Method for optimizing energy consumption and emissions of lithium battery recycling whole process based on digital twinning

Abstract

The application provides a lithium battery recycling full-process energy consumption and emission optimization method based on digital twinning, comprising: S1, initial scanning of the retired battery through a sensor device to obtain material profile characteristic data to generate a first data set; S2, classification of the positive electrode cobalt content according to the first data set, if higher than the cobalt content threshold, mark the high cobalt material group to generate a second data set; S3, determine the precious metal extraction process for the second data set to output a first process scheme; S4, extract the negative electrode graphite purity from the first data set, if lower than the graphite purity threshold, mark the low purity group to generate a third data set; S5, output a second process scheme according to the third data set matching the composite purification technical route; S6, obtain the electrolyte residual amount of the first data set, if exceeding the safety range, trigger the discharge processing module to generate a fourth data set; S7, form a final diversion processing path through the fourth data set combined with the first process scheme and the second process scheme, and output a comprehensive process execution plan.

Description

Technical Field

[0001] This invention relates to the field of lithium battery recycling technology, and in particular to a method for optimizing energy consumption and emissions throughout the entire lithium battery recycling process based on digital twins. Background Technology

[0002] Recycled lithium batteries are a key area for resource recycling and environmental protection. With the rapid development of new energy vehicles, the number of retired batteries is increasing dramatically. The demand for recycling limited precious metal resources such as cobalt and lithium, as well as graphite materials, makes efficient recycling extremely important. The recycling process must balance economic efficiency, safety, and resource utilization; otherwise, it will lead to the loss of precious metals, waste of graphite, and environmental pollution risks.

[0003] Current recycling methods primarily rely on experience-based judgment or fixed process flows for uniform dismantling and processing of batteries. This approach is ill-suited to the actual conditions of retired batteries, as different batteries exhibit varying cobalt content in the positive electrode, significant differences in graphite purity in the negative electrode, and varying amounts of residual electrolyte. This results in incomplete extraction of precious metals from some batteries under the same process, while others pose safety hazards due to electrolyte residue, or the graphite may be too impure to be recycled. Fixed processes cannot adjust the processing path according to the specific material characteristics of each batch of batteries, leading to low resource recovery rates and high processing costs.

[0004] These differences further amplify the technical contradictions throughout the recycling process. Batteries with high cobalt content are suitable for direct entry into efficient precious metal extraction processes, while forcing low-cobalt content batteries to use the same processes would significantly reduce extraction economics. Batteries with low graphite purity require additional composite purification steps; otherwise, the quality of the recycled graphite cannot meet requirements. Batteries with high residual electrolyte levels may pose a risk of combustion or leakage during subsequent disassembly or extraction if not prioritized for safe discharge treatment. These material characteristics interact with each other; for example, batteries with high residual electrolyte levels often present higher safety hazards, and directly diverting them to the precious metal extraction line could disrupt the entire production line. If low-purity graphite batteries are mixed with high-cobalt batteries, it will contaminate the extraction equipment or reduce overall precious metal production. These differences in characteristics make it difficult to precisely match process paths, and the three stages of safe treatment, precious metal extraction, and graphite regeneration cannot be coordinated.

[0005] How to achieve precise diversion and process matching of safe discharge, precious metal extraction and graphite regeneration based on the specific differences in cobalt content of the positive electrode, graphite purity of the negative electrode and residual electrolyte in the entire recycling process has become a key issue in improving resource utilization and reducing environmental risks. Summary of the Invention

[0006] To address the technical problems mentioned in the background section, this invention provides a method for optimizing energy consumption and emissions throughout the entire lithium battery recycling process based on digital twins, comprising: S1, performing an initial scan of retired batteries using sensor devices to obtain material profile characteristic data and generate a first dataset, wherein the material profile characteristic data includes the cobalt content of the positive electrode, the graphite purity of the negative electrode, and the residual amount of electrolyte; S2, classifying the cobalt content of the positive electrode according to a preset cobalt content threshold based on the first dataset, and marking high-cobalt material groups if the cobalt content is higher than the threshold to generate a second dataset; S3, calling a process mapping database to determine the precious metal extraction process based on the second dataset and outputting a first process scheme; S4, extracting the graphite purity of the negative electrode from the first dataset using a graded evaluation algorithm, and marking low-purity groups if the graphite purity is lower than the graphite purity threshold to generate a third dataset; S5, matching a composite purification technology route based on the third dataset and outputting a second process scheme; S6, obtaining the residual amount of electrolyte in the first dataset, and triggering a discharge processing module to generate a fourth dataset if the residual amount exceeds the safe range; S7, combining the fourth dataset with the first and second process schemes to form a final diversion processing path and outputting a comprehensive process execution plan.

