Capacity grading and grouping method, system and device for echelon utilization of batteries

Abstract

Provided is a capacity grading and grouping method for echelon utilization of batteries, comprising: performing capacity grading of battery cells, and recording voltage-sequence feature parameters to ensure that capacity grading conditions of the battery cells of the same specification are consistent and a process is continuous; importing the capacity grading data into a small-batch database, calculating a voltage-sequence mean sequence, and performing cosine similarity matching; and delivering or grouping, in the same batch, successfully matched battery cells.

Description

A method, system, and equipment for battery cascade utilization by capacity allocation and grouping. Technical Field

[0001] This application belongs to the field of battery protection technology, and in particular relates to a method, system and equipment for the capacity allocation and grouping of batteries for secondary utilization. Background Technology

[0002] Currently, the common method used by various companies in the market for capacity assessment is to select a voltage threshold range, perform charge and discharge operations on each cell, and record the energy released from the upper limit to the lower limit of the voltage threshold in order to calibrate the cell capacity.

[0003] However, this method cannot fully control the battery state. Although some cells show high consistency in parameters such as rated capacity and internal resistance, their consistency during operation is poor. In particular, under the long-term non-full charge and discharge conditions of some energy storage products, focusing only on static capacity assessment will result in inconsistencies between individual cells within the cluster during use. Such differences in dynamic parameter changes may affect the product's maintenance cycle and lifespan.

[0004] Static capacity grading methods cannot effectively capture the behavior of battery cells under different loads and depths of charge / discharge. Although the capacity and internal resistance of the battery show a certain consistency during capacity grading, in actual use, different battery cells may still exhibit different discharge characteristics due to slight differences in internal resistance, electrochemical characteristics, temperature distribution, and other factors. This difference, which accumulates gradually over long-term use, can lead to asynchrony in parameters such as voltage, current, and capacity of individual cells within the battery cluster, thus affecting the performance and lifespan of the entire battery pack. Especially in energy storage systems, batteries are often in a non-fully charged / discharged operating state, and the health status and aging process of the battery exhibit dynamic changes. Static capacity grading methods fail to fully reflect these dynamic changes, which may ultimately lead to inconsistencies among individual cells within the cluster. Summary of the Invention

[0005] To address the shortcomings of the existing technology, this application provides a method, system, and device for battery capacity allocation and grouping for secondary utilization, which optimizes the accuracy and efficiency of battery capacity allocation and grouping, ensures the consistency of battery performance during secondary utilization, and thus achieves more efficient and reliable battery grouping.

[0006] In a first aspect, a method for battery capacity allocation and grouping for cascade utilization, the method specifically includes:

[0007] Perform cell capacity grading and record the grading data; the grading data includes the compression sequence characteristic parameters.

[0008] The capacity classification data of each cell is imported into the small batch cell database. Based on the voltage sequence average value sequence calculated from the capacity classification data, the voltage sequence average value sequence of each voltage sequence characteristic parameter, such as current, time, temperature and pressure, is obtained, and cosine similarity is calculated. The small batch is preferably a cell from a nearby production batch or a cell from the same production batch. A nearby production batch can be a cell produced within 2 days or a few days, but is not limited to this.

[0009] Similarity is calculated based on the compressed mean sequence, and similarity feature matching is performed.

[0010] Successfully matched battery cells will be shipped in the same batch or grouped together.

[0011] The method proposed in this application comprehensively reflects the dynamic characteristics of the battery by collecting constant differential pressure time-series features, quantifies the battery characteristics by calculating the average voltage sequence, and groups batteries with similar performance together by similarity matching, thereby achieving more efficient and reliable battery grouping.

[0012] Preferably, the cell capacity grading process further includes: the cell capacity grading process is continuous, and capacity grading data is recorded by sampling triggered by a threshold or by sampling at a jump point; when the capacity grading is interrupted, the battery is discharged to the lower voltage limit and the capacity grading is performed again.

[0013] Preferably, the recorded capacity data specifically includes:

[0014] Score and voltage or pressure threshold range Voltage or pressure interval Then, during the charging process, you can get Each data frame, after being left to stand for the same time interval on cells from the same batch, is periodically discharged via a capacity grading device. From and let stand, discharge until Data frames at time The data sequence of the battery cell is obtained through a complete and continuous capacity testing process. The data sequence includes a frame, which simultaneously records the unique number of the battery cell; wherein the data sequence includes voltage, time, pressure, current and temperature as voltage sequence characteristic parameters.

