Battery cell sorting method and system of retired battery, electronic equipment and storage medium

Abstract

The invention provides a cell sorting method and system of a decommissioned battery, electronic equipment and a storage medium, and relates to the technical field of battery echelon utilization, the method comprises the following steps: respectively carrying out capacity increment test, charge state test and internal resistance test on a target decommissioned battery to obtain a capacity increment curve, a charge state value and an internal resistance value of each cell; calculating a comprehensive discriminant value of each battery cell according to the target characteristic parameter of the capacity increment curve, and removing the battery cells of which the comprehensive discriminant values are greater than a preset discriminant threshold value to obtain a target battery cell; and according to the charge state value and the internal resistance value of the target battery cell, carrying out battery cell category division to obtain a battery cell sorting result. The battery health degree is quantified through the capacity increment curve, the aged and abnormal battery cells are removed, and the screening precision is improved. And the consistency of the battery cells is comprehensively evaluated by combining the grouped charge state test and the internal resistance test, and the retired battery cells in different aging states are effectively identified.

Description

Technical Field

[0001] This application relates to the field of battery recycling technology, specifically to a method, system, electronic device, and storage medium for sorting cells of retired batteries. Background Technology

[0002] With the large-scale retirement of electric vehicle batteries, unprecedented challenges have arisen for battery reuse and resource recycling. Retired battery sorting is a core step in battery reuse, and precise sorting can significantly improve the consistency and safety of reconstituted battery packs.

[0003] Currently, the mainstream technologies for sorting retired batteries are mainly based on electrical performance testing or electrochemical impedance spectroscopy testing methods. However, their core bottleneck lies in their high dependence on the accuracy of the lifetime model and the consistency of the electrochemical impedance spectroscopy test, which leads to the problem of low accuracy in sorting retired batteries. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, this application provides a method, system, electronic device and storage medium for sorting cells of retired batteries, which effectively solves the problem of low accuracy in sorting retired batteries.

[0005] In a first aspect, this application provides a method for sorting the cells of retired batteries, the method comprising: Capacity increment test, state of charge test and internal resistance test are performed on the target retired batteries to obtain the capacity increment curve, state of charge value and internal resistance value of each cell; Calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and remove the cells whose comprehensive discrimination value is greater than the preset discrimination threshold to obtain the target cells; The target battery cell is classified into categories based on its state of charge and internal resistance, and the battery cell sorting results are obtained.

[0006] In an optional implementation, the target characteristic parameters include at least the peak voltage, peak area, and peak height of the target peak in the capacity increment curve.

[0007] In an optional implementation, the target peak is the third and / or fourth peak in the capacity increment curve.

[0008] In an optional implementation, the comprehensive discrimination value of each cell is calculated based on the target characteristic parameters of the capacity increment curve, including: The peak voltage, peak area, and peak height are calculated based on the capacity increment curve. A linear regression equation is obtained by performing multiple linear regression on the peak voltage, the peak area, and the peak height. Calculate the weighting coefficients corresponding to the peak voltage, peak area, and peak height in the linear regression equation; The capacity increment curves of each cell are compared with the target capacity increment curve of the target healthy cell to obtain the peak voltage difference, peak area difference, and peak height difference. The comprehensive discrimination value of each cell is calculated based on the peak voltage difference, the peak area difference, the peak height difference, and the corresponding weighting coefficient.

[0009] In an optional implementation, the formula for calculating the comprehensive discriminant value is as follows:

[0010] In the above formula, This represents the comprehensive discrimination value. This represents the peak voltage difference. w 1 represents the weighting coefficient corresponding to the peak voltage. This represents the difference in peak area. w 2 represents the weighting coefficient corresponding to the peak area. This represents the difference in peak height. w 3 represents the weighting coefficient corresponding to the peak height.

