Capacity grading and matching method for cascade utilization of batteries

Abstract

The invention relates to a capacity grading and matching method for gradient utilization of batteries, which comprises the following steps: carrying out capacity grading on formed battery cells, recording pressure sequence characteristic parameter data of capacity grading, ensuring high consistency of capacity grading conditions for the battery cells with the same specification, and certainly ensuring the continuity of the capacity grading process; importing the capacity grading data of each battery cell into a small-batch battery cell database, calculating a voltage sequence mean value sequence according to the capacity grading data, obtaining the voltage sequence mean value sequence, and performing cosine similarity calculation; carrying out constant pressure difference time sequence similarity calculation on each characteristic parameter, and selecting a similarity threshold value of a certain parameter to carry out similarity characteristic matching; and delivering or grouping the successfully matched battery cells in the same batch. The method can effectively reduce the bad matching phenomenon caused by performance difference between batteries, and further improves the matching quality of products and the consistency of battery cells in the battery pack, thereby improving the reliability and service life of the whole products.

Description

Technical Field

[0001] The present application belongs to the field of battery protection technology, and in particular, relates to a method for distributing and grouping batteries for cascade utilization. Background Art

[0002] At present, the capacity classification method commonly used by companies on the market is: select a voltage threshold range, charge and discharge each battery cell, and record the energy released from the upper limit to the lower limit of the voltage threshold to calibrate the battery cell capacity.

[0003] However, this method cannot fully control the battery status. Although some cells show high consistency in parameters such as rated capacity and internal resistance, the consistency in the process is poor, especially in the long-term non-full charge and discharge working state of some energy storage products. Only focusing on static capacity division will result in inconsistency of cells within the cluster during use. This difference in dynamic changes of parameters may affect the maintenance cycle and life of the product.

[0004] The static capacity sizing method cannot effectively capture the behavior of the battery cells under different loads and charge and discharge depths. Although the capacity and internal resistance of the battery show a certain consistency during capacity sizing, in actual use, different battery cells may still show different discharge characteristics due to slight differences in internal resistance, electrochemical properties, temperature distribution and other factors. This difference that gradually accumulates during long-term use will lead to the asynchrony of parameters such as voltage, current, and capacity of the cells in the battery cluster, thereby affecting the performance and life of the entire battery pack. Especially in energy storage systems, batteries are often in a non-full charge and discharge working state, and the health status and aging process of the battery will show dynamic changes. The static capacity sizing method fails to fully reflect this dynamic change, which may eventually lead to inconsistency of the cells within the cluster. Summary of the invention

[0005] In view of the defects of the above-mentioned prior art, the present application provides a method for capacity allocation and grouping of battery recycling, which optimizes the accuracy and efficiency of battery capacity allocation and grouping, ensures the consistency of battery performance during recycling, and thus achieves more efficient and reliable battery grouping.

[0006] In a first aspect, a method for distributing and grouping batteries for battery recycling is provided, the method specifically comprising:

[0007] S1: Perform cell capacity division and record capacity division data; the capacity division data includes pressure sequence characteristic parameters;

[0008] S2: Import the capacity data of each battery cell into the small batch battery cell database, obtain the pressure sequence mean sequence of each pressure sequence characteristic parameter of current, time, temperature and pressure according to the pressure sequence mean sequence calculated by the capacity data, and perform cosine similarity calculation; wherein, the small batch is preferably the battery cells of the adjacent production batch, or the battery cells in the same production batch, and the adjacent production batch can be the battery cells produced within 2 days or a few days, but is not limited to this.

[0009] S3: Calculate similarity based on the compression mean sequence and perform similarity feature matching;

[0010] S4: The successfully matched cells are shipped or grouped in the same batch;

[0011] The method proposed in the present application comprehensively reflects the dynamic characteristics of the battery by collecting the constant pressure difference time series characteristics, quantifies the battery characteristics by pressure sequence mean sequence calculation, and groups batteries with similar performance by similarity matching, thereby achieving more efficient and reliable battery grouping.

[0012] Preferably, the cell capacity division also includes: the cell capacity division process is continuous capacity division, and the capacity division data is recorded according to threshold trigger sampling or jump point sampling; when the capacity division is interrupted, the battery is discharged to the voltage lower limit, and the capacity division is performed again.

