Testing methods and related devices for lithium battery systems in thermal power units

Abstract

This invention relates to the field of system testing technology, specifically to a testing method and related apparatus for lithium battery systems in thermal power units. The method includes: obtaining trend characteristic factors and fluctuation characteristic factors for each type of data based on the trend changes of each data segment, the differences in trend changes between several data segments, and the distribution of all extreme points on the fitted curve of each data segment; constructing a feature space based on the trend characteristic factors and fluctuation characteristic factors for each type of data; obtaining the degree of increase in measurement time through the data distribution in the feature space; adjusting the preset measurement time according to the degree of increase in measurement time to obtain an adjusted measurement time; measuring and acquiring data using the adjusted measurement time; and performing lithium battery system testing based on the measured data. This invention improves the accuracy of lithium battery system testing.

Description

Technical Field

[0001] This invention relates to the field of system testing technology, and in particular to testing methods and related devices for lithium battery systems in thermal power units. Background Technology

[0002] In thermal power units, lithium battery systems are commonly used for power storage, emergency power, and battery energy storage systems (BESS). Effective testing of lithium battery systems is crucial to ensure their stability, reliability, and safety during long-term operation. In testing lithium battery systems in thermal power units, BMS (Battery Management System) communication refers to the data transmission and exchange between the BMS and other devices (such as batteries, inverters, and chargers). The main function of the BMS is to monitor and manage the battery's status, ensuring it operates within a safe range and optimizing its performance. This process requires communication between the BMS and external devices to exchange critical battery information and perform control operations, ensuring the efficient and safe operation of the entire battery system; therefore, BMS communication testing is extremely important.

[0003] In conventional BMS communication testing, sometimes only short-duration or limited-number tests are performed to quickly obtain results. However, short-duration tests are insufficient to reflect the system's performance under long-term operation. Especially under high load conditions, short-duration tests may overlook performance degradation during extended operation; single-test results may not be representative, particularly in complex or unstable communication environments. Short-duration or limited-number tests cannot accurately determine BMS communication rates and latency, thus reducing the accuracy of BMS communication testing. Summary of the Invention

[0004] This invention provides a testing method and related apparatus for lithium battery systems in thermal power units, which is used to solve existing problems.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] The first aspect of this invention is to provide a testing method for a lithium battery system in a thermal power unit, comprising:

[0007] Acquire several data segments of various data during the operation and testing of the lithium battery system;

[0008] Based on the trend changes of each data segment and the differences in trend changes between several data segments, the trend change characteristics of each data type are obtained; curve fitting is performed on all data in each data segment of each data type to obtain the fitted curve of each data segment of each data type; based on the distribution of all extreme points on the fitted curve of each data segment of each data type, the volatility factor of each data type is obtained.

[0009] The trend change characteristics of each data point are adjusted to obtain the trend characteristic factor of each data point, and the fluctuation factor of each data point is adjusted to obtain the fluctuation characteristic factor of each data point. Based on the trend characteristic factor and fluctuation characteristic factor of each data point, a feature space is constructed, and the degree of increase in measurement duration is obtained through the data distribution in the feature space.

[0010] The preset measurement duration is adjusted according to the increase in measurement duration to obtain the adjusted measurement duration. Data is then measured and acquired using the adjusted measurement duration, and the lithium battery system is tested based on the acquired data.

[0011] Furthermore, the data segments for acquiring various data during the operation and testing of the lithium battery system include:

[0012] Collect time-series data of various types of data; with a preset measurement duration as the time length for each data collection, randomly collect several data segments of the preset measurement duration for each type of data.

[0013] Furthermore, obtaining the trend change characteristics of each data type based on the trend changes of each data segment and the differences in trend changes between several data segments includes:

[0014]

[0015] In the formula, Represents the first of each data type The slope of each data segment Represents the first of each data type The slope of each data segment This represents the total number of data segments for each data type. It is the absolute value symbol. This indicates the trend change characteristics of each data point;

[0016] The slope of each data segment for each type of data is the slope between the start and end points of each data segment.

