Positive and negative battery system used in UPS scene and battery management method

Abstract

The invention discloses a positive and negative battery system used in a UPS scene and a battery management method, relates to the technical field of uninterruptible power supply management, and is used for solving the problem that a current battery management system lacks dynamic perception of multi-factor fusion and is easy to cause protection action lag. Measuring the bending degree of the battery frame, calculating the deformation quantity, and comprehensively analyzing the hidden overvoltage risk of the battery; detecting magnetic field intensity of a charging and discharging environment and internal resistance temperature of the battery, calculating temperature difference and electromagnetic interference quantity, and further evaluating apparent overvoltage risk; the false overvoltage index is generated by fusing invisible and explicit risks, and whether overvoltage protection is started or not is judged by combining the impulse voltage detected in real time, so that the battery overvoltage recognition accuracy is improved, and a protection mechanism is prevented from being triggered by mistake.

Description

Technical Field

[0001] The present invention relates to the technical field of uninterruptible power supply management, and more specifically, to a positive and negative battery system and a battery management method for use in a UPS scenario. Background Art

[0002] Uninterruptible power supply (UPS) systems, as backup power devices for critical loads, are widely used in data centers, medical equipment, communications rooms, and industrial automation. Batteries in UPS systems are core energy storage components, and their operating status directly impacts the system's power supply stability and safety. During actual operation, batteries are subject to a variety of complex environmental factors, such as mechanical stress, electromagnetic interference, and charge and discharge shocks. These factors can easily lead to hidden damage or overvoltage, which can cause serious consequences such as performance degradation, thermal runaway, and even fire and explosion.

[0003] The existing technology has the following deficiencies:

[0004] Current battery management systems lack a dynamic sensing mechanism that integrates multiple factors, including battery surface cracks, battery frame deformation, internal resistance temperature differences, and environmental electromagnetic interference. This makes it impossible to effectively assess both invisible and visible overvoltage risks in UPS applications, which can easily lead to delayed protection actions. Therefore, a positive and negative battery system and battery management method for UPS applications are proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a positive and negative battery system and a battery management method for a UPS scenario. By using an overvoltage risk modeling method based on the fusion of structural deformation identification, electromagnetic interference analysis and internal resistance thermal characteristics, the problems of difficulty in identifying invisible battery damage, weak overvoltage risk prediction ability and delayed overvoltage protection response proposed in the above-mentioned background technology are solved.

[0007] To achieve the above objectives, the present invention provides the following technical solution for a positive and negative battery system and a battery management method in a UPS scenario, comprising the following steps:

[0008] Step S1: Scan the battery surface to obtain the number of cracks on the battery surface, measure the curvature of the battery frame, and calculate the deformation of the battery frame according to the curvature of the battery frame;

[0009] Step S2: Analyze the hidden overvoltage risk of the battery by comprehensively considering the number of cracks on the battery surface and the deformation of the battery frame, detect the magnetic field strength of the battery charging and discharging environment, select a period of time to detect the battery internal resistance and temperature, and calculate the battery internal resistance temperature difference;

[0010] Step S3: Calculate the electromagnetic interference of the environment on the battery based on the magnetic field strength of the battery charging and discharging environment, and analyze the battery's apparent overvoltage risk in combination with the temperature difference of the battery's internal resistance;

[0011] Step S4: Generate a false overvoltage index for the battery based on the hidden overvoltage risk and visible overvoltage risk of the battery, detect the impact voltage during battery charging and discharging in real time, and determine whether to enable overvoltage protection for the battery based on the impact voltage and the false overvoltage index.

[0012] In a preferred embodiment, in step S1, an image of the battery surface structure is acquired, and the number of cracks on the battery surface is identified and counted in combination with image preprocessing operations;

[0013] The outer contour boundary of the battery frame is sampled at equal intervals to obtain the spatial coordinates of each sampling point, and the sampling points are fitted using the least squares method to form a target straight line.