[0013] Preferably, the record capacity data is specifically:

[0014] Score voltage or pressure threshold range X low ,X high , voltage or pressure interval X interval , then during the charging process, After the same batch of cells are left to rest for the same time interval, they are discharged regularly through the capacity division device. high -X interval and stand still, discharge to X low The data frame at Through a complete and continuous capacity division process, the data sequence of the battery cell is obtained. frame, and simultaneously records the unique number of the battery cell; wherein the data sequence includes pressure sequence characteristic parameters of at least voltage, time, pressure, current and temperature.

[0015] The step S2 further includes:

[0016] S21: import the data sequence of the unique number and capacity information of the battery cell into a small batch battery cell database; represent the small batch as Com;

[0017] S22: Each cell in a small batch of cells is represented as Cell i, i∈Com, assuming that there are a total number of monomers n, take the pressure sequence mean of the pressure sequence characteristic parameters of each battery cell in a small batch, and calculate the pressure sequence mean sequence based on the capacity division data. The formula is:

[0018]

[0019] Among them, Y is the pressure sequence parameters of temperature, current, time and pressure of each battery cell in a small batch, is the mean parameter of the Y parameter.

[0020] For time, a transformation is required. The initial time of the sub-capacity is time 0, and its time frame is converted into compression sequence data in seconds. For example, the time information of the first frame of compression sequence data of the sub-capacity of a certain monomer is 08:30:00:500ms on November 11, 2024, and the time information of the second frame of compression sequence data is 08:31:06:400ms on November 11, 2024. Then the conversion is: perform time conversion on the compression sequence mean of time, set the initial time of the sub-capacity to time 0, and convert the time frame into compression sequence data in seconds until the time compression sequence data is converted to frame.

[0021] Among them, Y is the pressure sequence mean of temperature, current, time and pressure of each battery cell in a small batch;

[0022] S23: Obtaining a pressure sequence mean sequence of temperature, current, time and pressure of each battery cell in a small batch;

[0023] S24: Use the cosine similarity algorithm to calculate the similarity between the current vector of each cell and the mean of the current sequence of a small batch, and set the current similarity threshold Sim I _t, when the cells in a small batch i Similarity with the mean of the current pressure sequence of a small batch Sim I _i<Sim I _t, the Cell i It is recorded as abnormal capacity division current and does not directly participate in the next step of similarity calculation and similarity feature matching. It returns to step S1 to re-divide it and enters the next small batch similarity calculation and similarity feature matching according to actual production.

[0024] The step S3 comprises:

[0025] S31: Assume that the number of cells in the small batch after step S2 is m, m≤n, and calculate the cosine similarity between the pressure sequence mean of temperature, current, time and pressure of each cell in the small batch and the pressure sequence characteristic parameter mean sequence of the small batch; obtain the similarity of the pressure sequence characteristic parameters of the cells, wherein the pressure sequence characteristic parameter similarity includes the time Sim t _i, pressure sequence temperature Sim T_i and pressure sequence Sim P _i;

[0026] S32: Calculate the matching similarity Sim_i according to the similarity of the compression feature parameters:

[0027] Sim_i=average(C t ×Sim t _i+C T ×Sim T _i+C P ×Sim P _i);

[0028] Among them, C t It's Sim t The weight of _i, C T It's Sim T The weight of _i, C P It's Sim P _i weight. Due to different requirements for each batch of cells, some may not be equipped with a single pressure sensor. In this case, the corresponding terms in the formula should be deleted, but at least keep Sim t The presence of the _i parameter. Preferred C t :C T :C P =2:1:1. It should be noted that simply increasing the parameters is also within the protection range.

[0029] S33: Perform similarity feature matching according to the matching similarity Sim_i.

[0030] Preferably, the S33 further includes:

[0031] S331: Set the Sim_i threshold Sim_t, sort the small batches Sim_i, select the single group quantity W and the matching group quantity G according to actual needs, and set the number of small batches that satisfy Sim_i≥Sim_t to m', and satisfy m'≤m≤n and WG≤m';

[0032] S332: Determine the grouping or whether to ship as the same batch based on the sorting result and Sim_t.

