Lithium manganese iron phosphate high-temperature solid-phase preparation method

Abstract

The invention discloses a lithium manganese iron phosphate high-temperature solid-phase preparation method, and relates to the technical field of battery material preparation, and the method comprises the following steps: carrying out purity detection and screening on raw materials to obtain a target preparation raw material set; calculating the theoretical dosage of the target preparation raw material set, and determining the stoichiometric ratio of the raw materials; weighing according to the stoichiometric ratio of the raw materials to obtain an intermediate composite precursor; constructing a staged heating sintering strategy; performing staged analysis, and determining a staged sintering strategy parameter set; and performing high-temperature solid-phase preparation and sampling monitoring on the intermediate composite precursor to obtain sample performance information, and performing lithium manganese iron phosphate optimization preparation on the sample performance information. The method solves the technical problems of low preparation efficiency and poor quality of lithium manganese iron phosphate caused by inaccurate raw material purity control, unreasonable stoichiometric ratio calculation and imperfect heating sintering strategy in the preparation process in the prior art, and achieves the technical effects of high-efficiency and high-quality preparation of lithium manganese iron phosphate and improvement of product performance.

Description

Technical Field

[0001] The present invention relates to the technical field of battery material preparation, and particularly to a method for preparing lithium iron manganese phosphate by high-temperature solid phase method Background Art

[0002] As a key energy storage device, the performance of lithium-ion batteries directly affects the development of many fields, such as electric vehicles, portable electronic devices, etc. Lithium iron manganese phosphate (LiFeMnPO4) has become a promising cathode material for lithium-ion batteries due to its high theoretical specific capacity, good thermal stability, and environmental friendliness, attracting much attention from researchers and the industrial community. However, the current preparation technology of lithium iron manganese phosphate still faces many challenges. On the one hand, in the raw material processing link, there is a lack of accurate and effective means to control the purity of raw materials such as manganese source, iron source, lithium source, and phosphorus source, resulting in the mixing of impurities and affecting the performance of the final product. Moreover, when calculating the chemical stoichiometric ratio of raw materials, due to the lack of scientific and reasonable methods, there are often proportional deviations, making the reaction unable to proceed fully, reducing the preparation efficiency and product quality. On the other hand, in the preparation process, the traditional heating and sintering strategy is relatively single, without fully considering the reaction characteristics of lithium iron manganese phosphate at different stages, and it is difficult to achieve an ideal crystal structure and material properties

[0003] The prior art has technical problems such as inaccurate control of raw material purity, unreasonable calculation of chemical stoichiometric ratio, and imperfect heating and sintering strategy in the preparation process, resulting in low preparation efficiency and poor quality of lithium iron manganese phosphate Summary of the Invention

[0004] The present application provides a method for preparing lithium iron manganese phosphate by high-temperature solid phase method, which is used to solve the technical problems in the prior art, such as inaccurate control of raw material purity, unreasonable calculation of chemical stoichiometric ratio, and imperfect heating and sintering strategy in the preparation process, resulting in low preparation efficiency and poor quality of lithium iron manganese phosphate

[0005] In view of the above problems, the present application provides a method for preparing lithium iron manganese phosphate by high-temperature solid phase method, and the method includes: The pre-prepared raw materials are subjected to purity detection and screening according to the purity application standard to obtain a target preparation raw material set, and the target preparation raw material set includes a manganese source, an iron source, a lithium source, and a phosphorus source; the theoretical usage amounts of the target preparation raw material set are calculated based on the target yield information of lithium iron phosphate manganese to determine the raw material stoichiometric ratio; the target preparation raw material set is weighed according to the raw material stoichiometric ratio, and a dispersant is added and mixed and put into a high-speed mixer for grinding pretreatment to obtain an intermediate composite precursor; a segmented heating sintering strategy is constructed, and the segmented heating sintering strategy includes a pre-sintering stage, a main sintering stage, and an annealing treatment stage; the intermediate composite precursor is subjected to staged analysis based on the segmented heating sintering strategy to determine a segmented sintering strategy parameter set; the intermediate composite precursor is subjected to high-temperature solid-phase preparation and sampling monitoring by using the segmented sintering strategy parameter set to obtain sample performance information, and the sample performance information is used for optimizing the preparation of lithium iron phosphate manganese by using a gas-phase deposition carbon coating method.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: The pre-prepared raw materials are subjected to purity detection and screening according to the purity application standard to obtain a target preparation raw material set; the theoretical usage amounts of the target preparation raw material set are calculated based on the target yield information of lithium iron phosphate manganese to determine the raw material stoichiometric ratio; the target preparation raw material set is weighed according to the raw material stoichiometric ratio, and a dispersant is added and mixed and put into a high-speed mixer for grinding pretreatment to obtain an intermediate composite precursor; a segmented heating sintering strategy is constructed; the intermediate composite precursor is subjected to staged analysis to determine a segmented sintering strategy parameter set; the intermediate composite precursor is subjected to high-temperature solid-phase preparation and sampling monitoring by using the segmented sintering strategy parameter set to obtain sample performance information, and the sample performance information is used for optimizing the preparation of lithium iron phosphate manganese by using a gas-phase deposition carbon coating method. The technical effect of efficient and high-quality preparation of lithium iron phosphate manganese is achieved, and the product performance is improved. Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0008] Figure 1 It is a schematic flowchart of a method for preparing lithium iron phosphate manganese by high-temperature solid phase provided by an embodiment of the present application; Figure 2 It is a schematic flowchart of obtaining an intermediate composite precursor in a method for preparing lithium iron phosphate manganese by high-temperature solid phase provided by an embodiment of the present application. Detailed Embodiments

[0009] This application provides a high-temperature solid-phase preparation method for lithium iron manganese phosphate, aiming to solve the technical problems in the prior art, such as inaccurate control of raw material purity, unreasonable calculation of stoichiometric ratio, and imperfect heating and sintering strategy in the preparation process, resulting in low preparation efficiency and poor quality of lithium iron manganese phosphate.

[0010] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0011] Embodiment, as Figure 1 shown, this application provides a high-temperature solid-phase preparation method for lithium iron manganese phosphate, and the method includes: Step S100: Perform purity detection and screening on the pre-prepared raw materials according to the purity application standard to obtain a target preparation raw material set, and the target preparation raw material set includes a manganese source, an iron source, a lithium source, and a phosphorus source.

[0012] Specifically, to perform purity detection and screening on the pre-prepared raw materials according to the purity application standard and obtain a target preparation raw material set, a variety of specific implementation means are required. For pre-prepared raw materials such as manganese source, iron source, lithium source, and phosphorus source, first use a spectroscopic analyzer to perform elemental analysis on them. The spectroscopic analyzer can accurately determine the content of various elements in the raw materials by measuring the light absorption or emission characteristics of the raw materials at specific wavelengths. For example, use an atomic absorption spectrometer (AAS) to determine the manganese element content in the manganese source. It atomizes the sample, makes the manganese atoms absorb light at a specific wavelength, and calculates the purity of manganese in the manganese source according to the relationship between the absorbance and the manganese element concentration. For possible impurities, use a mass spectrometer for detection. The mass spectrometer can ionize the sample molecules and determine the types and contents of impurities by measuring the mass-to-charge ratio of the ions. For example, to detect whether the lithium source contains impurity elements such as sodium and potassium, the mass spectrometer can accurately identify and quantify these impurities. In terms of chemical analysis, titrimetric analysis is used to determine the purity of certain raw materials. Taking the phosphorus source as an example, acid-base titration is used. By titrating the phosphorus source solution with a standard solution of known concentration, the content of the active ingredient in the phosphorus source is calculated according to the volume of the standard solution consumed, and then its purity is determined. During the screening process, the detection results of each raw material are compared with the pre-set purity application standard. For example, it is stipulated that the manganese content in the manganese source should reach more than 99%, and the iron purity in the iron source should be above 98.5%. Only the manganese source, iron source, lithium source, and phosphorus source that fully meet the purity standard will be selected to form the target preparation raw material set, providing a high-quality raw material basis for the subsequent preparation of lithium iron manganese phosphate.

[0013] Step S200: Calculate the theoretical dosage of the target preparation raw material set based on the target production information of lithium iron manganese phosphate, and determine the stoichiometric ratio of the raw materials.

