Battery parallel capacity grading method and system

Abstract

The embodiment of the invention provides a battery parallel capacity grading method and system, and relates to the technical field of battery capacity grading. The capacity grading method comprises the following steps: establishing a mathematical model of a battery parallel structure of a first-order RC equivalent circuit; obtaining detectable voltage and current data in the battery according to the mathematical model; obtaining an internal parameter value of each battery according to the voltage and current data; k-means clustering is carried out on each of the internal parameter values; and according to the classification number, carrying out comprehensive parameter capacity grading on the clustering result according to a preset rule. According to the method, only testing is needed in the middle section of the SOC, the cut-off voltage of the battery does not need to be judged, the data size can be reduced, dynamic capacity grading of the battery is achieved, the capacity grading cabinet only needs to be arranged in a constant-voltage mode, a complex working mode is not needed, and the capacity grading time and cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of battery capacity testing technology, and more specifically to a method and system for parallel battery capacity testing. Background Technology

[0002] During battery capacity testing, data from each testing point is obtained through computer management to determine the battery's quality level. This data volume is enormous and requires a lot of calculation, making the capacity testing process complex and time-consuming. Existing technologies typically require determining the battery's cutoff voltage and setting the working mode of the capacity testing equipment, which is complex to operate and follow, placing high demands on the performance of the capacity testing equipment. Summary of the Invention

[0003] The purpose of this invention is to provide a battery parallel capacity testing method and system. This method and system only requires testing in the middle of the SOC stage, without needing to determine the battery's cutoff voltage, which simplifies the amount of data and enables dynamic capacity testing of the battery. Furthermore, the capacity testing cabinet only needs to be set to constant voltage mode, without the need for complex operating modes, which can reduce capacity testing time and cost.

[0004] To achieve the above objectives, one embodiment of the present invention provides a battery parallel capacity testing method, the capacity testing method comprising: Establish a mathematical model of the parallel battery structure of the first-order RC equivalent circuit; The detectable voltage and current data inside the battery are obtained based on the mathematical model. The internal parameter values ​​of each battery are obtained based on the voltage and current data; Perform K-means clustering on each of the aforementioned intrinsic parameter values; Based on the number of categories, the clustering results are combined and classified according to preset rules.

[0005] Optionally, a mathematical model of the battery parallel structure of the first-order RC equivalent circuit is established, including: In the mathematical model, node 1 is taken as an equal current node; Treat the zero nodes in the mathematical model as equipotential nodes; The arrows connecting nodes 1 and 0 in the mathematical model are used as keys, and each key contains two variables: voltage and current. When there are vertical lines between the key and the connected node, it indicates that the key provides voltage to the connected node; When there are no vertical lines between the key and the connected node, it indicates that the key provides current to the connected node.

[0006] Optionally, the detectable voltage and current data inside the battery are obtained according to the mathematical model, including: According to formula (1), the current and voltage of node 1 marked A in the mathematical model are obtained. (1) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0007] Optionally, the detectable voltage and current data inside the battery are obtained according to the mathematical model, including: According to formula (2), the current and voltage of node 0 marked C in the mathematical model are obtained. (2) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0008] Optionally, the detectable voltage and current data inside the battery are obtained according to the mathematical model, including: According to formula (3), the current and voltage of node 0 marked B in the mathematical model are obtained. (3) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0009] Optionally, the internal parameter values ​​of each battery are obtained based on the voltage and current data, including: According to formula (4), the internal parameter values ​​of the first battery in the mathematical model are obtained. (4) in, This is the open-circuit voltage of the first battery. The charging and discharging voltage provided for the capacity divider operating in constant voltage mode, and this voltage value is constant. The ohmic internal resistance of the first battery. The polarization internal resistance of the first battery. This is the polarization capacitor of the first battery.

[0010] Optionally, the internal parameter values ​​of each battery are obtained based on the voltage and current data, including: Given three different constant voltage values ​​in the capacity-dividing cabinet to provide different charge and discharge voltages, obtain the ohmic internal resistance of the first battery. The polarization internal resistance of the first battery And the polarization capacitor of the first battery .

