Method for repairing lattice defects of lithium battery regenerated ternary cathode material by doping rare earth elements

Abstract

The application provides a method for repairing lattice defects of regenerated ternary cathode material of lithium battery by doping rare earth elements, comprising the following steps: S1, collecting surface and cross-section image data of the ternary cathode material sample, performing image processing, and obtaining a defect distribution mapping; S2, calculating the shallow defect density of the surface layer region and the deep defect density of the internal bulk phase region according to the defect distribution mapping, and determining the defect depth gradient value; S3, if the defect depth gradient value is greater than a preset depth gradient threshold value, then a high-concentration dopant dose is allocated to the surface layer region and an initial doping process is performed, and intermediate state data of the processed material is obtained; S4, if the surface layer defect repair rate in the intermediate state data reaches a preset standard, then the repair concentration threshold of the deep layer defect is determined, and a subsequent doping dose is adjusted to obtain a hierarchical concentration sequence; S5, a penetration doping process from the surface layer to the bulk phase is performed by using the hierarchical concentration sequence, and final material state data is obtained and a lattice structure integrity index is obtained.

Description

Technical Field

[0001] This invention relates to the field of lithium battery recycling technology, and in particular to a method for repairing lattice defects in regenerated ternary cathode materials of lithium batteries by doping with rare earth elements. Background Technology

[0002] Lithium-ion batteries are widely used in electric vehicles and energy storage systems. Ternary cathode materials are their core components, directly determining the battery's capacity, cycle life, and safety. As battery life increases, a large number of used lithium batteries are generated. How to efficiently recycle and regenerate these cathode materials has become a key direction in the field of resource recycling, directly related to the stability of raw material supply and environmental protection.

[0003] Current recycling methods typically employ a uniform approach to repair waste ternary cathode materials. However, during long-term charge-discharge cycles, the repeated insertion and extraction of lithium ions causes periodic changes in lattice parameters. These changes lead to structural stress concentration within the material, resulting in localized distortions and multi-layered defects extending from the surface to the bulk phase. These defects are not uniformly distributed; the surface layer is often more severely damaged, gradually weakening towards the interior. Existing uniform treatment methods struggle to simultaneously address the repair needs of defects at different depths, resulting in limited restoration of the material's structural integrity after repair.

[0004] The core challenge posed by this depth gradient distribution of lattice defects lies in the significant variation in defect density and severity as it extends from the surface into the bulk phase. Applying the same repair intensity may alleviate surface defects, but fail to reach deeper defects; conversely, insufficient repair may result in over-repair of the surface while leaving internal problems. This mismatch between defect depth and repair requirements makes it difficult to achieve a coordinated repair process from the outside in, hindering the restoration of the material's overall structure to a near-new state.

[0005] Therefore, how to accurately adjust the repair intensity based on the depth gradient of lattice defects from the surface to the bulk phase, so that severe surface defects can be fully repaired while mild deep defects can also be effectively penetrated and improved, has become a key issue in achieving high-quality regeneration of waste ternary cathode materials. Summary of the Invention

[0006] To address the technical problems mentioned in the background section, this invention provides a method for repairing lattice defects in regenerated ternary cathode materials for lithium batteries by doping with rare earth elements, comprising: S1, acquiring surface and cross-sectional image data of a ternary cathode material sample, performing image processing to obtain a defect distribution mapping map; S2, calculating the shallow defect density in the surface region and the deep defect density in the internal bulk region based on the defect distribution mapping map, and determining the defect depth gradient value; S3, if the defect depth gradient value is greater than a preset depth gradient threshold, allocating a high concentration doping dose to the surface region and performing an initial doping process to obtain intermediate state data of the processed material; S4, if the surface defect repair rate in the intermediate state data reaches a preset standard, determining the repair concentration threshold for deep defects, and adjusting the subsequent doping dose accordingly to obtain a graded concentration sequence; S5, using the graded concentration sequence to perform a penetration doping process from the surface to the bulk phase, obtaining final material state data and a lattice structure integrity index.

[0007] Optionally, step S1 includes: step S11, acquiring surface and cross-sectional image data of the ternary cathode material sample using a scanning electron microscope; step S12, using an image segmentation algorithm to separate the pixel sets of the surface region and the internal bulk region and obtain a defect distribution mapping map.

[0008] Optionally, step S11 includes: when acquiring images of ternary cathode materials, preparing the sample into a shape suitable for observation, including surface cleaning and cross-sectional cutting.

[0009] Optionally, step S12 includes: using an edge detection algorithm to identify the outer contour of the material particles, and determining the boundary between the surface region and the internal bulk region through gradient calculation and threshold processing.

[0010] Optionally, step S2 includes: step S21, calculating the shallow defect density in the surface region and the deep defect density in the internal bulk region based on the defect distribution mapping; step S22, determining the defect depth gradient value from the surface to the interior using density gradient analysis.

[0011] Optionally, step S22 includes: the density gradient analysis adopts a hierarchical statistical method to divide the ternary cathode material into multiple depth layers from the surface to the interior, calculate the defect density of each layer, fit these density data, and obtain the functional relationship between defect density and depth.

