Lithium battery consistency sorting method based on fuzzy clustering and electronic equipment

Abstract

The invention discloses a lithium battery consistency sorting method based on fuzzy clustering and electronic equipment. The method comprises the following steps: carrying out the same charging and discharging experiment on all battery monomers; battery sorting parameters are obtained, wherein the battery sorting parameters comprise charging capacity, discharging capacity, charging energy, discharging energy and the ratio of constant-current charging capacity to constant-voltage charging capacity; and based on the battery sorting parameters, carrying out consistency sorting on the batteries by adopting a fuzzy C-means algorithm fusing division clustering and fuzzy clustering. Through the synergistic effect of polarization characteristic parameters and fuzzy clustering, the sorting effect is obviously improved compared with that of a traditional method. The initial capacity of the sorted battery pack is improved, the capacity loss caused by grouping is obviously reduced, meanwhile, the charge-discharge voltage difference of each single battery in the battery pack is obviously reduced, the performance degradation of the battery pack is delayed, and the cycle life of the battery pack is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery sorting and grouping, and specifically relates to a lithium battery consistency sorting method and an electronic device based on fuzzy clustering. Background Art

[0002] One of the core challenges faced by lithium-ion battery packs in practical applications is the problem of consistency differences between individual batteries. Due to the inherent deviations in manufacturing processes and dynamic attenuation during use, even batteries produced in the same batch will have differences in parameters such as capacity, internal resistance, and aging rate. This difference will be amplified after grouping, resulting in the overall performance of the battery pack being significantly lower than that of individual batteries, and even causing safety hazards such as overcharging and over-discharging, accelerating capacity attenuation and the risk of thermal runaway. Obviously, the consistency difference is a key factor restricting the energy utilization rate, cycle life, and safety of lithium battery packs.

[0003] Currently, there are mainly three ways to improve the consistency of battery packs: process improvement, pre-grouping consistency sorting, and in-operation balancing management. Among them, process improvement is limited by cost and technical bottlenecks, while dynamic balancing management increases the system complexity. Therefore, precise sorting before grouping has become the most feasible solution. Currently, the industry generally adopts the parameter sorting method, that is, based on static parameter thresholds such as voltage, internal resistance, and capacity, combined with traditional statistical methods such as variance and standard deviation for grouping. Although this method is easy to operate, it only relies on limited static parameters and is difficult to comprehensively characterize the dynamic performance differences of batteries, resulting in insufficient sorting accuracy. Developing an advanced sorting method that integrates multi-dimensional parameters and intelligent algorithms has important engineering value for improving the performance of battery packs. Summary of the Invention

[0004] An object of the present invention is to provide a lithium battery consistency sorting method and an electronic device based on fuzzy clustering, which can solve the technical problems of the traditional lithium battery consistency sorting method in the prior art, such as single parameter dimension, rough sorting means, and inability to comprehensively reflect the differences in battery characteristics.

[0005] According to the first aspect of the present invention, a lithium battery consistency sorting method based on fuzzy clustering is provided, including:

[0006] Performing the same charge and discharge experiments on all battery monomers;

[0007] Obtaining battery sorting parameters, where the battery sorting parameters include charge capacity, discharge capacity, charge energy, discharge energy, and the ratio of constant current charge capacity to constant voltage charge capacity;

[0008] Based on the battery sorting parameters, using the fuzzy C-means algorithm that combines partitioning clustering and fuzzy clustering to perform consistency sorting on the batteries.

[0009] Optionally, performing the same charge and discharge experiments on all battery cells in the battery pack includes:

[0010] Fully charging all battery cells under the same conditions;

[0011] Performing constant current discharge on the battery at a rate of 2C at room temperature;

[0012] Stopping the discharge after the battery voltage reaches the discharge cut-off voltage;

[0013] Letting it stand for a preset time;

[0014] Performing constant current charging on the battery at a rate of 2C at room temperature;

[0015] Switching to constant voltage charging after the battery voltage reaches the charge cut-off voltage;

[0016] Ending the charging after the charging current drops to 0.05C.

