Method for optimizing synthesis process of lithium manganese iron phosphate battery material

Abstract

The invention discloses a synthesis process optimization method of a lithium manganese iron phosphate battery material, which belongs to the technical field of battery material synthesis, and comprises the following steps: taking a precursor preparation database as a liquid phase optimization domain; taking a predetermined uniformity characterization mechanism as a liquid phase optimization evaluation scheme, and performing global optimization of precursor liquid phase preparation control of the predetermined lithium iron manganese phosphate battery material in a liquid phase optimization domain; preparing a precursor according to the optimal liquid phase control decision; introducing a source evaluation strategy to evaluate and analyze the source feature information of the post-processing source; adjusting the initial uniformity index of the precursor by taking the source fitness coefficient as a weight; if the target uniformity index does not meet the predetermined uniformity constraint, calling a solid phase optimization strategy; and performing high-temperature curing treatment on the precursor based on a solid-phase optimization strategy to obtain the target lithium iron manganese phosphate battery material. According to the invention, the problems of unstable material performance, short cycle life and the like caused by difficulty in uniformity control in the synthesis process of the lithium manganese iron phosphate battery material in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery material synthesis, and in particular to a method for optimizing the synthesis process of lithium manganese iron phosphate battery materials. Background Art

[0002] Lithium manganese iron phosphate battery is an important energy storage solution in the fields of electric vehicles, energy storage systems, etc. The performance and stability of the battery are closely related to the synthesis quality of its materials, especially the uniformity has a crucial impact on the performance of the material.

[0003] At present, there are some bottlenecks in the synthesis process of lithium manganese iron phosphate batteries, especially in terms of uniformity control. Traditional synthesis methods can meet the process requirements to a certain extent, but usually rely on experience and trial and error, and cannot systematically optimize the various parameters in the synthesis process, resulting in unstable material performance and low production efficiency. Therefore, a new optimization method is urgently needed to improve the uniformity and overall performance of the material. Summary of the invention

[0004] The present application provides a method for optimizing the synthesis process of lithium iron manganese phosphate battery materials, aiming to solve the problem of uniformity control in the synthesis process of lithium iron manganese phosphate battery materials in the prior art, which leads to problems such as unstable material performance and short cycle life.

[0005] In view of the above problems, the present application provides a method for optimizing the synthesis process of lithium manganese iron phosphate battery materials.

[0006] The present application provides a method for optimizing the synthesis process of a lithium iron manganese phosphate battery material, the method comprising obtaining a precursor preparation database, and using the precursor preparation database as a liquid phase optimization domain; using a predetermined uniformity characterization mechanism as a liquid phase optimization evaluation scheme, and performing global optimization of liquid phase preparation control of a precursor of a predetermined lithium iron manganese phosphate battery material in the liquid phase optimization domain to obtain an optimal liquid phase control decision; preparing a precursor of the predetermined lithium iron manganese phosphate battery material according to the optimal liquid phase control decision; introducing a source evaluation strategy to evaluate and analyze source characteristic information of a post-processing source of the predetermined lithium iron manganese phosphate battery material to obtain a source fitness coefficient; adjusting an initial uniformity index of the precursor using the source fitness coefficient as a weight to obtain a target uniformity index; if the target uniformity index does not meet a predetermined uniformity constraint, calling a solid phase optimization strategy; and performing a high-temperature curing treatment on the precursor based on the solid phase optimization strategy to obtain a target lithium iron manganese phosphate battery material.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Due to the adoption of a technical solution based on liquid phase optimization and source evaluation strategy, the optimal liquid phase control decision is determined by first obtaining the precursor preparation database and performing global optimization in the liquid phase optimization domain, and then the characteristic analysis of the post-processing source is performed by introducing the source evaluation strategy, and the uniformity of the precursor is adjusted based on the source fitness coefficient, thereby achieving precise control of the synthesis process of lithium manganese iron phosphate battery materials. Through this solution, the problem of uniformity control in the synthesis process of lithium manganese iron phosphate battery materials in the prior art is effectively solved, which leads to problems such as unstable material performance and short cycle life, and achieves the technical effect of improving material uniformity, optimizing battery performance and extending cycle life.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a synthetic process optimization method for lithium manganese iron phosphate battery materials is provided for the embodiment of the present application; Figure 2 A flow chart of obtaining the optimal liquid phase control decision in a method for optimizing the synthesis process of lithium manganese iron phosphate battery materials is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The overall idea of ​​the technical solution provided by this application is as follows: The embodiment of the present application provides a method for optimizing the synthesis process of lithium manganese iron phosphate battery materials. First, the optimal liquid phase control strategy is determined by obtaining a precursor preparation database and performing global optimization; then, the post-processing source is evaluated using a source evaluation strategy, and the uniformity of the precursor is adjusted according to the source fitness coefficient. If the uniformity does not meet the predetermined requirements, further processing is performed through a solid phase optimization strategy to ultimately ensure that the uniformity and performance of the material reach the optimal state.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0012] Examples, such as Figure 1 As shown, the embodiment of the present application provides a method for optimizing the synthesis process of lithium manganese iron phosphate battery materials, the method comprising: Step S100: obtaining a precursor preparation database, and using the precursor preparation database as a liquid phase optimal domain.

[0013] Specifically, in materials science, precursors refer to raw materials or intermediates required to synthesize target materials. Specifically, a precursor preparation database refers to a database that stores detailed information and experimental data about the precursor synthesis process. The liquid phase optimal domain refers to the set of all possible synthesis variables and their ranges in the liquid phase synthesis process. Specifically, these variables include reaction temperature, solvent concentration, pH value, stirring speed, etc. The liquid phase optimal domain can be regarded as a space containing all possible synthesis conditions, and the optimization process is to find the best reaction conditions in this space.