[0012] Optionally, step S3 includes: step S31, predicting the penetration path of the dopant element after it attaches to the surface layer using a diffusion simulation algorithm, and obtaining a surface repair parameter set; step S32, using the surface repair parameter set to guide the initial doping process, and obtaining intermediate state data of the processed material; step S33, determining whether the surface defect repair rate in the intermediate state data reaches a preset standard, and obtaining a bulk penetration preparation signal.

[0013] Optionally, step S4 includes: step S41, extracting microcrack features of the internal bulk region from the defect distribution map based on the bulk penetration preparation signal; step S42, simulating the structural distortion caused by lithium-ion intercalation / deintercalation using a finite element analysis algorithm and determining the repair concentration threshold for deep defects; step S43, adjusting the subsequent doping dose based on the repair concentration threshold for deep defects and obtaining a graded concentration sequence; and step S44, determining whether each concentration value in the graded concentration sequence matches the defect depth gradient value to obtain an optimized graded doping scheme.

[0014] Optionally, step S5 includes: step S51, performing a penetration process from the outside to the inside using an optimized hierarchical doping scheme and obtaining final material state data; step S52, determining the overall defect repair uniformity in the final material state data and obtaining the lattice structure integrity index of the regenerated ternary cathode material.

[0015] Optionally, step S51 includes:

[0016] The doping and infiltration process employs multi-stage temperature control.

[0017] The first stage is low-temperature pretreatment, with the temperature controlled between 400 and 500 degrees Celsius;

[0018] The second stage is intermediate-temperature diffusion, where the temperature rises to 600 to 700 degrees Celsius;

[0019] The third stage is high-temperature solution treatment, with the temperature further increased to 750 to 850 degrees Celsius.

[0020] The technical solution provided by this invention has the following beneficial effects:

[0021] This invention discloses a method for repairing lattice defects in recycled ternary cathode materials from lithium-ion batteries by doping with rare earth elements. Addressing the multi-level defects in spent lithium-ion battery cathode materials caused by repeated lithium-ion insertion / extraction during long-term charge-discharge cycles, resulting in periodic changes in lattice parameters, structural stress concentration, and local distortion, this invention proposes a logically related business scenario solution: how to accurately identify and grade lattice defects from the surface to the bulk phase to restore the material's structural integrity. This invention uses image processing technology to accurately obtain defect distribution maps, combines scanning electron microscopy and image segmentation algorithms to analyze the surface and deep defect densities, and innovatively employs a graded doping strategy. The rare earth element doping concentration is dynamically adjusted according to the defect depth gradient value, from high-concentration surface repair to low-concentration deep penetration, ensuring comprehensive repair effects. Ultimately, this invention significantly improves the lattice structural integrity of the recycled ternary cathode material, restores its electrochemical performance, provides technical support for the efficient recycling of spent lithium-ion battery materials, and has significant economic and environmental value. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method for repairing lattice defects in regenerated ternary cathode materials for lithium batteries by doping with rare earth elements, according to the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0024] like Figure 1 As shown in the figure, this invention provides a method for repairing lattice defects in regenerated ternary cathode materials for lithium batteries by doping with rare earth elements. This method achieves comprehensive repair from the surface to the bulk phase by accurately identifying defect distribution characteristics and employing a hierarchical doping strategy, effectively restoring the integrity of the lattice structure of the regenerated ternary cathode material.

[0025] Specifically, the method includes: S1, acquiring surface and cross-sectional image data of a ternary cathode material sample, performing image processing, and obtaining a defect distribution mapping map. In one embodiment, the ternary cathode material sample is typically a spent lithium battery cathode material that has undergone multiple charge-discharge cycles, and its main components include nickel-cobalt-manganese ternary oxides. These materials will develop various types of lattice defects during long-term use, including surface oxide layer peeling, internal microcrack propagation, grain boundary separation, and active site failure. The formation mechanism of these defects mainly stems from the repeated insertion and extraction of lithium ions during charge and discharge, leading to periodic changes in lattice parameters, which in turn cause structural stress concentration and local lattice distortion. Specifically, obtaining the defect distribution mapping map requires comprehensive consideration of the multi-layered structural characteristics of the material. The surface region is typically 50 to 200 nanometers thick and mainly bears electrolyte corrosion and mechanical stress impact; therefore, surface defects often manifest as increased surface roughness, oxide deposition, and active coating peeling. The internal bulk region is mainly affected by the blockage of lithium-ion diffusion paths and the accumulation of lattice strain, with defect morphologies mainly consisting of microcrack networks, grain boundary voids, and phase separation.