[0014] In a preferred embodiment, in step S1, the shortest distance from each sampling point to the target straight line is averaged to obtain an average offset as the curvature of the battery frame;

[0015] The battery frame deformation is calculated based on the ratio of the curvature of the battery frame to the original design length of the frame.

[0016] In a preferred embodiment, in step S2, the number of cracks on the battery surface and the deformation of the battery frame are normalized;

[0017] A weighted linear function is constructed to obtain the reverse impact factor, and the reciprocal of the sum of the reverse impact factor and 1 is used as the battery invisible overvoltage risk score.

[0018] In a preferred embodiment, in step S2, within a preset acquisition period, the magnetic field strength value and direction in the battery charging and discharging environment are detected, and the magnetic field strength values ​​in each direction are combined into the magnetic field strength at the corresponding detection time using a vector synthesis method, and the average value at each time is calculated as the total magnetic field strength;

[0019] The battery internal resistance temperature is collected in the charging and discharging environment, the temperature at the start and end time of the collection cycle is recorded, and the difference is calculated as the battery internal resistance temperature difference.

[0020] In a preferred embodiment, in step S3, the electromagnetic closed area is obtained by a structural modeling tool, and the electromagnetic interference amount is calculated by Faraday's law of electromagnetic induction in combination with the total magnetic field strength;

[0021] The electromagnetic interference amount and the battery internal resistance temperature difference are normalized and then summed. Based on the summation result, a logistic regression model is constructed to calculate the battery visible overvoltage risk score.

[0022] In a preferred embodiment, in step S4, the battery invisible overvoltage risk and the battery visible overvoltage risk of n battery samples are collected, the corresponding information entropy values ​​are calculated respectively by the entropy weight method, the weights are calculated based on the information entropy values, and the battery invisible overvoltage risk score and the battery visible overvoltage risk score are weightedly summed with the corresponding weights to obtain the false overvoltage index.

[0023] In a preferred embodiment, in step S4, the voltage change during the battery charge and discharge process is monitored in real time, the maximum voltage and the voltage value with the highest frequency are recorded, and the difference between them is used to obtain the surge voltage value;

[0024] The preset voltage protection threshold and false overvoltage index threshold determine whether to enable overvoltage protection for the battery.

[0025] In a preferred embodiment, in step S4, if the surge voltage is less than or equal to the voltage protection threshold, the voltage protection is not activated;

[0026] If the surge voltage is greater than the voltage protection threshold, and the false overvoltage index is greater than the false overvoltage index threshold, the voltage protection will not be activated.

[0027] If the surge voltage is greater than the voltage protection threshold and the false overvoltage index is less than or equal to the false overvoltage index threshold, the voltage protection is turned on.

[0028] Technical effects and advantages of the present invention:

[0029] The present invention obtains the number of cracks on the battery surface by scanning the battery surface, measures the curvature of the battery frame, calculates the deformation of the battery frame according to the curvature of the battery frame, analyzes the invisible overvoltage risk of the battery based on the number of cracks on the battery surface, detects the magnetic field strength of the battery charging and discharging environment, detects the temperature of the battery internal resistance, calculates the temperature difference of the battery internal resistance, calculates the electromagnetic interference of the environment on the battery according to the magnetic field strength of the battery charging and discharging environment, analyzes the visible overvoltage risk of the battery based on the temperature difference of the battery internal resistance, generates the false overvoltage index of the battery based on the invisible overvoltage risk of the battery, detects the impact voltage of the battery during charging and discharging in real time, and determines whether to turn on overvoltage protection for the battery based on the false overvoltage index, thereby improving the accuracy of battery overvoltage identification and avoiding false triggering of the protection mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart for implementing the positive and negative battery systems and battery management method of the present invention in UPS scenarios.

