A method for predicting performance indexes of a ternary positive electrode material and a method for optimizing sintering conditions

Abstract

The application discloses a kind of prediction method of the performance index of ternary positive electrode material and the optimization method of sintering condition, belong to the technical field of ternary positive electrode material industry preparation.This prediction method of the performance index of ternary positive electrode material includes: obtaining the variation law of primary particle size index, establishing the primary particle prediction model of ternary positive electrode material based on grain growth kinetics equation, and then predicting the primary particle size of ternary positive electrode material.The optimization method of the sintering condition of ternary positive electrode material includes: with the least time to complete oxidation as optimization goal, with heating rate, oxygen vacancy final value, oxygen vacancy concentration prediction model, heating rate upper and lower bound and oxidation reaction starting stage as constraint condition to obtain optimal heating rate and optimal heating time.The prediction method proposed in the application can predict the primary particle size of ternary positive electrode material, and the prediction method is accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial preparation of ternary cathode materials, and particularly relates to a performance index prediction method and a sintering condition optimization method for ternary cathode materials. BACKGROUND

[0002] Lithium ion batteries have high specific capacity, no pollution and other advantages, and are one of the most important energy storage devices in the future, thus causing a research boom. The cathode material is an important component of a lithium ion battery, and has a great influence on the working voltage, capacity, service life and safety of the battery, and the cost of the cathode material accounts for 30% to 40% of the total cost of the battery. In recent years, the preparation of high-performance cathode materials with high capacity, good rate performance, long service life and low price has become an important research and development task.

[0003] Clean energy needs to be matched with good energy storage equipment to make the supply of new energy more stable and reliable, so it is very important to develop batteries with high energy density, long service life and environmental friendliness. Lithium ion batteries are environmentally friendly batteries, and can reduce carbon emissions by replacing traditional energy sources, but many factories ignore the high carbon emission in the process of preparation, and the production cost is high and causes energy waste, so reducing the sintering energy consumption of the roller kiln in the preparation process of ternary cathode materials has become an important target of energy saving and emission reduction.

[0004] In the industrial sintering process, the factory usually uses a roller kiln for a long time of uninterrupted high-temperature calcination process, including a temperature rising section, a constant temperature section and a temperature decreasing section, each temperature section is divided into multiple temperature zones, and the temperature and atmosphere of the temperature zones are coupled with each other. At the same time, the reactions occurring in each temperature zone are also different, and the quality of the sintered product is closely related to the temperature, atmosphere and sintering time of each temperature zone. After 22 hours of calcination and complex physical and chemical reaction changes, the desired sintered product can be finally obtained.

[0005] In the sintering process, the key performance index state directly affects the electrochemical performance of the material finished product. For example, in the actual process, the product electrochemical performance such as rate, capacity and specific surface area is often strictly required. Among them, the primary particle size is related to the rate, the roughness is related to the product capacity, and the oxygen defects are related to the specific surface area. Therefore, the primary particle size, the roughness and the oxygen vacancy can be selected as the performance measurement index of the product. However, there is no method for predicting the performance index of the sintering process of the ternary cathode material.

[0006] The sintering reaction process mainly includes three reaction stages, namely, a dehydration stage, an oxidation stage and a grain growth stage. However, the three reaction stages are coupled with each other, the reaction mechanism is complex, the micro reaction process is unclear, and a large amount of manpower and material resources are needed to find the optimal sintering condition meeting the requirements of different products. Meanwhile, the relationship between the temperature setting in the sintering condition and the performance indexes of the materials after sintering is complex, and it is difficult to quantitatively analyze, which brings great challenges to the determination of the initial sintering condition.

[0007] Therefore, the prior art does not study the prediction of the performance indexes in the sintering process of the ternary positive electrode material and the optimization of the initial sintering condition, and reasonably selects the sintering parameters, which is an urgent technical problem to be solved. SUMMARY

[0008] The present application aims to overcome the above technical deficiencies, and provide a performance index prediction method and a sintering condition optimization method for ternary positive electrode materials, which solve the technical problem of how to predict the performance indexes of ternary positive electrode materials in the prior art.

[0009] To achieve the above technical purposes, the technical scheme of the present application provides a performance index prediction method for ternary positive electrode materials, which includes the following steps:

[0010] According to the growth mechanism analysis and the corresponding scanning electron microscope experimental data, the change rule of the primary particle size index is obtained, a primary particle prediction model for ternary positive electrode materials based on the grain growth kinetics equation is established, and the primary particle size of the ternary positive electrode material is predicted.

[0011] The primary particle prediction model in the temperature rising stage is expressed as follows:

[0012] D0=a1-a2·T-a3·β+a4·T 2 -a5·T·β+a6·β 2 ;

[0013] Wherein, D0 is the average particle size of the grain at the end of the temperature rising stage, T is the sintering temperature, β is the temperature rising rate, and a1-a6 are constants.

[0014] The primary particle prediction model in the constant temperature stage is expressed as follows:

[0015] D=(D0 0.4908 +64.0383·t 1.2942 ·exp(-Q / R·T)) 1 / 0.4908

[0016] Wherein, D is the average grain size of the crystal grain after calcination for t time, nm; D0 is the average grain size of the crystal grain at the end of the temperature rising section, nm; Q represents the activation energy of the crystal grain growth; R is the ideal gas constant, which is 8.314 J / (mol·K); T is the sintering temperature, unit K; t is the constant temperature time, unit min.

[0017] Further, the method further comprises: obtaining the change rule of the oxygen vacancy concentration index according to the oxygen vacancy formation mechanism analysis combined with the corresponding EPR experimental data, obtaining the empirical formula of the relationship between the oxygen vacancy concentration and the sintering parameter, establishing the oxygen vacancy concentration prediction model, and then predicting the oxygen vacancy concentration according to the primary particle size.