[0011] In an optional implementation, the target battery cell is classified according to its state of charge and internal resistance to obtain a battery cell sorting result, including: Calculate the average value of the state of charge and the internal resistance of all the target cells to obtain the average state of charge and the average internal resistance. The target cells whose state of charge value is greater than or equal to the average state of charge value and whose internal resistance value is less than the average internal resistance value are classified into the first cell category. The target cells whose state of charge value is greater than or equal to the average state of charge value and whose internal resistance value is greater than or equal to the average internal resistance value are classified into a second cell category. The target cells whose state of charge value is less than the average state of charge value and whose internal resistance value is less than the average internal resistance value are classified into a third cell category. The target cells whose state of charge value is less than the average state of charge value and whose internal resistance value is greater than or equal to the average internal resistance value are classified into the fourth cell category.

[0012] In an optional implementation, the target retired battery is subjected to capacity increment testing, state of charge testing, and internal resistance testing to obtain the capacity increment curve, state of charge value, and internal resistance value of each cell, including: The target retired battery is sequentially charged, left to stand, and discharged. The corresponding voltage and capacity data of each cell are recorded, and the capacity increment curve of each cell is constructed based on the voltage and capacity data. The target retired battery is subjected to constant current charging and constant current discharging to obtain the static open circuit voltage of each cell after the discharge is completed. The state of charge value of each cell is estimated based on the static open circuit voltage. The state of charge of each cell in the target retired battery is adjusted to the target state of charge, and the cells are discharged at the target discharge rate for a preset time. The corresponding voltage change and current value of each cell are recorded, and the internal resistance value of each cell is calculated based on the voltage change and current value.

[0013] Secondly, this application provides a cell sorting system for retired batteries, the system comprising: The data acquisition module is used to perform capacity increment testing, state of charge testing and internal resistance testing on the target retired batteries to obtain the capacity increment curve, state of charge value and internal resistance value of each cell. The cell initial selection module is used to calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and to remove cells whose comprehensive discrimination value is greater than a preset discrimination threshold to obtain target cells; The cell sorting module is used to classify the cells according to their state of charge and internal resistance values ​​to obtain the cell sorting results.

[0014] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cell sorting method for retired batteries as described in any of the foregoing embodiments.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cell sorting method for retired batteries as described in any of the foregoing embodiments.

[0016] The cell sorting method, system, electronic equipment, and storage medium for retired batteries provided in this application quantify battery health through capacity increment curves and eliminate abnormally aged cells, thus improving screening accuracy. By combining group state-of-charge testing and internal resistance testing, the consistency of cells is comprehensively evaluated, effectively identifying retired battery cells in different aging states. Based on the correlation analysis of response characteristics and aging mechanisms under actual grouping conditions, the influence of model errors and test inconsistencies is avoided, significantly improving the grouping consistency of sorted cells, reducing the impact of voltage difference and temperature rise during charging and discharging, resulting in a clear gradient of state of charge between groups, and controlling the capacity range within an effective range, which is more conducive to the safety and cycle life improvement of retired batteries for secondary use. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a first schematic diagram of the cell sorting method for retired batteries provided in the embodiments of this application; Figure 2 This is a second schematic diagram of the cell sorting method for retired batteries provided in the embodiments of this application; Figure 3 This is a schematic diagram of the capacity increment curve of a lithium iron phosphate battery in an embodiment of this application; Figure 4 This is a third schematic diagram of the cell sorting method for retired batteries provided in the embodiments of this application; Figure 5 This is the fourth schematic diagram of the cell sorting method for retired batteries provided in the embodiments of this application; Figure 6 This is a schematic diagram of the cell sorting system for retired batteries provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0019] Explanation of key component symbols: 200. Cell sorting system for retired batteries; 210. Data acquisition module; 220. Cell initial selection module; 230. Cell sorting module; 300. Electronic equipment; 310. Processor; 320. Communication interface; 330. Memory; 340. Communication bus. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be further described clearly and completely below with reference to the accompanying drawings of the embodiments. It should be noted that the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0023] Currently, mainstream battery sorting technologies are mainly based on electrical performance testing or electrochemical impedance spectroscopy (EIS). However, these methods have significant technical bottlenecks: electrical performance testing methods are highly dependent on the accuracy of battery life degradation models, while battery aging mechanisms are highly complex, and existing models often fail to fully characterize the actual degradation trajectory, leading to systematic biases in lifespan predictions. EIS results are easily affected by factors such as ambient temperature fluctuations, testing equipment accuracy, and electrode contact impedance, making it difficult to guarantee the repeatability and consistency of test data, thus affecting the reliability of sorting results. Therefore, these technical bottlenecks result in low accuracy in the sorting of retired batteries.

