Preparation method for compacting nano-dispersion reinforced lithium iron phosphate particles
Through the mixed reaction of lithium source, iron source and phosphorus source solutions, nano-carbon tube dispersion and laser monitoring, combined with ultrasonic dispersion and spherical composite particles, the problem of insufficient compaction density of lithium iron phosphate particles is solved, and high-quality particle compaction and electrochemical performance improvement is achieved.
Patent Information
- Application Number
- CN202510549768.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-29
- Publication Date
- 2025-07-04
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the prior art, the compacting density of lithium iron phosphate particles cannot meet the requirements, the preparation reliability is low, and there are defects such as nanoparticles agglomeration, poor dispersion, and uneven particle size distribution, which makes it difficult to take into account the density and electrochemical stability of the compacted electrode.
The pH value and concentration of the lithium source, iron source and phosphorus source solution were mixed and reacted through a preset mixing reaction scheme, nanocarbon tubes were introduced, and lithium iron phosphate particles were dispersed by ultrasonic dispersion. The particle size distribution was monitored by laser particle size analyzer, the dispersion parameters were adjusted, spherical composite particles were constructed, sintered and carbon coated, and finally compacted and molded.
The compaction quality of lithium iron phosphate particles is improved, the structural uniformity and electrochemical stability of the particles are ensured, and the density of the electrodes and the connectivity of the conductive network are improved.
Smart Images
Figure CN120246970A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of lithium iron phosphate particle preparation, and particularly relates to a preparation method for compacting lithium iron phosphate particles strengthened by nano-dispersion. Background Art
[0002] Lithium iron phosphate (LiFePO4) is widely used in power batteries and energy storage systems due to its good thermal stability, environmental friendliness, long cycle life and cost advantages. However, the lithium iron phosphate material has inherent defects of low electronic conductivity and low lithium ion diffusion rate, which limits its performance under working conditions such as high-rate discharge. To improve its electrochemical performance, researchers generally use means such as doping, carbon coating and nanosizing to modify the lithium iron phosphate cathode material.
[0003] In the prior art, some studies use means such as ball milling and spray drying to prepare composite lithium iron phosphate materials. Although the compaction performance can be improved to a certain extent, there are still defects such as nanoparticle agglomeration, poor dispersion and uneven particle size distribution, resulting in difficulty in balancing the compaction electrode density and electrochemical stability. In addition, if the dispersion method and sintering process are not reasonably designed during the particle compaction process, problems such as uneven microstructure and discontinuous conductive network are likely to occur. Therefore, there is an urgent need to propose a preparation method for nano-dispersion strengthened lithium iron phosphate particles with uniform structure, good dispersion, high compaction density and stable electrochemical performance. Summary of the Invention
[0004] The present application provides a preparation method for compacting lithium iron phosphate particles strengthened by nano-dispersion, which is used to solve the technical problems that the compaction tightness of lithium iron phosphate particles in the prior art cannot meet the requirements and the preparation reliability is low.
[0005] In view of the above problems, the present application provides a preparation method for compacting lithium iron phosphate particles strengthened by nano-dispersion. The method includes: mixing and reacting the pH values and concentrations of lithium source, iron source and phosphorus source solutions according to a preset mixing reaction scheme, introducing carbon nanotubes, dispersing lithium iron phosphate particles by ultrasonic dispersion, and monitoring the particle size distribution by a laser particle size analyzer according to a preset monitoring frequency to obtain a sequence of particle size distribution results; identifying the distribution broadening scale of the sequence of particle size distribution results, and adjusting the dispersion parameters according to the identification results to obtain a first adjusted dispersion parameter; loading the first adjusted dispersion parameter into an ultrasonic dispersion controller for execution, constructing, sintering and carbon coating the dispersed mixed lithium iron phosphate particle assembly to obtain a pretreated spherical lithium iron phosphate particle assembly; compacting and molding the pretreated spherical lithium iron phosphate particle assembly, and verifying the compaction density by an electrode density detection device. If the compaction density verification is successful, a prepared strengthened lithium iron phosphate particle assembly is obtained.