[0026] Optionally, this step also includes: Step S11, acquiring surface and cross-sectional image data of the ternary cathode material sample using a scanning electron microscope (SEM). The working principle of the scanning electron microscope is based on the secondary electron signal generated by the interaction between a high-energy electron beam and the sample surface. When acquiring images of the ternary cathode material, the sample needs to be prepared into a suitable morphology for observation, including surface cleaning and cross-sectional cutting. Surface image acquisition mainly focuses on the morphological characteristics of material particles, the distribution of surface cracks, and the aggregation state between particles. Cross-sectional images expose the internal structure of the material through ion beam cutting or mechanical grinding, observing grain boundaries, pore distribution, and phase interface characteristics. In one possible implementation, the accelerating voltage of the scanning electron microscope is set to 5 kV to 15 kV, and the working distance is controlled within the range of 8 mm to 12 mm to obtain the best image resolution and depth of field. The sample surface needs to be conductive, usually by sputtering gold or carbon thin films to improve image quality. For cross-sectional observation, focused ion beam cutting technology can be used to prepare a smooth cross-section to avoid mechanical damage interfering with defect observation. For example, when acquiring surface images, the magnification is typically set to 1000x to 10000x to clearly display the microscopic defect features of the particle surface. The magnification of the cross-sectional image can be adjusted within the range of 500x to 5000x according to observation needs to ensure that macroscopic cracks and microscopic grain boundary structures can be observed simultaneously. Multiple fields of view need to be captured during image acquisition to obtain statistically significant defect distribution data. Step S12 involves using an image segmentation algorithm to separate the pixel sets of the surface region and the internal bulk region and obtain a defect distribution map. The core of the image segmentation algorithm lies in accurately identifying the boundary features and grayscale distribution differences of different regions. The surface region and the internal bulk region exhibit different contrast and texture features in scanning electron microscope images. The surface region typically has higher surface roughness and irregular edge contours, while the internal bulk region exhibits a relatively uniform grayscale distribution and a regular grain structure. In one embodiment, the image segmentation process first uses an edge detection algorithm to identify the outer contour of the material particles, and then determines the boundary between the surface region and the internal bulk region through gradient calculation and thresholding. Edge detection algorithms are based on the spatial rate of change of pixel grayscale values. When the grayscale difference between adjacent pixels exceeds a set grayscale difference threshold, the location is marked as an edge point. By connecting adjacent edge points to form continuous boundary lines, region segmentation is achieved. Specifically, the identification of surface regions needs to consider the morphological features and defect distribution patterns of the material surface. Surface defects mainly include surface cracks, oxide layer peeling, and wear marks on particle edges. These defects appear as dark areas with low grayscale values ​​in the image. By setting an appropriate grayscale threshold, these defective regions can be separated from the normal surface structure. The identification of defects in the internal bulk region is more complex, requiring comprehensive consideration of grain boundary features, porosity distribution, and phase separation phenomena.Preferably, the image segmentation algorithm further includes morphological processing steps to eliminate noise interference and fill small segmentation gaps. Morphological opening operations can remove isolated noise points, while closing operations can connect broken boundary lines. Through these processing steps, the final defect distribution map can accurately reflect the true defect state of the material. S2, based on the defect distribution map, the shallow defect density in the surface region and the deep defect density in the internal bulk region are calculated, and the defect depth gradient value is determined. The calculation of defect density is an important indicator for assessing the degree of material damage and directly affects the formulation of subsequent doping repair strategies. The shallow defect density reflects the degree of damage concentration on the material surface, while the deep defect density characterizes the integrity of the internal structure. The difference between the two forms the defect depth gradient, a parameter that is of great guiding significance for determining the penetration path and concentration distribution of dopant elements.

[0027] Optionally, this step further includes: step S21, calculating the shallow defect density of the surface region and the deep defect density of the internal bulk region based on the defect distribution map. The calculation of the shallow defect density is based on the statistical analysis of defect pixels in the surface region. In the defect distribution map, the defect region is usually represented by a set of pixels whose grayscale values ​​deviate significantly from the normal range. By counting the number of these abnormal pixels and dividing by the total number of pixels in the surface region, a numerical representation of the shallow defect density can be obtained. This calculation process needs to consider the weighting coefficients of different types of defects; for example, the influence of surface cracks is usually greater than that of slight surface roughening. In one possible implementation, the calculation of the shallow defect density also needs to consider the geometric feature parameters of the defects, including the length, width, and distribution density of the defects. The density calculation of linear crack defects is mainly based on the ratio of the total crack length to the area of ​​the observed region, while point defects are characterized by the ratio of the number of defects to the unit area. For defects with complex shapes, their proportion can be calculated by statistically analyzing the pixel area. The calculation of the deep defect density is relatively complex and requires extracting defect information of the internal structure from the cross-sectional image. Defects in the internal bulk region mainly include grain boundary cracks, pores, and phase separation regions. These defects exhibit different morphological characteristics and grayscale distributions in cross-sectional images. Grain boundary cracks typically appear as thin, dark lines, pores as circular or elliptical black areas, while phase separation regions have different grayscale values ​​and texture characteristics compared to the matrix material. Specifically, calculating the density of deep defects requires establishing a three-dimensional defect distribution model. By analyzing multiple cross-sectional images, the defect network structure inside the material can be reconstructed, and the volumetric defect density can then be calculated. This process involves spatial distribution statistics and connectivity analysis of defects, and needs to consider the differences in defect distribution at different depth levels. Step S22 uses density gradient analysis to determine the defect depth gradient value from the surface to the interior. Density gradient analysis is a quantitative assessment method based on the difference in defect density between shallow and deep layers. The defect depth gradient value reflects the spatial variation trend of defect distribution, which is of great significance for understanding the damage mechanism of materials and formulating repair strategies. When the gradient value is large, it indicates that the defects are mainly concentrated in the surface region, and the internal structure is relatively intact; when the gradient value is small, it indicates that the defects are relatively uniformly distributed throughout the material. In one embodiment, density gradient analysis employs a hierarchical statistical method, dividing the ternary cathode material into multiple depth layers from the surface to the interior, and calculating the defect density of each layer. By fitting these density data points, a functional relationship between defect density and depth can be obtained. The gradient value is the derivative of this function at a specific depth, reflecting the rate of change of defect density. For example, for typical regenerated ternary cathode materials, the defect density in the surface region is typically 2 to 5 times that of the internal bulk region. This difference mainly stems from the fact that the surface region is directly exposed to the electrolyte environment, enduring stronger chemical corrosion and mechanical impact.Density gradient analysis can determine the decay law of defect concentration, providing a quantitative basis for subsequent graded doping. It should be noted that the calculation of defect depth gradient values ​​also needs to consider the microstructural characteristics of the material. Different grain orientations and grain boundary densities affect the formation and propagation modes of defects, thus affecting the gradient distribution. Therefore, in actual calculations, it is necessary to combine the crystallographic information of the material for correction to improve the accuracy of the gradient values. S3, if the defect depth gradient value is greater than a preset depth gradient threshold, a high concentration of doping dose is allocated to the surface region and an initial doping process is performed to obtain intermediate state data of the processed material. When the defect depth gradient value exceeds the preset depth gradient threshold, it indicates that the damage to the material is mainly concentrated in the surface region. At this time, a high-concentration surface doping strategy can achieve efficient defect repair. The depth gradient threshold is determined based on statistical analysis of a large amount of experimental data and is usually set to a value between 0.3 and 0.7. The specific value needs to be adjusted according to the material type and application requirements.

