A lithium ion battery aging quantitative analysis method, device, equipment and medium

Abstract

The application is suitable for the technical field of battery, and provides a lithium ion battery aging quantitative analysis method, device, equipment and medium. The analysis method comprises: acquiring key parameters of a lithium ion battery; acquiring actual positive electrode voltage curves and actual negative electrode voltage curves of the lithium ion battery, acquiring positive electrode voltage curves and negative electrode voltage curves of the lithium ion battery after aging; acquiring simulation positive electrode voltage curves, simulation negative electrode voltage curves, simulation aging positive electrode voltage curves and simulation aging negative electrode voltage curves of the lithium ion battery; fitting multiple curves of the lithium ion battery to obtain full battery voltage curves; fitting multiple aging curves to obtain full battery aging voltage curves; analyzing values of the key parameters in the full battery voltage curves and the full battery aging curves to obtain a relationship between the key parameters and lithium ion battery aging. The analysis method can improve the accuracy of quantitative analysis of lithium ion battery aging.

Description

Technical Field

[0001] This application relates to the field of battery analysis technology, and in particular to a method, apparatus, equipment and medium for quantitative analysis of lithium-ion battery aging. Background Technology

[0002] Lithium-ion batteries are widely used in electronic devices, electric vehicles, and large-scale energy storage systems. As the demand for longer battery life increases, the safety and reliability of lithium-ion batteries are receiving increasing attention. Accelerated aging processes, such as aging tests at high temperatures and high current rates, are employed. For batteries after cycle aging, there are generally two methods for analysis. One method involves disassembling the battery and analyzing the changes in the physicochemical properties of each component to understand the aging mechanism. The other method uses the charge-discharge curves of actual batteries, analyzing the incremental capacity and differential voltage curves to understand the aging mechanism. Existing research reports have focused on explaining the mechanisms of one or more aging phenomena during battery aging, but have not provided a global analysis of the overall battery aging process. However, battery aging, increased battery impedance, power decay, and capacity decay are caused by multiple complex mechanisms; material parameters, storage conditions, and cycling conditions all affect battery life and performance. The aging process involves the solid phase, liquid phase, and interface electron and ion transport processes in areas such as the positive electrode, negative electrode, and separator. The changes in each part during the cycle aging process are also very different. Only by fully understanding the aging characteristics of each part can the safety and reliability of the battery be guaranteed.

[0003] Therefore, there is an urgent need to develop an effective method to match information obtained from disassembly tests with electrochemical curve information, and then quantitatively analyze the entire failure process of lithium-ion batteries to guide the design and development of lithium-ion batteries with long cycle life, good safety, and high reliability. However, existing technologies in this field suffer from poor accuracy in quantitative analysis of lithium-ion battery aging. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for quantitative analysis of lithium-ion battery aging, which can solve the problem of low accuracy in quantitative analysis of lithium-ion battery aging.

[0005] In a first aspect, embodiments of this application provide a method for quantitative analysis of lithium-ion battery aging, the method comprising:

[0006] To obtain the key parameters affecting the DC internal resistance of lithium-ion batteries;

[0007] Obtain the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each lithium-ion battery in the multiple lithium-ion batteries, obtain the positive electrode voltage curve and the negative electrode voltage curve after aging.

[0008] Obtain the simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging, and obtain the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging.

[0009] The actual positive electrode voltage curves of multiple lithium-ion batteries and the simulated positive electrode voltage curve of the target lithium-ion battery are fitted to obtain the positive electrode voltage curve of the target lithium-ion battery. The positive electrode voltage curve is obtained when the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve reaches the preset optimal fitting value.

[0010] The actual negative electrode voltage curves of multiple lithium-ion batteries and the simulated negative electrode voltage curve of the target lithium-ion battery are fitted to obtain the negative electrode voltage curve of the target lithium-ion battery. The negative electrode voltage curve is obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reaches the preset optimal fitting value.

[0011] Based on the positive electrode voltage curve and the negative electrode voltage curve, the full-cell voltage curve of the target lithium-ion battery is obtained;

[0012] The positive electrode voltage curves of multiple aged lithium-ion batteries and the simulated aged positive electrode curve of the target lithium-ion battery are fitted to obtain the positive electrode voltage curve of the target lithium-ion battery after aging. The positive electrode voltage curve after aging is obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aged positive electrode curve reaches the preset optimal fitting value.

[0013] The negative electrode voltage curves of multiple aged lithium-ion batteries and the simulated aged negative electrode curve of the target lithium-ion battery are fitted to obtain the negative electrode voltage curve of the target lithium-ion battery after aging. The negative electrode voltage curve after aging is obtained when the fitting variance between the negative electrode voltage curve after aging and the simulated aged negative electrode curve reaches the preset optimal fitting value.

[0014] Based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging, the full-cell aging voltage curve of the target lithium-ion battery is obtained.

[0015] The values ​​of key parameters in the full-cell voltage curve and full-cell aging curve are analyzed to obtain the relationship between key parameters and the aging of the target lithium-ion battery.

[0016] Secondly, embodiments of this application provide a quantitative analysis device for lithium-ion battery aging, comprising:

[0017] The acquisition module acquires key parameters that affect the DC internal resistance of lithium-ion batteries;

[0018] The actual module obtains the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each of the multiple lithium-ion batteries, obtains the positive electrode voltage curve and the negative electrode voltage curve after aging of the lithium-ion battery.

[0019] The simulation module obtains the simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging, and obtains the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging.

[0020] The first fitting module fits the actual positive electrode voltage curves of multiple lithium-ion batteries with the simulated positive electrode voltage curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery. The positive electrode voltage curve is obtained when the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve reaches the preset optimal fitting value.

[0021] The second fitting module fits the actual negative electrode voltage curves of multiple lithium-ion batteries with the simulated negative electrode voltage curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery. The negative electrode voltage curve is obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reaches the preset optimal fitting value.

[0022] The first calculation module obtains the full-cell voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve.

[0023] The third fitting module fits the positive electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged positive electrode curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery after aging. The positive electrode voltage curve after aging is obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aged positive electrode curve reaches the preset optimal fitting value.

[0024] The fourth fitting module fits the negative electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged negative electrode curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery after aging. The aged negative electrode voltage curve is obtained when the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode curve reaches the preset optimal fitting value.

[0025] The second calculation module obtains the full-cell aging voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging.

[0026] The analysis module analyzes the values ​​of key parameters in the full-cell voltage curve and the full-cell aging curve to obtain the relationship between key parameters and the aging of the target lithium-ion battery.

[0027] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned quantitative analysis method for lithium-ion battery aging.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned quantitative analysis method for lithium-ion battery aging.

