Lithium battery capacity and soc estimation method and device based on pseudo two-dimensional model and medium

Abstract

The application provides a lithium battery capacity and SOC estimation method and device based on a pseudo two-dimensional model and a medium. The method comprises the following steps: constructing a pseudo two-dimensional model of a lithium battery; based on the pseudo two-dimensional model, simulating and calculating the lithium battery according to a set constant-volume simulation working condition to obtain a voltage and current change curve with time and a real-time lithium ion concentration distribution inside the lithium battery; calculating the lithium battery capacity and the real-time SOC of the lithium battery based on the voltage and current change curve with time; fitting a mapping relationship between the real-time lithium ion concentration distribution and the real-time SOC of the lithium battery; and under any working condition, simulating and solving the lithium ion concentration distribution information based on the pseudo two-dimensional model of the lithium battery, and inputting the lithium ion concentration distribution information into the mapping relationship to solve the SOC of the lithium battery. The application can accurately estimate the capacity and SOC of the lithium battery.

Description

Technical Field

[0001] This application belongs to the field of lithium battery technology, and relates to a method, device and medium for estimating the capacity and SOC of lithium batteries based on a pseudo-two-dimensional model. Background Technology

[0002] Lithium-ion batteries possess advantages such as high energy density, high efficiency, long cycle life, and low self-discharge rate, leading to their widespread application in the new energy field, particularly in power storage and electric vehicles. However, the accuracy of lithium-ion battery capacity and SOC calculations, as well as inconsistencies in lithium-ion battery pack consistency, have long been obstacles to the industry's development. Capacity and SOC estimation are core technologies for ensuring the rational application of power storage and electric vehicles, and are also fundamental for the control, operation, monitoring, and maintenance of lithium-ion battery systems. In practical applications, these estimations exhibit time-varying characteristics, complex influencing factors, and uncertainties, resulting in difficulties in SOC estimation, low accuracy, and insufficient adaptability. Therefore, the value of high-precision, high-speed real-time lithium-ion battery SOC estimation is self-evident. Improving the accuracy of capacity and SOC estimation can ensure the safe and reliable operation of lithium-ion batteries and extend their lifespan, possessing significant engineering value for the large-scale application of lithium-ion batteries. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus and medium for estimating the capacity and SOC of lithium batteries based on a pseudo-two-dimensional model, so as to solve the problems existing in the prior art.

[0004] Firstly, this application provides a method for estimating the capacity and SOC of a lithium battery based on a pseudo-two-dimensional model. The method includes: constructing a pseudo-two-dimensional model of the lithium battery; simulating the lithium battery under set constant-capacity simulation conditions based on the pseudo-two-dimensional model to obtain voltage and current variation curves over time and the real-time lithium-ion concentration distribution inside the lithium battery; calculating the lithium battery capacity and real-time SOC based on the voltage and current variation curves over time; fitting the mapping relationship between the real-time lithium-ion concentration distribution and the real-time SOC of the lithium battery; and under any operating condition, simulating and solving for the lithium-ion concentration distribution information based on the pseudo-two-dimensional model of the lithium battery, and inputting the lithium-ion concentration distribution information into the mapping relationship to solve for the lithium battery SOC. In this application, by constructing a mapping relationship between the real-time lithium-ion concentration distribution inside the lithium battery and the SOC, the capacity and SOC of the lithium battery can be accurately estimated under any operating condition based on the mapping relationship. This effectively avoids extreme operating conditions such as overcharging and over-discharging of the lithium battery, and also strengthens the judgment of battery pack consistency, thereby effectively avoiding accidents such as fires and explosions caused by thermal abuse and thermal runaway of the lithium battery.

[0005] In one implementation of the first aspect, the pseudo-two-dimensional model includes: a solid-phase mass transfer equation, a solid-phase potential equation, a liquid-phase mass transfer equation, a liquid-phase potential equation, and the Butler-Folmer equation.

[0006] In one implementation of the first aspect, the simulation calculation of the lithium battery according to the set constant-capacity simulation conditions includes: starting from any operating condition, constant current charging to the upper cutoff voltage; constant voltage charging until the lithium battery reaches a fully charged state; starting from the fully charged state, constant current discharging at a preset constant-capacity current to the lower cutoff voltage, and stopping the discharge.