[0015] Import the unique serial number and capacity rating information of the battery cell into the small batch battery cell database; represent the small batch as... ;

[0016] Each individual cell in a small batch of battery cells is represented as... , Let the total number of units be... The average value of the voltage sequence characteristic parameters of each cell in a small batch is taken, and the average voltage sequence sequence is calculated based on the capacity grading data. The formula is as follows:

[0017] ;

[0018] in, This provides the voltage sequence parameters for temperature, current, time, and pressure of each cell in a small batch. for The mean of the parameters.

[0019] For time, a transformation is required. The initial time of the capacity allocation is set to 0. Its time frames are converted into compressed data in seconds. For example, if the first frame of the compressed data for a specific unit's capacity allocation is 00:00:00:500ms on November 11, 2024, and the second frame is 08:30:00:06:400ms on November 11, 2024, the transformation is as follows: The average compressed time value is converted, the initial time of the capacity allocation is set to 0, and the time frames are converted into compressed data in seconds. This process is repeated until the compressed data reaches the specified value. frame.

[0020] Where Y is the average value of temperature, current, time and pressure of each cell in the small batch;

[0021] Obtain the sequence of average values ​​of temperature, current, time, and pressure for each cell in a small batch;

[0022] The cosine similarity algorithm is used to calculate the similarity between the voltage sequence current vector of each cell and the average voltage sequence current of a small batch, and a current similarity threshold is set. When the cells in a small batch Similarity to the average current voltage sequence of small batches At that time, If the current is abnormal, it will not be directly involved in the next similarity calculation and similarity feature matching. Instead, it will be re-capacitated and then proceed to the next small batch for similarity calculation and similarity feature matching based on actual production.

[0023] Let the number of individual cells in a small batch be... The cosine similarity of the average values ​​of temperature, current, time, and pressure of each cell in a small batch with the average value sequence of the batch's sequence characteristic parameters is calculated; this yields the individual cell sequence characteristic parameter similarity, which includes time... Compression temperature and sequential pressure ;

[0024] The matching similarity is calculated based on the similarity of the compression feature parameters. :

[0025] ;

[0026] in, yes The weight, yes The weight, yes The weights. Since the requirements for each batch of battery cells differ, some may not be equipped with individual cell pressure sensors. In this case, the corresponding terms in the formula need to be reduced, but at least the following should be maintained. The existence of parameters. Preferred. It should be noted that simply adding parameters is also within the scope of protection.

[0027] Based on the matching similarity Perform similarity feature matching.

[0028] Preferred setting threshold and for small batches Sort the data and select the number of groups based on actual needs. and number of groups Suppose a certain small batch satisfies The number of And satisfy and ;

[0029] Based on the sorting results and The determination is made to confirm whether the items are grouped together or shipped in the same batch.

[0030] Preferably, for small batches The first and last single units in the sorted sequence do not meet the requirements of a complete set. In this case, selection can be achieved by prioritizing shipments to the beginning, end, or middle of the shipment; among these, the preferred method is... .

[0031] For individual cells that fail to be matched or shipped within a small batch, they are considered abnormal cells and can proceed to the next small batch for similarity calculation and similarity feature matching. The batch delay parameters at this point should be recorded. ,when When it is necessary to re-execute the capacity allocation, At that time, the battery cell was marked as a scrapped battery cell. and Selection needs to be based on actual production conditions, with the preferred option being... , .

[0032] Furthermore, small batches Perform Z-score normalization, and denote the normalized score. Recorded as ,Will Exceeding Individual cells within the specified range are designated as abnormal cells;

[0033] And for small batches Sort the data and select the number of groups based on actual needs. and number of groups Suppose a certain small batch satisfies The number of And satisfy and ;

[0034] For small batches The first and last single units in the sorted sequence do not meet the requirements of a complete set. When selecting, priority can be given to shipping to the beginning, end, or middle of the shipment.

[0035] For individual cells that fail to be matched or shipped within a small batch, they are considered abnormal cells and can proceed to the next small batch for similarity calculation and similarity feature matching. The batch delay parameters at this point should be recorded. ,when When it is necessary to re-execute the capacity allocation, At that time, the battery cell was marked as a scrapped battery cell; among them, and Selection needs to be based on actual production conditions, with the preferred option being... , .

