Screening method for echelon utilization of retired batteries

Abstract

The invention relates to the technical field of battery management, and discloses a screening method for echelon utilization of decommissioned batteries, which comprises the following steps: step 1, preliminary sorting: carrying out appearance image acquisition on the decommissioned batteries through an industrial camera, carrying out graying processing on the images by using an image processing algorithm, enhancing edge features, removing the batteries with cracks, bumps or leaks, and meanwhile, carrying out preliminary sorting on the decommissioned batteries; scanning the bar codes to obtain model information of the batteries, and classifying and grouping the batteries according to a model database; and 2, performance detection: carrying out performance test on the preliminarily sorted batteries, and measuring the open-circuit voltage of the batteries to calculate the residual capacity. Through double guarantee of thermal runaway monitoring and gas leakage detection, the temperature gradient change and gas leakage condition of the battery are monitored in real time, an isolation mechanism is automatically triggered in combination with a safety threshold value, accurate recognition and isolation of the high-risk battery are achieved, and the effects of remarkably improving the safety of the screening process and preventing and controlling accident risks are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a screening method for the cascade utilization of retired batteries. Background Art

[0002] With the rapid development of the new energy industry, lithium-ion batteries are widely used in electric vehicles, energy storage systems and portable electronic devices due to their high energy density, long cycle life and excellent charge and discharge performance. However, after long-term use, the performance of lithium-ion batteries will inevitably decay with the increase of time and cycle number. When they cannot meet the needs of the initial application scenario, they are considered retired batteries.

[0003] Studies have shown that most retired batteries have a certain amount of remaining capacity and health status, and can continue to be used in specific application scenarios, such as home energy storage, low-power backup power or industrial peak load regulation. This process, known as cascade utilization, can extend the service life of batteries and effectively alleviate the supply pressure of rare resources such as lithium and cobalt. At the same time, it reduces the pollution caused by discarded batteries to the environment, which meets the needs of building a resource-saving and environmentally friendly society. Therefore, the cascade utilization of retired batteries has gradually become an important technical approach to achieve resource recycling and carbon neutrality goals.

[0004] In order to realize the cascade utilization of retired batteries, screening is necessary to determine the remaining performance, safety status and applicable scenarios. Screening is the most critical part of the cascade utilization of retired batteries, which determines the efficiency and safety of cascade utilization.

[0005] Retired batteries have high safety risks due to aging and damage, such as thermal runaway, liquid leakage or gas leakage. Traditional screening methods usually rely on manual inspection or simple equipment, which cannot accurately identify batteries with potential safety risks, increasing the possibility of accidents.

[0006] The health status assessment of retired batteries requires the integration of multiple parameters. Traditional methods usually use a single parameter or qualitative judgment, lacking comprehensive analysis and dynamic correction of multiple parameters, resulting in deviations in screening results and failing to meet the precise matching requirements of cascade utilization scenarios.

[0007] The existing methods for screening and classifying retired batteries fail to fully consider the performance, safety and economic benefits of the batteries, resulting in serious waste of resources and affecting the overall efficiency and economic value of cascade utilization.

[0008] The existing screening process usually fails to systematically manage the performance data and classification results of batteries, the screening process lacks traceability, and it is impossible to effectively supervise and optimize the screening quality. At the same time, the screening data cannot provide support for subsequent cascade utilization scenario management and technical improvements.

[0009] Therefore, those skilled in the art provide a screening method for the recycling of retired batteries to solve the above-mentioned problems. Summary of the invention

[0010] In view of the deficiencies in the prior art, the present invention provides a screening method for the cascade utilization of retired batteries to solve the problems raised in the above background technology.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: A screening method for the cascade utilization of retired batteries, comprising:

[0012] Step 1: Preliminary sorting: Use an industrial camera to collect appearance images of retired batteries, use image processing algorithms to grayscale the images and enhance edge features, remove batteries with cracks, bulges or leaks, and scan barcodes to obtain battery model information, and classify and group the batteries according to the model database;