[0018] The oxygen vacancy concentration prediction model is represented as follows:

[0019] delta = p0*D 3 + p1*D 2 + p2*D + p3

[0020] Wherein, delta represents the oxygen vacancy concentration, D is the average grain size of the crystal grain after calcination for t time, and p0-p3 are constants.

[0021] Further, the method further comprises: combining the scanning electron microscope image experimental data, establishing the roughness size index prediction model according to the change rule of the roughness index, and then predicting the roughness according to the primary particle size.

[0022] The roughness size index prediction model is represented as follows:

[0023] R = q0*D 2 + q1*D + q2

[0024] Wherein, R is the roughness size, D is the average grain size of the crystal grain after calcination for t time, and q0-q2 are constants.

[0025] In addition, the application also proposes a sintering condition optimization method of a ternary positive electrode material, which comprises the following steps:

[0026] The sintering condition of the oxidation stage is optimized to complete the oxidation in the least time as the optimization target, and the optimal temperature rising rate and the optimal temperature rising time are obtained by solving the constraint conditions of the temperature rising rate, the oxygen vacancy final value, the oxygen vacancy concentration prediction model, the temperature rising rate upper and lower bounds and the oxidation reaction starting stage; the primary particle size predicted by the performance index prediction method of the ternary positive electrode material is further used to predict the oxygen vacancy concentration.

[0027] Further, the least time required for completing the oxidation of the oxidation stage is represented as

[0028] min J2 = t d ;

[0029] wherein, t d is the time spent for the completion of the oxidation reaction.

[0030] Further, in the temperature rising rate constraint, the temperature rising rate and the sintering time have a quantitative relationship, and the current sintering time is expressed as follows:

[0031] t = (T - T te ) / β2, t d = (T ye - T te ) / β2

[0032] wherein, T is the sintering temperature at the current time, T ye is the sintering temperature at the end of the oxidation reaction, β2 is the temperature rising rate in the oxidation stage, and t is the current sintering time;

[0033] In the oxygen vacancy final value constraint, in the oxidation stage, the oxygen vacancy is a structure index, and its final value needs to meet the process requirements, so

[0034] δ vmin ≤ δ vacancy (t d ) ≤ δ vmax

[0035] wherein, δ vmin and δ vmax are the process requirements for the oxygen vacancy at the end of the oxidation reaction, and δ vacancy (t d ) is calculated by the oxygen vacancy concentration prediction model;

[0036] In the oxidation reaction initial stage constraint, the oxidation stage needs to be completed within a certain time, and needs to meet:

[0037] t d min ≤ t d ≤ t d max

[0038] wherein, t d min and t d max respectively represent the maximum time of oxidation and the minimum time of oxidation;

[0039] In the upper and lower bound constraint, the temperature rising rate has a maximum value and a minimum value, and needs to meet:

[0040] β 2min ≤ β2 ≤ β 2max

[0041] wherein, β 2min represents the minimum value of the temperature rising rate, and β 2max represents the maximum value of the temperature rising rate;

[0042] By solving the above optimization problem, the optimal temperature rising rate and the optimal temperature rising time of the oxidation stage are obtained.

[0043] Further, the sintering conditions of the grain growth stage are also optimized, with the optimization goal being to complete the growth in the least time and at the lowest temperature, and with the final grain size, the primary particle prediction model, the roughness size index prediction model, the oxidation temperature, the upper and lower bounds of the platform temperature and the upper and lower bounds of the holding time as the constraint conditions to solve the optimal holding time and the platform temperature of the grain growth stage.

[0044] Further, the optimization goal is represented as

[0045] min J3=t b +T bw ;

[0046] Wherein, t b is the time spent in completing the grain growth, and T bw is the holding platform of the grain growth.

[0047] Further, in the final grain size constraint, the average particle size at the end of the grain growth stage needs to meet the process requirements, so the average particle size at the end of the grain growth stage needs to meet:

[0048] D bwmin ≤D bw (t b ,T bw )≤D bwmax

[0049] Wherein, D bwmin and D bwmax are the minimum and maximum grain sizes at the end of the grain growth, respectively.

[0050] In the primary particle prediction model constraint, the particle size change is related to the holding time, the platform temperature and the initial particle size, so the particle size is represented as:

[0051] D bw =f bw (t,T bw ,D0)

[0052] Wherein, D0 is the initial primary particle size of the grain, t represents the holding time, and T bw represents the platform temperature.

[0053] In the roughness size index prediction model constraint, the roughness size is related to the holding time, the platform temperature and the primary particle size in the constant temperature section, so the roughness is represented as:

[0054] R=f bw (β2,D bw ,t)

[0055] wherein, D bw is the primary particle size in the constant temperature stage, which is related to the constant temperature platform, the constant temperature time and the constant temperature initial particle size D bw = f bw (t, T bw , D0).

[0056] In the oxidation temperature constraint, the platform temperature needs to be greater than the temperature after the oxidation is completed, and there is a relationship as follows:

[0057] β1t w + β2t d ≤ T bw

[0058] In the upper and lower bound constraints, the platform temperature and the holding time both have maximum and minimum values, and the platform temperature and the holding time need to satisfy:

[0059]

[0060] By solving the above optimization problem, the optimal holding time and platform temperature in the grain growth stage are obtained.

[0061] Further, the ternary positive electrode material is LiNi 0.83 Co 0.11 Mn 0.06 O2.