[0014] Specifically, first clarify the basic chemical formula of lithium iron manganese phosphate: LiMn x Fe 1−x PO4. Look up the relative atomic masses of each element through the periodic table of chemical elements and calculate its molar mass M. Then, based on the known target production information W, obtain the target synthesis mole number n from the formula n = W / M. Taking lithium carbonate as the lithium source, manganese sulfate as the manganese source, ferrous sulfate as the iron source, and ammonium dihydrogen phosphate as the phosphorus source as an example, according to the stoichiometric relationship between each element in the chemical formula and lithium iron manganese phosphate, calculate that the dosage of the lithium source is n×molar mass of lithium carbonate / 2, the dosage of the manganese source is x×n×molar mass of manganese sulfate, the dosage of the iron source is (1 - x)×n×molar mass of ferrous sulfate, and the dosage of the phosphorus source is n×molar mass of ammonium dihydrogen phosphate, to obtain the set of theoretical raw material dosages. Due to the purity differences of the actual raw materials, obtain the purity information of each raw material, compare the set of theoretical raw material dosages with the purity information, obtain the dosage corrected by stoichiometry, and finally determine the accurate stoichiometric ratio of the raw materials to ensure that each raw material is put into production in a suitable proportion to meet the target production requirements.

[0015] Step S300: Weigh the target preparation raw material set according to the stoichiometric ratio of the raw materials, add a dispersant and mix them, then put them into a high-speed mixer for grinding pretreatment to obtain an intermediate composite precursor.

[0016] Specifically, to obtain the intermediate composite precursor, first use a high-precision electronic balance to accurately weigh the manganese source, iron source, lithium source, and phosphorus source in the target raw material set according to the determined stoichiometric ratio of the raw materials. To ensure the accuracy of weighing, calibrate the electronic balance before each weighing and select a suitable weighing container to reduce errors. Then, according to the capacity and performance of the high-speed mixer, as well as the characteristics of the raw materials, select a suitable dispersant. The addition amount of the dispersant needs to be strictly calculated, generally by referring to relevant literature and previous experimental data, to ensure that it can effectively reduce the agglomeration between raw material particles. Add the weighed raw materials and the dispersant together into the high-speed mixer and set appropriate grinding parameters. The grinding speed is usually adjusted according to the hardness and particle size requirements of the raw materials, generally between 1000 and 3000 revolutions per minute; the grinding duration is determined according to the uniformity of the raw material mixture, usually 1 to 3 hours; at the same time, select a suitable grinding medium, such as zirconia balls, whose size and filling amount will affect the grinding effect and need to be optimized according to the actual situation. During the grinding process, control the grinding temperature to avoid chemical reactions or performance changes of the raw materials due to too high temperature, and the temperature can be maintained stable through a circulating water cooling system or other means. After such grinding pretreatment, the raw materials are fully mixed and reach a suitable particle size, and finally the intermediate composite precursor is obtained.

[0017] Step S400: Construct a segmented heating sintering strategy, and the segmented heating sintering strategy includes a pre-sintering stage, a main sintering stage, and an annealing treatment stage.

[0018] Specifically, when constructing the segmented heating sintering strategy, for the pre-sintering stage, a large amount of historical sintering data is first deeply mined to analyze the key parameter ranges in this stage under different raw material characteristics and target product requirements. For example, the temperature is usually set at 200 - 400 °C and the time is 2 - 4 hours. The goal of this stage is to remove moisture, low-boiling-point impurities, and decompose unstable compounds in the raw materials. With the particle size, composition, and other characteristic information of the intermediate composite precursor as the constraint conditions, global optimization is carried out in the parameter space to obtain the optimal heating rate, holding temperature, time, and other parameters for pre-sintering. For the main sintering stage, a parameter space is also constructed based on historical data. The temperature in this stage is generally 600 - 800 °C and the time is 6 - 10 hours. The goal is to promote sufficient chemical reactions of the raw materials to form the target crystal structure. When performing global optimization, combined with the thermal stability and other characteristics of the intermediate composite precursor, appropriate main sintering strategy parameters are determined. For example, the heating rate needs to be precisely controlled to avoid excessive or uneven crystal growth. In the annealing treatment stage, the parameter ranges are clarified through data mining. The temperature usually slowly drops from the main sintering temperature to 200 - 300 °C and the time is 3 - 6 hours. The purpose is to eliminate internal stresses in the crystals and improve crystal defects. With the crystallinity and other characteristics of the intermediate composite precursor as the constraint, the strategy parameters for annealing treatment are optimized in the parameter space. Finally, the optimized parameters of the three stages are combined to form a complete segmented heating sintering strategy.

[0019] Step S500: Based on the segmented heating sintering strategy, perform staged analysis on the intermediate composite precursor to determine the segmented sintering strategy parameter set.

[0020] Specifically, to determine the parameter set of the segmented sintering strategy, it is necessary to conduct a staged analysis of the intermediate composite precursor based on the segmented heating sintering strategy. First, for the pre-sintering stage, the intermediate composite precursor is analyzed by a thermogravimetry-differential scanning calorimetry (TG-DSC) instrument to obtain information on its mass change and heat flow change at different temperatures. Combining historical pre-sintering data, the temperature range and heating rate range that can effectively remove moisture, low-boiling-point impurities, and decompose unstable compounds in this stage are determined. With the initial particle size, specific surface area, and other characteristics of the intermediate composite precursor as constraints, global optimization is carried out in the parameter space through a genetic algorithm to determine the optimal temperature, heating rate, and holding time in the pre-sintering stage. For the main sintering stage, an X-ray diffractometer (XRD) is used to analyze the phase transformation of the intermediate composite precursor during heating. Based on the parameter space constructed from historical data and combined with the chemical composition and thermal stability of the intermediate composite precursor, a simulated annealing algorithm is used for optimization to determine the main sintering temperature, heating rate, and holding duration that can enable the raw materials to fully react to form the target crystal structure. In the annealing treatment stage, a scanning electron microscope (SEM) is used to observe the microstructure change of the intermediate composite precursor. With the goal of reducing the internal stress of the crystal and improving crystal defects, combined with historical annealing data and the crystallinity and other characteristics of the intermediate composite precursor, a particle swarm algorithm is used for optimization in the parameter space to determine the cooling rate, final temperature, and holding time of the annealing treatment. Finally, the parameters determined in the three stages of pre-sintering, main sintering, and annealing treatment are integrated to form a complete parameter set of the segmented sintering strategy.

[0021] Step S600: Use the parameter set of the segmented sintering strategy to perform high-temperature solid-phase preparation and sampling monitoring on the intermediate composite precursor to obtain sample performance information, and use the gas-phase deposition carbon coating method to optimize the preparation of lithium iron phosphate manganese with the sample performance information.

[0022] Specifically, place the intermediate composite precursor in a high-temperature sintering furnace and precisely set the heating program according to the segmented sintering strategy parameter set. For example, according to the parameters of the pre-sintering stage, raise the furnace temperature to the pre-sintering temperature at a specific heating rate and maintain for a corresponding duration to remove impurities and unstable components in the precursor; then perform gradient heating according to the parameters of the main sintering stage to promote the solid-phase reaction of the raw materials to form the lithium iron manganese phosphate crystal structure; finally, cool down according to the parameters of the annealing treatment stage to eliminate the internal stress of the crystal and complete the high-temperature solid-phase preparation. During the preparation process, use a temperature sensor to monitor the furnace temperature in real time to ensure precise control of the temperature in each stage; at regular intervals, take a small amount of samples from the furnace through a special sampling device, analyze the crystal structure of the samples using an X-ray diffractometer (XRD), observe their micro-morphology using a scanning electron microscope (SEM), and test the electrochemical performance of the samples using an electrochemical workstation to obtain sample performance information. According to this performance information, if it is found that the conductivity, structural stability, etc. of the samples need to be improved, the gas-phase deposition carbon coating method is used for optimized preparation. Place the prepared samples in a chemical vapor deposition device, introduce carbon-containing gases (such as methane, acetylene, etc.), and under high temperature and specific reaction conditions, decompose the carbon source gas, and carbon atoms deposit and coat on the surface of the samples to form a uniform carbon layer, improving the conductivity, structural stability and other properties of the samples, and further improving the comprehensive quality of lithium iron manganese phosphate.

[0023] In a possible implementation manner, step S200 further includes: Step S210: Obtain the basic chemical formula of lithium iron manganese phosphate: LiMn x Fe 1−x PO4, where x is the molar ratio of Mn / Fe, and 0 ≤ x ≤ 1.

[0024] Step S220: Set the target output information of the lithium iron manganese phosphate as W, then the target output information = W = M(LiMn x Fe 1−x PO4)×n, where M is the molar mass, calculated according to different Mn / Fe ratios, and n is the target synthesis mole number.

[0025] Step S230: Calculate the theoretical usage of the target preparation raw material set based on W = M(LiMn x Fe 1−x PO4)×n to obtain the raw material theoretical usage set, and the raw material theoretical usage set is specifically: Mn source usage = x×n×M Mn源 , Fe source usage = (1−x)×n×M Fe源 , Li source usage = n×M Li源 , P source usage = n×M P源。

[0026] Step S240: Use the ratio of the theoretical raw material dosage set and the purity information of the target preparation raw material set as the metering correction dosage to determine the stoichiometric ratio of the raw materials.

