Internal resistance consistency evaluation method for dynamic reconfigurable battery module, and system

Abstract

An internal resistance consistency evaluation method for a dynamic reconfigurable battery module, and a system. The method comprises: calculating a current change rate by introducing the nominal capacity of a battery cell, and selecting the previous sampling moment of a sampling moment, at which the current change rate is higher than or equal to a target current change rate, as a target sampling moment (S3); when the number of target sampling moments is greater than a preset value, calculating the internal resistances of each battery cell at the target sampling moments on the basis of the target sampling moments, and constructing an internal resistance matrix (S4); and using the internal resistance matrix and an improved internal resistance consistency evaluation algorithm to calculate an internal resistance consistency result of the dynamic reconfigurable battery module (S5). In the present method, the current change rate is calculated by introducing the nominal capacity of the battery cell, and sampling moments are screened, thereby improving the universality of internal resistance consistency evaluation on batteries of different specifications, and improving the characterization capability of internal resistance consistency evaluation by introducing an improved algorithm for a transformation coefficient.

Description

A method and system for evaluating the internal resistance consistency of dynamically reconfigurable battery modules

[0001] This application claims priority to Chinese Patent Application No. 202410916895.7, filed on July 10, 2024, entitled "A Method and System for Evaluating the Internal Resistance Consistency of a Dynamically Reconfigurable Battery Module", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of power system energy storage, and in particular to a method and system for evaluating the internal resistance consistency of dynamically reconfigurable battery modules. Background Technology

[0003] With the continuous expansion of installed capacity of new energy power generation, the proportion of new energy power generation in the power grid is increasing. However, due to the small capacity, large number, and dispersed distribution of new energy power generation units, as well as their significant intermittency, volatility, and randomness, the high proportion of new energy grid connection inevitably brings unprecedented challenges to the power system's supply and demand balance, safety, and stability control. Energy storage systems are a key link in regulating the supply and demand imbalance between new energy power generation and the power system, as well as in energy management and optimization. Dynamically reconfigurable battery energy storage systems break the traditional fixed series-parallel connection application mode of batteries by adopting program-controlled flexible connections, fundamentally solving the "short-board effect" of battery systems, and realizing fine-grained control of battery system electrothermal integration and system-level intrinsic safety mechanisms. Dynamically reconfigurable battery energy storage technology provides a new technical path for building large-scale, highly safe, long-life, and low-cost battery energy storage systems, and is the future direction of the energy storage industry. The internal resistance of lithium batteries is affected by many factors, such as external factors like temperature, current, and load, as well as the battery's own material properties, structure, manufacturing process, and aging. By evaluating the consistency of the internal resistance of battery modules in an energy storage system, we can understand the inconsistencies between different battery cells, promptly identify and address inconsistent cells, and improve the operating efficiency and safety of the energy storage system.

[0004] In some cases, the evaluation of internal resistance consistency of dynamically reconfigurable battery storage has the following disadvantages: 1. Poor universality: The internal resistance consistency evaluation method only uses the absolute current difference to determine the target sampling time, which cannot be adapted to different models and specifications of battery modules. Different usage scenarios require adjustment of parameter thresholds, resulting in poor universality and accuracy; 2. Poor characterization ability: The interpretability of internal resistance consistency is poor, and it cannot accurately characterize the real internal resistance consistency results. Summary of the Invention

[0005] The purpose of this application is to provide a method for evaluating the internal resistance consistency of dynamically reconfigurable battery modules, which can improve the universality of internal resistance consistency evaluation on batteries of different specifications, and improve the characterization ability of internal resistance consistency evaluation by improving the algorithm.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] Firstly, this application provides a method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module, wherein the dynamically reconfigurable battery module includes multiple battery cells connected in series, comprising: acquiring the current of the dynamically reconfigurable energy storage battery module at different sampling times, and the voltage of the battery cells at different sampling times; calculating the current change rate at different sampling times using the current of the dynamically reconfigurable energy storage battery module at different sampling times; the current change rate is the absolute value of the ratio of the current difference between two adjacent sampling times to the nominal capacity of the battery cell; selecting the sampling time preceding the sampling time where the current change rate is greater than or equal to the target current change rate as the target sampling time; when the number of target sampling times is greater than a preset value, calculating the internal resistance of each battery cell at the target sampling time based on the target sampling time, and constructing an internal resistance matrix; using the internal resistance matrix, employing an improved internal resistance consistency evaluation algorithm to calculate the internal resistance consistency result of the dynamically reconfigurable battery module; the improved internal resistance consistency evaluation algorithm is constructed based on the internal resistance consistency transformation coefficient.