[0036] in: ;

[0037] In the formula, It is the inverse function of the Z-score error function.

[0038] The battery cascade utilization method for capacity allocation and grouping described in this application further includes: adjusting the voltage or pressure threshold range according to the degree of battery aging. The capacity data is used for the secondary use of individual cells within a battery cluster.

[0039] Among them, the quantity does not satisfy a complete set of quantities In such cases, selection can be achieved using methods such as prioritizing the first end, prioritizing the last end, or prioritizing the middle. It should be explained that prioritizing the first and last ends will retain individual cells that deviate from the overall similarity, facilitating subsequent re-grouping; prioritizing the middle ensures better product performance, but the risk of "abnormal cells" failing to be re-grouped and becoming "scrap cells" is higher.

[0040] In addition, this method can also adjust the voltage threshold range. The process is used for the secondary use of individual cells within the battery cluster, which is consistent with the capacity grading method. However, it is necessary to select a new voltage threshold range based on the degree of battery aging, which will not be elaborated further.

[0041] Secondly, this application proposes a battery tiered utilization capacity allocation and grouping system, which includes at least a domain controller. The domain controller is used to perform capacity allocation of battery cells, record capacity allocation data, obtain the average value sequence of each tiered characteristic parameter based on the capacity allocation data, and perform cosine similarity calculation and similarity feature matching. The successfully matched battery cells are shipped or grouped together in the same batch.

[0042] The domain controller includes at least a processor and a memory. The memory stores computer instructions for multiple functional modules that enable battery reuse and capacity allocation. The processor communicates with the memory via a bus and executes each computer instruction of the functional modules stored in the memory.

[0043] The plurality of functional modules include at least the following: a pressure sequence feature acquisition module: performing cell capacity grading and recording capacity grading data; the capacity grading data includes pressure sequence feature parameters; a mean processing module: importing the capacity grading data of each cell into a small batch cell database, calculating the pressure sequence mean sequence based on the pressure sequence mean sequence obtained from the capacity grading data, obtaining the pressure sequence mean sequence of each pressure sequence feature parameter such as current, time, temperature, and pressure, and performing cosine similarity calculation; a similarity matching module: performing similarity calculation based on the pressure sequence mean sequence and performing similarity feature matching; and a shipment and grouping module: shipping or grouping successfully matched cells as part of the same batch.

[0044] Thirdly, a battery cascade utilization capacity allocation and grouping device, comprising at least a battery cascade utilization capacity allocation and grouping system, wherein the battery cascade utilization capacity allocation and grouping system implements the steps of the battery cascade utilization capacity allocation and grouping method described in the first aspect.

[0045] This application proposes a method, system, and device for capacity grading and grouping of batteries for cascade utilization. By real-time monitoring and acquisition of the temporal characteristic parameters of voltage changes during the capacity grading process, the similarity between the voltage sequence mean values ​​of these parameters and other voltage sequence mean values ​​is calculated. Based on the similarity, cells are matched and grouped. Successfully matched cell batches are shipped or grouped, thereby achieving more precise capacity grading control and more efficient grouping operations. This effectively reduces grouping defects caused by performance differences between batteries, further improving the grouping quality of the product and the consistency of cells within the battery pack, thus enhancing the overall product reliability, stability, and lifespan.

[0046] Compared with the prior art, the advantages of this application are as follows:

[0047] 1. Ensure consistency between individual battery cells.

[0048] 2. Capture the dynamic changes in battery cell performance.

[0049] 3. Improve the consistency and reliability of battery packs.

[0050] 4. Adapt to battery aging and performance changes.

[0051] 5. Refined grouping and optimized management. Attached Figure Description

[0052] Figure 1 is a flowchart illustrating the battery cascade utilization and capacity allocation method according to an embodiment of this application.

[0053] Figure 2 is a flowchart illustrating the processing of capacity-divided data according to an embodiment of this application.

[0054] Figure 3 is a flowchart illustrating the similarity calculation and similarity feature matching process in an embodiment of this application.

[0055] Figure 4 is a flowchart illustrating the similarity feature matching process in an embodiment of this application.