[0006] In a possible implementation, the distribution broadening scale of the particle size distribution result sequence is identified, and the dispersion parameter is adjusted according to the identification result to obtain a first adjusted dispersion parameter, including: traversing the particle size distribution result sequence to identify the distribution broadening coefficient to obtain a first distribution broadening coefficient sequence; calculating the mean value of the first distribution broadening coefficient sequence to determine the distribution broadening scale; obtaining the current dispersion parameter, and adjusting the current dispersion parameter based on the distribution broadening scale to obtain the first adjusted dispersion parameter.
[0007] In a possible implementation, traversing the particle size distribution result sequence to identify the distribution broadening coefficient to obtain a first distribution broadening coefficient sequence includes: extracting a first particle size distribution result from the particle size distribution result sequence; extracting the leading particle size with the largest distribution quantity from the first particle size distribution result; constructing a leading neighborhood of the leading particle size according to a preset diffusion threshold, and calculating the leading neighborhood density of the leading neighborhood in combination with the first particle size distribution result; diffusing the leading neighborhood in the directions greater than and less than the leading particle size respectively according to the preset diffusion threshold to obtain a left-side diffused leading neighborhood and a right-side diffused leading neighborhood; performing diffusion analysis on the left-side diffused leading neighborhood and the right-side diffused leading neighborhood respectively to obtain a first distribution interval; identifying the first distribution interval based on a preset particle size distribution interval to determine the first distribution broadening coefficient; performing particle size distribution identification on the particle size distribution result sequence, and analyzing in combination with the preset particle size distribution interval to obtain a first distribution broadening coefficient sequence.
[0008] In a possible implementation, the present application further includes: judging whether the left-side diffusion leading neighborhood density of the left-side diffused leading neighborhood is greater than or equal to the leading neighborhood density, and if so, continuing to diffuse to the left until a preset stop condition is met to obtain a target left-side diffused leading neighborhood; judging whether the left-side diffusion leading neighborhood density of the right-side diffused leading neighborhood is greater than or equal to the leading neighborhood density, and if so, continuing to diffuse to the right until a preset stop condition is met to obtain a target right-side diffused leading neighborhood; extracting the left endpoint of the target left-side diffused leading neighborhood and the right endpoint of the target right-side diffused leading neighborhood to obtain a first distribution interval.
[0009] In a possible implementation, the particle sizes in the first particle size distribution result whose distances to the leading particle size are less than or equal to the preset diffusion threshold are added to the leading neighborhood.
[0010] In a possible implementation, the total number of particle sizes in the leading neighborhood is counted, and the statistical result is divided by the preset diffusion threshold to obtain the leading neighborhood density.
[0011] In a possible implementation, obtaining the current dispersion parameter, adjusting the current dispersion parameter based on the distribution broadening scale to obtain the first adjusted dispersion parameter includes: pre-constructing a parameter adjuster; using the parameter adjuster to identify the distribution broadening scale and the current dispersion parameter to obtain the first adjusted dispersion parameter.
[0012] In a possible implementation, constructing spherical composite particles from the aggregated mixed lithium iron phosphate particles after dispersion, using an on-line particle image analysis system to supervise and authenticate the construction process, and after the authentication passes, sintering and carbon coating the obtained micron-sized spherical composite particle aggregate in an inert atmosphere to obtain the pretreated spherical lithium iron phosphate particle aggregate.
[0013] In a possible implementation, compacting and forming the pretreated spherical lithium iron phosphate particle aggregate, using an electrode density detection device to authenticate the compacting density. If the compacting density authentication is successful, obtaining the prepared strengthened lithium iron phosphate particle aggregate, including: using the electrode density detection device to detect the density of the formed spherical lithium iron phosphate particle aggregate after compacting and forming to obtain a density detection result set; counting the proportion of particles meeting the requirements in the density detection result set and performing compacting density authentication based on a preset particle proportion threshold. When the statistical result is greater than or equal to the preset particle proportion threshold, the authentication result is that the compacting density authentication is successful.
