Lithium-ion battery safety warning method and storage medium based on cloud big data

Abstract

The present application provides a method and a storage medium for safety warning of lithium-ion batteries based on cloud big data. The method includes: obtaining target charging data of a lithium-ion battery recorded by cloud big data; wherein the target charging data includes continuous charging data segments when the voltage of the lithium-ion battery is in the plateau region; obtaining the monomer voltage at each moment within the continuous charging data segments; judging whether to give a safety warning to the lithium-ion battery according to the monomer voltage at each moment within the continuous charging data segments, so as to give a safety warning to the lithium-ion battery based on the continuous charging data segments when the voltage of the lithium-ion battery is in the plateau region, thereby reducing the influence of the initial capacity and the initial internal resistance difference of the battery cells on battery charging and improving the accuracy and reliability of battery warning.

Description

Technical Field

[0001] This application relates to the technical field of battery safety. Specifically, it relates to a lithium-ion battery safety warning method and a storage medium based on cloud big data. Background Art

[0002] Many safety hazards of lithium-ion batteries do not appear suddenly, but gradually deteriorate over time. This deterioration may involve multiple aspects such as the aging of battery materials, the change of electrolyte properties, and the loss of active substances. Under the combined action of these factors, the performance of the battery gradually deviates from the normal state. Minor abnormal changes inside the battery may indicate abnormal battery performance and are directly or indirectly reflected in its operating data. These data include but are not limited to the charging / discharging curve of the battery, the operating temperature range, and the cycle life performance. In order to effectively capture these safety risks, by long-term monitoring of the operating data of the battery, at the early stage of the occurrence of a safety fault, the subtle abnormal performance of the battery performance can be amplified, so as to identify its potential safety risks and issue an early warning.

[0003] Currently, traditional battery safety determination methods mainly rely on simple temperature monitoring and threshold judgment, lacking an accurate assessment of the battery state under complex working conditions, and there is a problem of low accuracy of the battery safety determination method. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a lithium-ion battery safety warning method and a storage medium based on cloud big data, so as to solve the problem that the current traditional battery safety determination method relies on simple temperature monitoring and threshold judgment, lacks an accurate assessment of the battery state under complex working conditions, and has a low accuracy of the battery safety determination method.

[0005] In the first aspect, the present invention provides a lithium-ion battery safety warning method based on cloud big data. The method includes: obtaining the target charging data of the lithium-ion battery recorded by the cloud big data; wherein the target charging data includes the continuous charging data segment when the voltage of the lithium-ion battery is in the plateau region; obtaining the single-cell voltage at each moment within the continuous charging data segment; and judging whether to issue a safety warning for the lithium-ion battery according to the single-cell voltage at each moment within the continuous charging data segment.

[0006] The above-mentioned lithium-ion battery safety warning method based on cloud big data. In this solution, the real-time charging data of the lithium-ion battery is recorded through cloud big data, and then continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region are obtained. Then, based on the monomer voltages at each moment within the continuous charging data segments, it is determined whether to issue a safety warning for the lithium-ion battery. Thus, the safety of the lithium-ion battery is warned based on the continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region, thereby reducing the impact on the charging of the lithium-ion battery caused by the differences in the initial capacities and initial internal resistances of each cell of the lithium-ion battery, and further improving the accuracy and reliability of the lithium-ion battery safety warning.

[0007] In an alternative implementation manner of the first aspect, determining whether to issue a safety warning based on the monomer voltages at each moment within the continuous charging data segments includes: calculating the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segments; determining whether there are monomer voltage differences exceeding a preset voltage difference threshold among the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segments; if it is determined that there are monomer voltage differences exceeding the preset voltage difference threshold, then it is determined to issue a safety warning for the lithium-ion battery.

[0008] In the above implementation manner, in this solution, the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segments are compared with the preset voltage difference threshold. When there are monomer voltage differences exceeding the preset voltage difference threshold, it indicates that there are fault risks during the charging process of the lithium-ion battery, and thus a safety warning is directly issued for the lithium-ion battery, thereby improving the efficiency of the lithium-ion battery safety warning.

[0009] In an alternative implementation manner of the first aspect, after determining whether there are monomer voltage differences exceeding the preset voltage difference threshold among the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segments, the method further includes: if it is determined that there are no monomer voltage differences exceeding the preset voltage difference threshold, then generating effective voltage segments for multiple time windows based on the monomer voltages at each moment within the continuous charging data segments; where the time window includes multiple moments within the continuous charging data segments, and the effective voltage segment is obtained after data enhancement through the monomer voltages at each moment within the continuous charging data segments; for each time window, calculating the monomer voltage entropy value sequence under the time window based on the effective voltage segment of the time window, and obtaining the monomer voltage entropy value sequences under each time window among the multiple time windows; and determining whether to issue a safety warning for the lithium-ion battery based on the monomer voltage entropy value sequences under each time window among the multiple time windows.

[0010] In the above embodiments, the magnitude of the voltage entropy value reflects the complexity and unpredictability of the battery voltage fluctuation. In this solution, effective voltage segments of multiple time windows are obtained by performing data augmentation on the single-cell voltage at each moment within a continuous charging data segment, and then the single-cell voltage entropy value sequence corresponding to each time window is calculated based on the effective voltage segments under each time window, so as to accurately reflect the voltage fluctuation of the corresponding single cell based on the voltage entropy, and further improve the accuracy of the safety warning of the lithium-ion battery.

[0011] In an alternative embodiment of the first aspect, generating effective voltage segments of multiple time windows according to the single-cell voltage at each moment within a continuous charging data segment includes: dividing the single-cell voltage at each moment within the continuous charging data segment into voltage segments of multiple time windows; calculating the median of the single-cell voltage and the standard deviation of the single-cell voltage within each of the multiple time windows, and generating an enhanced segment according to the median of the single-cell voltage and the standard deviation of the single-cell voltage within each time window; adding the enhanced segment before the voltage segment of the time window ranked first among the multiple time windows, and adding the enhanced segment after the voltage segment of the time window ranked last among the multiple time windows, to generate effective voltage segments of multiple time windows.