[0062] Compared with the prior art, the beneficial effects of the present application include: the present application proposes a prediction method for performance indicators of a ternary positive electrode material, which comprises: obtaining the change rule of the primary particle size indicator according to the growth mechanism analysis combined with the corresponding scanning electron microscope experimental data, establishing a primary particle prediction model of the ternary positive electrode material based on the grain growth kinetics equation, and then predicting the primary particle size of the ternary positive electrode material, and the prediction method is accurate. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 FIG. 1 is a segmented schematic diagram of the sintering process of the ternary positive electrode material of Example 1.

[0064] Figure 2 FIG. 4 is the selection of the optimal growth index n value of Example 1.

[0065] Figure 3 FIG. 5 is a comparison diagram of the primary particle size prediction results and experimental results under different sintering conditions of Example 1.

[0066] Figure 4 FIG. 6 is a comparison diagram of the oxygen vacancy concentration prediction results and experimental results under different sintering conditions of Example 1.

[0067] Figure 5The roughness prediction results of different sintering conditions in Example 1 are compared with the experimental results.

[0068] Figure 6 The experimental results after sintering according to the optimization method in Example 1 are shown in the figure ((a) is the given sintering schedule, (b) is the XRD test result, (c) is the EPR test result, and (d) is the SEM image test result). DETAILED DESCRIPTION

[0069] The specific embodiment provides a prediction method for performance indexes of a ternary positive electrode material, including the following steps:

[0070] According to the growth mechanism analysis combined with the corresponding scanning electron microscope experimental data, the variation law of the primary particle size index is obtained, a primary particle prediction model of the ternary positive electrode material based on the grain growth kinetics equation is established, and then the primary particle size of the ternary positive electrode material is predicted.

[0071] The primary particle prediction model of the temperature rising stage is as follows:

[0072] D0=a1-a2·T-a3·β+a4·T 2 -a5·T·β+a6·β 2 ;

[0073] Wherein, D0 is the average particle size of the grain at the end of the temperature rising stage, T is the sintering temperature, β is the temperature rising rate, and a1-a6 are constants;

[0074] The primary particle prediction model of the constant temperature stage is as follows:

[0075] D=(D0 0.4908 +64.0383·t 1.2942 ·exp(-Q / R·T)) 1 / 0.4908

[0076] Wherein, D is the average particle size of the grain after calcination for t time, nm; D0 is the average particle size of the grain at the end of the temperature rising stage, nm; R is the ideal gas constant, which is 8.314 J / (mol·K); T is the sintering temperature, unit K; t is the constant temperature time, unit min.

[0077] In some embodiments, the variation law of the oxygen vacancy concentration index is also obtained according to the oxygen vacancy formation mechanism analysis combined with the corresponding EPR experimental data, an empirical formula of the relationship between the oxygen vacancy concentration and the sintering parameters is obtained, an oxygen vacancy concentration prediction model is established, and then the oxygen vacancy concentration is predicted according to the primary particle size.

[0078] The oxygen vacancy concentration prediction model is as follows:

[0079] δ=p0·D3 + p1 · D 2 + p2 · D + p3

[0080] wherein, δ represents the oxygen vacancy concentration, D is the average grain size of the crystal grains after calcination for t time, p0-p3 are constants.

[0081] In some embodiments, a roughness size index prediction model is established according to the change rule of the roughness index based on the experimental data of the scanning electron microscope image, and then the roughness is predicted according to the primary particle size.

[0082] The roughness size index prediction model is expressed as follows:

[0083] R = q0 · D 2 + q1 · D + q2, wherein R is the roughness size, D is the average grain size of the crystal grains after calcination for t time, q0-q2 are constants.

[0084] The specific embodiment also proposes a sintering condition optimization method for a ternary positive electrode material, including the following steps:

[0085] The sintering conditions of the oxidation stage are optimized to complete the oxidation in the least time as the optimization goal, and the optimal heating rate and the optimal heating time are obtained by solving the constraints of the heating rate, the oxygen vacancy final value, the oxygen vacancy concentration prediction model, the upper and lower bounds of the heating rate, and the oxidation reaction starting stage; the oxygen vacancy concentration prediction model further predicts the oxygen vacancy concentration by predicting the primary particle size by the above-mentioned prediction method of the performance index of the ternary positive electrode material; the least time required for the oxidation stage to complete the oxidation is expressed as

[0086] min J2 = t d ;

[0087] wherein, t d is the time spent for the completion of the oxidation reaction;

[0088] In the heating rate constraint, there is a quantitative relationship between the heating rate and the sintering time, and the current sintering time is expressed as follows:

[0089] t = (T - T te ) / β2, t d = (T ye - T te ) / β2

[0090] wherein, T is the current sintering temperature, T ye is the sintering temperature at the end of the oxidation reaction, β2 is the heating rate of the oxidation stage, and t is the current sintering time.

[0091] In the final value constraint of oxygen vacancies, the final value of oxygen vacancies needs to meet the process requirements in the oxidation stage, so the final value of oxygen vacancies is a structural index, and therefore

[0092] δ vmin ≤δ vacancy (t d )≤δ vmax

[0093] wherein, δ vmin and δ vmax are the process requirements of oxygen vacancies at the end of the oxidation reaction, δ vacancy (t d ) is calculated by the oxygen vacancy concentration prediction model;

[0094] In the constraint of the initial stage of the oxidation reaction, the oxidation stage needs to be completed within a certain time, and needs to meet:

[0095] t d min ≤t d ≤t d max

[0096] wherein, t d min and t d max represent the maximum time of oxidation and the minimum time of oxidation, respectively;

[0097] In the upper and lower bound constraints, the maximum and minimum values of the heating rate exist, and need to meet:

[0098] β 2min ≤β2≤β 2max

[0099] wherein, β 2min represents the minimum value of the heating rate, and β 2max represents the maximum value of the heating rate;

[0100] By solving the above optimization problem, the optimal heating rate and the optimal heating time of the oxidation stage are obtained.

