A method, system, device and medium for sorting retired lithium-ion batteries

Abstract

The present invention relates to the field of battery recycling technology and discloses a method, system, equipment and medium for sorting retired lithium-ion batteries. The method comprises the following steps: obtaining usage data of retired lithium-ion batteries during service; obtaining a capacity loss curve for characterizing the capacity loss rate of the retired lithium-ion batteries during service, and a capacity increment curve for characterizing the internal aging mechanism of the retired lithium-ion batteries during service; inputting the capacity increment curve and the nonlinear loss region in the capacity loss curve of the retired lithium-ion batteries into a trained LSTM model to predict the future capacity decay path of the retired lithium-ion batteries, and identifying a mutation point in the capacity decay rate of the retired lithium-ion batteries from the capacity decay path; and evaluating the potential for secondary service of the retired lithium-ion batteries based on the remaining capacity of the retired lithium-ion batteries and the mutation point in the capacity decay rate of the retired lithium-ion batteries, thereby completing the sorting of the retired lithium-ion batteries.

Description

Technical Field

[0001] The present invention relates to the technical field of battery recycling, and in particular to a method, system, equipment and medium for sorting retired lithium-ion batteries. Background Art

[0002] Transportation is a key area of ​​carbon emissions. Carbon emissions can be reduced by promoting the electrification of the transportation sector and developing zero-emission cars, buses, and trucks. At the same time, carbon emissions can also be reduced by developing electric vehicles and replacing fuel-powered transportation with electric transportation as soon as possible. The number of electric vehicles has been growing rapidly, and lithium-ion batteries are widely used as on-board power batteries due to their high energy density and power density, as well as low self-discharge rate. As lithium-ion batteries age, the accumulation of internal side reaction products will reduce their performance. When the capacity of the on-board power battery is insufficient to support the driving range requirements of electric vehicle drivers, it will be withdrawn from on-board applications.

[0003] Power batteries are heavy assets. If retired lithium-ion batteries are directly discarded, the high costs will be borne directly by electric vehicle users or battery swap operators. However, the cascade utilization of retired lithium-ion batteries can significantly reduce the cost of battery applications on the vehicle side or improve the asset turnover rate of battery swap companies. This has a significant impact on both the daily use costs of electric vehicle users and the operations of battery swap companies. In addition, a large number of retired lithium-ion batteries will also place tremendous pressure on the environment. If the cascade utilization and recycling of retired lithium-ion batteries are ignored, the scrapping of electric vehicles each year will bring a large number of retired lithium-ion batteries, and the discarded battery cells will cause environmental pollution. Therefore, the recycling and cascade utilization of retired lithium-ion batteries is imminent.

[0004] With the increasing adoption of electric vehicles, the volume of retired lithium-ion batteries is enormous. Traditional sorting methods for retired lithium-ion batteries require complex testing of battery packs on dedicated test benches, which is time-consuming and inefficient. These methods are unable to cope with the large-scale sorting required for retired batteries. Furthermore, the low sorting efficiency of retired lithium-ion batteries hinders their further flow into the secondary market for cascade utilization. Summary of the Invention

[0005] The object of the present invention is to provide a method, system, device and medium for sorting retired lithium-ion batteries, which can solve the problem of low sorting efficiency of retired lithium-ion batteries.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for sorting retired lithium-ion batteries, comprising the following steps:

[0007] Obtain usage data of retired lithium-ion batteries during their service life;

[0008] Based on the usage data of retired lithium-ion batteries during service, a capacity loss curve is obtained to characterize the capacity loss rate of retired lithium-ion batteries during service, and a capacity increment curve is obtained to characterize the internal aging mechanism of retired lithium-ion batteries during service; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region;

[0009] The capacity increment curve and the nonlinear loss region in the capacity loss curve of retired lithium-ion batteries are input into a trained LSTM model to predict the future capacity decay path of the retired lithium-ion batteries and identify the mutation point of the capacity decay rate of the retired lithium-ion batteries from the capacity decay path. The LSTM model is trained using the nonlinear loss region in the capacity increment curve and capacity loss curve of several different retired lithium-ion batteries.

[0010] According to the remaining capacity of retired lithium-ion batteries and the mutation point of the capacity attenuation rate of retired lithium-ion batteries, the potential for secondary service of retired lithium-ion batteries is evaluated to complete the sorting of retired lithium-ion batteries.

[0011] Optionally, inputting the capacity increment curve and the nonlinear loss region in the capacity loss curve of the retired lithium-ion battery into a trained LSTM model to predict the future capacity attenuation path of the retired lithium-ion battery includes:

[0012] Perform one-dimensional linear fitting on the capacity loss curve of retired lithium-ion batteries to obtain the linear component of the linear loss region in the capacity loss curve;

[0013] Obtaining the health status of the retired lithium-ion battery according to the initial capacity and current capacity of the retired lithium-ion battery, and subtracting the component of the linear loss region in the capacity loss curve from the health status of the retired lithium-ion battery to obtain the nonlinear component of the nonlinear loss region in the capacity loss curve;

[0014] After normalizing the component of the nonlinear loss region in the capacity loss curve, multiply it by the exponential cycle life to obtain the nonlinear characteristics of the nonlinear component;

[0015] The capacity increment curve of retired lithium-ion batteries and the nonlinear characteristics of the nonlinear component are input into the trained LSTM model to predict the future capacity attenuation path of retired lithium-ion batteries.