[0014] First, it is necessary to collect experimental data related to precursor synthesis, which comes from previous experiments, literature or industrial practice, covering the preparation information of precursors under different reaction conditions. In order to facilitate subsequent optimization, all data need to be systematically stored in a database, including the parameter settings and corresponding results of each experiment. Then, based on the data in the precursor preparation database, define the liquid phase optimization domain. The liquid phase optimization domain represents all possible liquid phase reaction conditions and their ranges. This domain is usually a high-dimensional space, with each dimension representing a variable.

[0015] By systematically managing precursor preparation data and defining the liquid phase optimal search region, the uniformity of material synthesis can be greatly improved, experimental costs can be reduced, and ultimately battery performance can be improved.

[0016] Step S200: taking a predetermined uniformity characterization mechanism as a liquid phase optimization evaluation scheme, performing global optimization of a precursor liquid phase preparation control of a predetermined lithium manganese iron phosphate battery material in the liquid phase optimization domain to obtain an optimal liquid phase control decision.

[0017] Specifically, the uniformity characterization mechanism refers to the standards and methods used to measure the uniformity of materials during the precursor synthesis process. The uniformity is usually quantified by a series of physical and chemical indicators. The predetermined uniformity characterization mechanism refers to the uniformity measurement indicators and evaluation methods set in advance during the optimization process. The liquid phase optimization evaluation scheme refers to the standards and methods used to evaluate the effects of different liquid phase synthesis conditions. For example, the synthesis results can be evaluated based on indicators such as uniformity, reaction efficiency, and finished product quality to ensure the optimal process conditions. Global optimization refers to searching in the entire optimization space (i.e., the liquid phase optimization domain) to find the optimal solution, not just the local optimum. The global optimization method can avoid falling into the local optimal solution, thereby ensuring that the global optimal synthesis conditions are found. The optimal liquid phase control decision refers to the operating conditions that are most suitable for the synthesis precursor obtained through the liquid phase optimization process. This decision is the optimal combination of process parameters based on the optimization goal.

[0018] Before liquid phase optimization, it is necessary to first define the criteria for evaluating the uniformity of precursor synthesis, which is the predetermined uniformity characterization mechanism. Specifically, several different uniformity indicators can be selected to quantify the results, such as analyzing the uniformity of crystal structure by X-ray diffraction or checking the uniformity of particle size and distribution by scanning electron microscopy.

[0019] The construction of the liquid phase optimization domain involves all possible synthesis variables and their value ranges, such as reaction temperature, reaction time, solvent concentration, stirring rate, etc. Through the global optimization method, traversal and search are performed in this optimization domain.

[0020] Through the predetermined uniformity characterization mechanism and global optimization process, various process parameters can be optimized in liquid phase synthesis to ensure the best precursor material. Global optimization not only improves the uniformity of the material, but also reduces the human and material input in the experimental process through intelligent algorithms, thereby improving production efficiency.

[0021] Step S300: preparing a precursor of the predetermined lithium manganese iron phosphate battery material according to the optimal liquid phase control decision.

[0022] Specifically, the pre-ordered lithium iron manganese phosphate battery material refers to the lithium iron manganese phosphate battery material whose physical and chemical properties, crystal structure, electrochemical properties and other characteristics are set in advance during the synthesis process of the battery material. The pre-ordered lithium iron manganese phosphate battery material places more emphasis on ensuring that the target characteristics are achieved through precise control of its synthesis process.

[0023] According to the aforementioned liquid phase optimization process, the optimal liquid phase control decision has been obtained, which is usually obtained through a data-driven optimization algorithm. Once the optimal liquid phase control decision is determined, it can be applied to the actual precursor synthesis process. At this time, the required raw materials and liquid phase synthesis equipment need to be prepared. During the synthesis process, according to the optimal decision, key parameters such as reaction temperature, time, and raw material ratio are adjusted to ensure that the synthesized precursor meets the requirements.

[0024] After liquid phase synthesis, the obtained precursor needs to be further post-processed to finally obtain lithium manganese iron phosphate battery materials. The post-processing step will help the chemical substances in the precursor react to form the final battery material structure, further improving the performance of the battery material.

[0025] The implementation of the optimal liquid phase control decision ensures that the precursor has good uniformity during the liquid phase synthesis process, avoids material defects caused by unbalanced reaction conditions, and thus improves the quality of the final lithium manganese iron phosphate battery material. The precursor synthesis method based on the optimal liquid phase control decision not only improves the material quality and production efficiency, but also reduces the production cost, providing strong support for the efficient synthesis of lithium manganese iron phosphate battery materials.

[0026] Step S400: introducing a source assessment strategy to evaluate and analyze source characteristic information of the post-processing source of the predetermined lithium manganese iron phosphate battery material to obtain a source fitness coefficient.

[0027] Specifically, the source assessment strategy refers to a method for evaluating the source of raw materials (i.e., precursors and post-processing sources) during the material synthesis process, with the goal of selecting appropriate raw material sources and processing methods to optimize the quality of the synthesized materials. This strategy analyzes the characteristics of different raw material sources and evaluates the impact on the performance of the final product, thereby providing a more accurate selection of raw materials for the synthesis process. Post-processing sources refer to the raw materials or substances used in the further processing of lithium manganese iron phosphate battery materials after synthesis. Source characteristic information refers to the physicochemical properties of the post-processing source and its possible impact on the final material during the processing, such as composition, particle size, purity, reactivity, stability, etc. The source fitness coefficient is a value that measures the degree of adaptability of a specific post-processing source to the synthesis performance of the final material. This coefficient is usually obtained through experiments and data analysis, and reflects the contribution of a certain source to the predetermined target during the synthesis process. The higher the fitness coefficient, the more suitable the post-processing source is for synthesizing the target material.

