solid phase effective parameters of the battery to be simulated based on the environmental parameters; calculating the corresponding elements at each discrete point based on the solid phase effective parameters and the preset interval; constructing an initial matrix based on the elements at each discrete point

[0130] Exemplarily, the solid phase effective parameter can be calculated by the following formula:

[0131]

[0132] Where Ed represents the activation energy (constant), represents the reference temperature (constant), R represents the ideal gas state constant, and T represents the actual temperature of the lithium-ion battery (environmental parameter), which can be obtained through measurement.

[0133] In step S24, the first sub-solid phase concentration of the mth discrete point in the kth time step is calculated based on the solid phase concentration of the mth discrete point in the kth time step and the matrix of the kth time step. When k=0, the matrix of the kth time step is the initial matrix, and the solid phase concentration of the mth discrete point in the kth time step is the initial solid phase concentration.

[0134] Exemplarily, the first sub-solid phase concentration at the k+1th time step can be calculated based on the solid phase concentration at the kth time step. For the k+1th time step, the solid phase concentration at the kth time step is known. Even when k=0, the corresponding kth time step is the initial moment of the battery to be simulated. The initial solid phase concentration at this time is the initial solid phase concentration, and the matrix is ​​the initial matrix.

[0135] Separate order , for

[0136] The first sub-solid phase concentration at the k+1 time step:

[0137]

[0138] At this time, matrix A is the matrix corresponding to the kth time step.

[0139] In step S25, the second sub-solid phase concentration of the mth discrete point at the kth time step is calculated using the discrete state parameters of the mth discrete point at the kth time step, the environmental parameters and the matrix of the kth time step;

[0140] For example, when calculating the second sub-solid phase concentration at the k+1th time step, the coupling of other variables with the solid phase concentration needs to be taken into account, and therefore the discrete state parameters of the simulated battery at the kth time step (the state parameters are obtained after being discretized) need to be taken into account.

[0141] The second sub-solid phase concentration at the k+1 time step:

[0142]

[0143] in, Represents the state parameter of the mth discrete point at the k+1th time step.

[0144] That is to say, before calculating the concentration of the second sub-solid phase at the k+1th time step, it is necessary to determine the state parameters at the corresponding discrete points at the k+1th time step.

[0145] In some embodiments, the above step S25 can be implemented by the following steps: Figure 3 As shown:

[0146] Step S251, iterate the discrete state parameters of the mth discrete point at the kth time step to obtain the discrete state parameters of the mth discrete point at the k+1th time step.

[0147] Exemplarily, the discrete state parameters of the mth discrete point at the kth time step are carried forward to obtain the discrete state parameters of the mth discrete point at the k+1th time step. Specifically, in the embodiment of the present disclosure, Newton iteration can be used to obtain the discrete state parameters of the mth discrete point at the k+1th time step.

[0148] Specifically, step S251 can be implemented in the following manner: calculate the solid phase concentration corresponding to the mth discrete point at the pth iteration based on the discrete state parameter corresponding to the mth discrete point at the kth time step at the pth iteration; determine the increment based on the discrete state parameter corresponding to the mth discrete point at the pth iteration and the solid phase concentration corresponding to the mth discrete point at the pth iteration; determine the discrete state parameter of the mth discrete point at the k+1th time step based on the relationship between the discrete state parameter corresponding to the mth discrete point at the pth iteration and the pth iteration increment, and the preset threshold and the maximum number of iterations.

[0149] Exemplarily, in the Newton iteration process, iteration is performed for each discrete point respectively, and then the state parameter corresponding to each discrete point is obtained.

[0150] Among them, for the p-th iteration of the m-th discrete point, the solid phase concentration corresponding to the p-th iteration is calculated, and the calculation method of the solid phase concentration is .

[0151] After calculating the solid phase concentration in the p-th iteration of the m-th discrete point, the corresponding solid phase concentration increment is determined based on the solid phase concentration and the state parameter in the p-th iteration of the m-th discrete point, where the increment represents the change from the p-1-th iteration to the p-th iteration.