[0016] Optionally, before inputting the capacity increment curve of the retired lithium-ion battery and the nonlinear features of the nonlinear component into the trained LSTM model, the method further includes:

[0017] The initial capacity increment curve of the retired lithium-ion battery is obtained by using the constant current interval during the discharge process of the retired lithium-ion battery;

[0018] The SG filtering method is used to filter the initial capacity increment curve to obtain the capacity increment curve for input into the LSTM model.

[0019] Optionally, identifying a mutation point of the capacity decay rate of a retired lithium-ion battery from a capacity decay path includes:

[0020] The capacity of the retired lithium-ion battery is used to obtain a first derivative of the number of cycles of the retired lithium-ion battery, and a curve showing the capacity attenuation rate of the retired lithium-ion battery as a function of the number of cycles is obtained;

[0021] Using the curve of the capacity attenuation rate of retired lithium-ion batteries versus the number of cycles, a first derivative of the number of cycles of the retired lithium-ion batteries is obtained to obtain a curve of the capacity attenuation rate change rate of the retired lithium-ion batteries versus the number of cycles;

[0022] The mutation point of the capacity decay rate of retired lithium-ion batteries is obtained through the curve of the capacity decay rate change rate of retired lithium-ion batteries as a function of the number of cycles.

[0023] Optionally, the evaluating the potential for secondary service of retired lithium-ion batteries based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries includes:

[0024] If the remaining capacity of retired lithium-ion batteries is between 100% and 80%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can still be used in vehicle power battery scenarios;

[0025] If the remaining capacity of retired lithium-ion batteries is between 80% and 60%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can be used in public energy storage scenarios with clear operating conditions and good working environment;

[0026] If the remaining capacity of retired lithium-ion batteries is between 60% and 20%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can be used for home energy storage scenarios with low usage frequency and low energy density requirements;

[0027] If the remaining capacity of retired lithium-ion batteries is less than 20%, or if the capacity attenuation rate of the retired lithium-ion batteries has passed the mutation point, the retired lithium-ion batteries will enter the raw material recycling link and will have no value in being put into service again.

[0028] Optionally, the LSTM model is obtained by training mainly using capacity increment curves of several different retired lithium-ion batteries and supplemented by nonlinear loss regions in capacity loss curves.

[0029] Optionally, the usage data of the retired lithium-ion battery during its service period includes: the service time, charging current, charging voltage, charging power and charging temperature of the retired lithium-ion battery.

[0030] An embodiment of the present invention further provides a system for sorting retired lithium-ion batteries, comprising:

[0031] A battery data acquisition module is used to obtain usage data of retired lithium-ion batteries during their service period;

[0032] A data feature extraction module is used to obtain, based on the usage data of the retired lithium-ion batteries during service, a capacity loss curve for characterizing the capacity loss rate of the retired lithium-ion batteries during service, and a capacity increment curve for characterizing the internal aging mechanism of the retired lithium-ion batteries during service; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region;

[0033] The capacity decay prediction module is used to input the capacity increment curve and the nonlinear loss region in the capacity loss curve of retired lithium-ion batteries into a trained LSTM model to predict the future capacity decay path of retired lithium-ion batteries and identify the mutation point of the capacity decay rate of retired lithium-ion batteries from the capacity decay path. The LSTM model is trained using the nonlinear loss region in the capacity increment curve and capacity loss curve of several different retired lithium-ion batteries.

[0034] The retired battery sorting module is used to evaluate the potential of retired lithium-ion batteries for secondary service based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries, so as to complete the sorting of retired lithium-ion batteries.

[0035] An embodiment of the present invention also provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned method for sorting retired lithium-ion batteries.

[0036] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for sorting retired lithium-ion batteries when executed by a processor.

[0037] The method for sorting retired lithium-ion batteries provided by the present invention has at least the following beneficial effects:

[0038] The capacity loss curve and capacity increment curve of retired lithium-ion batteries are obtained by using the usage data of retired lithium-ion batteries during service. The capacity loss curve reflects the capacity loss rate of retired lithium-ion batteries during service, and the capacity increment curve reflects the internal aging mechanism of retired lithium-ion batteries during service. Among them, the nonlinear loss area in the capacity loss curve will greatly interfere with the estimation of the final capacity of the battery. Therefore, in the present invention, the capacity increment curves of several different retired lithium-ion batteries and the nonlinear loss area in the capacity loss curve are combined from the mechanism level of retired lithium-ion batteries and the external signal rheology to train an LSTM model, so that the LSTM model can effectively predict the future capacity decay path of retired lithium-ion batteries. At this time, the mutation point of the capacity decay rate of retired lithium-ion batteries is identified from the capacity decay path of retired lithium-ion batteries. Based on the remaining capacity of the retired lithium-ion batteries and the mutation point, the potential for the second service of the retired lithium-ion batteries is evaluated, thereby realizing the sorting of retired lithium-ion batteries, which not only has high sorting efficiency but also can effectively improve the utilization rate of retired lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments.

