A comprehensive recycling method and system for power batteries

Abstract

The present invention relates to the technical field of power batteries, and discloses a comprehensive recycling method and system for power batteries, which includes the following steps: S1: collecting performance parameters of a single battery in a retired power battery pack; S2: based on the performance parameters, reorganizing the single battery into a battery pack; S3: collecting the fitness index of each battery pack; S4: determining the weight of each fitness index for each application scenario; S5: calculating the fitness of each battery pack for each application scenario, and recommending an application scenario for each battery pack. The present invention realizes the precise adaptation of retired batteries, ensures that the reorganized battery pack can better meet various application requirements, improves the value of secondary utilization, and helps to promote the power battery recycling industry to develop in a more sustainable and competitive direction.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and in particular to a comprehensive recycling method and system for power batteries. Background Art

[0002] As the world pays more and more attention to renewable energy and sustainable development, the new energy vehicle industry is booming at an unprecedented speed. As the core component of new energy vehicles, the demand for power batteries has increased dramatically, and with it the large-scale production of retired power batteries. According to the International Energy Agency, the total amount of retired power batteries in the world will reach nearly 2 million tons by 2030, and this figure will soar to 7.8 million tons in 2040. Faced with such a huge volume of retired batteries, how to recycle and reuse them efficiently, environmentally friendly and economically has become a major issue of concern to the industry and even society. At present, the recycling of retired power batteries mainly adopts technical routes such as hydrometallurgy, pyrometallurgy, physical disassembly and direct recycling. In terms of business model, innovative practices such as "battery bank" and "battery swap model" have emerged, which realize the cascade utilization and value maximization of retired batteries through the full life cycle management of batteries.

[0003] Although the power battery recycling industry has made some progress, there are still many problems and challenges in actual operation. Although the policy level encourages the construction of a sound recycling system, in reality, the collection network of retired batteries is not yet sound, especially in remote areas and informal channels, and a large number of retired batteries have not been effectively recycled. In addition, the variety of battery models and specifications make it difficult to uniformly recycle and classify them. The lack of an efficient reverse logistics system and a reasonable recycling price mechanism is also an important factor restricting the improvement of the recycling rate. In the existing technology, only a rough capacity division is made for retired batteries, and there is no method to match the best application scenario for the retired batteries for secondary use. Retired power batteries do not completely lose their use value. Some batteries can be used in energy storage, backup power supply and other scenarios through testing, repair, reorganization and other methods to achieve cascade utilization. However, the standard system, quality certification, market acceptance and other aspects of cascade utilization are not yet mature, and the connection and coordination between them and resource recycling needs to be strengthened. How to scientifically evaluate the residual value of batteries and reasonably plan their cascade utilization and recycling paths is a major problem facing the industry.

[0004] For example, the Chinese patent with the authorization announcement number CN108448194B discloses a method for disassembling and recycling waste power battery packs for cascade utilization, including: testing the recycled waste power battery packs, including: reading the data of the battery management system of the waste power battery packs, and judging the performance indicators of the entire battery pack according to the read data; using an assembly line automated robot to decompose the waste power battery packs, including disassembling the waste power battery packs step by step until the single cells are removed; dividing the modules and single cells that can be used after the waste power battery packs are disassembled and sorted into groups for reorganization and cascade utilization; recycling the parts of the cells that can no longer be used for cascade utilization after the waste power battery packs are disassembled and sorted for material level. This invention has the characteristics of high recycling efficiency, energy saving and environmental protection, and no secondary pollution.

[0005] For example, a Chinese patent with the authorization announcement number CN1 11790645B discloses a method for sorting second-hand power batteries. Although the traditional method can accurately measure the capacity, internal resistance, self-discharge and other parameters of the battery, the test time is long, and a large amount of charging and discharging test equipment is also required, which results in a significant increase in the cost of sorting second-hand power batteries. The invention tests the open circuit voltage of the second-hand power battery, the battery voltage change value during charging or discharging, and the impedance value at different frequency points. On this basis, the corresponding deviation range is set for the test results of different parameters to achieve rapid sorting of second-hand power batteries. The invention sorts the second-hand power battery according to its open circuit voltage, the voltage change value during 10 minutes of charging and discharging, and the impedance value at high frequency, medium frequency and low frequency. The battery sorting work can be completed within 15 minutes, realizing the rapid sorting of second-hand power batteries and greatly shortening the sorting time of second-hand power batteries.