[0011] Optionally, K-means clustering is performed on each of the said intrinsic parameter values, including: Random selection There are 10 internal parameter values, each of which initially represents the center of a cluster; For each remaining internal parameter value, assign it to the nearest cluster based on its distance from the cluster center; Recalculate the average value for each cluster and update it with the new cluster centers; Return the step for each remaining intrinsic parameter value, based on its distance from the cluster center, to the nearest cluster, until the criterion function converges; After the criterion function converges, the intrinsic parameter values ​​are classified according to the clustering results. The intrinsic parameter values ​​include... The ohmic internal resistance of the battery Polarization internal resistance and polarization capacitor ; Obtain K-means clustering The ohmic internal resistance of the battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; Obtain K-means clustering The polarization internal resistance of the individual battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; Obtain K-means clustering The polarization capacitor of each battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class.

[0012] Optionally, based on the number of categories, the clustering results are subjected to comprehensive parameter scaling according to preset rules, including: The classification level is obtained according to formula (5). (5) in, Classification levels The minimum value in.

[0013] On the other hand, the present invention also provides a battery parallel capacity grading system, the capacity grading system including a processor for executing the capacity grading method as described above.

[0014] Through the above technical solution, this invention provides a battery parallel capacity assessment method and system. By establishing a mathematical model of the battery parallel structure with a first-order RC equivalent circuit, detectable voltage and current data within the batteries are obtained based on the mathematical model. Internal parameter values ​​for each battery are then obtained based on the voltage and current data. K-means clustering is performed on each internal parameter value, and the clustering results are combined according to preset rules for comprehensive capacity assessment based on the number of clusters. This capacity assessment method and system only requires testing in the middle of the SOC phase, eliminating the need to determine the battery's cutoff voltage. This simplifies the data volume and enables dynamic capacity assessment of the batteries. Furthermore, the capacity assessment cabinet only needs to be set to constant voltage mode, eliminating the need for complex operating modes and reducing capacity assessment time and cost.

[0015] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a battery parallel capacity-sharing method according to one embodiment of the present invention; Figure 2 This is a schematic diagram of a mathematical model of a parallel battery structure according to one embodiment of the present invention; Figure 3 This is a flowchart illustrating the establishment of a mathematical model for one embodiment of the present invention; Figure 4 This is a flowchart illustrating the acquisition of voltage and current data according to one embodiment of the present invention; Figure 5 This is a flowchart illustrating the process of obtaining internal parameter values ​​of a battery according to one embodiment of the present invention. Figure 6 This is a flowchart of clustering internal parameter values ​​according to one embodiment of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0018] In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0019] like Figure 1 The diagram shown is a flowchart of a battery parallel capacity-differentiating method according to one embodiment of the present invention. Figure 2 The diagram shown is a mathematical model of a parallel battery structure according to one embodiment of the present invention. Figure 1 In this context, the capacity-sharing method may include: In step S1, a mathematical model of the parallel battery structure of the first-order RC equivalent circuit is established; In step S2, detectable voltage and current data inside the battery are obtained based on the mathematical model; In step S3, the internal parameter values ​​of each battery are obtained based on the voltage and current data; In step S4, K-means clustering is performed on each intrinsic parameter value; In step S5, the clustering results are combined and divided according to preset rules based on the number of categories.

[0020] In steps S1 to S5, a mathematical model of the parallel battery structure with a first-order RC equivalent circuit is established. Based on this model, detectable voltage and current data within the batteries are obtained. Internal parameter values ​​for each battery are then acquired using this data. K-means clustering is performed on each internal parameter value. Based on the number of clusters, the clustering results are combined according to preset rules for capacity allocation. This capacity allocation method and system only requires testing in the middle of the SOC phase, eliminating the need to determine the battery's cutoff voltage. This simplifies the data volume and enables dynamic capacity allocation. Furthermore, the capacity allocation cabinet only needs to be set to constant voltage mode, eliminating the need for complex operating modes and reducing capacity allocation time and cost.