[0017] Optionally, performing consistency sorting on the batteries according to the battery sorting parameters includes:

[0018] Constructing a characteristic matrix of the batteries;

[0019] Setting sorting algorithm parameters, including the number of clusters, the fuzzy clustering index, and the algorithm iteration stop condition;

[0020] Calculating the cluster centers;

[0021] Calculating the current objective function value;

[0022] Judging whether to stop the iteration;

[0023] If not stopped, updating the membership matrix and recalculating the cluster centers;

[0024] If the iteration is stopped, outputting the sorting result.

[0025] Optionally, constructing the characteristic matrix of the batteries includes:

[0026] Normalizing the battery sorting parameters according to the following formula:

[0027] ;

[0028] Where is the k-th characteristic parameter of the i-th battery before normalization, is the k-th characteristic parameter of the i-th battery after normalization, is the maximum value among all the k-th parameters, is the minimum value among all the k-th parameters;

[0029] Form a feature matrix based on the standardized characteristic parameters :

[0030] ;

[0031] Among them, respectively represent the charging capacity, discharging capacity, charging energy, discharging energy, ratio of constant current charging capacity to constant voltage charging capacity of the nth battery after standardization, and n is the number of batteries in the battery pack.

[0032] Optionally, the setting of the sorting algorithm parameters includes:

[0033] Set the number of clusters according to the number of batteries and the number of categories;

[0034] The stopping condition for algorithm iteration is that the objective function is less than the threshold, and the calculation formula of the objective function is as follows:

[0035] ;

[0036] ;

[0037] Among them, is the objective function, m is the fuzzy clustering index, n is the number of batteries in the battery pack, c is the number of clusters, is the membership degree, indicating the similarity degree between the ith data and the jth category, represents the distance between the characteristics of the ith battery and the jth cluster center, is the kth eigenvalue that makes up the jth cluster center.

[0038] Optionally, the calculation formula of the cluster center is as follows:

[0039] ;

[0040] Among them, is the jth cluster center, m is the fuzzy clustering index, n is the number of batteries in the battery pack, is the membership degree, is the eigenvalue of the ith battery.

[0041] Optionally, the membership degree matrix is updated according to the following formula:

[0042] ;

[0043] Among them, is the membership degree, indicating the similarity degree between the ith data and the jth category, m is the fuzzy clustering index, c is the number of clusters, represents the distance between the characteristics of the ith battery and the jth cluster center, Represents the distance between the characteristics of the i-th battery and the k-th clustering center.

[0044] Optionally, after performing consistency sorting on the batteries according to the battery sorting parameters, the method further includes:

[0045] Performing a grouping charge-discharge experiment on the sorted batteries;

[0046] During the experiment, sampling the voltage and current of all batteries in the group at intervals of 0.1S;

[0047] Determining the performance parameters of the battery pack, including discharge capacity, discharge energy, standard deviation of average voltage, and range of average voltage;

[0048] Determining the performance of the battery pack according to the performance parameters of the battery pack.

[0049] Optionally, the performing a grouping charge-discharge experiment on the sorted batteries includes:

[0050] Fully charging all the batteries under the same conditions;

[0051] Connecting the sorted batteries in series into a group, and performing constant current discharge on the battery pack at a rate of 1C at room temperature until the voltage of a battery in the battery pack reaches the discharge cut-off voltage;

[0052] Standing for a preset time;

[0053] Performing constant current charging on the battery pack at a rate of 1C at room temperature until the voltage of a battery in the battery pack reaches the charge cut-off voltage.

[0054] According to a second aspect of the present invention, there is provided an electronic device, including a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, it implements the steps of a method for consistent sorting of lithium batteries based on fuzzy clustering as described in the first aspect of the present invention.

[0055] The beneficial effects of the present invention are as follows: Through the synergistic effect of polarization characteristic parameters and fuzzy clustering, the sorting effect of the present invention is significantly improved compared with traditional methods. The initial capacity of the battery pack after sorting is increased, the capacity loss caused by grouping is significantly reduced, and at the same time, the charge and discharge voltage differences of each single battery in the battery pack are significantly reduced, which helps to delay the performance decay of the battery pack and improve the cycle life of the battery pack. The present invention has strong engineering applicability. Compared with traditional methods, it does not require additional measurement of parameters such as battery internal resistance, parameter acquisition is simple, only a single charge-discharge experiment is required, the algorithm calculation burden is small, and it is easy to industrialize. Description of the Drawings

[0056] Figure 1 Is a flowchart of a method for consistent sorting of lithium batteries based on fuzzy clustering in an embodiment of the present invention. Detailed Implementation Modes

[0057] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and values set forth in these embodiments do not limit the scope of the present invention.