[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages: In this application, by mixing and reacting the pH values and concentrations of the lithium source, iron source, and phosphorus source solutions according to a preset mixing reaction scheme, introducing carbon nanotubes, dispersing the lithium iron phosphate particles by ultrasonic dispersion, and using a laser particle size analyzer to monitor the particle size distribution according to a preset monitoring frequency to obtain a particle size distribution result sequence, then identifying the distribution broadening scale of the particle size distribution result sequence, adjusting the dispersion parameter according to the identification result to obtain the first adjusted dispersion parameter, loading the first adjusted dispersion parameter into an ultrasonic dispersion controller for execution, constructing spherical composite particles, sintering, and carbon coating the aggregated mixed lithium iron phosphate particles after dispersion to obtain the pretreated spherical lithium iron phosphate particle aggregate, and then compacting and forming the pretreated spherical lithium iron phosphate particle aggregate, using an electrode density detection device to authenticate the compacting density. If the compacting density authentication is successful, the prepared strengthened lithium iron phosphate particle aggregate is obtained. The technical effect of improving the compacting quality of the lithium iron phosphate particles is achieved. Description of the Drawings
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.
[0016] Figure 1 It is a schematic flow chart of a preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles provided by an embodiment of the present application; Figure 2 It is a schematic flow chart of obtaining a first distribution interval in a preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles provided by an embodiment of the present application. Detailed implementation manners
[0017] The present application provides a preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles, which is used to solve the technical problems that the compaction tightness of lithium iron phosphate particles in the prior art cannot meet the requirements and the preparation reliability is low.
[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.
[0019] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.
[0020] Embodiment, as Figure 1 shown, the present application provides a preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles, wherein the method includes: S1: Mix and react the pH values and concentrations of the lithium source, iron source, and phosphorus source solutions according to a preset mixing reaction scheme, introduce carbon nanotubes, disperse the lithium iron phosphate particles by ultrasonic dispersion, and monitor the particle size distribution using a laser particle size analyzer according to a preset monitoring frequency to obtain a sequence of particle size distribution results; In a possible embodiment, the preset mixing reaction scheme refers to the process conditions and ratio parameters formulated in advance for synthesizing the lithium iron phosphate precursor, usually including the molar ratios of the Li source (such as LiOH or Li2CO3), Fe source (such as FeSO4 or FePO4), and P source (such as H3PO4), the pH control range (such as 6.0–7.5), the reaction temperature and time, etc. The ultrasonic dispersion method refers to using the cavitation effect of ultrasonic waves to generate intense microjets and shear forces in the liquid, thereby depolymerizing the aggregated nanoparticles and making them evenly distributed. The laser particle size analyzer is a particle size detection device based on the Mie scattering theory, which can collect the particle size distribution data in real time. The preset monitoring frequency is the time interval (such as once every 10 s) for collecting the particle size data preset by those skilled in the art, and is used to form a continuous analysis data stream. The particle size distribution result sequence reflects the change in the particle size distribution during the reaction process.
[0021] Preferably, according to the preset reaction ratio and process conditions in the preset mixing reaction scheme, the Li, Fe, and P sources are added to the reaction vessel at a predetermined concentration, and the pH of the system is controlled within a reasonable range to allow them to fully react to generate lithium iron phosphate precursor particles. Subsequently, a small amount (such as 1%–3% by mass fraction) of carbon nanotubes is introduced into the synthesis liquid, and the ultrasonic dispersion device is started to allow them to be fully depolymerized and evenly distributed in the liquid phase system to form a stable conductive network skeleton. To avoid deterioration of the material performance caused by agglomeration or incomplete dispersion of the carbon nanotubes, the laser particle size analyzer is also started simultaneously to collect the particle size data at the set frequency during the dispersion process, and a particle size distribution result sequence is dynamically generated. This achieves the technical effect of providing real-time monitoring data support for subsequent distribution broadening identification and feedback control.