[0007] Furthermore, the sensor equipment includes an X-ray fluorescence spectrometer, a Raman spectrometer, and an electrochemical impedance spectroscopy analyzer.

[0008] Furthermore, step S1 includes: step S11, where the sensor device performs non-destructive testing on the casing of the retired battery to identify the battery type and basic specifications; and step S12, where, after confirming the battery's safety status, in-depth material analysis is performed on the battery.

[0009] Furthermore, step S2 includes: step S21, extracting cathode cobalt content data from the first dataset and comparing it with a preset cobalt content threshold; step S22, establishing detailed profile information for the high cobalt material group.

[0010] Furthermore, the cobalt content threshold is set to 12%.

[0011] Furthermore, step S3 includes: step S31, the process mapping database performs process matching based on the specific characteristics of the high cobalt material group in the second dataset; step S32, determining the specific precious metal extraction process parameters.

[0012] Step S33: Generate a detailed execution plan for the first process scheme.

[0013] Furthermore, step S32 includes:

[0014] For high-cobalt materials with a cobalt content between 12% and 15%, a mild leaching process is adopted, using a low-concentration acidic solution and leaching for a long time at a relatively low temperature;

[0015] For high-cobalt materials with a cobalt content between 15% and 20%, an enhanced leaching process is adopted, using a higher concentration of leaching agent and a higher temperature for leaching.

[0016] Furthermore, step S4 includes: step S41, extracting the negative electrode graphite purity data of each retired battery sample from the first dataset; step S42, using a grading evaluation algorithm to perform multi-level comparisons of the standardized purity indicators.

[0017] In step S43, when generating the third dataset, not only are the low-purity group labels recorded, but the original detection data and evaluation process records are also associated.

[0018] Furthermore, step S6 includes: step S61, obtaining electrolyte residual data for each retired battery sample from the first dataset; step S62, the discharge processing module performing a safe discharge operation; and step S63, generating a fourth dataset to record the material state after safe processing.

[0019] Furthermore, step S7 includes: step S71, after confirming the safety status through the fourth dataset, inputting the high-cobalt material group into the processing path determined by the first process scheme; step S72, for the low-purity anode material group, inputting the composite purification path determined by the second process scheme; step S73, forming the final diversion processing path and outputting the comprehensive process execution plan.

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

[0021] This invention discloses a digital twin-based optimization method for the entire lithium battery recycling process. Addressing the challenges of diverse material compositions, significant differences in cobalt content and graphite purity, and prominent safety hazards posed by residual electrolyte in retired lithium batteries, traditional recycling processes struggle with precise diversion, resulting in low economic efficiency of precious metal extraction, high difficulty in graphite regeneration, and overall insufficient resource utilization. By constructing a digital twin model, the invention achieves intelligent management of the entire process. First, multi-modal sensors are used for non-destructive scanning to acquire material profile data such as cobalt content in the positive electrode, graphite purity in the negative electrode, and residual electrolyte, generating a first dataset. Then, based on threshold classification, groups of high-cobalt materials, low-purity graphite, and battery packs requiring safe discharge treatment are formed. Subsequently, a process mapping database is invoked to precisely match precious metal extraction processes and composite purification technologies. Combined with the results of safe discharge treatment, personalized diversion processing paths and comprehensive process execution plans are integrated to achieve efficient classification, safe treatment, and targeted recycling of retired batteries. This invention significantly improves the recovery rate and purity of precious metals and graphite, reduces processing costs and environmental risks, and achieves intelligent optimization and maximized resource utilization throughout the entire recycling process. Attached Figure Description

[0022] Figure 1 This is a flowchart of the energy consumption and emission optimization method for the entire lithium battery recycling process based on digital twins, as described in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for optimizing the entire process of lithium battery recycling based on digital twins. This method achieves intelligent management and optimization of the entire process of recycling retired lithium batteries by constructing a digital twin model, which significantly improves recycling efficiency and resource utilization.