[0029] The above-mentioned solution in this application has the following beneficial effects:

[0030] In the embodiments of this application, the accuracy of the positive electrode voltage curve of the target lithium-ion battery is greatly improved by fitting the actual positive electrode voltage curves of multiple lithium-ion batteries with the simulated positive electrode voltage curve of the target lithium-ion battery, and obtaining the positive electrode voltage curve of the target lithium-ion battery when the fitting variance of the two reaches a preset optimal fitting value. Similarly, the accuracy of the negative electrode voltage curve of the target lithium-ion battery is greatly improved by fitting the actual negative electrode voltage curves of multiple lithium-ion batteries with the simulated negative electrode voltage curve of the target lithium-ion battery, and obtaining the negative electrode voltage curve of the target lithium-ion battery when the fitting variance of the two reaches a preset optimal fitting value. This further greatly improves the accuracy of the full-cell voltage curve obtained based on the positive and negative electrode voltage curves. Furthermore, by fitting the simulated aging curve of the target lithium-ion battery with the voltage curves of multiple aged lithium-ion batteries, the accuracy of the full-cell aging voltage curve of the target lithium-ion battery is greatly improved. Therefore, when analyzing key parameters using the full-cell voltage curve and the full-cell aging voltage curve, the accuracy of the relationship between key parameters and battery aging is greatly improved, enhancing the precision of quantitative analysis of the battery.

[0031] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart of a quantitative analysis method for lithium-ion battery aging provided in an embodiment of this application;

[0034] Figure 2This is a structural diagram of a lithium-ion battery provided in an embodiment of this application;

[0035] Figure 3 A fitting flowchart provided for one embodiment of this application;

[0036] Figure 4 This is a schematic diagram illustrating the generation of the full-cell voltage curve and full-cell aging curve of the target lithium-ion battery provided in an embodiment of this application.

[0037] Figure 5 The full-cell voltage curve of the target lithium-ion battery provided in one embodiment of this application;

[0038] Figure 6 A voltage curve of a target lithium-ion battery provided in an embodiment of this application;

[0039] Figure 7 Temperature rise curve of a target lithium-ion battery provided in an embodiment of this application. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0041] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described feature, unit, step, or operation, but does not exclude the presence or addition of one or more other features, units, steps, or operations.

[0042] References to "some embodiments" and the like in this specification mean that one or more embodiments of this application include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, phrases such as "in some embodiments," "in other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiments, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0043] To address the issue of poor accuracy in existing quantitative analysis methods for lithium-ion battery aging, this application provides a method for quantitative analysis of lithium-ion battery aging.

[0044] The following is an exemplary description of the quantitative analysis method for lithium-ion battery aging provided in this application.

[0045] like Figure 1 As shown, the quantitative analysis method for lithium-ion battery aging provided in this application includes the following steps:

[0046] Step 101: Obtain the key parameters that affect the DC internal resistance of lithium-ion batteries.

[0047] The key parameters mentioned above can be: liquid phase conductivity, solid phase conductivity, electrode porosity, electrode tortuosity, membrane impedance, electrochemical reaction constant, electrochemical reaction activation energy, solid phase lithium ion concentration, liquid phase lithium ion concentration, solid phase lithium ion diffusion coefficient, and liquid phase lithium ion diffusion coefficient.

[0048] Obtaining the aforementioned key parameters allows for a more detailed analysis of the factors influencing the DC internal resistance of lithium-ion batteries. Subsequent analysis of lithium-ion battery aging based on these key parameters enables a comprehensive and detailed analysis of the relationship between each key parameter and lithium-ion battery aging.

[0049] In some embodiments of this application, the steps for obtaining key parameters affecting the DC internal resistance of a lithium-ion battery include:

[0050] The first step is to monitor the overpotential at different interfaces within the lithium-ion battery during charging and discharging. Based on the time response characteristics of ions and electrons during transport, the DC internal resistance of the lithium-ion battery is decomposed into an ohmic internal resistance R. ohm Electrochemical internal resistance R act and diffusion resistance R diffu .

[0051] The second step is to test the ohmic internal resistance R. ohm Numerical analysis was performed to obtain the ohmic internal resistance R. ohm Several key parameters:

[0052] R ohm ={κ, σ, ε, α, R} sf}

[0053] Where κ represents liquid phase conductivity, σ represents solid phase conductivity, ε represents electrode porosity, α represents electrode tortuosity, and R... sf Indicates membrane impedance;

[0054] The specific steps for numerical analysis of ohmic internal resistance are as follows: determine the source of ohmic internal resistance based on the structure of the lithium-ion battery, and deform and integrate the ohmic internal resistance within the structural range to obtain the internal resistance of different components. Based on the different internal resistance components in different structural ranges of the lithium-ion battery, the ohmic internal resistance in the lithium-ion battery can be decomposed. Analyze the expression of each internal resistance to obtain the key parameters affecting each internal resistance. The decomposition results and key parameters are shown in Table 1.

[0055]

[0056]

[0057] Table 1

[0058] Where, j tot j represents the total current density passing through a unit cross-sectional area. app h represents the applied current density. tot κ represents the total thickness of the battery. eff j represents the effective ionic conductivity of the electrolyte. L h represents the liquid phase current density. a σ represents the thickness of the negative electrode sheet. eff j represents the effective conductivity of the electrode material. s h represents the solid current density. s σ represents the thickness of the dividing region. Al L represents the ohmic internal resistance of the positive current collector. pos_cc Indicates the thickness of the positive electrode current collector, A S_pos σ represents the positive electrode area. Cu L represents the ohmic internal resistance of the negative electrode current collector. neg_cc A represents the thickness of the negative electrode current collector. S_neg σ represents the negative electrode area. tab_p V represents the conductivity of the positive electrode tab. tab_p A represents the volume of the positive electrode tab. tab_p σ represents the cross-sectional area of ​​the positive electrode tab. tab_n V represents the conductivity of the negative electrode tab. tab_n A represents the volume of the negative electrode tab. tab_n This represents the cross-sectional area of ​​the negative electrode tab.

[0059] The third step is to measure the electrochemical internal resistance R. act Numerical analysis was performed to obtain the electrochemical internal resistance R. act Several key parameters:

[0060] R act ={k,E a C s C e}

[0061] Where k represents the electrochemical reaction constant, E a C represents the activation energy of an electrochemical reaction. s C represents the solid-phase lithium-ion concentration. e Indicates the concentration of lithium ions in the liquid phase;

[0062] The specific steps for numerical analysis of electrochemical internal resistance are as follows: Based on the structure of the lithium-ion battery, determine the source of electrochemical internal resistance, and transform and integrate the electrochemical internal resistance within the structural range to obtain the internal resistance of different components. Based on the different internal resistance components in different structural ranges of the lithium-ion battery, the electrochemical internal resistance in the lithium-ion battery can be decomposed. Analyze the expression of each internal resistance and obtain the key parameters affecting each internal resistance. The decomposition results and key parameters are shown in Table 2.

[0063]

[0064] Table 2

[0065] Among them, the SEI film is the solid electrolyte interphase (SEI), the CEI film is the positive electrode solid electrolyte interphase (CEI), the LSV test is linear sweep voltammetry (LSV), and the electrochemical impedance spectroscopy (EIS) is used. loc Indicates current density, Represents solid-state potential. E represents the liquid phase potential. surf Indicates the surface potential of the electrode. The potential difference at the solid electrolyte interface membrane is represented by α, and the transfer coefficient is represented by α.

[0066] The third step is to adjust the diffusion resistance R. diffu Numerical analysis was performed to obtain the diffusion resistance R. diffu Several key parameters:

[0067] R diff u = {Ds, ε, De, α, C} s C e}

[0068] Where Ds represents the solid phase lithium-ion diffusion coefficient, and De represents the liquid phase lithium-ion diffusion coefficient;

[0069] The fourth step is to reduce the internal resistance R of the ohm. ohm Several key parameters, electrochemical internal resistance R act Several key parameters and diffusion resistance R diffu Several key parameters are also considered key parameters for lithium-ion batteries.