[0027] Specifically, lithium iron manganese phosphate is an important battery material, and its chemical composition has a decisive impact on performance. In this step, the basic chemical formula LiMn x Fe 1−x PO4 obtained is the core basis for the entire calculation of raw material dosage and subsequent preparation process. Among them, x represents the molar ratio of Mn / Fe, which is a crucial variable and its value range is between 0 and 1. When x = 0, it means that the material does not contain manganese element and is composed entirely of iron element to form the corresponding lithium iron phosphate; when x = 1, it means that the material only contains manganese element and is lithium manganese phosphate. In actual production, by adjusting the value of x, the relative content of manganese and iron in the material can be flexibly changed, thereby regulating the performance of lithium iron manganese phosphate to meet the diverse requirements of different application scenarios for battery materials in terms of energy density, charge and discharge performance, cycle stability, etc.

[0028] After clarifying the basic chemical formula LiMn x Fe 1−x PO4 of lithium iron manganese phosphate, in order to determine the raw material dosage required for preparation, a key parameter needs to be set, the target output information, which is set as W here. Target output information = W = M(LiMn x Fe 1−x PO4)×n. Among them, M represents the molar mass of lithium iron manganese phosphate. Since its molar mass will change with the change of the Mn / Fe ratio (i.e., the value of x), it needs to be accurately calculated according to different Mn / Fe ratios. And n represents the target synthesis mole number, which is determined according to actual production requirements and represents the amount of substance of lithium iron manganese phosphate expected to be synthesized. This formula closely links the target output W with the molar mass M and the target synthesis mole number n. Through this quantitative relationship, it lays a solid foundation for accurately calculating the theoretical dosage of each raw material in the target preparation raw material set in the subsequent steps, making the entire preparation process have a clear and accurate basis in the raw material preparation link and ensuring that the production process can proceed orderly according to the expected goal.

[0029] Based on this formula, a key calculation is carried out on the theoretical dosage of the target preparation raw material set. From the chemical formula LiMn x Fe 1−x PO4 of lithium iron manganese phosphate, it can be seen that the composition ratio of each element in it is fixed. In this formula, n is the target synthesis mole number, representing the amount of substance of lithium iron manganese phosphate expected to be prepared. Since every 1 mole of LiMn x Fe 1−xPO4, where the amount of substance of manganese element is \(x\) moles, the amount of substance of iron element is \((1 - x)\) moles, the amount of substance of lithium element is 1 mole, and the amount of substance of phosphorus element is 1 mole. Since the molar masses of each element are different, they are respectively denoted as \(M\) Mn源 、\(M\) Fe源 、\(M\) Li源 、\(M\) P源 。 Through stoichiometric relationships, the theoretical dosages of each raw material can be calculated. The theoretical dosage of the Mn source is \(x\times n\times M\) Mn源 , that is, the amount of substance of the Mn source required to produce the target output of lithium iron phosphate manganese (\(x\times n\)) multiplied by the molar mass \(M\) of the Mn source Mn源 ; Similarly, the dosage of the Fe source is \((1 - x)\times n\times M\) Fe源 , the dosage of the Li source is \(n\times M\) Li源 , and the dosage of the P source is \(n\times M\) P源 。 In this way, a set of theoretical raw material dosages including the theoretical dosages of the Mn source, Fe source, Li source, and P source is obtained, providing accurate data support for subsequent accurate weighing and preparation of raw materials, ensuring that in the actual preparation process, each raw material can be rationally proportioned according to stoichiometric relationships, thereby guaranteeing product quality and production efficiency.

[0030] After obtaining the set of theoretical raw material dosages, considering that the set of target preparation raw materials actually obtained is not 100% pure and there are certain impurities, in order to ensure that lithium iron phosphate manganese meeting the target output can be finally prepared, the raw material dosages are corrected. The set of theoretical raw material dosages is the calculation result based on ideal pure raw materials, while the set of target preparation raw materials has their respective purity information. Divide the theoretical dosages of each raw material (Mn source, Fe source, Li source, P source) in the set of theoretical raw material dosages by the purity of this raw material, and the obtained ratio is the dosage for metering correction. For example, if the theoretical dosage of the Mn source is \(m\) and its purity is \(p\%\), then the dosage for metering correction of the Mn source is \(m / (p\%)\). In this way, the corresponding dosage for metering correction is calculated for each raw material. Subsequently, based on these corrected dosages, the stoichiometric ratio of the raw materials can be accurately determined, that is, the precise proportioning relationship between each raw material, to ensure that in the actual preparation process, even if there are impurities in the raw materials, they can be put in according to the appropriate ratio, and finally lithium iron phosphate manganese with the target output can be successfully obtained.

[0031] In a possible implementation manner, as Figure 2 shown, step S300 further includes: Step S310: Obtain a set of mixing and grinding correlation factors according to the dispersant and the high-speed mixer, and the set of mixing and grinding correlation factors includes the dispersant addition amount, grinding speed, grinding duration, grinding medium, and grinding temperature.

[0032] Step S320: Crawl historical data based on the set of mixing and grinding correlation factors to obtain a lithium iron phosphate manganese mixing and grinding database.

[0033] Step S330: Traverse and match in the lithium iron manganese phosphate hybrid grinding database according to the precursor particle size target to determine the hybrid grinding process parameters.

[0034] Step S340: Mix the target preparation raw material set and the dispersant according to the hybrid grinding process parameters and put them into a high-speed mixer for grinding pretreatment to obtain an intermediate composite precursor.

[0035] Specifically, to obtain the hybrid grinding correlation factor set, the characteristics of the dispersant and the high-speed mixer need to be comprehensively considered. For the dispersant, refer to its product manual, technical data and past use experience to clarify the range of its optimal addition amount. For different types of dispersants, the optimal addition amounts in different systems vary greatly. According to the total mass of the target preparation raw material set, the addition amount range is initially determined according to a certain mass percentage, such as 0.5%-5%. For the high-speed mixer, check its operation manual and technical parameters to understand the adjustable range of its rotation speed. Generally, the rotation speed range of common high-speed mixers is 1000-3000 revolutions per minute, which provides a basis for determining the grinding rotation speed. At the same time, according to the performance of the high-speed mixer and the grinding experience of similar materials in the past, estimate the appropriate grinding duration, usually between 1-5 hours. In terms of grinding media, select appropriate grinding media such as zirconia balls and alumina balls according to the hardness and particle size requirements of the target preparation raw material set, and determine their sizes and filling amounts. For the grinding temperature, on the one hand, consider the heat dissipation capacity and temperature control range of the high-speed mixer itself, and on the other hand, combine the thermal stability of the raw materials, generally controlled between room temperature and 80°C to avoid raw material deterioration or dispersant failure caused by too high temperature. Through the above detailed analysis of the dispersant and the high-speed mixer, the hybrid grinding correlation factor set including the dispersant addition amount, grinding rotation speed, grinding duration, grinding media and grinding temperature is determined.

[0036] To build a database for the mixed grinding of lithium iron manganese phosphate, first, data crawler tools are used to extract data from document materials such as experimental records and production reports of past lithium iron manganese phosphate preparation projects. These materials detail the specific values of each factor in the set of factors related to mixed grinding during different batch production or experimental processes, including the amount of dispersant added, grinding speed, grinding duration, grinding medium, and grinding temperature. They also record the corresponding grinding results, such as the particle size distribution and compositional uniformity of the precursor. Through web crawler technology, publicly available data related to the mixed grinding of lithium iron manganese phosphate is searched on professional academic databases, industry forums, and relevant technical literature platforms. The data obtained from internal materials and web platforms is sorted and cleaned to remove duplicate, incorrect, or incomplete data. Then, it is stored in a unified data format to build a structured database for the mixed grinding of lithium iron manganese phosphate. This database can not only provide rich data support for subsequent determination of mixed grinding process parameters but also facilitate data analysis and comparison during subsequent research and production processes, providing a strong basis for optimizing the mixed grinding process.

[0037] Determine the mixed grinding process parameters based on a pre-set precursor particle size target. First, clarify the required range of precursor particle sizes, which is determined based on subsequent preparation processes and product performance requirements. Then, start a comprehensive traversal of the mixed grinding database. The database stores a large amount of precursor particle size data obtained from different combinations of factors related to mixed grinding (amount of dispersant added, grinding speed, grinding duration, grinding medium, and grinding temperature). During the traversal, the precursor particle size data in each set of records is compared with the target particle size, and the degree of difference between the two is calculated using a difference algorithm. For those records with the smallest difference from the target particle size, further analyze the corresponding combination of factors related to mixed grinding. If there are multiple sets of records with extremely small differences, other factors such as cost and efficiency are considered comprehensively. Finally, an optimal combination of factors related to mixed grinding is determined from these qualified records, that is, the mixed grinding process parameters are determined, and these parameters will be used to guide the actual raw material mixing and grinding operations in order to obtain an intermediate composite precursor that meets the particle size target.