[0025] Specifically, the method includes: S1, performing an initial scan of the retired battery using a sensor device to acquire material profile characteristic data and generate a first dataset, wherein the material profile characteristic data includes the cobalt content of the positive electrode, the purity of the graphite in the negative electrode, and the residual amount of electrolyte. In one embodiment, the sensor device employs multimodal detection technology to perform a comprehensive scanning analysis of the retired lithium battery. The sensor device includes various detection devices such as an X-ray fluorescence spectrometer, a Raman spectrometer, and an electrochemical impedance spectroscopy (EIS). The X-ray fluorescence spectrometer irradiates the positive electrode material of the battery with X-rays of a specific wavelength, exciting the cobalt element in the positive electrode material to produce characteristic fluorescence, and determines the precise value of the cobalt content of the positive electrode by analyzing the fluorescence intensity and wavelength. The Raman spectrometer uses a laser to excite the graphite material in the negative electrode to produce Raman scattering, and evaluates the crystallinity and purity level of the graphite by analyzing the peak position and intensity ratio of the scattering spectrum. The EIS measures the change in the internal resistance of the battery by applying a small-amplitude AC signal, and calculates the residual amount of electrolyte and the ion concentration distribution by combining impedance spectroscopy analysis. Specifically, the process of acquiring material profile characteristic data needs to consider the diversity and complexity of retired batteries. Lithium batteries of different brands, models, and service lives exhibit significant differences in material composition and degradation rates. For example, the cathode material of ternary lithium batteries typically contains three metal elements: nickel, cobalt, and manganese, with cobalt content usually between 10% and 20%, while the cathode material of lithium iron phosphate batteries does not contain cobalt. During the scanning process, sensor equipment needs to automatically adjust detection parameters and algorithm models according to the battery type to ensure the accuracy and reliability of data acquisition.

[0026] Optionally, this step may also include: step S11, where the sensor device performs non-destructive testing on the retired battery casing to identify the battery type and basic specifications.

[0027] Image recognition technology is used to analyze the markings on the battery casing, including key information such as manufacturer identification, model specifications, and production date. Simultaneously, ultrasonic testing technology is used to inspect the integrity of the battery casing, identifying any potential safety hazards such as damage, deformation, or leakage. These preliminary inspection results provide important reference for subsequent material profile analysis. Step S12: After confirming the battery's safety status, in-depth material analysis is performed on the battery.

[0028] First, the battery charge is reduced to a safe level through controlled discharge. Then, material composition analysis is performed in a dedicated, sealed testing chamber. The chamber is equipped with various sensors and analytical devices, enabling simultaneous multi-tasking. X-ray fluorescence spectrometry (XRF) is used for elemental analysis of the cathode material, obtaining the content distribution of metals such as cobalt, nickel, and manganese. Raman spectroscopy is used for structural analysis of the anode material, evaluating the interlayer spacing, defect density, and purity level of graphite. It should be noted that the first dataset was constructed using a standardized data format and storage structure. Each retired battery corresponds to a unique data record, containing complete information such as basic battery information, testing time, testing conditions, and material composition data. Cathode cobalt content data is recorded as a mass percentage with an accuracy of 0.1%. Anode graphite purity data includes multiple indicators such as graphitization degree, specific surface area, and particle size distribution. Electrolyte residual data includes not only the total volume but also detailed information such as electrolyte ion concentration, pH value, and impurity content. S2, based on the first dataset, the cathode cobalt content is classified using a preset cobalt content threshold. If the content exceeds the threshold, it is labeled as a high-cobalt material group, generating the second dataset. In one embodiment, the cobalt content threshold is determined based on economic benefit analysis and technical feasibility assessment. Through statistical analysis of a large number of retired battery samples, combined with the current market price of cobalt metal and extraction costs, a cobalt content threshold of 12% is determined. When the cobalt content in the cathode is higher than 12%, precious metal extraction has good economic benefits, covering extraction costs and generating reasonable profits. When the cobalt content is lower than 12%, direct precious metal extraction is less cost-effective, making other recycling methods more suitable. Specifically, the threshold classification algorithm employs a multi-level judgment mechanism to ensure the accuracy and reliability of the classification. First, the cathode cobalt content data in the first dataset is cleaned and outlier detected, eliminating obviously erroneous detection results. Then, statistical analysis methods are used to calculate the cobalt content confidence interval for each battery sample, and samples whose confidence intervals cross the threshold are subject to secondary detection and confirmation. Finally, the final classification is performed based on the confirmed cobalt content data to ensure the reliability of the classification results. Step S21: Extract the cathode cobalt content data from the first dataset and compare it with the preset cobalt content threshold for analysis.