[0013] Step 2, performance testing: Perform performance testing on the preliminarily sorted batteries, measure the open circuit voltage of the batteries to calculate the remaining capacity, detect the AC impedance spectrum of the batteries to determine the internal resistance, and record the number of battery cycles and historical usage data as input parameters for health status assessment;

[0014] Step 3: Health status assessment: Based on the open circuit voltage, remaining capacity, internal resistance and cycle number data obtained from performance testing, combined with the historical usage records of the battery, a mathematical model is used to calculate the health status of the battery and quantify the degree of battery attenuation for cascade utilization scenario adaptation;

[0015] Step 4, safety assessment: Conduct thermal runaway monitoring on batteries that have completed health status assessment, use a thermal imager to collect temperature data on the battery surface and calculate the temperature gradient. At the same time, use a gas sensor to detect the leakage of combustible gas around the battery, and isolate the identified batteries with safety hazards, and do not enter the subsequent screening process;

[0016] Step 5, Intelligent Classification: For batteries that have passed the safety assessment, the batteries are divided into high-performance group, medium-performance group and low-performance group according to health status, remaining capacity and internal resistance parameters, and matched to the cascade utilization scheme in energy storage applications, low-power equipment or material recycling scenarios. At the same time, the classification information is recorded;

[0017] Step 6, data management: After intelligent classification, the battery model information, performance test results, health status assessment results, safety assessment results and classification results are stored in the industrial database to form a traceable screening data chain, providing data support for the execution and management of subsequent cascade utilization.

[0018] Preferably, in step 2, the remaining capacity is calculated by the open circuit voltage, and the formula is:

[0019]

[0020] Among them, SoC is the remaining capacity of the battery, V OC is the open circuit voltage of the battery,

[0021] V min and V max is the minimum and maximum operating voltage of the battery;

[0022] In step 2, when calculating the remaining capacity, the open circuit voltage is corrected for temperature, and the correction formula is: SoC corrected =SoC+kT-T ref ,

[0023] Among them, SoC corrected is the corrected remaining capacity, T is the current ambient temperature of the battery, T ref is the reference temperature, k is the temperature compensation coefficient, and SoC is the remaining capacity of the battery;

[0024] In step 2, the internal resistance is detected by AC impedance spectroscopy, and the total impedance is determined by the following formula:

[0025]

[0026] Where Zω is the total impedance of the battery, R s is the ohmic internal resistance, R ct is the charge transfer resistance, C dl is the double layer capacitance, ω is the angular frequency of the AC signal, and j is the imaginary unit.

[0027] Preferably, the parameters of the internal resistance are obtained by least squares fitting, and the fitting formula is:

[0028]

[0029] in, is the measured impedance value, is the impedance value calculated by the model, and N is the total number of measurement frequency points.

[0030] Preferably, in step 3, the health status is evaluated by a mathematical model, and the calculation formula is:

[0031]

[0032] Among them, SoH is the health state of the battery, C used is the cumulative usage capacity of the battery, C rated is the rated capacity of the battery.

[0033] Preferably, the health status assessment combines the open circuit voltage and internal resistance change of the battery, and the assessment formula is:

[0034] SoH adjusted =SoH-m·R current -R baseline ,

[0035] Among them, SoH adjusted is the corrected health status, R current is the internal resistance currently detected, R baseline is the rated internal resistance, m is the influence coefficient of impedance on health state, and SoH is the health state of the battery.

[0036] Preferably, in step 4, the monitoring of thermal runaway is based on the temperature gradient on the battery surface, and the calculation formula is:

[0037] ΔT=T max -T arg ,

[0038] Where ΔT is the temperature gradient, T max is the maximum temperature of the battery surface, T arg is the average temperature of the battery surface;

[0039] If the temperature gradient ΔT exceeds the preset threshold, the system automatically triggers the isolation process and records the safety assessment results of the relevant batteries through the industrial database.