[0017] Further, curve fitting is performed on all data in each data segment for each type of data to obtain a fitted curve for each data segment of each type of data. Based on the distribution of all extreme points on the fitted curve of each data segment of each type of data, the fluctuation factor of each type of data is obtained, including:

[0018] Curve fitting is performed on all data in each data segment for each type of data using the least squares method to obtain the fitted curve for each data segment of each type of data; the extreme points on the fitted curve for each data segment of each type of data are obtained; where the extreme points include maximum points and minimum points;

[0019]

[0020] In the formula, Represents the first of each data type The first data segment A maximum value, Represents the first of each data type The mean of all maxima in a data segment Represents the first of each data type The total number of all maxima in each data segment Represents the first of each data type The first data segment A minimum value, Represents the first of each data type The mean of all the minimum values ​​in a data segment Represents the first of each data type The total number of all local minima in each data segment This represents the total number of data segments for each data type. The volatility factor represents the volatility of each data point. It is the absolute value symbol.

[0021] Furthermore, adjusting the trend change characteristics of each data point to obtain a trend characteristic factor for each data point, and adjusting the volatility factor for each data point to obtain a volatility characteristic factor for each data point, includes:

[0022]

[0023] In the formula, This indicates the trend change characteristics of each data point. This represents the trend characteristic factor for each type of data. Represents an exponential function with the natural constant as its base;

[0024]

[0025] In the formula, The volatility factor represents the volatility of each data point. This represents the volatility characteristic factor for each type of data. This represents the linear normalization function.

[0026] Furthermore, the step of constructing a feature space based on the trend characteristic factor and fluctuation characteristic factor of each type of data, and obtaining the degree of increase in measurement duration through the data distribution in the feature space, includes:

[0027] A feature space is constructed using the trend characteristic factor of each data type as the horizontal axis and the fluctuation characteristic factor of each data type as the vertical axis. The trend characteristic factors and fluctuation characteristic factors of various data types are mapped onto the feature space to obtain several data points. All data points in the feature space are clustered using the K-means clustering algorithm to obtain several clusters.

[0028] Obtain the centroid of each cluster in the feature space, and record the product of the trend feature factor and the fluctuation feature factor corresponding to the centroid of each cluster as the degree of increase of each cluster; denote the cluster with the largest degree of increase among all clusters as the target cluster.

[0029]

[0030] In the formula, This represents the number of all data points in the target cluster. This represents the total number of data points in the feature space. This indicates the degree of increase in measurement duration.

[0031] Further, the step of adjusting the preset measurement duration according to the increase in measurement duration to obtain the adjusted measurement duration includes:

[0032]

[0033] In the formula, Indicates the degree of increase in measurement duration. Indicates the preset measurement duration. This indicates the adjusted measurement duration.

[0034] A second aspect of the present invention is to provide a testing apparatus for lithium battery systems in thermal power units, comprising:

[0035] Data acquisition module: Used to acquire several data segments of various data during the operation and testing of the lithium battery system;

[0036] Trend and volatility analysis module: used to obtain the trend change characteristics of each data type based on the trend changes of each data segment and the differences in trend changes between several data segments; to perform curve fitting on all data in each data segment of each data type to obtain the fitted curve of each data segment of each data type; and to obtain the volatility factor of each data type based on the distribution of all extreme points on the fitted curve of each data segment of each data type.

[0037] The degree of increase analysis module is used to adjust the trend change characteristics of each type of data to obtain the trend characteristic factor of each type of data, and to adjust the fluctuation factor of each type of data to obtain the fluctuation characteristic factor of each type of data; based on the trend characteristic factor and fluctuation characteristic factor of each type of data, a feature space is constructed, and the degree of increase in measurement duration is obtained through the data distribution in the feature space;

[0038] Measurement duration adjustment module: This module is used to adjust the preset measurement duration according to the increase in measurement duration, obtain the adjusted measurement duration, acquire data through the adjusted measurement duration, and perform lithium battery system testing based on the acquired data.

[0039] A third aspect of the present invention is to provide an electronic device, 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 a method for testing a lithium battery system in a thermal power unit.

[0040] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a testing method for a lithium battery system in a thermal power unit.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: First, by obtaining the trend change characteristics of each data segment and the difference in trend changes between several data segments, the accuracy of trend analysis is improved. Second, curve fitting is performed on all data in each data segment of each data type to obtain the fitted curve for each data segment. Third, based on the distribution of all extreme points on the fitted curve of each data segment, the fluctuation factor of each data type is obtained, improving the accuracy of fluctuation analysis. Fourth, the trend change characteristics of each data type are adjusted to obtain the trend characteristic factor, and the fluctuation factor is adjusted to obtain the fluctuation characteristic factor. Fifth, the influence of differences between different data categories is eliminated. Sixth, a feature space is constructed based on the trend characteristic factor and fluctuation characteristic factor of each data type. The degree of increase in measurement time is obtained through the data distribution in the feature space, improving the accuracy of measurement time adjustment analysis. Seventh, the preset measurement time is adjusted according to the degree of increase in measurement time to obtain the adjusted measurement time. Data is measured and acquired using the adjusted measurement time, and lithium battery system testing is performed based on the acquired data, improving the accuracy of lithium battery system testing. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This invention provides a flowchart illustrating the steps of a testing method for a lithium battery system in a thermal power unit.