[0012] In the above embodiments, in this solution, an enhanced segment is generated according to the median of the single-cell voltage and the standard deviation of the single-cell voltage within each time window, and then the enhanced segment is added before the voltage segment of the time window ranked first among the multiple time windows, and the enhanced segment is added after the voltage segment of the time window ranked last among the multiple time windows, to generate effective voltage segments of multiple time windows, so as to ensure equal use of all charging data within the continuous charging data segment through data augmentation, and further improve the accuracy of the warning of the lithium-ion battery.

[0013] In an alternative embodiment of the first aspect, for each time window, calculating the single-cell voltage entropy value sequence under the time window based on the effective voltage segment of the time window, and obtaining the single-cell voltage entropy value sequence under each of the multiple time windows includes: for each time window, obtaining the maximum value of the single-cell voltage and the minimum value of the single-cell voltage in the effective voltage segment of the time window; taking the minimum value of the single-cell voltage as the starting point, forming multiple voltage intervals at intervals of a preset voltage difference until the voltage intervals cover the maximum value of the single-cell voltage of the time window; counting the frequency of each single-cell voltage falling within each voltage interval within the time window, to obtain the probability distribution matrix corresponding to the time window; calculating the single-cell voltage entropy value sequence under the time window according to the probability distribution matrix, to obtain the single-cell voltage entropy value sequence under each of the multiple time windows.

[0014] In the above embodiments, when calculating the monomer voltage entropy sequence, the present solution calculates the probability distribution by dividing the monomer voltage into intervals, rather than directly using the original voltage value to calculate the probability distribution, thereby reducing the influence caused by hardware sampling errors, etc., and further improving the accuracy of early warning.

[0015] In an alternative embodiment of the first aspect, to determine whether to issue a safety warning for a lithium-ion battery based on the monomer voltage entropy value sequence under each time window among multiple time windows, it includes: standardizing and taking the absolute value of the monomer voltage entropy value sequence under each time window using the standard score to obtain the maximum standard score under each time window; obtaining a standard score threshold; wherein, the standard score threshold is determined according to the distribution of the monomer voltage entropy value sequences of all lithium-ion batteries stored in the database; and determining whether to issue a safety warning for the lithium-ion battery based on the maximum standard scores under multiple time windows and the standard score threshold.

[0016] In an alternative embodiment of the first aspect, to determine whether to issue a safety warning for a lithium-ion battery based on the maximum standard scores under multiple time windows and the standard score threshold, it includes: determining whether the number of times the maximum standard score under multiple time windows is greater than the standard score threshold exceeds a preset number; if it is determined that the number of times the maximum standard score under multiple time windows is greater than the standard score threshold exceeds the preset number, then it is determined to issue a safety warning for the lithium-ion battery.

[0017] In the above embodiments, the present solution issues a safety warning for the lithium-ion battery based on the fact that the number of times the maximum standard score under multiple time windows is greater than the standard score threshold exceeds the preset number. In this way, if the voltage value of the lithium-ion battery fluctuates violently too many times under different time windows, it indicates that there is a high probability that the lithium-ion battery will malfunction. Therefore, a safety warning is issued for the lithium-ion battery, thereby improving the reliability and accuracy of the safety warning for the lithium-ion battery.

[0018] In an alternative embodiment of the first aspect, the method further includes: if it is determined that the number of times the maximum standard score under multiple time windows is not more than the preset number, then it is determined not to issue a safety warning for the lithium-ion battery.

[0019] In an alternative embodiment of the first aspect, the method further includes: obtaining the monomer voltage entropy value sequence of the lithium-ion battery currently calculated; storing the monomer voltage entropy value sequence of the lithium-ion battery currently calculated in the database; and updating the standard score threshold according to the monomer voltage entropy value sequences of all lithium-ion batteries stored in the database.

[0020] In the above embodiments, based on the sequence of entropy values of the monomer voltages of all the lithium-ion batteries recorded, the standard score threshold is updated, thereby realizing the dynamic update of the standard threshold score, so as to avoid the influence of seasonal environmental factors and battery aging factors on the charging of lithium-ion batteries, and further improve the accuracy and reliability of the safety warning of lithium-ion batteries.

[0021] In a second aspect, the present application provides a lithium-ion battery safety warning device based on cloud big data. The device includes an acquisition module and a determination module. The acquisition module is used to acquire the target charging data of the lithium-ion battery recorded in the cloud big data. Among them, the target charging data includes continuous charging data segments when the voltage of the lithium-ion battery is in the plateau region; and the monomer voltage at each moment within the continuous charging data segment is acquired. The determination module is used to determine whether to give a safety warning to the lithium-ion battery according to the monomer voltage at each moment within the continuous charging data segment.

[0022] For the above-designed lithium-ion battery safety warning device based on cloud big data, in this solution, the real-time charging data of the lithium-ion battery is recorded through cloud big data, then a continuous charging data segment when the voltage of the lithium-ion battery is in the plateau region is acquired, and then it is determined whether to give a safety warning to the lithium-ion battery according to the monomer voltage at each moment within the continuous charging data segment. Thus, a safety warning is given to the lithium-ion battery based on the continuous charging data segment when the voltage of the lithium-ion battery is in the plateau region, thereby reducing the influence of differences in the initial capacity and initial internal resistance of each cell of the lithium-ion battery on the charging of the lithium-ion battery, and further improving the accuracy and reliability of the safety warning of the lithium-ion battery.

[0023] In an alternative embodiment of the second aspect, the determination module is specifically configured to calculate the monomer voltage difference between the monomer voltages at each moment within the continuous charging data segment according to the monomer voltage at each moment within the continuous charging data segment; determine whether there is a monomer voltage difference exceeding a preset voltage difference threshold among the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segment; if it is determined that there is a monomer voltage difference exceeding the preset voltage difference threshold, it is determined to give a safety warning to the lithium-ion battery.