[0024] Example 1 This application provides a method for sorting the cells of retired batteries, which effectively solves the problem of low accuracy in sorting retired batteries. Figure 1 This is a first schematic diagram of the process flow for sorting the cells of retired batteries provided in this application embodiment, as shown below. Figure 1 As shown, the method includes the following steps: S100: Perform capacity increment test, state of charge test and internal resistance test on the target retired battery to obtain the capacity increment curve, state of charge value and internal resistance value of each cell.

[0025] In this embodiment of the application, the target retired battery can be a lithium iron phosphate battery. The retired lithium iron phosphate battery cells are sorted based on the grouping consistency and battery aging mechanism by performing capacity increment testing, state of charge testing and internal resistance testing on the grouped retired lithium iron phosphate battery cells. Figure 2 This is a second schematic diagram of the cell sorting method for retired batteries provided in this application embodiment, as shown below. Figure 2 As shown, the testing of the target retired battery specifically includes the following steps: S110. The target retired battery is charged, left to stand, and discharged sequentially. The corresponding voltage and capacity data of each cell are recorded, and the capacity increment curve of each cell is constructed based on the voltage and capacity data.

[0026] For example, taking a retired lithium iron phosphate battery as an example, it is first charged at a constant current rate of 1 / 3C to 3.65V, then switched to constant voltage charging until the current naturally decays to 0.02C, ensuring that the internal reaction of the battery is fully balanced and improving the accuracy of the state of charge. The battery is then left to stand for 1 hour to eliminate polarization effects and stabilize the internal potential of the cell, providing consistent initial conditions for the subsequent discharge process. Next, it is discharged at an extremely low constant current rate of 0.04C to 2.5V. Low-rate discharge effectively reduces ohmic losses and concentration polarization, improving voltage sampling accuracy and thus more accurately reflecting the material phase transition characteristics. Throughout the entire discharge process, the corresponding voltage data of each cell is recorded in real time. V With capacity data Q The corresponding voltage data is obtained through differentiation. dQ / dV The values ​​are then used to construct a capacity increment curve. Under the same operating conditions, by collecting cycle aging data of retired lithium iron phosphate batteries, the data corresponding to each cycle is extracted to construct a capacity increment curve, and the battery capacity is collected to calculate the true health status.

[0027] Based on this, the capacity increment curve can clearly show the electrochemical reaction peaks during the lithium-ion insertion and extraction process, especially highlighting the phase transition platform of the graphite anode in the lithium iron phosphate and graphite systems at different stages. This can then be used to identify the position and morphological changes of the target peak, providing a high-resolution basis for health status assessment and sorting based on aging mechanisms.

[0028] S120. Perform constant current charging and constant current discharging on the target retired battery to obtain the static open circuit voltage of each cell after the discharge is completed, and estimate the state of charge value of each cell based on the static open circuit voltage.

[0029] For example, to accurately obtain the state of charge (SOC) of each cell in a retired lithium iron phosphate (LFP) battery, the retired LFP battery is first discharged at a constant current of 1 / 3C to 2V to achieve deep discharge and eliminate initial state differences. Then, using the parallel channel function of the charge / discharge equipment, multiple cells are connected in parallel for a unified charge / discharge test: charging is stopped at a constant current of 1 / 3C when any cell voltage reaches 3.65V, and then discharging at a constant current of 1 / 3C until any cell voltage drops to 2V. The total capacity of the group is recorded. After discharge, all cells are left to stand for a sufficient time, typically more than one hour, until polarization dissipates. Then, the open-circuit voltage of each cell is measured. Because the open-circuit voltage-SOC curve of a lithium iron phosphate battery has good repeatability and characteristic plateau, the actual SOC value of each cell in its current state can be deduced from the measured open-circuit voltage using the pre-calibrated correspondence between the open-circuit voltage and SOC curves.