[0028] Optionally, this step also includes: Step S31, predicting the penetration path of the dopant element after it adheres to the surface layer using a diffusion simulation algorithm, and obtaining a set of surface repair parameters. The diffusion simulation algorithm predicts the transport behavior of the dopant element in the material based on Fick's diffusion law and the microstructural characteristics of the material. Rare earth dopant elements such as lanthanum, cerium, and yttrium have unique electronic structures and ionic radii, which can effectively fill lattice vacancies, stabilize grain boundary structures, and improve the structural stability of the material. The diffusion simulation needs to consider the diffusion coefficient of the dopant element, the lattice parameters of the material, and process conditions such as temperature and time. In one possible implementation, the diffusion simulation algorithm uses the finite difference method to solve the diffusion equation. The ternary cathode material is discretized into a three-dimensional mesh structure, where each mesh point represents a microscopic volume unit. The concentration distribution of the dopant element evolves gradually through iterative calculations until a steady-state distribution is reached. Appropriate boundary conditions need to be set during the simulation, including the surface doping concentration, the initial internal concentration, and the treatment of the diffusion boundary. Specifically, the diffusion path of rare earth elements in the ternary cathode material mainly follows grain boundaries and defect channels. The irregularity of atomic arrangement in grain boundary regions provides a rapid diffusion channel for dopant elements. Defect regions such as vacancies, dislocations, and grain boundary cracks further promote the penetration of dopant elements. By simulating these preferred diffusion paths, the spatial distribution and concentration gradient of dopant elements can be predicted. The surface repair parameter set includes key process parameters such as doping temperature, doping time, dopant element concentration, and atmosphere control. The doping temperature is usually set in the range of 600 to 800 degrees Celsius to ensure sufficient diffusion of dopant elements while avoiding damage to the material structure caused by excessively high temperatures. The doping time is determined based on diffusion simulation results and is generally 2 to 8 hours. The dopant element concentration needs to be optimized according to the defect density and repair requirements; too low a concentration cannot achieve effective repair, while too high a concentration may cause new structural defects. Step S32: The surface repair parameter set is used to guide the initial doping process, and intermediate state data of the processed material are obtained. The initial doping process is a key step in the entire repair process and directly determines the repair effect of surface defects. The doping process is typically carried out in a controlled atmosphere furnace, where precise control of temperature, time, and atmosphere composition ensures uniform distribution and effective solid solution of the dopant elements. Dopant elements can be introduced into the material surface using various methods such as solid-phase diffusion, vapor deposition, or solution impregnation. In one embodiment, the solid-phase diffusion method involves thoroughly mixing rare earth oxide powder with regenerated ternary cathode material and then heat-treating it at high temperatures. The rare earth oxides decompose at high temperatures, releasing active rare earth ions, which penetrate to defect sites on the material surface via solid-state diffusion. The mixing ratio needs to be precisely controlled based on the defect density and repair requirements; typically, the molar ratio of rare earth elements is controlled within the range of 0.5% to 3%. The vapor deposition method, on the other hand, forms a uniform doped layer on the material surface by evaporating rare earth metals or compounds.This method enables more precise concentration control and uniform distribution, making it particularly suitable for applications with high surface defect density. The deposition process must be carried out in a vacuum or inert gas environment to avoid the impact of oxidation reactions on the doping effect. The intermediate state data of the processed material includes multiple aspects such as the degree of surface defect repair, dopant element distribution, lattice parameter changes, and electrochemical performance indicators. These data are obtained through various characterization methods, including scanning electron microscopy, X-ray diffraction analysis, and electrochemical impedance spectroscopy. The intermediate state data provides important feedback information for subsequent deep doping. Step S33 determines whether the surface defect repair rate in the intermediate state data meets the preset standard and obtains a bulk phase penetration preparation signal. The surface defect repair rate is a core indicator for evaluating the initial doping effect, calculated by comparing the defect distribution mapping before and after doping. The repair rate is calculated based on the reduction in defect area or number, usually expressed as a percentage. The preset standard is determined according to the material's application requirements and compatibility with subsequent processes, generally requiring a surface defect repair rate of over 70%. In one possible implementation, the surface defect repair rate is evaluated using a quantitative image analysis method. By comparing scanning electron microscope images before and after doping, the pixel changes in the defect area are statistically analyzed, and the repair rate is calculated. Simultaneously, the repair quality assessment needs to be considered, including the structural integrity of the repaired area, the bonding strength with the matrix material, and the degree of recovery of electrochemical activity. When the surface defect repair rate reaches a preset standard, the system generates a bulk penetration preparation signal, initiating preparation for the deep doping stage. This signal not only indicates the successful completion of surface repair but also provides favorable starting conditions for subsequent bulk penetration. Effective surface repair provides a smoother diffusion channel for further penetration of dopant elements into the interior. In step S4, if the surface defect repair rate in the intermediate state data reaches a preset standard, the repair concentration threshold for deep defects is determined, and the subsequent doping dosage is adjusted accordingly to obtain a graded concentration sequence. Based on the successful completion of surface repair, the repair of deep defects requires a more refined concentration control strategy. Due to their deep location and complex distribution, the repair process for deep defects needs to consider the long-distance diffusion and multi-level penetration of dopant elements. Determining the repair concentration threshold is a key technical step in this stage, directly affecting the effect of subsequent graded doping and the overall performance recovery of the material.