[0056] Figure 5 is a framework diagram of a battery cascade utilization and capacity allocation system shown in an embodiment of this application.

[0057] Figure 6 is a framework diagram of a domain controller shown in an embodiment of this application.

[0058] Figure 7 is a frame diagram of the memory shown in an embodiment of this application.

[0059] Figure 8 is a framework diagram of a battery cascade utilization and capacity allocation device shown in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0061] This invention proposes a method, system, and device for capacity grading and grouping batteries for tiered utilization. Unlike traditional methods that rely on "static capacity grading information," this invention focuses on the "dynamic consistency" of various parameters during the capacity grading process. Traditional methods typically depend on preset static parameters, neglecting changes in battery performance parameters during actual operation, which can lead to inaccuracies in capacity grading and imbalances in the grouping process.

[0062] The battery tiered utilization capacity matching method of the present invention ensures that these parameters remain dynamically consistent during the capacity matching process by real-time monitoring and adjustment of key parameters such as voltage sequence current, voltage sequence pressure, and voltage sequence temperature of the battery during the capacity matching process, thereby achieving more precise capacity matching control and more efficient matching operation.

[0063] Example 1:

[0064] As shown in Figure 1, this application provides a method for battery capacity allocation and grouping for cascade utilization, the method specifically including:

[0065] S1: Perform cell capacity assessment and record the capacity assessment data; the capacity assessment data includes the compression sequence characteristic parameters;

[0066] S2: Import the capacity classification data of each cell into the small batch cell database, and obtain the pressure sequence average value sequence of each pressure sequence characteristic parameter such as current, time, temperature and pressure based on the pressure sequence average value sequence calculated from the capacity classification data, and perform cosine similarity calculation.

[0067] S3: Calculate the similarity based on the compressed mean sequence and perform similarity feature matching;

[0068] S4: Successfully matched battery cells will be shipped in the same batch or grouped together;

[0069] It is important to emphasize that, in this application, "small batch" refers to a subset of the same batch. In mass production, due to limitations in storage space or production lines, products from the same batch are typically divided into multiple "small batches" for management and shipment. These "small batches" are actually subsets of the "same batch" of products. Although they originate from the same production batch, the production and distribution process may need to be carried out in several stages to facilitate warehousing, transportation, and shipping arrangements. The production and shipment times of each small batch may differ, and they are physically separated from each other. Such batching operations can sometimes present coordination and quality control challenges. Therefore, although these "small batches" belong to the same "batch," their shipments and deliveries may be distributed across different time points.

[0070] In this embodiment, the dynamic characteristics of the battery are fully reflected by collecting constant voltage difference time-series characteristics. The battery characteristics are quantified by calculating the average voltage sequence. Batteries with similar performance are grouped together by similarity matching. This can effectively reduce the poor matching phenomenon caused by the performance difference between batteries, further improve the matching quality of the product and the consistency of the cells in the battery pack, thereby improving the overall product reliability and service life.

[0071] The cell capacity grading process also includes: the cell capacity grading process is continuous, and the capacity grading data is recorded by sampling triggered by a threshold or by sampling at a jump point; when the capacity grading is interrupted, the battery is discharged to the lower voltage limit and the capacity grading is performed again.

[0072] In this embodiment, threshold-triggered sampling or transition point sampling can be selected according to the application:

[0073]

[0074] Preferably, the recorded capacity data specifically includes:

[0075] Score and voltage or pressure threshold range Voltage or pressure interval Then, during the charging process, you can get Each data frame, after being left to stand for the same time interval on cells from the same batch, is periodically discharged via a capacity grading device. From and let stand, discharge until Data frames at time The data sequence of the battery cell is obtained through a complete and continuous capacity testing process. The data sequence includes, but is not limited to, voltage, time, pressure, current and temperature as voltage sequence characteristic parameters.

[0076] In this embodiment, for common lithium iron phosphate cells, it is preferred that... , , All data are recorded in international units, with time recordings preferably accurate to 1ms. For data recording and transition processes, "threshold-triggered sampling" or "transition point sampling" is used to ensure that data changes are recorded equally effectively.