[0058] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way serves as a limitation to the present invention, its application or use.

[0059] Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification. In all examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Thus, other examples of exemplary embodiments may have different values.

[0060] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, further discussion thereof in subsequent drawings is not necessary.

[0061] In the description of the present invention, features related to the terms "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "a plurality of" means two or more. In addition, "and / or" in the specification means at least one of the connected objects, and the character " / " generally means an "or" relationship between the associated objects before and after.

[0062] This embodiment introduces a method for sorting the consistency of lithium batteries based on fuzzy clustering, including steps 1100 - 1300.

[0063] Step 1100: Perform the same charge and discharge experiments on all battery cells.

[0064] In this embodiment, step 1100 includes steps 1110 - 1170.

[0065] Step 1110: Charge all battery cells to full charge under the same conditions.

[0066] Step 1120: Discharge the battery at a constant current rate of 2C at room temperature.

[0067] Step 1130: Stop discharging after the battery voltage reaches the discharge cut-off voltage.

[0068] Step 1140: Stand still for a preset time.

[0069] Step 1150: Constant current charge the battery at a rate of 2C at room temperature.

[0070] Step 1160: Switch to constant voltage charging after the battery voltage reaches the charging cut-off voltage.

[0071] Step 1170: End the charging after the charging current drops to 0.05C.

[0072] Taking 36 6Ah IFR32700 lithium iron phosphate batteries as an example, the battery parameters are shown in Table 1. First, fully charge all the lithium batteries to be tested under the same conditions to ensure consistent initial states. Then, constant current discharge the battery at a rate of 2C (12A) at room temperature until the battery voltage reaches the discharge cut-off voltage of 2.0V. Let it stand for 10 minutes. Then, constant current charge the battery at a rate of 2C (12A) at room temperature. When the battery voltage reaches the charging cut-off voltage of 3.65V, switch to constant voltage charging and end the charging when the charging current drops to 0.05C (0.3A).

[0073] Table 1 IFR32700 battery parameters

[0074]

[0075] Step 1200: Obtain battery sorting parameters, where the battery sorting parameters include charging capacity, discharging capacity, charging energy, discharging energy, and the ratio of constant current charging capacity to constant voltage charging capacity.

[0076] During the whole experiment, sample the voltage and current data of all batteries at intervals of 0.1s. After obtaining the charge and discharge voltage and current data of all batteries, the charge and discharge capacities, charge and discharge energies, and CC / CV parameters required for sorting can be calculated.

[0077] Step 1300: Based on the battery sorting parameters, perform consistent sorting of the batteries using a fuzzy C-means algorithm that combines partition clustering and fuzzy clustering.

[0078] For the sorting requirements of lithium-ion batteries, the present invention first selects charging capacity, discharging capacity, charging energy, discharging energy, and the ratio of constant current charging capacity to constant voltage charging capacity (The ratio of constantcurrent charging capacity to constant voltage charging capacity, CC / CV) as the core sorting parameters based on theoretical and experimental analyses. These parameters can be synchronously obtained through a single charge and discharge experiment, with both high efficiency of data acquisition and engineering practicability.

[0079] Among them, the charging capacity, discharging capacity, charging energy, and discharging energy directly reflect the basic performance of the battery, avoiding the one-sidedness of traditional single voltage or internal resistance parameters; while the CC / CV parameter, as an innovative parameter, comprehensively characterizes the polarization characteristics and internal resistance distribution of the battery by quantifying the capacity ratio of the constant current (polarization formation) and constant voltage (polarization elimination) stages, including ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance, replacing the traditional complex internal resistance measurement. Since the current is large and the efficiency is high during the constant current charging process, while the current is small and the efficiency is low during the constant voltage charging process, this parameter can also reflect the charging efficiency of the battery, thereby indirectly reflecting the performance differences of the battery under dynamic working conditions.