[0024] In an alternative embodiment of the second aspect, the determination module is further specifically configured to, if it is determined that there is no single-cell voltage difference exceeding the preset voltage difference threshold, generate effective voltage segments for multiple time windows based on the single-cell voltage at each moment in the continuous charging data segment; wherein, the time window includes multiple moments in the continuous charging data segment, and the effective voltage segment is obtained by enhancing the data of the single-cell voltage at each moment in the continuous charging data segment; for each time window, calculate the single-cell voltage entropy value sequence under the time window based on the effective voltage segment of the time window, and obtain the single-cell voltage entropy value sequence under each time window among the multiple time windows; and determine whether to issue a safety warning for the lithium-ion battery according to the single-cell voltage entropy value sequence under each time window among the multiple time windows.

[0025] In an alternative embodiment of the second aspect, the determination module is further specifically configured to divide the single-cell voltage at each moment in the continuous charging data segment into voltage segments for multiple time windows; calculate the median and standard deviation of the single-cell voltage in each time window among the multiple time windows, and generate an enhanced segment according to the median and standard deviation of the single-cell voltage in each time window; add the enhanced segment before the voltage segment of the time window ranked first among the multiple time windows, and add the enhanced segment after the voltage segment of the time window ranked last among the multiple time windows, so as to generate effective voltage segments for multiple time windows.

[0026] In an alternative embodiment of the second aspect, the determination module is further specifically configured to, for each time window, obtain the maximum single-cell voltage and the minimum single-cell voltage in the effective voltage segment of the time window; form multiple voltage intervals with the minimum single-cell voltage as the starting point and a preset voltage difference as the interval until the voltage intervals cover the maximum single-cell voltage of the time window; count the frequencies of each single-cell voltage falling into each voltage interval within the time window, and obtain the probability distribution matrix corresponding to the time window; calculate the single-cell voltage entropy value sequence under the time window according to the probability distribution matrix, and obtain the single-cell voltage entropy value sequence under each time window among the multiple time windows.

[0027] In an alternative embodiment of the second aspect, the determination module is further specifically configured to standardize and take the absolute value of the single-cell voltage entropy value sequence under each time window by using the standard score to obtain the maximum standard score under each time window; obtain the standard score threshold; wherein, the standard score threshold is determined according to the distribution of the single-cell voltage entropy value sequences of all lithium-ion batteries stored in the database; and determine whether to issue a safety warning for the lithium-ion battery according to the maximum standard scores under the multiple time windows and the standard score threshold.

[0028] In an alternative embodiment of the second aspect, the determination module is further specifically configured to determine whether the number of times the maximum standard score in multiple time windows is greater than the standard score threshold exceeds a preset number; if it is determined that the number of times the maximum standard score in multiple time windows is greater than the standard score threshold exceeds the preset number, then it is determined to issue a safety warning for the lithium-ion battery.

[0029] In an alternative embodiment of the second aspect, the determination module is further specifically configured to determine that no safety warning is issued for the lithium-ion battery if it is determined that the number of times the maximum standard score in multiple time windows is not greater than the standard score threshold does not exceed the preset number.

[0030] In an alternative embodiment of the second aspect, the acquisition module is further configured to acquire the sequence of entropy values of the individual cell voltages of the lithium-ion battery currently calculated; the device further includes a storage module and an update module, the storage module is configured to store the sequence of entropy values of the individual cell voltages of the lithium-ion battery currently calculated in a database; the update module is configured to update the standard score threshold according to the sequences of entropy values of the individual cell voltages of all lithium-ion batteries stored in the database.

[0031] In a third aspect, the present invention provides an electronic device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it executes any one of the optional methods in the first aspect.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it executes any one of the optional methods in the first aspect.

[0033] In a fifth aspect, the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, it executes the steps of any one of the optional methods in the first aspect.

[0034] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically described below. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation of the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 The first flowchart of the lithium-ion battery safety warning method based on cloud big data provided by the embodiments of the present application;

[0037] Figure 2 An example diagram of the platform area of the lithium-ion battery provided by the embodiments of the present application;

[0038] Figure 3 The second flowchart of the lithium-ion battery safety warning method based on cloud big data provided by the embodiments of the present application;

[0039] Figure 4 The third flowchart of the lithium-ion battery safety warning method based on cloud big data provided by the embodiments of the present application;

[0040] Figure 5 The fourth flowchart of the lithium-ion battery safety warning method based on cloud big data provided by the embodiments of the present application;

[0041] Figure 6 The fifth flowchart of the lithium-ion battery safety warning method based on cloud big data provided by the embodiments of the present application;

[0042] Figure 7 The structural schematic diagram of the lithium-ion battery safety warning device based on cloud big data provided by the embodiments of the present application;

[0043] Figure 8 The structural schematic diagram of the electronic device provided by the embodiments of the present application.

[0044] Icons: 700 - Acquisition module; 710 - Determination module; 720 - Storage module; 730 - Update module; 8 - Electronic device; 801 - Processor; 802 - Memory; 803 - Communication bus. Detailed implementation manners

[0045] Next, embodiments of the technical solutions of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above accompanying drawings are intended to cover non-exclusive inclusion.

[0047] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0048] Reference to "embodiment" in this text means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase does not necessarily refer to the same embodiment when it appears in various positions in the specification, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0049] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this text generally represents an "or" relationship between the associated objects before and after.

[0050] In the description of the embodiments of the present application, the term "a plurality of" means more than two (including two). Similarly, "a plurality of groups" means more than two groups (including two groups), and "a plurality of pieces" means more than two pieces (including two pieces).

[0051] In the description of the embodiments of the present application, technical terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the embodiments of the present application and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the embodiments of the present application.