[0040] Figure 1 is a flow chart of a method for sorting retired lithium-ion batteries according to one embodiment of the present invention;

[0041] Figure 2 (a) is a schematic diagram of a capacity loss curve provided according to an embodiment of the present invention;

[0042] Figure 2 (b) is a schematic diagram of a UC feature provided according to an embodiment of the present invention;

[0043] Figure 3 (a) is a schematic diagram of an IC curve before and after SG filtering according to an embodiment of the present invention;

[0044] Figure 3 (b) is a schematic diagram of an IC complete curve with different cycle numbers provided according to an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram comparing capacity attenuation paths of multiple batteries provided according to an embodiment of the present invention;

[0046] Figure 5 1. A schematic diagram of a battery decommissioning point and a battery diving point according to an embodiment of the present invention;

[0047] Figure 6 is a schematic diagram of a battery sorting scheme provided according to an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of a method for sorting retired lithium-ion batteries according to an embodiment of the present invention is provided. Figure 1 ;

[0049] Figure 8 A schematic diagram of a method for sorting retired lithium-ion batteries according to an embodiment of the present invention is provided. Figure 2 ;

[0050] Figure 9 Schematic diagram of a capacity attenuation path provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in the embodiments of the present invention, many technical details are provided to enable the reader to better understand the present invention. However, even without these technical details and the various changes and modifications based on the following embodiments, the technical solutions claimed in the present invention can be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with each other and referenced to each other under the premise that there is no contradiction.

[0052] Due to the lack of effective methods for sorting retired lithium-ion batteries, most battery packs are subject to a blanket scrapping and recycling process, eliminating their potential for reuse. Furthermore, the diverse battery pack structures of electric vehicles make it difficult to establish efficient battery pack disassembly lines and impose high labor costs. Disassembling and recycling all battery packs without undergoing inspection and sorting results in low profits for integrated recycling companies. Furthermore, due to the low efficiency of cell recycling, most battery recycling currently only reaches the module level, leaving most cells destined for disposal and the ongoing risk of environmental pollution. Furthermore, since the cells cannot pass through the recycling process and re-enter the life cycle of new batteries as raw materials, this increases the carbon cost of the battery's entire life cycle.

[0053] Therefore, the present invention proposes an efficient sorting solution for retired lithium-ion batteries that does not rely on bench testing and structural disassembly, providing a feasible option for the cascade utilization of retired lithium-ion batteries, thereby reserving a more sufficient time window for subsequent recycling and utilization, significantly reducing the processing pressure caused by the centralized retirement of batteries, and ensuring that each batch of retired lithium-ion batteries can be processed in a timely and effective manner.

[0054] One embodiment of the present invention relates to a method for sorting retired lithium-ion batteries. The implementation details of the method for sorting retired lithium-ion batteries of this embodiment are described in detail below. The following content is only provided for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0055] The specific process of the method for sorting retired lithium-ion batteries of this embodiment can be as follows: Figure 1 Shown, including:

[0056] Step 101: Obtain usage data of retired lithium-ion batteries during their service period.

[0057] Specifically, the first step is to obtain the usage data of the battery during its service period. The main application target is automotive lithium-ion power batteries. The data during its service period needs to be provided by the data center of the car manufacturer, and the usage data of retired lithium-ion batteries during their service period mainly includes: the service time, charging current, charging voltage, charging power and charging temperature of the retired lithium-ion batteries.

[0058] Step 102: Based on the usage data of the retired lithium-ion battery during its service period, a capacity loss curve is obtained for characterizing the capacity loss rate of the retired lithium-ion battery during its service period, and a capacity increment curve is obtained for characterizing the internal aging mechanism of the retired lithium-ion battery during its service period; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region.

[0059] In step 103, the capacity increment curve and the nonlinear loss area in the capacity loss curve of the retired lithium-ion battery are input into the trained LSTM model to predict the future capacity decay path of the retired lithium-ion battery, and identify the mutation point of the capacity decay rate of the retired lithium-ion battery from the capacity decay path; wherein the LSTM model is trained by the nonlinear loss area in the capacity increment curve and the capacity loss curve of several different retired lithium-ion batteries.