[0070] The specific steps for numerical analysis of diffusion resistance are as follows: determine the source of diffusion resistance based on the lithium-ion battery structure, and deform and integrate the electrochemical resistance within the structural range to obtain the resistance of different components. Based on the different internal resistance components in different structural ranges of the lithium-ion battery, the diffusion resistance in the lithium-ion battery can be decomposed. Analyze the expression of each internal resistance and obtain the key parameters affecting each internal resistance. The decomposition results and key parameters are shown in Table 3.

[0071]

[0072] Table 3

[0073] Among them, GITT stands for Galvanostatic Intermittent Titration Technique, PITT stands for Potentiostatic Intermittent Titration Technique, CV stands for Cyclic Voltammetry, PRT stands for Battery Safety Testing, and E... surf E represents the surface potential of the electrode. ave The electrode average potential is represented by R, the gas constant is represented by T, and the temperature is represented by c. L The value represents the lithium-ion concentration in the electrolyte, F represents the Faraday constant, and κ represents the lithium-ion concentration in the electrolyte. c This represents the conductivity of the electrolyte when the lithium ion concentration is c.

[0074] It should be noted that the time response characteristics of ions and electrons during transport are the changes in potential over time. Since a single key parameter can affect multiple internal resistances, the ohmic internal resistance, electrochemical internal resistance, and diffusion internal resistance share the same key parameter.

[0075] It is worth mentioning that by numerically analyzing the expression for internal resistance, the key parameters of lithium-ion batteries can be obtained, and the parameters affecting lithium-ion batteries can be finely divided.

[0076] The above lithium-ion battery will be explained by way of example with a specific example below.

[0077] like Figure 2As shown, a lithium-ion battery includes a negative electrode current collector, a negative electrode, a passivation film for the negative electrode (i.e., the aforementioned solid electrolyte interface film), an electrode solution containing multiple components for the negative electrode, a separator, an electrolyte containing multiple components for the positive electrode, a passivation film for the positive electrode, a positive electrode, and a positive electrode current collector. Electrons are transferred from the negative electrode tab to the positive electrode tab, forming a voltage. Simultaneously, lithium ions travel from the electrode solution of the negative electrode through the separator to the electrolyte of the positive electrode. The overpotential range of the positive electrode current collector is 0 to 771, and the overpotential of the positive electrode... The overpotential range for the positive electrode passivation film is 771 to 772; the overpotential range for the positive electrode electrolyte is 773 to 774; the overpotential range for the separator is 774 to 775; the overpotential range for the negative electrode electrode solution is 775 to 776; the overpotential range for the negative electrode passivation film is 776 to 777; the overpotential range for the negative electrode is 777 to 778; the overpotential range for the negative electrode current collector is 778 to 779; and the thickness of the negative electrode sheet is h. a The spacing thickness is h s The total thickness of the negative electrode, separator, and positive electrode is h. tot .

[0078] Step 102: Obtain the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each lithium-ion battery in the multiple lithium-ion batteries, obtain the positive electrode voltage curve and the negative electrode voltage curve after aging of the lithium-ion battery.

[0079] In some embodiments of this application, the actual positive electrode voltage curve and the actual negative electrode voltage curve of the lithium-ion battery are obtained by performing actual tests on each of the multiple lithium-ion batteries (such as using common testing devices such as multimeters).

[0080] It should be noted that all the voltage curves obtained from the above actual tests are of the same type, such as the Hybrid Pulse Power Characterization (HPPC) curve.

[0081] It is worth mentioning that by aging lithium-ion batteries, data on lithium-ion batteries under two different conditions can be obtained.

[0082] Step 103: Obtain the simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging, and obtain the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging.

[0083] In some embodiments of this application, for each lithium-ion battery, the lithium-ion battery is rapidly aged through methods such as disassembly, and the aged lithium-ion battery is subjected to actual testing to obtain the positive electrode voltage curve and negative electrode voltage curve of the aged lithium-ion battery. By inputting the data of the target lithium-ion battery under normal conditions into computer software (such as COMSOL simulation software) for simulation, simulated positive electrode voltage curve and simulated negative electrode voltage curve are obtained. By adjusting the data of the lithium-ion battery, simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the lithium-ion battery under aged conditions are obtained.

[0084] It should be noted that the target lithium-ion battery mentioned above is a specific type of lithium-ion battery that requires aging analysis, and this target lithium-ion battery belongs to the same type of lithium-ion battery as the aforementioned lithium-ion batteries (such as lithium-ion batteries used in automobiles, lithium-ion batteries used in watches, etc.). All the voltage curves obtained from the simulations are of the same type, such as hybrid pulse power characteristic curves.

[0085] It is worth mentioning that by simulating the target lithium-ion battery, the voltage curves of the positive and negative electrodes of the target lithium-ion battery under ideal conditions can be obtained.

[0086] Step 104: Fit the actual positive electrode voltage curves of multiple lithium-ion batteries and the simulated positive electrode voltage curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery.

[0087] The above positive voltage curve was obtained when the fitting variance between the actual positive voltage curve and the simulated positive voltage curve reached the preset optimal fitting value.

[0088] Specifically, through the formula:

[0089]

[0090] Calculate the average value of the actual positive electrode voltage curves of multiple lithium-ion batteries at time t.

[0091]

[0092] in, Let represent the value of the actual positive electrode voltage curve of the nth lithium-ion battery at time t, where n = 1, 2, ..., N, N represents the total number of lithium-ion batteries, and t = 1, 2, ..., T, T represents the time T.

[0093] Through the formula:

[0094]

[0095]

[0096] Calculate the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve.

[0097] in, This represents the variance of the fit between the actual positive electrode voltage curve and the simulated positive electrode voltage curve at time t. This represents the value of the simulated positive electrode voltage curve at time t.

[0098] Determine the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve. If the preset optimal fitting value is reached, then the simulated positive electrode voltage curve will be used as the positive electrode voltage curve of the target lithium-ion battery.

[0099] Otherwise, the average value of the actual positive electrode voltage curves of multiple lithium-ion batteries at time t. Within a preset range, adjust the value of the simulated positive electrode voltage curve at time t, and use the adjusted value of the simulated positive electrode voltage curve at time t as the actual value of the simulated positive electrode voltage curve at time t. Then, calculate the fitting variance between the actual and simulated positive electrode voltage curves using a formula. Determine the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve. The steps to determine whether the preset optimal fit value has been achieved.

[0100] It should be noted that the above-mentioned preset optimal fit value is based on the optimal fit degree. The optimal fit degree is the expected optimal degree of overlap under the mutual constraints of the parameters between the two curves. The fit variance is used to express the fit degree in numerical form. The closer the value of the fit variance is to the preset optimal fit value, the better the fit degree.

[0101] It is worth mentioning that by fitting the actual positive electrode voltage curve and the simulated positive electrode voltage curve, the data of the positive electrode of the target lithium-ion battery under normal conditions can be corrected, thereby improving the accuracy of the data.