[0028] In the synthesis process of lithium manganese iron phosphate battery materials, in addition to the liquid phase control strategy, the selection of post-processing sources also needs to be considered, because different post-processing sources have a direct impact on the final performance of the material. The source evaluation strategy first measures the effect of these post-processing sources by setting a set of indicators, such as the chemical composition, reactivity, purity, etc. of the source. These characteristic information can help determine which sources are most suitable for improving the electrochemical performance and stability of the material under specific synthesis conditions.

[0029] Perform detailed characterization of each post-processing source. This source characterization information includes, but is not limited to: the chemical composition, particle size distribution, reactivity, thermal stability, etc. of the source. For example, if a certain metal doping source is used, their purity, solubility, and reaction mechanism with the precursor are required. By analyzing this source characterization information, it is possible to predict the effect of the post-processing source on the precursor material during the processing, such as whether it can effectively improve the crystal structure or electrochemical properties of the material.

[0030] The source characteristic information is correlated with the target performance of the lithium manganese iron phosphate battery material. For example, the addition of a certain doping source may increase the specific capacity of the material, but it may also affect its rate performance or cycle stability. Through multiple experiments and data modeling, a source fitness coefficient is obtained. This coefficient is usually measured based on the impact of the post-processing source on the final material. The higher the fitness coefficient, the better the use effect of the source.

[0031] After obtaining the source fitness coefficient, the appropriate post-processing source can be selected according to the coefficient. Only sources with higher source fitness coefficients can play a better role in the synthesis process, ensuring that the final lithium manganese iron phosphate battery material meets the predetermined electrochemical performance requirements.

[0032] The introduction of source evaluation strategy can scientifically evaluate the characteristics of different post-processing sources, and help select the best source by calculating the source fitness coefficient, thereby optimizing the electrochemical performance of the material. For complex material synthesis systems, source evaluation strategy can effectively reduce experimental complexity and trial and error costs.

[0033] Step S500: adjusting the initial uniformity index of the precursor using the source fitness coefficient as a weight to obtain a target uniformity index.

[0034] Specifically, the initial uniformity index is a preliminary uniformity index calculated based on the initial liquid phase control strategy (e.g., the uniformity index corresponding to the optimal liquid phase control decision), which represents the uniformity level of the material under the liquid phase control decision.

[0035] First, the initial uniformity index is adjusted using the obtained source fitness coefficient as a weight. Specifically, the source fitness coefficient reflects the contribution of different sources to the material properties, so sources with higher source fitness coefficients will play a greater role in the adjustment process. For example, if a carbon source or doping source significantly improves the uniformity of the material, then the fitness coefficient of the source is higher and it will play a more important role in adjusting the initial uniformity index.

[0036] Assuming that the uniformity index corresponding to the optimal liquid phase control decision is the initial uniformity index, this initial value is adjusted using the weighted average method according to the fitness coefficient of each source. The target uniformity index is the uniformity index after the above weighted adjustment, reflecting the ideal uniformity level after weighting based on the source fitness coefficient. It serves as a guiding value for subsequent experiments to ensure that the final material synthesis has the best uniformity.

[0037] Through this step, the target uniformity index finally obtained represents a more refined liquid phase control strategy, which will directly improve the uniformity of lithium manganese iron phosphate battery materials, and thus improve its electrochemical performance, specific capacity, cycle stability and other key indicators.

[0038] Step S600: If the target uniformity index does not satisfy the predetermined uniformity constraint, a solid phase optimization strategy is invoked.

[0039] Specifically, the predetermined uniformity constraint refers to the minimum uniformity requirement set according to the design requirements or quality standards. The solid phase optimization strategy refers to the optimization of the solid phase process when the liquid phase control does not meet the uniformity requirements. Solid phase optimization may include adjusting the solid phase reaction temperature, time, atmosphere, additives and other factors of the material to further improve the uniformity of the material.

[0040] First, a target uniformity index is obtained through liquid phase optimization adjustment, which indicates the uniformity level of the material. However, if the target uniformity index fails to meet the predetermined uniformity requirements (i.e., the predetermined uniformity constraints are not met), further optimization measures need to be introduced. In this case, the system will trigger the solid phase optimization strategy to further improve the uniformity of the material by adjusting the parameters in the solid phase process.

[0041] First, the calculated target uniformity index is compared with the predetermined uniformity constraint. If the target uniformity index is lower than the predetermined uniformity constraint, it means that the liquid phase preparation process has not yet reached the ideal uniformity level. At this time, a subsequent solid phase optimization strategy needs to be adopted for further adjustment. When the liquid phase optimization fails to meet the uniformity constraint, the pre-set solid phase optimization strategy is called. These solid phase optimization strategies include adjustments to the solid phase reaction conditions during material post-processing, such as temperature, time, atmosphere, additives, etc. Depending on the situation where the target uniformity index does not meet the predetermined constraints, the solid phase optimization strategy may also include adjusting the reaction time so that the material has more reaction opportunities during the solid phase process, thereby improving the uniformity of the crystal structure. In addition, the atmosphere in the solid phase process (such as oxygen, nitrogen or inert gas, etc.) will also affect the uniformity of the material. By adjusting the atmosphere and optimizing the reaction environment, the uniformity can also be further improved. After the solid phase optimization strategy is determined, experiments are conducted on the material according to the new reaction conditions to measure its uniformity and other related properties. By implementing the solid phase optimization strategy, even if the liquid phase control process fails to meet the predetermined uniformity constraints, the uniformity and performance of the material can be further improved by optimizing factors such as temperature, time, and atmosphere in the solid phase process. Not only does it improve the uniformity of the battery material, but it also enhances its electrochemical properties, such as specific capacity, rate performance, and cycle stability, making the lithium manganese iron phosphate battery material more in line with high performance requirements.

[0042] Step S700: performing high temperature curing treatment on the precursor based on the solid phase optimization strategy to obtain the target lithium manganese iron phosphate battery material.

[0043] Specifically, high temperature curing refers to the thermal treatment of the precursor under high temperature conditions, usually involving calcination or curing. During this process, the chemical reactions and crystal growth in the precursor change, transforming it into a material with the target properties.