[0060] Specifically, let's first explain the training process of the LSTM model:

[0061] Because the capacity loss curve of lithium-ion batteries during aging exhibits certain nonlinear characteristics, algorithms trained solely using battery capacity and battery service time data are inaccurate for predicting the final stage of the battery capacity loss curve. Therefore, based on the original data, this embodiment utilizes the battery's internal aging mechanism and mathematical statistics to construct two artificial features: the incremental capacity feature (IC) and the nonlinear correction feature (UC). These two features are then combined for model training.

[0062] Regarding the UC characteristics, that is, the characteristics of the nonlinear loss area in the capacity loss curve:

[0063] For lithium-ion batteries, the capacity loss curve shows two obvious stages: Figure 2 As shown in (a), the smooth curve in the figure shows the capacity of the battery during each discharge cycle, and the curve with dots shows the capacity loss during each cycle. The capacity loss curve is divided into two stages. The first stage is the linear degradation zone, in which the capacity of the battery decays approximately linearly, and the capacity loss rate remains at a relatively gentle and stable level. The second stage shows an accelerated decrease in capacity, which is the nonlinear decay zone, and the capacity loss rate begins to rise. This type of nonlinear decay at the end of battery aging will greatly interfere with the estimation of the battery's final capacity. Therefore, identifying the nonlinear loss zone in the capacity loss curve and introducing it into the neural network as an independent feature after separation is particularly important for identifying the future capacity decay path of the battery capacity.

[0064] In a specific implementation, when the nonlinear loss region in the capacity loss curve is used for model training, a one-dimensional linear fit is first performed on the capacity loss curve of the retired lithium-ion battery to obtain the linear component of the linear loss region in the capacity loss curve. Then, the health status of the retired lithium-ion battery is obtained based on the initial capacity and current capacity of the retired lithium-ion battery, as shown in the following formula:

[0065]

[0066] Where SOH(t) represents the health status of retired lithium-ion batteries at cycle number t, and the SOH curve of the entire life cycle (from 1 to 0.8) can be decomposed into linear and nonlinear components.

[0067] Therefore, by subtracting the linear loss region component of the capacity loss curve from the health status of retired lithium-ion batteries, the nonlinear component of the nonlinear loss region of the capacity loss curve can be obtained. That is, the nonlinear component in the SOH curve of the entire life cycle can be obtained by calculating the residual, as shown in the following formula:

[0068]

[0069] Next, the component of the nonlinear loss region in the capacity loss curve is normalized and multiplied by the exponential cycle life to obtain the nonlinear characteristics of the nonlinear component, as shown in the following formula:

[0070]

[0071] Where R(t) represents the percentage of the remaining number of cycles to the end of life (EOL) of the current cycle, ranging from 1 to 0. In order to maintain the same range as the battery SOH to obtain accurate prediction, UCexp (t) is mapped to the interval [0.8, 1]. In geometric theory, UC exp (t) gets its maximum value at the inflection point, by changing UC exp (t) is set to 1 to obtain the UC(t) curve, such as Figure 2 (b) shown.

[0072] but, Figure 2 (b) shows the characteristics of the nonlinear loss region in the capacity loss curve described in this embodiment (ie, the nonlinear characteristics of the nonlinear component).

[0073] Regarding the IC characteristics, that is, the characteristics of the capacity increment curve of retired lithium-ion batteries:

[0074] From a statistical point of view, UC gives the probability of nonlinear attenuation through the change of battery aging rate, but ignores the internal aging mechanism of the battery. It is difficult to combine the mechanism level with the external signal rheology of the battery to predict the capacity change trend. Therefore, UC is not suitable as a single feature training network. IC is a method widely used to describe the battery aging process. It analyzes the battery aging mechanism from the electrode level. The IC curve represents the rate of change of capacity with voltage evolution during constant current charging / charging and discharging. In this embodiment, the IC curve is used to describe the attenuation process of the internal material, and is used to combine with the nonlinear characteristics of the nonlinear component for model training.

[0075] However, in a low-temperature environment, the constant current process of fast charging is very short, and constant voltage charging will be quickly entered. Therefore, this embodiment first uses the constant current interval during the discharge process of the retired lithium-ion battery to obtain the initial capacity increment curve of the retired lithium-ion battery, as shown in the following formula:

[0076]

[0077] Where Q represents the battery capacity at time t, V represents the voltage, and t represents the sampling time.

[0078] In practice, it is difficult to obtain the peak value of IC because the parameter value is easily disturbed by measurement noise in the charging platform area. To solve this problem, the SG filtering method is used to filter the initial capacity increment curve to obtain the capacity increment IC curve for model training.

[0079] like Figure 3 As shown, Figure 3 (a) shows the IC curves before and after SG filtering. It can be seen that the IC curve after filtering is smooth and can be used to extract aging features. The complete IC curves with different cycle numbers are shown in Figure 2. Figure 3(b) shows that the IC curves of the battery at different cycles have similar shapes, and the peaks in the curves have unique heights and positions, reflecting the changes in the electrode materials during the battery charging and discharging process.