[0033] Preferably, the S332 further includes:

[0034] For the monomers at the head and tail of the small batch Sim_i sorting, when the quantity does not meet the complete set quantity W, the selection can be achieved by giving priority to the head end, the end end, or the middle end; among them, Sim_t=0.99997 is preferred.

[0035] For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when

[0036] D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t When the battery is marked as scrapped. batch_t and k t It needs to be selected in combination with actual production, and the preferred D batch_t =3, k t =3.

[0037] Furthermore, the S332 further includes:

[0038] Normalize the Z-score of the small batch of Sim_i, record the normalized Sim_i as Sim_i', and record Sim_i' beyond (-N threshold ,N threshold ) range is recorded as abnormal cells;

[0039] And sort the small batches Sim_i', select the number of single groups W and the number of matching groups G according to actual needs, and assume that a small batch satisfies Sim_i∈(-N threshold ,N threshold ) is m', and satisfies m'≤m≤n and WG≤m';

[0040] For the monomers at the head and tail of the small batch Sim_i', when the quantity does not meet the complete set of quantity W, the selection can be achieved by giving priority to the head end, the tail end, or the middle end;

[0041] For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when

[0042] D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t When D batch_t and k t It needs to be selected in combination with actual production, and the preferred Dbatch_t =3, k t =3.

[0043] in:

[0044] Where erf -1 It is the inverse function of the Z-score error function.

[0045] The battery recycling method described in the present application also includes: adjusting the voltage or pressure threshold range X according to the battery aging degree. low ,X high and capacity data for the cascade utilization of single cells in a battery cluster.

[0046] Among them, when the quantity does not meet the complete set quantity W, the selection can be achieved by giving priority to the first end, the last end, or the middle end. Explanation: The two methods of giving priority to the first end and the last end will retain the single cells that deviate from the overall similarity, which is convenient for subsequent re-grouping; the method of giving priority to the middle end can ensure better product performance, but the risk of "abnormal cells" failing to be re-grouped and becoming "scrapped cells" is higher.

[0047] In addition, this method can also adjust the voltage threshold range [V low ,V high ] and process are used for the cascade utilization of single cells in the battery cluster, which is consistent with the capacity division method, except that a new voltage threshold range needs to be selected according to the degree of battery aging, which will not be elaborated.

[0048] The present application proposes a method for capacity division and grouping of battery recycling, which monitors and collects the time series characteristic parameters of the voltage change of the battery during the capacity division process in real time, calculates the similarity between the pressure sequence mean sequence and the pressure sequence mean sequence of these parameters, matches and groups them according to the similarity, and ships or groups the successfully matched battery cell batches, thereby achieving more accurate capacity division control and more efficient grouping operation. It effectively reduces the phenomenon of poor grouping caused by performance differences between batteries, further improves the product grouping quality and the consistency of the battery cells in the battery pack, thereby improving the reliability, stability and service life of the overall product.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. Ensure consistency between battery cells.

[0051] 2. Capture the dynamic changes in battery cell performance.

[0052] 3. Improve the consistency and reliability of battery packs.

[0053] 4. Adapt to battery aging and performance changes.

[0054] 5. Refined grouping and optimized management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the battery recycling capacity distribution method shown in an embodiment of the present application.

[0056] Figure 2 It is a flow chart of processing capacity sharing data according to an embodiment of the present application.

[0057] Figure 3 It is a flow chart of similarity calculation and similarity feature matching shown in an embodiment of the present application.

[0058] Figure 4 It is a similarity feature matching flow chart shown in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme will be clearly and completely described below in conjunction with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] This case proposes a battery capacity division and grouping method for cascade utilization, which is different from the traditional "static capacity division information" method. The present invention focuses on the "dynamic consistency" of various parameters of the battery during the capacity division process. Traditional methods usually rely on preset static parameters and ignore the changes in performance parameters of the battery during actual operation, which may lead to inaccurate capacity division operations and imbalance in the grouping process.

[0061] The capacity division and grouping method for battery recycling of the present invention ensures that these parameters maintain dynamic consistency during the capacity division process by real-time monitoring and adjusting key parameters such as the pressure sequence current, pressure sequence pressure, and pressure sequence temperature of the battery during the capacity division process, thereby achieving more precise capacity division control and more efficient grouping operation.