[0006] The above patents all have the problem raised by this background technology: they only divide the capacity of retired batteries, and fail to match the retired batteries for secondary use with the best application scenarios.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0008] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide a comprehensive recycling method and system for power batteries, achieve precise adaptation of retired batteries, ensure that the reorganized battery pack can better meet various application requirements, improve the value of secondary utilization, and promote the power battery recycling industry to develop in a more sustainable and competitive direction.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In one aspect, the present invention provides a comprehensive recycling method for power batteries, comprising the following steps:

[0011] S1: Collect the performance parameters of a single battery in a retired power battery pack;

[0012] S2: Based on the performance parameters, reorganize the single battery into a battery pack;

[0013] S3: Collect fitness indicators of each battery pack;

[0014] S4: Determine the weight of each fitness indicator for each application scenario;

[0015] S5: Calculate the adaptability of each battery pack for each application scenario, and recommend an application scenario for each battery pack.

[0016] As a preferred scheme of the comprehensive recycling method of a power battery described in the present invention, the performance parameters include rated voltage, charging cut-off voltage, discharging cut-off voltage, open circuit voltage, rated capacity, remaining capacity, total internal resistance, ohmic internal resistance, number of cycles, thermal stability parameters, and capacity attenuation coefficient.

[0017] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the method for collecting the total internal resistance and the ohmic internal resistance is as follows:

[0018] The battery is pulse-discharged, and the battery terminal voltage is continuously recorded to obtain a terminal voltage change curve. The terminal voltage at the moment before the pulse discharge is read from the terminal voltage change curve, which is recorded as U A , read the terminal voltage at the beginning of the pulse discharge, recorded as U B , read the terminal voltage just before the end of the pulse discharge, recorded as U C , read the terminal voltage at the end of the pulse discharge, recorded as U D ; The calculation formula of the total internal resistance is as follows:

[0019]

[0020] Where, R represents the total internal resistance; I represents the pulse discharge current;

[0021] The calculation formula of the ohmic internal resistance is as follows:

[0022]

[0023] Among them, R0 represents the ohmic internal resistance.

[0024] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the thermal stability parameter is collected by the following method: a temperature rise test is performed on the battery, and the battery temperature is continuously monitored; the thermal stability parameter is calculated by the following formula:

[0025]

[0026] Among them, T S Represents the thermal stability parameter of the battery; α and β are weight parameters; ΔT C ΔT is the maximum temperature rise threshold of the battery during charging; CA ΔT is the maximum temperature rise of the battery during charging; D ΔT is the maximum temperature rise threshold of the battery during discharge; DA It is the maximum temperature rise of the battery during discharge.

[0027] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the method for collecting the capacity attenuation coefficient is as follows:

[0028] Obtain historical SOH data of each battery through BMS;

[0029] Construct the capacity attenuation model for each battery, the equation is as follows:

[0030] C(t)=1-λ·exp(-k·t)+ε;

[0031] Among them, C(t) represents the capacity parameter of any battery at time t, expressed as SOH;

[0032] λ is the proportionality coefficient, ε is the error coefficient, and k is the capacity attenuation coefficient;

[0033] Through parameter fitting, a set of optimal solutions for λ, ε, and k are obtained and saved.

[0034] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the method of reorganizing a single battery into a battery pack is as follows:

[0035] S201: Standardizing the performance parameters of a single battery and forming a performance parameter vector;

[0036] S202: Determine the number of clusters K, that is, the number of clusters into which a single battery is divided;

[0037] S203: randomly selecting K battery performance parameter vectors as cluster centers of K clusters;

[0038] S204: Divide each battery into a cluster with the closest performance distance;

[0039] The performance distance is the Euclidean distance between the performance parameter vector of any battery and the cluster center of any cluster;

[0040] S205: updating the cluster center of each cluster to the mean vector of the performance parameter vectors of all batteries in the cluster;

[0041] S206: repeating steps S204-S205 until the change rate of the cluster center of any cluster is less than a preset threshold;

[0042] The change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration;

[0043] S207: Combine the batteries in each cluster together to form a modular battery pack.