[0077] As shown in Figure 2, step S2 further includes:

[0078] S21; Import the unique serial number and capacity information data sequence of the battery cell into the small batch battery cell database; represent the small batch as... ;

[0079] S22; Represent each individual cell in a small batch of battery cells as follows: , Let the total number of units be... The average value of the voltage sequence characteristic parameters of each cell in a small batch is taken, and the average voltage sequence sequence is calculated based on the capacity grading data. The formula is as follows:

[0080] ;

[0081] in, This provides the voltage sequence parameters for temperature, current, time, and pressure of each cell in a small batch. for The sequence of mean values ​​of the parameters.

[0082] For time, a transformation is required. The initial time of the capacity allocation is set to 0. Its time frames are converted into compressed data in seconds. For example, if the first frame of the compressed data for a specific unit is 08:30:00:500ms on November 11, 2024, and the second frame is 08:31:06:400ms on November 11, 2024, then the transformation is as follows: The average compressed time value is converted, setting the initial time of the capacity allocation to 0, and the time frames are converted into compressed data in seconds (65.900). This process continues until the compressed data is converted to... frame.

[0083] Based on the above data processing flow, the average voltage sequence values ​​of various characteristic parameters such as current, time, temperature, and pressure are obtained, which are the average voltage sequence values ​​of the batch of cells.

[0084] Where Y represents the pressure sequence parameters of temperature, current, time, and pressure for each cell in a small batch;

[0085] S23: Obtain the sequence of average values ​​of temperature, current, time, and pressure for each cell in a small batch;

[0086] S24: Use the cosine similarity algorithm to calculate the similarity between the current vector of each cell and the average current of a small batch, and set a current similarity threshold. When the cells in a small batch Similarity to the average current voltage sequence of small batches At that time, This is recorded as an abnormal capacity balancing current and does not directly participate in the next similarity calculation and similarity feature matching. It returns to step S1 to re-balance the capacity and proceeds to the next small batch similarity calculation and similarity feature matching based on actual production. Preferably, .

[0087] Preferably, as shown in Figure 3, step S3 includes:

[0088] S31: Let the number of individual cells in the small batch after step S2 be... The cosine similarity of the average values ​​of temperature, current, time, and pressure of each cell in a small batch with the average value sequence of the batch's sequence characteristic parameters is calculated; this yields the individual cell sequence characteristic parameter similarity, which includes time... Compression temperature and sequential pressure ;

[0089] S32: Calculate the matching similarity based on the similarity of the compression feature parameters. :

[0090] ;

[0091] in, yes The weight, yes The weight, yes The weight.

[0092] Since the requirements for each batch of battery cells differ, some may not be equipped with individual cell pressure sensors. In this case, the corresponding terms in the formula need to be reduced, but at least the following should be maintained: The existence of parameters. Preferred. It should be noted that simply adding parameters is also within the scope of protection.

[0093] S33: Based on the matching similarity Perform similarity feature matching.

[0094] Preferably, as shown in FIG4, S33 further includes:

[0095] S331: Setting threshold and for small batches Sort the data and select the number of groups based on actual needs. and number of groups Suppose a certain small batch satisfies The number of And satisfy and ;

[0096] S332: Based on the sorting results and The determination is made to confirm whether the items are grouped together or shipped in the same batch.

[0097] Preferably, S332 further includes:

[0098] For small batches The first and last single units in the sorted sequence do not meet the requirements of a complete set. In this case, selection can be achieved by prioritizing shipments to the beginning, end, or middle of the shipment; among these, the preferred method is... .

[0099] For individual cells that fail to be matched or shipped within a small batch, they are considered abnormal cells. They can proceed to the next small batch similarity calculation and return to S2 for similarity feature matching, recording the batch delay parameters at this point. ,when When it is necessary to return to S1 to perform capacity allocation, At that time, the battery cell was marked as a scrapped battery cell. and Selection needs to be based on actual production conditions, with the preferred option being... , .

[0100] Furthermore, S332 also includes:

[0101] small batches Perform Z-score normalization, and denote the normalized score. Recorded as ,Will Exceeding Individual cells within the specified range are designated as abnormal cells;

[0102] And for small batches Sort the data and select the number of groups based on actual needs. and number of groups Suppose a certain small batch satisfies The number of And satisfy and ;

[0103] For small batches The first and last single units in the sorted sequence do not meet the requirements of a complete set. When selecting, priority can be given to shipping to the beginning, end, or middle of the shipment.