[0038] After determining the hybrid grinding process parameters, the actual grinding pretreatment operation begins. First, according to the dispersant addition amount in the determined hybrid grinding process parameters, use a high-precision measuring instrument, such as an electronic balance or a micropipette, to accurately weigh the corresponding mass of the dispersant. Then, put the previously screened set of target preparation raw materials, namely manganese source, iron source, lithium source, and phosphorus source, into the grinding chamber of the high-speed mixer in sequence according to their respective measurements. Subsequently, add the weighed dispersant into it as well. Start the high-speed mixer, set the grinding speed according to the hybrid grinding process parameters, and use the motor drive device of the high-speed mixer to make the grinding components reach the predetermined speed. For example, if the parameter is set to 2000 revolutions per minute, the motor will drive the stirring paddle or grinding medium to rotate at this speed at high speed. At the same time, start timing according to the set grinding duration. During the entire grinding process, keep the grinding temperature stable within the range specified by the process parameters through the temperature control system, for example, maintain it at about 50 °C, to avoid affecting the raw material performance and dispersion effect due to temperature changes. The grinding medium makes full contact, collision, and friction with the raw materials under high-speed rotation. After the set grinding duration, such as 3 hours, the raw materials are evenly dispersed and refined under the action of the dispersant, and are fully mixed to form an intermediate composite precursor, providing a material basis that meets the requirements for the subsequent preparation process.

[0039] In a possible implementation manner, step S300 further includes: Step S350: Mix and put the set of target preparation raw materials and the dispersant into a high-speed mixer for grinding pretreatment using the hybrid grinding process parameters to obtain a prefabricated precursor.

[0040] Step S360: Use a laser particle size analyzer to detect the particle size distribution of the prefabricated precursor to obtain precursor particle size distribution information.

[0041] Step S370: Based on the precursor particle size distribution information, correct and optimize the hybrid grinding process parameters, determine the optimized grinding process parameters, and execute the optimized grinding process parameters through the high-speed mixer to obtain an intermediate composite precursor.

[0042] Specifically, after obtaining the hybrid grinding process parameters, the crucial grinding pretreatment step is initiated. First, an extremely precise electronic balance is used to weigh the manganese source, iron source, lithium source, and phosphorus source in the target raw material set for preparation one by one according to the exact ratio specified by the process parameters. Meanwhile, according to the established dosage of the dispersant, a professional measuring tool is used to accurately measure the corresponding dispersant. The weighed raw materials of various types and the dispersant are carefully poured into the grinding cavity of the high-speed mixer in sequence. The high-speed mixer is equipped with a motor system that can precisely adjust the rotation speed. According to the rotation speed set by the hybrid grinding process parameters, for example, the rotation speed is adjusted to 2500 revolutions per minute. After starting the motor, the motor drives the internal stirring blades or grinding media to rotate at high speed. During the grinding process, according to the duration set by the process parameters, for example, the grinding duration is set to 2 hours, continuous mixing and grinding operations are carried out on the raw materials. Throughout the process, a temperature control system is also used to ensure that the temperature in the grinding cavity is stable within the appropriate range specified by the process parameters, such as maintained at about 60 °C, to avoid affecting the raw material properties and dispersion effect due to temperature fluctuations. Through such rigorous operations, the target raw material set for preparation is fully mixed and ground to a suitable particle size under the action of the dispersant, and finally a prefabricated precursor is successfully obtained, providing qualified initial materials for the subsequent preparation process.

[0043] Prepare an appropriate amount of prefabricated precursor samples and uniformly disperse them in a suitable dispersion medium, such as deionized water or a specific organic solvent, to ensure that the precursor particles are in a single-particle dispersion state in the dispersion medium and avoid agglomeration. Subsequently, the dispersion liquid containing the prefabricated precursor is injected into the sample cell of the laser particle size analyzer. A stable and high-intensity laser beam is emitted inside the laser particle size analyzer. When the laser beam irradiates the prefabricated precursor particles in the sample cell, a light scattering phenomenon occurs. Particles of different particle sizes will scatter the laser to different angles. The larger the particle size, the smaller the scattering angle of the scattered light; the smaller the particle size, the larger the scattering angle of the scattered light. The laser particle size analyzer is equipped with a series of high-precision light detectors distributed at different angles to collect these scattered light signals. The built-in analysis software of the instrument will quickly and accurately calculate the scattered light intensity and angle data collected by the detectors according to relevant algorithms such as the Mie scattering theory, so as to obtain the content ratio of particles of different particle sizes in the prefabricated precursor, and finally generate detailed precursor particle size distribution information, including key data such as the particle size distribution curve, average particle size, and particle size range, providing an important basis for the optimization of subsequent process parameters.

[0044] Import the precursor particle size distribution information obtained by the laser particle size analyzer into professional data analysis software, such as SAS or SPSS. In the software, analyze the key data such as particle size distribution curve, average particle size, particle size range, etc. in depth, and compare them with the preset ideal particle size standard in detail. If the average particle size exceeds the standard range, such as being too large, increase the grinding speed appropriately through the intelligent control panel of the high-speed mixer, increase by 200-300 rpm each time, and keep records. If the particle size distribution range is too wide, indicating that the particle size difference is large, use a high-precision electronic scale to increase the amount of dispersant added according to a certain proportion, increase by 0.1%-0.3% each time, and mark it in detail in the operation record. For grinding media, the staff will judge its wear and grinding effect based on experience. If the effect is not good, replace it with a more suitable type in time, such as replacing a 5mm diameter alumina ball with a 3mm zirconia ball, and adjust its filling amount to the appropriate proportion. For the grinding time, according to the timing system of the high-speed mixer, extend or shorten it by 15-30 minutes each time for trial. After each parameter adjustment, a small-scale grinding experiment will be conducted again, and the particle size distribution of the precursor will be detected again with a laser particle size analyzer. After multiple experiments and parameter fine-tuning, when the particle size distribution information is close to or reaches the ideal standard, the grinding process optimization parameters are determined. Finally, the target preparation raw material set and dispersant are added to the high-speed mixer in a predetermined proportion, and the machine is operated strictly according to the optimized parameters, including precise speed, duration, dispersant dosage, grinding medium specifications and filling amount. After sufficient mixing and grinding, an intermediate composite precursor that meets the requirements is obtained.

[0045] In a possible implementation, step S370 further includes: Step S371: Analyze the correction direction of the mixed grinding process parameters based on the precursor particle size distribution information to determine the correction direction of the grinding parameters.

[0046] Step S372: performing variation correction on the mixed grinding process parameters according to the grinding parameter correction direction to obtain a plurality of grinding process parameters.

[0047] Step S373: Simulate and optimize the multiple grinding process parameters to determine the optimized grinding process parameters.

[0048] Specifically, the precursor particle size distribution information obtained by the laser particle size analyzer is imported into data analysis software, such as SAS or SPSS. In the software, first carefully observe the shape of the particle size distribution curve. If the curve is wide and flat, it means that the particle size distribution range is wide and the particle size difference is large. Compare the average particle size value with the pre-set ideal average particle size. If the actual average particle size is larger, it indicates that the overall degree of grinding of the particles is insufficient. Then check the particle size range. If it exceeds the ideal range, it means that the discrete degree of the particle size does not meet the requirements. Based on these analyses, if the average particle size is too large, the direction of correction of the grinding parameters is to increase the grinding speed and enhance the mechanical force during the grinding process to promote more sufficient particle refinement; it can also be to extend the grinding time to give the particles more time to be ground to the appropriate size. If the particle size distribution range is too wide, the correction direction may be to adjust the amount of dispersant added. If insufficient dispersant causes particle agglomeration, adding dispersant appropriately can improve the dispersion effect, so that the particles can be more evenly stressed during the grinding process; or change the type or filling amount of grinding media, such as replacing grinding media with smaller particle sizes with larger particle sizes, changing the selectivity of grinding, and thus optimizing the particle size distribution. Through such a detailed analysis, the correction direction of the grinding parameters is finally determined, providing clear guidance for subsequent parameter adjustments.