[0152] The increment is determined in the following way: based on the discrete state parameters corresponding to the mth discrete point at the pth iteration and the solid phase concentration corresponding to the mth discrete point at the pth iteration, the residual vector and Jacobian matrix of the mth discrete point at the pth iteration are calculated; the ratio of the Jacobian matrix to the residual vector is used as the increment of the pth iteration.

[0153] For example, the residual vector F and the Jacobian matrix . Increment delta = J / F.

[0154] The discrete state parameter of the mth discrete point at the k+1th time step is determined by the following method: the discrete state parameter corresponding to the mth discrete point at the pth iteration is subtracted from the increment to obtain the discrete state parameter of the mth discrete point at the p+1th iteration;

[0155] If the ratio of the increment of the p-th iteration to the discrete state parameter of the m-th discrete point in the k-th time step at the p-th iteration is less than the preset threshold, and the p-th iteration has not reached the maximum number of iterations, the corresponding discrete state parameter of the m-th discrete point at the p-th iteration is used as the discrete state parameter of the m-th discrete point in the k+1-th time step.

[0156] Exemplarily, the discrete state parameter corresponding to the mth discrete point at the pth iteration is:

[0157]

[0158] Update the number of iterations p, and the residual res = ||delta|| / ||U||.

[0159] If the residual res is less than the preset threshold and the pth iteration reaches the maximum number of iterations, the calculation is terminated, and the corresponding discrete state parameter of the mth discrete point at the pth iteration is used as the discrete state parameter of the mth discrete point at the k+1th time step.

[0160] Step S252, using the discrete state parameters of the mth discrete point at the k+1th time step, the solid phase effective parameters at the k+1th time step and the matrix at the k+1th time step, calculate the second sub-solid phase concentration, and the matrix at the k+1th time step is obtained based on the solid phase effective parameters at the kth time step and the matrix at the kth time step.

[0161] Exemplarily, the second sub-solid phase concentration is:

[0162]

[0163] From the above process, we can see that Through Newton iteration calculation, is the solid phase effective parameter at the k+1th time step, Known, the corresponding second sub-solid phase concentration can be calculated.

[0164] It should be noted that the solid-phase effective parameters of the k+1th time step here are determined based on the solid-phase effective parameters of the kth time step. Specifically, if the ratio of the solid-phase effective parameters of the k-1th time step to the solid-phase effective parameters of the kth time step is greater than the preset allowable error, the solid-phase effective parameters of the kth time step will be used as the solid-phase effective parameters of the k+1th time step; if the ratio of the solid-phase effective parameters of the k-1th time step to the solid-phase effective parameters of the kth time step is less than or equal to the preset allowable error, the solid-phase effective parameters of the k-1th time step will be used as the solid-phase effective parameters of the k+1th time step.

[0165] That is, after the solid phase concentration and the corresponding state parameters corresponding to the kth time step have been calculated at the kth time step, if the ratio of the solid phase effective parameters at the k-1th time step to the solid phase effective parameters at the kth time step is greater than the preset allowable error, the solid phase effective parameters are updated and the solid phase effective parameters at the kth time step are used as the solid phase effective parameters at the k+1th time step.

[0166] In step S26, if the k+1th time step is the maximum time step, the state parameters of the battery to be simulated are obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration and the discrete state parameters of each discrete point.

[0167] Exemplarily, if the k+1th time step is the maximum time step, it means that the final stage of the simulation has been reached, and the state parameters of the battery to be simulated can be obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration and the discrete state parameters of each discrete point.

[0168] If the k+1th time step is not the maximum time step, the first sub-solid phase concentration, the second sub-solid phase concentration and the discrete state parameters of each discrete point in the subsequent time steps will continue to be calculated until the maximum time step is reached.