[0031] Figure 2 This is a schematic diagram of the steps of the positive and negative battery system and battery management method of the present invention used in UPS scenarios. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] Example 1

[0034] See also Figures 1 to 2 , a positive and negative battery system and a battery management method for a UPS scenario, comprising the following steps:

[0035] Step S1: Scan the battery surface to obtain the number of cracks on the battery surface, measure the curvature of the battery frame, and calculate the deformation of the battery frame according to the curvature of the battery frame;

[0036] Step S2: Analyze the hidden overvoltage risk of the battery by comprehensively considering the number of cracks on the battery surface and the deformation of the battery frame, detect the magnetic field strength of the battery charging and discharging environment, select a period of time to detect the battery internal resistance temperature, and calculate the battery internal resistance temperature difference; Step S3: Calculate the electromagnetic interference of the environment on the battery based on the magnetic field strength of the battery charging and discharging environment, and analyze the visible overvoltage risk of the battery by combining the battery internal resistance temperature difference;

[0037] Step S4: Generate a false overvoltage index for the battery based on the hidden overvoltage risk and visible overvoltage risk of the battery, detect the impact voltage during battery charging and discharging in real time, and determine whether to enable overvoltage protection for the battery based on the impact voltage and the false overvoltage index.

[0038] The specific implementation is as follows:

[0039] In step S1, the battery surface is clearly scanned by a high-resolution industrial camera to obtain an image of the battery surface structure. The image is preprocessed to enhance the image features of cracks on the battery surface. The contour detection method is used to identify and analyze the crack areas on the battery surface in the image. The continuous dark and elongated areas in the image are marked as crack areas. The number of crack areas is counted as the number of cracks on the battery surface.

[0040] The laser ranging sensor is used to perform equidistant spatial sampling of the outer contour of the battery frame, and the spatial coordinates of each sampling point are obtained and merged into a sampling point set: P = {( , , )|1,2,3...N}, where N is the number of sampling points, , , are the coordinate values ​​of the i-th sampling point in space;

[0041] The least squares method is used to fit the reference straight line to the sampling point set to form the target straight line: ,in, is the preset fitting starting point, d is the direction vector, t is the linear parameter, and L is the target line.

[0042] It needs to be explained that the preset fitting starting point is to use the spatial centroid of the sampling point set as the starting point of the straight line; the direction vector is the direction of the target straight line, which is the fitting direction of the changing trend of the sampling point set; the linear parameter refers to the component of the straight line direction vector, which determines the fitting result and is convenient for solving using linear algebra methods; the direction vector and the preset fitting starting point make the sum of the squares of the distances from all sampling points to the target straight line as small as possible, and the specific settings are made by professionals.

[0043] The average offset is calculated by the shortest distance from each sampling point to the target straight line, and the average offset is used as the curvature of the battery frame:

[0044] Calculate the shortest distance from each sampling point to the target line: ,in, is the vertical distance from the i-th sampling point to the target line. The average offset of each sampling point is calculated to obtain the curvature of the battery frame: ,in, is the curvature of the battery frame.

[0045] The battery frame deformation is calculated based on the ratio of the battery frame curvature to the original length of the battery frame: ,in, is the original length of the battery frame, is the battery frame deformation.

[0046] This step obtains the number of cracks by scanning the battery surface, measures the curvature of the battery frame by fitting a straight line based on the spatial sampling points, and further calculates the deformation variable to quantify the degree of deformation of the battery structure, accurately reflecting the structural changes caused by battery stress or aging, and providing key basic data for subsequent invisible overvoltage risk analysis.