[0052] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and can be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0053] Many safety hazards of lithium-ion batteries do not appear suddenly, but gradually deteriorate over time. This deterioration may involve multiple aspects such as the aging of battery materials, the change of electrolyte properties, and the loss of active substances. Under the combined action of these factors, the performance of the battery gradually deviates from the normal state. Minor abnormal changes inside the battery may indicate abnormal battery performance and be directly or indirectly reflected in its operating data. These data include but are not limited to the charge / discharge curve of the battery, the operating temperature range, and the cycle life performance. To effectively capture these safety risks, the operating data of the battery can be monitored for a long time. In the early stage of a safety failure, the subtle abnormal performance of the battery can be amplified to identify its potential safety risks and issue early warnings.

[0054] Currently, traditional battery safety determination methods mainly rely on simple temperature monitoring and threshold judgment, lacking an accurate assessment of the battery state under complex working conditions, and there is a problem of low accuracy in battery safety determination methods.

[0055] Based on the above problems, this application designs a lithium-ion battery safety warning method and storage medium based on cloud big data. The cloud big data platform can monitor the usage status and various performance parameters of the battery in real time and store all historical data. It is a database that preserves the full life cycle record of lithium-ion batteries from production to use until retirement and recycling, and covers a large number of batteries, with the characteristics of strong real-time performance, large storage capacity, and wide coverage. With the help of cloud big data, the current operating performance of the battery can be obtained in real time to make a real-time diagnosis of its performance, and the change of its usage performance can also be evaluated by analyzing the past data performance of the battery.

[0056] This solution records the real-time charging data of lithium-ion batteries through cloud big data, then obtains continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region, and then determines whether to issue a safety warning for the lithium-ion battery based on the cell voltage at each moment within the continuous charging data segment. Thus, by using the original data of the battery voltage recorded by cloud big data, while reducing the types of data sources relied on, it also strongly guarantees data reliability. And based on the continuous charging data segment where the voltage of the lithium-ion battery is in the plateau region, a safety warning is issued for the lithium-ion battery, thereby reducing the impact on the charging of the lithium-ion battery caused by the differences in the initial capacity and initial internal resistance of each cell of the lithium-ion battery, and further improving the accuracy and reliability of the safety warning of the lithium-ion battery. Secondly, this solution also compares the cell voltage difference between the cell voltages at each moment within the continuous charging data segment with a preset voltage difference threshold. In the case where there is a cell voltage difference exceeding the preset voltage difference threshold, a safety warning is directly issued for the lithium-ion battery, thereby improving the efficiency of the safety warning of the lithium-ion battery. In addition, in the case where there is no cell voltage exceeding the preset voltage difference threshold, this solution realizes the safety warning of the lithium-ion battery by calculating the cell voltage entropy, thereby further improving the accuracy and reliability of the safety warning of the lithium-ion battery. Finally, by considering the longitudinal comparison relationship of the lithium-ion battery cells themselves on the time axis and the lateral comparison relationship between different lithium-ion battery cells, a standard score threshold for comprehensive evaluation is set to give an early warning when the battery has a safety risk.

[0057] Based on the above idea, this application first provides a method for safety warning of lithium-ion batteries based on cloud big data. This method can be applied to computing devices, which include but are not limited to computers, servers, chips, and cloud servers, etc. As Figure 1 shown, the method for safety warning of lithium-ion batteries based on cloud big data can be implemented in the following ways, including:

[0058] Step S100: Obtain the target charging data of the lithium-ion battery recorded by cloud big data, where the target charging data includes continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region.

[0059] Step S110: Obtain the cell voltage at each moment within the continuous charging data segment.

[0060] Step S120: Determine whether to issue a safety warning for the lithium-ion battery based on the cell voltage at each moment within the continuous charging data segment.

[0061] In the above embodiment, the target charging data selected for the safety warning of this solution includes continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region. This plateau region represents a relatively stable area that appears on the curve of voltage changing with time (or capacity) during the charge and discharge process of the battery. Specifically, as a possible implementation, the cloud big data platform can monitor the usage status and various performance parameters of the lithium-ion battery in real time. This solution can obtain the changes in the voltage and capacity of the battery monitored in real time during the charging process recorded by the cloud big data platform. When it is detected that the battery voltage enters the plateau region, the charging data of the lithium-ion battery is started to be recorded and obtained until it is detected that the battery voltage leaves the plateau region.

[0062] As another possible implementation, this solution can perform an open-circuit voltage test on the lithium-ion battery in advance, and calibrate the SOC interval of the plateau region of the lithium-ion battery through the open-circuit voltage test result curve. For example, as Figure 2 shown, and are the plateau regions of this lithium-ion battery. On this basis, for step S100, this solution can obtain the real-time charging data of the lithium-ion battery recorded by the cloud big data platform during the charging process. This real-time charging data includes the real-time current, real-time SOC, cell voltage, and cell temperature of this lithium-ion battery, etc. Then, the charging data with the real-time SOC value of the lithium-ion battery in the SOC interval of the plateau region is intercepted from the real-time charging data, so as to obtain the target charging data of the lithium-ion battery.

[0063] In the case of obtaining the target charging data of the lithium-ion battery through the above method, this solution can directly obtain the cell voltage of the lithium-ion battery at each moment within the continuous charging data segment.

[0064] Specifically, this solution can represent the cell voltage of the lithium-ion battery at each moment within a continuous charging data segment through the following formula:

[0065] ;

[0066] where, represents the cell voltage matrix of the continuous charging data segment , represents the voltage sequence of the battery cells within this segment , represents the voltage value of the battery cell at the th moment, is the number of battery cells in the lithium-ion battery pack, is the number of data frames (number of moments) within the current segment.

[0067] When the cell voltages at each moment within the continuous charging data segment are obtained in the above manner, this solution can determine whether to issue a safety warning for the lithium-ion battery based on the cell voltages at each moment within the continuous charging data segment.

[0068] The above-described lithium-ion battery safety warning method based on cloud big data records the real-time charging data of the lithium-ion battery through cloud big data, then obtains the continuous charging data segment where the voltage of the lithium-ion battery is in the plateau region, and then determines whether to issue a safety warning for the lithium-ion battery based on the cell voltages at each moment within the continuous charging data segment. Thus, a safety warning for the lithium-ion battery is issued based on the continuous charging data segment where the voltage of the lithium-ion battery is in the plateau region, thereby reducing the impact on the charging of the lithium-ion battery caused by the differences in the initial capacities and initial internal resistances of the individual cells of the lithium-ion battery, and further improving the accuracy and reliability of the lithium-ion battery safety warning.