[0169] The state parameter includes at least one of solid phase concentration, terminal voltage and temperature, and the discrete state parameter also includes potential. Specifically, step S26 includes at least one of the following:

[0170] The first sub-solid phase concentration is subtracted from the second sub-solid phase concentration to obtain the solid phase concentration of the mth discrete point at the k+1th time step;

[0171] The potential of the discrete point corresponding to the positive electrode of the battery to be simulated is subtracted from the potential of the discrete point corresponding to the negative electrode of the battery to be simulated to obtain the terminal voltage of the battery to be simulated;

[0172] The heat of the battery to be simulated is calculated based on the discrete state parameters of the k+1th time step; and the temperature of the battery to be simulated is determined according to the heat and the battery material of the battery to be simulated.

[0173] As shown in Table 1, some parameters are used to compare the effects of the battery state simulation method in the embodiment of the present disclosure and the traditional open source software LIONSIMBA.

[0174] Table 1

[0175]

[0176] like Figure 4 As shown, it is a comparison chart of the battery status simulation effect of the embodiment of the present disclosure and the traditional open source software LIONSIMBA. Figure 4 The three curves in FIG. 1 represent the voltage response of the battery at different discharge rates under the above materials, and the voltage response corresponding to the solution in the prior art. Figure 4 In the above, Decoupled is the voltage response calculated by the battery state simulation method in this application, and Comsol is the voltage response obtained by the method in the prior art. Figure 4 It can be seen that the two curves are very close, indicating that the calculation accuracy of the battery state simulation method in this application can reach the calculation accuracy in the prior art. On this basis, as shown in Table 2, Decoupled is the battery state simulation method in this application, and LIONSIMBA is the method in the prior art. In Table 2, under the same number of cells, the time used by the battery state simulation method in this application is significantly reduced than that of the LIONSIMBA method in the prior art. Figure 4 As shown in Table 2, the calculation accuracy of this scheme can achieve the same accuracy as the prior art, and the calculation time is less than that of the related algorithms in the prior art.

[0177] Table 2

[0178]

[0179] It should be noted that:

[0180] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0181] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.

[0182] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0183] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A battery state simulation method, characterized in that: The method comprises: Acquire environmental parameters and initial state parameters of the battery to be simulated, wherein the initial state parameters include initial solid phase concentration; In the direction from the positive electrode to the negative electrode of the battery to be simulated, discretizing the initial state parameter into a plurality of discrete state parameters at preset intervals; An initial matrix is ​​calculated based on the environmental parameters and the preset interval; According to the solid phase concentration of the mth discrete point at the kth time step and the matrix of the kth time step, the first sub-solid phase concentration of the mth discrete point at the k+1th time step is calculated, when k=0, the matrix of the kth time step is the initial matrix, the solid phase concentration of the mth discrete point at the kth time step is the initial solid phase concentration, and k is an integer greater than or equal to 0; The second sub-solid phase concentration of the m-th discrete point in the k+1-th time step is calculated by using the discrete state parameter of the m-th discrete point in the k-th time step, the environmental parameter and the matrix of the k-th time step; If the k+1th time step is the maximum time step, based on the first sub-solid phase concentration, the second sub-solid phase concentration and the discrete state parameters of each discrete point, the state parameters of the battery to be simulated are obtained; The method of calculating the second sub-solid phase concentration of the mth discrete point at the k+1th time step by using the discrete state parameter of the mth discrete point at the kth time step, the environmental parameter and the matrix of the kth time step comprises: Iterating the discrete state parameter of the mth discrete point at the kth time step to obtain the discrete state parameter of the mth discrete point at the k+1th time step; The second sub-solid phase concentration is calculated using the discrete state parameters of the m-th discrete point in the k+1-th time step, the solid phase effective parameters in the k+1-th time step and the matrix in the k+1-th time step, wherein the matrix in the k+1-th time step is obtained according to the solid phase effective parameters in the k-th time step and the matrix in the k-th time step; Before calculating the second sub-solid phase concentration using the discrete state parameter of the mth discrete point at the k+1th time step, the solid phase effective parameter at the k+1th time step and the matrix at the k+1th time step, the method further includes: If the ratio of the solid phase effective parameter at the k-1th time step to the solid phase effective parameter at the kth time step is greater than a preset allowable error, the solid phase effective parameter at the kth time step is used as the solid phase effective parameter at the k+1th time step; If the ratio of the solid phase effective parameter at the k-1th time step to the solid phase effective parameter at the kth time step is less than or equal to the preset allowable error, the solid phase effective parameter at the k-1th time step is used as the solid phase effective parameter at the k+1th time step; The state parameter includes at least one of a solid phase concentration, a terminal voltage, and a temperature, and the discrete state parameter also includes an electric potential. The state parameter of the battery to be simulated is obtained based on the first sub-solid phase concentration, the second sub-solid phase concentration, and the discrete state parameters of each discrete point, and includes at least one of the following: The first sub-solid phase concentration is subtracted from the second sub-solid phase concentration to obtain the solid phase concentration of the mth discrete point in the k+1th time step; The terminal voltage of the battery to be simulated is obtained by subtracting the potential of the discrete point corresponding to the negative electrode of the battery to be simulated from the potential of the discrete point corresponding to the positive electrode of the battery to be simulated; Based on the discrete state parameters of the k+1th time step, the heat of the battery to be simulated is calculated; and according to the heat and the battery material of the battery to be simulated, the temperature of the battery to be simulated is determined.