[0044] Figure 2 This invention provides a schematic diagram of the module flow of a testing device for lithium battery systems in thermal power units. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0047] To address the problems existing in the background technology, this study designs a testing method and related device for lithium battery systems in thermal power units, which has significant practical implications.

[0048] like Figure 1 As shown, the first aspect of the present invention is to provide a testing method for a lithium battery system in a thermal power unit, comprising the following steps:

[0049] Step S001: Collect several data segments of various data during the operation and testing of the lithium battery system.

[0050] It should be noted that when testing the communication performance of a lithium battery system, some data typically only show changes over a long period of time, and the corresponding changes cannot be determined in a short period of time. Therefore, it is necessary to collect various data from the lithium battery system during operation and testing, and analyze the changes in these data.

[0051] Specifically, time-series data of various types of data are collected; with a preset measurement duration. To determine the duration of each data collection session, randomly collect each type of data several times for a preset measurement duration. The data segment; wherein, in this embodiment, a preset measurement duration is used. The following description uses hours as an example, where the preset measurement duration is specified in this embodiment. No specific restrictions are imposed; implementers can decide based on the specific circumstances.

[0052] At this point, we have obtained several data segments for each type of data.

[0053] Step S002: Analyze the trend changes and fluctuations of all data segments for each type of data to obtain the trend change characteristics and fluctuation factors for each type of data.

[0054] It should be noted that some data do not show characteristics in a short period of time, but only show certain characteristics over a long period of time. Furthermore, some data may fluctuate briefly due to noise interference in a short period of time. When the test time is short, the data may be considered abnormal. Therefore, the measurement time is adjusted by using the variation characteristics of multiple data over multiple short periods of time.

[0055] It should be further noted that when measuring over a short period of time, if the data shows a certain trend change, but the corresponding trend change is relatively slow, it cannot be detected by short-term measurement. That is, these long-term, slow changes cannot be captured in short-term testing. Therefore, for data with slow trend changes, a longer measurement period should be given in order to better monitor the trend change characteristics of the data.

[0056] Specifically, based on the trend changes of each data segment and the differences in trend changes between several data segments, the trend change characteristics of each data type are obtained; these trend change characteristics are specifically expressed by the following formula:

[0057]

[0058] In the formula, Represents the first of each data type The slope of each data segment Represents the first of each data type The slope of each data segment This represents the total number of data segments for each data type. It is the absolute value symbol. This represents the trend change characteristics of each data type. The slope of each data segment for each data type is the slope between the start and end points of that segment.

[0059] in, This represents the mean of the absolute values ​​of the slopes across all data segments for each data type. The smaller the mean of the absolute values ​​of the slopes across all data segments for each data type, the smaller the trend change characteristic of that data type. This indicates a slower trend, meaning that these long-term, slow changes cannot be captured in a short period, thus requiring a longer measurement time. The slower the change, the longer the required measurement time. Conversely, the larger the mean of the absolute values ​​of the slopes across all data segments for each data type, the stronger the trend change characteristic of that data type. This indicates that information can be captured better in a short period, requiring less or less additional measurement time. This indicates the difference in slope between adjacent data segments for each type of data. The smaller the difference in slope between adjacent data segments, the slower the change trend of each type of data, and the more necessary it is to increase the measurement time. Conversely, the larger the difference in slope between adjacent data segments, the less necessary it is to increase the measurement time.

[0060] Thus, the trend change characteristics of each type of data are obtained.

[0061] It should be noted that the system may be subject to external or internal noise interference during normal operation, which may cause abnormal fluctuations in the data. If the measurement is performed in a short period of time, it is difficult to distinguish whether the abnormal fluctuations caused by noise interference are caused by noise or normal fluctuations within the system.

[0062] It should be further noted that the normal fluctuations of lithium battery systems follow a certain pattern, while the abnormal fluctuations caused by noise interference are irregular. Therefore, the analysis is based on the distribution pattern of data in each data segment of each type of data.