[0040] Preferably, in step 5, the intelligent classification is implemented by a multi-objective optimization algorithm, and the calculation formula of the fitness is:

[0041] Fit=w1·Performance+w2·Safety+w3·Economic,

[0042] Among them, Fit is the adaptability of the battery to the cascade utilization scenario, Performance is the performance score of the battery, Safety is the safety assessment score of the battery, Economic is the economic benefit score of cascade utilization, and w1, w2, and w3 are weight factors for each score.

[0043] Preferably, the safety assessment score in the fitness calculation is determined by the following formula:

[0044] Safety=δ1·1-ΔT / T max +δ2·1-C leak / C threshold ,

[0045] Among them, Safety is the safety assessment score, ΔT is the temperature gradient, T max is the maximum surface temperature, C leakis the detected gas concentration, C threshold is the safety threshold of gas leakage,

[0046] δ1 and δ2 are weight factors for safety assessment.

[0047] Preferably, the economic benefit score in the fitness calculation is determined by the following formula:

[0048] Economic=ResidualValue-ProcessingCost,

[0049] Among them, Economic is the economic benefit score, ResidualValue is the residual value of the battery, and ProcessingCost is the screening and processing cost of the battery.

[0050] Preferably, the multi-objective optimization algorithm is implemented by a genetic algorithm, and the optimized fitness objective function is:

[0051]

[0052] Where M is the number of optional cascade utilization scenarios, Fit i is the adaptability value of the i-th tier utilization scenario.

[0053] The present invention provides a screening method for the cascade utilization of retired batteries. It has the following beneficial effects:

[0054] 1. The present invention uses the dual protection of thermal runaway monitoring and gas leakage detection to monitor the temperature gradient changes and gas leakage of the battery in real time, and automatically triggers the isolation mechanism in combination with the safety threshold to achieve accurate identification and isolation of high-risk batteries, thereby significantly improving the safety of the screening process and preventing and controlling accident risks.

[0055] 2. The present invention evaluates the health status through a dynamic correction formula, combines the comprehensive analysis of open circuit voltage, internal resistance and historical data, and optimizes the calculation of internal resistance parameters by least squares fitting, to achieve high-precision quantitative evaluation of battery health status and performance, thereby improving screening accuracy and reliability of health status judgment.

[0056] 3. The present invention incorporates the performance score, safety and economic benefits of the battery into the fitness calculation through a multi-objective optimization algorithm, and rationally matches the battery to energy storage applications, low-power equipment or material recycling scenarios based on this, thereby achieving optimal allocation of resources and improving the efficiency of cascade utilization and resource utilization.

[0057] 4. The present invention constructs an industrial database to systematically store the model information, performance test data, health status assessment results, safety assessment records and classification results of each battery in the screening process, thereby achieving comprehensive tracing and problem analysis of the screening process, and supporting process optimization, problem tracing and tiered utilization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0059] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0060] The present invention is described in detail below in conjunction with the accompanying drawings:

[0061] Example:

[0062] Please refer to the attached Figure 1 The embodiment of the present invention provides a screening method for the cascade utilization of retired batteries, comprising:

[0063] Step 1: Preliminary sorting: Use an industrial camera to collect appearance images of retired batteries, use image processing algorithms to grayscale the images and enhance edge features, remove batteries with cracks, bulges or leaks, and scan barcodes to obtain battery model information, and classify and group the batteries according to the model database;

[0064] Step 2, performance testing: Perform performance testing on the preliminarily sorted batteries, measure the open circuit voltage of the batteries to calculate the remaining capacity, detect the AC impedance spectrum of the batteries to determine the internal resistance, and record the number of battery cycles and historical usage data as input parameters for health status assessment;

[0065] Step 3: Health status assessment: Based on the open circuit voltage, remaining capacity, internal resistance and cycle number data obtained from performance testing, combined with the historical usage records of the battery, a mathematical model is used to calculate the health status of the battery and quantify the degree of battery attenuation for cascade utilization scenario adaptation;

[0066] Step 4, safety assessment: Conduct thermal runaway monitoring on batteries that have completed health status assessment, use a thermal imager to collect temperature data on the battery surface and calculate the temperature gradient. At the same time, use a gas sensor to detect the leakage of combustible gas around the battery, and isolate the identified batteries with safety hazards, and do not enter the subsequent screening process;