[0069] In an alternative implementation manner of this embodiment, for step S120, this solution can implement whether to issue a safety warning for the lithium-ion battery in the following manner, as Figure 3 shown, including:

[0070] Step S300: Calculate the cell voltage difference between the cell voltages at each moment within the continuous charging data segment based on the cell voltages at each moment within the continuous charging data segment.

[0071] Step S310: Determine whether there is a cell voltage difference exceeding a preset voltage difference threshold among the cell voltage differences between the cell voltages at each moment within the continuous charging data segment. If there is, go to step S320; if not, go to step S400.

[0072] Step S320: Determine to issue a safety warning for the lithium-ion battery.

[0073] In the above implementation manner, the cell voltage difference of the lithium-ion battery refers to the voltage difference between different individual cells in a lithium-ion battery pack composed of multiple individual cells connected in series or in parallel. And an excessive cell voltage difference between the battery cells inside the lithium-ion battery may indicate a fault risk of the lithium-ion battery. In this regard, in this implementation manner, first, the cell voltage difference between the cell voltages at each moment within the continuous charging data segment is calculated based on the cell voltages at each moment within the continuous charging data segment, and then it is determined whether there is a cell voltage difference exceeding the preset voltage difference threshold among the cell voltage differences between the cell voltages at each moment within the continuous charging data segment. If there is a cell voltage difference exceeding the preset voltage difference threshold, it means that the lithium-ion battery has a fault with an excessive cell voltage difference, which further indicates a fault risk during the charging process of the lithium-ion battery. Therefore, this solution issues a safety warning for the lithium-ion battery.

[0074] Specifically, assume that the monomer voltage at each moment within the continuous charging data segment is as described above In this case, the monomer voltage difference at the i-th moment calculated by this solution can be:

[0075] ;

[0076] The sequence of monomer voltage differences within this continuous charging data segment is:

[0077] ;

[0078] Based on the above, this solution pre-sets a preset voltage difference threshold , and determines whether there is a monomer voltage difference exceeding in it. If there is any monomer voltage difference exceeding , a safety warning will be given to the lithium-ion battery.

[0079] In the above implementation, this solution compares the monomer voltage difference between the monomer voltages at each moment within the continuous charging data segment with the preset voltage difference threshold. In the case where there is a monomer voltage difference exceeding the preset voltage difference threshold, it indicates that there is a fault risk during the charging process of the lithium-ion battery, and then a safety warning is directly given to the lithium-ion battery, thereby improving the efficiency of the safety warning of the lithium-ion battery.

[0080] In an alternative implementation of this embodiment, when this solution determines that there is no monomer voltage difference exceeding the preset voltage difference threshold among the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segment, this solution can achieve the safety warning of the lithium-ion battery through the following method, as Figure 4 shown, including:

[0081] Step S400: Generate effective voltage segments for multiple time windows based on the monomer voltage at each moment within the continuous charging data segment.

[0082] Step S410: For each time window, calculate the sequence of monomer voltage entropy values under the time window based on the effective voltage segment of the time window, and obtain the sequence of monomer voltage entropy values under each time window among multiple time windows.

[0083] Step S420: Determine whether to give a safety warning to the lithium-ion battery according to the sequence of monomer voltage entropy values under each time window among multiple time windows.

[0084] The magnitude of the voltage entropy reflects the complexity and unpredictability of the battery voltage fluctuations. If the voltage change of the lithium-ion battery is relatively regular and stable, its voltage entropy value is low; conversely, if the voltage fluctuates violently and without obvious rules, the voltage entropy value is high. Therefore, the voltage fluctuation situation during the charging process of the lithium-ion battery can be realized by the magnitude of the voltage entropy, and then early warning of the charging process of the lithium-ion battery can be carried out.

[0085] In the above embodiment, this solution calculates the voltage entropy of consecutive charging data segments with a fixed time window. This time window can include multiple moments within the consecutive charging data segments. After the calculation of the current window is completed in this solution, the single-cell voltage entropy of the next time window is calculated by moving the set step length along the time axis, and so on until the end of the time window covers the last moment of the current consecutive charging data segment. However, during the calculation process, the charging data at the first moment is only included in the calculation once in the first time window, and the charging data at the last moment is also only included in the calculation once in the last time window. However, the data at other moments is included in the calculation at least twice. In this way, the data in the first time window and the last time window is used for calculation fewer times than the data in other windows. To ensure equal use of all charging data and improve the accuracy of lithium-ion battery safety early warning, this solution can first perform data enhancement on the single-cell voltage at each moment within the consecutive charging data segments, so as to obtain effective voltage segments for multiple time windows.

[0086] Specifically, this solution can achieve data enhancement in the following way: First, this solution can divide the single-cell voltage at each moment within the consecutive charging data segments into voltage segments for multiple time windows, then calculate the median of the single-cell voltage and the standard deviation of the single-cell voltage within each time window among the multiple time windows. According to the median of the single-cell voltage and the standard deviation of the single-cell voltage within each time window, an enhanced segment is generated. Finally, the enhanced segment is added before the voltage segment of the time window ranked first among the multiple time windows, and the enhanced segment is added after the voltage segment of the time window ranked last among the multiple time windows to generate effective voltage segments for multiple time windows.

[0087] As a specific example, the single-cell voltage at each moment within the aforementioned consecutive charging data segments is In this case, the effective voltage segments for multiple time windows after data enhancement are:

[0088] ;

[0089] Among them, the superscript represents the original segment the first time window, represents the original segment the last time window, Represents the median voltage of cell j within the window. Represents the standard deviation of the voltage of cell j within the window. Represents a random number between 0 and 1, and L is the length of the time window.