[0044] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the fitness index includes a power index, an energy index, a reliability index, a peak load index, an energy storage index, and a cycle life index; wherein:

[0045] The power index is collected by measuring the power density of the battery pack as the power index; the energy index is collected by measuring the total capacity of the battery pack as the energy index; the reliability index is collected by measuring the thermal stability parameter of the battery pack as the reliability index; the peak load index is collected by measuring the peak power of the battery pack as the peak load index; the energy storage index is collected by measuring the charge and discharge efficiency of the battery pack as the energy storage index; the formula is as follows:

[0046]

[0047] Among them, η c Indicates the charge and discharge efficiency of the battery pack, E d Indicates the discharge energy of the battery pack; E i Indicates the charging energy of the battery pack;

[0048] The method for collecting the cycle life index is to predict through a neural network model; the neural network model is one of a long short-term memory network, a gated recurrent unit, a deep neural network, and a convolutional neural network, and the input is the power index, energy index, reliability index, peak load index, energy storage index, number of batteries in the battery pack, and performance parameters of each battery in the battery pack, and the output is the cycle life of the battery pack;

[0049] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the method for determining the weight of each fitness index for each application scenario is as follows:

[0050] A hierarchical model is constructed for each application scenario, including a target layer and a criterion layer, where the target layer is the highest match between the battery pack and the application scenario, and the criterion layer is composed of the fitness indicators of the battery pack;

[0051] The 1-9 scaling method is used to quantify the relative importance of any two fitness indicators in the criterion layer to the target layer, and the judgment matrix of the criterion layer is constructed;

[0052] Calculating the maximum eigenvalue of the judgment matrix and performing a consistency check;

[0053] If the consistency check passes, the eigenvector corresponding to the maximum eigenvalue is extracted, and each element in the eigenvector is the weight of the corresponding fitness index; if the consistency check fails, the judgment matrix is ​​reconstructed, and the consistency check is performed after calculating the maximum eigenvalue until the consistency check passes.

[0054] As a preferred solution of the comprehensive recycling method of a power battery described in the present invention, the calculation formula of the adaptability of the battery pack for each application scenario is as follows:

[0055]

[0056] Among them, F i represents the adaptability of any battery pack to the i-th application scenario, the value range of i is 1, 2, ..., m, where m is the number of application scenarios; w ij represents the weight of the jth fitness index for the ith application scenario; f j represents the value of the jth fitness index; max(·) represents the maximum value of the function in the brackets;

[0057] Sort the adaptability of any battery pack for each application scenario in descending order, and select the n application scenarios with the largest adaptability as the recommended application scenarios of the battery pack; n is a positive integer.

[0058] In a second aspect, the present invention provides a comprehensive recycling system for power batteries, including a data acquisition module, a battery reorganization module, a hierarchical analysis module, a scenario recommendation module, a database module, and a user interface module; wherein:

[0059] The data acquisition module is used to collect the performance parameters of a single battery in the retired power battery pack and the fitness index of the reorganized battery pack;

[0060] The battery reorganization module is used to reorganize a single battery in a retired power battery pack;

[0061] The hierarchical analysis module is used to set the weight of fitness indicators for each application scenario;

[0062] The scenario recommendation module is used to calculate the adaptability of each battery pack for each application scenario and recommend an application scenario for each battery pack;

[0063] The database module is used to store and manage retired battery pack and single cell performance parameters, battery pack reorganization information, battery pack fitness indicators, and battery pack fitness for application scenarios;

[0064] The user interface module is used to provide a human-computer interaction interface for operators to input operating instructions, view data, monitor system operating status, and view application scenario recommendation results.

[0065] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0066] By collecting the performance parameters of a single battery in a retired power battery pack and reorganizing the battery based on these parameters, the performance of the reorganized battery pack is optimized and the reuse rate of retired battery resources is improved. The individual differences of the single battery are fully considered, and the "one-size-fits-all" recycling treatment is avoided, so that the battery resources that may have been underestimated or wasted can be maximized and utilized, providing a practical solution to the problem of large-scale recycling of retired power batteries, which is of great value in promoting technological progress and industrial upgrading in the power battery recycling industry.

[0067] The present invention introduces a fitness index system to carry out customized evaluation and matching for different application scenarios. By quantitatively calculating the fitness of each battery pack in a specific application scenario and combining the weight of the application scenario, the accurate adaptation of retired batteries is achieved, ensuring that the reorganized battery pack can better meet various application requirements, increasing the value of secondary utilization, and is expected to reduce recycling costs, increase economic benefits, and promote the development of the power battery recycling industry in a more sustainable and competitive direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0069] Figure 1 A flow chart of the comprehensive recycling method for power batteries provided by the present invention;

[0070] Figure 2 A flow chart of a method for reassembling a single battery into a battery pack provided by the present invention;