[0044] After liquid phase optimization and source evaluation, a precursor containing elements such as manganese, iron and phosphorus was obtained. The precursor usually exists in the form of a certain compound, such as manganese salt, iron salt and phosphate, which will be converted into the crystal structure of lithium iron manganese phosphate during solid phase treatment. According to the solid phase optimization strategy, the precursor will be cured at a specific temperature, time and atmosphere. During the curing process, the chemical substances in the precursor will decompose and react, and finally form a stable lithium iron manganese phosphate compound. Temperature is a key factor in the curing process. Too low temperature may lead to incomplete reaction, and too high temperature may lead to excessive grain size or excessive sintering of particles, reducing the uniformity and performance of the material. Therefore, the choice of temperature needs to balance the uniformity and performance requirements of the material. Under the selected high temperature conditions, the precursor will undergo a step-by-step reaction process to form lithium iron manganese phosphate material. During this process, elements such as manganese, iron, and phosphorus in the precursor will combine into a certain proportion of lithium iron manganese phosphate crystals. This process may also involve crystal growth to improve the structural uniformity of the material. After the curing process is completed, the material needs to be cooled to ensure the stability of the crystal and avoid material structure damage caused by rapid cooling. During the cooling process, the material's crystal lattice is further stabilized, ensuring that the final material's properties meet expectations.

[0045] The high-temperature curing treatment based on the solid phase optimization strategy can significantly improve the uniformity and electrochemical performance of lithium manganese iron phosphate battery materials. Adjusting the curing temperature, time, atmosphere and other parameters effectively promotes the process of precursor conversion to target material, making the crystal structure of the material more uniform, and improving the specific capacity and cycle stability of the battery.

[0046] Further, such as Figure 2 As shown, a predetermined uniformity characterization mechanism is used as a liquid phase optimization evaluation scheme, and a global optimization of the liquid phase preparation control of the precursor of the predetermined lithium manganese iron phosphate battery material is performed in the liquid phase optimization domain to obtain an optimal liquid phase control decision, including: extracting a first preparation data group in the liquid phase optimization domain, the first preparation data group including a first liquid phase control strategy and a first uniformity test data; evaluating and analyzing the first uniformity test data based on the liquid phase optimization evaluation scheme to obtain a first uniformity index; arranging the first liquid phase control strategies in descending order based on the first uniformity index to obtain a descending list of liquid phase control parameters; and taking the top liquid phase control strategy in the descending list of liquid phase control parameters as the optimal liquid phase control decision.

[0047] Specifically, the first preparation data group refers to the relevant data set of the liquid phase preparation of the precursor collected in the liquid phase optimization domain. It includes the liquid phase control strategy under specific reaction conditions (such as temperature, concentration, stirring rate and other control parameters) and uniformity test data (that is, data for evaluating the uniformity of the synthesized material under the reaction conditions, such as particle size distribution, chemical uniformity, etc.). The first liquid phase control strategy refers to the strategy of specific reaction control conditions adopted in the liquid phase preparation process, including multiple experimental parameters such as temperature, solvent concentration, stirring rate, reaction time, etc. The first uniformity test data refers to the uniformity evaluation data for the precursor synthesized under a certain liquid phase control strategy. The descending list of liquid phase control parameters refers to sorting the liquid phase control strategies according to the uniformity evaluation results to obtain a list in which the liquid phase control strategies with better uniformity performance are ranked first.

[0048] In the liquid phase optimization domain, liquid phase synthesis of the precursor is carried out under different experimental conditions, and the control strategy and corresponding uniformity test data of each experiment are recorded. For example, for different temperature, concentration, stirring rate and other conditions, the corresponding test results are obtained. The data in the first preparation data group include: the first liquid phase control strategy: for example, the temperature selected in the experiment is 85°C, the solvent concentration is 0.5mol / L, and the stirring rate is 300rpm. The first uniformity test data: for example, under this condition, the coefficient of variation of the particle size distribution of the material is 4%, and the uniformity index is 92%.

[0049] The first uniformity test data is analyzed using a predetermined uniformity characterization mechanism. The uniformity evaluation scheme may include: Particle size distribution: Test the particle size distribution of the material and calculate its coefficient of variation. The smaller the coefficient of variation, the better the uniformity. Chemical uniformity: Use energy spectrum analysis to test the distribution uniformity of elements in the material. Microstructural uniformity: Observe the surface microstructure of the material through a scanning electron microscope and analyze its uniformity.

[0050] Arrange all liquid phase control strategies in descending order according to their corresponding uniformity index, and select the strategy with the best performance. For example, if there are multiple liquid phase control strategies, their uniformity test results can be sorted: control strategy A: uniformity index 92%, control strategy B: uniformity index 89%, control strategy C: uniformity index 85%; put the control strategy with the highest uniformity index (such as control strategy A) in front to obtain a descending list of liquid phase control parameters. Select the top liquid phase control strategy from the descending list as the optimal liquid phase control decision. For example, assuming that control strategy A performs best, its corresponding reaction conditions (such as temperature, concentration, stirring rate) are the optimal liquid phase control decision.

[0051] Through this step, the most suitable liquid phase preparation control conditions can be effectively selected to ensure that the synthesized precursor reaches the best state in terms of uniformity. This not only improves the performance of the material, but also reduces unnecessary attempts and resource waste during the experiment.

[0052] Furthermore, the first preparation data group refers to a data group for liquid phase preparation of a precursor of a first lithium iron manganese phosphate battery material, wherein the first lithium iron manganese phosphate battery material has an identification of a first post-processing source, and the first post-processing source complies with a predetermined source fitness constraint.