[0029] Optionally, this step further includes: step S41, extracting microcrack features of the internal bulk region from the defect distribution map based on the bulk penetration preparation signal. In one embodiment, the extraction of microcrack features requires comprehensive analysis of various defect morphologies in the cross-sectional image. Microcracks in the internal bulk region mainly include grain boundary cracks, transgranular cracks, and microcrack networks caused by stress concentration. These cracks exhibit different geometric features and spatial distribution patterns in scanning electron microscope images, requiring identification and quantification through specialized image analysis algorithms. Grain boundary cracks typically extend along grain boundaries, exhibiting tortuous linear characteristics, with crack widths generally ranging from a few nanometers to tens of nanometers. The formation of these cracks mainly stems from the difference in thermal expansion coefficients between different grains and the non-uniform distribution of grain boundary energy. During charge-discharge cycles, the repeated insertion and extraction of lithium ions leads to periodic changes in lattice parameters, resulting in stress concentration at grain boundaries, ultimately leading to crack initiation and propagation. Transgranular cracks, on the other hand, directly penetrate the interior of the grains, typically exhibiting relatively straight linear characteristics. The formation of these types of cracks is closely related to dislocation movement and slip system activation within the grains. When the stress on the material exceeds the yield strength of the grains, dislocations begin to move and accumulate at obstacles, forming stress concentration points, which in turn trigger transgranular cracks. The extraction of microcrack characteristics employs a multi-scale analysis method. First, the macroscopic distribution pattern of cracks is observed under low magnification, and then the detailed morphological characteristics of individual cracks are analyzed under high magnification. Through statistical analysis, key parameters such as crack length distribution, orientation distribution, and density distribution can be obtained. These parameters provide a quantitative basis for subsequent repair strategy formulation. Specifically, microcrack characteristic parameters include crack length, crack width, crack orientation angle, crack density, and crack connectivity. Crack length reflects the severity of damage; long cracks usually require higher concentrations of dopant elements for effective repair. Crack width determines the ease of dopant penetration; wide cracks provide a more convenient diffusion channel for dopant elements. Crack orientation angle affects the diffusion directionality of dopant elements and needs to be considered in the doping process design. Step S42 involves using the finite element method (FEM) to simulate the structural distortion caused by lithium-ion deintercalation and determine the repair concentration threshold for deep defects. The FEM is a numerical engineering analysis method that discretizes a continuous material structure into a finite number of elements, solves for the mechanical response of each element, and thus obtains the stress-strain distribution of the overall structure. In the analysis of lithium-ion battery cathode materials, the FEM can accurately simulate the volume changes and stress evolution during lithium-ion deintercalation, providing theoretical guidance for defect repair. Structural distortion caused by lithium-ion deintercalation is one of the main reasons for the performance degradation of ternary cathode materials. During charging, lithium ions are extracted from the cathode material, leading to lattice contraction and structural rearrangement. During discharging, lithium ions are reintercalated, causing lattice expansion.This periodic volume change generates a complex stress field within the material. When the stress exceeds the material's fracture strength, microcracks and other structural defects form. In one possible implementation, the finite element model represents the microstructure of the ternary cathode material as a three-dimensional mesh, with each mesh element representing a micro-volume. The model needs to define mechanical parameters such as the material's elastic modulus, Poisson's ratio, and coefficient of thermal expansion, as well as the relationship between lithium-ion concentration and lattice parameters. By applying boundary conditions with varying lithium-ion concentration, the stress distribution and deformation modes within the material can be calculated. Simulation results of structural distortion show that stress concentration mainly occurs in grain boundary regions, phase interfaces, and at the tips of existing defects. The stress levels in these regions are typically 2 to 5 times higher than in the matrix material, becoming preferred locations for the initiation of new defects. By analyzing stress distribution contour maps, the possible propagation paths of defects and the final damage modes can be predicted. The repair concentration threshold for deep defects refers to the minimum dopant element concentration required to effectively repair a specific defect. Determining this parameter requires comprehensive consideration of the defect's geometric characteristics, location depth, and surrounding stress environment. For regions with severe stress concentration, higher dopant concentrations are needed to achieve effective structural stability. For deeper defects, the diffusion resistance and concentration decay effect of the dopant element also need to be considered. The calculation of the repair concentration threshold is based on the thermodynamic and kinetic analysis of defect repair. From a thermodynamic perspective, the solid solution of the dopant element at the defect site needs to overcome a certain energy barrier; the higher the concentration, the greater