[0049] Once the direction of the grinding parameter correction is determined, the mixed grinding process parameters will be modified accordingly. Assuming that the correction direction is to increase the grinding speed, the current high-speed mixer speed is used as the starting point, and the speed setting is gradually increased according to the predetermined speed increment, such as increasing by 150 rpm each time, so as to generate multiple process parameter versions with different speeds. If the amount of dispersant added is to be adjusted, the amount of dispersant added is changed by increasing or decreasing by 0.2% each time based on the existing amount added, and then a series of parameter combinations with different amounts of dispersant are obtained. For the grinding time, if the correction direction is to extend it, it is based on the current time and increases by 20 minutes each time; if it is to be shortened, it is reduced by 10 minutes each time, so as to construct multiple parameter settings with different time lengths. In terms of grinding media, if you plan to change its type, such as replacing alumina balls with zirconia balls, and at the same time adjust its filling amount according to a certain proportion, such as increasing or decreasing by 5% each time, to form multiple parameter schemes covering different grinding media types and filling amounts. By making regular changes and adjustments to the key factors in the mixed grinding process parameters, such as rotation speed, dispersant dosage, grinding time, grinding medium type and filling amount, according to the correction direction of the grinding parameters, we can eventually obtain a large number of different combinations of grinding process parameters, providing rich data samples for subsequent simulation optimization.

[0050] To simulate and optimize multiple grinding process parameters and determine the optimized grinding process parameters, a multi-objective genetic algorithm combined with computational fluid dynamics (CFD) simulation is used. First, multiple grinding process parameters, such as the dosage of dispersant, grinding speed, grinding duration, type and filling amount of grinding media, etc., are encoded as chromosomes in the genetic algorithm. Multiple objective functions are set, including minimizing the error between the precursor particle size distribution and the target value, minimizing the grinding energy consumption, and maximizing the grinding efficiency. Using CFD software, according to the process parameters corresponding to each chromosome, the flow, particle collision, and dispersion of raw materials in the high-speed mixer are simulated to obtain the simulation result data under each parameter combination, which is used as the input value of the objective function. The genetic algorithm starts to run, and a certain number of chromosomes are randomly initialized to form an initial population. For each chromosome in the population, the objective function value is obtained through CFD simulation, and the fitness is calculated. The higher the fitness, the better the parameter combination. The selection operation is adopted, and some chromosomes are selected from the population as parents according to the fitness. The chromosomes with higher fitness have a greater probability of being selected. Then, the crossover operation is carried out, and some genes of the parent chromosomes are exchanged to generate offspring chromosomes, increasing the diversity of the population. Then, the mutation operation is carried out to change some genes of the offspring chromosomes with a certain probability to avoid the algorithm falling into a local optimum. The selection, crossover, and mutation operations are repeated, and the population is continuously iteratively updated. In each iteration, the chromosome with the highest fitness and its corresponding process parameter combination are recorded. When the number of iterations reaches the preset value or the improvement of the fitness is less than a certain threshold, the algorithm stops. At this time, the grinding process parameters corresponding to the chromosome with the highest fitness are the optimized grinding process parameters determined by simulation optimization.

[0051] In a possible implementation manner, step S373 further includes: Step S3731: Based on the lithium iron manganese phosphate hybrid grinding database, perform particle size simulation training to construct a lithium iron manganese phosphate hybrid grinding simulation module.

[0052] Step S3732: Use the lithium iron manganese phosphate hybrid grinding simulation module to perform simulation optimization on the multiple grinding process parameters to determine the optimized grinding process parameters.

[0053] Specifically, based on the lithium iron manganese phosphate hybrid grinding database, particle size simulation training is carried out to construct a lithium iron manganese phosphate hybrid grinding simulation module, using an integrated learning algorithm that combines deep neural networks and random forests. First, key data are extracted from the database, including the dispersant addition amount, grinding speed, grinding duration, grinding medium type and filling amount, etc. as input features, and the corresponding precursor particle size data as output labels. The data are divided into a training set and a test set according to a certain ratio (such as 8:2). For the deep neural network part, a fully connected network structure with multiple hidden layers is constructed. The number of neurons in the input layer is the same as the number of input features. The ReLU activation function is used in the hidden layer to introduce non-linearity and enhance the expression ability of the model. The number of neurons in the output layer corresponds to the dimension of the output label, that is, the particle size-related index. During the training process, the Adam optimizer, a variant algorithm of stochastic gradient descent (SGD), is used to update the network parameters to minimize the mean square error loss function between the predicted particle size and the actual particle size. At the same time, the random forest algorithm is introduced. A random forest consists of multiple decision trees. During training, multiple subsets are formed by sampling the training set with replacement, and each subset trains a decision tree. Each node of the decision tree determines the best partition by randomly selecting some features to increase the diversity and generalization ability of the model. In the integration stage, the prediction results of the deep neural network and the random forest are weighted and fused. Through cross-validation, the weighting coefficients are adjusted to minimize the error of the fused prediction results on the test set. After multiple iterative trainings and parameter optimizations, a lithium iron manganese phosphate hybrid grinding simulation module that can accurately simulate the particle size change during the lithium iron manganese phosphate hybrid grinding process is finally constructed.

[0054] Encode each grinding process parameter, such as the amount of dispersant added, grinding speed, duration, and parameters related to grinding media, into the position vector of the particle. Each particle represents a possible combination of grinding process parameters. Set multiple optimization objectives, including minimizing the difference between the precursor particle size distribution and the ideal distribution, which is measured by calculating the root mean square error (RMSE) between the two; simultaneously pursuing the lowest grinding energy consumption, where the energy consumption value can be calculated according to the relationship model between energy consumption and process parameters in the hybrid grinding simulation module; and maximizing the grinding efficiency, which is characterized by the amount of material reaching the target particle size per unit time. Initialize the particle swarm, randomly generate the positions and velocities of the particles. In each iteration, the particles update their velocities and positions based on their own historical best positions (pbest) and the global best position (gbest). The velocity update formula combines the particle's own cognition, social learning, and random factors to balance the global search and local search capabilities. For each particle, input the grinding process parameters corresponding to its position vector into the lithium iron manganese phosphate hybrid grinding simulation module to obtain the target values under this parameter combination. According to these target values, use the non-dominated sorting method to sort the particles, divide all particles into different non-dominated fronts, and the particles in the better front have better comprehensive performance. To maintain the diversity of the population, use the crowding distance calculation to select particles with uniform distribution and better performance as the new global best position. After multiple rounds of iteration, the particles gradually gather towards the optimal solution region, and finally determine the optimal grinding process parameter combination that achieves a balance among multiple objectives, that is, the optimized parameters of the grinding process.

[0055] In a possible implementation manner, step S500 further includes: Step S510: Based on the segmented temperature rise sintering strategy, perform historical data mining respectively to construct a pre-sintering parameter space, a main sintering parameter space, and an annealing treatment parameter space.

[0056] Step S520: According to the segmented temperature rise sintering strategy, determine the pre-sintering target, the main sintering target, and the annealing treatment target.

[0057] Step S530: Based on the pre-sintering target, the main sintering target, and the annealing treatment target, use the characteristic information of the intermediate composite precursor as the optimization constraint condition to perform global optimization in the pre-sintering parameter space, the main sintering parameter space, and the annealing treatment parameter space respectively, and obtain the pre-sintering strategy parameters, the main sintering strategy parameters, and the annealing treatment strategy parameters.

[0058] Step S540: According to the pre-sintering strategy parameters, the main sintering strategy parameters, and the annealing treatment strategy parameters, combine to obtain the segmented sintering strategy parameter set.

[0059] Specifically, work is carried out around the established segmented heating sintering strategy. First, deeply explore the historical database of the lithium iron manganese phosphate sintering process accumulated over a long time, which covers a large number of sintering experiments and production data of different batches. According to the three key stages of pre-sintering, main sintering, and annealing treatment in the segmented heating sintering strategy, classify the data in detail. For the pre-sintering stage, accurately screen out various parameters related to pre-sintering from a large amount of historical data, such as the heating rate during each pre-sintering, which determines how fast the material is heated in the initial stage; the pre-sintering temperature, which affects the degree of the preliminary reaction of the precursor; and the pre-sintering duration, the length of which determines the sufficiency of the pre-sintering reaction. Organize and summarize these parameters to construct a pre-sintering parameter space, which contains the value ranges and combinations of all past pre-sintering parameters. Similarly, for the main sintering stage, extract the main sintering temperature, which is a key parameter determining the formation of the material's crystal structure; the holding duration, which has an important impact on the densification degree of the material; and the sintering atmosphere. Different atmospheres (such as inert gases, reducing gases, etc.) will change the chemical properties of the material. Integrate the main sintering parameters to form a main sintering parameter space. In the annealing treatment stage, explore the annealing temperature, which can effectively eliminate the internal stress of the material; the cooling rate, which affects the final microstructure of the material; and the annealing duration, which determines the effect of stress elimination and structure stabilization. Through sorting out these annealing treatment parameters, construct an annealing treatment parameter space. Through this series of operations, a rich and comprehensive data basis is provided for subsequent process optimization.

