Battery management method for positive and negative battery system in ups scenario

Abstract

The application discloses a battery management method for a positive and negative battery system in a UPS (Uninterruptible Power Supply) scene, relates to the technical field of uninterruptible power supply management, and aims to solve the problem that the current battery management system lacks dynamic perception of multi-factor fusion and is prone to cause lag of protection action. The number of cracks on the surface of a battery is acquired through scanning, the bending degree of a battery frame is measured and a deformation variable is calculated, and the invisible overvoltage risk of the battery is comprehensively analyzed. The magnetic field strength and the internal resistance temperature of the battery in the charging and discharging environment are detected, the temperature difference and the electromagnetic interference quantity are calculated, and then the visible overvoltage risk is evaluated. The invisible and visible risks are fused to generate a false overvoltage index, and the impact voltage quantity detected in real time is combined to determine whether to start overvoltage protection, so that the overvoltage identification accuracy of the battery is improved, and the protection mechanism is prevented from being triggered mistakenly.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of uninterruptible power supply management, and more particularly, to a battery management method for a positive and negative battery system in a UPS scenario. BACKGROUND

[0002] An uninterruptible power supply (UPS) system is widely used in data centers, medical equipment, communication machine rooms, and industrial automation scenarios as a backup power supply device for critical loads. The battery in the UPS system is a core energy storage component, and its operating state directly affects the stability and safety of the system. In actual operation, the battery is affected by various complex environmental factors, such as mechanical stress, electromagnetic interference, and charge-discharge impact, which can easily cause hidden damage or overvoltage risks, and further lead to performance degradation, thermal runaway, and even fire and explosion.

[0003] The prior art has the following disadvantages:

[0004] Current battery management systems lack a dynamic sensing mechanism that integrates multiple factors such as battery surface cracks, battery frame deformation, internal resistance temperature difference, and environmental electromagnetic interference, and cannot effectively assess the hidden and visible overvoltage risks of the battery in the UPS scenario, which can easily cause protection action lag. Therefore, a battery management method for a positive and negative battery system in a UPS scenario is proposed.

[0005] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a battery management method for a positive and negative battery system in a UPS scenario, which uses an overvoltage risk modeling method based on structural deformation recognition, electromagnetic interference analysis, and internal resistance thermal characteristic integration to solve the problems of difficult identification of battery hidden damage, weak overvoltage risk prediction ability, and overvoltage protection response lag mentioned in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions, a battery management method for a positive and negative battery system in a UPS scenario, comprising the following steps:

[0008] Step S1: scanning the battery surface to obtain the number of battery surface cracks, measuring the battery frame curvature, and calculating the battery frame deformation according to the battery frame curvature;

[0009] Step S2: analyzing the hidden overvoltage risk of the battery by integrating the number of battery surface cracks and the battery frame deformation, detecting the magnetic field strength of the battery charge-discharge environment, selecting a period of time to detect the battery internal resistance temperature, and calculating the battery internal resistance temperature difference;

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

[0011] Step S4: Generate the false overvoltage index of the battery by comprehensively considering the battery hidden overvoltage risk and the battery apparent overvoltage risk, detect the impact voltage amount of the battery in real time during charging and discharging, and judge whether to enable the overvoltage protection for the battery in combination with the impact voltage amount and the false overvoltage index.

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

[0013] The outer contour boundary of the battery frame is equally spaced sampled to obtain the spatial coordinates of each sampling point, and the least square method is used to fit the sampling points to form a target straight line.

[0014] In a preferred embodiment, in step S1, the average offset is obtained by mean calculation of the shortest distance from each sampling point to the target straight line, as the bending degree of the battery frame;

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

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

[0017] The inverse influence factor is obtained by using a weighted linear function, and the reciprocal of the sum of the inverse influence factor and 1 is used as the battery hidden overvoltage risk score.