[0022] S2: Identify the distribution broadening scale of the particle size distribution result sequence, and adjust the dispersion parameters according to the identification result to obtain the first adjusted dispersion parameter; In a possible embodiment, the first adjusted dispersion parameter is an optimal set of dispersion operation parameters generated by feedback based on the analysis result of distribution broadening, and is used to guide the dynamic regulation of the dispersion process, including ultrasonic power, action time, frequency, or dispersant concentration, etc.
[0023] Preferably, first, traverse and analyze the sequence of particle size distribution results obtained in the previous stage, and analyze and quantify whether there is a trend of too wide particle size distribution or particle agglomeration in the current system by using a preset particle size distribution range. When a broadening phenomenon is identified (such as D90–D10 exceeding 300 nm, or the distribution pattern showing a skewed bimodal), combine the parameter adjuster and the current dispersion parameters of the current operation to generate the first adjusted dispersion parameters. Exemplarily, if it is identified that the degree of particle agglomeration increases, the system can automatically increase the ultrasonic power (such as from 200 W to 300 W), or extend the dispersion time (such as from 2 min to 4 min), or appropriately add a surfactant (such as 0.5 wt% polyvinylpyrrolidone). By establishing a closed-loop path of particle size distribution, discrimination model, feedback parameter adjustment, and dispersion enhancement, the uniform distribution effect of the nano-conductive phase is significantly improved, which is an important link to ensure the final compaction density and conductivity network connectivity of the material.
[0024] Further, identify the broadening scale of the particle size distribution result sequence, and adjust the dispersion parameters according to the identification result to obtain the first adjusted dispersion parameters. Step S2 of the embodiment of the present application further includes: Traverse the particle size distribution result sequence to identify the broadening coefficient, and obtain the first broadening coefficient sequence; Calculate the mean value of the first broadening coefficient sequence to determine the broadening scale; Obtain the current dispersion parameters, and adjust the current dispersion parameters based on the broadening scale to obtain the first adjusted dispersion parameters.
[0025] In a possible embodiment, the first broadening coefficient sequence is a sequence formed by the broadening coefficients calculated for each group of particle size distribution results over time, which reflects the fluctuation of the particle size distribution change during the mixing reaction process. The current dispersion parameters refer to the parameter combination used in the current stage (such as ultrasonic power, action duration, etc.).
[0026] First, analyze the general particle size distribution of each particle size distribution result in the particle size distribution result sequence, determine the interval with the most common particle size distribution when the laser particle size analyzer analyzes each time, identify the deviation by combining the preset particle size distribution range, and determine the particle distribution uniformity. Furthermore, calculate the mean value of the first broadening coefficient sequence to determine the general broadening degree considering the measurement error, and use the calculation result as the broadening scale. Furthermore, analyze the broadening scale by using the parameter adjuster to obtain the first adjusted dispersion parameters. Through the multi-point identification of dispersion uniformity and the parameter closed-loop control ability, the technical effect of ensuring that the nano-conductive phase can be more stably embedded into the composite particle microstructure and providing a structural basis for subsequent compaction and carbon coating is achieved.
[0027] Further, traverse the particle size distribution result sequence to identify the distribution broadening coefficient, and obtain the first distribution broadening coefficient sequence. Step S2 of the embodiment of the present application further includes: Extract the first particle size distribution result from the particle size distribution result sequence; Extract the leading particle size with the largest distribution quantity from the first particle size distribution result; Construct a leading neighborhood of the leading particle size according to a preset diffusion threshold, and calculate the leading neighborhood density of the leading neighborhood in combination with the first particle size distribution result; Diffuse the leading neighborhood in the directions greater than and less than the leading particle size respectively according to the preset diffusion threshold to obtain a left-side diffused leading neighborhood and a right-side diffused leading neighborhood; Conduct diffusion analysis on the left-side diffused leading neighborhood and the right-side diffused leading neighborhood respectively to obtain a first distribution interval; Identify the first distribution interval based on a preset particle size distribution interval to determine the first distribution broadening coefficient; Conduct particle size distribution identification on the particle size distribution result sequence, and analyze it in combination with the preset particle size distribution interval to obtain the first distribution broadening coefficient sequence.