[0053] Specifically, the first lithium iron manganese phosphate battery material is the preliminary product of the lithium iron manganese phosphate battery material first synthesized in the liquid phase preparation process. It is a precursor generated by certain experimental control conditions based on the reactants of the liquid phase synthesis reaction. It still needs to undergo subsequent processing to become the final battery material. The post-processing source refers to the raw material or process for post-processing the synthesized lithium iron manganese phosphate battery material. The post-processing usually includes heating, doping, ball milling, coating and other processes. These post-processing are necessary steps to optimize material properties and improve battery performance. The first post-processing source is the raw material or strategy for material post-processing selected for the first experimental stage. The predetermined source fitness constraint refers to some standards or restrictions that must be met when selecting a post-processing source. These standards are usually set according to the performance requirements of the material, production process requirements or environmental factors.

[0054] In the liquid phase synthesis stage, the first batch of lithium iron manganese phosphate battery materials are synthesized by setting different liquid phase control strategies. During the synthesis process, it is necessary to record the performance data of various experimental conditions and the final product in real time, especially the uniformity test data, which constitute the first preparation data set. After completion, the synthesized first lithium iron manganese phosphate battery material will be post-processed to improve its performance. The first post-processing source refers to the material or process used for post-processing. For example, a cobalt-doped source is selected as a post-processing source to enhance the conductivity and stability of the lithium iron manganese phosphate battery material. When selecting the first post-processing source, it is necessary to ensure that it meets the predetermined fitness standards. Including the chemical purity, reactivity, environmental protection requirements, etc. of the post-processing source. Only post-processing sources that meet the constraints will be selected to ensure the stability of the material and battery performance in subsequent processing.

[0055] By selecting post-processing sources that meet the predetermined source fitness constraints, the final performance of the material is optimized, the stability and conductivity of the battery are improved, and the efficient application of the material is ensured.

[0056] Furthermore, the first uniformity test data is evaluated and analyzed based on the liquid phase optimization evaluation scheme to obtain a first uniformity index, including: reading a predetermined uniformity characterization indicator, and traversing and matching the predetermined uniformity characterization indicator in the first uniformity test data to obtain a first uniformity characterization parameter; performing coefficient of variation weighting processing on the normalized first uniformity characterization parameter according to the liquid phase optimization evaluation scheme to obtain the first uniformity index.

[0057] Specifically, uniformity characterization indicators refer to parameters used to describe and evaluate the different physical and chemical properties, structural properties, and electrochemical properties of material uniformity. These indicators reflect the uniformity of the material during synthesis and its performance in different applications. Common uniformity characterization indicators include: physical and chemical properties: such as particle size distribution, solubility, melting point, etc. Crystal structure: such as crystal phase purity, lattice parameters, crystal defect density, etc., these indicators describe the order and stability of the material at the microscopic level. Particle size morphology: such as particle size distribution, specific surface area, particle morphology, etc., these indicators describe the macroscopic morphology of the material and affect the reactivity of the material. Chemical composition: such as element content, valence distribution, etc., these indicators describe the chemical composition of the material and its relative distribution, affecting the stability and function of the material. Electrochemical performance: such as specific capacity, rate performance, cycle stability, voltage platform, internal resistance, etc., these indicators describe the actual performance of the material in battery applications. The first uniformity characterization parameter refers to a comprehensive parameter obtained by matching and analyzing the first uniformity test data according to the uniformity characterization indicator. It is the quantified result of multiple uniformity characteristics, used to represent the comprehensive level of various uniformity indicators in the material synthesis process. The first uniformity index is a comprehensive result after weighted processing of the coefficient of variation, which is used to quantify the uniformity of the material. The lower the uniformity index, the better the uniformity of the material.

[0058] First, determine a set of uniformity characterization indicators related to material properties, including crystal structure, particle size distribution, chemical composition, and electrochemical properties. These characterization indicators are preset based on the application requirements of the material. For example, if the goal is to prepare high-performance battery materials, more emphasis may be placed on electrochemical properties and crystal structure. Match the first uniformity test data obtained during the experiment with the above-mentioned predetermined uniformity characterization indicators. Through this process, a set of information reflecting the uniformity of various indicators can be obtained. The matched data is processed to obtain a comprehensive quantitative result of each characterization indicator (i.e., the first uniformity characterization parameter).

[0059] Next, according to the predetermined liquid phase optimization evaluation scheme, the normalized first uniformity characterization parameters are weighted. The coefficient of variation indicates the degree of dispersion of each parameter. After weighting, the contribution of different indicators will be adjusted according to their importance. Finally, the weighted results of the coefficient of variation are combined to obtain the first uniformity index. The value of the uniformity index reflects the uniformity level of the entire material batch. The lower the index, the more uniform the performance parameters of the material and the better the performance consistency.

[0060] By processing multi-dimensional test data, the uniformity of the material can be accurately evaluated. This provides a quantitative basis for subsequent optimization. Through the uniformity index obtained, researchers can determine which factors in the synthesis process have a greater impact on uniformity, and thus adjust the liquid phase preparation process, such as changing the reaction temperature, solvent concentration, etc.

[0061] Furthermore, after taking the top liquid phase control strategy in the descending list of the liquid phase control parameters as the optimal liquid phase control decision, it also includes: forming a liquid phase control index set based on the liquid phase optimization domain; extracting the first liquid phase indicator in the liquid phase control index set, wherein the first liquid phase indicator has an identifier of a first parameter threshold; generating a first value parameter set according to a first mapping relationship between the first liquid phase indicator and the first parameter threshold; obtaining any liquid phase control strategy based on any value parameter in the first value parameter set; activating a uniformity prediction model to perform predictive analysis on the arbitrary liquid phase control strategy to obtain any uniformity index; and adding any prepared data group formed by the arbitrary liquid phase control strategy and the arbitrary uniformity index as a derivative data group to the liquid phase optimization domain.