[0101] In some embodiments, the sintering conditions of the grain growth stage are also optimized, with the minimum time and the lowest temperature as the optimization objective, and the final value of the grain size, the primary particle prediction model, the roughness size index prediction model, the oxidation temperature, the upper and lower bounds of the platform temperature, and the upper and lower bounds of the holding time as the constraint conditions to solve the optimal holding time and the platform temperature of the grain growth stage.

[0102] The optimization objective is represented as

[0103] min J3=t b +T bw ;

[0104] wherein, t bT is the time it takes for grain growth to complete. bw It serves as a heat preservation platform for grain growth.

[0105] In some embodiments, the final grain size constraint requires that the final average grain size meet process requirements during the grain growth stage. Therefore, the final average grain size must satisfy the following:

[0106] D bwmin ≤D bw (t b ,T bw )≤D bwmax

[0107] Among them, D bwmin and D bwmax These represent the minimum and maximum grain size at the end of grain growth, respectively.

[0108] In the single-stage particle prediction model constraints, the particle size variation is related to the holding time, the platform temperature, and the initial particle size. Therefore, the particle size is expressed as:

[0109] D bw =f bw (t,T bw ,D0)

[0110] Where D0 is the initial primary grain size, t represents the holding time, and T bw , indicates the platform temperature;

[0111] In the roughness magnitude prediction model constraints, during the isothermal section, the roughness magnitude is related to the holding time, the plateau temperature, and the primary particle size. Therefore, the roughness is expressed as:

[0112] R = f bw (β2,D bw ,t)

[0113] Among them, D bw This refers to the particle size during the isothermal stage, and its value is related to the isothermal plateau, isothermal time, and initial particle size during isothermal control. bw =f bw (t,T bw ,D0).

[0114] In the oxidation temperature constraint, the plateau temperature needs to be higher than the temperature after oxidation is completed, according to the following relationship:

[0115] β1t w +β2t d ≤T bw

[0116] Within the upper and lower bound constraints, both the platform temperature and the holding time have maximum and minimum values. The platform temperature and the holding time must satisfy the following:

[0117]

[0118] By solving the above optimization problem, the optimal holding time and platform temperature in the grain growth stage are obtained.

[0119] In some embodiments, the ternary positive electrode material is LiNi 0.83 Co 0.11 Mn 0.06 O2.

[0120] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0121] Example 1

[0122] (1) Based on the chemical reaction mechanism and expert knowledge, the physical and chemical reactions in the sintering process of the ternary positive electrode material are segmented;

[0123] In the sintering process of the positive electrode material, LiOH·H2O and Ni 0.83 Co 0.11 Mn 0.06 (OH)2 are mixed, assembled and loaded into the kiln according to a certain stoichiometric ratio, and are conveyed from the kiln head to the kiln tail at a certain speed. The two raw materials undergo complex chemical reactions in the roller kiln, and finally obtain the high-nickel positive electrode material LiNi 0.83 Co 0.11 Mn 0.06 O2 product. The overall reaction equation of the process is shown as equation (23).

[0124]

[0125] The reactions occurring in the sintering process mainly include thermal decomposition and dehydration reactions, resulting in mass loss and phase change, oxidation reactions resulting in mass gain, phase and structure change, and high-temperature grain growth in the constant temperature stage. In the sintering process of high-energy density materials, thermal decomposition of LiOH·H2O occurs first and ends at about 60℃. Co 3+ and Mn 4+ are oxidized preferentially at about 60-200℃, oxygen carries away hydrogen atoms while lithium atoms are inserted into the vacancies generated by dehydrogenation, which is essentially equivalent to proton exchange. Since the mass of hydrogen atoms and lithium atoms is similar, the overall mass is basically unchanged. Co 2+ is oxidized to Co 4+Dehydration of the precursor occurs at about 200-400℃, the mass decreases greatly, and the mass decrease rate will mutate at a certain moment due to the generation of by-products. High-temperature oxidation of nickel occurs at about 400-650℃, Ni 2+ is oxidized to Ni 3+ After reaching the constant temperature section, the crystal grains grow at high temperature, and further structural ordering is generated.

[0126] The chemical reaction formulas of each part are shown in formulas (24)-(27).

[0127] LiOH·H2O→LiOH+H2O (25)

[0128] Ni x Co y Mn 1-x-y (OH)2→Ni x Co y Mn 1-x-y O+H2O (26)

[0129] 0.5LiOH→0.5Li2O+H2O (27)

[0130] Ni x Co y Mn 1-x-y O+0.5Li2O+0.25O2→LiNi x Co y Mn 1-x-y O2 (28)

[0131] The specific sections in the sintering process are shown in Table 1. Figure 1

[0132] (2) According to the results of the sections, the different physicochemical reaction stages are researched, the performance indicators corresponding to each reaction stage are found, and the influencing factors of each performance indicator are explored;

[0133] ① Crystal grain growth

[0134] In the sintering process, the generated new substances gradually crystallize, and the crystal grain growth phenomenon occurs, and the crystal grain growth process is also a process in which part of the crystal grains shrink or disappear, and the result is that the average crystal grain size continuously grows. This crystal grain growth is not the result of small crystal grains sticking together to form large crystal grains, but the result of crystal grain migration. The driving force of crystal grain migration is the difference in Gibbs free energy of the substances on both sides of the crystal grain, and the phenomenon of crystal grain moving to the curvature center occurs.