[0028] Further, as Figure 2 shown, step S2 of the embodiment of the present application further includes: Judge whether the left-side diffused leading neighborhood density of the left-side diffused leading neighborhood is greater than or equal to the leading neighborhood density. If so, continue to diffuse to the left until the preset stop condition is met to obtain the target left-side diffused leading neighborhood; Judge whether the left-side diffused leading neighborhood density of the right-side diffused leading neighborhood is greater than or equal to the leading neighborhood density. If so, continue to diffuse to the right until the preset stop condition is met to obtain the target right-side diffused leading neighborhood; Extract the left endpoint of the target left-side diffused leading neighborhood and the right endpoint of the target right-side diffused leading neighborhood to obtain the first distribution interval.
[0029] Further, add the particle sizes in the first particle size distribution result whose distance to the leading particle size is less than or equal to the preset diffusion threshold to the leading neighborhood.
[0030] Further, count the total number of particle sizes in the leading neighborhood, and divide the statistical result by the preset diffusion threshold to obtain the leading neighborhood density.
[0031] In a possible embodiment, the first particle size distribution result refers to the complete particle size statistical information (such as particle size and corresponding occurrence frequency or proportion) at a specific time point extracted from the entire particle size distribution result sequence. The leading particle size refers to the particle size value with the largest number of occurrences in the distribution result, which can also be understood as the position of the main peak; the leading neighborhood is an initial particle size window area constructed based on the center of the leading particle size and according to a preset diffusion threshold (Δ), usually representing the particle size within the range of ±Δ. Wherein, the preset diffusion threshold is the particle size amplitude for a single diffusion preset by those skilled in the art. Preferably, the particle sizes in the first particle size distribution result whose distance to the leading particle size is less than or equal to the preset diffusion threshold are added to the leading neighborhood to obtain the leading neighborhood. The leading neighborhood density reflects the concentration degree of this area. Optionally, by counting the total number of particle sizes in the leading neighborhood and dividing the statistical result by the preset diffusion threshold, the leading neighborhood density is obtained.
[0032] By gradually expanding the leading neighborhood to the left (small particle size) and right (large particle size) respectively according to the preset diffusion threshold, the particle distribution extension boundary is searched, and finally the first distribution interval is formed. Wherein, the first distribution interval is the particle size interval with a relatively common distribution in the first particle size distribution result. The preset particle size distribution interval is the particle size interval that those skilled in the art set for lithium iron phosphate particles to meet during the dispersion stage. The first distribution broadening coefficient reflects the degree of satisfaction of the current particle size distribution relative to the requirement. By dividing the deviation value between the first distribution interval and the particle size distribution interval by the interval length of the particle size distribution interval, the first distribution broadening coefficient is obtained. Among them, the larger the first distribution broadening coefficient, the lower the dispersion degree. Furthermore, based on the same principle as obtaining the first distribution broadening coefficient, the particle size distribution result sequence is identified for particle size distribution, and analyzed in combination with the preset particle size distribution interval to obtain the first distribution broadening coefficient sequence.
[0033] Preferably, in order to judge whether there is a wide diffusion trend of particles, the leading neighborhood is gradually expanded to the left and right (i.e., in the direction of small particle size and large particle size) respectively according to the same diffusion threshold to construct a left-side diffusion leading neighborhood and a right-side diffusion leading neighborhood. In each step of expansion, the density of the current diffusion neighborhood needs to be calculated. Optionally, the density calculation is performed based on the same obtaining method as obtaining the leading neighborhood density. And it is compared with the original leading neighborhood density. If the density of the diffusion area is still greater than or equal to the leading neighborhood density, it indicates that there is a significant particle group in this direction, and the expansion will continue until the preset stop condition is met (such as the density drops below the threshold preset by those skilled in the art or the particle size exceeds the limit). The continuous particle size interval composed of the finally obtained left and right endpoints is the first distribution interval, and its interval width is the particle size diffusion degree.