[0071] Figure 3 This is a schematic structural diagram of the power battery comprehensive recycling system provided by the present invention. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0073] Example 1

[0074] This embodiment introduces a comprehensive recycling method for power batteries. Figure 1 , the method comprises the following steps:

[0075] S1: Collect the performance parameters of a single battery in a retired power battery pack;

[0076] The performance parameters include rated voltage, charge cut-off voltage, discharge cut-off voltage, open circuit voltage, rated capacity, remaining capacity, total internal resistance, ohmic internal resistance, number of cycles, thermal stability parameters, and capacity attenuation coefficient;

[0077] The remaining capacity is collected in the following manner:

[0078] Discharge the battery at a constant current of 1C to the battery's discharge cut-off voltage, then stop discharging and let it stand for one hour;

[0079] The battery is charged at a constant current at a discharge rate of 1C until the battery charging cut-off voltage is reached;

[0080] Charge the battery with constant voltage until the charging current decreases to 0.05C, then stop charging and let it stand for one hour;

[0081] Then, perform constant current discharge at a discharge rate of 1C until the battery reaches the discharge cut-off voltage, and record the total discharge amount during the discharge process;

[0082] Repeat the above process three times, and average the total discharge amounts of the three times to get the remaining capacity of the battery.

[0083] The remaining capacity refers to the amount of electricity that the battery can actually discharge. The rated capacity refers to the capacity marked on the nameplate by the manufacturer. The actual capacity of retired batteries will gradually decrease as the usage time and the number of charge and discharge cycles increase, which is very different from the rated capacity of new batteries.

[0084] The method for collecting the total internal resistance and the ohmic internal resistance is as follows:

[0085] The battery is pulse-discharged, and the battery terminal voltage is continuously recorded to obtain a terminal voltage change curve. The terminal voltage at the moment before the pulse discharge is read from the terminal voltage change curve, and recorded as UA , read the terminal voltage at the beginning of the pulse discharge, recorded as U B , read the terminal voltage just before the end of the pulse discharge, recorded as U C , read the terminal voltage at the end of the pulse discharge, recorded as U D ; The calculation formula of the total internal resistance is as follows:

[0086]

[0087] Where, I represents the pulse discharge current;

[0088] The calculation formula of the ohmic internal resistance is as follows:

[0089]

[0090] Among them, R0 represents the ohmic internal resistance;

[0091] If the internal resistance is too large, the heat generated by the battery will increase during operation, thus affecting the working efficiency and life of the battery. When the internal resistance increases to a certain extent, the battery's ability to store and release energy will be weakened, so the internal resistance of retired batteries is also an important indicator parameter for judging their recycling value.

[0092] The number of cycles is collected by reading through BMS;

[0093] After multiple charge and discharge cycles, the performance of retired batteries will gradually decline. Due to the barrel effect, the actual performance of the battery pack is affected by the smallest capacity battery. Retired batteries with similar cycle life can effectively improve battery utilization.

[0094] The thermal stability parameter is collected by performing a temperature rise test on the battery and continuously monitoring the battery temperature. The thermal stability parameter is calculated by the following formula:

[0095]

[0096] Among them, T S represents the thermal stability parameter of the battery; α and β are weight parameters, which are set by technicians in this field according to actual needs, and the sum of the two is 1; ΔT C ΔT is the maximum temperature rise threshold of the battery during charging, which is determined by the technical parameters of the battery; CA ΔT is the maximum temperature rise of the battery during charging; D ΔT is the maximum temperature rise threshold of the battery during discharge, which is determined by the technical parameters of the battery; DA is the maximum temperature rise of the battery during discharge;

[0097] Thermal stability parameter T SIt can simultaneously reflect the thermal stability of the battery during charging and discharging, and help to comprehensively evaluate the thermal safety of the battery under different operating conditions.

[0098] The method for collecting the capacity attenuation coefficient is as follows:

[0099] Obtain historical SOH data of each battery through BMS;

[0100] Construct the capacity attenuation model for each battery, the equation is as follows:

[0101] C(t)=1-λ·exp(-k·t)+ε;

[0102] Among them, C(t) represents the capacity parameter of any battery at time t, expressed as SOH;

[0103] λ is the proportionality coefficient, ε is the error coefficient, and k is the capacity attenuation coefficient;

[0104] Through the parameter fitting method, a set of optimal solutions of λ, ε, and k are obtained and saved;

[0105] The capacity decay coefficient k can be used to quantify the difference between the capacity decay curves of different batteries.