[0104] For individual cells that fail to be matched or shipped within a small batch, they are considered abnormal cells. They can proceed to the next small batch similarity calculation and return to S2 for similarity feature matching, recording the batch delay parameters at this point. ,when When it is necessary to return to S1 to perform capacity allocation, At that time, the battery cell was marked as a scrapped battery cell; among them, and Selection needs to be based on actual production conditions, with the preferred option being... , .

[0105] in: ;

[0106] In the formula, It is the inverse function of the Z-score error function.

[0107] The battery cascade utilization method for capacity allocation and grouping described in this application further includes: adjusting the voltage or pressure threshold range according to the degree of battery aging. The capacity data is used for the secondary use of individual cells within a battery cluster.

[0108] Among them, the quantity does not satisfy a complete set of quantities In such cases, selection can be achieved using methods such as prioritizing the first end, prioritizing the last end, or prioritizing the middle. It should be explained that prioritizing the first and last ends will retain individual cells that deviate from the overall similarity, facilitating subsequent re-grouping; prioritizing the middle ensures better product performance, but the risk of "abnormal cells" failing to be re-grouped and becoming "scrap cells" is higher.

[0109] In addition, this method can also adjust the voltage threshold range. The process is used for the secondary use of individual cells within the battery cluster, which is consistent with the capacity grading method. However, it is necessary to select a new voltage threshold range based on the degree of battery aging, which will not be elaborated further.

[0110] Example 2:

[0111] As shown in Figure 5, this application proposes a battery capacity allocation and grouping system 10 for cascaded battery utilization. As shown in Figure 6, the battery capacity allocation and grouping system 10 for cascaded battery utilization includes at least a domain controller 100. The domain controller 100 is used to perform capacity allocation of battery cells, record capacity allocation data, obtain the average value sequence of each voltage sequence characteristic parameter based on the capacity allocation data, and perform cosine similarity calculation and similarity feature matching. The successfully matched battery cells are shipped or grouped together in the same batch.

[0112] The domain controller 100 includes at least a processor 120 and a memory 110. The memory 110 is used to store computer instructions for multiple functional modules of battery cascade utilization and capacity allocation groups. The processor 120 communicates with the memory 110 through a bus 130 and is used to execute each computer instruction of the functional modules stored in the memory 110.

[0113] As shown in Figure 7, the plurality of functional modules include at least: a pressure sequence feature acquisition module 111: performing cell capacity grading and recording capacity grading data; the capacity grading data includes pressure sequence feature parameters; a mean processing module 112: importing the capacity grading data of each cell into a small batch cell database, obtaining the pressure sequence mean sequence of current, time, temperature and pressure for each pressure sequence feature parameter based on the pressure sequence mean sequence calculated from the capacity grading data, and performing cosine similarity calculation; a similarity matching module 113: performing similarity calculation based on the pressure sequence mean sequence and performing similarity feature matching; and a shipment and grouping module 114: shipping or grouping successfully matched cells as part of the same batch.

[0114] In some embodiments, the memory 110 and the processor 120 are interconnected via a bus 130; the processor 120 may be one or more CPUs. If the processor 120 is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processor 120 is used to control various functional modules of the electronic device and process signals.

[0115] The memory 110 includes, but is not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), and CD-ROM (Compact Disc Read-Only Memory). The memory 110 is used to store computer programs, operating systems, various applications, and data, such as storing computer programs for implementing the battery cascade utilization and capacity allocation method.

[0116] Example 3:

[0117] As shown in Figure 8, a battery grading and grouping device for cascaded utilization includes at least a battery grading and grouping system 10. The battery grading and grouping system 10 implements the steps of the battery grading and grouping method described in Embodiment 1. By real-time monitoring and collecting the time-series characteristic parameters of voltage changes during the grading process, the similarity between the voltage sequence mean value sequence and the voltage sequence mean value sequence of these parameters is calculated. Based on the similarity, matching and grouping are performed. Successfully matched cell batches are shipped or grouped, thereby achieving more precise grading control and more efficient grouping operations. This effectively reduces grouping defects caused by performance differences between batteries, further improving the grouping quality of the product and the consistency of cells within the battery pack, thereby enhancing the overall product reliability, stability, and service life.