[0135] ​The grain growth process of ternary cathode material runs through the sintering process of the material and affects the electrochemical performance of the sintered product. In the sintering process, it is mainly divided into the grain growth process in the heating section and the grain growth process in the constant temperature section. The grain growth process in the heating section has a primary particle formation at about 200 degrees. With the increase of temperature, the morphology of the primary particles changes from flaky to brick-shaped and then to rice-shaped, and the number of primary particles decreases from more to less and the size increases. At about 800 degrees, the heating process ends and enters the constant temperature stage. The constant temperature stage supports the continuous growth of the grains through the heat preservation time of several hours, which is the main process of grain growth. The grains continuously fuse and grow, and finally reach the required grain size of the finished product.

[0136] In the grain growth process of ternary cathode material, the main factors affecting the growth of particles are the nature of the material, the sintering temperature, the heat preservation time, the heating rate, etc. Among them, the sintering temperature has the most significant effect on the growth of particles, while the sintering time and the heating rate have relatively small effects. If the heating rate is too high, the grains do not have enough time to grow completely. During the heat preservation process, the external energy is continuously input to provide the grains with continuous fusion and growth.

[0137] ②Oxygen vacancy

[0138] In the sintering process of metal oxides or other oxygen-containing compounds, the change of material structure will produce the tendency of conversion from high-valence metal ions to low-valence metal ions, resulting in the decrease of the total positive charge. According to the principle of charge conservation, oxygen will be precipitated from the lattice to balance the potential, leading to the generation of oxygen vacancies, as shown in Figure 3 The general oxygen defect equation is as follows:

[0139]

[0140] Among them, O represents the oxygen in the lattice, and O - represents the formed oxygen vacancy.

[0141] In ternary cathode material, the change rule of oxygen vacancy concentration is closely related to the factors such as heating rate and heating temperature. Under a certain oxygen partial pressure and heating rate, the oxygen vacancy concentration will show a low-high-low trend. First, due to the high concentration of reactants and low sintering temperature, the oxygen partial pressure in the sintering environment ensures that the material has a certain oxygen vacancy concentration. With the continuous increase of temperature, the concentration of reactants decreases but still maintains at a high level, and the oxygen vacancy concentration rises to a high level. Then, the concentration of reactants continuously decreases until the reaction is complete, and the temperature gradually reaches the constant temperature platform. The number of vacancies is limited by the number of reactants, so the oxygen vacancy concentration reaches a low level and approaches to disappear.

[0142] ③Roughness

[0143] In the process of sintering ternary cathode material, first, primary particles are generated, and roughness measures the unevenness of small intervals and tiny peaks and valleys on the surface of primary particles. The smaller the roughness, the smoother the surface. The more complete the reaction, the smaller the roughness of primary particles. The change of surface roughness of high-energy-density cathode material is an important factor affecting the specific surface area of finished products, and affects the final quality of products.

[0144] The embodiment proposes a prediction method for performance indicators of ternary cathode material products, which comprises the following steps:

[0145] According to the analysis of growth mechanism and the corresponding scanning electron microscope (SEM) experimental data, the change rule of the primary particle size index is found, and a primary particle prediction model of ternary cathode material based on the grain growth kinetics equation is established.

[0146] Further, the data model is used to predict the primary particle size in the heating section. Since the particle growth in the heating section is closely related to the initial primary particle size of the precursor, the heating rate and the platform temperature, the three elements are used as inputs, the average particle size of the primary particles is used as output from room temperature to the platform temperature point, the following relationship is established, and is used as the primary particle growth rule in the heating section.

[0147] D0=a1-a2·T-a3·β+a4·T 2 -a5·T·β+a6·β 2 ; (30)

[0148] Wherein, D0 is the average particle size of the grain at the end of the heating section, T is the sintering temperature, β is the heating rate, and a1-a6 are constants.

[0149] In the constant temperature section, there is a clear change in grain growth particle size and a clear mechanism, so the grain growth kinetics model is used to predict the size of the primary particle size:

[0150]

[0151] In the formula, D is the average particle size of the grain after calcination for t time, nm; D0 is the average particle size of the grain at the end of the heating section, nm; n is the grain growth index, k0 is the grain growth rate constant affected by diffusion; E is the grain growth activation energy, kJ / mol; R is the ideal gas constant, which is 8.314 J / (mol·K); T is the sintering temperature, K; t is the constant temperature time, min.

[0152] In order to obtain the constant shown in formula (30), take the logarithm of both sides of the equation, and obtain:

[0153]

[0154] Since the values ​​of n, k0, and E in equation (31) cannot be directly determined by parameter identification, we first assume the value of n, such as 0.1, 0.2, 0.3, ..., 1 respectively. Then, we use SEM image data to fit to determine the values ​​of k0, E and error, and take the sum of squares of the error as a function of n. With the goal of minimizing the sum of squares of the regression error, we obtain the value of n.

[0155] When the heat preservation time is constant, take the partial derivative of 1 / T in equation (31):

[0156]

[0157] In the formula, The least squares method can be used to identify and obtain... The average slope of the curve relating the two is l.

[0158] When the sintering temperature is constant, take the partial derivative of ln t in equation (31):

[0159]

[0160] pass The average slope m can be obtained from the linear relationship curve between ln t and ln t.

[0161] By fitting SEM image data from the isothermal range, the values ​​of k0, E, m, and error are determined. First, different values ​​of n (n = 0-1) are selected, and the curve of the regression error sum of squares versus n is plotted. Then, the value of n that minimizes the regression error sum of squares is obtained through fitting. Figure 2 As shown in the figure. Substituting the above parameters into the growth kinetics equation, we can obtain the grain growth variation law in the isothermal section, as shown in the following equation.