[0062] As attached Figure 1 As shown, the present application provides a method for dividing and grouping batteries for cascade utilization, and the method specifically includes:

[0063] S1: Perform cell capacity division and record capacity division data; the capacity division data includes pressure sequence characteristic parameters;

[0064] S2: Importing the capacity data of each battery cell into the small batch battery cell database, obtaining the pressure sequence mean sequence of each pressure sequence characteristic parameter of current, time, temperature and pressure according to the pressure sequence mean sequence calculated from the capacity data, and performing cosine similarity calculation;

[0065] S3: Calculate similarity based on the compression mean sequence and perform similarity feature matching;

[0066] S4: The successfully matched cells are shipped or grouped in the same batch;

[0067] Among them, it should be emphasized that in this application, small batches are subsets of the same batch. In the process of mass production, due to the limitations of storage space or production lines and other sites, the same batch of products is usually divided into multiple "small batches" for management and shipment. These "small batches" are actually subsets of the "same batch" products. Although they come from the same production batch, in order to facilitate warehousing, transportation and shipment arrangements, the production and distribution process may need to be divided into several times. The production and shipment time of each small batch may be different, and they are physically separated from each other. Such batch operations sometimes bring certain coordination and quality control challenges. Therefore, although these "small batches" belong to the same "batch", their shipment and delivery may be distributed at different time nodes.

[0068] In this embodiment, the dynamic characteristics of the battery are fully reflected by collecting constant pressure difference time series characteristics, the battery characteristics are quantified by pressure sequence mean sequence calculation, and batteries with similar performance are grouped together through similarity matching. This can effectively reduce the poor matching phenomenon caused by performance differences between batteries, further improve the product matching quality and the consistency of the battery cells in the battery pack, thereby improving the reliability and service life of the overall product.

[0069] The cell capacity division also includes: the cell capacity division process is continuous capacity division, and the capacity division data is recorded according to threshold trigger sampling or jump point sampling; when the capacity division is interrupted, the battery is discharged to the voltage lower limit, and the capacity division is performed again.

[0070] In this embodiment, threshold trigger sampling or transition point sampling can be selected according to the application situation:

[0071]

[0072] Preferably, the record capacity data is specifically:

[0073] Score voltage or pressure threshold range [V low ,V high ], voltage or pressure interval V interval , then during the charging process, After the same batch of cells are left to rest for the same time interval, they are discharged regularly through the capacity division device. high -V interval and leave it to stand, discharge to V low The data frame at Through a complete and continuous capacity division process, the data sequence of the battery cell is obtained. frame, and simultaneously records the unique number of the battery cell; wherein the data sequence includes pressure sequence characteristic parameters of at least voltage, time, pressure, current and temperature, but is not limited thereto.

[0074] In this embodiment, for common lithium iron phosphate batteries, V low =2.5, V high =3.65, V interval =0.001, where international units are used. The time record is preferably accurate to 1ms. For the data recording and jump process, "threshold trigger sampling" or "jump point sampling" is used to ensure that data changes are equally effectively recorded.

[0075] like Figure 2 As shown, the step S2 further includes:

[0076] S21: import the data sequence of the unique number and capacity information of the battery cell into a small batch battery cell database; represent the small batch as Com;

[0077] S22: Each cell in a small batch of cells is represented as Cell i , i∈Com, assuming that there are a total number of monomers n, take the pressure sequence mean of the pressure sequence characteristic parameters of each battery cell in a small batch, and calculate the pressure sequence mean sequence based on the capacity division data. The formula is:

[0078]

[0079] Among them, Y is the pressure sequence parameters of temperature, current, time and pressure of each battery cell in a small batch, is the mean sequence of the Y parameters.

[0080] For time, a transformation is required. The initial time of the sub-capacity is time 0, and its time frame is converted into compression sequence data in seconds. For example, the time information of the first frame of compression sequence data of the sub-capacity of a certain monomer is 08:30:00:500ms on November 11, 2024, and the time information of the second frame of compression sequence data is 08:31:06:400ms on November 11, 2024. Then the conversion is: perform time conversion on the compression sequence mean of time, set the initial time of the sub-capacity to time 0, and convert the time frame into compression sequence data in seconds 65.900, until the time compression sequence data is converted to frame.