[0030] Based on this, group testing of battery cells effectively avoids the problem of low efficiency in single-cell testing. At the same time, group testing reflects the consistency of performance in actual use, and the accuracy of state of charge estimation is improved by measuring the static open-circuit voltage, providing a reliable data foundation for subsequent multi-dimensional grouping based on state of charge and internal resistance.

[0031] S130. Adjust the state of charge of each cell in the target retired battery to the target state of charge, discharge at the target discharge rate for a preset time, record the corresponding voltage change and current value of each cell, and calculate the internal resistance value of each cell based on the voltage change and current value.

[0032] For example, to accurately assess the internal resistance characteristics of retired lithium iron phosphate (LFP) batteries, the state of charge (SOC) of each cell in the LFP battery is uniformly adjusted to a target SOC, which can be 30%. A 30% SOC falls within a relatively flat voltage plateau region of the LFP battery, exhibiting moderate sensitivity to external disturbances. This eliminates the influence of SOC differences on internal resistance measurement, ensuring data comparability and improving repeatability and consistency. Then, a 1.5C discharge rate is used as the target discharge rate, and a pulse discharge is performed for 10 seconds. The voltage change and actual discharge current value are recorded instantaneously before and after the start of discharge. The 1.5C discharge rate balances signal strength and battery safety, and the preset 10-second duration is sufficient to reflect the resistance response dominated by ohmic polarization while avoiding excessive interference from concentration polarization. Based on Ohm's law, the internal resistance value of each cell can be accurately calculated using the voltage change and actual discharge current value.

[0033] Based on this, the internal resistance value obtained by the test not only reflects the aging state of the physical structure such as the electrolyte, separator and electrode interface of the cell, but also indirectly indicates the aging mechanism such as active lithium loss and ohmic impedance increase, providing key parameter support for subsequent multi-dimensional sorting by combining capacity increment curve and state of charge value.

[0034] S200. Calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and remove the cells whose comprehensive discrimination value is greater than the preset discrimination threshold to obtain the target cells.

[0035] Understandably, batteries undergo a degradation and aging process during long-term use. This is mainly manifested in the formation and thickening of the solid electrolyte interfacial film on the positive and negative electrode surfaces, the loss of battery active materials, and lithium plating on the negative electrode. These aging phenomena can generally be classified into three aging mechanisms: loss of cyclic lithium ions, loss of electrode active materials, and increase in ohmic internal resistance. These aging mechanisms cause changes in the peaks and their positions in the corresponding capacity increment curves. Therefore, changes in the peaks in the capacity increment curves can reflect the aging trend of the battery.

[0036] Figure 3 This is a schematic diagram of the capacity increment curve of a lithium iron phosphate battery in an embodiment of this application, as shown below. Figure 3 As shown, the negative electrode of a lithium iron phosphate (LFP) battery is graphite. During the lithium intercalation process of the graphite negative electrode in an LFP battery, the capacity increment curve exhibits multiple peaks, corresponding to different stages of lithium intercalation phase transitions between graphite layers. Among them, peaks 1 to 4 represent different stages of lithium ion intercalation into the graphite structure from low voltage to high voltage. However, peaks 1 and 2 typically correspond to surface side reactions, SEI film formation, or lithium intercalation behavior at a few edge sites. These signals are weak and easily affected by test conditions, resulting in poor repeatability and difficulty in consistently extracting effective features. In contrast, peaks 3 and 4 correspond to the key phase transition process of deep lithium intercalation in the main graphite structure. These peaks have high signal intensity, obvious features, and strong repeatability, and can more accurately reflect the intrinsic aging state of the electrode material. Furthermore, with battery cycle degradation, aging mechanisms such as reversible lithium loss, active material degradation, and SEI thickening significantly affect the peak position, height, area, and voltage shift of peaks 3 and 4. Therefore, by monitoring the dynamic changes of peaks 3 and 4, the aging trend and health status of batteries can be sensitively captured, providing a scientific basis for refined sorting based on aging mechanisms.