[0029] The comparison process employs an automated algorithm, enabling rapid processing of large volumes of battery data. The algorithm first reads the cobalt content value of the cathode for each battery sample in the first dataset, and then compares it with a preset cobalt content threshold of 12%. For battery samples with cobalt content exceeding the threshold, the algorithm automatically adds a "high cobalt material group" label and records the classification time and basis. Step S22 establishes detailed profile information for the high cobalt material group.

[0030] In addition to basic cobalt content data, other important battery information needs to be recorded, including battery capacity, service life, degradation level, and the content of other metal elements. This information is crucial for subsequent precious metal extraction process selection and parameter optimization. For example, battery samples with high nickel content may require different separation techniques during precious metal extraction, and differences in manganese content will also affect the selection of extraction processes. It should be noted that the generation of the second dataset includes not only basic information about the high-cobalt material group but also an association index with the process mapping database. Each high-cobalt material group sample corresponds to a unique process mapping identifier, used for rapid subsequent invocation of the corresponding precious metal extraction process. This design significantly improves data processing efficiency and reduces redundant calculations and query time. S3, the process mapping database is invoked for the second dataset to determine the precious metal extraction process and output the first process scheme. In one embodiment, the process mapping database is a comprehensive technical database containing multiple precious metal extraction processes. The database stores optimized extraction processes for different cobalt content ranges, different battery types, and different material compositions. Each process includes detailed operating steps, process parameters, equipment requirements, safety measures, and other complete information. The database employs a hierarchical structure, categorizing and indexing materials according to their properties to quickly match the most suitable extraction process. Specifically, the precious metal extraction process mainly includes four core stages: pretreatment, leaching, separation, and purification. The pretreatment stage includes physical processing steps such as battery disassembly, material separation, and grinding. The leaching stage uses chemical solvents to dissolve precious metals such as cobalt from the cathode material; commonly used leaching agents include sulfuric acid, hydrochloric acid, and citric acid. The separation stage uses methods such as precipitation, extraction, and ion exchange to separate different metal elements. The purification stage uses techniques such as electrolysis and crystallization to obtain high-purity metal products.

[0031] Optionally, this step also includes: step S31, whereby the process mapping database performs process matching based on the specific characteristics of the high-cobalt material group in the second dataset.

[0032] The matching algorithm first analyzes key parameters such as cobalt, nickel, and manganese content for each battery sample, and then searches the database for the most matching process template. The matching process considers multiple factors, including extraction efficiency, cost control, environmental impact, and safety risks. The algorithm employs a weighted scoring mechanism to calculate a comprehensive score for each available process, selecting the process with the highest score as the recommended solution. Step S32 determines the specific precious metal extraction process parameters.

[0033] Based on the matched process template and the actual characteristics of the battery samples, the process parameters are finely adjusted. For example, for ternary lithium battery cathode materials with a cobalt content of 15%, a sulfuric acid leaching process is recommended, with a leaching temperature of 80 degrees Celsius, a leaching time of 4 hours, and a sulfuric acid concentration of 2 mol / L. For high-cobalt batteries with a cobalt content of 18%, the leaching temperature can be appropriately increased to 90 degrees Celsius, and the leaching time extended to 6 hours to improve the cobalt extraction rate. For example, the process mapping database provides multiple optional extraction paths for different types of high-cobalt material groups. For material groups with a cobalt content between 12% and 15%, a mild leaching process is recommended, using a lower concentration of acidic solution and a longer leaching time at a relatively low temperature. This approach can reduce equipment corrosion and environmental pollution while ensuring extraction efficiency. For material groups with a cobalt content between 15% and 20%, an enhanced leaching process can be used, employing a higher concentration of leaching agent and a higher operating temperature, enabling efficient extraction in a shorter time. Step S33: Generate a detailed execution plan for the first process scheme.