[0060] According to the established segmented heating sintering strategy, determine the specific goals of each stage. For the pre-sintering stage, the goal is to raise the temperature to an appropriate range at a specific heating rate and maintain it for a certain duration, initially removing volatile impurities in the intermediate composite precursor, such as residual organic solvents or moisture, etc., and at the same time promoting partial solid-phase reactions of the precursor, adjusting the crystal structure, laying a good foundation for the subsequent main sintering process, and making the precursor reach a state more conducive to deep sintering. The goal of the main sintering stage is to enable the atoms inside the material to fully diffuse and rearrange under high-temperature conditions by precisely controlling the sintering temperature and holding duration, promoting the growth and densification of lithium iron manganese phosphate crystals, forming a sintered body that meets the ideal crystal structure and performance requirements, and meeting the index requirements of the material in terms of electrochemical performance, etc. For the annealing treatment stage, the goal is to slowly cool the sintered material to an appropriate annealing temperature and hold it, effectively eliminating the internal stress generated during the main sintering process, stabilizing the crystal structure, further improving the structural stability and performance consistency of the material, and ensuring that the final product can maintain stable and reliable performance in different environments.

[0061] In order to determine the optimal strategy parameters for each stage, the goals and constraints are comprehensively considered for global optimization. Based on the pre-sintering target, the main sintering target and the annealing treatment target, the characteristic information of the intermediate composite precursor, such as particle size, component ratio, specific surface area, etc., is used as a strict optimization constraint. In the pre-sintering parameter space, a global optimization algorithm, such as a genetic algorithm, is used. The algorithm simulates the biological evolution process, encodes various parameters of pre-sintering (such as heating rate, pre-sintering temperature, pre-sintering time, etc.) to form individuals, and many individuals constitute a population. The algorithm performs selection, crossover and mutation operations in the population, continuously iterates and evolves, and combines the characteristic constraints of the intermediate composite precursor to screen out the parameter combination that can maximize the pre-sintering target (such as effectively removing impurities and adjusting the crystal structure), thereby obtaining the pre-sintering strategy parameters. For the main sintering parameter space, the global optimization algorithm is also used. Guided by the main sintering goal (such as forming an ideal crystal structure and achieving material densification), the main sintering temperature, holding time, sintering atmosphere and other parameters are optimized while satisfying the characteristics of the intermediate composite precursor to determine the main sintering strategy parameters. In the annealing parameter space, the same optimization idea is still used. According to the annealing treatment goal (such as eliminating internal stress and stabilizing the crystal structure), combined with the constraints, the annealing temperature, cooling rate, annealing time and other parameters are searched to finally determine the annealing treatment strategy parameters. Through such a comprehensive and accurate optimization process, it is ensured that the parameters of each stage can reach the optimal configuration, providing reliable guidance for the subsequent sintering process.

[0062] The pre-sintering strategy parameters are sorted out, covering the key parameters of the pre-sintering stage, such as heating rate, pre-sintering temperature and pre-sintering time, and their specific values ​​and precise settings are clarified. Next, focus on the main sintering strategy parameters, involving important parameters such as the main sintering temperature, insulation time and sintering atmosphere, and accurately record these parameters and standardize their values. Subsequently, the annealing treatment strategy parameters, including annealing temperature, cooling rate and annealing time, are also sorted out in detail. After completing the sorting of the parameters of each stage, these parameters are arranged and combined in sequence according to the order of pre-sintering, main sintering and annealing in the staged heating sintering strategy. The parameters of each stage are integrated in a clear and orderly manner to form a complete and comprehensive set of staged sintering strategy parameters. This parameter set clearly defines the specific operating parameters for each stage of the entire sintering process, and provides an accurate and operational guidance plan for the actual lithium manganese iron phosphate sintering production, ensuring that the sintering process can proceed smoothly according to the predetermined goals, thereby producing products that meet quality requirements.

[0063] In a possible implementation, step S530 further includes: Step S531: Based on the pre-sintering parameter space, the main sintering parameter space, and the annealing treatment parameter space, decompose and fit the evaluation indicators for the pre-sintering target, the main sintering target, and the annealing treatment target, and construct a pre-sintering fitness evaluation function, a main sintering fitness evaluation function, and an annealing treatment fitness evaluation function.

[0064] Step S532: Use the characteristic information of the intermediate composite precursor as the optimization constraint conditions to perform matching and partitioning in the pre-sintering parameter space, the main sintering parameter space, and the annealing treatment parameter space respectively, to obtain a pre-sintering matching parameter set, a main sintering matching parameter set, and an annealing treatment matching parameter set.

[0065] Step S533: Based on the pre-sintering fitness evaluation function, the main sintering fitness evaluation function, and the annealing treatment fitness evaluation function, perform global optimization in the pre-sintering matching parameter set, the main sintering matching parameter set, and the annealing treatment matching parameter set respectively, to obtain the pre-sintering strategy parameters, the main sintering strategy parameters, and the annealing treatment strategy parameters.

[0066] Specifically, for the pre-sintering target, with the help of chemical analysis such as spectroscopic analysis and thermogravimetric analysis, clarify the impurity residue amount, and use X-ray diffraction (XRD) to obtain the change rate of crystal structure parameters. These two indicators are used as the key to evaluating the effects of pre-sintering on removing impurities and adjusting the crystal structure. In the pre-sintering parameter space, which includes parameters such as heating rate, pre-sintering temperature, and pre-sintering duration, use the multiple nonlinear regression method. Based on historical data, correlate these parameters with the evaluation indicators, and through optimization, determine the weight coefficients and the form of the nonlinear function to construct the pre-sintering fitness evaluation function. For the main sintering target, determine crystal structure indicators such as crystal integrity and lattice constant accuracy through XRD and transmission electron microscopy (TEM), and use the Archimedes method to measure the density and calculate the relative error from the theoretical density as the material densification indicator. In the main sintering parameter space that includes parameters such as main sintering temperature, holding duration, and sintering atmosphere, also use regression analysis, and combine a large amount of experimental data to train and optimize the weight coefficients and the nonlinear function to construct the main sintering fitness evaluation function. In terms of annealing treatment, characterize the degree of internal stress elimination through the broadening of the XRD peak or the change in the Raman spectrum peak position, and measure the structural stability using the change rate of crystal structure parameters obtained from long-term structure monitoring. In the annealing treatment parameter space that includes parameters such as annealing temperature, cooling rate, and annealing duration, use regression analysis, learn and optimize according to historical annealing experimental data, and construct the annealing treatment fitness evaluation function to provide a quantitative basis for subsequent parameter optimization.

[0067] The characteristics information of the intermediate composite precursor and the parameter space at each stage are matched and divided by using the support vector machine (SVM) algorithm. First, the characteristic data such as the particle size distribution, composition ratio, and specific surface area of the intermediate composite precursor are standardized and transformed into a numerical vector form recognizable by the algorithm. In the pre-sintering parameter space, parameters such as the heating rate, pre-sintering temperature, and pre-sintering duration are also organized into corresponding vectors. Using the SVM algorithm to construct a classification model, with the characteristic vector of the intermediate composite precursor as the input and the pre-sintering parameter vector as the output label, it is trained with a large amount of historical data to enable the model to learn the mapping relationship between the two. After training, for the new characteristic data of the intermediate composite precursor, the model predicts the pre-sintering parameter combination that matches it, and the combinations that meet the requirements are screened to form a pre-sintering matching parameter set. In the main sintering parameter space, parameters such as the main sintering temperature, holding duration, and sintering atmosphere are also vectorized, and the trained main sintering matching model based on SVM is used to predict and screen the appropriate main sintering parameter combination according to the characteristic data of the intermediate composite precursor to obtain the main sintering matching parameter set. In the annealing treatment parameter space, similar operations are performed on parameters such as the annealing temperature, cooling rate, and annealing duration. By using the SVM algorithm to construct an annealing treatment matching model, the corresponding annealing treatment parameter combination is screened according to the characteristics of the intermediate composite precursor, and finally an annealing treatment matching parameter set is formed.