[0034] Further, obtain the current dispersion parameter, adjust the current dispersion parameter based on the distribution broadening scale to obtain the first adjusted dispersion parameter. Step S2 of the embodiment of the present application further includes: Pre-construct a parameter adjuster; Use the parameter adjuster to identify the distribution broadening scale and the current dispersion parameter to obtain the first adjusted dispersion parameter.
[0035] In a possible embodiment, obtain multiple sample distribution broadening scales and multiple sample current dispersion parameters as training data, use the training data to perform supervised training on a framework constructed based on a feedforward neural network, and adjust and update the network parameters of the framework according to the output results during training until convergence to obtain a trained parameter adjuster. Use the parameter adjuster to identify the distribution broadening scale and the current dispersion parameter to obtain the first adjusted dispersion parameter. The technical effect of improving the parameter adjustment efficiency and the degree of intelligence is achieved.
[0036] S3: Load the first adjusted dispersion parameter into an ultrasonic dispersion controller for execution, perform spherical composite particle construction, sintering, and carbon coating on the dispersed mixed lithium iron phosphate particle assembly to obtain a pretreated spherical lithium iron phosphate particle assembly; Further, perform spherical composite particle construction on the dispersed mixed lithium iron phosphate particle assembly, use an online particle image analysis system to supervise and authenticate the construction process. After the authentication passes, perform sintering and carbon coating on the obtained micron-sized spherical composite particle assembly in an inert atmosphere to obtain the pretreated spherical lithium iron phosphate particle assembly.
[0037] In a possible embodiment, the online particle image analysis system is a visual detection device that can collect particle morphology images in real time and analyze their particle roundness, diameter, and agglomeration conditions. The lithium iron phosphate mixed system after nano-uniform dispersion treatment is further processed into spherical composite particles with good compressibility and electrochemical performance, and carbon coating is completed through heat treatment. First, apply the first adjusted dispersion parameter to the ultrasonic dispersion system to achieve enhanced control of the distribution effect of subsequent batches of carbon nanotubes. The adjusted particle system enters the spherical construction stage. Usually, a spray drying process is used to atomize the mixed slurry to form particles and dry them into micron-sized spherical composite particles. To ensure the morphological consistency and fluidity of the particle structure, the online particle image analysis system is synchronously connected during the construction process to supervise and analyze the sphericity, size distribution, and agglomeration degree of the particles. When the system determines that the overall morphology of the particles meets the set standards (such as roundness > 0.85 and the average diameter is within the range of 3μm ± 0.5μm), it is allowed to enter the next sintering process.
[0038] The sintering stage is carried out in an inert atmosphere (such as argon), controlling the temperature between 650 and 750 °C to complete the transformation of the crystal form from amorphous to orthorhombic olivine structure. At the same time, a carbon source material (such as precursors like glucose and caramel) is applied for in-situ carbon coating. This process not only enhances the particle conductivity but also improves the interface contact efficiency, providing structural and performance guarantees for subsequent compaction and electrode forming. It achieves the technical effect of making the finally obtained aggregate of pretreated spherical lithium iron phosphate particles have a controllable particle size, a dense structure, and a continuous carbon layer.
[0039] S4: Compact and form the aggregate of the pretreated spherical lithium iron phosphate particles, and use an electrode density detection device to conduct compaction density certification. If the compaction density certification is successful, obtain the prepared enhanced aggregate of lithium iron phosphate particles.
[0040] Further, when compacting and forming the aggregate of the pretreated spherical lithium iron phosphate particles and using an electrode density detection device to conduct compaction density certification, if the compaction density certification is successful and the prepared enhanced aggregate of lithium iron phosphate particles is obtained, step S4 of the embodiment of the present application further includes: Use the electrode density detection device to detect the density of the formed spherical lithium iron phosphate particle aggregate after compaction to obtain a density detection result set; Statistically analyze the proportion of particles that meet the requirements in the density detection result set, and conduct compaction density certification based on a preset particle proportion threshold. When the statistical result is greater than or equal to the preset particle proportion threshold, the certification result is that the compaction density certification is successful.