[0047] It should be noted that a high-resolution industrial camera is a special imaging device used in industrial inspection scenarios with a million-pixel image acquisition capability. It can clearly scan the battery surface and obtain the structural features of the battery surface. The preprocessing operation includes grayscale, filtering and noise reduction, contrast enhancement, edge detection and other steps, which can effectively suppress background texture interference and improve the continuity and clarity of the crack boundary. The contour detection method refers to an image recognition technology that obtains the external contour of the target with a closed boundary structure in the image through edge extraction and connected boundary analysis during the image processing process. In the embodiment of the present invention, the contour detection method is mainly used to identify the outer boundary of the crack area on the battery surface, and realize the spatial positioning, contour modeling and quantitative statistics of the crack target. The laser ranging sensor is a precision sensor that uses a laser beam for non-contact distance measurement, which can accurately collect multiple three-dimensional spatial sampling points at the boundary of the battery frame. The least squares method is used to construct an optimal fitting function based on a given set of discrete data points, so that the function is as close as possible to all data points. The original length of the battery frame is obtained through the battery product technical manual, which includes the standard geometric dimensions of the frame.

[0048] In step S2, the number of battery surface cracks and battery frame deformation are comprehensively considered to analyze the battery invisible overvoltage risk by constructing a logistic regression model, reflecting the possibility of errors or deviations in the impact voltage measurement due to abnormal battery structure;

[0049] The number of battery surface cracks and battery frame deformation are normalized respectively, and the reverse impact factor is obtained by constructing a linear function. The battery invisible overvoltage risk score is calculated based on the reverse impact factor:

[0050] Construct a linear function to obtain the reverse impact factor: ,in, and is the preset weight coefficient, x is the number of cracks on the battery surface, and U is the reverse impact factor;

[0051] Calculate the battery invisible overvoltage risk score based on the reverse impact factor: , where S is the battery invisible overvoltage risk score.

[0052] When there are a large number of cracks on the battery surface and the battery frame deformation is large, the battery structure deformation and crack distribution tend to be uniform, which helps to alleviate local voltage fluctuations and reduce the possibility of impulse voltage measurement errors, and the invisible overvoltage risk score is low; on the contrary, when the number of cracks is small and the deformation is small, local voltage anomalies are more likely to be ignored, and the invisible overvoltage risk score is relatively high.

[0053] The collection cycle is preset, and the magnetic field sensor is used to detect the magnetic field strength of the battery charging and discharging environment in real time during the collection cycle. The amplitude and direction of the battery magnetic field strength are recorded. The total magnetic field strength is: ,in, 、 、 are the magnetic field intensity amplitudes in the x, y, and z directions, respectively. For the During the acquisition period, the average value of the magnetic field strength at each moment is calculated as the total magnetic field strength and recorded as .

[0054] During the charge and discharge process, the temperature sensor is used to collect the battery internal resistance temperature, record the temperature at the start and end of the collection cycle, and calculate the temperature difference: ,in, is the temperature at the start of the acquisition period, is the temperature at the end of the acquisition period, is the battery internal resistance temperature difference;

[0055] The greater the temperature difference of the battery internal resistance, the stronger the thermal response of the battery internal resistance will be under the same current, which is more likely to cause instantaneous voltage drop jitter, thereby amplifying the probability of voltage misjudgment.

[0056] By comprehensively analyzing the number of cracks on the battery surface and the deformation of the battery frame, a battery invisible overvoltage risk scoring model is constructed, the environmental magnetic field strength is detected, and the battery internal resistance temperature changes are collected during a specific time period to reflect the battery structural state and its thermal response characteristics. This helps to identify impact voltage measurement deviations caused by local stress or thermal inhomogeneity, and improve the accuracy of invisible overvoltage risk identification and the timeliness of system protection.

[0057] It should be noted that the magnetic field sensor is an electronic device used to detect and measure the strength of the magnetic field. It can sense the spatial magnetic field generated by the current changes during the charging and discharging process of the battery. The temperature sensor is used to detect the temperature changes in specific areas within the battery structure in real time, and is used to reflect the heat generation inside the battery.