[0090] In the case of obtaining valid voltage segments of multiple time windows through the above method, this solution can calculate the sequence of entropy values of the cell voltage under each time window based on the valid voltage segments of the time window for each time window, and obtain the sequence of entropy values of the cell voltage under each time window among multiple time windows.

[0091] Specifically, this solution can first, for each time window, obtain the maximum and minimum cell voltages in the valid voltage segment of the time window, and then, starting from the minimum cell voltage, form multiple voltage intervals at intervals of a preset voltage difference until the voltage intervals cover the maximum cell voltage of the time window; then count the frequencies of each cell voltage within the time window falling into each voltage interval to obtain the probability distribution matrix corresponding to the time window; finally, calculate the sequence of entropy values of the cell voltage under the time window based on the probability distribution matrix, and obtain the sequence of entropy values of the cell voltage under each time window among multiple time windows.

[0092] The process of calculating the sequence of entropy values of the cell voltage under each time window can be illustrated by the following example:

[0093] For the valid voltage segments of multiple time windows after data augmentation , the cell voltage of a lithium-ion battery within a time window can be expressed as:

[0094]

[0095] where the superscript represents the L-th time window of the valid voltage segments of multiple time windows .

[0096] For a time window voltage matrix , calculate the entropy value of the voltage of each cell under this time window. The calculation method is:

[0097] The maximum and minimum values of all cell voltages within

[0098] ;

[0099] ;

[0100] Starting from the minimum cell voltage within the time window, at an interval of form voltage intervals:

[0101] ;

[0102] until covering the maximum value of the single - cell voltage within the time window , count the frequency of each single - cell voltage falling within each interval within the time window, and obtain a probability distribution matrix:

[0103] ;

[0104] wherein, represents the probability that the voltage of single - cell j within the time window of the effective segment q falls within the k - th voltage interval, represents the number of voltage intervals; Then, calculate the entropy value sequence of the single - cell voltage for the current window according to the probability matrix:

[0105] ;

[0106] ;

[0107] ;

[0108] In the case of obtaining the entropy value sequence of the single - cell voltage for each time window among multiple time windows through the above - mentioned method, this solution then determines whether to give a safety warning for the lithium - ion battery according to the entropy value sequence of the single - cell voltage for each time window among multiple time windows.

[0109] In the above - mentioned implementation manner, when calculating the entropy sequence of the single - cell voltage, this solution calculates the probability distribution by dividing the single - cell voltage into intervals, rather than directly using the original voltage value to calculate the probability distribution, thereby reducing the influence caused by hardware sampling errors, etc., and further improving the accuracy of the warning.

[0110] For step S420, this solution can determine whether to give a safety warning for the lithium - ion battery based on the entropy value sequence of the single - cell voltage for each time window among multiple time windows in the following way, as Figure 5 shown, including:

[0111] Step S500: Standardize the entropy value sequence of the single - cell voltage for each time window using the standard score and take the absolute value to obtain the maximum value of the standard score for each time window.

[0112] Step S510: Obtain the standard score threshold.

[0113] Step S520: Determine whether to give a safety warning for the lithium - ion battery according to the maximum value of the standard scores for multiple time windows and the standard score threshold.

[0114] In the above embodiments, the present solution can standardize the monomer voltage entropy value sequence by using the standard score method to obtain the maximum standard score under each time window. Among them, the maximum standard score represents the value with the largest standard deviation of the monomer voltage entropy value in the monomer voltage entropy value sequence under this time window being higher than the average value, that is, the value with the largest deviation relative to the average value.

[0115] On this basis, the present solution can obtain a standard score threshold, which can be determined according to the distribution of the monomer voltage entropy value sequences of all lithium-ion batteries stored in the cloud database, and then determine whether to give a safety warning to the lithium-ion battery according to the maximum standard scores under multiple time windows and the standard score threshold.

[0116] Specifically, as a possible implementation manner, the present solution can determine whether the number of times the maximum standard scores under multiple time windows are greater than the standard score threshold exceeds a preset number. If it is determined that the number of times the maximum standard scores under multiple time windows are greater than the standard score threshold exceeds the preset number, it indicates that the voltage of the lithium-ion battery fluctuates too violently, and there is a high probability that the lithium-ion battery will malfunction. Therefore, a safety warning is given to the lithium-ion battery; if it is determined that the number of times the maximum standard scores under multiple time windows are greater than the standard score threshold does not exceed the preset number, it indicates that the voltage of the lithium-ion battery is normal during the charging process. Therefore, no safety warning is given to the lithium-ion battery.

[0117] Furthermore, in order to further improve the reliability and accuracy of the safety warning of the lithium-ion battery, the present solution can also obtain the monomer number corresponding to the maximum standard score greater than the standard score threshold under each time window, which characterizes the battery monomer with the most violent and irregular voltage fluctuation under this time window, and then determine to give a safety warning to the lithium-ion battery and send a warning message. The warning message includes a warning reminder and the monomer number corresponding to the maximum standard score greater than the standard score threshold. Among them, the warning reminder can be a text message reminder or a buzzer alarm, etc.

[0118] If it is determined that the number of times the maximum standard scores under multiple time windows are greater than the standard score threshold does not exceed the preset number, it indicates that the fluctuation of the voltage entropy value may be caused by reasonable fluctuations during the charging process, so it is determined not to give a safety warning to the lithium-ion battery.

[0119] Among them, as a possible implementation, the preset number required for the foregoing determination in this solution can be determined according to the length of the time window. For example, if the length of the time window is the L moment, then the preset number can be set to L; as another possible implementation, the preset number required for the determination in this solution can also be determined according to the number of time windows. For example, if the number of time windows is N, then the preset number can be set to N / 2. In this way, assuming that the number of times the maximum standard score is greater than the standard score threshold exceeds the preset number, it means that the maximum standard score under more than half of the time windows is greater than the standard score threshold.

[0120] In an alternative implementation of this embodiment, this solution can also update the standard score threshold described above in real time, as Figure 6 shown, and specifically may include the following steps:

[0121] Step S600: Obtain the sequence of entropy values of the single-cell voltage of the lithium-ion battery calculated currently.