[0080] The characteristics of the nonlinear loss region in the capacity loss curves and the characteristics of the capacity increment curves of several different retired lithium-ion batteries are used to train a long short-term memory (LSTM) network to obtain the final LSTM model.

[0081] The following describes the training process of the LSTM model:

[0082] First, during the training process of the LSTM model, the training is mainly based on the capacity increment curves of several different retired lithium-ion batteries, and supplemented by the nonlinear loss area in the capacity loss curve.

[0083] However, when the capacity of an on-board power battery decays to 80% of its initial capacity, its driving range cannot meet the driver's needs. When it decays to 70% of its initial capacity, it will no longer be used in vehicles. To simulate the actual application scenario, this example divides the battery aging data obtained in the experiment into training and validation sets in a ratio of 3:7. Only the first 30% of the capacity decay data is used to train the model and verify the prediction effect of the capacity decay path of the later model.

[0084] Furthermore, because lithium-ion battery data is collected during the charge and discharge cycles, it constitutes time series data. Long-term Memory (LSTM) is widely used to process time series data, using a recurrent unit structure instead of the state unit of a classic RNN. By introducing input, forget, and output gate mechanisms, LSTM effectively controls the flow of information and retains important historical information, thereby improving model performance.

[0085] This embodiment also introduces the attention mechanism, a machine learning technique that helps neural networks focus on important features, thereby improving model performance and generalization. In a neural network, each feature has a different impact on the result, but typically only a set of features dominates the output. The attention mechanism learns based on the attention level of individual features in the sequence and integrates features based on this attention level. Even if only a single feature is input to the LSTM network, the attention mechanism can still effectively capture key information in the time series data by weighting different time steps when processing time series data.

[0086] After the LSTM model is trained using the above method, the capacity increment curve and the nonlinear loss area in the capacity loss curve of the retired lithium-ion batteries to be sorted are input into the trained LSTM model, and the future capacity attenuation path of the retired lithium-ion batteries can be predicted.

[0087] like Figure 4 The figure shows the variation of the single-cycle available capacity of retired lithium-ion batteries (i.e., capacity decay path) obtained on a test bench. During the experiment, each battery was operated at -10°C and charged at constant currents of 20A (battery #1 and #2), 15A (battery #5 and #6), and 10A (battery #9 and #10), respectively, until the voltage reached 4.2V, when constant voltage charging was switched. The figure shows that the capacity decay paths of batteries #1 and #2 are nearly straight, demonstrating typical linear capacity decay; however, the remaining four batteries all exhibit accelerated capacity decay. For example, after the 27th cycle, the capacity decay rate of battery #1 increased significantly, resulting in a rapid decrease in the battery's available capacity. Furthermore, the number of cycles required for battery #1 to reach 80% of its initial capacity is far less than for batteries with linear decay. Subsequent capacity decay will continue at a high rate, making it unsuitable for cascade reuse. Instead, it is recommended that it be scrapped and recycled for key materials.

[0088] In other words, accurately predicting the battery capacity attenuation path and identifying the mutation point of the battery capacity attenuation rate, that is, the battery performance diving point, are the key to pre-selecting and sorting retired batteries for cascade utilization.

[0089] Among them, when identifying the mutation point of the capacity decay rate of the retired lithium-ion battery from the capacity decay path, the capacity of the retired lithium-ion battery is first used to find the first derivative of the number of cycles (i.e., working cycle) of the retired lithium-ion battery to obtain a curve showing that the capacity decay rate of the retired lithium-ion battery varies with the number of cycles. Then, the curve showing that the capacity decay rate of the retired lithium-ion battery varies with the number of cycles is used to find the first derivative of the number of cycles of the retired lithium-ion battery to obtain a curve showing that the capacity decay rate change rate (i.e., acceleration) of the retired lithium-ion battery varies with the number of cycles. Finally, the mutation point of the capacity decay rate of the retired lithium-ion battery is obtained through the curve showing that the capacity decay rate change rate of the retired lithium-ion battery varies with the number of cycles.

[0090] Before reaching the capacity decay rate mutation point, the rate of change of the capacity decay rate is near 0. After reaching the capacity decay rate mutation point, the rate of change of the capacity decay rate increases and then returns to near 0. This process reflects that the battery capacity decay rate remains basically constant before the performance drop point, and the decay rate increases after the performance drop point and gradually stabilizes at a higher rate. Therefore, this embodiment uses the second derivative of capacity with respect to the number of cycles (duty cycle), that is, the rate of change of the capacity decay rate, as an indicator for finding the mutation point of the capacity decay rate of retired lithium-ion batteries.

[0091] Step 104 : evaluating the potential of the retired lithium-ion batteries for secondary service based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries, so as to complete the sorting of the retired lithium-ion batteries.