[0106] One of the main functions of the battery management system BMS is to monitor and manage the status of each battery cell in real time to ensure the safe and efficient operation of the battery system and extend its service life. Therefore, BMS usually records the SOH (battery state of health) of a single battery and the overall SOH of the entire power battery pack. Since one definition of SOH is the percentage of remaining capacity to rated capacity, SOH can reflect the capacity information of the battery to a certain extent. Therefore, this embodiment uses SOH as a characteristic parameter representing the capacity of a single battery.

[0107] As the usage time or the number of charge and discharge cycles changes, the battery capacity will gradually decrease. In practical applications, due to the influence of test conditions, measurement errors and other factors, directly depicting these discrete measurement data points may not be sufficient to clearly show the trend or law of capacity decay. Therefore, in order to obtain a more accurate, smooth capacity decay trend curve that can reflect the inherent decay law, this embodiment performs data fitting on the original data to obtain a continuous capacity decay curve, which helps to better understand the changing characteristics of battery performance over time, predict future capacity status, and optimize battery usage strategies and maintenance plans.

[0108] S2: Based on the performance parameters, reorganize the single battery into a battery pack; the method is to integrate the batteries with similar performance into the same modular battery pack through the clustering algorithm according to the battery performance evaluation results, so as to achieve cross-batch battery integration. Automatically match the optimal battery combination strategy to minimize the performance difference between the cells in the battery pack and maintain the consistency and stability of the overall performance of the battery pack; refer to Figure 2 , the specific steps are as follows:

[0109] S201: Standardize the performance parameters of a single battery and form a performance parameter vector; for example, convert each performance parameter into a Z-score standardized value to ensure that different parameters are compared on the same scale.

[0110] S202: Determine the number of clusters K, that is, the number of clusters into which a single battery is divided; determine the appropriate number of clusters based on the requirements of the battery pack and expert experience.

[0111] S203: randomly selecting K battery performance parameter vectors as cluster centers of K clusters;

[0112] S204: Divide each battery into a cluster with the closest performance distance;

[0113] The performance distance is the Euclidean distance between the performance parameter vector of any battery and the cluster center of any cluster;

[0114] S205: updating the cluster center of each cluster to the mean vector of the performance parameter vectors of all batteries in the cluster;

[0115] S206: repeating steps S204-S205 until the change rate of the cluster center of any cluster is less than a preset threshold;

[0116] The change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration; the threshold of the change rate is set according to actual needs.

[0117] S207: Combine the batteries in each cluster together to form a modular battery pack.

[0118] S3: Collect fitness indicators of each battery pack;

[0119] The fitness index includes power index, energy index, reliability index, peak load index, energy storage index, and cycle life index; wherein:

[0120] The power index collection method is to measure the power density of the battery pack as a power index; in some application scenarios that require a large amount of power in a short period of time, such as the acceleration phase of large vehicles, emergency power supply systems, short-term peak load compensation, etc., it is necessary to select a battery pack with high power density and excellent dynamic response performance to meet the requirements of instantaneous large current discharge. Power density refers to the maximum power that a battery can provide per unit mass or unit volume, which determines the amount of electrical energy that the battery can provide in a short period of time.

[0121] The energy index collection method is to measure the total capacity of the battery pack as an energy index; in application scenarios such as energy storage power stations, drone endurance, and off-grid microgrids that require long-term power supply or carry large energy reserves, it is necessary to select a battery pack with a larger capacity to meet the energy supply needs of long working hours or long-distance movement. The total capacity is the total energy that the battery pack can provide, usually expressed in watt-hours or kilowatt-hours, and is used to evaluate how long or how far the battery can meet the usage needs.

[0122] The reliability index collection method is to measure the thermal stability parameters of the battery pack as a reliability index; for scenarios with high requirements for power supply stability and safety, such as medical equipment and uninterruptible power supplies for data centers, it is necessary to ensure the reliability of the battery system in long-term stable operation and reduce the probability of failure.

[0123] The peak load index is collected by measuring the peak power of the battery pack as the peak load index; for application scenarios with high peak load requirements, such as fast charging stations and the start-up of large industrial equipment, it is necessary to select a battery pack with strong instantaneous discharge capability. Peak power refers to the maximum discharge power allowed by the battery, that is, the product of the maximum current and voltage output in a short period of time.