[0122] Step S610: Store the sequence of entropy values of the single-cell voltage of the lithium-ion battery calculated currently in the database.

[0123] Step S620: Update the standard score threshold according to the sequences of entropy values of the single-cell voltages of all lithium-ion batteries stored in the database.

[0124] In the above implementation, the cloud big data can record and store the sequence of entropy values of the single-cell voltage during each calculation process of the lithium-ion battery, and then update the standard score threshold based on the sequences of entropy values of the single-cell voltages of all recorded lithium-ion batteries, thereby realizing the dynamic update of the standard threshold score, so as to avoid the influence of seasonal environmental factors and battery aging factors on the charging of lithium-ion batteries, and further improve the accuracy and reliability of the safety warning of lithium-ion batteries. Specifically, this solution can use the mean or standard deviation of the sequences of entropy values of all recorded lithium-ion batteries as the standard threshold score, or calculate the corresponding standard score sequence for each sequence of entropy values of the single-cell voltage based on the sequences of entropy values of all recorded lithium-ion batteries, and then use the mean or standard deviation of all standard score sequences as the standard threshold score.

[0125] Figure 7 shows a schematic structural block diagram of a lithium-ion battery safety warning device provided by this application. It should be understood that this device is applied to the computing device described above, and this device is connected to Figure 1 and 6Corresponding to the method embodiment executed in, the steps involved in the aforementioned method can be executed. The specific functions of the device can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device. Specifically, the device includes: an acquisition module 700 and a determination module 710, the acquisition module 700 is used to obtain the target charging data of the lithium-ion battery recorded in the cloud big data; wherein the target charging data includes a continuous charging data segment in which the lithium-ion battery voltage is located in the platform area; and the single cell voltage at each moment in the continuous charging data segment is obtained; the determination module 710 is used to determine whether to issue a safety warning to the lithium-ion battery according to the single cell voltage at each moment in the continuous charging data segment.

[0126] The above-designed lithium-ion battery safety warning device based on cloud big data, this scheme records the real-time charging data of the lithium-ion battery through cloud big data, and then obtains the continuous charging data segment in which the voltage of the lithium-ion battery is in the platform area, and then judges whether to issue a safety warning for the lithium-ion battery according to the single cell voltage at each moment in the continuous charging data segment, thereby issuing a safety warning for the lithium-ion battery based on the continuous charging data segment in which the voltage of the lithium-ion battery is in the platform area, thereby reducing the impact of the differences in the initial capacity and initial internal resistance of each cell of the lithium-ion battery on the charging of the lithium-ion battery, thereby improving the accuracy and reliability of the lithium-ion battery safety warning.

[0127] In an optional implementation of the present embodiment, the determination module 710 is specifically used to calculate the cell voltage difference between the cell voltages at each moment in the continuous charging data segment based on the cell voltages at each moment in the continuous charging data segment; determine whether there is a cell pressure difference between the cell voltages at each moment in the continuous charging data segment that exceeds a preset pressure difference threshold; if it is determined that there is a cell pressure difference that exceeds the preset pressure difference threshold, determine to issue a safety warning for the lithium-ion battery.

[0128] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to, if it is determined that there is no cell voltage difference exceeding the preset voltage difference threshold, generate effective voltage segments for multiple time windows according to the cell voltages at each moment in the continuous charging data segment; wherein, the time window includes multiple moments in the continuous charging data segment, and the effective voltage segment is obtained by performing data enhancement on the cell voltages at each moment in the continuous charging data segment; for each time window, calculate the cell voltage entropy value sequence under the time window based on the effective voltage segment of the time window, and obtain the cell voltage entropy value sequence under each time window among multiple time windows; and determine whether to issue a safety warning for the lithium-ion battery according to the cell voltage entropy value sequences under each time window among multiple time windows.

[0129] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to divide the cell voltages at each moment in the continuous charging data segment into voltage segments for multiple time windows; calculate the median and standard deviation of the cell voltages within each time window among multiple time windows, and generate an enhanced segment according to the median and standard deviation of the cell voltages within each time window; add the enhanced segment before the voltage segment of the time window ranked first among multiple time windows, and add the enhanced segment after the voltage segment of the time window ranked last among multiple time windows, so as to generate effective voltage segments for multiple time windows.

[0130] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to, for each time window, obtain the maximum cell voltage and the minimum cell voltage in the effective voltage segment of the time window; form multiple voltage intervals at intervals of a preset voltage difference starting from the minimum cell voltage until the voltage intervals cover the maximum cell voltage of the time window; count the frequencies of each cell voltage falling into each voltage interval within the time window, and obtain the probability distribution matrix corresponding to the time window; calculate the cell voltage entropy value sequence under the time window according to the probability distribution matrix, and obtain the cell voltage entropy value sequence under each time window among multiple time windows.

[0131] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to standardize and take the absolute value of the cell voltage entropy value sequence under each time window by using the standard score, and obtain the maximum standard score value under each time window; obtain the standard score threshold; wherein, the standard score threshold is determined according to the distribution of the cell voltage entropy value sequences of all lithium-ion batteries stored in the database; and determine whether to issue a safety warning for the lithium-ion battery according to the maximum standard score values under multiple time windows and the standard score threshold.

[0132] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to determine whether the number of times the maximum standard score in multiple time windows is greater than the standard score threshold exceeds a preset number; if it is determined that the number of times the maximum standard score in multiple time windows is greater than the standard score threshold exceeds the preset number, then it is determined to issue a safety warning for the lithium-ion battery.

[0133] In an alternative implementation of this embodiment, the determination module 710 is further specifically configured to determine that no safety warning is issued for the lithium-ion battery if it is determined that the number of times the maximum standard score in multiple time windows is not greater than the standard score threshold does not exceed the preset number.