[0092] Specifically, if the remaining capacity of retired lithium-ion batteries is between 100% and 80% and has not passed the mutation point of the capacity decay rate of retired lithium-ion batteries, the retired lithium-ion batteries can still be used in vehicle power battery scenarios; if the remaining capacity of retired lithium-ion batteries is between 80% and 60% and has not passed the mutation point of the capacity decay rate of retired lithium-ion batteries, the retired lithium-ion batteries can be used in public energy storage scenarios with clear operating conditions and good working environment; if the remaining capacity of retired lithium-ion batteries is between 60% and 20% and has not passed the mutation point of the capacity decay rate of retired lithium-ion batteries, the retired lithium-ion batteries can be used in household energy storage scenarios with low usage frequency and low energy density requirements; if the remaining capacity of retired lithium-ion batteries is less than 20%, or has passed the mutation point of the capacity decay rate of service lithium-ion batteries, the retired lithium-ion batteries enter the raw material recycling link and have no value in re-service.

[0093] In the specific implementation, such as Figure 5 As shown in the figure, by analyzing the retirement point of the power battery (the standard is usually 70% of the initial capacity or 80% of the initial capacity) and the mutation point of the capacity attenuation rate of the retired lithium-ion battery, the relative position of the mutation point and the retirement point can be obtained to evaluate the potential of the battery for secondary service and realize pre-selection and sorting. The sorting scheme is as follows: Figure 6 shown.

[0094] In this embodiment, the capacity loss curve and capacity increment curve of retired lithium-ion batteries are obtained from the usage data of retired lithium-ion batteries during their service. The capacity loss curve reflects the capacity loss rate of retired lithium-ion batteries during service, and the capacity increment curve reflects the internal aging mechanism of retired lithium-ion batteries during service. The nonlinear loss region in the capacity loss curve can significantly interfere with the estimation of the battery's final capacity. Therefore, the present invention combines the capacity increment curves and the nonlinear loss region in the capacity loss curve of several different retired lithium-ion batteries. From the perspective of the retired lithium-ion battery mechanism and the external signal rheology, an LSTM model is trained. This allows the LSTM model to effectively predict the future capacity decay path of retired lithium-ion batteries. In this case, the capacity decay rate mutation point of the retired lithium-ion battery is identified from the capacity decay path of the retired lithium-ion battery. Based on the remaining capacity of the retired lithium-ion battery and the mutation point, the potential for the retired lithium-ion battery to be re-serviced is assessed, thereby achieving sorting of retired lithium-ion batteries. This not only improves sorting efficiency but also effectively improves the utilization rate of retired lithium-ion batteries.

[0095] In one embodiment, the implementation process of the method for sorting retired lithium-ion batteries of the present invention is as follows: Figure 7 and Figure 8 As shown in the figure, it includes the following steps: (1) obtaining usage data of the battery during service; (2) constructing artificial features; (3) processing the data set as model input; (4) neural network model prediction; (5) obtaining battery life attenuation path analysis and cascade utilization recommendations.

[0096] (1) Obtaining battery usage data during service

[0097] Battery life data is used to generate input training data for subsequent models. It primarily includes battery life, charging current, charging voltage, charging capacity, and charging temperature. This data is collected by the new energy vehicle's onboard battery management system and transmitted to the vehicle manufacturer or a new energy big data center. This data must be collected by the user; the system proposed in this invention does not include the hardware infrastructure for collecting battery life data.

[0098] (2) Constructing artificial features

[0099] According to the formula in the detailed technical solution of the invention, the battery incremental capacity characteristics (IC) and nonlinear correction characteristics (UC) are processed. This step is implemented in the processor module of the system of the present invention, and the processing results are stored in the system storage unit.

[0100] (3) Processing the data set as model input

[0101] Based on the processed artificial features, the peak value and peak position of the IC feature for each battery operating cycle, as well as the probability of rapid battery capacity decline given by the UC feature for each battery operating cycle, are extracted. These two features are organized into a data set for input into the model: a data set of IC peak value, IC peak position, and UC probability for each battery operating cycle. This step is accomplished through interaction between the storage unit and the processor of the system of the present invention.

[0102] (4) Neural network model prediction

[0103] By transferring the input data set into the neural network model, the battery capacity decay curve can be predicted, such as Figure 9 As shown in the figure. The circles in the figure represent the actual capacity collected by the battery management system. Among them, EOL = 0.8 means that the battery is retired from the vehicle application scenario when it reaches 80% of its initial capacity. In this case, the service data of the first 20% of the battery life cycle is input into the neural network model to obtain the decay curve from 80% to 60% capacity, as shown by the solid line in the figure below. It can be seen that the neural network model accurately predicts the battery's capacity decay trajectory (data with a battery capacity below 80% is not required in actual applications and is used here only to demonstrate the accuracy).

[0104] (5) Analysis of battery life attenuation paths and recommendations for cascade utilization

[0105] Analysis of battery life and capacity decay results generated by the neural network model indicates that the batteries in this case exhibit linear decay after reaching 60% of their initial capacity, with no performance drop-off. Based on the rapid sorting criteria for cascade utilization outlined in the detailed technical proposal, the proposed system recommends the battery's cascade utilization as a public energy storage battery, such as in power plants.