[0124] The energy storage index collection method is to measure the charge and discharge efficiency of the battery pack as the energy storage index; the formula is as follows:

[0125]

[0126] Among them, η c Indicates the charge and discharge efficiency of the battery pack, E d Indicates the discharge energy of the battery pack; the calculation method is to connect a fully charged battery pack to a load and supply power to the load, collect the average discharge power during the process of the battery pack being discharged, and multiply it by the discharge time to obtain the discharge energy; E i Indicates the charging energy of the battery pack. The calculation method is to charge the empty battery pack through an external power source, collect the average power during the process of charging the battery pack, and multiply it by the charging time to get the charging energy;

[0127] In energy storage projects, battery packs need to be charged and discharged for a long time and many times. The higher the charging and discharging efficiency, the less loss there is in the energy storage conversion process.

[0128] The cycle life index is collected by prediction through a neural network model; in application scenarios with frequent charging and discharging or expected long service life (such as energy storage power stations, electric vehicle power batteries, etc.), it is necessary to select battery packs with longer cycle life to reduce replacement costs. Cycle life refers to the number of complete charge and discharge cycles a battery can go through before its performance drops to a certain threshold.

[0129] The neural network model is one of a long short-term memory network, a gated recurrent unit, a deep neural network, and a convolutional neural network, and the input is the power index, energy index, reliability index, peak load index, energy storage index, number of batteries in the battery pack, and performance parameters of each battery in the battery pack, and the output is the cycle life of the battery pack;

[0130] S4: Determine the weight of each fitness indicator for each application scenario;

[0131] The application scenarios include but are not limited to backup power supply for communication base stations, large-scale grid energy storage, household energy storage, emergency rescue power supply, low-speed electric vehicles, new energy power generation, lighting power supply, disassembly and recycling, etc. For example, China Tower Group has more than 2 million base stations and has a huge demand for batteries. By recycling retired batteries in their communication base stations as backup power supplies, it is possible to recycle and reuse retired batteries and alleviate recycling pressure. For another example, for vehicles with low power requirements and slow driving speeds, such as golf carts, sightseeing cars, and elderly scooters, retired batteries can be reassembled as drive batteries to reduce costs after meeting relevant performance standards.

[0132] The importance of each fitness indicator in each application scenario is quantified by the hierarchical analysis method, and the weight of each fitness indicator for each application scenario is obtained. The specific method is as follows:

[0133] A hierarchical model is constructed for each application scenario, including a target layer and a criterion layer, where the target layer is the highest match between the battery pack and the application scenario, and the criterion layer is composed of the fitness indicators of the battery pack;

[0134] The 1-9 scaling method is used to quantify the relative importance of any two fitness indicators in the criterion layer to the target layer, and the judgment matrix of the criterion layer is constructed;

[0135] Calculating the maximum eigenvalue of the judgment matrix and performing a consistency check;

[0136] If the consistency check passes, the eigenvector corresponding to the maximum eigenvalue is extracted, and each element in the eigenvector is the weight of the corresponding fitness index; if the consistency check fails, the judgment matrix is ​​reconstructed, and the consistency check is performed after calculating the maximum eigenvalue until the consistency check passes.

[0137] Each value of the eigenvector corresponding to the maximum eigenroot represents the relative weight of the corresponding fitness indicator to the target layer. The larger the relative weight, the more important the fitness indicator is in the corresponding application scenario.

[0138] Through the above steps, the weight of each indicator in the battery pack application scenario can be determined systematically and quantitatively, thereby helping to scientifically evaluate and select appropriate battery pack solutions.

[0139] S5: Calculate the adaptability of each battery pack for each application scenario, and recommend an application scenario for each battery pack.

[0140] The calculation formula of the adaptability of the battery pack for each application scenario is as follows:

[0141]

[0142] Among them, F i represents the adaptability of any battery pack to the i-th application scenario, the value range of i is 1, 2, ..., m, where m is the number of application scenarios; w ij represents the weight of the jth fitness index for the ith application scenario; f j represents the value of the jth fitness index; max(·) represents the maximum value of the function in the brackets.

[0143] Sort the fitness of any battery pack for each application scenario in descending order, and select the n application scenarios with the largest fitness as the recommended application scenarios of the battery pack; n is a positive integer. List the recommended application scenarios of each battery and the fitness of each application scenario in descending order of fitness.

[0144] The above method performs dynamic scheduling according to the actual needs of the application scenario, realizing the refined docking of the reorganized battery pack of retired power with the specific application scenario, ensuring that while meeting the needs of the application scenario, the utilization rate and service life of the battery pack are maximized, ultimately achieving the goal of energy saving, efficiency improvement and cost reduction.