[0018] In a preferred embodiment, in step S2, the magnetic field strength value and direction in the battery charging and discharging environment are detected within a preset collection period, the magnetic field strength values in each direction are combined into the magnetic field strength at the corresponding detection time through vector synthesis method, and the average value of 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 temperatures at the start time and the end time of the collection period are recorded, and the difference value is calculated as the battery internal resistance temperature difference.

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

[0021] The electromagnetic interference quantity and the battery internal resistance temperature difference are normalized and summed, and a logistic regression model is constructed based on the sum to calculate the battery overtension risk score.

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

[0023] In a preferred embodiment, 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 the impact voltage quantity.

[0024] The preset voltage protection threshold and the false overtension index threshold are used to determine whether to start the overvoltage protection for the battery.

[0025] In a preferred embodiment, in step S4, if the impact voltage quantity is less than or equal to the voltage protection threshold, the voltage protection is not started.

[0026] If the impact voltage quantity is greater than the voltage protection threshold, and the false overtension index is greater than the false overtension index threshold, the voltage protection is not started.

[0027] If the impact voltage quantity is greater than the voltage protection threshold, and the false overtension index is less than or equal to the false overtension index threshold, the voltage protection is started.

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

[0029] The present application obtains the number of cracks on the surface of the battery by scanning the surface of the battery, measures the bending degree of the battery frame, calculates the deformation of the battery frame according to the bending degree of the battery frame, analyzes the battery overtension risk by comprehensively considering the number of cracks on the surface of the battery, detects the magnetic field intensity of the battery charging and discharging environment, detects the battery internal resistance temperature, calculates the battery internal resistance temperature difference, calculates the electromagnetic interference quantity of the battery according to the magnetic field intensity of the battery charging and discharging environment, analyzes the battery overtension risk by combining the temperature difference of the battery internal resistance, generates the false overtension index of the battery by comprehensively considering the battery overtension risk, detects the impact voltage quantity during the battery charging and discharging in real time, and judges whether to start the overvoltage protection for the battery by combining the false overtension index, thereby improving the accuracy of the battery overvoltage identification and avoiding false triggering of the protection mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0030] Fig. 1 The present application is a battery management method for the implementation flowchart of the positive and negative battery system in the UPS scenario.

[0031] Fig. 2The schematic diagram of the steps of the battery management method for the positive and negative battery system in the UPS scenario of the application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0033] Embodiment 1

[0034] Please refer to Figs. 1-2 , the battery management method for the positive and negative battery system in the UPS scenario, comprising the following steps:

[0035] Step S1: scanning the surface of the battery to obtain the number of surface cracks of the battery, measuring the bending degree of the battery frame, and calculating the deformation of the battery frame according to the bending degree of the battery frame;

[0036] Step S2: analyzing the hidden overvoltage risk of the battery by comprehensively considering the number of surface cracks of the battery and the deformation of the battery frame, detecting the magnetic field strength of the battery charging and discharging environment, selecting a period of time to detect the temperature of the battery internal resistance, and calculating the temperature difference of the battery internal resistance; Step S3: calculating the electromagnetic interference amount of the environment on the battery according to the magnetic field strength of the battery charging and discharging environment, and analyzing the visible overvoltage risk of the battery in combination with the temperature difference of the battery internal resistance;

[0037] Step S4: generating the false overvoltage index of the battery by comprehensively considering the hidden overvoltage risk of the battery and the visible overvoltage risk of the battery, detecting the impact voltage amount in real time when the battery is charging and discharging, and judging whether to start the overvoltage protection for the battery in combination with the impact voltage amount and the false overvoltage index.

[0038] The implementation is as follows:

[0039] In step S1, the surface of the battery is clearly scanned by a high-resolution industrial camera to obtain the image of the surface structure of the battery. The image is preprocessed to strengthen the image features of the surface cracks of the battery. The crack area in the image is identified and analyzed by combining the contour detection method. The continuous dark and slender area in the image is marked as the crack area, and the number of crack areas is the number of surface cracks of the battery.