[0106] The method for sorting retired lithium-ion batteries of this embodiment has the following beneficial effects:

[0107] (1) Rapid sorting improves corporate profits

[0108] The current market lacks systems that support rapid and accurate sorting, resulting in inefficient and unprofitable cascade utilization methods. This rapid lithium-ion battery sorting technology and system, which considers performance drop points, uses built-in model algorithms to accurately analyze the spatiotemporal relationship between battery performance drop points and retirement points, enabling efficient cascade utilization of retired batteries. This significantly reduces the processing pressure associated with centralized battery retirement, ensuring that each batch of batteries is processed promptly and effectively, thereby increasing profitability.

[0109] (2) Efficient cascade utilization helps environmental protection

[0110] The cascaded utilization of retired batteries can extend their service life, reduce the demand for lithium-ion batteries, and minimize carbon emissions from production. Furthermore, cascaded utilization of battery energy storage can smooth out peaks and valleys in renewable energy generation, reducing carbon emissions from fossil fuels. Recycling metal materials from scrapped batteries can significantly reduce carbon emissions from the mining, smelting, and transportation of raw materials, while also preventing environmental pollution from hazardous materials such as heavy metals and promoting the reuse of precious metals. According to the inventors' estimates, this system can help integrated recycling companies save nearly 45% in material and labor costs and help reduce carbon emissions by approximately 40% over the battery's lifecycle.

[0111] The steps of the various methods above are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are within the scope of protection of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of the invention.

[0112] Another embodiment of the present invention relates to a system for sorting retired lithium-ion batteries. The following describes in detail the implementation details of the retired lithium-ion battery sorting system of this embodiment. The following content is only provided for ease of understanding and is not required for implementing this solution. The retired lithium-ion battery sorting system of this embodiment includes:

[0113] A battery data acquisition module is used to obtain usage data of retired lithium-ion batteries during their service period;

[0114] A data feature extraction module is used to obtain, based on the usage data of the retired lithium-ion batteries during service, a capacity loss curve for characterizing the capacity loss rate of the retired lithium-ion batteries during service, and a capacity increment curve for characterizing the internal aging mechanism of the retired lithium-ion batteries during service; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region;

[0115] The capacity decay prediction module is used to input the capacity increment curve and the nonlinear loss region in the capacity loss curve of retired lithium-ion batteries into a trained LSTM model to predict the future capacity decay path of retired lithium-ion batteries and identify the mutation point of the capacity decay rate of retired lithium-ion batteries from the capacity decay path. The LSTM model is trained using the nonlinear loss region in the capacity increment curve and capacity loss curve of several different retired lithium-ion batteries.

[0116] The retired battery sorting module is used to evaluate the potential of retired lithium-ion batteries for secondary service based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries, so as to complete the sorting of retired lithium-ion batteries.

[0117] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and this embodiment can be implemented in conjunction with the above-mentioned method embodiment. The relevant technical details and technical effects mentioned in the above-mentioned embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned embodiment.

[0118] It is worth noting that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of the present invention, this embodiment does not include units that are not closely related to solving the technical problems proposed by the present invention. However, this does not mean that other units do not exist in this embodiment.

[0119] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for sorting retired lithium-ion batteries in the above-mentioned embodiments.

[0120] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0121] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0122] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0123] That is, those skilled in the art will understand that all or part of the steps in the above-described method embodiments can be implemented by instructing related hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (such as a microcontroller or chip) or a processor to execute all or part of the steps in the method embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0124] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for sorting retired lithium-ion batteries, characterized in that: include: Obtain usage data of retired lithium-ion batteries during their service life; Based on the usage data of retired lithium-ion batteries during service, a capacity loss curve is obtained to characterize the capacity loss rate of retired lithium-ion batteries during service, and a capacity increment curve is obtained to characterize the internal aging mechanism of retired lithium-ion batteries during service; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region; The capacity increment curve and the nonlinear loss region in the capacity loss curve of retired lithium-ion batteries are input into a trained LSTM model to predict the future capacity decay path of the retired lithium-ion batteries and identify the mutation point of the capacity decay rate of the retired lithium-ion batteries from the capacity decay path. The LSTM model is trained using the nonlinear loss region in the capacity increment curve and capacity loss curve of several different retired lithium-ion batteries. According to the remaining capacity of retired lithium-ion batteries and the mutation point of the capacity attenuation rate of retired lithium-ion batteries, the potential for secondary service of retired lithium-ion batteries is evaluated to complete the sorting of retired lithium-ion batteries.