[0067] Step 5, Intelligent Classification: For batteries that have passed the safety assessment, the batteries are divided into high-performance group, medium-performance group and low-performance group according to health status, remaining capacity and internal resistance parameters, and matched to the cascade utilization scheme in energy storage applications, low-power equipment or material recycling scenarios. At the same time, the classification information is recorded;

[0068] Step 6, data management: After intelligent classification, the battery model information, performance test results, health status assessment results, safety assessment results and classification results are stored in the industrial database to form a traceable screening data chain, providing data support for the execution and management of subsequent cascade utilization.

[0069] Step 1 Benefits: Industrial cameras combined with image processing algorithms can quickly detect and remove batteries with appearance defects, effectively improving the safety of the screening process. By scanning barcodes and grouping model databases, the consistency management of battery models is achieved, which facilitates the standardization and process optimization of subsequent performance tests. Preliminary elimination of worthless or dangerous batteries reduces unnecessary testing steps and improves overall screening efficiency.

[0070] Step 2 Benefits: Measuring the open circuit voltage to calculate the remaining capacity provides a quantitative basis for evaluating the availability of the battery. Detecting the AC impedance spectrum to calculate the internal resistance accurately reflects the aging degree and electrochemical performance changes of the battery. Recording the number of cycles and historical usage data provides more comprehensive and accurate input data for health status assessment and improves the reliability of the assessment model.

[0071] Step 3 Benefits: Calculate the health status through mathematical models, quantitatively evaluate the battery's attenuation, and provide a scientific basis for the adaptation of the second-life scenario. Comprehensively analyze multiple performance parameters to improve the accuracy of judging the true health status of the battery. Provide health status data to support subsequent classification decisions and ensure the quality and reliability of second-life products.

[0072] Step 4 Benefits: Thermal runaway monitoring can identify the potential thermal runaway risk of the battery in advance by calculating the temperature gradient in real time, ensuring the safety of the screening process. Combustible gas leak detection further improves the comprehensiveness of the safety assessment, especially for high-risk retired batteries. Batteries with safety hazards are isolated to avoid entering the subsequent screening process, reducing the overall risk of the screening system.

[0073] Step 5 Benefits: By using health status, remaining capacity and internal resistance parameters, the batteries are divided into high-performance, medium-performance and low-performance groups to achieve reasonable allocation of cascade utilization resources. Matching to specific energy storage applications, low-power equipment or material recycling scenarios ensures that the use value of retired batteries is maximized. Recording classification information facilitates subsequent utilization management and performance tracking.

[0074] Step 6 Benefits: The battery model, performance test, health status, safety assessment and classification results are stored in the industrial database to form a complete data chain and achieve traceability management of the entire process. Systematic management of data facilitates statistical analysis and provides a basis for optimizing the screening process and improving screening efficiency. The data supports the planning and management of subsequent cascade utilization scenarios and improves the intelligence level of the retired battery utilization system.

[0075] In summary, this method constructs a complete set of retired battery recycling screening process through the steps of preliminary sorting, performance testing, health status assessment, safety assessment, intelligent classification and data management. Each step has a clear purpose and function, and is closely connected to each other, forming an efficient, safe and intelligent screening system.

[0076] In step 2, the remaining capacity is calculated by the open circuit voltage using the formula:

[0077]

[0078] Among them, SoC is the remaining capacity of the battery, V OC is the open circuit voltage of the battery,

[0079] V min and V max is the minimum and maximum operating voltage of the battery;

[0080] In step 2, when calculating the remaining capacity, the open circuit voltage is corrected for temperature. The correction formula is: SoC corrected =SoC+kT-T ref ,

[0081] Among them, SoC corrected is the corrected remaining capacity, T is the current ambient temperature of the battery, T ref is the reference temperature, k is the temperature compensation coefficient, and SoC is the remaining capacity of the battery;

[0082] In step 2, the internal resistance is detected by AC impedance spectroscopy, and the total impedance is determined using the following formula:

[0083]

[0084] Where Zω is the total impedance of the battery, R s is the ohmic internal resistance, R ct is the charge transfer resistance, Cdl is the double layer capacitance, ω is the angular frequency of the AC signal, and j is the imaginary unit.