[0058] In step S3, since the current during the battery charging and discharging process will cause magnetic field fluctuations, the electromagnetic interference amount of the environment on the battery is calculated according to the magnetic field strength of the battery charging and discharging environment;

[0059] According to the battery design and wiring structure, the closed area enclosed by the positive and negative current paths of the battery is obtained through the structural modeling tool, recorded as A, and the electromagnetic interference amount is calculated using Faraday's law of electromagnetic induction: , where f is the magnetic field frequency, is the angle between the magnetic field direction and the loop plane, is the electromagnetic interference amount;

[0060] For example, =0.001T, f=50Hz, A=0.01m², assuming the magnetic field is perpendicular to the circuit plane =1, ;

[0061] The greater the amount of electromagnetic interference, the more dramatic the changes in the magnetic field of the battery environment, thereby disturbing the normal change trajectory of the battery terminal voltage and causing abnormal fluctuations.

[0062] The electromagnetic interference amount is combined with the battery internal resistance temperature difference to analyze the battery's apparent overvoltage risk by constructing a logic model, reflecting the possibility of actual abnormal fluctuations in the battery terminal voltage due to the combined effect of external electromagnetic disturbances and internal thermal imbalance.

[0063] The electromagnetic interference and internal resistance temperature difference are normalized respectively, and the sum is used as the logistic regression parameter of visible overvoltage. The logistic regression model is constructed using the logistic regression parameter of visible overvoltage: , where z is the logistic regression parameter of visible overvoltage, e is the natural base, and L is the calculation result of the logistic regression model corresponding to visible overvoltage. The calculation result of the logistic regression model of visible overvoltage is used as the battery visible overvoltage risk score.

[0064] When the electromagnetic interference is large and the battery's internal resistance-temperature difference is large, the superposition effect of electromagnetic and thermal disturbances inside the battery is significant, which can easily lead to increased voltage fluctuations, and the apparent overvoltage risk score is high; conversely, when the electromagnetic interference is small and the internal resistance-temperature difference is small, the battery voltage fluctuation is relatively stable, and the apparent overvoltage risk score is relatively low.

[0065] By calculating the corresponding electromagnetic interference amount and combining it with the temperature difference change of the battery's internal resistance, the obvious overvoltage risk of the battery during operation is comprehensively analyzed, and abnormal voltage behavior caused by environmental magnetic field disturbances and internal thermal fluctuations is identified. This effectively improves the ability to identify overvoltage risks under the influence of dynamic electromagnetic and thermal interactions, providing an accurate basis for subsequent overvoltage protection decisions.

[0066] It should be noted that the structural modeling tool is a software system used to construct, simulate and analyze the geometric shape and physical properties of engineering structures. It can be used to calculate the loop area enclosed by the positive and negative electrode circuits in the battery.

[0067] In step S4, the battery invisible overvoltage risk and battery visible overvoltage risk of n battery samples are collected, and their corresponding information entropy values ​​are calculated respectively by the entropy weight method. The weights are calculated based on the information entropy values, and the battery invisible overvoltage risk score and the battery visible overvoltage risk score are weighted according to their weights to obtain the false overvoltage index. The specific steps are as follows:

[0068] Calculate the proportional weight as: ,in, is the battery invisible overvoltage risk score of the i-th battery sample, is the proportional weight of the battery invisible overvoltage risk score of the i-th battery sample;

[0069] Calculate information entropy: , Information entropy for scoring battery invisible overvoltage risk;

[0070] Repeat the above steps to get Information entropy for battery overvoltage risk scoring;

[0071] Calculate the entropy weight coefficients separately: , ,in, and are the battery invisible overvoltage risk score and the battery invisible overvoltage risk score weight respectively;

[0072] Calculate the false overvoltage index: ,in, It is the false overvoltage index.

[0073] The voltage change of the battery during the charge and discharge process is monitored in real time by a high-speed sampling voltage sensor.