[0021] In Figure 1 In the method shown, step S1 can be used to establish a mathematical model of the parallel battery structure of the first-order RC equivalent circuit. Figure 2 This is a schematic diagram of the mathematical model. The specific methods for establishing the mathematical model can take many forms known to those skilled in the art. In one example of this invention, establishing the mathematical model may include, for example... Figure 3 The steps shown are in this Figure 3 In this context, step S1 may further include: In step S11, node 1 in the mathematical model is taken as an equicurrent node; In step S12, the zero node in the mathematical model is taken as the equipotential node; In step S13, the arrows connecting nodes 1 and 0 in the mathematical model are used as keys, and each key contains two variables: voltage and current. In step S14, when there are vertical lines on the key and the connected node, it indicates that the key provides voltage to the connected node; In step S15, when there are no vertical lines on the key and the connected node, it indicates that the key provides current to the connected node.

[0022] like Figure 2 The mathematical model shown is in this Figure 2 In the diagram, node 1 can be considered an equipotential node, and node 0 can be considered an equipotential node. The arrows connecting nodes 1 and 0 can be considered bonds. Each bond contains two variables: voltage and current. For example, the voltage and current of bond 1-1 are respectively... and The voltage and current of keys 1-2 are respectively and Similarly, the voltages and currents of keys 1-7 are respectively and The direction of the arrow indicates the reference direction of voltage and current. When there are vertical lines on the key and the connected node, it indicates that the key provides voltage to the connected node; when there are no vertical lines on the key and the connected node, it indicates that the key provides current to the connected node. Figure 2 Taking node 1, marked A, as an example, the keys connected to it are 1-1, 1-2, 1-3, and 1-6. Among them, key 1-2 is connected to the node without a vertical line, so key 1-2 provides current to the node, and the other keys provide voltage to the node.

[0023] Let there be a total If the batteries are connected in series, then the first one can also be determined. The battery's contacts and the voltage and current at each contact. Step S2 can be used to obtain detectable voltage and current data inside the battery based on a mathematical model. Specific methods for obtaining the voltage and current data can be various and known to those skilled in the art. In one example of the invention, obtaining the voltage and current data may include, for example... Figure 4 The steps shown are in this Figure 4 In this context, step S2 may further include: In step S21, the current and voltage of node 1 marked A in the mathematical model are obtained according to formula (1). (1) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0024] In step S22, the current and voltage at node C (marked in the mathematical model) are obtained according to formula (2). (2) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0025] In step S23, the current and voltage at node 0 marked B in the mathematical model are obtained according to formula (3). (3) in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

[0026] Since node 1 represents an equicurrent node, the currents on all bonds connected to that node are equal, and the vector sum of the voltages is 0. Therefore, in Figure 2 In the mathematical model shown, Figure 2 The parameter value of node 1, marked A, can be determined according to formula (1). Since node 0 represents an equipotential node, the voltage on all bonds connected to this node is equal, and the vector sum of the currents is 0. Therefore, in Figure 2 In the mathematical model shown, Figure 2 The parameter values ​​for node 0, marked C, can be determined according to formula (2). Therefore, in Figure 2 In the mathematical model shown, Figure 2 The parameter value of node 0 marked as B can be determined according to formula (3).

[0027] In Figure 2 middle, for The open-circuit voltage of each battery. for The ohmic internal resistance of a battery for The polarization internal resistance of a battery. for The polarization capacitor of a battery. The charging and discharging voltage provided to the capacity grading cabinet operating in constant voltage mode is constant; therefore, the capacity grading cabinet only needs to be set to constant voltage mode. Step S3 can be used to obtain the internal parameter values ​​of each battery based on voltage and current data. The specific method for obtaining the internal parameter values ​​of each battery can be of various forms known to those skilled in the art. In one example of the present invention, obtaining the internal parameter values ​​of each battery may include, for example... Figure 5 The steps shown are in this Figure 5 In this context, step S3 may further include: In step S31, the internal parameter values ​​of the first battery in the mathematical model are obtained according to formula (4). (4) in, This is the open-circuit voltage of the first battery. The charging and discharging voltage provided for the capacity divider operating in constant voltage mode, and this voltage value is constant. The ohmic internal resistance of the first battery. The polarization internal resistance of the first battery. This is the polarization capacitor of the first battery.