[0040] The outer contour boundary of the battery frame is sampled at equal intervals by a laser ranging sensor to obtain the spatial coordinates of each sampling point, which are combined into a sampling point set: P={( , , )|1,2,3...N}, wherein N is the number of sampling points, Xi, Yi, Zi are the coordinate values of the i-th sampling point in space, respectively;

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

[0042] It needs to be explained that the preset fitting starting point is to take 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 change 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 by linear algebra method; the direction vector and the preset fitting starting point make the sum of the squared distances of all sampling points to the target straight line minimum, which is set by professionals.

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

[0044] The shortest distance of each sampling point to the target straight line is calculated: wherein, is the perpendicular distance of the i-th sampling point to the target straight line, and the average offset of each sampling point is calculated to obtain the bending degree of the battery frame: wherein, is the bending degree of the battery frame.

[0045] The battery frame deformation variable is calculated based on the ratio of the battery frame bending degree to the original length of the battery frame: wherein, is the original length of the battery frame, is the battery frame deformation variable.

[0046] This step obtains the number of cracks by scanning the battery surface, measures the bending degree of the battery frame based on the fitting straight line method of spatial sampling points, further calculates the deformation variable, which is used to quantify the deformation degree of the battery structure, accurately reflects the structural changes caused by the stress or aging of the battery, and provides key basic data for subsequent invisible overpressure risk analysis.

[0047] ​​It should be noted that the high-resolution industrial camera is a special imaging device used in industrial detection scenarios, with the ability to collect images at the level of millions of pixels, which can clearly scan the surface of the battery and obtain the structural features of the surface of the battery; the preprocessing operation includes steps such as graying, filtering and denoising, contrast enhancement, and edge detection, 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 in the image processing process, in the embodiment of the present application, the contour detection method is mainly used to identify the shape boundary of the crack region on the surface of the battery, and realize the spatial positioning, contour modeling and quantity 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 space 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 from 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 the battery frame deformation are analyzed by constructing a logistic regression model to analyze the hidden overvoltage risk of the battery, which reflects the possibility of error or deviation in the measurement of impact voltage caused by abnormal battery structure;

[0049] The number of battery surface cracks and the battery frame deformation are normalized, and a linear function is used to obtain the reverse influence factor, and the hidden overvoltage risk score of the battery is calculated based on the reverse influence factor:

[0050] A linear function is constructed to obtain the reverse influence factor: , wherein, and are preset weight coefficients, x is the number of battery surface cracks, and U is the reverse influence factor.

[0051] The hidden overvoltage risk score of the battery is calculated based on the reverse influence factor: , wherein S is the hidden overvoltage risk score of the battery.

[0052] When the number of battery surface cracks is large 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 measurement error of impact voltage, so the hidden overvoltage risk score is low; on the contrary, when the number of cracks is small and the deformation is small, local voltage abnormalities are more likely to be ignored, so the hidden overvoltage risk score is relatively high.

[0053] A preset collection period is set, the magnetic field strength of the battery charging and discharging environment in the collection period is detected in real time by the magnetic field sensor, and the amplitude and direction of the battery magnetic field strength are recorded, and the total magnetic field strength is: ,in, , , These represent the magnetic field strength amplitudes along the x, y, and z axes, respectively. For the first The magnetic field strength at each moment; within the acquisition period, the average magnetic field strength at each moment is calculated as the total magnetic field strength, denoted as . .

[0054] During charging and discharging, the battery's internal resistance temperature is collected using a temperature sensor. The temperatures at the start and end of the collection cycle are recorded, and the temperature difference is calculated. ,in, The temperature at the start of the data collection cycle. The temperature at the end of the data collection cycle. This refers to the temperature difference between the battery's internal resistance and its internal resistance.

[0055] The greater the temperature difference between the battery's internal resistance and the more intense the thermal response of the battery's internal resistance under the same current, the more likely it is to cause instantaneous voltage drop fluctuations, thereby amplifying the probability of voltage misjudgment.