[0085] The parameters of the internal resistance are obtained by least squares fitting, and the fitting formula is:

[0086]

[0087] in, is the measured impedance value, is the impedance value calculated by the model, and N is the total number of measurement frequency points.

[0088] Benefits of calculating remaining capacity by open circuit voltage: The linear relationship between open circuit voltage and remaining capacity can be used to quickly calculate the remaining capacity of the battery without complex discharge testing. The calculation can be completed by measuring the open circuit voltage of the battery, reducing the equipment and steps required for testing. It provides key input data for subsequent health status assessment.

[0089] Benefits of open circuit voltage temperature correction: By correcting the effect of temperature on open circuit voltage, the accuracy of remaining capacity calculation can be maintained at different ambient temperatures. The error caused by temperature changes is eliminated, making the calculation result close to the actual remaining capacity of the battery. The correction formula makes the algorithm more stable and suitable for a variety of usage scenarios and battery types.

[0090] Benefits of internal resistance detection: By calculating the ohmic internal resistance and charge transfer resistance, the aging state of the battery can be accurately evaluated. The AC impedance spectroscopy technology provides impedance values ​​at multiple frequency points, which can fully reflect the electrochemical performance of the battery. It is suitable for batteries of different chemical compositions and specifications and has a wide range of applicability.

[0091] Benefits of least squares fitting: Minimize the error between the measured value and the model value to ensure the accuracy of the impedance parameter calculation results. The parameters obtained through fitting are more in line with reality and can improve the true reflection of battery performance. The fitting process is implemented through software to reduce human errors and improve efficiency.

[0092] In summary, in step 2, the performance test combines the open circuit voltage to calculate the remaining capacity, temperature correction, AC impedance spectrum to detect the internal resistance, and the least squares method to fit the optimized parameters, which has the following comprehensive benefits:

[0093] Using open circuit voltage and impedance spectroscopy technology, key performance parameters can be quickly obtained, saving time and cost of traditional testing.

[0094] Through temperature correction and fitting optimization, the accuracy of detection data can be improved, providing a reliable basis for subsequent evaluation and classification.

[0095] In step 3, the health status is evaluated by a mathematical model, and the calculation formula is:

[0096]

[0097] Among them, SoH is the health state of the battery, C used is the cumulative usage capacity of the battery, C rated is the rated capacity of the battery.

[0098] The health status assessment combines the open circuit voltage and internal resistance change of the battery, and the assessment formula is:

[0099] SoH adjusted =SoH-m·R current -R baseline ,

[0100] Among them, SoH adjusted is the corrected health status, R current is the internal resistance currently detected, R baseline is the rated internal resistance, m is the influence coefficient of impedance on health state, and SoH is the health state of the battery.

[0101] Benefits of basic health status assessment: By comparing the cumulative usage capacity with the rated capacity, the capacity attenuation of the battery can be directly quantified. The rated capacity and cumulative usage capacity of the battery must be known, that is, the basic calculation of the health status can be completed quickly. The calculation results directly indicate whether the battery has the basic conditions for cascade utilization.

[0102] Benefits of health status combined with open circuit voltage and internal resistance change correction evaluation: Introducing internal resistance change, combined with the actual open circuit voltage of the battery, corrects the deviation caused by a single parameter in the health status calculation and improves the accuracy of the calculation results. Internal resistance is one of the important indicators of battery aging. Including internal resistance changes in the calculation can accurately reflect the performance of the battery at different aging stages. When the detection conditions change, the health status calculation can be dynamically adjusted to ensure that the evaluation results are closer to reality.

[0103] To summarize the overall benefits:

[0104] Comprehensive assessment of the battery's remaining capacity, internal resistance change and health status ensures full coverage of the assessment.