[0007] In one implementation of the first aspect, the lithium battery capacity is calculated using the following formula: Q total =I cap ·t, where Q total For lithium battery capacity, I cap Let t be the constant current, and t be the time taken for constant current discharge at the preset constant current to the lower cutoff voltage. In this implementation, compared to actually measuring the battery capacity in the laboratory, the lithium battery capacity is calculated through simulation using a pseudo-two-dimensional model combined with appropriate numerical analysis methods. This method is low-cost, fast, and yields high-accuracy calculation results.

[0008] In one implementation of the first aspect, determining the real-time SOC of the lithium battery includes calculating the real-time SOC of the lithium battery using the following formula during the process of constant current discharge from a fully charged state to the lower cutoff voltage using a preset constant current: Where soc(t) is the SOC value of the lithium battery at time t, soc(t0) is the initial SOC value of the lithium battery at time t0, soc(t0) = 100%, Q total For lithium battery capacity, I cap Let η be the constant current and η be the coulombic efficiency. In this implementation, the method for calculating the real-time SOC of the lithium battery, compared to empirical models such as equivalent circuit models, starts from first principles and explains the SOC of the lithium battery from a more microscopic perspective through the distribution of lithium ions inside the battery, resulting in more accurate calculation results.

[0009] In one implementation of the first aspect, obtaining the real-time lithium-ion concentration distribution inside the lithium battery includes: from a fully charged state, constant current discharge is performed at a preset constant capacity current until the lower cutoff voltage, and the solid-phase lithium-ion concentration distribution of the positive or negative electrode of the lithium battery is simulated and calculated based on the pseudo two-dimensional model.

[0010] In one implementation of the first aspect, fitting the mapping relationship between the real-time lithium-ion concentration distribution and the real-time SOC of the lithium battery includes: obtaining the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery based on the solid-phase lithium-ion concentration distribution of the positive or negative electrode; establishing a one-to-one correspondence between the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery and the real-time SOC of the lithium battery in the time dimension, and fitting the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery and the real-time SOC of the lithium battery to obtain the mapping relationship.

[0011] In one implementation of the first aspect, determining the average solid lithium-ion concentration of the positive or negative electrode of the lithium battery based on the solid lithium-ion concentration distribution includes: for the positive or negative electrode of the lithium battery, determining the average value of the solid lithium-ion concentration distribution on the r-axis in that electrode. Among them, c r-average Let V be the average solid-phase lithium ion concentration along the r-axis, V be the volume of the solid-phase lithium ion particles in the electrode, r be the radius of the solid-phase lithium ion particles in the electrode, and c be the average solid-phase lithium ion concentration along the r-axis. s Let dr be the solid-phase lithium ion concentration distribution in the electrode, and dr be the length of each discrete unit on the r-axis. The average value of the solid-phase lithium ion concentration distribution on the r-axis is then averaged on the x-axis. Among them, c volume-average Let L be the average solid-phase lithium-ion concentration in the electrode, L be the thickness of the electrode, and dx be the length of each discrete unit along the x-axis. This implementation is not limited to common averaging methods but employs a more comprehensive and multi-dimensional averaging approach. This approach offers greater compatibility with different numerical analysis methods for partial differential equations and different discretization methods, improving computational accuracy and yielding more precise SOC results. It effectively avoids extreme operating conditions, protecting users' lives and property in various practical lithium battery application scenarios.

[0012] Secondly, this application provides a lithium battery capacity and SOC estimation device based on a pseudo-two-dimensional model. The device includes: a memory configured to store a computer program; and a processor configured to invoke the computer program to execute the lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to the first aspect of this application.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed to implement the lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to the first aspect of this application.