[0074] The difference between the maximum voltage value recorded during the monitoring process and the voltage value with the highest frequency is used as the impulse voltage to quantify the degree of voltage mutation;

[0075] The preset voltage protection threshold and false overvoltage index threshold are used to determine whether to enable overvoltage protection for the battery:

[0076] If the surge voltage is less than or equal to the voltage protection threshold, the voltage protection will not be activated;

[0077] If the surge voltage is greater than the voltage protection threshold, and the false overvoltage index is greater than the false overvoltage index threshold, the voltage protection will not be activated.

[0078] If the surge voltage is greater than the voltage protection threshold and the false overvoltage index is less than or equal to the false overvoltage index threshold, the voltage protection is turned on.

[0079] When the surge voltage exceeds the voltage protection threshold and the false overvoltage index is lower than the false overvoltage index threshold, it indicates that the current voltage anomaly poses an actual overvoltage risk, and the voltage protection measures are activated. When the surge voltage exceeds the voltage protection threshold, but the false overvoltage index is higher than the false overvoltage index threshold, it indicates that the voltage change may be caused by non-hazardous factors such as structural stress buffering and electromagnetic disturbance. To prevent unnecessary protection operations caused by misjudgment, the voltage protection measures are not activated.

[0080] Real-time battery surge voltage measurement is collected and combined with the false overvoltage index to determine whether to enable overvoltage protection. When the surge voltage exceeds the protection threshold but the false overvoltage index is high, it indicates that the anomaly is not a true overvoltage, thus avoiding false protection, improving overvoltage identification accuracy, and reducing misjudgments.

[0081] It should be noted that the entropy weight method is an objective weighting method, which determines the weight of the indicator by analyzing the information entropy of the data itself, avoiding the deviation of subjective assignment; the high-speed sampling voltage sensor is a sensing device that can continuously collect voltage changes at an extremely high frequency, and is used to monitor the voltage fluctuations of the battery during the charging and discharging process in real time; the preset voltage protection threshold refers to the voltage change critical value set by the battery to trigger the overvoltage protection mechanism. The false overvoltage index threshold is used to determine whether the voltage anomaly is the risk of non-real overvoltage behavior caused by structural or measurement deviations. It is specifically set by professionals and will not be elaborated here.

[0082] Multi-dimensional fusion identification mechanism: By comprehensively modeling the hidden and visible battery overvoltage risks, objective weight allocation is achieved based on the entropy weight method to generate a false overvoltage index, significantly improving the accuracy of identifying battery overvoltage risks.

[0083] Dynamic judgment mechanism: A high-speed sampling voltage sensor is introduced to monitor the surge voltage during the charging and discharging process in real time. It then uses the pseudo-overvoltage index as a dual condition to determine whether to initiate overvoltage protection, effectively avoiding false protection caused by non-real overvoltage factors such as structural stress and electromagnetic disturbances.

[0084] Improve identification accuracy: Use information entropy theory to determine indicator weights, reduce the interference of human experience errors, and combine the impact voltage change to accurately distinguish the risk attributes of abnormal voltage changes, thereby improving the intelligence and reliability of the battery management system;

[0085] Enhance system safety and stability: By scientifically judging the protection trigger conditions, timely response to real overvoltage is achieved, effective fault tolerance for non-dangerous disturbances is achieved, unnecessary protection actions are reduced, the overall system stability is improved, battery life is extended, and the safe operation of the UPS system is guaranteed.

[0086] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0087] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0088] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0089] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0092] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0093] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0094] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0095] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A positive and negative battery system and battery management method for UPS, characterized by: The following steps are involved: Step S1: Scan the battery surface to obtain the number of cracks on the battery surface, measure the curvature of the battery frame, and calculate the deformation of the battery frame according to the curvature of the battery frame; Step S2: Analyze the hidden overvoltage risk of the battery by comprehensively considering the number of cracks on the battery surface and the deformation of the battery frame, detect the magnetic field strength of the battery charging and discharging environment, select a period of time to detect the battery internal resistance and temperature, and calculate the battery internal resistance temperature difference; Step S3: Calculate the electromagnetic interference of the environment on the battery based on the magnetic field strength of the battery charging and discharging environment, and analyze the battery's apparent overvoltage risk in combination with the temperature difference of the battery's internal resistance; Step S4: Generate a false overvoltage index for the battery based on the hidden overvoltage risk and visible overvoltage risk of the battery, detect the impact voltage during battery charging and discharging in real time, and determine whether to enable overvoltage protection for the battery based on the impact voltage and the false overvoltage index.