[0102] For example, the preset range of the above average value can be 0.5 to 1.5 times the average value, such as the average value of the actual positive electrode voltage curves of multiple lithium-ion batteries at time t. If the value is 2 volts (V), the preset range is 1V to 3V. If the value of the simulated positive voltage curve at time t is 1.2V and the fitting variance is 0.8, which does not reach the preset optimal fitting value of 0.9, then the value of the simulated negative voltage curve at time t is adjusted within the range of 1V to 3V, and the fitting variance is recalculated.

[0103] Step 105: Fit the actual negative electrode voltage curves of multiple lithium-ion batteries and the simulated negative electrode voltage curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery.

[0104] The above negative electrode voltage curve was obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reached the preset optimal fitting value.

[0105] Specifically, through the formula:

[0106]

[0107] Calculate the average value of the actual negative electrode voltage curves of multiple lithium-ion batteries at time t.

[0108]

[0109] in, The value of the actual negative electrode voltage curve of the nth lithium-ion battery at time t is represented by n = 1, 2, ..., N, where N represents the total number of lithium-ion batteries, and t = 1, 2, ..., T, where T represents the time t.

[0110] Through the formula:

[0111]

[0112]

[0113] Calculate the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve.

[0114] in, This represents the variance of the fit between the actual negative electrode voltage curve and the simulated negative electrode voltage curve at time t. This represents the value of the simulated negative electrode voltage curve at time t.

[0115] Determine the variance of the fit between the actual negative electrode voltage curve and the simulated negative electrode voltage curve. If the preset optimal fitting value is reached, then the simulated negative electrode voltage curve will be used as the negative electrode voltage curve of the target lithium-ion battery.

[0116] Otherwise, the average value of the actual negative electrode voltage curves of multiple lithium-ion batteries at time t. Within a preset range, adjust the value of the simulated negative electrode voltage curve at time t, and use the adjusted value of the simulated negative electrode voltage curve at time t as the actual value of the simulated negative electrode voltage curve at time t. Then, calculate the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve using a formula. Determine the variance of the fit between the actual negative electrode voltage curve and the simulated negative electrode voltage curve. The steps to determine whether the preset optimal fit value has been achieved.

[0117] It is worth mentioning that by fitting the actual negative electrode voltage curve and the simulated negative electrode voltage curve, the data of the negative electrode of the target lithium-ion battery under normal conditions can be corrected, thereby improving the accuracy of the data.

[0118] Step 106: Obtain the full-cell voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve.

[0119] In some embodiments of this application, the above-mentioned full-cell voltage curve is as follows:

[0120]

[0121] Among them, E cell (t) represents the full cell voltage. This represents the value of the positive electrode voltage curve of the target lithium-ion battery at time t. Let t represent the value of the negative electrode voltage curve of the target lithium-ion battery at time t, where t = 1, 2, ..., T, and T represents the Tth time.

[0122] It is worth mentioning that by fitting the two voltage curves of the positive electrode and the two voltage curves of the negative electrode of the lithium-ion battery under normal conditions, the data of the positive and negative electrodes of the target lithium-ion battery under normal conditions can be corrected separately, thereby improving the accuracy of the data contained in the full-cell voltage curve.

[0123] The above steps will be illustrated with a specific example below.

[0124] like Figure 3 As shown, a is the curve to be fitted, where E pos HPPCexp represents the actual positive voltage HPPC curve, E pos HPPCsim represents the simulated positive electrode voltage HPPC curve, where b is the curve to be fitted (curve 2), and E neg HPPCexp represents the actual negative electrode HPPC curve, E neg HPPCsim represents the simulated negative electrode HPPC curve, where c is the fitted curve, and E cell HPPCexp represents the HPPC curve obtained experimentally in a full cell, E cellHPPCsim represents the HPPC curve obtained by simulation method in full cell. The horizontal axis is time, starting from 0 seconds (s) in a, with each division representing 100 seconds; starting from -50 seconds (s) in b, with each division representing 50 seconds; and starting from 0 seconds (s) in c, with each division representing 100 seconds. The vertical axis is voltage, starting from 4.00 volts (V) in a, with each division representing 0.02V; starting from 0.09V (V) in b, with each division representing 0.01V; and starting from 3.88V (V) in c, with each division representing 0.04V.

[0125] Step 107: Fit the positive electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged positive electrode curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery after aging.

[0126] The above-mentioned positive electrode voltage curve after aging was obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aging positive electrode curve reached the preset optimal fitting value.

[0127] Specifically, through the formula:

[0128]

[0129] Calculate the average value of the positive electrode voltage curves of multiple lithium-ion batteries after aging, taken at time t.

[0130] in, This represents the value of the positive electrode voltage curve of the nth aged lithium-ion battery sample at time t, where n = 1, 2, ..., N, N represents the total number of lithium-ion battery samples, and t = 1, 2, ..., T, T represents the time T.

[0131] Through the formula:

[0132]

[0133]

[0134] Calculate the fitting variance between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve.

[0135]

[0136] in, This represents the variance of the fit between the aged cathode voltage curve and the simulated aged cathode voltage curve at time t. The value of the simulated aging positive electrode voltage curve of an aging lithium-ion battery at time t.

[0137] Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. If the preset optimal fitting value is reached, then the simulated aging positive electrode voltage curve will be used as the positive electrode voltage curve of the target lithium-ion battery after aging.

[0138] Otherwise, the average value of the positive electrode voltage curves of multiple lithium-ion batteries after aging at time t. Within a preset range, adjust the value of the simulated aging positive electrode voltage curve at time t, and use the adjusted value of the simulated aging positive electrode voltage curve at time t as the value of the simulated aging positive electrode voltage curve at time t. Then, calculate the fitting variance between the aged positive electrode voltage curve and the simulated aging positive electrode voltage curve using a formula. Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. The steps to determine whether the preset optimal fit value has been achieved.

[0139] It is worth mentioning that by fitting the voltage curve of the aged cathode with the simulated aged cathode curve, the data of the cathode of the target lithium-ion battery under aging conditions can be corrected, thereby improving the accuracy of the data.

[0140] Step 108: Fit the negative electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged negative electrode curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery after aging.

[0141] The above-mentioned negative electrode voltage curve after aging was obtained when the fitting variance between the negative electrode voltage curve after aging and the simulated aging negative electrode curve reached the preset optimal fitting value.

[0142] Specifically, through the formula:

[0143]

[0144] Calculate the average value of the negative electrode voltage curves of multiple lithium-ion batteries after aging, taken at time t.

[0145] in, This represents the value of the negative electrode voltage curve of the nth aged lithium-ion battery sample at time t.

[0146] Through the formula:

[0147]

[0148]

[0149] Calculate the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve.

[0150]

[0151] in, This represents the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve at time t. This represents the value of the simulated aging negative electrode voltage curve at time t.

[0152] Determine the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. If the preset optimal fitting value is reached, then the simulated aging negative electrode voltage curve will be used as the negative electrode voltage curve of the target lithium-ion battery after aging.

[0153] Otherwise, the average value of the negative electrode voltage curve at time t after aging of multiple lithium-ion batteries. Within a preset range, adjust the value of the simulated aging negative electrode voltage curve at time t, and use the adjusted value of the simulated aging negative electrode voltage curve at time t as the value of the simulated aging negative electrode voltage curve at time t. Then, calculate the fitting variance between the aged negative electrode voltage curve and the simulated aging negative electrode voltage curve. Determine the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. The steps to determine whether the preset optimal fit value has been achieved.