[0037] Therefore, in the embodiments of this application, the third peak and / or the fourth peak in the capacity increment curve can be selected as the target peak, and the peak voltage, peak area and peak height of the target peak can be used as target characteristic parameters. Figure 4 This is a third schematic diagram of the process flow for sorting the cells of retired batteries provided in this application embodiment, as shown below. Figure 4 The calculation of the comprehensive discrimination value of each cell based on the target characteristic parameters specifically includes the following steps: S210. Calculate the peak voltage, peak area, and peak height based on the capacity increment curve.

[0038] In this embodiment, the peak of the target peak in the capacity increment curve is located. The voltage value on the horizontal axis corresponding to this peak is the peak voltage of the target peak, and the maximum value on the vertical axis of the peak is the peak height. It reflects the kinetic difficulty of the electrochemical reaction. The decrease in peak height is usually directly related to aging mechanisms such as loss of active lithium ions, reduction of electrode active surface area, or decrease in solid-phase diffusion coefficient. The peak area is obtained by integrating the individual target peak within its voltage window. The decay of the peak area directly quantifies the total loss of active material participating in a specific reaction and is the most direct manifestation of capacity decay.

[0039] S220. Perform multiple linear regression based on peak voltage, peak area, and peak height to obtain the linear regression equation.

[0040] In this embodiment, the peak voltage of the third and / or fourth peak is obtained based on the capacity increment curve corresponding to each cycle. V Peak area A Peak height H Using the cell health status as the dependent variable, peak voltage V Peak area A Peak height H Using multiple linear regression analysis as the independent variable, the optimal mathematical model was fitted, and the following linear regression equation was obtained:

[0041] In the above formula, SOH Indicates the health status of the battery cell. V Indicates peak voltage. A Indicates the peak area. H Indicates peak height, w 1 represents the weighting coefficient corresponding to the peak voltage. w 2 represents the weighting coefficient corresponding to the peak area. w 3 represents the weighting coefficient corresponding to the peak height. b This represents the intercept.

[0042] S230. Calculate the weighting coefficients corresponding to peak voltage, peak area and peak height in the linear regression equation.

[0043] In this embodiment, combining the actual health status of retired lithium iron phosphate batteries under different cycle periods with the corresponding target characteristic parameters, the least squares method is used to perform regression analysis on multiple sets of data to solve for the optimal weight coefficients in the linear regression equation. w 1. w 2 and w 3. To minimize the error between the predicted cell health status and the actual health status, thereby reflecting the contribution weight of each target characteristic parameter to the health status.

[0044] S240. Compare the capacity increment curve of each cell with the target capacity increment curve of the target healthy cell to obtain the peak voltage difference, peak area difference, and peak height difference.

[0045] In this embodiment, a fresh, healthy cell of the same model as those in a retired lithium iron phosphate battery is selected as the target healthy cell. Capacity increment testing is performed under the same operating conditions to obtain a target capacity increment curve. Based on this curve, the target peak voltage, target peak area, and target peak height are calculated for each target peak. The peak voltage, peak area, and peak height of the target peak in the capacity increment curve of each cell in the retired lithium iron phosphate battery are compared with the target peak voltage, target peak area, and target peak height to obtain the corresponding peak voltage difference, peak area difference, and peak height difference.

[0046] S250. Calculate the comprehensive discrimination value of each cell based on the peak voltage difference, peak area difference, peak height difference and corresponding weighting coefficient.