[0014] As described above, the lithium battery capacity and SOC estimation method, device and medium based on pseudo-two-dimensional model described in this application have the following beneficial effects: By constructing a mapping relationship between the real-time lithium ion concentration distribution inside the lithium battery and the SOC, the lithium battery capacity and SOC can be accurately estimated under any operating condition based on the mapping relationship. This can effectively avoid the occurrence of extreme operating conditions such as overcharging and over-discharging of lithium batteries, and can also strengthen the judgment of battery pack consistency, thereby effectively avoiding accidents such as fire and explosion caused by thermal abuse and thermal runaway of lithium batteries. Attached Figure Description

[0015] Figure 1 The flowchart shown is a flowchart of the lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model described in the embodiments of this application.

[0016] Figure 2 The diagram shown is a flowchart illustrating the specific operating conditions of the constant volume simulation in this embodiment of the application.

[0017] Figure 3a The figure shows the voltage change curve over time during the constant-capacity simulation process in this application embodiment.

[0018] Figure 3b The figure shows the current change curve over time during the constant-capacity simulation process in this application embodiment.

[0019] Figure 4 The diagram shows the relationship between SOC and the average solid-phase lithium-ion concentration of the negative electrode in the embodiments of this application, and a schematic diagram of its linear fitting.

[0020] Figure 5 The diagram shown is a schematic diagram of a lithium battery capacity and SOC estimation device based on a pseudo-two-dimensional model in an embodiment of this application.

[0021] Component designation explanation

[0022] 1. A device for estimating lithium battery capacity and SOC based on a pseudo-two-dimensional model

[0023] 11. Memory

[0024] 12 processors

[0025] Steps S1 to S5 Detailed Implementation

[0026] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0027] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0028] Common battery management systems (BMS) simulation models for lithium batteries include equivalent circuit (ECM) models, pseudo-two-dimensional (P2D) models, and single-particle (SPM) models. Among these, the P2D model has the highest computational complexity, accuracy, and predictability. It describes the electrochemical reactions occurring inside the lithium-ion battery through a series of partial differential equations, including lithium-ion diffusion in the solid-liquid phase, lithium-ion migration in the electrolyte, and charge transfer at the solid-liquid interface. It is suitable for the design and performance simulation of electrodes or individual cells, making it an ideal electrochemical model for studying the internal physicochemical properties of lithium batteries. These electrochemical models involve the coupling between multiple physical fields, including electric and concentration fields, and contain a large number of partial differential equations. Appropriate numerical analysis methods, such as the finite difference method, finite element method, or spectroscopic methods, are required for solving these models.

[0029] The capacity and state of charge (SOC) of a lithium battery are closely related to the physical quantities inside the battery, with the relationship being particularly significant for the specific physical quantity of lithium ion concentration. By leveraging the high precision of the aforementioned electrochemical models and solution methods, the capacity of a lithium battery can be accurately calculated, and information on the distribution of lithium ions inside the battery can be obtained from any state. This allows for the effective prediction of the SOC in the corresponding state, avoiding extreme conditions such as overcharging, over-discharging, thermal abuse, and thermal runaway, and preventing the occurrence of sudden accidents.

[0030] Based on the above, the following embodiments of this application provide a method, apparatus and medium for estimating lithium battery capacity and SOC based on a pseudo-two-dimensional model. The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0031] like Figure 1 As shown, this embodiment provides a method for estimating the capacity and SOC of a lithium battery based on a pseudo-two-dimensional model. The method includes the following steps S1 to S5.

[0032] S1. Construct a pseudo-two-dimensional model of a lithium battery.

[0033] The pseudo-two-dimensional model in step S1 contains four partial differential equations and one algebraic equation. The four partial differential equations are, in order, the solid-phase mass transfer equation, the solid-phase potential equation, the liquid-phase mass transfer equation, and the liquid-phase potential equation. The algebraic equation is the Butler-Volmer equation. Details are as follows:

[0034] 1. Solid-state mass transfer, according to Fick's second law, we know that:

[0035]

[0036] Among them, c s Let be the solid-phase lithium ion concentration, t be time, r be the radius of the reacting particles, and D be the solid-phase lithium ion concentration. s is the lithium-ion solid-phase diffusion coefficient.