[0034] The execution plan includes a complete set of content such as a process flow diagram, a list of operation steps, equipment configuration requirements, a raw material consumption list, product quality standards, and safe operating procedures. The process flow diagram graphically displays each stage of the extraction process and the material flow, facilitating understanding and execution by operators. The list of operation steps details the specific requirements of each operation, including the operation sequence, operation parameters, and precautions. S4, the purity of the negative electrode graphite is extracted from the first dataset using a grading evaluation algorithm. If the purity is below the graphite purity threshold, it is marked as a low-purity group, generating a third dataset. In one embodiment, the grading evaluation algorithm uses a multi-dimensional comprehensive analysis method to assess the purity of the negative electrode graphite. The purity of the negative electrode graphite is not just a single percentage of graphite content, but also includes several key indicators such as graphitization degree, impurity content, particle uniformity, and specific surface area. These indicators collectively determine whether the graphite material is suitable for direct recycling or requires deep purification. By weighting and synthesizing these indicators, the grading evaluation algorithm can more accurately reflect the actual recycling value and processing difficulty of the negative electrode material. Specifically, the grading evaluation algorithm first preprocesses the anode graphite purity-related data in the first dataset, including data standardization, missing value imputation, and outlier removal. Then, it scores each indicator according to preset weighting coefficients to obtain a comprehensive purity score. The preset threshold is usually set at a comprehensive score of 80. When the comprehensive score is below 80, the anode material is considered to require composite purification treatment to restore its electrochemical performance and structural integrity.

[0035] Optionally, this step may also include: step S41, extracting negative electrode graphite purity data from the first dataset for each retired battery sample.

[0036] Specifically, these data mainly come from Raman spectroscopy and X-ray diffraction analysis results, including key parameters such as the intensity ratio of graphite characteristic peaks, the intensity ratio of D peak to G peak, and interplanar spacing. The algorithm converts these raw detection data into standardized purity indices, such as graphitization degree expressed as a percentage and impurity content expressed as a mass fraction. In step S42, the hierarchical evaluation algorithm performs multi-level comparisons of the standardized purity indices.

[0037] In one embodiment, the grading evaluation algorithm first performs preliminary screening using a single threshold, for example, materials with a graphitization degree below 95% are considered potentially low-purity materials. Then, in the comprehensive evaluation stage, the algorithm performs a weighted summation of indicators such as graphitization degree, impurity content, and particle uniformity according to preset weights. The weighting coefficients can be adjusted according to the recycling target; for example, when prioritizing electrochemical performance recovery, the weight of graphitization degree is increased. It should be noted that the grading evaluation algorithm also considers the impact of the aging state of the negative electrode material on purity evaluation. During the use of retired batteries, a solid electrolyte interface film forms on the surface of the negative electrode graphite, leading to a decrease in apparent purity. The algorithm corrects and compensates for the purity data by analyzing the thickness and composition of the surface film, ensuring that the evaluation results reflect the intrinsic purity of the material rather than the artificial purity caused by surface contamination. For example, for a group of retired negative electrode materials from electric vehicle power batteries, the detection found a graphitization degree of 93%, an impurity content of 2.5%, and good particle uniformity. The grading and evaluation algorithm calculated a comprehensive score of 78, which is below the threshold of 80. Therefore, it is marked as a low-purity group and needs to enter the composite purification process. Another group, anode materials from consumer electronics batteries, has a graphitization degree of 97% and an impurity content of only 0.8%, with a comprehensive score of 92, which is above the threshold and can directly enter the recycling path. In step S43, when generating the third dataset, not only is the low-purity group label recorded, but the original detection data and evaluation process records are also linked.

[0038] The third dataset contains detailed purity indicators, aging characteristics, and battery usage history for each low-purity sample. This information provides crucial information for selecting subsequent composite purification technology routes, enabling more targeted process matching. Step S5: Based on the third dataset, a second process scheme is output by matching the composite purification technology route. In one embodiment, the composite purification technology route for purifying low-purity negative electrode graphite employs a multi-stage treatment method combining physical and chemical processes. Common technology routes include combinations of high-temperature heat treatment, acid-base washing, flotation separation, and surface modification. These processes effectively remove impurities, repair the layered structure of graphite, and restore the electrochemical performance of the material, enabling low-purity graphite to meet recycling standards. Specifically, the selection of the composite purification technology route is based on the specific characteristics of the low-purity group in the third dataset. For example, when the impurities are mainly metallic elements, acid washing is preferred; when there is a large amount of organic contamination on the surface, high-temperature heat treatment is preferred; when there are intercalations between graphite layers, flotation and chemical stripping processes need to be combined. The matching process for technical routes is achieved through a combination of rule bases and case bases to ensure that the selected solutions have high applicability and success rate. Step S51: Based on the distribution characteristics of purity indicators in the third dataset, candidate solutions are selected from the pre-set technical route base.

[0039] The technology route library contains a variety of validated composite purification combinations, such as high-temperature heat treatment combined with acid washing, high-temperature heat treatment combined with flotation, and chemical exfoliation combined with surface modification. Each combination corresponds to specific applicable conditions and expected results. Step S52 involves an adaptability evaluation of the candidate solutions.