[0105] Combined with multi-parameter calculations, the accuracy of health status assessment can be improved to avoid biased results caused by simple single parameter assessment.

[0106] By correcting the formula, the health status assessment can dynamically reflect the changes in performance parameters during battery aging and adapt to the actual conditions of different batteries.

[0107] Health status assessment is one of the core steps in screening retired batteries for cascade utilization, providing a scientific basis for subsequent cascade utilization scenario matching and reliable data support for intelligent classification.

[0108] In step 4, thermal runaway monitoring is based on the temperature gradient on the battery surface, and the calculation formula is:

[0109] ΔT=T max -T arg ,

[0110] Where ΔT is the temperature gradient, T max is the maximum temperature of the battery surface, T arg is the average temperature of the battery surface;

[0111] If the temperature gradient ΔT exceeds the preset threshold, the system automatically triggers the isolation process and records the safety assessment results of the relevant batteries through the industrial database.

[0112] Benefits of temperature gradient calculation: By changing the temperature gradient, it is possible to quickly determine whether there is a local overheating area on the battery surface and preliminarily identify the risk of thermal runaway. Temperature gradient calculation is simple and applicable to many types of batteries. Through real-time collection and calculation, an alarm can be quickly issued before thermal runaway occurs, improving the system's responsiveness.

[0113] Benefits of temperature gradient threshold-triggered isolation: Using the temperature gradient threshold as a criterion for safety assessment can effectively screen out batteries with thermal runaway risks and prevent them from entering subsequent processes. By isolating high-risk batteries in real time, accidents caused by thermal runaway during screening or transportation can be avoided, thereby improving the safety of the overall screening operation. At the same time as isolation, the relevant safety assessment data is recorded in the industrial database to facilitate subsequent analysis of the causes of risky batteries and provide a basis for optimizing the screening process.

[0114] In summary, thermal runaway monitoring achieves efficient risk identification and safety control through temperature gradient calculation and threshold judgment. Specifically:

[0115] Through rapid calculation of temperature gradients, potential thermal runaway problems can be discovered and addressed in a timely manner, shortening reaction time.

[0116] Triggering the isolation process effectively prevents risky batteries from entering subsequent links and ensures the safety of the overall screening process.

[0117] By recording the safety assessment results, a complete data chain is formed to provide data support for eliminating thermal runaway risks and optimizing operations.

[0118] Thermal runaway monitoring is an indispensable part of the screening process, ensuring the safety of the screening process and providing a scientific basis for the subsequent treatment of risky batteries. Combining multiple functions of real-time monitoring, threshold judgment and data management, thermal runaway monitoring effectively improves the reliability and safety of the retired battery screening system.

[0119] In step 5, intelligent classification is achieved through a multi-objective optimization algorithm, and the calculation formula for fitness is:

[0120] Fit=w1·Performance+w2·Safety+w3·Economic,

[0121] Among them, Fit is the adaptability of the battery to the cascade utilization scenario, Performance is the performance score of the battery, Safety is the safety assessment score of the battery, Economic is the economic benefit score of cascade utilization, and w1, w2, and w3 are weight factors for each score.

[0122] The safety assessment score in the fitness calculation is determined by the following formula:

[0123] Safety=δ1·1-ΔT / T max +δ2·1-C leak / C threshold ,

[0124] Among them, Safety is the safety assessment score, ΔT is the temperature gradient, T max is the maximum surface temperature, C leak is the detected gas concentration, C threshold is the safety threshold of gas leakage,

[0125] δ1 and δ2 are weight factors for safety assessment.

[0126] The economic benefit score in the fitness calculation is determined by the following formula:

[0127] Economic=ResidualValue-ProcessingCost,

[0128] Among them, Economic is the economic benefit score, ResidualValue is the residual value of the battery, and ProcessingCost is the screening and processing cost of the battery.

[0129] The multi-objective optimization algorithm is implemented by genetic algorithm, and the optimized fitness objective function is:

[0130]

[0131] Where M is the number of optional cascade utilization scenarios, Fit i is the adaptability value of the i-th tier utilization scenario.