[0008] Secondly, this application provides a computer system, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module.

[0009] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and system for evaluating the internal resistance consistency of a dynamically reconfigurable battery module. By introducing the nominal capacity of the battery cell to calculate the current change rate, and selecting the sampling time before the sampling time where the current change rate is greater than or equal to the target current change rate as the target sampling time, this solves the problem that the traditional internal resistance consistency evaluation method only uses the absolute current difference to determine the target sampling time, which cannot be adapted to different models and specifications of battery modules. This improves the universality of internal resistance consistency evaluation on different specifications of batteries. Furthermore, by introducing an improved algorithm with transformation coefficients, the characterization ability of internal resistance consistency evaluation is improved, and the interpretability of internal resistance consistency evaluation is enhanced. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 is a flowchart illustrating a method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to an embodiment of this application.

[0012] Figure 2 is a voltage curve of a single battery cell at different sampling times provided in the embodiments of this application.

[0013] Figure 3 shows the current curves of the dynamically reconfigurable energy storage battery module at different sampling times provided in the embodiments of this application.

[0014] Figure 4 shows the internal resistance curves of a single battery cell at different sampling times provided in the embodiments of this application.

[0015] Figure 5 is a bar chart showing the voltage consistency evaluation results of different dynamically reconfigurable battery modules provided in the embodiments of this application.

[0016] Figure 6 is a schematic diagram of the structure of a computer system provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1, as shown in Figure 1, provides a method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module. The dynamically reconfigurable battery module includes multiple battery cells connected in series, and includes the following steps.

[0020] S1. As shown in Figures 2 and 3, obtain the current of the dynamically reconfigurable energy storage battery module at different sampling times, and the voltage of the individual battery cells at different sampling times.

[0021] Voltage data was obtained from the PXI7065 battery data acquisition card, and current data was acquired through the LEM HTB1000-P Hall effect current sensor.

[0022] In practical applications, the current of the dynamically reconfigurable energy storage battery module at different sampling times and the voltage of individual battery cells at different sampling times can be obtained from the input operating data of the dynamically reconfigurable energy storage battery module. Then, a current vector is constructed based on the current of the dynamically reconfigurable energy storage battery module at different sampling times, and a voltage matrix is ​​constructed based on the voltage of the individual battery cells at different sampling times.

[0023] Where, the current vector I = (I1, I2, ..., I m ) is a 1-row, m-column vector, where m is the latest sampling time; I i The voltage matrix represents the current value of the dynamically reconfigurable battery module at the i-th sampling time, 1≤i≤m; It is an m x n matrix, where V ij Let represent the voltage of the j-th battery cell at the i-th sampling time, where 1≤i≤m and 1≤j≤n.

[0024] S2. Calculate the current change factor at different sampling times using the current of the dynamically reconfigurable energy storage battery module; the current change factor is the absolute value of the ratio of the current difference between two adjacent sampling times to the nominal capacity of the battery cell.

[0025] S3. Select the sampling time preceding the sampling time where the current change rate is greater than or equal to the target current change rate as the target sampling time.

[0026] Step S3 includes the following steps.

[0027] S31. Iterate through the current at each sampling time, determine whether the i-th sampling time is before the latest sampling time, and obtain the first judgment result.

[0028] S32. If the first judgment result is yes, then determine whether the absolute value of the ratio of the current difference at the (i+1)th sampling time to the current at the ith sampling time to the nominal capacity of the battery cell is greater than or equal to the target current change rate, and obtain the second judgment result.

[0029] S33. If the second judgment result is negative, then i = i + 1, and return "traverse the current at each sampling time and determine whether the i-th sampling time is before the latest sampling time".

[0030] S34. If the second judgment result is yes, then the i-th sampling time is output as the target sampling time, i = i + 1, and the function "traverses the current at each sampling time and determines whether the i-th sampling time is before the latest sampling time" is returned.

[0031] S35. If the first judgment result is negative, output all target sampling times.

[0032] Furthermore, the formula for calculating the current change factor is as follows.