[0028] In step S32, three different constant voltage values ​​are given to the capacity-dividing cabinet to provide different charging and discharging voltages, and the ohmic internal resistance of the first battery is obtained. The polarization internal resistance of the first battery And the polarization capacitor of the first battery .

[0029] For the first series-connected battery, the internal parameter values ​​can be obtained as shown in formula (4). According to formulas (1) to (3), The charging and discharging voltage provided for the capacity divider operating in constant voltage mode is a known quantity and is constant. This is the open-circuit voltage, which is also a known quantity. The current in a single battery branch is a known quantity, which can be measured. Therefore, the only unknown quantities are the battery parameters. , and Therefore, by providing three different constant voltage values ​​for the capacity-dividing cabinet, different charge and discharge voltages can be supplied, and the ohmic internal resistance of the first battery can be obtained. The polarization internal resistance of the first battery And the polarization capacitor of the first battery .

[0030] Step S4 can be used to perform K-means clustering on each intrinsic parameter value. The specific method for clustering each intrinsic parameter value can be of various forms known to those skilled in the art. In one example of the present invention, the method for clustering each intrinsic parameter value may include, for example... Figure 6 The steps shown are in this Figure 6 In this context, step S4 may further include: In step S41, randomly select There are 10 internal parameter values, each of which initially represents the center of a cluster; In step S42, for each remaining internal parameter value, it is assigned to the nearest cluster based on its distance from the cluster center; In step S43, the average value of each cluster is recalculated and updated to the new cluster center; In step S44, for each remaining intrinsic parameter value, it is returned and assigned to the nearest cluster based on its distance from the cluster center, until the criterion function converges; In step S45, after the criterion function converges, the intrinsic parameter values ​​are classified according to the clustering results. The intrinsic parameter values ​​include... The ohmic internal resistance of the battery Polarization internal resistance and polarization capacitor ; In step S46, obtain the K-means clustered data. The ohmic internal resistance of the battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; In step S47, obtain the K-means clustered data. The polarization internal resistance of the individual battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; In step S48, obtain the K-means clustered data. The polarization capacitor of each battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class.

[0031] Step S5 can be used to perform comprehensive parameter allocation on the clustering results according to preset rules based on the number of categories. Because They are not necessarily equal; the minimum value of the classification level needs to be obtained according to formula (5). (5) in, Classification levels The minimum value in.

[0032] Table 1

[0033] The rules are shown in Table 1. Based on the table, batteries can be divided into 2 categories. This allows for different levels of capacity allocation for parallel batteries.

[0034] On the other hand, the present invention also provides a battery parallel capacity grading system, the capacity grading system including a processor for executing the capacity grading method as described above.

[0035] Through the above technical solution, this invention provides a battery parallel capacity assessment method and system. By establishing a mathematical model of the battery parallel structure with a first-order RC equivalent circuit, detectable voltage and current data within the batteries are obtained based on the mathematical model. Internal parameter values ​​for each battery are then obtained based on the voltage and current data. K-means clustering is performed on each internal parameter value, and the clustering results are combined according to preset rules for comprehensive capacity assessment based on the number of clusters. This capacity assessment method and system only requires testing in the middle of the SOC phase, eliminating the need to determine the battery's cutoff voltage. This simplifies the data volume and enables dynamic capacity assessment of the batteries. Furthermore, the capacity assessment cabinet only needs to be set to constant voltage mode, eliminating the need for complex operating modes and reducing capacity assessment time and cost.