[0132] The fitness calculation benefits of the multi-objective optimization algorithm: The fitness calculation takes into account the core indicators of performance, safety and economic benefits at the same time to ensure the comprehensiveness of the allocation results. The weight factor can be adjusted according to the needs of the specific tiered utilization scenario to adapt to different application goals. The contribution of each indicator is quantified through a mathematical model to avoid interference from human factors in the classification results.

[0133] Benefits of safety assessment scoring: Combined with key indicators of temperature gradient and gas leakage concentration, the potential safety risks of batteries are comprehensively assessed. Safety scores are quantified through mathematical formulas to improve the sensitivity and judgment of safety issues during the classification process.

[0134] Benefits of economic efficiency scoring: By comprehensively considering residual value and disposal costs, the economic feasibility of the cascade utilization scenario is ensured. Batteries with high economic efficiency scores are preferentially allocated to cascade utilization scenarios with higher value, optimizing resource allocation.

[0135] Benefits of genetic algorithms: Genetic algorithms can find the most adaptive classification scheme through population iteration and fitness function optimization. In multi-objective optimization problems, genetic algorithms can quickly process a large number of allocation schemes. When input data changes, genetic algorithms can flexibly adjust the population to maintain the dynamic nature of the optimization results.

[0136] In summary, intelligent classification is achieved through a multi-objective optimization algorithm, the core of which is the calculation of fitness and the optimization of genetic algorithms. In the fitness calculation, the performance, safety and economic benefit scores take into account the electrochemical performance, safety risks and economic benefits of the battery.

[0137] Comprehensive benefits:

[0138] The fitness calculation integrates the dimensions of performance, safety and economic benefit, providing a scientific basis for the matching of tiered utilization scenarios.

[0139] Through global optimization of genetic algorithms, each battery is allocated to the most appropriate cascade utilization scenario to maximize resource utilization efficiency.

[0140] The ability to adjust the weight factors enables the classification algorithm to adapt to the needs of different scenarios and has high applicability.

[0141] Intelligent classification is a key step in the recycling process of retired batteries. Its scientificity, precision and flexibility directly determine the efficiency and economic value of resource utilization.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A screening method for the cascade utilization of retired batteries, characterized in that: include: Step 1: Preliminary sorting: Use an industrial camera to collect appearance images of retired batteries, use image processing algorithms to grayscale the images and enhance edge features, remove batteries with cracks, bulges or leaks, and scan barcodes to obtain battery model information, and classify and group the batteries according to the model database; Step 2, performance testing: Perform performance testing on the preliminarily sorted batteries, measure the open circuit voltage of the batteries to calculate the remaining capacity, detect the AC impedance spectrum of the batteries to determine the internal resistance, and record the number of battery cycles and historical usage data as input parameters for health status assessment; Step 3: Health status assessment: Based on the open circuit voltage, remaining capacity, internal resistance and cycle number data obtained from performance testing, combined with the historical usage records of the battery, a mathematical model is used to calculate the health status of the battery and quantify the degree of battery attenuation for cascade utilization scenario adaptation; Step 4, safety assessment: Conduct thermal runaway monitoring on batteries that have completed health status assessment, use a thermal imager to collect temperature data on the battery surface and calculate the temperature gradient. At the same time, use a gas sensor to detect the leakage of combustible gas around the battery, and isolate the identified batteries with safety hazards, and do not enter the subsequent screening process; Step 5, Intelligent Classification: For batteries that have passed the safety assessment, the batteries are divided into high-performance group, medium-performance group and low-performance group according to health status, remaining capacity and internal resistance parameters, and matched to the cascade utilization scheme in energy storage applications, low-power equipment or material recycling scenarios. At the same time, the classification information is recorded; Step 6, data management: After intelligent classification, the battery model information, performance test results, health status assessment results, safety assessment results and classification results are stored in the industrial database to form a traceable screening data chain, providing data support for the execution and management of subsequent cascade utilization.