the driving force for solid solution. From a kinetic perspective, the dopant element needs to diffuse to reach the defect site; the longer the diffusion distance, the greater the required concentration gradient. By establishing a corresponding mathematical model, the repair concentration threshold corresponding to different defect types and locations can be quantitatively calculated. Step S43: Adjust the subsequent doping dose based on the repair concentration threshold of the deep defect and obtain a hierarchical concentration sequence. The design of the hierarchical concentration sequence is a key technical step in achieving efficient deep repair. Unlike surface repair which uses a single high concentration, deep repair requires designing multiple concentration gradients based on the spatial distribution and repair difficulty of the defect to ensure that the dopant element can penetrate layer by layer and achieve effective repair at each depth level. In one embodiment, the hierarchical concentration sequence is designed using a decreasing gradient mode, that is, the doping concentration gradually decreases from the surface to the interior. This design considers the diffusion characteristics and concentration decay law of the dopant element. The surface region, being in direct contact with the dopant source, can withstand higher doping concentrations, while the inner regions require moderate concentrations to avoid side effects caused by overdoping. The specific values ​​of the concentration sequence need to be determined based on the repair concentration threshold and diffusion simulation results. For example, for the surface region, the doping concentration can be set to 1.5 to 2 times the repair concentration threshold to ensure sufficient repair. For the middle layer region, the concentration can be set to 1.2 to 1.5 times the repair concentration threshold, ensuring repair effectiveness while avoiding excessive concentration. For the deep region, the concentration is close to the concentration threshold to achieve precise repair.Adjusting the doping dosage involves not only the concentration value but also the optimization of doping time and temperature. Different doping depths may require different process conditions to achieve the best repair effect. Shallow doping can use relatively low temperatures and short times, while deep doping requires higher temperatures and longer times to promote long-distance diffusion of the dopant element. The hierarchical concentration sequence also needs to consider the synergistic effect of different rare earth elements. In practical applications, composite doping of multiple rare earth elements can be used to achieve synergistic repair by utilizing the characteristics of different elements. For example, lanthanum has a large ionic radius, which is suitable for filling large lattice vacancies; cerium has a variable valence state, which can stabilize oxygen vacancies; yttrium has a moderate ionic radius and good chemical stability, which is suitable as a grain boundary stabilizer. Step S44: Determine whether the concentration values ​​in the hierarchical concentration sequence match the defect depth gradient values ​​to obtain the optimized hierarchical doping scheme. Determining the concentration matching is an important step in ensuring the effectiveness of the doping scheme. The defect depth gradient value reflects the spatial variation law of defect distribution, while the hierarchical concentration sequence reflects the spatial distribution of the doping strategy. The degree of matching between the two directly affects the uniformity and integrity of the repair effect. In one possible implementation, matching is determined using a combination of numerical comparison and trend analysis. First, the defect depth gradient values ​​at different depths are calculated and then compared with the corresponding doping concentrations. Ideally, regions with high defect density should correspond to higher doping concentrations, and regions with low defect density should correspond to lower doping concentrations. Matching assessment also needs to consider the diffusion characteristics and repair mechanisms of the dopant elements. Different types of defects have different sensitivities to doping concentration. Point defects such as vacancies and interstitial atoms are relatively easy to repair, requiring only low doping concentrations; line defects such as dislocations require medium doping concentrations; and surface defects such as grain boundaries and phase interfaces require higher doping concentrations to achieve effective stabilization. When a mismatch is found between the concentration sequence and the defect gradient, corresponding adjustments and optimizations are necessary. Adjustment strategies include fine-tuning the concentration values, redesigning the concentration distribution, and correcting the doping process parameters. The optimization process uses an iterative method, undergoing multiple adjustments and verifications to ultimately obtain the optimal matching effect. The optimized hierarchical doping scheme includes not only the concentration distribution but also detailed process flows and operating parameters. The scheme clearly specifies the temperature, time, atmosphere conditions, and the method of introducing dopant elements for each doping stage. Precise control of these parameters is crucial for achieving the desired repair effect. Step S5 involves using the graded concentration sequence to perform a penetration doping process from the surface to the bulk phase, obtaining final material state data and lattice structure integrity indicators. The penetration doping process is the core of the entire repair process, requiring precise control of the spatial distribution and temporal evolution of the doping elements. Penetration from the surface to the bulk phase is a complex multiphysics coupling process involving diffusion mass transfer, heat conduction, and chemical reactions.