[0154] It is worth mentioning that by fitting the voltage curve of the aged negative electrode with the simulated aged negative electrode curve, the data of the negative electrode of the target lithium-ion battery under aging condition can be corrected, thereby improving the accuracy of the data.

[0155] Step 109: Based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging, obtain the full-cell aging voltage curve of the target lithium-ion battery.

[0156] In some embodiments, the above-mentioned full-cell aging voltage curve is as follows:

[0157]

[0158] Among them, E acell (t) represents the aging voltage of the full battery. This represents the value of the positive electrode voltage curve of the target lithium-ion battery at time t after aging. The value of the negative electrode voltage curve of the target lithium-ion battery after aging is represented at time t, where t = 1, 2, ..., T, and T represents the Tth time.

[0159] It is worth mentioning that by fitting two voltage curves of the positive electrode and two voltage curves of the negative electrode of a lithium-ion battery under aging conditions, the data of the positive and negative electrodes of the target lithium-ion battery under aging conditions can be corrected separately, thereby improving the accuracy of the data contained in the full-cell aging voltage curve.

[0160] The above steps will be explained by way of example with a specific example below.

[0161] like Figure 4 As shown, a represents the positive electrode voltage curve (Epos Fresh) and the aged positive electrode voltage curve (Epos Ageing) of the target lithium-ion battery; b represents the negative electrode voltage curve (Eeg Fresh) and the aged negative electrode voltage curve (Eeg Ageing) of the target lithium-ion battery; and c represents the full cell curve (Ecell Fresh) and the full cell aging curve (Ecell Ageing). The horizontal axis represents time, starting from 0 seconds (s), with each division representing 100 seconds. The vertical axis represents voltage, starting from 4.02 volts (V) in a, with each division representing 0.02V in b, starting from 0.10V, with each division representing 0.02V in c, and starting from 3.88V, with each division representing 0.04V in c.

[0162] Step 110: Analyze the values ​​of key parameters in the full-cell voltage curve and the full-cell aging curve to obtain the relationship between key parameters and the aging of the target lithium-ion battery.

[0163] In some embodiments of this application, an electrochemical-thermal coupling model can be constructed for the target lithium-ion battery to obtain the performance curve of the target lithium-ion battery, thereby obtaining the relationship between key parameters and the aging of the target lithium-ion battery.

[0164] Specifically, for each key parameter, the value of that parameter in the full-cell aging voltage curve, along with the values ​​of other key parameters in the full-cell voltage curve, are input into the electrochemical-thermal coupling model to generate a performance curve, thereby obtaining the relationship between the parameter and the aging of the target lithium-ion battery (this type of lithium-ion battery).

[0165] Specifically, the ranges of ohmic internal resistance, electrochemical internal resistance, and diffusion internal resistance are mapped onto the full-cell curve and the full-cell aging curve. Then, numerical probes can be used to obtain the values ​​of key parameters from the full-cell curve and the full-cell aging curve respectively. The changes of key parameters are obtained by comparing the values ​​of key parameters in the full-cell voltage curve and the full-cell aging voltage curve, as shown in Table 4.

[0166]

[0167]

[0168]

[0169] Table 4

[0170] Among them, the preliminary verification of BET is the preliminary verification of specific surface area, t + k represents the lithium-ion transference number. a k represents the positive electrode reaction rate constant. c E represents the rate constant of the negative electrode reaction. a α represents the activation energy of solid-state diffusion. a α represents the positive electrode transfer coefficient. c D represents the negative electrode transfer coefficient. s，a D represents the solid-phase diffusion coefficient of lithium ions in the negative electrode. s，c D represents the solid-phase diffusion coefficient of lithium ions in the cathode. e，a D represents the liquid-phase diffusion coefficient of lithium ions in the negative electrode. e，c D represents the liquid-phase diffusion coefficient of lithium ions in the cathode. e This represents the liquid phase diffusion coefficient.

[0171] The values ​​of all key parameters in the full-cell voltage curve are input into the electrochemical-thermal coupling model to obtain the performance curve of the target lithium-ion battery under normal conditions. The values ​​of one or more key parameters are changed and replaced with the values ​​of the key parameters in the full-cell aging voltage curve to obtain the performance curve of the target lithium-ion battery after aging. By analyzing the two performance curves, the relationship between key parameters and lithium-ion battery aging is obtained.

[0172] It should be noted that the above performance curves can be one or more of the following: voltage curve, temperature rise curve, and lithium ion concentration distribution curve of a lithium-ion battery.

[0173] It is worth mentioning that by fitting the simulated aging curve of the target lithium-ion battery with the voltage curves of multiple lithium-ion batteries after aging, the accuracy of the full-cell aging voltage curve of the target lithium-ion battery is greatly improved. As a result, when using the full-cell voltage curve and the full-cell aging voltage curve to analyze key parameters, the accuracy of the relationship between key parameters and battery aging can be greatly improved, and the precision of quantitative analysis of the battery can be increased.

[0174] The steps for obtaining the values ​​of the key parameters are explained below with a specific example.

[0175] like Figure 5 As shown, in the full-cell voltage curve Ecell of a fresh cell (i.e., a lithium-ion battery under normal conditions as described above), the ohmic internal resistance R... ohm (i.e., R_ohm in the figure), electrochemical internal resistance R act(i.e., R_act in the figure) and diffusion resistance R diffu (i.e., R_Diffu in the figure) are mapped to their corresponding value ranges. Using a numerical probe, the values ​​of liquid phase conductivity κ, solid phase conductivity σ, electrode porosity ε, electrode tortuosity α, and film impedance δ are obtained from the range of ohmic internal resistance. sf The value of the electrochemical reaction constant k is obtained from the range of values ​​of the electrochemical internal resistance, and the activation energy E of the electrochemical reaction is obtained. a The value of k sf The passivation film formation reaction rate constant is obtained from the range of diffusion resistance values. The solid phase lithium ion diffusion coefficient Ds and the liquid phase lithium ion diffusion coefficient De are obtained from the range of diffusion resistance values. The electrode porosity ε and the electrode tortuosity α are obtained from the range of diffusion resistance values. The full cell voltage curve is the HPPC curve. ΔOCV represents the change in the full cell equilibrium potential. The horizontal axis is time, starting from 0 seconds, with each division representing 100 seconds (s). The vertical axis is voltage, starting from 3.85 volts (V), with each division representing 0.05 V.

[0176] For example, the values ​​of key parameters of lithium-ion batteries obtained through actual testing are shown in Table 5.

[0177]

[0178]

[0179] Table 5

[0180] The key parameter values ​​obtained from the full-cell curve after curve fitting are shown in Table 6.

[0181]

[0182]

[0183] Table 6

[0184] It can be seen that the accuracy of the key parameters obtained from the full-cell curve has increased.

[0185] The above performance curves will be explained by way of example with a specific example below.