2. The method for sorting retired lithium-ion batteries according to claim 1, characterized in that: The capacity increment curve and the nonlinear loss area in the capacity loss curve of the retired lithium-ion battery are input into the trained LSTM model to predict the future capacity attenuation path of the retired lithium-ion battery, including: Perform one-dimensional linear fitting on the capacity loss curve of retired lithium-ion batteries to obtain the linear component of the linear loss region in the capacity loss curve; Obtaining the health status of the retired lithium-ion battery according to the initial capacity and current capacity of the retired lithium-ion battery, and subtracting the component of the linear loss region in the capacity loss curve from the health status of the retired lithium-ion battery to obtain the nonlinear component of the nonlinear loss region in the capacity loss curve; After normalizing the component of the nonlinear loss region in the capacity loss curve, multiply it by the exponential cycle life to obtain the nonlinear characteristics of the nonlinear component; The capacity increment curve of retired lithium-ion batteries and the nonlinear characteristics of the nonlinear component are input into the trained LSTM model to predict the future capacity attenuation path of retired lithium-ion batteries.

3. The method for sorting retired lithium-ion batteries according to claim 2, characterized in that: Before inputting the capacity increment curve of the retired lithium-ion battery and the nonlinear features of the nonlinear component into the trained LSTM model, the method further includes: The initial capacity increment curve of the retired lithium-ion battery is obtained by using the constant current interval during the discharge process of the retired lithium-ion battery; The SG filtering method is used to filter the initial capacity increment curve to obtain the capacity increment curve for input into the LSTM model.

4. The method for sorting retired lithium-ion batteries according to claim 1, characterized in that: The method of identifying a mutation point of the capacity decay rate of a retired lithium-ion battery from a capacity decay path includes: The capacity of the retired lithium-ion battery is used to obtain a first derivative of the number of cycles of the retired lithium-ion battery, and a curve showing the capacity attenuation rate of the retired lithium-ion battery as a function of the number of cycles is obtained; Using the curve of the capacity attenuation rate of retired lithium-ion batteries versus the number of cycles, a first derivative of the number of cycles of the retired lithium-ion batteries is obtained to obtain a curve of the capacity attenuation rate change rate of the retired lithium-ion batteries versus the number of cycles; The mutation point of the capacity decay rate of retired lithium-ion batteries is obtained through the curve of the capacity decay rate change rate of retired lithium-ion batteries as a function of the number of cycles.

5. The method for sorting retired lithium-ion batteries according to claim 1, characterized in that: The method of evaluating the potential for secondary service of retired lithium-ion batteries based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries includes: If the remaining capacity of retired lithium-ion batteries is between 100% and 80%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can still be used in vehicle power battery scenarios; If the remaining capacity of retired lithium-ion batteries is between 80% and 60%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can be used in public energy storage scenarios with clear operating conditions and good working environment; If the remaining capacity of retired lithium-ion batteries is between 60% and 20%, and there is no mutation point in the capacity attenuation rate of retired lithium-ion batteries, then retired lithium-ion batteries can be used for home energy storage scenarios with low usage frequency and low energy density requirements; If the remaining capacity of retired lithium-ion batteries is less than 20%, or if the capacity attenuation rate of the retired lithium-ion batteries has passed the mutation point, the retired lithium-ion batteries will enter the raw material recycling link and will have no value in being put into service again.

6. The method for sorting retired lithium-ion batteries according to claim 1, characterized in that: The LSTM model is obtained by training based on the capacity increment curves of several different retired lithium-ion batteries and the nonlinear loss region in the capacity loss curve as an auxiliary.

7. The method for sorting retired lithium-ion batteries according to any one of claims 1 to 6, characterized in that: The usage data of the retired lithium-ion battery during its service period includes: the service time, charging current, charging voltage, charging power and charging temperature of the retired lithium-ion battery.

8. A sorting system for retired lithium-ion batteries, characterized in that: include: A battery data acquisition module is used to obtain usage data of retired lithium-ion batteries during their service period; A data feature extraction module is used to obtain, based on the usage data of the retired lithium-ion batteries during service, a capacity loss curve for characterizing the capacity loss rate of the retired lithium-ion batteries during service, and a capacity increment curve for characterizing the internal aging mechanism of the retired lithium-ion batteries during service; wherein the capacity loss curve includes a linear loss region and a nonlinear loss region; The capacity decay prediction module is used to input the capacity increment curve and the nonlinear loss region in the capacity loss curve of retired lithium-ion batteries into a trained LSTM model to predict the future capacity decay path of retired lithium-ion batteries and identify the mutation point of the capacity decay rate of retired lithium-ion batteries from the capacity decay path. The LSTM model is trained using the nonlinear loss region in the capacity increment curve and capacity loss curve of several different retired lithium-ion batteries. The retired battery sorting module is used to evaluate the potential of retired lithium-ion batteries for secondary service based on the remaining capacity of the retired lithium-ion batteries and the mutation point of the capacity attenuation rate of the retired lithium-ion batteries, so as to complete the sorting of retired lithium-ion batteries.

9. A computer device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for sorting retired lithium-ion batteries as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for sorting retired lithium-ion batteries according to any one of claims 1 to 7 is implemented.

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