[0068] Using the particle swarm optimization algorithm, for the pre-sintering matching parameter set, first, the pre-sintering fitness evaluation function is taken as the optimization objective, and each parameter combination in the pre-sintering matching parameter set is regarded as a particle in the particle swarm. The position of the particle represents a set of pre-sintering parameters (such as heating rate, pre-sintering temperature, pre-sintering duration). Initialize the particle swarm and randomly assign the initial position and velocity to each particle. In each iteration, calculate the fitness value of each particle according to the pre-sintering fitness evaluation function. The higher the fitness value, the better the parameter combination. Each particle records its own historical best position (pbest), and at the same time, the group records the global best position (gbest). The particle updates its velocity according to the velocity update formula based on its own cognition, social learning, and random factors, and then updates its position according to the new velocity. After multiple iterations, the particles gradually converge to the optimal solution region. When the preset number of iterations is met or the fitness improvement is less than the threshold, the algorithm stops. The parameter combination corresponding to the global best position at this time is the pre-sintering strategy parameter. For the main sintering matching parameter set and the annealing treatment matching parameter set, the same particle swarm optimization algorithm is used. In the main sintering matching parameter set, with the main sintering fitness evaluation function as the goal, optimize parameters such as the main sintering temperature, holding duration, and sintering atmosphere to determine the main sintering strategy parameter. In the annealing treatment matching parameter set, according to the annealing treatment fitness evaluation function, optimize parameters such as the annealing temperature, cooling rate, and annealing duration to obtain the annealing treatment strategy parameter. In this way, global optimization is effectively achieved within each matching parameter set, and the optimal strategy parameters for each stage are obtained.

[0069] In a possible implementation manner, step S600 further includes: Step S610: Generate a sintering gradient temperature control curve according to the pre-sintering strategy parameter, the main sintering strategy parameter, and the annealing treatment strategy parameter.

[0070] Step S620: Divide the sintering gradient temperature control curve into stages to obtain a pre-sintering temperature change curve, a main sintering temperature change curve, and an annealing temperature change curve. Among them, the pre-sintering temperature change curve is a heating change curve, the main sintering temperature change curve is a gradient heating change curve, and the annealing temperature change curve is a cooling change curve.

[0071] Step S630: Use the pre-sintering temperature change curve, the main sintering temperature change curve, and the annealing temperature change curve to perform high-temperature solid-phase temperature control and preparation optimization on the intermediate composite precursor.

[0072] Specifically, the strategy parameters of the three stages of pre-sintering, main sintering, and annealing are used as the key basis for generating the sintering gradient temperature control curve. For the pre-sintering strategy parameters, the heating rate determines how fast the temperature rises, the starting temperature sets the initial temperature point of pre-sintering, and the pre-sintering duration specifies the time for which this heating process lasts. For example, if the pre-sintering heating rate is 5 °C per minute, the starting temperature is 150 °C, and the duration is 2 hours, at the beginning stage of generating the curve, the temperature starts to rise from 150 °C at a rate of 5 °C per minute and lasts for 120 minutes. In terms of the main sintering strategy parameters, the heating rate determines the heating speed in the early stage of the main sintering stage, the main sintering temperature is the temperature that needs to be reached and maintained in this stage, and the holding duration specifies the time duration at this temperature. Assuming that the main sintering heating rate is 3 °C per minute, the main sintering temperature is 800 °C, and the holding duration is 3 hours, after the pre-sintering is completed, the curve will continue to rise to 800 °C at a rate of 3 °C per minute and maintain at 800 °C for 180 minutes. The cooling rate in the annealing treatment strategy parameters determines how fast the temperature drops, and the annealing duration is the total duration of the cooling process. If the cooling rate is 4 °C per minute and the annealing duration is 2.5 hours, after the main sintering is completed, the curve starts from the main sintering temperature and cools at a rate of 4 °C per minute for 150 minutes. By integrating the parameters of these different stages in chronological order and using specialized data plotting software such as Origin, MATLAB plotting toolbox, etc., a curve of temperature change over time is accurately plotted, and this curve is the complete sintering gradient temperature control curve, providing intuitive and accurate temperature control guidance for subsequent sintering operations.

[0073] Import the file (such as a CSV file) that records the sintering gradient temperature control curve data into the Python environment, and use the Pandas library to read the data. This library can efficiently process tabular data. After the data is read, according to the start and end time points in the pre-sintering strategy parameters, locate the corresponding row data in the dataset and extract this part of the data. Then, use the Matplotlib library to plot the pre-sintering temperature change curve. Matplotlib is a powerful plotting tool. Use the time column in the extracted data as the abscissa and the temperature column as the ordinate. The plotted curve shows an upward trend, which is the pre-sintering temperature change curve. For the main sintering temperature change curve, according to the start and end times of the main sintering stage, filter out the corresponding data segment in the original dataset. The main sintering process involves gradient heating and heat preservation. When plotting, analyze the data in the heating stage to determine the gradient change rule, and then combine the data at the heat preservation time point. Use Matplotlib to plot the main sintering temperature change curve with gradient heating and including the isothermal stage. Finally, process the annealing temperature change curve. According to the start and end times in the annealing treatment strategy parameters, intercept the corresponding data from the dataset. Since annealing is a cooling process, also use Matplotlib to plot the curve with time as the abscissa and temperature as the ordinate. This curve shows a downward trend, which is the annealing temperature change curve. Through the collaborative operation of these Python libraries, the phased division of the sintering gradient temperature control curve is accurately achieved, and the temperature change curves required for each stage are obtained.

[0074] Place the intermediate composite precursor in the reaction chamber. According to the pre-sintering temperature change curve, accurately control the temperature of the equipment so that the temperature gradually rises at the set heating rate and complete the pre-sintering process within the specified pre-sintering duration to initially remove impurities and adjust the crystal structure. Then, according to the main sintering temperature change curve, control the temperature to rise to the main sintering temperature in a gradient manner and keep the temperature for the corresponding duration at this temperature to promote the full crystallization and densification of the material. Finally, according to the annealing temperature change curve, reduce the temperature at the set cooling rate to complete the annealing treatment, eliminate internal stress, and stabilize the crystal structure. Through such precise high-temperature solid-phase temperature control, the preparation process of lithium iron phosphate manganese is optimized to improve product quality.

[0075] In a possible implementation manner, step S630 further includes: Step S631: Use the pre-sintering temperature change curve, the main sintering temperature change curve, and the annealing temperature change curve to perform high-temperature solid-phase temperature control on the intermediate composite precursor to obtain the composition structure information of the prefabricated lithium iron phosphate manganese.

[0076] Step S632: Obtain the gradient heating slope information of the main sintering temperature change curve.

[0077] Step S633: Determine the temperature rise adjustment threshold based on the composition and structure information of the prefabricated lithium iron manganese phosphate, and adjust the gradient heating slope information of the main sintering temperature change curve based on the temperature rise adjustment threshold.

[0078] Specifically, use a high-temperature solid-state reaction device to place the intermediate composite precursor in the reaction chamber. Through the temperature control system of the device, strictly perform the heating operation according to the heating rate, starting temperature, and duration specified by the pre-sintering temperature change curve to complete the pre-sintering process, initially remove impurities, and adjust the crystal structure. Then, according to the main sintering temperature change curve, raise the temperature to the main sintering temperature at a specific gradient heating rate and maintain it for a certain period to promote the full crystallization and densification of the material. Finally, according to the annealing temperature change curve, lower the temperature at the set cooling rate to complete the annealing treatment to eliminate internal stress and stabilize the crystal structure. After the entire temperature control process is completed, use various analytical and testing methods such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) to detect the obtained prefabricated lithium iron manganese phosphate, so as to obtain its composition and structure information, including crystal structure, element composition, grain size, etc.

[0079] Import the data of the main sintering temperature change curve into data processing software, such as Origin or MATLAB. Assume that the data of the main sintering temperature change curve has time as the horizontal axis and temperature as the vertical axis, and the data is stored in the software in the form of discrete points. Use the built-in functions of the software or write corresponding program codes to perform the slope calculation operation. Taking Origin software as an example, its built-in derivative analysis tool can be used. This tool approximates the slope of each point on the curve by dividing the difference in temperature values between adjacent data points by the difference in corresponding time values. For the data points in the gradient heating stage, the software will calculate the slope within each interval in turn according to the set step size. For example, starting from the first data point of the main sintering heating, select two adjacent data points, calculate the ratio of the temperature difference to the time difference to obtain the slope value of this small interval. Then, move to the next set of adjacent data points at a fixed step size and repeat the calculation process until the data processing of the entire gradient heating stage is completed. Organize the calculated series of slope values into a list form to obtain the gradient heating slope information of the main sintering temperature change curve, providing key data support for the subsequent adjustment of the curve.