[0047] In this embodiment, the formula for calculating the comprehensive discriminant value is as follows:

[0048] In the above formula, This represents the comprehensive discriminant value. Indicates the peak voltage difference. w 1 represents the weighting coefficient corresponding to the peak voltage. This represents the difference in peak area. w 2 represents the weighting coefficient corresponding to the peak area. Indicates the difference in peak height. w 3 represents the weighting coefficient corresponding to the peak height.

[0049] After obtaining the comprehensive discrimination value of each cell, the cells are eliminated according to the preset discrimination threshold corresponding to the target peak. Cells with comprehensive discrimination values ​​greater than the preset discrimination threshold in the retired lithium iron phosphate batteries are eliminated, and the remaining cells are used as target cells.

[0050] Based on this, a comprehensive discrimination value is obtained by calculating and processing the capacity increment curves of each cell. Cells are then judged and eliminated based on this comprehensive discrimination value, effectively identifying and excluding cells with significant aging and poor consistency from retired batteries. By fully utilizing the sensitivity of the capacity increment curves to intrinsic degradation mechanisms such as electrode material aging and lithium loss, sorting accuracy is improved, ensuring that the remaining target cells have higher consistency in cycle life, capacity retention, and internal resistance characteristics. This lays the foundation for the performance stability and safety of battery packs in subsequent cascade utilization.

[0051] S300. Based on the state of charge and internal resistance of the target cells, the cells are classified into categories to obtain the cell sorting results.

[0052] In this embodiment of the application, the target battery cell is further classified based on its state of charge and internal resistance. Figure 5 This is the fourth schematic diagram of the cell sorting method for retired batteries provided in the embodiments of this application, as shown below. Figure 5 As shown, the specific steps for classifying battery cells include the following: S310. Calculate the average value of the state of charge and internal resistance of all target cells to obtain the average state of charge and average internal resistance.

[0053] Understandably, by summing the state of charge and internal resistance values ​​of each target cell and then dividing by the number of target cells, the corresponding average state of charge and average internal resistance values ​​can be obtained.

[0054] S320. Target cells with a state of charge value greater than or equal to the average state of charge value and an internal resistance value less than the average internal resistance value are classified as the first cell category.

[0055] In this application, the cells corresponding to the first cell category have a high state of charge and low internal resistance, indicating that they have high capacity retention, low polarization, high utilization of active materials, and low aging, thus having the highest cell health.

[0056] S330. Target cells with a state of charge value greater than or equal to the average state of charge value and an internal resistance value greater than or equal to the average internal resistance value are classified as the second cell category.

[0057] In the embodiments of this application, although the cells corresponding to the second cell category have a higher state of charge, their internal resistance is relatively high, indicating that there is a certain ohmic loss or electrolyte deterioration, resulting in slightly poorer power performance and cycle life, thus the cell health is second best.

[0058] S340. Target cells with a state of charge value less than the average state of charge value and an internal resistance value less than the average internal resistance value are classified as the third cell category.

[0059] In the embodiments of this application, the cells corresponding to the third cell category have a lower state of charge but lower internal resistance, which may be due to more reversible lithium loss or insufficient charging. As a result, the capacity performance is limited, and the cell health is lower than that of the first two cell categories.

[0060] S350. Target cells with a state of charge value less than the average state of charge value and an internal resistance value greater than or equal to the average internal resistance value are classified as the fourth cell category.

[0061] In this embodiment, the cells corresponding to the fourth cell category have both low charge state and high internal resistance, reflecting severe capacity decay and impedance rise dual aging characteristics, with the worst overall performance and therefore the lowest cell health.

[0062] Understandably, the above-mentioned cell classification method combines two key health indicators, namely capacity status and power characteristics, to obtain cell sorting results. This can comprehensively reflect the actual health level of cells in retired batteries, achieve refined grading before secondary use, and improve the safety and consistency of the system after assembly.

[0063] The cell sorting method for retired batteries provided in this application quantifies battery health through capacity increment curves and removes abnormally aged cells, thus improving screening accuracy. By combining group state-of-charge testing and internal resistance testing, the consistency of cells is comprehensively evaluated, effectively identifying retired battery cells in different aging states. Based on the correlation analysis between response characteristics and aging mechanisms under actual group operating conditions, the method avoids the influence of model errors and test inconsistencies.