[0062] Specifically, the liquid phase control index set refers to a set of indicators used to describe various control parameters and operating characteristics in the liquid phase synthesis process. It is usually directly related to the synthesis quality, uniformity, performance, etc. of the material. Common liquid phase control indicators include temperature, solvent concentration, reaction time, stirring rate, etc. The first liquid phase index refers to a specific indicator extracted from the liquid phase control index set, which is used to describe a key control variable in the liquid phase synthesis process. For example, reaction temperature, pH value, stirring rate, etc., these indicators usually directly affect the uniformity of the precursor and the quality of the battery material. The first parameter threshold refers to the threshold value associated with a liquid phase indicator, which usually represents the maximum or minimum range allowed for the indicator in practical applications. The first mapping relationship refers to the relationship between a liquid phase indicator and its threshold value, which helps determine how the indicator value corresponds to a specific control strategy in the liquid phase preparation process. The role of the mapping relationship is to compare the actual value of an indicator with the set threshold value to determine the control strategy of the indicator. The first value parameter set is a set of optional parameter values ​​generated based on the mapping relationship between the first liquid phase indicator and its threshold value. These parameter values ​​represent potential choices for liquid phase control strategies. The uniformity prediction model is a mathematical model used to predict the uniformity of precursors, usually based on historical data, experimental data or machine learning algorithms. The model can predict the uniformity of the final material based on the liquid phase control strategy, thereby providing a basis for control decisions. Derivative data sets refer to data sets generated by predictive analysis or other methods. These data sets contain new liquid phase control strategies and corresponding uniformity indices. Derivative data sets help expand the liquid phase optimization domain so that the optimization process can cover a wider range of liquid phase control strategies.

[0063] First, the liquid phase control indicators that have a great influence on the uniformity and quality of the material are clarified, such as temperature, stirring rate, solvent concentration, reaction time, etc., which are collectively referred to as the liquid phase control indicator set. In the liquid phase control indicator set, a key control indicator is selected as the first liquid phase indicator. For example, assuming that the reaction temperature is selected as the first liquid phase indicator, the reaction temperature has an important influence on the crystal structure and particle size distribution of the material during the synthesis process. Next, a first parameter threshold is set for the liquid phase indicator. For example, the threshold of the reaction temperature is set to 250°C, which means that if the reaction temperature exceeds 250°C, the material performance may be degraded.

[0064] According to the mapping relationship between the first liquid phase index and the first parameter threshold, a set of suitable liquid phase control strategy parameter sets can be generated. Subsequently, based on the generated first value parameter set, any parameter value can be selected to form a new liquid phase control strategy. For example, 245°C is selected as the control temperature to obtain the corresponding liquid phase control strategy. By activating the uniformity prediction model, the uniformity of the material under the control strategy can be predicted. The liquid phase control strategy is combined with the uniformity index to construct a new derivative data group, which is added to the liquid phase optimization domain. The addition of the derivative data group expands the optimization space, makes the liquid phase optimization domain richer, and provides more options for further optimization decisions.

[0065] By generating and adding new derivative data sets, the liquid phase optimization domain is enriched, providing more space for finding the best liquid phase control strategy. Based on the analysis of the uniformity prediction model, the impact of the liquid phase control strategy on the uniformity of the material can be predicted more accurately, thereby improving the optimization effect. The liquid phase preparation parameters are continuously adjusted and optimized to achieve the best quality and uniformity of the final material, thereby improving the performance of lithium manganese iron phosphate battery materials and extending the service life of the battery.

[0066] Furthermore, the uniformity prediction model is activated to perform predictive analysis on the arbitrary liquid phase control strategy to obtain an arbitrary uniformity index, including: forming a training data group based on the first liquid phase control strategy and the first uniformity index; performing machine supervised learning on the training data group and training to obtain the uniformity prediction model.

[0067] Specifically, the liquid phase control strategy and the corresponding uniformity index are used as the basis of the training data set. Each piece of data represents the corresponding relationship between a liquid phase control strategy and uniformity. Next, the training data set is trained using a machine learning algorithm. Taking the support vector machine as an example, the various parameters of the liquid phase control strategy are first used as input features, and the uniformity index is used as the target output. By learning the training data, the SVM model will find the optimal mapping relationship between the input features and the output uniformity index. During the machine supervised learning process, the model will continuously adjust its parameters to reduce the prediction error. Through multiple rounds of iterative training, the model can eventually give the corresponding uniformity prediction results for any liquid phase control strategy. The model training is completed and used to predict the new liquid phase control strategy.

[0068] By activating the uniformity prediction model, the uniformity index prediction under any liquid phase control strategy can be obtained. Specifically, through machine learning prediction, it is possible to determine which liquid phase control strategies can achieve the best uniformity in a shorter time, reducing a lot of trial and error processes. By learning historical data, the model can continuously update the optimization plan, avoid the limitations of traditional empirical rules, and provide a more scientific and accurate optimization path. Overall, the use of a uniformity prediction model can not only improve the efficiency of the preparation process, but also significantly optimize the performance of the material, and promote the high-quality, large-scale production of lithium manganese iron phosphate battery materials.

[0069] Furthermore, a source assessment strategy is introduced to evaluate and analyze the source characteristic information of the post-processing source of the predetermined lithium manganese iron phosphate battery material to obtain a source fitness coefficient, including: obtaining a predetermined source characteristic index stored in the source assessment strategy; based on the predetermined source characteristic index, collecting source characteristics of the carbon source and the doping source in the post-processing source in turn to obtain carbon characteristic information and doping characteristic information, respectively; the carbon characteristic information and the doping characteristic information constitute the source characteristic information.

[0070] Specifically, the predetermined source characteristic index refers to the standardized index defined in the source assessment strategy for evaluating source performance. It is used to quantify and describe the characteristics of different sources for comparison and selection. Common source characteristic indicators include elemental composition, reaction activity, physical and chemical properties, etc., based on which it can be accurately judged whether a certain source is suitable for use under specific conditions. Carbon source: In the synthesis of lithium manganese iron phosphate battery materials, carbon sources are usually used as additives to improve the conductivity of the material or affect its crystal structure. Common carbon sources include glucose, sucrose, activated carbon, etc. Doping source: Doping source refers to different elements or compounds added during the material synthesis process to adjust the performance of the material. For example, metal salts such as cobalt, nickel, and manganese can be used as doping sources to affect the structure and electrochemical properties of battery materials by adjusting the type or content of elements.