[0134] In an alternative implementation of this embodiment, the acquisition module 700 is further configured to acquire the sequence of entropy values of the single-cell voltages of the lithium-ion battery calculated currently; the device further includes a storage module 720 and an update module 730, where the storage module 720 is configured to store the sequence of entropy values of the single-cell voltages of the lithium-ion battery calculated currently in a database; the update module 730 is configured to update the standard score threshold according to the sequences of entropy values of the single-cell voltages of all the lithium-ion batteries stored in the database.

[0135] According to some embodiments of the present application, as Figure 8 shown, the present application provides an electronic device 8, including: a processor 801 and a memory 802, the processor 801 and the memory 802 are interconnected and communicate with each other through a communication bus 803 and / or other forms of connection mechanisms (not shown), the memory 802 stores a computer program executable by the processor 801, and when the computing device runs, the processor 801 executes the computer program to execute the method of any optional implementation manner when executed, for example, steps S100 to S120: acquiring target charging data of the lithium-ion battery recorded in cloud big data, where the target charging data includes continuous charging data segments where the voltage of the lithium-ion battery is in the plateau region; acquiring the single-cell voltage at each moment within the continuous charging data segment; and determining whether to issue a safety warning for the lithium-ion battery according to the single-cell voltage at each moment within the continuous charging data segment.

[0136] The present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the method in any of the foregoing optional implementation manners is executed.

[0137] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0138] The present application provides a computer program product, which when running on a computer, causes the computer to execute the method in any of the optional implementation manners.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be covered by the scope of the claims and the description of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present application is not limited to the specific embodiments disclosed in the text, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for safety warning of lithium-ion batteries based on cloud big data, characterized in that, The method includes: Obtaining target charging data of a lithium-ion battery recorded in cloud big data; wherein, the target charging data includes continuous charging data segments when the voltage of the lithium-ion battery is in the plateau region; Obtaining the monomer voltage at each moment within the continuous charging data segment; Judging whether to give a safety warning for the lithium-ion battery according to the monomer voltage at each moment within the continuous charging data segment; Calculating the monomer voltage difference between the monomer voltages at each moment within the continuous charging data segment according to the monomer voltage at each moment within the continuous charging data segment; Judging whether there is a monomer voltage difference exceeding a preset voltage difference threshold among the monomer voltage differences between the monomer voltages at each moment within the continuous charging data segment; If it is determined that there is a monomer voltage difference exceeding the preset voltage difference threshold, determining to give a safety warning for the lithium-ion battery; If it is determined that there is no monomer voltage difference exceeding the preset voltage difference threshold, generating effective voltage segments of multiple time windows according to the monomer voltage at each moment within the continuous charging data segment; wherein, the time window includes multiple moments within the continuous charging data segment, and the effective voltage segment is obtained after data enhancement by the monomer voltage at each moment within the continuous charging data segment; For each time window, calculating the monomer voltage entropy value sequence under the time window based on the effective voltage segment of the time window, and obtaining the monomer voltage entropy value sequence under each time window among multiple time windows; Judging whether to give a safety warning for the lithium-ion battery according to the monomer voltage entropy value sequence under each time window among multiple time windows; The generating effective voltage segments of multiple time windows according to the monomer voltage at each moment within the continuous charging data segment includes: Dividing the monomer voltage at each moment within the continuous charging data segment into voltage segments of multiple time windows; Calculating the median and standard deviation of the monomer voltage within each time window among multiple time windows, and generating an enhanced segment according to the median and standard deviation of the monomer voltage within each time window; Adding the enhanced segment before the voltage segment of the time window ranked first among the multiple time windows, and adding the enhanced segment after the voltage segment of the time window ranked last among the multiple time windows, to generate effective voltage segments of multiple time windows.

2. The method according to claim 1, characterized in that The calculating the monomer voltage entropy value sequence under the time window based on the effective voltage segment of the time window for each time window, and obtaining the monomer voltage entropy value sequence under each time window among multiple time windows includes: For each time window, obtaining the maximum monomer voltage and the minimum monomer voltage in the effective voltage segment of the time window; Taking the minimum monomer voltage as the starting point, forming multiple voltage intervals at intervals of a preset voltage difference until the voltage intervals cover the maximum monomer voltage of the time window; Counting the frequency of each monomer voltage falling within each voltage interval within the time window, to obtain the probability distribution matrix corresponding to the time window; Calculate the monomer voltage entropy value sequence under the time window according to the probability distribution matrix, and obtain the monomer voltage entropy value sequence under each time window in multiple time windows.

3. The method according to claim 1, characterized in that, Judging whether to give a safety warning to the lithium-ion battery according to the monomer voltage entropy value sequence under each time window in multiple time windows includes: Standardize the monomer voltage entropy value sequence under each time window by using the standard score and take the absolute value to obtain the maximum standard score value under each time window; Obtain the standard score threshold; wherein, the standard score threshold is determined according to the distribution of the monomer voltage entropy value sequences of all lithium-ion batteries stored in the database; Judge whether to give a safety warning to the lithium-ion battery according to the maximum standard score values under multiple time windows and the standard score threshold.

4. The method according to claim 3, wherein Judging whether to give a safety warning to the lithium-ion battery according to the maximum standard score values under multiple time windows and the standard score threshold includes: Judge whether the number of times that the maximum standard score value under the multiple time windows is greater than the standard score threshold exceeds a preset number of times; If it is determined that the number of times that the maximum standard score value under the multiple time windows is greater than the standard score threshold exceeds the preset number of times, it is determined to give a safety warning to the lithium-ion battery.

5. The method according to claim 4, characterized in that, The method further includes: If it is determined that the number of times that the maximum standard score value under the multiple time windows is greater than the standard score threshold does not exceed the preset number of times, it is determined not to give a safety warning to the lithium-ion battery.

6. The method according to claim 3, wherein The method further includes: Obtain the monomer voltage entropy value sequence of the lithium-ion battery currently calculated; Store the monomer voltage entropy value sequence of the lithium-ion battery currently calculated in the database; Update the standard score threshold according to the monomer voltage entropy value sequences of all lithium-ion batteries stored in the database.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.

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