[0186] The voltage curve of the target lithium-ion battery is as follows: Figure 6 As shown, Fresh is the voltage curve of the target lithium-ion battery under normal conditions, κ is the voltage curve of the target lithium-ion battery after liquid phase conductivity aging, and σ pos The voltage curve of the target lithium-ion battery after aging of the positive electrode solid-phase conductivity is given by σ. neg The voltage curve of the target lithium-ion battery after aging of the negative electrode solid-phase conductivity is shown in Figure D. posEa represents the voltage curve of the target lithium-ion battery after aging, where the positive electrode solid-phase diffusion coefficient is [value missing]. D_neg The voltage curve of the target lithium-ion battery after aging, Ea, represents the activation energy of lithium-ion diffusion in the negative electrode solid phase. D_pos The activation energy for lithium-ion diffusion in the positive electrode is the voltage curve of the target lithium-ion battery after aging, k. neg Ea represents the voltage curve of the target lithium-ion battery after aging, where the negative electrode electrochemical reaction rate constant is the constant. k_pos The voltage curve of the target lithium-ion battery after aging is the activation energy of the positive electrode electrochemical reaction, Ea. k_neg The voltage curve of the target lithium-ion battery after aging, representing the activation energy of the negative electrode electrochemical reaction, is shown in Rsf. pos This is the temperature rise curve of the target lithium-ion battery after positive electrode film impedance aging, Rsf neg This is the temperature rise curve of the target lithium-ion battery after negative electrode film impedance aging, D neg The voltage curve of the target lithium-ion battery after aging is shown in k, representing the negative electrode solid-phase diffusion coefficient. pos The voltage curve of the target lithium-ion battery after aging, representing the positive electrode electrochemical reaction rate constant, is shown. (Recyclable Li) + The voltage curve of the target lithium-ion battery after aging is shown in Figure 1. Ageing is the voltage curve of the target lithium-ion battery after aging. The horizontal axis represents time, starting from 0 seconds and each division is 1000 seconds (s). The vertical axis represents voltage, starting from 2.8 volts (V) and each division is 0.05V.

[0187] The temperature rise curve of the target lithium-ion battery is as follows: Figure 7 As shown, Fresh is the temperature rise curve of the target lithium-ion battery under normal conditions, κ is the temperature rise curve of the target lithium-ion battery after liquid phase conductivity aging, and σ pos The temperature rise curve of the target lithium-ion battery after aging of the positive electrode solid-phase conductivity is shown, σ. neg The temperature rise curve of the target lithium-ion battery after aging of the negative electrode solid-phase conductivity is shown in Figure D. pos The temperature rise curve of the target lithium-ion battery after aging is Ea, representing the positive electrode solid-phase diffusion coefficient. D_neg The temperature rise curve of the target lithium-ion battery after aging is shown in Ea, representing the activation energy of lithium-ion diffusion in the negative electrode solid phase. D_pos The activation energy for lithium-ion diffusion in the positive electrode is shown in the temperature rise curve of the target lithium-ion battery after aging, k. neg The temperature rise curve of the target lithium-ion battery after aging is Ea, representing the negative electrode electrochemical reaction rate constant. k_pos The temperature rise curve of the target lithium-ion battery after aging is Ea, representing the activation energy of the positive electrode electrochemical reaction. k_neg The temperature rise curve of the target lithium-ion battery after aging, representing the activation energy of the negative electrode electrochemical reaction, is shown in Rsf. posThis is the temperature rise curve of the target lithium-ion battery after positive electrode film impedance aging, Rsf neg This is the temperature rise curve of the target lithium-ion battery after negative electrode film impedance aging, D neg The temperature rise curve of the target lithium-ion battery after aging is shown in k, representing the negative electrode solid-phase diffusion coefficient. pos The temperature rise curve of the target lithium-ion battery after aging is shown as the positive electrode electrochemical reaction rate constant. (Recyclable Li) + The voltage curve of the target lithium-ion battery after aging is shown in Figure 1. Ageing is shown in Figure 2. The horizontal axis represents time, starting from 0 seconds and each division is 1000 seconds (s). The vertical axis represents temperature, starting from 0 degrees Celsius (°C) and each division is 5°C.

[0188] This shows that aging caused by various factors leads to a decrease in the voltage and capacity of lithium-ion batteries. Among these, the reduction in cyclic lithium, the aging of the positive electrode electrochemical reaction constant, and the aging of the negative electrode solid-phase lithium-ion diffusion coefficient have the most significant impact on voltage output capability. Figure 7 The aging of different factors shown will cause the temperature rise during battery discharge to increase. Among them, the reduction of cyclic lithium, the aging of the positive electrode electrochemical reaction constant, and the aging of the negative electrode film impedance have the most significant impact on the battery temperature rise.

[0189] Furthermore, the quantitative analysis method for lithium-ion battery aging in this application can be applied to the battery performance prediction process in scenarios such as electronic products, new energy vehicles, and large-scale energy storage power stations.

[0190] The following is an exemplary description of the lithium-ion battery aging quantitative analysis device provided in this application.

[0191] This application provides a quantitative analysis device for lithium-ion battery aging, which includes:

[0192] The acquisition module acquires key parameters that affect the DC internal resistance of lithium-ion batteries;

[0193] The actual module obtains the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each of the multiple lithium-ion batteries, obtains the positive electrode voltage curve and the negative electrode voltage curve after aging of the lithium-ion battery.

[0194] The simulation module obtains the simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging, and obtains the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging.

[0195] The first fitting module fits the actual positive electrode voltage curves of multiple lithium-ion batteries with the simulated positive electrode voltage curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery. The positive electrode voltage curve is obtained when the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve reaches the preset optimal fitting value.

[0196] The second fitting module fits the actual negative electrode voltage curves of multiple lithium-ion batteries with the simulated negative electrode voltage curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery. The negative electrode voltage curve is obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reaches the preset optimal fitting value.

[0197] The first calculation module obtains the full-cell voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve.

[0198] The third fitting module fits the positive electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged positive electrode curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery after aging. The positive electrode voltage curve after aging is obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aged positive electrode curve reaches the preset optimal fitting value.

[0199] The fourth fitting module fits the negative electrode voltage curves of multiple aged lithium-ion batteries with the simulated aged negative electrode curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery after aging. The aged negative electrode voltage curve is obtained when the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode curve reaches the preset optimal fitting value.

[0200] The second calculation module obtains the full-cell aging voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging.

[0201] The analysis module analyzes the values ​​of key parameters in the full-cell voltage curve and the full-cell aging curve to obtain the relationship between key parameters and the aging of the target lithium-ion battery.

[0202] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0203] An embodiment of this application provides a terminal device. The terminal device D10 of this embodiment includes: at least one processor D100, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100. When the processor D100 executes the computer program D102, it implements the steps in any of the above method embodiments, which can improve the accuracy of quantitative analysis of batteries.

[0204] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0205] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0206] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0207] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0208] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to the lithium-ion battery aging quantitative analysis method apparatus / terminal device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0209] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0210] Those skilled in the art will 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 or a combination of computer software and electronic hardware. 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.

[0211] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention.