[0162] D=(D0 0.4908 +64.0383·t 1.2942 ·exp(-Q / R·T)) 1 / 0.4908 (35)

[0163] By inputting different sintering condition parameters, grain growth evolution curves under different conditions can be obtained, such as... Figure 3 As shown, the predicted parameters are basically consistent with the actual parameters of the experimental results. (4) Based on the analysis of the oxygen vacancy formation mechanism and the corresponding EPR experimental data, we can find the variation law of the oxygen vacancy concentration index, find the empirical formula of the relationship between oxygen vacancy concentration and sintering parameters, and establish an oxygen vacancy concentration prediction model.

[0164] Because oxygen vacancy belongs to structural parameters, it is difficult to measure quantitatively, and there is no suitable mechanism equation to describe this index, so the relative quantitative analysis of oxygen vacancy is carried out with the help of EPR experiment and the above oxygen vacancy generation principle. The peak value of EPR experiment test result is taken as the relative concentration of oxygen vacancy, and it is normalized.

[0165] According to the mechanism analysis, the relationship between the oxygen vacancy concentration and the heating rate, sintering temperature is found respectively, and the empirical formula of the change of oxygen vacancy concentration is constructed, as shown in the following formula:

[0166] δ(β,T)=a·exp(-((β-b) 2 / (2·c 2 )))+d·exp(-((T-e) 2 / (2·f 2 ))) (36)

[0167] Wherein, a, b, c, d, e, f are constants, δ is the oxygen vacancy concentration, β is the heating rate, and T is the platform temperature.

[0168] According to the evolution of the oxygen vacancy concentration formula, the change relationship of oxygen vacancy under different heating rates and different sintering temperatures is obtained, as shown in the following formula: Figure 4 The predicted parameters are basically consistent with the true parameters of the experimental results. It is also inseparable from the process of primary particle growth, and there is the following relationship:

[0169] δ=p0·D 3 +p1·D 2 +p2·D+p3 (37)

[0170] (5) According to the roughness generation reason combined with the corresponding scanning electron microscope (SEM) experimental data, the change rule of roughness index is found, and the roughness size index prediction model is established;

[0171] Convert the input SEM image to a gray surface image: let f(x,y) be a surface in three-dimensional space, and the height of the point on the surface is the gray value of the pixel corresponding to the position in the image.

[0172] Suppose a point in the surface is taken as the center, for the pixel points with a distance greater than r from the center point, a plane with a thickness of 2r is used to cover, then the surface area of the surface can be obtained by dividing the volume between the upper and lower surfaces of the covering plane by 2r, and the plane is called a carpet.

[0173] Suppose the image f(x,y) is at a scale r, and the upper and lower surfaces are represented as U r (x,y) and B r (x,y) respectively, then the upper and lower surfaces can be represented as:

[0174]

[0175] wherein U0(x,y) is the upper surface, B0(x,y) is the lower surface, f(x,y) is the surface of the three-dimensional space, d[(x,y),(m,n)] is the distance from point (x,y) to point (m,n), U r (x,y) is the upper surface of the space formed by the carpet cover, B r (x,y) is the lower surface of the space formed by the carpet cover.

[0176] The volume of the space formed by the carpet cover and the area of the surface are calculated as shown in equations (39)-(40).

[0177]

[0178]

[0179] wherein V r is the volume of the space formed by the carpet cover, S r is the area of the surface, U r (x,y) is the upper surface of the space formed by the carpet cover, B r (x,y) is the lower surface of the space formed by the carpet cover.

[0180] The fractal dimension of the SEM image is formula (41),

[0181] log(S r )=(3-D)log(r)+log(c) (41)

[0182] wherein S r is the area of the surface, D is the carpet dimension, c is a constant, and r is the interval value.

[0183] The roughness of the high-energy density positive electrode material in the constant temperature section shows a trend of first decreasing and then increasing. Due to the increase of the constant temperature time, the primary particles on the surface of the precursor continuously fuse and grow, and the surface is continuously smoothed. With further constant temperature, the structure may change due to the reduction of Ni ions, resulting in the generation and precipitation of residual lithium, and thus the surface roughness increases. Therefore, there is a critical value in the change process of the roughness in the constant temperature section. Therefore, the roughness and the size of the primary particles have the following relationship:

[0184] R=q0·D 2 +q1·D+q2 (42)

[0185] The fractal dimension is used to characterize the roughness change in the constant temperature section. The closer the fractal dimension is to 3, the greater the roughness. As shown in formula (43), the change of the roughness conforms to the actual law. Figure 5 ​

[0186] The embodiment also proposes an optimization method of sintering conditions of the ternary positive electrode material, comprising:

[0187] (6) According to the multi-stage performance index prediction model, the constraint condition of actual production, the multi-objective optimization problem of the ternary positive electrode material is described.

[0188] The performance index of each reaction stage in the sintering process is different, and its range is determined according to different product requirements. On the basis of ensuring that all key performance indexes can meet the better range, a system for the fastest completion of sintering is the target of the multi-temperature zone optimal sintering condition collaborative optimization.

[0189] 1) Oxidation stage

[0190] ① Optimization target

[0191] Further, the sintering conditions of the oxidation stage are optimized. In the oxidation stage, the main purpose is to complete the oxidation with the least time, so the optimization target can be expressed as

[0192] min J2=t d (43)

[0193] Where t d is the time spent for the completion of the oxidation reaction.

[0194] ② Constraint condition

[0195] Rising rate constraint - the rising rate and the sintering time have a quantitative relationship, and there is

[0196] t=(T-T te ) / β2,t d =(T ye -T te ) / β2 (44)

[0197] Where T is the current sintering temperature, T ye is the sintering temperature at the end of the oxidation reaction, β2 is the oxidation stage rising rate, and t is the current sintering time.