[0081] Based on the above data processing flow, the pressure sequence mean of each characteristic parameter such as current, time, temperature, pressure, etc. is obtained, which is the average pressure sequence level of the batch of battery cells.

[0082] Among them, Y is the pressure sequence parameters of temperature, current, time and pressure of each battery cell in a small batch;

[0083] S23: Obtaining a pressure sequence mean sequence of temperature, current, time and pressure of each battery cell in a small batch;

[0084] S24: Use the cosine similarity algorithm to calculate the similarity between the current vector of each cell and the mean of the current sequence of a small batch, and set the current similarity threshold Sim I _t, when the cells in a small batch i Similarity with the mean of the current pressure sequence of a small batch Sim I _i<Sim I _t, the Cell i It is recorded as abnormal current of capacity division, and does not directly participate in the next similarity calculation and similarity feature matching. It returns to step S1 to divide it again, and enters the next small batch similarity calculation and similarity feature matching according to actual production. I _t=0.995.

[0085] Preferably, Figure 3 As shown, the step S3 includes:

[0086] S31: Assume that the number of cells in the small batch after step S2 is m, m≤n, and calculate the cosine similarity between the pressure sequence mean of temperature, current, time and pressure of each cell in the small batch and the pressure sequence characteristic parameter mean sequence of the small batch; obtain the similarity of the pressure sequence characteristic parameters of the cells, wherein the pressure sequence characteristic parameter similarity includes the time Sim t _i, pressure sequence temperature Sim T _i and pressure sequence Sim P _i;

[0087] S32: Calculate the matching similarity Sim_i according to the similarity of the compression feature parameters:

[0088] Sim_i=average(C t ×Sim t _i+C T ×Sim T _i+C P ×Sim P _i);

[0089] Among them, C t It's Sim t The weight of _i, CT It's Sim T The weight of _i, C P It's Sim P The weight of _i.

[0090] Among them, due to the different requirements of each batch of batteries, some may not be equipped with a single pressure sensor. In this case, the corresponding items in the formula should be deleted, but at least keep Sim t The presence of the _i parameter. Preferred C t :C T :C P =2:1:1. It should be noted that simply increasing the parameters is also within the protection range.

[0091] S33: Perform similarity feature matching according to the matching similarity Sim_i.

[0092] Preferably, Figure 4 As shown, the S33 further includes:

[0093] S331: Set the Sim_i threshold Sim_t, sort the small batches Sim_i, select the single group quantity W and the matching group quantity G according to actual needs, and set the number of small batches that satisfy Sim_i≥Sim_t to m', and satisfy m'≤m≤n and WG≤m';

[0094] S332: Determine the grouping or whether to ship as the same batch based on the sorting result and Sim_t.

[0095] Preferably, the S332 further includes:

[0096] For the monomers at the head and tail of the small batch Sim_i sorting, when the quantity does not meet the complete set quantity W, the selection can be achieved by giving priority to the head end, the end end, or the middle end; among them, Sim_t=0.99997 is preferred.

[0097] For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when

[0098] D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t When the battery is marked as scrapped. batch_t and k t It needs to be selected in combination with actual production, and the preferred Dbatch_t =3, k t =3.

[0099] Furthermore, the S332 further includes:

[0100] Normalize the Z-score of the small batch of Sim_i, record the normalized Sim_i as Sim_i', and record Sim_i' beyond (-N threshold ,N threshold ) range is recorded as abnormal cells;

[0101] And sort the small batches Sim_i', select the number of single groups W and the number of matching groups G according to actual needs, and assume that a small batch satisfies Sim_i∈(-N threshold ,N threshold ) is m', and satisfies m'≤m≤n and WG≤m';

[0102] For the monomers at the head and tail of the small batch Sim_i', when the quantity does not meet the complete set of quantity W, the selection can be achieved by giving priority to the head end, the tail end, or the middle end;

[0103] For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when

[0104] D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t When D batch_t and k t It needs to be selected in combination with actual production, and the preferred D batch_t =3, k t =3.

[0105] in:

[0106] Where erf -1 It is the inverse function of the Z-score error function.