[0036] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0037] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0038] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0040] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0041] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0042] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0043] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0044] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for parallel capacity testing of batteries, characterized in that, The capacity-sharing method includes: Establish a mathematical model of the parallel battery structure of the first-order RC equivalent circuit; The detectable voltage and current data inside the battery are obtained based on the mathematical model. The internal parameter values ​​of each battery are obtained based on the voltage and current data; Perform K-means clustering on each of the aforementioned intrinsic parameter values; Based on the number of categories, the clustering results are combined and classified according to preset rules.

2. The capacity-sharing method according to claim 1, characterized in that, A mathematical model of the parallel battery structure with a first-order RC equivalent circuit is established, including: In the mathematical model, node 1 is taken as an equal current node; Treat the zero nodes in the mathematical model as equipotential nodes; The arrows connecting nodes 1 and 0 in the mathematical model are used as keys, and each key contains two variables: voltage and current. When there are vertical lines between the key and the connected node, it indicates that the key provides voltage to the connected node; When there are no vertical lines between the key and the connected node, it indicates that the key provides current to the connected node.

3. The capacity-sharing method according to claim 2, characterized in that, Based on the mathematical model, detectable voltage and current data inside the battery are obtained, including: According to formula (1), the current and voltage of node 1 marked A in the mathematical model are obtained. ，（1） in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

4. The capacity-sharing method according to claim 3, characterized in that, Based on the mathematical model, detectable voltage and current data inside the battery are obtained, including: According to formula (2), the current and voltage of node 0 marked C in the mathematical model are obtained. ，（2） in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

5. The capacity-sharing method according to claim 4, characterized in that, Based on the mathematical model, detectable voltage and current data inside the battery are obtained, including: According to formula (3), the current and voltage of node 0 marked B in the mathematical model are obtained. ，（3） in, For single cell batteries The current in the branch circuit, The number of batteries is a positive integer. For single cell batteries The voltage of the branch circuit.

6. The capacity-sharing method according to claim 5, characterized in that, Based on the voltage and current data, obtain the internal parameter values ​​of each battery, including: According to formula (4), the internal parameter values ​​of the first battery in the mathematical model are obtained. ，（4） in, This is the open-circuit voltage of the first battery. The charging and discharging voltage provided for the capacity divider operating in constant voltage mode, and this voltage value is constant. The internal resistance of the first battery is ohmic. The polarization internal resistance of the first battery. This is the polarization capacitor of the first battery.

7. The capacity-sharing method according to claim 6, characterized in that, Based on the voltage and current data, obtain the internal parameter values ​​of each battery, including: Given three different constant voltage values ​​in the capacity-dividing cabinet to provide different charge and discharge voltages, obtain the ohmic internal resistance of the first battery. The polarization internal resistance of the first battery And the polarization capacitor of the first battery .

8. The capacity-sharing method according to claim 7, characterized in that, K-means clustering is performed on each of the said intrinsic parameter values, including: Random selection There are 10 internal parameter values, each of which initially represents the center of a cluster; For each remaining internal parameter value, assign it to the nearest cluster based on its distance from the cluster center; Recalculate the average value for each cluster and update it with the new cluster centers; Return the step for each remaining intrinsic parameter value, based on its distance from the cluster center, to the nearest cluster, until the criterion function converges; After the criterion function converges, the intrinsic parameter values ​​are classified according to the clustering results. The intrinsic parameter values ​​include... The ohmic internal resistance of the battery Polarization internal resistance and polarization capacitor ; Obtain K-means clustering The ohmic internal resistance of the battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; Obtain K-means clustering The polarization internal resistance of the individual battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class; Obtain K-means clustering The polarization capacitor of each battery ,make Divided into The classes are categorized into levels 1, 2, etc., based on their values ​​from smallest to largest. class.

9. The capacity-sharing method according to claim 8, characterized in that, Based on the number of categories, the clustering results are combined and divided according to preset rules, including: The classification level is obtained according to formula (5). ，（5） in, Classification levels The minimum value in.

10. A battery parallel capacity-sharing system, characterized in that, The capacity partitioning system includes a processor for executing the capacity partitioning method as described in any one of claims 1 to 9.

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