[0040] The evaluation process considers key factors such as the average degree of graphitization, main impurity types, and particle size distribution of the low-purity group. For example, when the average degree of graphitization is below 90% and the impurities are mainly metals such as copper and aluminum, a composite route is recommended: first, acid pickling to remove metal impurities, followed by high-temperature heat treatment to repair the structure. This sequence avoids the formation of difficult-to-remove compounds from the reaction of metal impurities with graphite at high temperatures. It should be noted that the generation of the second process scheme also includes preliminary optimization of process parameters. Based on the sample characteristics in the third dataset, initial settings are made for parameters such as operating temperature, processing time, and reagent concentration for each process stage. For example, the high-temperature heat treatment temperature can be set in the range of 2000 to 2800 degrees Celsius, with the specific value determined according to the graphitization recovery requirements. The acid concentration and soaking time in the acid pickling process are adjusted according to the impurity content. For example, for a group of low-purity anode materials with a graphitization degree of 92% and a high copper impurity content, the matching composite purification technology route is as follows: first, copper impurities are removed by soaking in dilute sulfuric acid; then, the graphite structure is repaired by high-temperature heat treatment at 2500 degrees Celsius; and finally, surface fluorination modification is performed to improve the material stability. This route can increase the graphite purity from 92% to over 99%, meeting the requirements of high-end regenerated battery anode materials. In step S53, when outputting the second process scheme, a complete process execution document is generated. The document includes a process flow diagram, operating parameters for each stage, a list of reagents used, equipment requirements, quality control points, waste treatment measures, etc. The process flow diagram clearly shows the flow path of materials between each processing unit, which is convenient for production personnel to understand and operate. In step S6, the electrolyte residue amount in the first dataset is obtained. If it exceeds the safe range, the discharge processing module is triggered to generate a fourth dataset. In one embodiment, the safe range of electrolyte residue amount is determined according to industry standards and actual operating experience. The safe range is typically set at no more than 0.5% of the total battery weight for residual electrolyte, or at least the concentration of flammable materials in the residual electrolyte is below the safe concentration threshold. When the residual amount exceeds the safe range, the retired battery poses a risk of spontaneous combustion or explosion and must undergo safety treatment first. Specifically, the discharge processing module uses a combination of controlled discharge and an inert environment to ensure that the battery does not experience thermal runaway during processing. The discharge process is carried out by connecting a dedicated discharge device and slowly discharging with a small current until the voltage is below the safe voltage threshold. This is done simultaneously within a sealed inert gas protection chamber to prevent contact with air and potential reactions. Step S61: Obtain the residual electrolyte amount data for each retired battery sample from the first dataset.

[0041] Specifically, these data are mainly obtained through electrochemical impedance spectroscopy and gas sampling analysis, which can accurately reflect the volume, composition, and activity of the residual electrolyte. The algorithm compares the residual amount data with a preset safety range, and samples exceeding the range automatically trigger the discharge treatment process. In step S62, the discharge treatment module performs a safe discharge operation.

[0042] First, the battery is placed in a sealed chamber filled with nitrogen or argon gas, and then discharged at a constant low current through a precisely controlled discharge circuit. During discharge, parameters such as battery temperature, voltage, and internal pressure are monitored in real time. If any abnormality occurs, discharge is immediately stopped and the cooling system is activated. After discharge, the battery undergoes residual electrolyte extraction and neutralization. It should be noted that the discharge processing module also adjusts the processing parameters based on the material profile characteristics data in the first dataset. For example, for batteries with a high cobalt content in the positive electrode, the discharge current needs to be lower to avoid irreversible reactions of cobalt compounds during discharge. For batteries with low graphite purity in the negative electrode, the discharge time can be appropriately extended to ensure sufficient electrolyte reaction and consumption. For instance, a batch of retired batteries was found to have an average residual electrolyte content of 0.8%, exceeding the safe range of 0.5%. After the discharge processing module was activated, the battery was placed in a nitrogen-protected environment and discharged at a current of 0.1C until the voltage dropped below 2.5 volts. During discharge, the battery temperature was consistently controlled below 30 degrees Celsius to avoid heat accumulation. After processing, the residual electrolyte content was reduced to 0.2%, meeting the safety standard. Step S63: Generate a fourth dataset to record the material state after safe treatment. The fourth dataset contains complete information such as residual electrolyte, depth of discharge, treatment time, and temperature profile. This information provides a safety guarantee for the integration of subsequent process schemes, ensuring that the high-cobalt extraction and composite purification processes are carried out under safe conditions. Step S7: By combining the fourth dataset with the first and second process schemes, a final diversion processing path is formed, and a comprehensive process execution plan is output. In one embodiment, the integration of the final diversion processing path adopts a modular design approach, organically combining the precious metal extraction path for the high-cobalt material group, the composite purification path for the low-purity anode, and the processing paths for other materials after safe treatment. The integration process ensures that each path is coordinated in terms of time, equipment, and material flow, achieving efficient operation of the entire process. Specifically, the comprehensive process execution plan is compiled on a batch-by-batch basis. For the same batch of batteries, they enter different processing paths according to their classification labels. After safe treatment, the batteries are diverted according to their high-cobalt or low-purity properties: high-cobalt materials enter the precious metal extraction line, low-purity anodes enter the composite purification line, and other materials enter the conventional recycling line. Step S71: After confirming the safety status through the fourth dataset, input the high cobalt material group into the processing path determined by the first process scheme.