Claims

1. A method for quantitative analysis of lithium-ion battery aging, characterized in that, include: To obtain the key parameters affecting the DC internal resistance of lithium-ion batteries; Obtain the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each of the multiple lithium-ion batteries, obtain the positive electrode voltage curve and the negative electrode voltage curve after aging of the lithium-ion battery. The simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging are obtained, and the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging are obtained. The actual positive electrode voltage curves of the plurality of lithium-ion batteries and the simulated positive electrode voltage curve of the target lithium-ion battery are fitted to obtain the positive electrode voltage curve of the target lithium-ion battery; the positive electrode voltage curve is obtained when the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve reaches a preset optimal fitting value; The actual negative electrode voltage curves of the plurality of lithium-ion batteries and the simulated negative electrode voltage curve of the target lithium-ion battery are fitted to obtain the negative electrode voltage curve of the target lithium-ion battery; the negative electrode voltage curve is obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reaches the preset optimal fitting value; Based on the positive electrode voltage curve and the negative electrode voltage curve, the full-cell voltage curve of the target lithium-ion battery is obtained; The positive electrode voltage curves of the multiple lithium-ion batteries after aging and the simulated aging positive electrode curve of the target lithium-ion battery are fitted to obtain the positive electrode voltage curve of the target lithium-ion battery after aging; the positive electrode voltage curve after aging is obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aging positive electrode curve reaches the preset optimal fitting value. The negative electrode voltage curves of the multiple lithium-ion batteries after aging and the simulated aging negative electrode curve of the target lithium-ion battery are fitted to obtain the negative electrode voltage curve of the target lithium-ion battery after aging; the negative electrode voltage curve after aging is obtained when the fitting variance between the negative electrode voltage curve after aging and the simulated aging negative electrode curve reaches the preset optimal fitting value. Based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging, the full-cell aging voltage curve of the target lithium-ion battery is obtained. The values ​​of the key parameters in the full-cell voltage curve and the full-cell aging voltage curve are analyzed to obtain the relationship between the key parameters and the aging of the target lithium-ion battery. The step of fitting the aged positive electrode voltage curves of the plurality of lithium-ion batteries with the simulated aged positive electrode curve of the target lithium-ion battery to obtain the aged positive electrode voltage curve of the target lithium-ion battery includes: Through the formula: Calculate the positive electrode voltage curves of the multiple lithium-ion batteries after aging in the first... The average of the values ​​at each time point ; in, Indicates the first The positive electrode voltage curve of the aged lithium-ion battery sample is shown in the first... The value at each moment, , This represents the total number of lithium-ion battery samples. , Indicates the first At that moment; Through the formula: Calculate the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve at a given time point. The simulated aging positive electrode voltage curve of the aged lithium-ion battery represents the voltage curve at the [missing information - likely a date or time]. The value at each moment; Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. Whether the preset optimal fitting value is reached, if so, the simulated aging positive electrode voltage curve is used as the positive electrode voltage curve of the target lithium-ion battery after aging. Otherwise, the positive electrode voltage curves of the plurality of lithium-ion batteries after aging are in the first... The average of the values ​​at each time point Within a preset range, the simulated aging positive electrode voltage curve is adjusted in the first... The value at time n is used, and the adjusted simulated aging positive electrode voltage curve is used at time n. The value at time n is used as the simulated aging positive electrode voltage curve at time n. The values ​​at each moment are returned, and the fitting variance between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve is calculated using the formula. Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. The step of determining whether the preset optimal fitting value has been reached; The process of fitting the aged negative electrode voltage curves of the plurality of lithium-ion batteries with the simulated aged negative electrode curve of the target lithium-ion battery to obtain the aged negative electrode voltage curve of the target lithium-ion battery includes: Through the formula: Calculate the negative electrode voltage curves of the multiple lithium-ion batteries after aging in the first... The average of the values ​​at each time point ; in, Indicates the first The negative electrode voltage curve of the aged lithium-ion battery sample after aging is shown in the first... The value at each moment; Through the formula: Calculate the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve at each time point. The simulated aging negative electrode voltage curve indicates that the voltage curve is at the first... The value at each moment; Determine the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. Whether the preset optimal fitting value is reached, if so, the simulated aging negative electrode voltage curve is used as the negative electrode voltage curve of the target lithium-ion battery after aging. Otherwise, the negative electrode voltage curves of the plurality of lithium-ion batteries after aging are in the first... The average of the values ​​at each time point Within a preset range, the simulated aging negative electrode voltage curve is adjusted in the first... The value at time n is used, and the adjusted simulated aging negative electrode voltage curve is used at time n. The value at time n is used as the simulated aging negative electrode voltage curve at time n. The values ​​at each time point are returned, and the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve is calculated. Determine the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. The step of determining whether the preset optimal fit value has been reached.

2. The method for quantitative analysis of lithium-ion battery aging according to claim 1, characterized in that, The key parameters affecting the DC internal resistance of lithium-ion batteries include: The DC internal resistance of the lithium-ion battery is decomposed into ohmic internal resistance. Electrochemical internal resistance and diffusion resistance ; Regarding the ohmic internal resistance Numerical analysis was performed to obtain the ohmic internal resistance. Several key parameters: in, Indicates liquid phase conductivity. Indicates solid-state conductivity. Indicates electrode porosity. Indicates electrode tortuosity. Indicates membrane impedance; Regarding the electrochemical internal resistance Numerical analysis was performed to obtain the electrochemical internal resistance. Several key parameters: in, Represents the electrochemical reaction constant. Indicates the activation energy of an electrochemical reaction. Indicates the concentration of lithium ions in the solid phase. Indicates the concentration of lithium ions in the liquid phase; Regarding the diffusion internal resistance Numerical analysis was performed to obtain the diffusion resistance. Several key parameters: in, This represents the solid-phase lithium-ion diffusion coefficient. Indicates the diffusion coefficient of lithium ions in the liquid phase; The ohmic internal resistance Several key parameters, the electrochemical internal resistance Several key parameters and the diffusion resistance Several key parameters are used as key parameters of the lithium-ion battery.

3. The method for quantitative analysis of lithium-ion battery aging according to claim 1, characterized in that, The process of fitting the actual positive electrode voltage curves of the plurality of lithium-ion batteries and the simulated positive electrode voltage curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery includes: Through the formula: Calculate the actual positive electrode voltage curves of the multiple lithium-ion batteries in the first... The average of the values ​​at each time point ; in, Indicates the first The actual positive electrode voltage curve of the lithium-ion battery is on the [number]th [year]. The value at each moment, , This indicates the total number of lithium-ion batteries. , Indicates the first At that moment; Through the formula: Calculate the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve at each time point. The simulated positive electrode voltage curve indicates that the voltage curve is at the first... The value at each moment; Determine the variance of the fit between the actual positive electrode voltage curve and the simulated positive electrode voltage curve. Whether the preset optimal fitting value has been reached, if so, then the simulated positive electrode voltage curve is used as the positive electrode voltage curve of the target lithium-ion battery; Otherwise, in the actual positive electrode voltage curves of the plurality of lithium-ion batteries, the first... The average of the values ​​at each time point Within a preset range, the simulated positive electrode voltage curve is adjusted in the first... The value at time n is used, and the adjusted simulated positive electrode voltage curve is used at time n. The value at time n is used as the simulated positive electrode voltage curve at time n. The values ​​at each moment are returned, and the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve is calculated using the formula. Determine the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve. The step of determining whether the preset optimal fit value has been reached.