[0037] 2. Solid-state potential: According to Kirchhoff's current law and Ohm's law, we know that:

[0038]

[0039] Where x is the x-axis coordinate point in the pseudo-two-dimensional model. φ is the effective conductivity of the solid phase. s Let be the solid-state potential, 'a' be the specific surface area of ​​the lithium battery, 'F' be the Faraday constant (typically taken as 96485), and 'j' be the solid-state potential. n This refers to the lithium-ion flux.

[0040] 3. Liquid phase mass transfer

[0041]

[0042] Where, ε e c is the integral number of the electrolytic liquid in the electrode. e Let be the concentration of lithium ions in the liquid phase, t be time, and x be the x-axis coordinate in the pseudo-two-dimensional model. Let be the effective diffusion coefficient of lithium ions in the liquid phase, α be the specific surface area of ​​the lithium battery, and t be the effective diffusion coefficient of lithium ions in the liquid phase. c j is the cation transfer number. n This refers to the lithium-ion flux.

[0043] 4. Liquid phase potential

[0044]

[0045] Where, φ e Let be the liquid phase potential, x be the x-axis coordinate point in the pseudo-two-dimensional model, and i be the liquid phase potential. e Let κ be the liquid phase current density. eff c represents the effective conductivity of lithium-ion liquid phase.e Where is the liquid-phase lithium-ion concentration, R is the universal gas constant (typically taken as 8.314), T is the temperature of the lithium battery, F is the Faraday constant (typically taken as 96485), and t c It is the cation transfer number. This is the thermodynamic factor (a constant related to the liquid-phase average molar activity coefficient, typically taken as 1). This is the partial derivative of the logarithmic function of the liquid phase potential with respect to the x-axis coordinates.

[0046] 5. Butler-Folmer equation

[0047]

[0048] Where i0 is the electrode exchange current density, c e c represents the concentration of lithium ions in the liquid phase. s,max c represents the maximum concentration of lithium ions in the solid phase. ss α represents the surface concentration of the reactant particles in the solid phase. a α c is the electrode reaction conversion coefficient.

[0049] The five equations mentioned above can be used to accurately calculate the internal state of a lithium battery and the specific values ​​of each physical quantity. It should be noted that boundary conditions that conform to the actual physical meaning are required for the solution to work properly. For example, the gradient of the lithium ion concentration field is 0 at the initial moment, some physical quantities are equal at the interface between the solid and liquid phases, and the relationship between the internal current and the operating current of the external circuit at the boundary where the positive and negative electrodes are connected to the external circuit.

[0050] S2. Based on the pseudo-two-dimensional model, the lithium battery is simulated according to the set constant-capacity simulation conditions to obtain the voltage and current change curves over time and the real-time lithium ion concentration distribution inside the lithium battery.

[0051] The flowchart of the specific operating conditions for the constant volume simulation in step S2 is as follows: Figure 2 As shown, it includes:

[0052] 1. Starting from any operating condition, charge at a constant current (CC) to the upper cutoff voltage;

[0053] 2. Charge under constant voltage (CV) until the current is ≤C / 20, then stop charging. At this point, the battery is considered to be fully charged, i.e., SOC = 100%.

[0054] 3. Start constant current (CC) discharge with the set constant current (usually 1C current) until the lower cutoff voltage is reached, then stop discharging. At this point, the battery is considered to be fully discharged and SOC = 0.

[0055] Taking a lithium cobalt oxide LCO battery as an example, with upper and lower cutoff voltages of 4.1V and 2.5V respectively, the voltage-time curves during the entire constant-capacity simulation process described above are as follows: Figure 3a As shown in the figure, the current-time variation curve during the constant-capacity simulation is as follows: Figure 3b As shown.

[0056] In addition, in step S2, obtaining the real-time lithium-ion concentration distribution inside the lithium battery includes: from the fully charged state, constant current discharge with a preset constant current until the lower cutoff voltage, the solid-phase lithium-ion concentration distribution of the positive or negative electrode of the lithium battery is simulated and calculated based on the pseudo two-dimensional model.

[0057] S3. Calculate the lithium battery capacity and real-time SOC of the lithium battery based on the voltage and current change curves over time.