[0063] Specifically, curve fitting is performed on all data in each data segment for each data type using the least squares method to obtain a fitted curve for each data segment of each data type; extreme points on the fitted curve for each data segment of each data type are obtained, where extreme points include maxima and minima; wherein, the least squares method is a known technique. Based on the distribution of all extreme points on the fitted curve for each data segment of each data type, the volatility factor for each data type is obtained; the volatility factor is specifically expressed by the formula:

[0064]

[0065] In the formula, Represents the first of each data type The first data segment A maximum value, Represents the first of each data type The mean of all maxima in a data segment Represents the first of each data type The total number of all maxima in each data segment Represents the first of each data type The first data segment A minimum value, Represents the first of each data type The mean of all the minimum values ​​in a data segment Represents the first of each data type The total number of all local minima in each data segment This represents the total number of data segments for each data type. The volatility factor represents the volatility of each data point. It is the absolute value symbol.

[0066] in, This represents the difference between each maximum value in each data segment of each data type and the mean of all maximum values. The larger the difference, the larger the volatility factor of each data type, indicating that the data fluctuations in the data segment are less regular; conversely, the smaller the difference, the smaller the volatility factor of each data type, indicating that the data fluctuations in the data segment are more regular. This represents the difference between each minimum value in each data segment and the mean of all minimum values ​​for each type of data. The larger the difference, the larger the volatility factor for each type of data, indicating that the data fluctuations in the data segment are less regular; conversely, the smaller the difference, the smaller the volatility factor for each type of data, indicating that the data fluctuations in the data segment are more regular.

[0067] Thus, the volatility factor for each type of data is obtained.

[0068] Step S003: Analyze the degree of increase in measurement duration by analyzing the trend change characteristics and fluctuation factors of each type of data.

[0069] It should be noted that the smaller the trend change characteristic of each data point, the more likely it is to have a slow long-term trend; the larger the fluctuation factor of each data point, the more likely it is to have abnormal data fluctuations caused by noise interference. Therefore, for both smaller trend change characteristics and larger fluctuation factors, it is necessary to increase the measurement time to ensure a more accurate analysis of the specific situation of the data.

[0070] It should be further noted that since there are multiple data sets, and the trend characteristics and volatility factors corresponding to different data sets are different, the measurement duration can be adjusted by observing the overall changes in the multiple data sets.

[0071] Specifically, a negative correlation mapping is performed on the trend change characteristics of each type of data to obtain the trend characteristic factor for each type of data. The trend characteristic factor is specifically expressed by the following formula:

[0072]

[0073] In the formula, This indicates the trend change characteristics of each data point. This represents the trend characteristic factor for each type of data. This represents an exponential function with the natural constant as its base.

[0074] It should be noted that, in order to eliminate the impact of different data due to differences in units and specific changes, the fluctuation factor of each data needs to be normalized to reduce the influence.

[0075] Specifically, the normalization of the volatility factor for each type of data is expressed by the following formula:

[0076]

[0077] In the formula, The volatility factor represents the volatility of each data point. This represents the volatility characteristic factor for each type of data. This represents the linear normalization function.

[0078] A feature space is constructed using the trend characteristic factor of each data type as the horizontal axis and the volatility characteristic factor of each data type as the vertical axis. The trend and volatility characteristic factors of various data types are mapped onto the feature space to obtain several data points. All data points in the feature space are then clustered using the K-means clustering algorithm to obtain several clusters. The number of clusters is determined using the elbow method. Both the K-means clustering algorithm and the elbow method are well-known techniques and will not be elaborated upon here.

[0079] It should be noted that in the feature space, the larger the trend feature factor and fluctuation feature factor corresponding to the data point, the more the measurement time needs to be increased. Therefore, the degree to which the measurement time needs to be increased can be determined by analyzing the distribution of clusters in the feature space.

[0080] Specifically, the centroid of each cluster in the feature space is obtained, and the product of the trend feature factor and the fluctuation feature factor corresponding to the centroid of each cluster is recorded as the degree of increase of each cluster; the cluster with the largest degree of increase among all clusters is recorded as the target cluster.

[0081] It should be further noted that the target cluster represents the cluster corresponding to the larger the trend characteristic factor and volatility characteristic factor. The more data points in the target cluster, the greater the need to increase the measurement duration, and vice versa.

[0082] Specifically, the increase in measurement time is obtained by measuring the proportion of data points in the target cluster among all data points; the increase in measurement time is expressed by the following formula:

[0083]

[0084] In the formula, This represents the number of all data points in the target cluster. This represents the total number of data points in the feature space. This indicates the degree of increase in measurement duration.