[0043] The pretreatment, leaching, separation, and purification steps in the first process scheme are executed sequentially, while the actual operating parameters and intermediate product states of each step are recorded. This information is fed back to the digital twin model for real-time optimization of process parameters. In step S72, for low-purity anode materials, the composite purification path determined by the second process scheme is input.

[0044] The process involves sequential steps such as high-temperature heat treatment, pickling, and surface modification. Buffer zones and quality inspection points are established between each step to ensure that only materials meeting intermediate quality standards proceed to the next stage. This design effectively improves the final product's yield. It's worth noting that resource sharing and energy coupling are also considered during the integration process. For example, the pickling wastewater from the precious metal extraction process, after neutralization, can be used in the pre-cleaning stage of negative electrode composite purification. The waste heat generated from high-temperature heat treatment can be used to preheat the leaching solution, achieving tiered energy utilization. This coupling design reduces overall energy consumption and processing costs. For instance, after sorting 1000 retired batteries, 300 are labeled as high-cobalt material groups, 500 are low-purity negative electrodes, and the remaining 200 are conventional materials. The integrated process execution plan schedules the high-cobalt group to enter the sulfuric acid leaching extraction line, with a daily processing capacity matching 300 batteries. The low-purity group enters the high-temperature heat treatment combined with pickling purification line, with the processing cycle coordinated with the high-cobalt line to ensure balanced production line operation. Step S73: Form the final diversion processing path and output the integrated process execution plan.

[0045] The execution plan includes a complete set of components, such as a general process flow diagram, batch processing schedule, equipment occupancy plan, personnel allocation plan, quality control system, safety management measures, and environmental protection requirements. The general process flow diagram clearly shows the entire chain path and branch conditions from initial scanning to final product output, facilitating production management and traceability. In one implementation, the integrated process execution plan also integrates a digital twin monitoring system. The system collects operational data from each process step in real time, compares and analyzes it with the planned parameters, automatically alarms when deviations are detected, and proposes adjustment suggestions. This closed-loop management approach ensures that the entire process is strictly executed according to the optimized path, maximizing recovery efficiency and resource utilization. In one embodiment, the process mapping database stores the precious metal extraction processes corresponding to the high-cobalt material group and the composite purification technology routes corresponding to the low-purity group. These processes and routes have undergone extensive experimental verification and industrial practice, forming standardized process templates. The database is updated regularly to incorporate the latest technological advancements and optimization results, ensuring that the matching schemes are always at an advanced level. Then, the corresponding processes and routes are invoked based on the second and third datasets, respectively. During the integration phase, the system automatically combines the first and second process schemes with the safety status information of the fourth dataset to form a personalized processing path for each battery sample. This refined triage method can maximize the recovery rate of precious metals and graphite materials and reduce resource waste. It should be noted that the entire optimization method, supported by digital twin technology, achieves a two-way mapping from physical batteries to a virtual model. The detection data of the physical batteries updates the digital twin model in real time, and the model generates process schemes based on the optimization algorithm, providing feedback to guide the physical processing. This virtual-physical interaction mechanism can continuously improve the intelligence level and adaptability of the entire recycling process. In one embodiment, for large-scale recycling scenarios, this method supports parallel processing of multiple batches. Batteries from different batches can simultaneously enter the initial scanning stage and be dynamically allocated to different processing lines based on the classification results. The system coordinates the operating load of each line through a central scheduling module to ensure that the overall capacity utilization rate remains within a reasonable range. Specifically, when the daily processing capacity of the recycling plant is 5000 batteries, the system can predict the daily high cobalt ratio and low purity ratio based on historical data and adjust equipment configuration and personnel arrangements in advance. For example, when a high proportion of cobalt is predicted, the number of leaching line shifts is increased; when a high proportion of low purity is predicted, fuel supply to the high-temperature furnace is prioritized. This predictive scheduling can significantly reduce waiting time and improve overall processing efficiency.