[0080] In-depth analysis is carried out based on the obtained composition and structure information of the prefabricated lithium iron manganese phosphate. With the help of X-ray diffraction (XRD) technology, crystal structure parameters such as lattice constants and crystal phase compositions can be accurately obtained; through scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), the element distribution and grain size can be clearly understood. If the XRD results show defects in the crystal structure, such as large lattice distortions, or SEM-EDS analysis reveals uneven element distribution and significant differences in grain size, a reasonable temperature rise adjustment threshold will be determined by combining past experimental data and materials science theoretical knowledge. This threshold stipulates the adjustable range of the gradient heating rate of the main sintering temperature change curve, for example, between -20% and +30%. Subsequently, based on this heating adjustment threshold, the gradient heating rate information of the previously obtained main sintering temperature change curve is adjusted. If it is found that the crystal growth is slow, the slope will be appropriately increased within the threshold range to accelerate the heating rate and promote crystal growth and densification; if local overheating leads to element volatilization or abnormal grain growth, the slope will be decreased within the threshold to reduce the heating rate to ensure the uniformity and stability of the material composition and structure. Through such precise adjustment, the main sintering process better meets the actual requirements of the prefabricated lithium iron manganese phosphate, further optimizing its preparation process.

[0081] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0083] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A high-temperature solid-phase preparation method for lithium manganese iron phosphate, characterized in that: The method comprises: Purity testing and screening of the pre-prepared raw materials according to the purity application standard to obtain a target preparation raw material set, wherein the target preparation raw material set includes a manganese source, an iron source, a lithium source and a phosphorus source; The theoretical amount of the target preparation raw material set is calculated according to the target output information of lithium iron manganese phosphate to determine the raw material stoichiometric ratio; The target preparation raw material set is weighed according to the raw material stoichiometric ratio, and a dispersant is added to the mixture, and the mixture is put into a high-speed mixer for grinding pretreatment to obtain an intermediate composite precursor; Constructing a staged temperature rise sintering strategy, wherein the staged temperature rise sintering strategy includes a pre-sintering stage, a main sintering stage and an annealing treatment stage; Based on the staged temperature rise sintering strategy, the intermediate composite precursor is analyzed in stages to determine a staged sintering strategy parameter set; The segmented sintering strategy parameter set is used to perform high-temperature solid-phase preparation and sampling monitoring on the intermediate composite precursor to obtain sample performance information, and the vapor deposition carbon coating method is used to optimize the preparation of lithium manganese iron phosphate based on the sample performance information.

2. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 1, characterized in that: Determining the stoichiometric ratio of raw materials comprises: Get the basic chemical formula of lithium iron manganese phosphate: LiMn x Fe 1−x PO4, wherein x is the molar ratio of Mn / Fe and 0≤x≤1; Assume that the target production information of lithium manganese iron phosphate is W, then the target production information = W = M (LiMn x Fe 1−x PO4)×n, where M is the molar mass, calculated according to different Mn / Fe ratios, and n is the target synthesis mole number; Based on the W=M(LiMn x Fe 1−x PO4)×n calculates the theoretical amount of the target preparation raw material set to obtain a raw material theoretical amount set, and the raw material theoretical amount set is specifically: Mn source amount = x×n×M Mn源 , Fe source dosage = (1−x)×n×M Fe源 , Li source dosage = n×M Li源 , P source usage = n × M P源 ; The ratio of the theoretical raw material dosage set to the purity information of the target preparation raw material set is used as the dosage correction dosage to determine the raw material stoichiometric ratio.

3. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 1, characterized in that: The intermediate composite precursor is obtained, comprising: According to the dispersant and the high-speed mixer, a set of mixed grinding related factors is obtained, wherein the set of mixed grinding related factors includes the amount of dispersant added, grinding speed, grinding time, grinding medium and grinding temperature; Based on the mixed grinding associated factor set, historical data is crawled to obtain a manganese iron phosphate lithium mixed grinding database; Determine the mixed grinding process parameters by traversing and matching the precursor particle size target in the lithium manganese iron phosphate mixed grinding database; The target raw material set and the dispersant are mixed and put into a high-speed mixer for grinding pretreatment using the mixed grinding process parameters to obtain an intermediate composite precursor.

4. A high temperature solid phase preparation method for lithium manganese iron phosphate as claimed in claim 3, characterized in that: The intermediate composite precursor is obtained, comprising: The target raw material set and the dispersant are mixed and put into a high-speed mixer for grinding pretreatment by using the mixing and grinding process parameters to obtain a prefabricated precursor; Using a laser particle size analyzer to detect the particle size distribution of the prefabricated precursor to obtain the precursor particle size distribution information; The mixed grinding process parameters are corrected and optimized based on the precursor particle size distribution information, the grinding process optimization parameters are determined, and the grinding process optimization parameters are executed by the high-speed mixer to obtain an intermediate composite precursor.

5. A high temperature solid phase preparation method for lithium manganese iron phosphate as claimed in claim 4, characterized in that: The step of determining the grinding process optimization parameters comprises: Analyzing the correction direction of the mixed grinding process parameters based on the precursor particle size distribution information to determine the correction direction of the grinding parameters; Performing variation correction on the mixed grinding process parameters according to the grinding parameter correction direction to obtain a plurality of grinding process parameters; The plurality of grinding process parameters are simulated and optimized to determine the grinding process optimization parameters.

6. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 5, characterized in that: The step of determining the grinding process optimization parameters comprises: Based on the lithium manganese iron phosphate mixed grinding database, particle size simulation training is performed to construct a lithium manganese iron phosphate mixed grinding simulation module; The manganese iron lithium phosphate mixed grinding simulation module is used to simulate and optimize the multiple grinding process parameters to determine the grinding process optimization parameters.

7. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 1, characterized in that: The step of determining the staged sintering strategy parameter set comprises: Based on the staged temperature rise sintering strategy, historical data mining is performed to construct a pre-sintering parameter space, a main sintering parameter space and an annealing treatment parameter space; According to the staged temperature rise sintering strategy, determining the pre-sintering target, the main sintering target and the annealing treatment target; Based on the pre-sintering target, the main sintering target and the annealing target, the characteristic information of the intermediate composite precursor is used as the optimization constraint condition to perform global optimization in the pre-sintering parameter space, the main sintering parameter space and the annealing parameter space to obtain the pre-sintering strategy parameters, the main sintering strategy parameters and the annealing strategy parameters; The staged sintering strategy parameter set is obtained by combining the pre-sintering strategy parameters, the main sintering strategy parameters and the annealing treatment strategy parameters.

8. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 7, characterized in that: The obtaining of the pre-sintering strategy parameters, the main sintering strategy parameters and the annealing strategy parameters comprises: Based on the pre-sintering parameter space, the main sintering parameter space and the annealing parameter space, the pre-sintering target, the main sintering target and the annealing target are decomposed and fitted with evaluation indicators to construct a pre-sintering fitness evaluation function, a main sintering fitness evaluation function and an annealing fitness evaluation function; Using the characteristic information of the intermediate composite precursor as optimization constraints to perform matching and division in the pre-sintering parameter space, the main sintering parameter space and the annealing parameter space, respectively, to obtain a pre-sintering matching parameter set, a main sintering matching parameter set and an annealing matching parameter set; Based on the pre-sintering fitness evaluation function, the main sintering fitness evaluation function and the annealing treatment fitness evaluation function, global optimization is performed in the pre-sintering matching parameter set, the main sintering matching parameter set and the annealing treatment matching parameter set to obtain the pre-sintering strategy parameters, the main sintering strategy parameters and the annealing treatment strategy parameters.

9. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 8, characterized in that: The step of using the segmented sintering strategy parameter set to perform high temperature solid phase preparation on the intermediate composite precursor comprises: Generating a sintering gradient temperature control curve according to the pre-sintering strategy parameters, the main sintering strategy parameters and the annealing treatment strategy parameters; The sintering gradient temperature control curve is divided into stages to obtain a pre-sintering temperature change curve, a main sintering temperature change curve and an annealing temperature change curve, wherein the pre-sintering temperature change curve is a temperature increase change curve, the main sintering temperature change curve is a gradient temperature increase change curve, and the annealing temperature change curve is a temperature decrease change curve; The pre-sintering temperature variation curve, the main sintering temperature variation curve and the annealing temperature variation curve are used to perform high-temperature solid phase temperature control and preparation optimization on the intermediate composite precursor.

10. A high temperature solid phase preparation method for lithium manganese iron phosphate according to claim 9, characterized in that: The method of using the pre-sintering temperature variation curve, the main sintering temperature variation curve and the annealing temperature variation curve to perform high-temperature solid phase temperature control and preparation optimization on the intermediate composite precursor includes: The pre-sintering temperature variation curve, the main sintering temperature variation curve and the annealing temperature variation curve are used to control the high-temperature solid phase temperature of the intermediate composite precursor to obtain the composition structure information of the prefabricated lithium iron manganese phosphate; Obtaining the gradient temperature rise slope information of the main sintering temperature change curve; A temperature rise adjustment threshold is determined according to the component structure information of the prefabricated lithium manganese iron phosphate, and the gradient temperature rise slope information of the main sintering temperature change curve is adjusted based on the temperature rise adjustment threshold.

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