[0085] At this point, the degree of increase in measurement duration is obtained.

[0086] Step S004: Adjust the preset measurement duration according to the increase in measurement duration to obtain the adjusted measurement duration. Use the adjusted measurement duration to measure and acquire data, and use the acquired data to test the lithium battery system.

[0087] The preset measurement duration is adjusted according to the increase in measurement duration to obtain the adjusted measurement duration; the adjusted measurement duration is expressed by the formula as follows:

[0088]

[0089] In the formula, Indicates the degree of increase in measurement duration. Indicates the preset measurement duration. This indicates the adjusted measurement duration.

[0090] The greater the increase in measurement duration, the longer the adjusted measurement duration.

[0091] The experimental simulation data in this embodiment are as follows:

[0092] In this embodiment, the measurement duration is preset. Let's take an hour as an example to illustrate;

[0093] The simulation data for the trend characteristic factor and volatility factor of each data type during the simulation process are shown in Table 1 below:

[0094] Table 1

[0095]

[0096] Based on the trend characteristic factors and fluctuation characteristic factors in Table 1, 10 data points are obtained by mapping them onto the feature space. All data points are clustered, and a target cluster is selected. The number of data points in the target cluster is 2. Therefore, the increase in measurement time is 0.2 by comparing the number of data points in the target cluster with the total number of data points in the feature space. Finally, the preset measurement time is adjusted to obtain an adjusted measurement time of 12 hours.

[0097] Measurement data was collected using both a preset measurement duration and an adjusted measurement duration. The simulated test errors obtained from the collected data were 15.5% and 8.2%, respectively. The 15.5% error represents the traditional simulation test error with a fixed duration, while the 8.2% error represents the simulation test error with the dynamically adjusted measurement duration of this invention. The simulated test errors show a significant reduction in error and higher accuracy.

[0098] Data is acquired by measuring the adjusted measurement duration, and the lithium battery system is tested based on the acquired data.

[0099] like Figure 2 As shown, a second aspect of the present invention provides a testing apparatus for lithium battery systems in thermal power units, comprising the following steps:

[0100] Data acquisition module 101: Used to acquire several data segments of various data during the operation and testing of the lithium battery system;

[0101] Trend and volatility analysis module 102: used to obtain the trend change characteristics of each data type based on the trend change of each data segment and the difference in trend change between several data segments; to perform curve fitting on all data in each data segment of each data type to obtain the fitting curve of each data segment of each data type; and to obtain the volatility factor of each data type based on the distribution of all extreme points on the fitting curve of each data segment of each data type.

[0102] The degree of increase analysis module 103 is used to adjust the trend change characteristics of each type of data to obtain the trend characteristic factor of each type of data, and to adjust the fluctuation factor of each type of data to obtain the fluctuation characteristic factor of each type of data; based on the trend characteristic factor and fluctuation characteristic factor of each type of data, a feature space is constructed, and the degree of increase in measurement duration is obtained through the data distribution in the feature space;

[0103] Measurement duration adjustment module 104: used to adjust the preset measurement duration according to the increase of the measurement duration, obtain the adjusted measurement duration, measure and acquire data through the adjusted measurement duration, and perform lithium battery system testing based on the measured data.

[0104] A third aspect of the present invention is to provide an electronic device, 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 a method for testing a lithium battery system in a thermal power unit.

[0105] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a testing method for a lithium battery system in a thermal power unit.

[0106] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, etc.) containing computer-usable program code.

[0107] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] 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.