[0198] Oxygen vacancy final value constraint - in the oxidation stage, the oxygen vacancy is a structure index, and its final value needs to meet the process requirements, so

[0199] δ vmin ≤δ vacancy (t d )≤δ vmax (45)

[0200] Where δ vmin and δ vmax are the process requirements of the oxygen vacancy at the end of the oxidation reaction, and δ vacancy (td ) calculated by the oxygen vacancy concentration prediction model.

[0201] Oxygen vacancy concentration prediction model constraints - The change of oxygen vacancy is related to the primary particle size, heating rate and sintering time of the material in the oxidation stage, so there are

[0202] δ vacancy = f yh (β2, D yh , t) (46)

[0203] where D yh is the primary particle size in the oxidation stage, the value of which is related to the heating rate and reaction time in the oxidation stage D yh = f yd (β2, t).

[0204] Oxidation reaction starting stage constraints - The oxidation stage requires completion within a certain time

[0205] t d min ≤ t d ≤ t d max (47)

[0206] where t d min and t d max are the maximum and minimum time of oxidation.

[0207] Upper and lower bound constraints - There are maximum and minimum values of the heating rate, which are

[0208] β 2min ≤ β2≤ β 2max (48)

[0209] By solving the above optimization problem, the optimal heating rate and heating time of the oxidation stage can be obtained.

[0210] 2) Grain growth stage

[0211] ① Optimization goal

[0212] Further, the sintering conditions of the grain growth stage are optimized. In the particle size growth stage, the main purpose is to complete the growth with the least time and the lowest temperature, so the optimization goal can be expressed as

[0213] min J3 = t b + T bw (49)

[0214] where t b is the time spent to complete the grain growth, and T bw is the grain growth holding platform.

[0215] ② Constraint conditions

[0216] Grain size final value constraint - the average grain size final value needs to meet the process requirement at the end of grain growth, so

[0217] D bwmin ≤D bw (t b ,T bw )≤D bwmax (50)

[0218] where D bwmin and D bwmax are the process requirements at the end of grain growth.

[0219] Primary particle prediction model constraint - the change of grain size is related to holding time, plateau temperature and initial grain size, so there is

[0220] D bw =f bw (t,T bw ,D0) (51)

[0221] where D0is the initial primary particle size.

[0222] Roughness size index prediction model constraint - at the constant temperature stage, the roughness size is related to holding time, plateau temperature and primary particle size, so there is

[0223] R=f bw (β2,D bw ,t) (52)

[0224] where D bw is the primary particle size at the constant temperature stage, which is related to the constant temperature plateau, constant temperature time and constant temperature initial grain size D bw =f bw (t,T bw ,D0).

[0225] Oxidation temperature constraint - the plateau temperature needs to be greater than the temperature after oxidation is completed, so there is

[0226] β1t w +β2t d ≤T bw (53)

[0227] Upper and lower bound constraints - the plateau temperature and holding time both have maximum and minimum values, so there is

[0228]

[0229] By solving the above optimization problem, the optimal holding time and plateau temperature of the grain growth stage can be obtained.

[0230] The lower the lithium / nickel cation mixing parameter is, the lower the oxygen vacancy concentration is, the grain size and roughness meet the product demand range, and the industrial production effect is better. In order to ensure that the performance index meets the actual demand and the sintering time is the shortest to ensure the lowest energy consumption, a plurality of qualified sintering systems can be obtained according to the above optimization method. After the material is sintered according to the given sintering condition, the sintered product is detected to obtain the performance index result of the sintered product. Figure 6 The XRD pattern, EPR result and SEM grain image of the finished product under the sintering scheme are displayed, and are compared with the prediction result of the model, and the result basically agrees with the prediction result of the established model, the particle morphology and structure also meet the sintered product standard, fully illustrating the accuracy and feasibility of the optimization method of the initial sintering condition of the ternary positive electrode material proposed in the application.

[0231] The application provides a prediction method of performance indexes of ternary positive electrode material products and an optimization method of initial sintering conditions, physical and chemical reactions in a sintering process of ternary positive electrode materials are segmented based on chemical reaction mechanism and knowledge; different physical and chemical reaction stages are researched according to the segmentation result, and corresponding performance indexes of each reaction stage are found; the change rule of different key performance indexes and the relationship between the sintering conditions are determined according to the mechanism analysis of each segment and the related experimental data, and a related prediction model is established; a multi-objective optimization model of ternary positive electrode materials is established according to the multi-segment performance index prediction model and the constraint conditions of actual production. The prediction method of performance indexes of ternary positive electrode material products and the optimization method of initial sintering conditions provided by the application creatively predict key performance indexes in a sintering process of ternary positive electrode materials, not only model the performance indexes in the whole process of the sintering process, but also can multi-objectively optimize the sintering system according to the performance index prediction model, and provide guiding opinions for subsequent optimization control and micro-macro combination by giving reference to macroscopic phenomena from microscopic processes.

[0232] The specific embodiments of the application described above do not constitute a limitation on the protection scope of the application. Any various other corresponding changes and modifications made according to the technical concept of the application shall be included in the protection scope of the claims of the application.