2. The method for screening retired batteries for cascade utilization according to claim 1, characterized in that: In step 2, the remaining capacity is calculated by the open circuit voltage, and the formula is: Among them, SoC is the remaining capacity of the battery, V OC is the open circuit voltage of the battery, V min and V max is the minimum and maximum operating voltage of the battery; In step 2, when calculating the remaining capacity, the open circuit voltage is corrected for temperature, and the correction formula is: SoC corrected =SoC+k(TT ref ), Among them, SoC corrected is the corrected remaining capacity, T is the current ambient temperature of the battery, T ref is the reference temperature, k is the temperature compensation coefficient, and SoC is the remaining capacity of the battery; In step 2, the internal resistance is detected by AC impedance spectroscopy, and the total impedance is determined by the following formula: Where Z(ω) is the total impedance of the battery, R s is the ohmic internal resistance, R ct is the charge transfer resistance, C dl is the double layer capacitance, ω is the angular frequency of the AC signal, and j is the imaginary unit.

3. The screening method for the cascade utilization of retired batteries according to claim 2 is characterized in that: The parameters of the internal resistance are obtained by least squares fitting, and the fitting formula is: in, is the measured impedance value, is the impedance value calculated by the model, and N is the total number of measurement frequency points.

4. The screening method for the cascade utilization of retired batteries according to claim 1 is characterized in that: In step 3, the health status is evaluated by a mathematical model, and the calculation formula is: Among them, SoH is the health state of the battery, C used is the cumulative usage capacity of the battery, C rated is the rated capacity of the battery.

5. The screening method for the cascade utilization of retired batteries according to claim 4 is characterized in that: The health status assessment combines the open circuit voltage and internal resistance change of the battery, and the assessment formula is: SoH adjusted =SoH-m·(R current -R baseline ), Among them, SoH adjusted is the corrected health status, R current is the internal resistance currently detected, R baseline is the rated internal resistance, m is the influence coefficient of impedance on health state, and SoH is the health state of the battery.

6. The screening method for the cascade utilization of retired batteries according to claim 1 is characterized in that: In step 4, the monitoring of thermal runaway is based on the temperature gradient on the battery surface, and the calculation formula is: ΔT=T max -T arg , Where ΔT is the temperature gradient, T max is the maximum temperature of the battery surface, T arg is the average temperature of the battery surface; If the temperature gradient ΔT exceeds the preset threshold, the system automatically triggers the isolation process and records the safety assessment results of the relevant batteries through the industrial database.

7. The method for screening retired batteries for cascade utilization according to claim 1, characterized in that: In step 5, intelligent classification is achieved through a multi-objective optimization algorithm, and the calculation formula of the fitness is: Fit=w1·Performance+w2·Safety+w3·Economic, Among them, Fit is the adaptability of the battery to the cascade utilization scenario, Performance is the performance score of the battery, Safety is the safety assessment score of the battery, and Economic is the economic benefit score of the cascade utilization. w1, w2, and w3 are weight factors for each score.

8. The method for screening retired batteries for cascade utilization according to claim 7, characterized in that: The safety assessment score in the fitness calculation is determined by the following formula: Safety=δ1·(1-ΔT / T max )+δ2·(1-C leak / C threshold ), Among them, Safety is the safety assessment score, ΔT is the temperature gradient, T max is the maximum surface temperature, C leak is the detected gas concentration, C threshold is the safety threshold of gas leakage, δ1 and δ2 are weight factors for safety assessment.

9. The method for screening retired batteries for cascade utilization according to claim 7, characterized in that: The economic benefit score in the fitness calculation is determined by the following formula: Economic=ResidualValue-ProcessingCost, Among them, Economic is the economic benefit score, ResidualValue is the residual value of the battery, and ProcessingCost is the screening and processing cost of the battery.

10. The method for screening retired batteries for cascade utilization according to claim 7, characterized in that: The multi-objective optimization algorithm is implemented by a genetic algorithm, and the optimized fitness objective function is: Where M is the number of optional cascade utilization scenarios, Fit i is the adaptability value of the i-th tier utilization scenario.

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