4. The method for quantitative analysis of lithium-ion battery aging according to claim 1, characterized in that, The process of fitting the actual negative electrode voltage curves of the plurality of lithium-ion batteries and the simulated negative electrode voltage curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery includes: Through the formula: Calculate the actual negative electrode voltage curves of the multiple lithium-ion batteries in the first... The average of the values ​​at each time point ; in, Indicates the first The actual negative electrode voltage curve of the lithium-ion battery is on the [number]th [year]. The value at each moment, , This indicates the total number of lithium-ion batteries. , Indicates the first At that moment; Through the formula: Calculate the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve at each time point. The simulated negative electrode voltage curve indicates that the curve is in the first... The value at each moment; Determine the variance of the fit between the actual negative electrode voltage curve and the simulated negative electrode voltage curve. Whether the preset optimal fitting value has been reached, if so, then the simulated negative electrode voltage curve is used as the negative electrode voltage curve of the target lithium-ion battery; Otherwise, in the actual negative electrode voltage curves of the plurality of lithium-ion batteries, in the ... The average of the values ​​at each time point Within a preset range, adjust the simulated negative electrode voltage curve at the first... The value at time n is used, and the adjusted simulated negative electrode voltage curve is then used at time n. The value at time n is used as the simulated negative electrode voltage curve at time n. The values ​​at each moment are returned, and the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve is calculated using the formula. Determine the variance of the fit between the actual negative electrode voltage curve and the simulated negative electrode voltage curve. The step of determining whether the preset optimal fit value has been reached.

5. The method for quantitative analysis of lithium-ion battery aging according to claim 1, characterized in that, The full battery voltage curve is as follows: in, Indicates the full battery voltage. The positive electrode voltage curve of the target lithium-ion battery is shown in the first... The value at each moment, The negative electrode voltage curve of the target lithium-ion battery is shown in the first... The value at each moment, , Indicates the first That moment.

6. The method for quantitative analysis of lithium-ion battery aging according to claim 1, characterized in that, The aging voltage curve of the full battery is as follows: in, Indicates the aging voltage of the full battery. The positive electrode voltage curve of the target lithium-ion battery after aging is shown in the first... The value at each moment, The negative electrode voltage curve of the target lithium-ion battery after aging is shown in the first... The value at each moment, , Indicates the first That moment.

7. A quantitative analysis device for lithium-ion battery aging, characterized in that, include: The acquisition module acquires key parameters that affect the DC internal resistance of lithium-ion batteries; The actual module obtains the actual positive electrode voltage curve and the actual negative electrode voltage curve of multiple lithium-ion batteries, and for each of the multiple lithium-ion batteries, obtains the positive electrode voltage curve and the negative electrode voltage curve after aging of the lithium-ion battery. The simulation module obtains the simulated positive electrode voltage curve and simulated negative electrode voltage curve of the target lithium-ion battery before aging, and obtains the simulated aged positive electrode voltage curve and simulated aged negative electrode voltage curve of the target lithium-ion battery after aging. The first fitting module fits the actual positive electrode voltage curves of the plurality of lithium-ion batteries and the simulated positive electrode voltage curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery; the positive electrode voltage curve is obtained when the fitting variance between the actual positive electrode voltage curve and the simulated positive electrode voltage curve reaches a preset optimal fitting value; The second fitting module fits the actual negative electrode voltage curves of the plurality of lithium-ion batteries and the simulated negative electrode voltage curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery; the negative electrode voltage curve is obtained when the fitting variance between the actual negative electrode voltage curve and the simulated negative electrode voltage curve reaches the preset optimal fitting value. The first calculation module obtains the full-cell voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve. The third fitting module fits the positive electrode voltage curves of the multiple lithium-ion batteries after aging with the simulated aging positive electrode curve of the target lithium-ion battery to obtain the positive electrode voltage curve of the target lithium-ion battery after aging; the positive electrode voltage curve after aging is obtained when the fitting variance between the positive electrode voltage curve after aging and the simulated aging positive electrode curve reaches the preset optimal fitting value. The fourth fitting module fits the negative electrode voltage curves of the multiple lithium-ion batteries after aging with the simulated aging negative electrode curve of the target lithium-ion battery to obtain the negative electrode voltage curve of the target lithium-ion battery after aging; the negative electrode voltage curve after aging is obtained when the fitting variance between the negative electrode voltage curve after aging and the simulated aging negative electrode curve reaches the preset optimal fitting value. The second calculation module obtains the full-cell aging voltage curve of the target lithium-ion battery based on the positive electrode voltage curve and the negative electrode voltage curve of the target lithium-ion battery after aging. The analysis module analyzes the values ​​of the key parameters in the full-cell voltage curve and the full-cell aging voltage curve to obtain the relationship between the key parameters and the aging of the target lithium-ion battery. Specifically, the third fitting module is used to implement: Through the formula: Calculate the positive electrode voltage curves of the multiple lithium-ion batteries after aging in the first... The average of the values ​​at each time point ; in, Indicates the first The positive electrode voltage curve of the aged lithium-ion battery sample is shown in the first... The value at each moment, , This represents the total number of lithium-ion battery samples. , Indicates the first At that moment; Through the formula: Calculate the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve at a given time point. The simulated aging positive electrode voltage curve of the aged lithium-ion battery represents the voltage curve at the [missing information - likely a date or time]. The value at each moment; Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. Whether the preset optimal fitting value is reached, if so, the simulated aging positive electrode voltage curve is used as the positive electrode voltage curve of the target lithium-ion battery after aging. Otherwise, the positive electrode voltage curves of the plurality of lithium-ion batteries after aging are in the first... The average of the values ​​at each time point Within a preset range, the simulated aging positive electrode voltage curve is adjusted in the first... The value at time n is used, and the adjusted simulated aging positive electrode voltage curve is used at time n. The value at time n is used as the simulated aging positive electrode voltage curve at time n. The values ​​at each moment are returned, and the fitting variance between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve is calculated using the formula. Determine the variance of the fit between the aged positive electrode voltage curve and the simulated aged positive electrode voltage curve. The step of determining whether the preset optimal fitting value has been reached; The process of fitting the aged negative electrode voltage curves of the plurality of lithium-ion batteries with the simulated aged negative electrode curve of the target lithium-ion battery to obtain the aged negative electrode voltage curve of the target lithium-ion battery includes: Through the formula: Calculate the negative electrode voltage curves of the multiple lithium-ion batteries after aging in the first... The average of the values ​​at each time point ; in, Indicates the first The negative electrode voltage curve of the aged lithium-ion battery sample after aging is shown in the first... The value at each moment; Through the formula: Calculate the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. ; in, Indicates the first The value of the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve at each time point. The simulated aging negative electrode voltage curve indicates that the voltage curve is at the first... The value at each moment; Determine the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. Whether the preset optimal fitting value is reached, if so, the simulated aging negative electrode voltage curve is used as the negative electrode voltage curve of the target lithium-ion battery after aging. Otherwise, the negative electrode voltage curves of the plurality of lithium-ion batteries after aging are in the first... The average of the values ​​at each time point Within a preset range, the simulated aging negative electrode voltage curve is adjusted in the first... The value at time n is used, and the adjusted simulated aging negative electrode voltage curve is used at time n. The value at time n is used as the simulated aging negative electrode voltage curve at time n. The values ​​at each time point are returned, and the variance of the fit between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve is calculated. Determine the fitting variance between the aged negative electrode voltage curve and the simulated aged negative electrode voltage curve. The step of determining whether the preset optimal fit value has been reached.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the quantitative analysis method for lithium-ion battery aging as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the quantitative analysis method for lithium-ion battery aging as described in any one of claims 1 to 6.

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