[0033] Where Q is the current change factor, I i+1 For the current of the dynamically reconfigurable battery module at the (i+1)th target sampling time, I i Let E be the current of the dynamically reconfigurable battery module at the i-th target sampling time, and E be the nominal capacity of the battery cell. The nominal capacity E is related to the structure and battery specifications of the dynamically reconfigurable energy storage battery module, and the unit is Ah.

[0034] In one embodiment, the target current change factor θ = 0.1, and the threshold value for the number of internal resistance sampling times ρ = 20 when calculating the internal resistance consistency result.

[0035] Among them, θ and ρ are variable parameters that can be adjusted according to different application scenarios. For example, in a battery module consisting of 16 280Ah lithium iron phosphate batteries connected in series, θ can take the range of [0.1,1] and ρ can take the range of ≥20.

[0036] S4. As shown in Figure 4, when the number of target sampling times is greater than the preset value, the internal resistance of each battery cell at the target sampling time is calculated based on the target sampling time, and an internal resistance matrix is ​​constructed.

[0037] Furthermore, the formula for the internal resistance of each battery cell at the target sampling time is as follows.

[0038] Among them, R ij Let K be the internal resistance of the j-th battery cell at the i-th target sampling time, and K be the target voltage matrix. (i+1)j K represents the voltage of the j-th battery cell at the (i+1)-th target sampling time. ij I represents the voltage of the j-th battery cell at the i-th target sampling time, and I is the current vector. i+1 For the current of the dynamically reconfigurable battery module at the (i+1)th target sampling time, I i Let |·| be the current of the dynamically reconfigurable battery module at the i-th target sampling time, and |·| be the absolute value.

[0039] In one implementation, if the number of target sampling moments is less than or equal to a preset value, then the number of target sampling moments is insufficient, the number of battery cells needs to be increased, and the algorithm ends.

[0040] Furthermore, the expression for the internal resistance matrix is ​​as follows.

[0041] Where R is the internal resistance matrix, R 11 R is the internal resistance of the first battery cell at the first target sampling time. 1jR is the internal resistance of the j-th battery cell at the first target sampling time. 1n R is the internal resistance of the nth battery cell at the first target sampling time. i1 R is the internal resistance of the first battery cell at the i-th target sampling time. ij Let R be the internal resistance of the j-th battery cell at the i-th target sampling time. in Let R be the internal resistance of the nth battery cell at the i-th target sampling time. k1 R is the internal resistance of the first battery cell at the k-th target sampling time. kj Let R be the internal resistance of the j-th battery cell at the k-th target sampling time. kn Let be the internal resistance of the nth battery cell at the kth target sampling time.

[0042] Step S4 includes the following steps.

[0043] S41. Select the current of the dynamically reconfigurable battery module at the target sampling time and construct the current vector.

[0044] S42. Select the voltage of the target battery cell at the sampling time and construct the target voltage matrix.

[0045] S43. Based on the current vector and the target voltage matrix, calculate the internal resistance of each battery cell at the target sampling time in sequence, and construct the internal resistance matrix.

[0046] In practical applications, the list of internal resistances of all battery cells at the target sampling time is converted into an internal resistance matrix of K rows and N columns. Then, the average internal resistance of each battery cell is calculated to form an average internal resistance vector.

[0047] in, The average internal resistance of the first battery cell. Let j be the average internal resistance of the j-th battery cell. Let be the average internal resistance of the nth battery cell.

[0048] S5. As shown in Figure 5, the internal resistance consistency evaluation algorithm is used to calculate the internal resistance consistency result of the dynamically reconfigurable battery module using the internal resistance matrix. The improved internal resistance consistency evaluation algorithm is constructed based on the internal resistance consistency transformation coefficient.

[0049] Step S5 includes the following steps.

[0050] S51. Using the internal resistance matrix, calculate the average internal resistance of each battery cell.

[0051] Furthermore, the formula for calculating the average internal resistance of each battery cell is as follows.

[0052] in, Let R be the average internal resistance of the j-th battery cell at different sampling times. ij Let be the internal resistance of the j-th battery cell at the i-th target sampling time.

[0053] S52. Using the following formula, based on the average internal resistance of each battery cell, and employing the improved internal resistance consistency evaluation algorithm, the internal resistance consistency result of the dynamically reconfigurable battery module is calculated.