Claims

1. A method for predicting performance index of a ternary cathode material, characterized in that, The method comprises the following steps: According to the growth mechanism analysis combined with the corresponding scanning electron microscope image experimental data, the variation law of the primary particle size index is obtained, a primary particle prediction model of the ternary positive electrode material based on the grain growth kinetics equation is established, and the primary particle size of the ternary positive electrode material is predicted; The primary particle prediction model of the temperature rising stage is expressed as follows: wherein, D0 is the average grain size of the crystal grains at the end of the temperature increase section, Tsis the sintering temperature, is the temperature increase rate, is a constant; The primary particle prediction model of the constant temperature stage is expressed as follows: wherein, is the average particle diameter of the crystal grains after calcination for a certain time, nm; is the average particle diameter of the crystal grains at the end of the temperature increase section, nm; Q represents the activation energy for crystal growth; is the average particle diameter of the crystal grains at the end of the temperature increase section, nm; Q represents the activation energy for crystal growth; is the ideal gas constant, and is 8.314 J / (mol·K); is the sintering temperature, in K; is the isothermal time, in min; Further comprising: according to the oxygen vacancy formation mechanism analysis combined with the corresponding EPR experimental data, the variation law of the oxygen vacancy concentration index is obtained, an empirical formula of the relationship between the oxygen vacancy concentration and the sintering parameters is obtained, an oxygen vacancy concentration prediction model is established, and the oxygen vacancy concentration is predicted according to the primary particle size; The oxygen vacancy concentration prediction model is expressed as follows: wherein, represents the oxygen vacancy concentration, is the average particle diameter of the crystal grains after calcination for time, is a constant. 2.The method for predicting performance index of ternary cathode material according to claim 1, characterized in that, Further comprising: Combined with the scanning electron microscope image experimental data, according to the variation law of the roughness index, a roughness size index prediction model is established, and the roughness is predicted according to the primary particle size; The roughness size index prediction model is expressed as follows: ； wherein R is a roughness size, is the average grain size of the crystallites after time calcination, is a constant.

3. The method of predicting performance indicators of a ternary cathode material according to any one of claims 1-2, characterized in that, The ternary cathode material is LiNi 0.83 Co 0.11 Mn 0.06 O2.

4. A method for optimizing sintering conditions of a ternary cathode material, characterized in that, The method comprises the following steps: The sintering conditions of the oxidation stage are optimized, the minimum time is taken to complete oxidation as the optimization target, the optimal heating rate and the optimal heating time are obtained by solving the constraints of the heating rate, the final value of the oxygen vacancy, the oxygen vacancy concentration prediction model, the upper and lower bounds of the heating rate and the oxidation reaction starting stage, the oxygen vacancy concentration is further predicted according to the primary particle size obtained by the performance index prediction method of the ternary positive electrode material according to any one of claims 1-3. 5.The method of optimizing sintering conditions of a ternary cathode material according to claim 4, characterized in that, The minimum time required for the oxidation stage to complete oxidation is expressed as ； wherein, is the time taken for the oxidation reaction to complete. 6.The method of optimizing sintering conditions of a ternary cathode material according to claim 4, characterized in that, In the heating rate constraint, there is a quantitative relationship between the heating rate and the sintering time, and the current sintering time is expressed as follows: ； wherein, is the sintering temperature at the current time, is the sintering temperature at the end of the oxidation reaction, is the temperature increase rate in the oxidation stage, and t is the current sintering time; In the final value constraint of the oxygen vacancy, the oxygen vacancy is a structure index in the oxidation stage, and its final value needs to meet the process requirements, so wherein, and is the process requirement of oxygen vacancies at the end of the oxidation reaction, calculated by the oxygen vacancy concentration prediction model; In the oxidation reaction starting stage constraint, the oxidation stage requires to be completed within a certain time, and needs to meet: wherein, and respectively represent the maximum time of oxidation and the minimum time of oxidation. In the upper and lower bound constraint, the heating rate has a maximum value and a minimum value, and needs to meet: wherein represents a minimum value of the temperature increase rate, represents a maximum value of the temperature increase rate; By solving the above optimization problem, the optimal heating rate and the optimal heating time of the oxidation stage are obtained. 7.The method of optimizing sintering conditions of a ternary cathode material according to claim 4, characterized in that, Further comprising optimizing the sintering conditions of the grain growth stage, taking the minimum time and the lowest temperature to complete growth as the optimization target, and solving the optimal holding time and the platform temperature of the grain growth stage by taking the final value of the grain size, the primary particle prediction model, the roughness size index prediction model, the oxidation temperature, the upper and lower bounds of the platform temperature and the upper and lower bounds of the holding time as the constraint conditions. 8.The method of optimizing sintering conditions of a ternary cathode material according to claim 7, characterized in that, The optimization target is expressed as ； wherein, the time taken for the grain growth to complete, is the hold time for the grain growth. 9.The method of claim 7, wherein the sintering condition of the ternary cathode material is optimized by using a sintering temperature of 800-1000 ℃ and a sintering time of 5-20 hours. In the final value constraint of the grain size, the final value of the average particle size needs to meet the process requirements in the grain growth stage, so the final value of the average particle size needs to meet: wherein, and are the minimum and maximum grain size at the end of grain growth, respectively; In the primary particle prediction model constraint, the particle size change is related to the holding time, the platform temperature and the initial particle size, so the particle size is expressed as: wherein, D0 is the initial primary particle size of the crystalline grain, denotes the holding time, denotes the plateau temperature; In the roughness size index prediction model constraint, in the constant temperature stage, the roughness size is related to the holding time, the platform temperature and the primary particle size, so the roughness is expressed as: wherein is the once particle size at the isothermal stage, the value of which is related to the isothermal plateau, the isothermal time and the isothermal initial particle diameter ; In the oxidation temperature constraint, the platform temperature needs to be greater than the temperature after the oxidation is completed, and there is the following relationship: In the upper and lower bound constraints, the platform temperature and the holding time length both have maximum and minimum values, and the platform temperature and the holding time length need to satisfy: ； By solving the above optimization problem, the optimal holding time and platform temperature in the grain growth stage are obtained. By solving the above optimization problem, the optimal holding time and platform temperature in the grain growth stage are obtained.

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