[0107] The battery recycling method described in the present application also includes: adjusting the voltage or pressure threshold range X according to the battery aging degree. low ,X high and capacity data for the cascade utilization of single cells in a battery cluster.

[0108] Among them, when the quantity does not meet the complete set quantity W, the selection can be achieved by giving priority to the first end, the last end, or the middle end. Explanation: The two methods of giving priority to the first end and the last end will retain the single cells that deviate from the overall similarity, which is convenient for subsequent re-grouping; the method of giving priority to the middle end can ensure better product performance, but the risk of "abnormal cells" failing to be re-grouped and becoming "scrapped cells" is higher.

[0109] In addition, this method can also adjust the voltage threshold range [V low ,V high ] and process are used for the cascade utilization of single cells in the battery cluster, which is consistent with the capacity division method, except that a new voltage threshold range needs to be selected according to the degree of battery aging, which will not be elaborated.

[0110] The battery cascade utilization capacity division and grouping method of the present invention can achieve better capacity division, grouping and small batch shipment, and ensure that these parameters maintain dynamic consistency during the capacity division process by real-time monitoring and adjusting the key parameters such as the pressure sequence current, pressure sequence pressure, and pressure sequence temperature of the battery during the capacity division process, thereby achieving more accurate capacity division control and more efficient grouping operation. It can effectively reduce the poor grouping phenomenon caused by performance differences between batteries, further improve the product grouping quality and the consistency of the battery cells in the battery pack, thereby improving the reliability and service life of the overall product.

[0111] At the same time, this application is not only applicable to the initial capacity division and grouping operations, but also has important later application value. It can be used for the cascade utilization of single cells in a battery cluster. Through cascade utilization, the remaining life of the battery is fully utilized, thereby reducing resource waste and further extending the overall service life of the battery cluster. This new capacity division and grouping method provides a more flexible and sustainable battery management solution, meeting the needs of modern battery production and use for efficiency, reliability and environmental protection.

[0112] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0113] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0115] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to embodiments of the present invention. The present invention can also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0117] Although the present invention is described in conjunction with the above specific embodiments, it is obvious that those skilled in the art can make many substitutions, modifications and changes according to the above content. Therefore, all such substitutions, modifications and changes are included in the spirit and scope of the appended claims.

Claims

1. A battery capacity allocation method for battery recycling, characterized in that: S1: Perform cell capacity division and record capacity division data; the capacity division data includes pressure sequence characteristic parameters; S2: Import the capacity data of each battery cell into the small batch battery cell database, obtain the pressure sequence mean sequence of each pressure sequence characteristic parameter of current, time, temperature and pressure according to the pressure sequence mean sequence calculated by the capacity data, and perform cosine similarity calculation; wherein, the small batch is at least the battery cells of the adjacent production batch, or the battery cells in the same production batch, and the adjacent production batch can be the battery cells produced within the next 2 days or a few days; S3: Calculate similarity based on the compression mean sequence and perform similarity feature matching; S4: The successfully matched cells are shipped or grouped in the same batch.

2. A battery capacity allocation and grouping method for battery recycling according to claim 1, characterized in that: The cell capacity division also includes: the cell capacity division process is continuous capacity division, and the capacity division data is recorded according to the threshold trigger sampling or the jump point sampling; when the capacity division is interrupted, The battery is discharged to the lower voltage limit and re-capacitated.

3. A battery recycling method according to claim 2, characterized in that: The record capacity data is specifically: Score voltage or pressure threshold range X low ,X high , voltage or pressure interval X interval , then during the charging process, After the same batch of cells are left to rest for the same time interval, they are discharged regularly through the capacity division device. high -X interval and stand still, discharge to X low The data frame at Through a complete and continuous capacity division process, the data sequence of the battery cell is obtained. frame, and simultaneously records the unique number of the battery cell; wherein the data sequence includes pressure sequence characteristic parameters of at least voltage, time, pressure, current and temperature.