[0109] 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.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for testing a lithium battery system in a thermal power generating unit, characterized in that, The method comprises the following steps: obtaining a plurality of data segments of various data of a lithium battery system in a running test; obtaining a trend change characteristic of each kind of data according to a trend change of each data segment of each kind of data, and a difference in trend change between the plurality of data segments, comprising: In the formula, represents the slope of the first data segment of each data, represents the slope of the first data segment of each data, represents the slope of the first data segment of each data, represents the slope of the first data segment of each data, represents the total number of all data segments of each data, is an absolute value symbol, represents the trend change characteristics of each data; wherein the slope of each data segment of each data is the slope between the starting point and the ending point in each data segment. performing curve fitting on all data in each data segment of each kind of data to obtain a fitting curve of each data segment of each kind of data, and obtaining a fluctuation factor of each kind of data according to a distribution of all extreme points on the fitting curve of each data segment of each kind of data, comprising: performing curve fitting on all data in each data segment of each kind of data by a least square method to obtain a fitting curve of each data segment of each kind of data; and obtaining extreme points on the fitting curve of each data segment of each kind of data; wherein the extreme points include maximum points and minimum points; In the formula, Represents the first of each data type The first data segment A maximum value, Represents the first of each data type The mean of all maxima in a data segment Represents the first of each data type The total number of all maxima in each data segment Represents the first of each data type The first data segment A minimum value, Represents the first of each data type The mean of all the minimum values ​​in a data segment Represents the first of each data type The total number of all local minima in each data segment This represents the total number of data segments for each data type. This represents the volatility factor for each type of data. It is the absolute value symbol; adjusting the trend change characteristic of each kind of data to obtain a trend characteristic factor of each kind of data, adjusting the fluctuation factor of each kind of data to obtain a fluctuation characteristic factor of each kind of data; constructing a characteristic space according to the trend characteristic factor and the fluctuation characteristic factor of each kind of data, and obtaining an increase degree of a measurement duration through data distribution in the characteristic space; adjusting a preset measurement duration according to the increase degree of the measurement duration to obtain an adjusted measurement duration, performing data measurement through the adjusted measurement duration, and performing lithium battery system test according to the measured data.

2. The method of claim 1, wherein: The method comprises the following steps: collecting time sequence data of various data; and randomly collecting data segments of a preset measurement duration for each kind of data several times, with the preset measurement duration as a time length for each data collection.

3. The method of claim 1, wherein: The method comprises the following steps: In the formula, represents the trend change characteristics of each data, represents the trend characteristics factor of each data, represents an exponential function with a natural constant as the base; wherein denotes a fluctuation factor for each data, denotes a fluctuation characteristic factor for each data, denotes a linear normalization function.

4. The method of claim 1, wherein: The method comprises the following steps: constructing a characteristic space with the trend characteristic factor of each kind of data as a horizontal axis and the fluctuation characteristic factor of each kind of data as a vertical axis; mapping the trend characteristic factor and the fluctuation characteristic factor of various data in the characteristic space to obtain a plurality of data points; and clustering all data points in the characteristic space through a K-means clustering algorithm to obtain a plurality of clusters; obtaining a centroid point of each cluster in the characteristic space, and recording a product result between the trend characteristic factor and the fluctuation characteristic factor corresponding to the centroid point of each cluster as an increase degree of each cluster; and recording a cluster with the largest increase degree in all clusters as a target cluster. wherein, denotes the number of all data points in the target cluster, denotes the total number of all data points in the feature space, denotes the increasing degree of the measurement duration.

5. The method of claim 1, wherein: The method comprises the following steps: In the formula, represents the degree of increase in the measurement duration, represents the preset measurement duration, represents the adjusted measurement duration.

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The curve fitting is performed on all data in each data segment of each data to obtain a fitting curve of each data segment of each data, and a fluctuation factor of each data is obtained according to the distribution of all extreme points on the fitting curve of each data segment of each data, including: The curve fitting is performed on all data in each data segment of each data to obtain a fitting curve of each data segment of each data; extreme points on the fitting curve of each data segment of each data are obtained; wherein the extreme points include maximum points and minimum points; In the formula, Represents the first of each data type The first data segment A maximum value, Represents the first of each data type The mean of all maxima in a data segment Represents the first of each data type The total number of all maxima in each data segment Represents the first of each data type The first data segment A minimum value, Represents the first of each data type The mean of all the minimum values ​​in a data segment Represents the first of each data type The total number of all local minima in each data segment This represents the total number of data segments for each data type. This represents the volatility factor for each type of data. It is the absolute value symbol; The increasing degree analysis module is configured to adjust the trend change characteristics of each data to obtain a trend characteristic factor of each data, adjust the fluctuation factor of each data to obtain a fluctuation characteristic factor of each data, construct a feature space according to the trend characteristic factor and the fluctuation characteristic factor of each data, and obtain the increasing degree of the measurement time length through the data distribution in the feature space; The measurement time length adjustment module is configured to adjust the preset measurement time length according to the increasing degree of the measurement time length to obtain an adjusted measurement time length, perform data measurement and acquisition through the adjusted measurement time length, and perform lithium battery system testing according to the measured data.

7. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method for testing the lithium battery system in the thermal power generating unit according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method for testing the lithium battery system in the thermal power generating unit according to any one of claims 1-5.

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