[0145] Example 2

[0146] This embodiment is the second embodiment of the present invention; it is based on the same inventive concept as embodiment 1, Figure 3This embodiment introduces a comprehensive recycling system for power batteries, including: a data acquisition module, a battery reorganization module, a hierarchical analysis module, a scenario recommendation module, a database module, and a user interface module; wherein:

[0147] The data acquisition module is used to collect the performance parameters of a single battery in the retired power battery pack and the fitness index of the reorganized battery pack;

[0148] The battery reorganization module is used to reorganize the single batteries in the retired power battery pack; based on the single battery performance parameters provided by the data acquisition module, the clustering algorithm is used to divide the single battery into the optimal combination to optimize the energy efficiency, safety, service life, etc. of the battery pack. This module also has the function of dynamically adjusting the battery series and parallel structure to ensure that the reorganized battery pack meets the preset technical requirements.

[0149] The hierarchical analysis module is used to set the weight of fitness indicators for each application scenario; the weight of fitness indicators is set for different application scenarios based on factors such as market demand, policy orientation, technology development trends, and expert experience.

[0150] The scenario recommendation module is used to calculate the fitness of each battery pack for each application scenario and recommend application scenarios for each battery pack; based on the fitness index score of the battery pack and the weight of each application scenario, the comprehensive fitness of each battery pack for each application scenario is calculated. Then, the best application scenario is recommended for each battery pack based on the comprehensive fitness ranking. In addition, this module also provides an explanation function for the recommendation results, that is, to clearly explain the reasons for the recommendation and enhance the transparency and credibility of the decision.

[0151] The database module is used to store and manage retired battery packs and single-cell performance parameters, battery pack reorganization information, fitness indicators, application scenario weights, and battery pack fitness for application scenarios. It supports data query, backup, recovery, update and other functions to ensure data security and integrity.

[0152] The user interface module is used to provide a human-computer interaction interface for operators to input operation instructions, view data, monitor system operation status, and view application scenario recommendation results. It displays the work progress and results of each module and supports data visualization for easy user understanding and operation.

[0153] The specific functions of the above modules are realized by referring to the relevant contents of the comprehensive recycling method of power batteries described in Example 1, which will not be elaborated here.

[0154] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A comprehensive recycling method for power batteries, characterized in that: The following steps are involved: S1: Collect the performance parameters of a single battery in a retired power battery pack; S2: Based on the performance parameters, the single battery is reassembled into a battery pack as follows: S201: Standardizing the performance parameters of a single battery and forming a performance parameter vector; S202: Determine the number of clusters K, that is, the number of clusters into which a single battery is divided; S203: randomly selecting K battery performance parameter vectors as cluster centers of K clusters; S204: Divide each battery into a cluster with the closest performance distance; The performance distance is the Euclidean distance between the performance parameter vector of any battery and the cluster center of any cluster; S205: updating the cluster center of each cluster to the mean vector of the performance parameter vectors of all batteries in the cluster; S206: repeating steps S204-S205 until the change rate of the cluster center of any cluster is less than a preset threshold; The change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration; S207: combining batteries in each cluster to form a modular battery pack; S3: Collect fitness indicators of each battery pack; The fitness index includes power index, energy index, reliability index, peak load index, energy storage index, and cycle life index; wherein: The power index is collected by measuring the power density of the battery pack as the power index; the energy index is collected by measuring the total capacity of the battery pack as the energy index; the reliability index is collected by measuring the thermal stability parameter of the battery pack as the reliability index; the peak load index is collected by measuring the peak power of the battery pack as the peak load index; the energy storage index is collected by measuring the charge and discharge efficiency of the battery pack as the energy storage index; the formula is as follows: Among them, η c Indicates the charge and discharge efficiency of the battery pack, E d Indicates the discharge energy of the battery pack; E i Indicates the charging energy of the battery pack; The method for collecting the cycle life index is to predict through a neural network model; the neural network model is one of a long short-term memory network, a gated recurrent unit, a deep neural network, and a convolutional neural network, and the input is the power index, energy index, reliability index, peak load index, energy storage index, number of batteries in the battery pack, and performance parameters of each battery in the battery pack, and the output is the cycle life of the battery pack; S4: Determine the weight of each fitness indicator for each application scenario as follows: A hierarchical model is constructed for each application scenario, including a target layer and a criterion layer, where the target layer is the highest match between the battery pack and the application scenario, and the criterion layer is composed of the fitness indicators of the battery pack; The 1-9 scaling method is used to quantify the relative importance of any two fitness indicators in the criterion layer to the target layer, and the judgment matrix of the criterion layer is constructed; Calculating the maximum eigenvalue of the judgment matrix and performing a consistency check; If the consistency test is passed, the eigenvector corresponding to the maximum eigenvalue is extracted, and each element in the eigenvector is the weight of the corresponding fitness index; if the consistency test is not passed, the judgment matrix is ​​reconstructed, and the consistency test is performed after calculating the maximum eigenvalue until the consistency test is passed; S5: Calculate the adaptability of each battery pack for each application scenario, and recommend an application scenario for each battery pack; The calculation formula of the adaptability of the battery pack for each application scenario is as follows: Among them, F i represents the adaptability of any battery pack to the i-th application scenario, the value range of i is 1, 2, ..., m, where m is the number of application scenarios; w ij represents the weight of the jth fitness index for the ith application scenario; f j represents the value of the jth fitness index; max(·) represents the maximum value of the function in the brackets; Sort the adaptability of any battery pack for each application scenario in descending order, and select the n application scenarios with the largest adaptability as the recommended application scenarios of the battery pack; n is a positive integer.