[0046] The above are only some preferred embodiments of the present invention, but the present invention is not limited thereto, and many improvements and modifications can be made. Any improvements and modifications made based on the basic principles of the present invention should be considered to fall within the protection scope of the present invention.

Claims

1. A method for optimizing energy consumption and emissions throughout the entire lithium battery recycling process based on digital twins, characterized in that, include: S1. Initially scan the retired battery using sensor equipment to obtain material profile characteristic data and generate a first dataset. The material profile characteristic data includes the cobalt content of the positive electrode, the purity of the graphite of the negative electrode, and the residual amount of electrolyte. S2. Based on the first dataset, classify the cobalt content of the positive electrode using a preset cobalt content threshold. If the cobalt content is higher than the threshold, mark it as a high-cobalt material group to generate a second dataset. S3. For the second dataset, call the process mapping database to determine the precious metal extraction process and output a first process scheme. S4. Extract the graphite purity of the negative electrode from the first dataset using a graded evaluation algorithm. If the purity is lower than the graphite purity threshold, mark it as a low-purity group to generate a third dataset. S5. Based on the third dataset, match a composite purification technology route and output a second process scheme. S6. Obtain the residual amount of electrolyte from the first dataset. If it exceeds the safe range, trigger the discharge processing module to generate a fourth dataset. S7. Combine the fourth dataset with the first and second process schemes to form the final diversion processing path and output a comprehensive process execution plan.

2. The method as described in claim 1, characterized in that, The sensor equipment includes an X-ray fluorescence spectrometer, a Raman spectrometer, and an electrochemical impedance spectroscopy instrument.

3. The method as described in claim 2, characterized in that, Step S1 includes: Step S11, the sensor device performs non-destructive testing on the casing of the retired battery to identify the battery type and basic specifications; Step S12, after confirming the battery's safety status, the battery undergoes in-depth material analysis.

4. The method as described in claim 1, characterized in that, Step S2 includes: Step S21, extracting cathode cobalt content data from the first dataset and comparing it with a preset cobalt content threshold; Step S22, establishing detailed profile information for the high cobalt material group.

5. The method as described in claim 4, characterized in that, The cobalt content threshold is set at 12%.

6. The method as described in claim 1, characterized in that, Step S3 includes: Step S31, the process mapping database performs process matching based on the specific characteristics of the high cobalt material group in the second dataset; Step S32, the specific precious metal extraction process parameters are determined. Step S33: Generate a detailed execution plan for the first process scheme.

7. The method as described in claim 6, characterized in that, Step S32 includes: For high-cobalt materials with a cobalt content between 12% and 15%, a mild leaching process is adopted, using a low-concentration acidic solution and leaching for a long time at a relatively low temperature; For high-cobalt materials with a cobalt content between 15% and 20%, an enhanced leaching process is adopted, using a higher concentration of leaching agent and a higher temperature for leaching.

8. The method as described in claim 1, characterized in that, Step S4 includes: Step S41, extracting the negative electrode graphite purity data of each retired battery sample from the first dataset; Step S42, using a grading evaluation algorithm to perform multi-level comparisons of the standardized purity indicators. In step S43, when generating the third dataset, not only are the low-purity group labels recorded, but the original detection data and evaluation process records are also associated.

9. The method as described in claim 1, characterized in that, Step S6 includes: Step S61, obtaining electrolyte residual data for each retired battery sample from the first dataset; Step S62, the discharge processing module performing a safe discharge operation; Step S63, generating a fourth dataset to record the material state after safe processing.

10. The method as described in claim 1, characterized in that, Step S7 includes: Step S71, after confirming the safety status through the fourth dataset, inputting the high cobalt material group into the processing path determined by the first process scheme; Step S72, for the low purity negative electrode material, inputting the composite purification path determined by the second process scheme; Step S73, forming the final diversion processing path and outputting the comprehensive process execution plan.

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