[0080] Based on theoretical and experimental analysis, in addition to selecting static parameters such as charging capacity, discharging capacity, charging energy, and discharging energy, the present invention innovatively introduces the ratio of constant current charging capacity to constant voltage charging capacity to quantify the polarization characteristics of the battery and comprehensively characterize the static and dynamic performance of the battery. Subsequently, all sorting parameters are extracted through only one charge-discharge experiment, and the data acquisition process is simple. Finally, based on the sampled battery parameter data, the fuzzy C-means algorithm that combines partition clustering and fuzzy clustering is used to optimize the classification boundary through the membership function, and batteries with as high a consistency as possible are sorted to form a battery pack, ensuring the performance and cycle life of the battery pack.

[0081] In this embodiment, step 1300 includes steps 1310-1370.

[0082] Step 1310: Construct the feature matrix of the battery.

[0083] In order to eliminate the dimensional difference between each feature parameter, the battery sorting parameters need to be standardized according to the following formula:

[0084] ;

[0085] Among them, is the k-th feature parameter of the i-th battery before standardization, is the k-th feature parameter of the i-th battery after standardization, is the maximum value among all the k-th parameters, is the minimum value among all the k-th parameters.

[0086] Subsequently, the standardized battery feature parameters are combined to form a feature matrix X that can represent all battery features, thereby serving as the input of the sorting algorithm.

[0087] According to the standardized feature parameters, a feature matrix is formed :

[0088] ;

[0089] Among them, respectively represent the charging capacity, discharging capacity, charging energy, discharging energy, ratio of constant current charging capacity to constant voltage charging capacity of the nth battery after standardization, where n is the number of batteries in the battery pack.

[0090] Step 1320: Set the sorting algorithm parameters, including the number of clusters, fuzzy clustering index, and algorithm iteration stop condition.

[0091] First, set the number of clusters c. Set the number of clusters according to the number of batteries and the number of categories, and it can be set based on the number of batteries and the number of categories that need to be sorted in actual applications. The fuzzy clustering index m is generally taken as 2.

[0092] The algorithm iteration stop condition is that the objective function is less than the threshold. The calculation formula of the objective function is as follows:

[0093] ;

[0094] ;

[0095] Among them, is the objective function, m is the fuzzy clustering index, n is the number of batteries in the battery pack, c is the number of clusters, represents the distance between the feature of the ith battery and the jth cluster center, is the kth eigenvalue that makes up the jth cluster center. is the membership degree, indicating the similarity degree between the ith data and the jth class, and satisfies the normalization condition:

[0096] ;

[0097] The membership degree matrix can be initialized by randomly assigning values by the computer.

[0098] Step 1330: Calculate the cluster centers.

[0099] The calculation formula of the cluster centers is as follows:

[0100] ;

[0101] Among them, is the jth cluster center, m is the fuzzy clustering index, n is the number of batteries in the battery pack, is the membership degree, is the eigenvalue of the ith battery.

[0102] Step 1340: Calculate the current objective function value.

[0103] Calculate the current objective function value according to the above objective function calculation formula.

[0104] Step 1350: Determine whether to stop iteration.

[0105] Step 1360: If not stopped, update the membership matrix and recalculate the cluster centers.

[0106] The membership matrix is updated according to the following formula:

[0107] ;

[0108] where is the membership degree, representing the similarity degree between the i-th data and the j-th class, m is the fuzzy clustering index, c is the number of clusters, represents the distance between the feature of the i-th battery and the j-th cluster center, represents the distance between the feature of the i-th battery and the k-th cluster center.

[0109] If the algorithm is not stopped, after updating the membership matrix, return to Step 1330 to recalculate the cluster centers.

[0110] Step 1370: If the iteration is stopped, output the sorting result.

[0111] After the algorithm stops iterating, based on the latest membership matrix, each battery is assigned to the class with the largest membership degree, and then the sorting result is output.

[0112] In this embodiment, in order to verify the effect of the sorting method proposed by the present invention, a lithium battery matching charge and discharge experiment is designed, which specifically includes Steps 2100 - 2400.

[0113] Step 2100: Conduct a matching charge and discharge experiment on the sorted batteries.