[0054] Where S represents the internal resistance consistency result of the dynamically reconfigurable battery module, τ represents the consistency score internal resistance consistency transformation coefficient, cv is a normalized measure of the dispersion of the probability distribution of the internal resistance of a single battery cell, used to evaluate internal resistance consistency, τ is used to characterize the penalty imposed on cv, the normalized measure of the dispersion of the probability distribution of a single battery cell, in the internal resistance consistency result, σ is the standard deviation of the mean internal resistance of all battery cells, and μ is the mean internal resistance of all battery cells. The average internal resistance of the j-th battery cell, k is the total number of target sampling times, and n is the number of battery cells.

[0055] After obtaining the internal resistance consistency results, the following applications can be carried out:

[0056] 1) Health status assessment: Identify cells with abnormal internal resistance, predict battery aging trends, and optimize maintenance strategies.

[0057] 2) Dynamic reconfiguration optimization: Adjust the module topology in real time based on the difference in internal resistance (series-parallel switching) to improve overall efficiency and energy utilization.

[0058] 3) Balance Management: Activate the active balance circuit in a targeted manner to reduce capacity decay caused by inconsistent internal resistance.

[0059] 4) Safety warning: A sudden increase in internal resistance may indicate the risk of thermal runaway and trigger the BMS protection mechanism.

[0060] 5) Cascade utilization screening: Quickly screen batteries with consistent internal resistance from retired modules to reduce the cost of refactoring.

[0061] In one implementation, since the cv and internal resistance consistency are not linearly related under normal circumstances, an internal resistance consistency scoring formula is used to map the cv value to a percentage score range to evaluate the internal resistance consistency.

[0062] Furthermore, the consistency score transformation coefficient τ∈{2,4,6}, and the internal resistance consistency transformation coefficient τ can be calculated using the formula... "OK" indicates that the number of individual battery cells in the dynamically reconfigurable battery module is divided by 4 and rounded down.

[0063] This application provides a method and system for evaluating the internal resistance consistency of dynamically reconfigurable battery modules. By introducing the nominal capacity of individual battery cells to calculate the current change rate, and selecting the sampling time preceding the sampling time where the current change rate is greater than or equal to the target current change rate as the target sampling time, this solves the problem of traditional internal resistance consistency evaluation methods that only use absolute current differences to determine the target sampling time, which cannot be adapted to different battery module models and specifications. This improves the universality of internal resistance consistency evaluation across different battery specifications. Furthermore, by introducing an improved algorithm with transformation coefficients, the representational ability and interpretability of internal resistance consistency evaluation are enhanced. In addition, this application ensures the number of internal resistance sampling times when calculating internal resistance consistency by setting a preset value for the target sampling time, improving the accuracy and robustness of the internal resistance consistency evaluation. Vector and matrix parallel calculations improve the efficiency of internal resistance and internal resistance consistency score calculations. Simultaneously, the pulse charge-discharge mode of the dynamically reconfigurable energy storage system can increase the sampling times for internal resistance calculation, further improving the accuracy and robustness of the evaluation.

[0064] Example 2 provides a computer system, which can be a server or a terminal, and its internal structure is shown in Figure 6. The computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores video tag processing data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module.

[0065] Those skilled in the art will understand that the structure shown in Figure 6 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0066] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0067] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module, characterized in that, The dynamically reconfigurable battery module comprises multiple battery cells connected in series. The method for evaluating the internal resistance consistency of the dynamically reconfigurable battery module includes: Obtain the current of the dynamically reconfigurable energy storage battery module at different sampling times, and the voltage of the individual battery cells at different sampling times; The current change factor at different sampling times is calculated using the current of the dynamically reconfigurable energy storage battery module; the current change factor is the absolute value of the ratio of the current difference between two adjacent sampling times to the nominal capacity of the battery cell. The sampling time preceding the sampling time where the current change rate is greater than or equal to the target current change rate is selected as the target sampling time. When the number of target sampling times is greater than the preset value, the internal resistance of each battery cell at the target sampling time is calculated based on the target sampling time, and an internal resistance matrix is ​​constructed. Using the internal resistance matrix and an improved internal resistance consistency evaluation algorithm, the internal resistance consistency result of the dynamically reconfigurable battery module is calculated; the improved internal resistance consistency evaluation algorithm is constructed based on the internal resistance consistency transformation coefficient.

2. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, The sampling time preceding the sampling time where the current change rate is greater than or equal to the target current change rate is selected as the target sampling time, including: Iterate through the current at each sampling time, determine whether the i-th sampling time is before the latest sampling time, and obtain the first judgment result; If the first judgment result is yes, then determine whether the absolute value of the ratio of the current difference between the (i+1)th sampling time and the current at the ith sampling time to the nominal capacity of the battery cell is greater than or equal to the target current change rate, and obtain the second judgment result. If the second judgment result is negative, then i = i + 1, and return "traverse the current at each sampling time and determine whether the i-th sampling time is before the latest sampling time"; If the second judgment result is yes, then the i-th sampling time is output as the target sampling time, i = i + 1, and the function "traverses the current at each sampling time and determines whether the i-th sampling time is before the latest sampling time" is returned. If the first judgment result is negative, output all target sampling times.

3. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, The formula for calculating the current change factor is as follows: Where Q is the current change factor, I i+1 For the current of the dynamically reconfigurable battery module at the (i+1)th target sampling time, I i Let E be the current of the dynamically reconfigurable battery module at the i-th target sampling time, and E be the nominal capacity of the battery cell.

4. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, Calculate the internal resistance of each battery cell at the target sampling time, and construct an internal resistance matrix, including: Select the current of the dynamically reconfigurable battery module at the target sampling time and construct a current vector; Select the voltage of the target battery cell at the target sampling time and construct the target voltage matrix; Based on the current vector and the target voltage matrix, the internal resistance of each battery cell at the target sampling time is calculated sequentially, and an internal resistance matrix is ​​constructed.

5. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, The formula for the internal resistance of each battery cell at the target sampling time is as follows: Among them, R ij Let K be the internal resistance of the j-th battery cell at the i-th target sampling time, and K be the target voltage matrix. (i+1)j K represents the voltage of the j-th battery cell at the (i+1)-th target sampling time. ij I represents the voltage of the j-th battery cell at the i-th target sampling time, and I is the current vector. i+1 For the current of the dynamically reconfigurable battery module at the (i+1)th target sampling time, I i Let |·| be the current of the dynamically reconfigurable battery module at the i-th target sampling time, and |·| be the absolute value.

6. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, The expression for the internal resistance matrix is ​​as follows: Where R is the internal resistance matrix, R 11 R is the internal resistance of the first battery cell at the first target sampling time. 1j R is the internal resistance of the j-th battery cell at the first target sampling time. 1n R is the internal resistance of the nth battery cell at the first target sampling time. i1 R is the internal resistance of the first battery cell at the i-th target sampling time. ij Let R be the internal resistance of the j-th battery cell at the i-th target sampling time. in Let R be the internal resistance of the nth battery cell at the i-th target sampling time. k1 R is the internal resistance of the first battery cell at the k-th target sampling time. kj Let R be the internal resistance of the j-th battery cell at the k-th target sampling time. kn Let be the internal resistance of the nth battery cell at the kth target sampling time.

7. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 1, characterized in that, Using the internal resistance matrix and an improved internal resistance consistency evaluation algorithm, the internal resistance consistency results of the dynamically reconfigurable battery module are calculated, including: The average internal resistance of each battery cell is calculated using the internal resistance matrix. The internal resistance consistency result of the dynamically reconfigurable battery module is calculated using the following formula, based on the average internal resistance of each battery cell and the improved internal resistance consistency evaluation algorithm. Where S represents the internal resistance consistency result of the dynamically reconfigurable battery module, τ is the consistency score internal resistance consistency transformation coefficient, cv is a normalized measure of the dispersion of the probability distribution of the internal resistance of a single battery cell, used to evaluate internal resistance consistency, τ is used to characterize the penalty imposed on cv, the normalized measure of the dispersion of the probability distribution of a single battery cell, in the internal resistance consistency result, σ is the standard deviation of the mean internal resistance of all battery cells, and μ is the mean internal resistance of all battery cells. The average internal resistance of the j-th battery cell, k is the total number of target sampling times, and n is the number of battery cells.

8. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 7, characterized in that, The formula for calculating the average internal resistance of each battery cell is as follows: in, Let R be the average internal resistance of the j-th battery cell at different sampling times. ij Let be the internal resistance of the j-th battery cell at the i-th target sampling time.

9. The method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to claim 7, characterized in that, The consistency score transformation coefficient τ∈{2,4,6}, and the internal resistance consistency transformation coefficient τ is based on the formula... Sure.

10. A computer system, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method for evaluating the internal resistance consistency of a dynamically reconfigurable battery module according to any one of claims 1-9.

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