[0071] First, the predetermined source characteristic indicators are extracted from the source assessment strategy, which are usually defined according to the target performance of the lithium manganese iron phosphate battery material. For example, the elemental composition, reactivity, thermal stability, etc. of the source may be included. Through these predetermined indicators, the characteristic information of the source can be effectively collected in the subsequent steps. Next, based on the predetermined source characteristic indicators, the characteristics of the two key sources are collected separately, and the characteristic information collected from the carbon source and the doping source are integrated to form complete source characteristic information. The purpose is to provide comprehensive characteristic data for subsequent evaluation and optimization.

[0072] Finally, the source fitness coefficient can be calculated based on the source characteristic information of the carbon source and the doping source. This coefficient reflects the contribution of each source to the final performance of the lithium manganese iron phosphate battery material. The higher the fitness coefficient, the better the synthesis effect of the source on the material. The source fitness coefficient is usually calculated through experiments and modeling, which can provide a basis for selecting appropriate post-processing sources.

[0073] By introducing a source evaluation strategy, the carbon source and doping source can be systematically characterized and the fitness coefficient can be calculated for each source, thereby ensuring that the most appropriate source is selected to optimize material performance. By accurately evaluating source characteristics and fitness coefficients, key properties such as specific capacity, cycle life, and rate performance of lithium manganese iron phosphate battery materials can be effectively improved to meet actual application requirements.

[0074] Furthermore, based on the solid phase optimization strategy, the precursor is subjected to high temperature curing treatment to obtain the target lithium iron manganese phosphate battery material, including: extracting the predetermined ball milling decision in the solid phase optimization strategy; ball milling the powder of the precursor and the predetermined sucrose powder according to the predetermined ball milling decision to obtain the target lithium iron manganese phosphate battery material; wherein the predetermined ball milling decision includes a predetermined ball milling time, a predetermined ball milling particle size and a predetermined ball milling temperature.

[0075] Specifically, sucrose powder is an auxiliary material commonly used in solid phase synthesis. Here, it is used as an additive for ball milling with precursor powder. Sucrose can improve particle dispersibility and reduce friction loss during ball milling, and help improve the performance of the final material through certain reactions.

[0076] In the solid phase optimization strategy, the parameter settings of the ball milling process are determined, that is, the predetermined ball milling decision. It includes three key parameters: ball milling time, ball milling particle size and ball milling temperature. These parameters have a crucial impact on the ball milling effect and the quality of the final material. For example, a longer ball milling time helps to improve the uniformity of the powder, but too long ball milling time may lead to excessive refinement of the material or even particle breakage; ball milling particle size affects the particle size distribution and reaction rate; ball milling temperature may affect the rate of certain chemical reactions and even affect the morphology and properties of the material.

[0077] According to the extracted predetermined ball milling decision, the precursor powder and sucrose powder are mixed in a specific ratio and ball milled. During the ball milling process, the steel balls are hit and rubbed in the container through high-speed rotation to fully mix the precursor powder and sucrose powder. The purpose of ball milling is to improve the uniformity of the powder, promote the reaction of chemical components, and at the same time refine the powder particles, increase the surface area of ​​the material, and further enhance its electrochemical performance.

[0078] After ball milling and mixing, the materials enter the solid phase reaction stage (such as high temperature curing or calcination), and the final lithium manganese iron phosphate battery material is obtained through further heat treatment. At this stage, the ball milling process has significantly improved the uniformity of the precursor, making the reaction process smoother, and the final synthesized battery material has better electrochemical performance.

[0079] Finally, through ball milling and subsequent high-temperature treatment, the target lithium manganese iron phosphate battery materials were obtained, which have good electrochemical properties, such as higher specific capacity, better rate performance and cycle stability. These properties make the material suitable for the production of high-performance lithium batteries.

[0080] By ball-milling the precursors with predetermined ball-milling decisions, the particle dispersion and reaction uniformity of the precursors can be significantly improved. During the ball-milling process, sucrose powder is used as an auxiliary material to help improve the dispersion between particles, avoid particle aggregation, and thus improve the uniformity and electrochemical performance of the material. Experiments have shown that the materials that have been optimized for ball milling have significantly improved the specific capacity, rate performance, and cycle stability of the battery compared to the unoptimized materials. Not only does it improve the performance of battery materials, it also improves production efficiency and material consistency.

[0081] In summary, the method for optimizing the synthesis process of lithium manganese iron phosphate battery materials provided in the embodiments of the present application has the following technical effects: 1. Through this synthesis process optimization method, the controllability and efficiency of the synthesis process of lithium manganese iron phosphate battery materials can be significantly improved. By combining liquid phase optimization and solid phase optimization strategies, the uniformity and consistency of the material can be ensured, the variability in the production process can be reduced, and the electrochemical properties of the material, such as specific capacity and rate performance, can be improved. Overall process optimization can improve the cycle stability and service life of the battery, while reducing manufacturing costs and improving the production efficiency of lithium manganese iron phosphate batteries.

[0082] 2. This optimization method combines liquid phase optimization with uniformity testing and uses control strategies to optimize the synthesis process of lithium manganese iron phosphate battery materials, thereby effectively improving the uniformity of the materials. By arranging the liquid phase control strategies in descending order, the best liquid phase control scheme can be found for different synthesis conditions, thereby improving the performance stability of the materials, reducing the uncertainty in the production process, ensuring the consistency and high performance of the final materials, and enhancing the cycle stability and discharge efficiency of the battery.