[0058] The capacity of a lithium battery is calculated using the following formula:

[0059] Q total =I cap ·t

[0060] Among them, Q total For lithium battery capacity, I cap I is the constant current, and t is the time taken for constant current discharge at the preset constant current to reach the lower cutoff voltage. cap A current of 1C is generally used, t is usually taken as the unit of hours, and Q is... total The calculation result is in ampere-hours, i.e., [A·h].

[0061] Determining the real-time SOC of a lithium battery involves constant current discharge from a fully charged state to the lower cutoff voltage using the following formula:

[0062]

[0063] Where soc(t) is the SOC value of the lithium battery at time t, soc(t0) is the initial SOC value of the lithium battery at time t0, soc(t0) = 100%, Q total For lithium battery capacity, I cap Let η be the constant current and η be the coulombic efficiency.

[0064] Using a lithium iron phosphate (LFP) battery as measured data, the capacity of the LFP battery was calculated using the above process and simplified formula, yielding a result of approximately 10⁵ [A·h]. The calculated result showed a high degree of fit with the measured data, demonstrating that the lithium battery capacity estimation method in this invention has high calculation accuracy.

[0065] S4. Fit the mapping relationship between the real-time lithium-ion concentration distribution and the real-time SOC of the lithium battery.

[0066] Specifically, step S4 includes steps S41 and S42:

[0067] S41. Calculate the average solid lithium ion concentration of the positive or negative electrode of the lithium battery based on the solid lithium ion concentration distribution of the positive or negative electrode.

[0068] The pseudo-two-dimensional model, as the name suggests, is not two-dimensional in the traditional sense (two dimensions: x and y). Its "pseudo-two-dimensionality" lies in the fact that the model possesses both a macroscopic x-axis representing the three domains of the lithium battery (positive electrode, separator, and negative electrode) and a microscopic r-axis representing the radius of the solid reactive particles, which are approximately spherical. When spatially discretizing the pseudo-two-dimensional model, both the x-axis and r-axis dimensions need to be discretized separately. Therefore, to obtain the average solid lithium ion concentration of the positive or negative electrode of a lithium battery, the average value along the r-axis radius is first calculated. Then, the r-axis average value is mapped to the x-axis discrete grid, and the average value of the r-axis average values ​​of each discrete unit on the x-axis is calculated. The result equals the volume average value, which is the final average calculated value. In other words, it is equivalent to performing an average calculation along both the x-axis and r-axis.

[0069] Specifically: For the positive or negative electrode of a lithium battery, calculate the average value of the solid-phase lithium ion concentration distribution along the r-axis in that electrode:

[0070]

[0071] Among them, c r-average Let V be the average solid-phase lithium ion concentration along the r-axis, V be the volume of the solid-phase lithium ion particles in the electrode, r be the radius of the solid-phase lithium ion particles in the electrode, and c be the average solid-phase lithium ion concentration along the r-axis. s dr represents the solid-phase lithium ion concentration distribution in the electrode, and dr is the length of each discrete unit on the r-axis.

[0072] The average value of the solid lithium ion concentration distribution in the electrode on the r-axis is averaged on the x-axis:

[0073]

[0074] Among them, c volume-average dx represents the average solid-phase lithium ion concentration in the electrode, L represents the thickness of the electrode, and dx represents the length of each discrete unit on the x-axis.

[0075] It should be noted that the above formula has different uses in different numerical calculation methods.

[0076] It is clear from the above formula that, assuming the pseudo-two-dimensional model has sufficiently high accuracy, the discretization parameters dx and dr become the key points for calculating the average. If the finite difference method is used to solve the partial differential equations in the pseudo-two-dimensional model, a uniformly divided grid is generally used, where each grid cell has an equal length. Then, the above formula can be simplified to the most basic arithmetic mean, which is the sum of all data points divided by the number of data points. The simplified formula is shown below:

[0077]

[0078] Among them, c average c is the average concentration. s Let n be the concentration distribution of the reaction particles (i.e., the concentration distribution of solid-phase lithium ions in the electrode), and c be the concentration distribution of the reaction particles. s The total number of data points.