[0030] Optionally, this step also includes: Step S51, performing a penetration process from the outside to the inside using an optimized hierarchical doping scheme and obtaining the final material state data. The implementation of the doping penetration process requires a multi-stage temperature program and precise time control. The first stage is a low-temperature pretreatment, with the temperature controlled within the range of 400 to 500 degrees Celsius. The main purpose is to activate the material surface, creating favorable conditions for subsequent high-temperature doping. In this stage, rare earth dopants begin to undergo chemical adsorption and initial diffusion on the material surface, forming the initial distribution of the concentration gradient. The second stage is a medium-temperature diffusion, with the temperature increased to 600 to 700 degrees Celsius, where the dopants begin to substantially penetrate into the material interior. Within this temperature range, the atomic diffusion coefficient increases significantly, and the dopants can overcome grain boundary resistance and diffuse deeper along defect channels. The diffusion process follows a concentration gradient-driven law, with dopants migrating from high-concentration regions to low-concentration regions, gradually establishing a concentration distribution from the surface to the interior. The third stage is high-temperature solution treatment, with temperatures further increased to 750 to 850 degrees Celsius, ensuring sufficient solution and structural stability of the dopant element at the target location. Under high-temperature conditions, lattice vibrations intensify, providing ample thermal activation energy for dopant element displacement and interstitial solution. Simultaneously, the high temperature promotes defect healing and structural rearrangement, achieving a comprehensive recovery of material properties. In one embodiment, the infiltration process also requires appropriate mechanical treatment, such as slight vibration or pressure application, to promote uniform distribution of the dopant element. Mechanical action can break down surface oxide layers and contaminants, providing more unobstructed channels for dopant element infiltration. Simultaneously, moderate mechanical stress can activate dislocation movement within the material, providing additional driving force for dopant element diffusion. Finally, obtaining the material state data requires comprehensive analysis using multiple characterization methods. X-ray diffraction analysis is used to detect changes in lattice parameters and the evolution of phase composition. By comparing the diffraction patterns before and after doping, the degree of dopant element solution and the recovery of lattice distortion can be determined. Scanning electron microscopy (SEM) is used to observe the morphological characteristics of defect repair, including the degree of crack healing, surface smoothness, and improvement in particle morphology. Transmission electron microscopy (TEM) provides atomic-scale structural information, allowing direct observation of the dopant element's occupancy in the crystal lattice and local structural changes. High-resolution TEM imaging clearly shows the repair process of grain boundary structures and the elimination of lattice defects. Electron energy loss spectroscopy (EEGS) analysis determines the valence state and chemical environment of the dopant element, verifying the correctness of the doping mechanism. Electrochemical performance testing is the most direct means of evaluating the repair effect, including cyclic voltammetry, constant current charge-discharge testing, and electrochemical impedance spectroscopy (EIS). By comparing electrochemical performance parameters before and after repair, such as specific capacity, cycle stability, and rate performance, the effectiveness of the repair process can be quantitatively evaluated. Step S52 determines the overall defect repair uniformity in the final material state data and obtains the lattice structure integrity index of the regenerated ternary cathode material.Overall defect repair uniformity is a comprehensive indicator for evaluating repair quality, reflecting the consistency of repair effects across different regions and depths of the material. Uniformity assessment requires comprehensive consideration of multiple aspects, including the spatial distribution of defect repair rate, the concentration distribution of dopant elements, and the uniformity of material properties. In one possible implementation, repair uniformity is calculated using statistical analysis methods. A variance analysis is performed on data from multiple test points to obtain a quantitative index of uniformity. The selection of test points needs to cover different regions of the material, including the surface, intermediate, and deep layers, as well as different spatial locations, such as particle centers, particle edges, and interparticle interfaces. The spatial distribution of defect repair rate is evaluated by comparing the change in defect density before and after repair. Ideally, the repair rate in each region should be close to the target value, and the differences between them should be controlled within a reasonable range. If the repair rate in some regions is significantly lower, it indicates that the doping process needs further optimization, which may require adjusting the doping concentration, extending the doping time, or increasing the doping temperature. The uniformity of the dopant element concentration distribution is detected using energy dispersive spectroscopy (EDS) or mass spectrometry (MS). Uniform doping distribution is a prerequisite for achieving consistent repair results. If dopant elements are found to be clustered or absent in certain regions, the cause needs to be analyzed and the doping process improved. Common causes of inhomogeneity include excessive diffusion resistance, uneven temperature distribution, and insufficient doping time. The lattice structure integrity index is the ultimate evaluation standard for material repair quality, comprehensively reflecting the degree of lattice defect repair, restoration of structural stability, and improvement in electrochemical performance. The integrity index is calculated based on a weighted average of multiple sub-indices, including lattice parameter restoration degree, defect density reduction rate, improvement in structural order, and improvement in electrochemical performance. The lattice parameter restoration degree is determined by X-ray diffraction analysis, comparing the closeness of the repaired lattice parameters to theoretical values. A fully repaired material should have lattice parameters close to those of fresh material; the smaller the deviation, the better the repair effect. The defect density reduction rate reflects the degree of elimination of various defects, including the comprehensive improvement of point defects, line defects, and surface defects. The improvement in structural order is evaluated by Raman or infrared spectroscopy analysis; an ordered lattice structure has characteristic vibrational modes and sharp spectral peaks. During the repair process, as defects are eliminated and the structure is improved, the intensity of spectral peaks increases and the full width at half maximum (FWHM) decreases, indicating an improvement in structural order. The electrochemical performance improvement rate is the most direct indicator of the repair effect, including recovery of specific capacity, extension of cycle life, and improvement in rate performance. By comparing the performance with that of fresh material, the degree of performance recovery of the repaired material can be determined. An excellent repair process should be able to restore the material's performance to more than 80% of that of fresh material. It should be noted that the evaluation of lattice structure integrity also needs to consider the long-term stability of the material. The repaired material should maintain stable performance during subsequent use, without rapid degradation or abnormal behavior. Therefore, integrity assessment also includes accelerated aging tests and long-term cycling tests to ensure the durability of the repair effect.