4. A battery capacity allocation and grouping method for battery recycling according to claim 3, characterized in that: The step S2 further includes: S21: import the data sequence of the unique number and capacity information of the battery cell into a small batch battery cell database; represent the small batch as Com; S22: Each cell in a small batch of cells is represented as Cell i , i∈Com, assuming that there are a total number of monomers n, take the pressure sequence mean of temperature, current, time and pressure of each battery cell in a small batch, and calculate the pressure sequence mean sequence based on the capacity distribution data. The formula is: Among them, Y is the pressure sequence parameters of temperature, current, time and pressure of each battery cell in a small batch, is the mean sequence of Y parameters; S23: Obtaining a pressure sequence mean sequence of temperature, current, time and pressure of each battery cell in a small batch; S24: Use the cosine similarity algorithm to calculate the similarity between the current vector of each cell and the mean of the current sequence of a small batch, and set the current similarity threshold Sim I _t, when the cells in a small batch i Similarity with the mean of the current pressure sequence of a small batch Sim I _i<Sim I _t, the Cell i It is recorded as abnormal capacity division current and does not directly participate in the next step of similarity calculation and similarity feature matching. It returns to step S1 to re-divide it and enters the next small batch similarity calculation and similarity feature matching according to actual production.

5. A battery capacity allocation and grouping method for battery recycling according to claim 4, characterized in that: The step S22 further includes: Perform time conversion on the time compression sequence mean, set the initial time of the distribution as time 0, and convert the time frame into compression sequence data in seconds until the time compression sequence data is converted to frame.

6. A battery capacity allocation and grouping method for battery recycling according to claim 5, characterized in that: The step S3 comprises: S31: Assume that the number of cells in the small batch after step S2 is m, m≤n, and calculate the cosine similarity between the pressure sequence mean of temperature, current, time and pressure of each cell in the small batch and the pressure sequence characteristic parameter mean sequence of the small batch; obtain the similarity of the pressure sequence characteristic parameters of the cells, wherein the pressure sequence characteristic parameter similarity includes the time Sim t _i, pressure sequence temperature Sim T _i and pressure sequence Sim P _i; S32: Calculate the matching similarity Sim_i according to the similarity of the compression feature parameters: Sim_i=average(C t ×Sim t _i+C T ×Sim T _i+C P ×Sim P _i); Among them, C t It's Sim t The weight of _i, C T It's Sim T The weight of _i, C P It's Sim P _i's weight; S33: Perform similarity feature matching according to the matching similarity Sim_i.

7. A battery capacity allocation and grouping method for battery recycling according to claim 6, characterized in that: The S33 further includes: S331: Set the Sim_i threshold Sim_t, sort the small batches Sim_i, select the single group quantity W and the matching group quantity G according to actual needs, and set the number of small batches that satisfy Sim_i≥Sim_t to m', and satisfy m'≤m≤n and WG≤m'; S332: Determine the grouping or whether to ship as the same batch based on the sorting result and Sim_t.

8. A battery capacity allocation and grouping method for battery recycling according to claim 7, characterized in that: The S332 further includes: For the monomers at the head and tail of the small batch Sim_i, when the quantity does not meet the complete set quantity W, the selection can be achieved by giving priority to the head end, the tail end or the middle end; For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t , the battery cell is marked as scrapped battery cell.

9. A battery capacity allocation and grouping method for battery recycling according to claim 7, characterized in that: The S332 further includes: Normalize the Z-score of the small batch of Sim_i, record the normalized Sim_i as Sim_i', and record Sim_i' beyond (-N threshold ,N threshold ) range is recorded as abnormal cells; And sort the small batches Sim_i', select the number of single groups W and the number of matching groups G according to actual needs, and assume that a small batch satisfies Sim_i∈(-N threshold ,N threshold ) is m', and satisfies m'≤m≤n and WG≤m'; For the monomers at the head and tail of the small batch Sim_i', when the quantity does not meet the complete set of quantity W, the selection can be achieved by giving priority to the head end, the tail end or the middle end; For cells that cannot be assembled or shipped in a small batch, they are abnormal cells. They can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter D at this time. batch =D batch +1, when D batch = k × D batch_t ,k∈N + ,k<k t It is necessary to return to S1 to perform capacity division. When k = k t When the battery is marked as scrapped; in: Where erf -1 It is the inverse function of the Z-score error function.

10. A battery capacity allocation and grouping method for battery recycling according to any one of claims 1 to 9, characterized in that: Also includes: Adjust the voltage or pressure threshold range X according to the battery aging degree low ,X high and capacity data for the cascade utilization of single cells in a battery cluster.

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