[0118] The battery grading and grouping method, system, and equipment described in this invention enable optimized grading and grouping for small-batch shipments. By real-time monitoring and adjustment of key parameters such as voltage-sequence current, voltage-sequence voltage, and voltage-sequence temperature during the grading process, these parameters are ensured to maintain dynamic consistency, thereby achieving more precise grading control and more efficient grouping operations. This effectively reduces grouping defects caused by performance differences between batteries, further improving the grouping quality of the product and the consistency of cells within the battery pack, thus enhancing the overall product reliability and lifespan.

[0119] Furthermore, this application is not only applicable to initial capacity testing and grouping operations, but also has significant value for later applications. It can be used for the secondary utilization of individual cells within a battery cluster. Through secondary utilization, the remaining lifespan of the battery is fully utilized, thereby reducing resource waste and further extending the overall lifespan of the battery cluster. This novel capacity testing and grouping method provides a more flexible and sustainable battery management solution, meeting the demands for high efficiency, reliability, and environmental protection in modern battery production and use.

[0120] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0122] 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 merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0123] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors 120, or a combination thereof. Those skilled in the art will understand that microprocessors 120 or digital signal processors 120 (DSPs) 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 an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0125] Although the description of the invention has been given in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.

Claims

1. A method for allocating and grouping batteries for cascaded utilization, wherein, Perform cell capacity grading and record the grading data; the grading data includes the pressure sequence characteristic parameter (S1); The capacity classification data of each cell is imported into the small batch cell database. Based on the voltage sequence average value sequence calculated from the capacity classification data, the voltage sequence average value sequence of each voltage sequence characteristic parameter, such as current, time, temperature and pressure, is obtained, and cosine similarity is calculated. Among them, the small batch is at least the cells of the adjacent production batch, or the cells of the same production batch. The adjacent production batch can be the cells produced within the next 2 days or several days (S2). Similarity is calculated based on the compressed mean sequence, and similarity feature matching is performed (S3). Successfully matched cells are shipped in the same batch or grouped together (S4).

2. The method of claim 1, wherein, The cell capacity assessment process further includes: the cell capacity assessment is a continuous process, and capacity assessment data is recorded by sampling triggered by a threshold or by sampling at a jump point; when a capacity assessment interruption occurs, The battery is discharged to its lower voltage limit and then re-calibrated.

3. The battery capacity allocation method for cascade utilization according to claim 2, wherein, The recorded capacity data specifically includes: Score keeping voltage or pressure threshold range , voltage or pressure interval then during the charging process one data frame, after the same batch of cells rest for the same time interval, through the sizing device timing discharge. Will be from and left to stand, discharged to time data frame one, through a complete continuous process of dispensing, get the data sequence of the battery cell The data sequence includes a frame, which simultaneously records the unique number of the battery cell; wherein the data sequence includes voltage, time, pressure, current and temperature as voltage sequence characteristic parameters.

4. The battery capacity allocation method for cascade utilization according to claim 3, wherein, Step S2 further includes: The unique number of the battery cell and the data sequence of the data of the capacity information are introduced into the small batch battery cell database; the small batch is expressed as (S21)； Each individual cell in a small batch of cells is represented as ， , let the total number of monomers be The average values ​​of temperature, current, time, and pressure of each cell in a small batch are taken as the voltage sequence average value sequence. The formula for calculating the voltage sequence average value sequence based on the capacity grading data is (S22): ； wherein, temperature, current, time and pressure sequence parameters for small batches of individual cells, For The mean sequence of parameters; Obtain the average sequence of temperature, current, time and pressure of each cell in a small batch (S23); The cosine similarity algorithm is used to calculate the similarity between the voltage sequence current vector of each battery cell and the small batch current voltage sequence average, and a current similarity threshold is set when the cells within the small batch Similarity to small batch current order mean at the time of If the current is abnormal, it will not directly participate in the next step of similarity calculation and similarity feature matching. Return to step S1 to re-divide the current and proceed to the next small batch similarity calculation and similarity feature matching according to the actual production (S24).

5. The method of claim 4, wherein, Step S22 further includes: The time-converted time series average is time-converted, and the initial time of the volume is set as 0 time. The time frame is converted into a volume series data in seconds until the time series data is converted to frame.