[0031] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A method for repairing lattice defects in regenerated ternary cathode materials for lithium batteries by doping with rare earth elements, characterized in that, include: S1. Collect surface and cross-sectional image data of ternary cathode material samples, perform image processing, and obtain a defect distribution map; S2. Calculate the shallow defect density in the surface region and the deep defect density in the internal bulk region based on the defect distribution map, and determine the defect depth gradient value; S3. If the defect depth gradient value is greater than a preset depth gradient threshold, allocate a high concentration doping dose to the surface region and perform an initial doping process to obtain processed intermediate state data of the material; S4. If the surface defect repair rate in the intermediate state data reaches a preset standard, determine the repair concentration threshold for deep defects, and adjust the subsequent doping dose accordingly to obtain a graded concentration sequence; S5. Use the graded concentration sequence to perform a penetration doping process from the surface to the bulk phase, obtain the final material state data, and obtain a lattice structure integrity index.

2. The method as described in claim 1, characterized in that, Step S1 includes: Step S11, acquiring surface and cross-sectional image data of the ternary cathode material sample using a scanning electron microscope; Step S12, using an image segmentation algorithm to separate the pixel sets of the surface region and the internal bulk region and obtain a defect distribution mapping map.

3. The method as described in claim 2, characterized in that, Step S11 includes: when acquiring images of ternary cathode materials, preparing the sample into a shape suitable for observation, including surface cleaning and cross-sectional cutting.

4. The method as described in claim 3, characterized in that, Step S12 includes: using an edge detection algorithm to identify the outer contour of material particles, and determining the boundary between the surface region and the internal bulk region through gradient calculation and threshold processing.

5. The method as described in claim 1, characterized in that, Step S2 includes: Step S21, calculating the shallow defect density in the surface region and the deep defect density in the internal bulk region based on the defect distribution mapping; Step S22, determining the defect depth gradient value from the surface to the interior using density gradient analysis.

6. The method as described in claim 5, characterized in that, Step S22 includes: the density gradient analysis adopts a hierarchical statistical method to divide the ternary cathode material into multiple depth layers from the surface to the interior, calculate the defect density of each layer, fit these density data, and obtain the functional relationship between defect density and depth.

7. The method as described in claim 1, characterized in that, Step S3 includes: Step S31, predicting the penetration path of the dopant element after it attaches to the surface layer using a diffusion simulation algorithm, and obtaining a surface repair parameter set; Step S32, using the surface repair parameter set to guide the initial doping process, and obtaining intermediate state data of the processed material; Step S33, determining whether the surface defect repair rate in the intermediate state data reaches a preset standard, and obtaining a bulk phase penetration preparation signal.

8. The method as described in claim 1, characterized in that, Step S4 includes: Step S41, extracting microcrack features of the internal bulk region from the defect distribution map based on the bulk penetration preparation signal; Step S42, simulating the structural distortion caused by lithium-ion intercalation / deintercalation using a finite element analysis algorithm and determining the repair concentration threshold for deep defects; Step S43, adjusting the subsequent doping dose based on the repair concentration threshold for deep defects and obtaining a graded concentration sequence; Step S44, determining whether each concentration value in the graded concentration sequence matches the defect depth gradient value, and obtaining an optimized graded doping scheme.

9. The method as described in claim 1, characterized in that, Step S5 includes: Step S51, performing a penetration process from the outside to the inside using an optimized hierarchical doping scheme and obtaining the final material state data; Step S52, determining the overall defect repair uniformity in the final material state data and obtaining the lattice structure integrity index of the regenerated ternary cathode material.

10. The method as described in claim 9, characterized in that, Step S51 includes: The doping and infiltration process employs multi-stage temperature control. The first stage is low-temperature pretreatment, with the temperature controlled between 400 and 500 degrees Celsius; The second stage is intermediate-temperature diffusion, where the temperature rises to 600 to 700 degrees Celsius; The third stage is high-temperature solution treatment, with the temperature further increased to 750 to 850 degrees Celsius.

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