[0041] In a possible embodiment, the pretreated spherical lithium iron phosphate particles will enter the compaction process. Usually, the material will be made into a slurry with an appropriate amount of binder and solvent and then coated on a metal current collector (such as aluminum foil), and then compacted and formed through a rolling process, making the particles arranged tightly and the porosity reduced, thereby improving the energy density and consistency of the electrode.
[0042] To verify whether the compaction process meets the target performance, an electrode density detection device is deployed to sample the density of the electrode after compaction. For example, multiple compaction areas can be extracted from different positions in an interval sampling manner, and the single-point density is calculated using the volume and mass values measured by the device to form a density detection result set. Statistically analyze the samples in the set to determine the proportion of samples with a density value higher than 2.3 g / cm³. If this proportion exceeds the set preset particle proportion threshold (such as 90%), it is considered that the overall compaction process is stable and the density meets the standard, and the certification result is that the compaction density certification is successful, and finally the prepared enhanced aggregate of lithium iron phosphate particles is obtained. It achieves the technical effect of improving the ability to identify local anomalies, equipment fluctuations, or uneven material distribution, and strengthening the stability and traceability of production line quality control.
[0043] In summary, the embodiments of the present application at least have the following technical effects: In the present application, the pH values and concentrations of lithium source, iron source, and phosphorus source solutions are mixed and reacted according to a preset mixing reaction scheme, carbon nanotubes are introduced, the lithium iron phosphate particles are dispersed by ultrasonic dispersion, and the particle size distribution is monitored by a laser particle size analyzer according to a preset monitoring frequency to obtain a sequence of particle size distribution results. Then, the broadening scale of the particle size distribution results sequence is identified, and the dispersion parameters are adjusted according to the identification results to obtain the first adjusted dispersion parameter. The first adjusted dispersion parameter is loaded into the ultrasonic dispersion controller for execution. The mixed lithium iron phosphate particle assembly after dispersion is subjected to spherical composite particle construction, sintering, and carbon coating to obtain a pretreated spherical lithium iron phosphate particle assembly. Furthermore, the pretreated spherical lithium iron phosphate particle assembly is compacted and formed, and the compacting density is verified by an electrode density detection device. If the compacting density verification is successful, a prepared strengthened lithium iron phosphate particle assembly is obtained. The technical effect of improving the compacting quality of lithium iron phosphate particles is achieved.
[0044] It should be noted that the above sequence 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 describes specific embodiments of this specification. In addition, 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.
[0045] 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 principle of the present application shall be included in the protection scope of the present application.
[0046] 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 preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles, characterized in that, The method includes: According to a preset mixing reaction scheme, mix and react the pH values and concentrations of lithium source, iron source, and phosphorus source solutions, introduce carbon nanotubes, disperse lithium iron phosphate particles by ultrasonic dispersion, and monitor the particle size distribution using a laser particle size analyzer according to a preset monitoring frequency to obtain a sequence of particle size distribution results; Identify the distribution broadening scale of the sequence of particle size distribution results, and adjust the dispersion parameters according to the identification result to obtain a first adjusted dispersion parameter; Based on the first adjusted dispersion parameter, load it into an ultrasonic dispersion controller for execution, construct, sinter, and carbon coat the dispersed mixed lithium iron phosphate particle assembly to obtain a pretreated spherical lithium iron phosphate particle assembly; Compactly mold the pretreated spherical lithium iron phosphate particle assembly, and use an electrode density detection device to authenticate the compacted density. If the compacted density authentication is successful, obtain a prepared strengthened lithium iron phosphate particle assembly.
2. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles as described in claim 1, characterized in that, Identifying the distribution broadening scale of the sequence of particle size distribution results, and adjusting the dispersion parameters according to the identification result to obtain a first adjusted dispersion parameter, includes: Traverse the sequence of particle size distribution results to identify the distribution broadening coefficient to obtain a first sequence of distribution broadening coefficients; Calculate the mean value of the first sequence of distribution broadening coefficients to determine the distribution broadening scale; Obtain the current dispersion parameter, and adjust the current dispersion parameter based on the distribution broadening scale to obtain the first adjusted dispersion parameter.
3. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles as described in claim 2, characterized in that, Traversing the sequence of particle size distribution results to identify the distribution broadening coefficient to obtain a first sequence of distribution broadening coefficients, includes: Extract a first particle size distribution result from the sequence of particle size distribution results; Extract the leading particle size with the largest distribution quantity from the first particle size distribution result; Construct a leading neighborhood of the leading particle size according to a preset diffusion threshold, and calculate the leading neighborhood density of the leading neighborhood in combination with the first particle size distribution result; Diffuse the leading neighborhood in the directions greater than and less than the leading particle size respectively according to a preset diffusion threshold to obtain a left-diffused leading neighborhood and a right-diffused leading neighborhood; Conduct diffusion analysis on the left-diffused leading neighborhood and the right-diffused leading neighborhood respectively to obtain a first distribution interval; Identify the first distribution interval based on a preset particle size distribution interval to determine the first distribution broadening coefficient; Identify the particle size distribution of the sequence of particle size distribution results, and analyze it in combination with a preset particle size distribution interval to obtain a first sequence of distribution broadening coefficients.
4. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles according to claim 3, wherein, Includes: Judge whether the left-diffused leading neighborhood density of the left-diffused leading neighborhood is greater than or equal to the leading neighborhood density. If so, continue to diffuse to the left until a preset stop condition is met to obtain a target left-diffused leading neighborhood; Judge whether the left-diffused leading neighborhood density of the right-diffused leading neighborhood is greater than or equal to the leading neighborhood density. If so, continue to diffuse to the right until a preset stop condition is met to obtain a target right-diffused leading neighborhood; Extract the left endpoint of the left diffusion leading neighborhood of the target and the right endpoint of the right diffusion leading neighborhood of the target to obtain a first distribution interval.
5. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles according to claim 3, characterized in that, Add the particle sizes in the first particle size distribution result whose distances to the leading particle size are less than or equal to the preset diffusion threshold into the leading neighborhood.
6. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles as described in claim 3, wherein, Count the total number of particle sizes in the leading neighborhood, and divide the statistical result by the preset diffusion threshold to obtain the leading neighborhood density.
7. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles as described in claim 2, wherein Obtain the current dispersion parameter, and adjust the current dispersion parameter based on the distribution broadening scale to obtain the first adjusted dispersion parameter, including: Pre-construct a parameter adjuster; Use the parameter adjuster to identify the distribution broadening scale and the current dispersion parameter to obtain the first adjusted dispersion parameter.
8. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles according to claim 1, wherein Construct spherical composite particles from the mixture of lithium iron phosphate particles after dispersion, and use an online particle image analysis system to supervise and authenticate the construction process. After the authentication passes, sinter and carbon coat the obtained collection of micron-sized spherical composite particles in an inert atmosphere to obtain the pretreated spherical lithium iron phosphate particle collection.
9. The preparation method for compacting nano-dispersion strengthened lithium iron phosphate particles as described in claim 1, characterized in that, Compact and form the pretreated spherical lithium iron phosphate particle collection, and use an electrode density detection device to authenticate the compaction density. If the compaction density authentication is successful, obtain the prepared strengthened lithium iron phosphate particle collection, including: Use the electrode density detection device to detect the density of the formed spherical lithium iron phosphate particle collection after compaction and forming to obtain a density detection result set; Count the proportion of particles that meet the requirements in the density detection result set, and conduct compaction density authentication based on a preset particle proportion threshold. When the statistical result is greater than or equal to the preset particle proportion threshold, the authentication result is that the compaction density authentication is successful.