6. A method for battery capacity allocation and grouping for cascade utilization according to claim 5, wherein, Step S3 includes: Let the number of battery cell monomers in the small batch after the S2 step be The temperature, current, time, and pressure sequence mean values of each battery cell in the small batch are calculated with the cosine similarity of the small batch pressure sequence characteristic parameter mean value sequence; the single cell pressure sequence characteristic parameter similarity is obtained, which includes time , pressure sequence temperature and pressure sequence pressure (S31)； According to the press sequence characteristic parameter similarity, the matching similarity is calculated （S32）； According to the matching similarity Perform similarity feature matching (S33).

7. A method for battery capacity allocation and grouping for cascade utilization according to claim 6, wherein, The matching similarity : , in which is the weight of the object, is the weight of the object, is weighting (S32) the plurality of features; wherein 。 8. The method of claim 7, wherein, Step S33 further includes: Setting Threshold value and to small batches Ranking, select single group size based on actual needs and the number of permutations , let a certain mini-batch satisfy the number of the , and satisfies and (S331)； According to the ranking result and The determination is made to confirm whether the items are grouped together or shipped in the same batch (S332).

9. The method of claim 8, wherein, Step S332 further includes: For small batches sorting the monomers at the beginning and end of the sequence, in an amount that does not complete a full set of the number When selecting, priority can be given to shipping to the beginning, end, or middle of the shipment. For the single body which fails to match or shipment within the small batch, it is an abnormal battery, which can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter at this time when When the time needs to be returned to S1 to perform the sub-packaging again, when At that time, the battery cell was marked as a scrapped battery cell.

10. The method of claim 9, wherein, Step S332 further includes: Small batches of Z-score normalization was performed, and the normalized values were recorded as denoted as ,Will exceeding Individual cells within the specified range are designated as abnormal cells; and to small batches Ranking, select single group size based on actual needs and the number of permutations , let a certain mini-batch satisfy the number of the , and satisfies and ； For small batches sorting the monomers at the beginning and end of the sequence, in an amount that does not complete a full set of the number When selecting, priority can be given to shipping to the beginning, end, or middle of the shipment. For the single body which fails to match or shipment within the small batch, it is an abnormal battery, which can enter the next small batch similarity calculation and return to S2 for similarity feature matching, and record the batch delay parameter at this time when When the time needs to be returned to S1 to perform the sub-packaging again, when At that time, the battery cell was marked as a scrapped battery cell; wherein: ； In the formulae, It is the inverse function of the Z-score error function.

11. A method of partial capacity grouping of battery cascade utilization according to claim 1, wherein, Also included: adjusting voltage or pressure threshold range according to battery aging degree And grading data for the battery cluster within the single battery for the ladder utilization.

12. A battery cascade utilization capacity allocation and grouping system (10) includes at least a domain controller (100), wherein the domain controller (100) is used to perform capacity allocation of battery cells, record capacity allocation data, obtain the average value sequence of each pressure sequence characteristic parameter according to the capacity allocation data, and perform cosine similarity calculation and similarity feature matching, and ship or group the successfully matched battery cells as the same batch.

13. The battery cascade utilization grouping system (10) according to claim 12, wherein the domain controller (100) includes at least a processor (120) and a memory (110). The memory (110) is used to store computer instructions for multiple functional modules of the battery cascade utilization group. The processor (120) communicates with the memory (110) via a bus to execute each computer instruction of the functional modules stored in the memory (110).

14. The battery cascade utilization and grouping system (10) according to claim 12, wherein the plurality of functional modules include at least: The cell compression sequence feature acquisition module (111) performs cell capacity grading and records the capacity grading data; the capacity grading data includes cell compression sequence feature parameters; Mean processing module (112): Imports the capacity data of each cell into the small batch cell database, calculates the pressure sequence mean sequence based on the capacity data, obtains the pressure sequence mean sequence of each pressure sequence characteristic parameter such as current, time, temperature and pressure, and performs cosine similarity calculation. Similarity matching module (113): calculates similarity based on the compressed mean sequence and performs similarity feature matching; Shipment matching module (114): Matches successfully matched cells as part of the same batch of shipments or groups.

15. A battery cascade utilization partial capacity matching and grouping device, wherein, It includes at least a battery capacity allocation system (10) for secondary battery utilization, which implements the steps of the battery capacity allocation method as described in claim 1.

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