[0114] All batteries are fully charged under the same conditions; the sorted batteries are connected in series into a group, and the battery group is discharged at a constant current of 1C at room temperature until the voltage of a battery in the battery group reaches the discharge cut-off voltage; stand for a preset time; charge the battery group at a constant current of 1C at room temperature until the voltage of a battery in the battery group reaches the charge cut-off voltage.

[0115] Step 2200: Sample the voltage and current of all batteries in the group at intervals of 0.1S during the experiment.

[0116] Step 2300: Determine the performance parameters of the battery group, including discharge capacity, discharge energy, standard deviation of average voltage, and range of average voltage.

[0117] Step 2400: Determine the performance of the battery group according to the performance parameters of the battery group.

[0118] Here, the performance of the battery pack is evaluated through four indicators: the discharge capacity of the battery pack, the discharge energy, the standard deviation of the average voltage, and the range of the average voltage during the charge and discharge process. Among them, the calculation formulas for the standard deviation of the average voltage SD and the range of the average voltage SR are as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] Among them, represents the voltage of the i-th battery at the k-th sampling point, represents the average voltage of all batteries at the k-th sampling point, n represents the number of batteries in the group, and N represents the total number of sampling points.

[0124] To fully demonstrate the superiority of the proposed sorting method, here, the proposed sorting method of the present invention and the traditional sorting method (battery capacity difference ≤ 3%; internal resistance difference ≤ 5%; average discharge voltage difference ≤ 5%) are respectively used to sort the 36 brand-new 6Ah IFR32700 lithium batteries mentioned above. Four lithium batteries are selected and connected in series to form a battery pack by both methods. Through the above lithium battery matching charge and discharge experiments, the performance parameters of the battery packs under the two sorting methods are shown in Table 2.

[0125] Table 2 Experimental results of battery matching with different sorting methods

[0126]

[0127] Based on Table 2, it can be seen that the initial capacity of the battery pack sorted by the sorting method proposed in the present invention is significantly greater than that of the battery pack sorted by the traditional method. Compared with the nominal capacity, the reduction in the capacity of the battery group decreases from 0.15 Ah of the traditional method to less than 0.1 Ah, a reduction of about 33%. At the same time, the initial energy output of the battery pack is also increased by 1.6% compared with the traditional sorting. In addition, compared with the traditional method, the standard deviation of the average voltage within the battery pack during the charge and discharge process of the battery pack sorted by the proposed sorting method is reduced by 24.5%, and the range of the average voltage is reduced by 26%. And the sorting method proposed in the present invention does not require measuring the internal resistance of the battery, and the implementation is simpler.

[0128] This embodiment introduces an electronic device, including a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, it realizes the steps of a lithium battery consistency sorting method based on fuzzy clustering as described in any embodiment of the present invention.

[0129] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and not for limiting the scope of the present invention. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the present invention.

[0130] Those of ordinary skill in the art can realize that the modules and algorithm steps described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0131] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices and equipment described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0132] In the embodiments provided in the present 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 example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical or other forms.

[0133] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0134] In addition, the various functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0135] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0136] The above description is only for the preferred embodiments of this application and the explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solution formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in this application.

[0137] It should be understood that the magnitudes of the sequence numbers of the steps in the content of the present invention and the embodiments do not absolutely mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. For the purpose of illustration and description, the foregoing description of the implementation of the present disclosure has been given. The foregoing description is not exhaustive and does not intend to limit the present disclosure to the exact form disclosed. According to the above teachings, various deformations and modifications may exist, or various deformations and modifications may be obtained from the practice of the present disclosure. These embodiments are selected and described to illustrate the principles of the present disclosure and its practical applications, so that those skilled in the art can utilize the present disclosure in various embodiments and various modifications suitable for the specific use conceived.

Claims

1. A method for sorting the consistency of lithium batteries based on fuzzy clustering, characterized in that Including: Performing the same charge and discharge experiments on all battery cells; Obtaining battery sorting parameters, where the battery sorting parameters include charge capacity, discharge capacity, charge energy, discharge energy, and the ratio of constant current charge capacity to constant voltage charge capacity; Based on the battery sorting parameters, using a fuzzy C-means algorithm that combines partition clustering and fuzzy clustering to perform consistency sorting on the batteries.