2. A comprehensive recycling method for power batteries according to claim 1, characterized in that: The performance parameters include rated voltage, charge cut-off voltage, discharge cut-off voltage, open circuit voltage, rated capacity, remaining capacity, total internal resistance, ohmic internal resistance, number of cycles, thermal stability parameters, and capacity attenuation coefficient.

3. A comprehensive recycling method for power batteries according to claim 2, characterized in that: The method for collecting the total internal resistance and the ohmic internal resistance is as follows: Perform pulse discharge on the battery, continuously record the battery terminal voltage, and obtain the terminal voltage change curve. Read the terminal voltage at the moment before the pulse discharge from the terminal voltage change curve, and record it as U A , read the terminal voltage at the beginning of the pulse discharge, recorded as U B , read the terminal voltage just before the end of the pulse discharge, recorded as U C , read the terminal voltage at the end of the pulse discharge, denoted as U D ; The calculation formula of the total internal resistance is as follows: Where, R represents the total internal resistance; I represents the pulse discharge current; The calculation formula of the ohmic internal resistance is as follows: Among them, R0 represents the ohmic internal resistance.

4. A comprehensive recycling method for power batteries according to claim 2, characterized in that: The thermal stability parameter is collected by performing a temperature rise test on the battery and continuously monitoring the battery temperature. The thermal stability parameter is calculated by the following formula: Among them, T S Represents the thermal stability parameter of the battery; α and β are weight parameters; ΔT C ΔT is the maximum temperature rise threshold of the battery during charging; CA ΔT is the maximum temperature rise of the battery during charging; D ΔT is the maximum temperature rise threshold of the battery during discharge; DA It is the maximum temperature rise of the battery during discharge.

5. A comprehensive recycling method for power batteries as claimed in claim 2, characterized in that: The method for collecting the capacity attenuation coefficient is as follows: Obtain historical SOH data of each battery through BMS; Construct the capacity attenuation model for each battery, the equation is as follows: C(t)=1-λ·exp(-k·t)+ε; Where C(t) represents the capacity parameter of any battery at time t, expressed as SOH; λ is the proportionality coefficient, ε is the error coefficient, and k is the capacity attenuation coefficient; Through parameter fitting, a set of optimal solutions for λ, ε, and k are obtained and saved.

6. A comprehensive recycling system for power batteries, which is implemented based on a comprehensive recycling method for power batteries according to any one of claims 1 to 5, characterized in that: It includes data acquisition module, battery reorganization module, hierarchical analysis module, scenario recommendation module, database module and user interface module; among which: The data acquisition module is used to collect the performance parameters of a single battery in the retired power battery pack and the fitness index of the reorganized battery pack; The battery reorganization module is used to reorganize a single battery in a retired power battery pack; The hierarchical analysis module is used to set the weight of fitness indicators for each application scenario; The scenario recommendation module is used to calculate the adaptability of each battery pack for each application scenario and recommend an application scenario for each battery pack; The database module is used to store and manage retired battery pack and single cell performance parameters, battery pack reorganization information, battery pack fitness indicators, and battery pack fitness for application scenarios; The user interface module is used to provide a human-computer interaction interface for operators to input operating instructions, view data, monitor system operating status, and view application scenario recommendation results.

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