[0064] Example 2 Based on the same technical concept as Embodiment 1 above, this application provides a cell sorting system for retired batteries. Figure 6 This is a schematic diagram of the cell sorting system for retired batteries provided in an embodiment of this application, as shown below. Figure 6 As shown, the cell sorting system 200 for the retired battery includes: The data acquisition module 210 is used to perform capacity increment testing, state of charge testing and internal resistance testing on the target retired battery, and obtain the capacity increment curve, state of charge value and internal resistance value of each cell.

[0065] The cell initial selection module 220 is used to calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and to remove cells with a comprehensive discrimination value greater than a preset discrimination threshold to obtain the target cells.

[0066] The cell sorting module 230 is used to classify cells according to their state of charge and internal resistance values ​​to obtain cell sorting results.

[0067] The battery cell sorting system for retired batteries provided in this application significantly improves the consistency of the sorted cells, reduces the impact of voltage difference and temperature rise during charging and discharging, and makes the state of charge gradient between groups obvious, with the capacity difference controlled within an effective range, which is more conducive to the safety and cycle life improvement of retired batteries for secondary use.

[0068] It is understood that the implementation method of the cell sorting method for retired batteries in Embodiment 1 above is also applicable to this embodiment and can achieve the same technical effect, so it will not be described again here.

[0069] Example 3 Based on the same concept, this application also provides an electronic device. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown, the electronic device 300 may include a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute the steps of the cell sorting method for retired batteries as described in the above embodiments. For example, this includes: S100: Perform capacity increment test, state of charge test and internal resistance test on the target retired battery respectively to obtain the capacity increment curve, state of charge value and internal resistance value of each cell. S200: Calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and remove the cells whose comprehensive discrimination value is greater than the preset discrimination threshold to obtain the target cells; S300. Based on the state of charge and internal resistance of the target cells, the cells are classified into categories to obtain the cell sorting results.

[0070] The processor 310 can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0071] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The memory 330 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0073] Example 4 Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program containing at least one piece of code executable by a master control device to control the master control device to implement the steps of the cell sorting method for retired batteries as described in the above embodiments. For example, it includes: S100: Perform capacity increment test, state of charge test and internal resistance test on the target retired battery respectively to obtain the capacity increment curve, state of charge value and internal resistance value of each cell. S200: Calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and remove the cells whose comprehensive discrimination value is greater than the preset discrimination threshold to obtain the target cells; S300. Based on the state of charge and internal resistance of the target cells, the cells are classified into categories to obtain the cell sorting results.

[0074] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0075] The computer program may be stored, in whole or in part, on a computer-readable storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0076] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0077] In summary, the cell sorting method, system, electronic equipment, and storage medium for retired batteries provided in this application quantify battery health through capacity increment curves and eliminate abnormally aged cells, thus improving screening accuracy. By combining group state-of-charge testing and internal resistance testing, the consistency of cells is comprehensively evaluated, effectively identifying retired battery cells in different aging states. Based on the correlation analysis between response characteristics and aging mechanisms under actual grouping conditions, the influence of model errors and test inconsistencies is avoided, significantly improving the grouping consistency of sorted cells, reducing the impact of voltage difference and temperature rise during charging and discharging, resulting in a clear gradient of state of charge between groups, and controlling the capacity difference within an effective range, which is more conducive to the safety and cycle life improvement of retired batteries for secondary use.

[0078] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0079] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for sorting the cells of retired batteries, characterized in that, The method includes: Capacity increment test, state of charge test and internal resistance test are performed on the target retired batteries to obtain the capacity increment curve, state of charge value and internal resistance value of each cell; Calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and remove the cells whose comprehensive discrimination value is greater than the preset discrimination threshold to obtain the target cells; The target battery cell is classified into categories based on its state of charge and internal resistance, and the battery cell sorting results are obtained.