[0079] However, if the Chebyshev spectral method is used to solve the partial differential equations in the pseudo-two-dimensional model, according to the definition of the Chebyshev spectral method, the Chebyshev point is defined by the following formula:

[0080] x j =cos(jπ / N),j=0,1,…,N

[0081] Where, x j Let be the coordinates of the j-th Chebyshev point, and N be the number of grid cells. As can be seen from the above, the grid division is non-uniform during the Chebyshev spectral method, meaning dx is not equal everywhere. Therefore, if the Chebyshev spectral method is used to simply calculate the arithmetic mean, a large error will occur. It is necessary to calculate a weighted average by multiplying the data of each discrete cell by the corresponding weight based on the dx distribution of the Chebyshev points.

[0082] S42. The average solid-phase lithium ion concentration of the positive or negative electrode of the lithium battery is mapped one-to-one with the real-time SOC of the lithium battery in the time dimension, and the average solid-phase lithium ion concentration of the positive or negative electrode of the lithium battery and the real-time SOC of the lithium battery are fitted to obtain the mapping relationship.

[0083] Specifically, constant current (CC) discharge is started with a set constant current (generally 1C current). During the process of discharging to the lower cutoff voltage, it involves charging to SOC = 100% and then discharging to SOC = 0. During this process, the concentration data of negative electrode reaction particles are extracted at equal time intervals. For each moment, the formula in step S3 is used to calculate the average concentration of negative electrode reaction particles and the SOC at that moment. A one-to-one mapping relationship between SOC and average negative electrode concentration is established. This relationship is then linearly fitted to obtain the expression of the linear relationship.

[0084] For example, ideally, starting at time t0, constant current (CC) discharge begins from 100% SOC, with the current set to 1C. Discharge continues until t = 3600s, or one hour, at which point the discharge stops, precisely at the lower cutoff voltage. If the time step is set to dt = 1s, then step S2 will extract 3600 time points, 3600 concentration distributions of negative electrode reactants, and 3600 SOC values. The concentration distributions of negative electrode reactants at each time point are averaged using the method in step S3 to obtain an average concentration data with the same length as the SOC data. This achieves a one-to-one mapping effect, allowing the prior expression for SOC and average concentration to be derived.

[0085] Taking a lithium iron phosphate battery as an example, using the calculation method in this invention, the relationship between SOC and the average solid-phase lithium-ion concentration of the negative electrode, and its linear fitting, are as follows, under the condition of a constant current of 1C. Figure 4 As shown.

[0086] S5. Under any operating condition, the lithium-ion concentration distribution information is solved by simulation based on the pseudo two-dimensional model of the lithium battery, and the lithium-ion concentration distribution information is input into the mapping relationship to solve the SOC of the lithium battery.

[0087] Taking a certain lithium iron phosphate (LFP) battery as an example, several operating conditions are arbitrarily selected. This embodiment lists three operating conditions, as shown in Table 1:

[0088] Table 1. Average solid-phase lithium-ion concentration and SOC value of several negative electrodes

[0089] Average solid-phase lithium-ion concentration at the negative electrode SOC Operating Condition #1 19717.2418997 86.14％ Operating Condition #2 10802.0962255 44.47％ Operating Condition #3 4865.4313183 16.69％

[0090] The aforementioned method for accurately estimating SOC has significant value in practical applications. For example, in the use of automotive lithium-ion batteries, under certain operating conditions, the battery management system might calculate an SOC of 30%, while the actual SOC might be less than 20%. Such inaccurate calculations would send incorrect information to the user, preventing them from charging in a timely manner. Similarly, in large-scale lithium battery energy storage power stations, inaccurate SOC results during charging, displaying 85% when the actual charge might be close to 100%, could lead to overcharging. In severe cases, this could even cause sudden accidents such as swelling, deformation, fire, or explosion of the lithium battery pack.

[0091] like Figure 5 As shown, this embodiment provides a lithium battery capacity and SOC estimation device based on a pseudo-two-dimensional model. The lithium battery capacity and SOC estimation device 1 based on the pseudo-two-dimensional model includes: a memory 11 configured to store a computer program; and a processor 12 configured to call the computer program to execute the above-described lithium battery capacity and SOC estimation method based on the pseudo-two-dimensional model.