[0083] 3. By training a uniformity prediction model based on machine supervised learning, the uniformity of lithium manganese iron phosphate battery materials can be effectively predicted and optimized. This method can adjust the variables in the production process in real time by establishing an accurate training data set and combining predictive analysis to ensure that the uniformity and performance of the final material are in the optimal state. Through this optimization, the electrochemical performance of the material is improved, and the cycle performance and specific capacity of the battery are enhanced to meet the requirements of high-performance batteries.

[0084] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0085] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. 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 equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the synthesis process of lithium manganese iron phosphate battery materials, characterized in that: include: Obtaining a precursor preparation database, and using the precursor preparation database as a liquid phase optimal domain; Taking a predetermined uniformity characterization mechanism as a liquid phase optimization evaluation scheme, a global optimization of the liquid phase preparation control of a predetermined lithium manganese iron phosphate battery material precursor is performed in the liquid phase optimization domain to obtain an optimal liquid phase control decision; Preparing a precursor of the predetermined lithium manganese iron phosphate battery material according to the optimal liquid phase control decision; Introducing a source assessment strategy to evaluate and analyze source characteristic information of the post-processing source of the predetermined lithium manganese iron phosphate battery material to obtain a source fitness coefficient; The initial uniformity index of the precursor is adjusted by using the source fitness coefficient as a weight to obtain a target uniformity index; If the target uniformity index does not satisfy the predetermined uniformity constraint, calling a solid phase optimization strategy; Based on the solid phase optimization strategy, the precursor is subjected to high temperature curing treatment to obtain the target lithium manganese iron phosphate battery material.

2. The method for optimizing the synthesis process of lithium iron manganese phosphate battery material according to claim 1, characterized in that: Taking the predetermined uniformity characterization mechanism as the liquid phase optimization evaluation scheme, a global optimization of the liquid phase preparation control of the precursor of the predetermined lithium manganese iron phosphate battery material is performed in the liquid phase optimization domain to obtain the optimal liquid phase control decision, including: Extracting a first preparation data group in the liquid phase optimization domain, wherein the first preparation data group includes a first liquid phase control strategy and first uniformity test data; Based on the liquid phase optimization evaluation scheme, the first uniformity test data is evaluated and analyzed to obtain a first uniformity index; Arranging the first liquid phase control strategies in descending order based on the first uniformity index to obtain a descending list of liquid phase control parameters; The top liquid phase control strategy in the descending list of liquid phase control parameters is taken as the optimal liquid phase control decision.

3. The method for optimizing the synthesis process of lithium manganese iron phosphate battery material according to claim 2, characterized in that: The first preparation data group refers to a data group for liquid phase preparation of a precursor of a first lithium manganese iron phosphate battery material, wherein the first lithium manganese iron phosphate battery material has an identifier of a first post-processing source, and the first post-processing source complies with a predetermined source fitness constraint.

4. The method for optimizing the synthesis process of lithium iron manganese phosphate battery material according to claim 2, characterized in that: The first uniformity test data is evaluated and analyzed based on the liquid phase optimization evaluation scheme to obtain a first uniformity index, including: Reading a predetermined uniformity characterization index, and traversing and matching the predetermined uniformity characterization index in the first uniformity test data to obtain a first uniformity characterization parameter; The first uniformity index is obtained by performing a weighted processing of the coefficient of variation on the normalized first uniformity characterization parameter according to the liquid phase optimization evaluation scheme.

5. The method for optimizing the synthesis process of lithium iron manganese phosphate battery material according to claim 2, characterized in that: After taking the top liquid phase control strategy in the descending list of liquid phase control parameters as the optimal liquid phase control decision, the method further includes: Establishing a liquid phase control index set based on the liquid phase optimal domain; Extracting a first liquid phase indicator from the liquid phase control indicator set, wherein the first liquid phase indicator has an identifier of a first parameter threshold; Generate a first value parameter set according to a first mapping relationship between the first liquid phase indicator and the first parameter threshold; Obtaining any liquid phase control strategy based on any value parameter in the first value parameter set; Activating the uniformity prediction model to perform prediction analysis on the arbitrary liquid phase control strategy to obtain an arbitrary uniformity index; Any preparation data set formed by the arbitrary liquid phase control strategy and the arbitrary uniformity index is added as a derivative data set to the liquid phase optimization domain.

6. The method for optimizing the synthesis process of lithium manganese iron phosphate battery material according to claim 5, characterized in that: The uniformity prediction model is activated to perform prediction analysis on the arbitrary liquid phase control strategy to obtain any uniformity index, including: forming a training data set based on the first liquid phase control strategy and the first uniformity index; The training data set is subjected to machine supervised learning and trained to obtain the uniformity prediction model.

7. The method for optimizing the synthesis process of lithium manganese iron phosphate battery material according to claim 1, characterized in that: The source assessment strategy is introduced to evaluate and analyze the source characteristic information of the post-processing source of the predetermined manganese iron phosphate lithium battery material to obtain the source fitness coefficient, including: Obtaining a predetermined source characteristic index stored in the source assessment strategy; Based on the predetermined source characteristic index, source characteristics of the carbon source and the doping source in the post-processing source are collected in sequence to obtain carbon characteristic information and doping characteristic information respectively; The carbon characteristic information and the doping characteristic information constitute the source characteristic information.

8. The method for optimizing the synthesis process of lithium manganese iron phosphate battery material according to claim 1, characterized in that: Based on the solid phase optimization strategy, the precursor is subjected to high temperature curing treatment to obtain the target lithium manganese iron phosphate battery material, including: extracting predetermined ball milling decisions in the solid phase optimization strategy; According to the predetermined ball milling decision, the powder of the precursor and the predetermined sucrose powder are ball milled and mixed to obtain the target lithium manganese iron phosphate battery material; The predetermined ball milling decision includes a predetermined ball milling time, a predetermined ball milling particle size and a predetermined ball milling temperature.

Images