2. The positive and negative battery system and battery management method for UPS according to claim 1, characterized in that: In step S1, an image of the battery surface structure is acquired, and the number of cracks on the battery surface is identified and counted in combination with image preprocessing operations; The outer contour boundary of the battery frame is sampled at equal intervals to obtain the spatial coordinates of each sampling point, and the sampling points are fitted using the least squares method to form a target straight line.

3. The positive and negative battery system and battery management method for UPS according to claim 2, characterized in that: In step S1, the shortest distance from each sampling point to the target straight line is averaged to obtain an average offset, which is used as the curvature of the battery frame; The battery frame deformation is calculated based on the ratio of the curvature of the battery frame to the original design length of the frame.

4. The positive and negative battery system and battery management method for UPS according to claim 3, characterized in that: In step S2, the number of cracks on the battery surface and the deformation of the battery frame are normalized; A weighted linear function is constructed to obtain the reverse impact factor, and the reciprocal of the sum of the reverse impact factor and 1 is used as the battery invisible overvoltage risk score.

5. The positive and negative battery system and battery management method for UPS according to claim 4, characterized in that: In step S2, within a preset acquisition period, the magnetic field strength and direction in the battery charging and discharging environment are detected, and the magnetic field strength values ​​in each direction are combined into the magnetic field strength at the corresponding detection time using a vector synthesis method. The average value at each time is calculated as the total magnetic field strength; The battery internal resistance temperature is collected in the charging and discharging environment, the temperature at the start and end time of the collection cycle is recorded, and the difference is calculated as the battery internal resistance temperature difference.

6. The positive and negative battery system and battery management method for UPS according to claim 5, characterized in that: In step S3, the electromagnetic closed area is obtained by a structural modeling tool, and the electromagnetic interference amount is calculated by Faraday's law of electromagnetic induction in combination with the total magnetic field strength; The electromagnetic interference amount and the battery internal resistance temperature difference are normalized and then summed. Based on the summation result, a logistic regression model is constructed to calculate the battery visible overvoltage risk score.

7. The positive and negative battery system and battery management method for UPS according to claim 6, characterized in that: In step S4, the battery invisible overvoltage risk and battery visible overvoltage risk of n battery samples are collected, and the corresponding information entropy values ​​are calculated respectively by the entropy weight method. The weights are calculated based on the information entropy values, and the battery invisible overvoltage risk score and the battery visible overvoltage risk score are weighted and summed with the corresponding weights to obtain the false overvoltage index.

8. The positive and negative battery system and battery management method for UPS according to claim 7, characterized in that: In step S4, the voltage change during the battery charging and discharging process is monitored in real time, the maximum voltage and the voltage value with the highest frequency are recorded, and the difference between them is used to obtain the surge voltage value; The preset voltage protection threshold and false overvoltage index threshold determine whether to enable overvoltage protection for the battery.

9. The positive and negative battery system and battery management method for UPS according to claim 8, characterized in that: In step S4, if the surge voltage is less than or equal to the voltage protection threshold, the voltage protection is not activated; If the surge voltage is greater than the voltage protection threshold, and the false overvoltage index is greater than the false overvoltage index threshold, the voltage protection will not be activated. If the surge voltage is greater than the voltage protection threshold and the false overvoltage index is less than or equal to the false overvoltage index threshold, the voltage protection is turned on.

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