[0092] Preferably, the memory 11 includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0093] Preferably, the processor 12 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0094] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0095] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0096] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0097] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0098] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0099] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0100] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0101] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for estimating the capacity and SOC of a lithium battery based on a pseudo-two-dimensional model, characterized in that, The method includes: Construct a pseudo-two-dimensional model of a lithium battery; Based on the pseudo-two-dimensional model, the lithium battery is simulated according to the set constant-capacity simulation conditions to obtain the voltage and current change curves over time and the real-time lithium ion concentration distribution inside the lithium battery. The lithium battery capacity and real-time SOC of the lithium battery are determined based on the voltage and current change curves over time. The capacity of a lithium battery is calculated using the following formula: in, For lithium battery capacity, For constant current, t The time taken to discharge at a preset constant current to the lower cutoff voltage; Fit the mapping relationship between the real-time lithium-ion concentration distribution and the real-time SOC of the lithium battery; Fitting the mapping relationship between the real-time lithium-ion concentration distribution and the real-time SOC of the lithium battery includes: calculating the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery based on the solid-phase lithium-ion concentration distribution of the positive or negative electrode; establishing a one-to-one correspondence between the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery and the real-time SOC of the lithium battery in the time dimension; and fitting the average solid-phase lithium-ion concentration of the positive or negative electrode of the lithium battery and the real-time SOC of the lithium battery to obtain the mapping relationship. Determining the average solid-phase lithium-ion concentration of a lithium battery's positive or negative electrode based on its solid-phase lithium-ion concentration distribution includes: for the positive or negative electrode, calculating the average solid-phase lithium-ion concentration distribution along the r-axis. in, This represents the average solid-phase lithium-ion concentration along the r-axis. V The volume of the solid-phase lithium-ion particles in the positive or negative electrode. r The radius length of the solid-phase lithium-ion particles in the positive or negative electrode. This refers to the solid-phase lithium ion concentration distribution in the positive or negative electrode. dr Let be the length of each discrete element on the r-axis; The average value of the solid lithium ion concentration distribution in the positive or negative electrode along the r-axis is... x Calculate the average value on the axis: in, The average solid-phase lithium ion concentration in the positive or negative electrode. L The thickness of the positive electrode or the negative electrode. for x The length of each discrete unit on the axis; under any working condition, the lithium-ion concentration distribution information is solved by simulation based on the pseudo two-dimensional model of the lithium battery, and the lithium-ion concentration distribution information is input into the mapping relationship to solve the lithium battery SOC.

2. The lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to claim 1, characterized in that, The pseudo-two-dimensional model includes: solid-phase mass transfer equation, solid-phase potential equation, liquid-phase mass transfer equation, liquid-phase potential equation, and Butler-Folmer equation.

3. The lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to claim 1, characterized in that, The simulation calculations for the lithium battery based on the set constant-capacity simulation conditions include: Starting from any operating condition, charge at a constant current until the upper cutoff voltage; Constant voltage charging until the lithium battery is fully charged; Starting from a fully charged state, the system discharges at a preset constant current until the lower cutoff voltage, at which point the discharge stops.

4. The lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to claim 3, characterized in that, Determining the real-time SOC of a lithium battery involves constant current discharge from a fully charged state to the lower cutoff voltage using the following formula: in, for t The SOC value of the lithium battery at all times. The initial state of charge (SOC) of the lithium battery. , For lithium battery capacity, For constant current, For Coulomb efficiency.

5. The lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to claim 3, characterized in that, Obtaining the real-time lithium-ion concentration distribution inside a lithium battery includes: from a fully charged state, constant current discharge is performed at a preset constant capacity current until the lower cutoff voltage, and the solid-phase lithium-ion concentration distribution of the positive or negative electrode of the lithium battery is simulated and calculated based on the pseudo-two-dimensional model.

6. A lithium battery capacity and SOC estimation device based on a pseudo-two-dimensional model, characterized in that, The device includes: Memory, configured to store computer programs; and The processor is configured to invoke the computer program to execute the lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed to implement the lithium battery capacity and SOC estimation method based on a pseudo-two-dimensional model according to any one of claims 1 to 5.

Images