2. The cell sorting method for retired batteries according to claim 1, characterized in that, The target characteristic parameters include at least the peak voltage, peak area, and peak height of the target peak in the capacity increment curve.

3. The cell sorting method for retired batteries according to claim 2, characterized in that, The target peak is the third and / or fourth peak in the capacity increment curve.

4. The cell sorting method for retired batteries according to claim 2, characterized in that, The comprehensive discrimination value of each cell is calculated based on the target characteristic parameters of the capacity increment curve, including: The peak voltage, peak area, and peak height are calculated based on the capacity increment curve. A linear regression equation is obtained by performing multiple linear regression on the peak voltage, the peak area, and the peak height. Calculate the weighting coefficients corresponding to the peak voltage, peak area, and peak height in the linear regression equation; The capacity increment curves of each cell are compared with the target capacity increment curve of the target healthy cell to obtain the peak voltage difference, peak area difference, and peak height difference. The comprehensive discrimination value of each cell is calculated based on the peak voltage difference, the peak area difference, the peak height difference, and the corresponding weighting coefficient.

5. The cell sorting method for retired batteries according to claim 4, characterized in that, The formula for calculating the comprehensive discriminant value is as follows: In the above formula, This represents the comprehensive discrimination value. This represents the peak voltage difference. w 1 represents the weighting coefficient corresponding to the peak voltage. This represents the difference in peak area. w 2 represents the weighting coefficient corresponding to the peak area. This represents the difference in peak height. w 3 represents the weighting coefficient corresponding to the peak height.

6. The cell sorting method for retired batteries according to claim 1, characterized in that, Based on the state of charge (SOC) value and internal resistance value of the target battery cell, the battery cell is classified into categories to obtain the battery cell sorting results, including: Calculate the average value of the state of charge and the internal resistance of all the target cells to obtain the average state of charge and the average internal resistance. The target cells whose state of charge value is greater than or equal to the average state of charge value and whose internal resistance value is less than the average internal resistance value are classified into the first cell category. The target cells whose state of charge value is greater than or equal to the average state of charge value and whose internal resistance value is greater than or equal to the average internal resistance value are classified into a second cell category. The target cells whose state of charge value is less than the average state of charge value and whose internal resistance value is less than the average internal resistance value are classified into a third cell category. The target cells whose state of charge value is less than the average state of charge value and whose internal resistance value is greater than or equal to the average internal resistance value are classified into the fourth cell category.

7. The cell sorting method for retired batteries according to claim 1, characterized in that, Capacity increment testing, state of charge testing, and internal resistance testing were performed on the target retired batteries to obtain the capacity increment curve, state of charge value, and internal resistance value of each cell, including: The target retired battery is sequentially charged, left to stand, and discharged. The corresponding voltage and capacity data of each cell are recorded, and the capacity increment curve of each cell is constructed based on the voltage and capacity data. The target retired battery is subjected to constant current charging and constant current discharging to obtain the static open circuit voltage of each cell after the discharge is completed. The state of charge value of each cell is estimated based on the static open circuit voltage. The state of charge of each cell in the target retired battery is adjusted to the target state of charge, and the cells are discharged at the target discharge rate for a preset time. The corresponding voltage change and current value of each cell are recorded, and the internal resistance value of each cell is calculated based on the voltage change and current value.

8. A cell sorting system for retired batteries, characterized in that, The system includes: The data acquisition module is used to perform capacity increment testing, state of charge testing and internal resistance testing on the target retired batteries to obtain the capacity increment curve, state of charge value and internal resistance value of each cell. The cell initial selection module is used to calculate the comprehensive discrimination value of each cell based on the target characteristic parameters of the capacity increment curve, and to remove cells whose comprehensive discrimination value is greater than a preset discrimination threshold to obtain target cells; The cell sorting module is used to classify the cells according to their state of charge and internal resistance values ​​to obtain the cell sorting results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the cell sorting method for retired batteries as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cell sorting method for retired batteries as described in any one of claims 1-7.

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