2. The method according to claim 1, characterized in that The calculating and obtaining the initial matrix based on the environmental parameters and the preset interval includes: Calculate the solid phase effective parameters of the battery to be simulated based on the environmental parameters; Based on the solid phase effective parameters and the preset interval, calculating the corresponding elements at each discrete point; An initial matrix is ​​constructed based on the elements at each discrete point.

3. The method according to claim 1, characterized in that The discrete state parameter of the mth discrete point at the kth time step is iterated to obtain the discrete state parameter of the mth discrete point at the k+1th time step, including: According to the discrete state parameter corresponding to the mth discrete point at the pth iteration in the kth time step, calculate the solid phase concentration corresponding to the mth discrete point at the pth iteration, where P is an integer greater than or equal to 0; Determine the increment based on the discrete state parameter corresponding to the m-th discrete point at the p-th iteration and the solid phase concentration corresponding to the m-th discrete point at the p-th iteration; Based on the discrete state parameter corresponding to the mth discrete point at the pth iteration and the pth iteration increment, and the relationship between the preset threshold and the maximum number of iterations, the discrete state parameter of the mth discrete point at the k+1th time step is determined.

4. The method according to claim 3, characterized in that The step of determining the increment based on the discrete state parameter corresponding to the m-th discrete point at the p-th iteration and the solid phase concentration corresponding to the m-th discrete point at the p-th iteration includes: Based on the discrete state parameter corresponding to the m-th discrete point at the p-th iteration and the solid phase concentration corresponding to the m-th discrete point at the p-th iteration, calculate the residual vector and Jacobian matrix of the m-th discrete point at the p-th iteration; The ratio of the Jacobian matrix and the residual vector is used as the increment of the p-th iteration.

5. The method according to claim 3, characterized in that: The determining of the discrete state parameter of the mth discrete point at the k+1th time step based on the relationship between the discrete state parameter corresponding to the mth discrete point at the pth iteration and the pth iteration increment, a preset threshold and a maximum number of iterations includes: The discrete state parameter corresponding to the m-th discrete point at the p-th iteration is subtracted from the increment to obtain the discrete state parameter of the m-th discrete point at the p+1-th iteration; If the ratio of the p-th iteration increment to the discrete state parameter of the m-th discrete point in the k-th time step at the p-th iteration is less than a preset threshold, and the p-th iteration has not reached the maximum number of iterations, the corresponding discrete state parameter of the m-th discrete point at the p-th iteration is used as the discrete state parameter of the m-th discrete point in the k+1-th time step.

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