2. The method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 1, characterized in that The performing the same charge and discharge experiments on all battery cells in the battery pack includes: Fully charging all battery cells under the same conditions; Performing constant current discharge on the batteries at a rate of 2C at room temperature; Stopping the discharge after the battery voltage reaches the discharge cut-off voltage; Standing for a preset time; Performing constant current charging on the batteries at a rate of 2C at room temperature; Switching to constant voltage charging after the battery voltage reaches the charge cut-off voltage; Ending the charging after the charging current drops to 0.05C.

3. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 1, characterized in that The performing consistency sorting on the batteries according to the battery sorting parameters includes: Constructing a feature matrix of the batteries; Setting sorting algorithm parameters, including the number of clusters, the fuzzy clustering index, and the algorithm iteration stop condition; Calculating the cluster centers; Calculating the current objective function value; Judging whether to stop the iteration; If not stopped, updating the membership matrix and recalculating the cluster centers; If the iteration is stopped, outputting the sorting result.

4. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 3, characterized in that, The constructing a feature matrix of the batteries includes: Normalizing the battery sorting parameters according to the following formula: ； Among them, is the k-th characteristic parameter of the i-th battery before standardization, is the k-th characteristic parameter of the i-th battery after standardization, is the maximum value among all the k-th parameters, is the minimum value among all the k-th parameters; Form a feature matrix based on the standardized feature parameters : ； Among them, respectively represent the charging capacity, discharging capacity, charging energy, discharging energy, and the ratio of constant-current charging capacity to constant-voltage charging capacity of the nth battery after standardization, where n is the number of batteries in the battery pack.

5. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 4, characterized in that The setting the sorting algorithm parameters includes: Setting the number of clusters according to the number of batteries and the number of categories; The algorithm iteration stop condition is that the objective function is less than a threshold, and the objective function calculation formula is as follows: ； ； Among them, is the objective function, m is the fuzzy clustering index, n is the number of batteries in the battery pack, c is the number of clusters, is the membership degree, representing the similarity degree between the i-th data and the j-th class, represents the distance between the characteristics of the i-th battery and the j-th cluster center, is the k-th eigenvalue that makes up the j-th cluster center.

6. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 5, characterized in that The calculation formula for the cluster centers is as follows: ； Among them, is the j-th clustering center, m is the fuzzy clustering index, n is the number of batteries in the battery pack, is the membership degree, is the eigenvalue of the i-th battery.

7. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 4, characterized in that The membership matrix is updated according to the following formula: ； Among them, is the membership degree, indicating the similarity degree between the i-th data and the j-th class, m is the fuzzy clustering index, c is the number of clusters, represents the distance between the feature of the i-th battery and the j-th cluster center, represents the distance between the feature of the i-th battery and the k-th cluster center.

8. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 1, characterized in that After performing consistency sorting on the batteries according to the battery sorting parameters, the method further includes: Performing a grouped charge and discharge experiment on the sorted batteries; Sampling the voltages and currents of all batteries in the group at intervals of 0.1S during the experiment; Determining the performance parameters of the battery pack, including discharge capacity, discharge energy, average voltage standard deviation, and average voltage range; Determining the performance of the battery pack according to the performance parameters of the battery pack.

9. A method for sorting the consistency of lithium batteries based on fuzzy clustering according to claim 8, characterized in that, The performing a grouped charge and discharge experiment on the sorted batteries includes: Fully charging all the batteries under the same conditions; Connecting the sorted batteries in series into a group, and performing constant current discharge on the battery group at a rate of 1C at room temperature until the voltage of a battery in the battery group reaches the discharge cut-off voltage; Standing for a preset time; Performing constant current charging on the battery group at a rate of 1C at room temperature until the voltage of a battery in the battery group reaches the charge cut-off voltage.

10. An electronic device, characterized in that, Including a processor and a memory, the memory stores a program or instructions that can run on the processor, and when the program or instructions are executed by the processor, the steps of a lithium battery consistency sorting method based on fuzzy clustering as described in any one of claims 1 to 9 are implemented.

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