[0056] By comprehensively analyzing the number of surface cracks and the deformation of the battery frame, a hidden overvoltage risk scoring model for the battery is constructed. The ambient magnetic field strength is detected, and the temperature change of the battery internal resistance is collected at a specific time period to reflect the battery structural state and its thermal response characteristics. This helps to identify the measurement deviation of the impact voltage caused by local stress or thermal inhomogeneity, and improves the accuracy of hidden overvoltage risk identification and the timeliness of system protection.

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

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

[0059] Based on the battery design and wiring structure, the closed area enclosed by the current paths of the positive and negative electrodes of the battery is obtained using structural modeling tools and denoted as A. The electromagnetic interference is then calculated using Faraday's law of electromagnetic induction. Where f is the magnetic field frequency, The angle between the direction of the magnetic field and the loop plane. This refers to electromagnetic interference.

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

[0061] The greater the electromagnetic interference quantity, the more intense the magnetic field change in the environment where the battery is located, thereby disturbing the normal change trajectory of the battery terminal voltage and forming abnormal fluctuations.

[0062] The electromagnetic interference quantity and the internal resistance temperature difference are combined to analyze the battery overtension risk by constructing a logic model, reflecting the possibility of actual abnormal fluctuations of the battery terminal voltage caused by the combined effects of external electromagnetic disturbance and internal thermal imbalance.

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

[0064] When the electromagnetic interference quantity is large and the internal resistance temperature difference is large, the internal electromagnetic and thermal disturbance superposition effect of the battery is significant, which easily leads to intensified voltage fluctuations, and the overtension risk score is high. Conversely, when the electromagnetic interference quantity is small and the internal resistance temperature difference is small, the battery voltage fluctuation is stable, and the overtension risk score is relatively low.

[0065] By calculating the corresponding electromagnetic interference quantity and combining the internal resistance temperature difference, the overtension risk of the battery during operation is comprehensively analyzed, the abnormal voltage behavior caused by environmental magnetic field disturbance and internal thermal fluctuation is identified, and the overvoltage risk identification ability under the influence of dynamic electromagnetic and thermal interaction is effectively improved, providing accurate basis for subsequent overvoltage protection decision.

[0066] It should be noted that the structural modeling tool is a software system for building, simulating and analyzing the geometry and physical properties of engineering structures, which can be used to calculate the loop area surrounded by the positive and negative loops in the battery.

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

[0068] The proportion weight is calculated as: wherein, is the battery overtension risk score of the i-th battery sample, is the proportion weight of the battery overtension risk score of the i-th battery sample.

[0069] Calculate the information entropy of the battery hidden overvoltage risk score; , The information entropy of the battery hidden overvoltage risk score;

[0070] Repeat the above steps to obtain The information entropy of the battery hidden overvoltage risk score;

[0071] Calculate the entropy weight coefficient respectively: , wherein, and are the weights of the battery hidden overvoltage risk score and the battery hidden overvoltage risk score respectively;

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

[0073] Real-time monitoring of the voltage change of the battery during charging and discharging by high-speed sampling voltage sensor,

[0074] The difference between the maximum voltage recorded during monitoring and the voltage value with the highest frequency is taken as the impact voltage amount, which is used to quantify the degree of voltage mutation;

[0075] Determine whether to start overvoltage protection for the battery by presetting the voltage protection threshold and the false overvoltage index threshold:

[0076] If the impact voltage amount is less than or equal to the voltage protection threshold, do not start the voltage protection;

[0077] If the impact voltage amount is greater than the voltage protection threshold, and the false overvoltage index is greater than the false overvoltage index threshold, do not start the voltage protection;

[0078] If the impact voltage amount is greater than the voltage protection threshold, and the false overvoltage index is less than or equal to the false overvoltage index threshold, start the voltage protection.

[0079] When the impact voltage amount exceeds the voltage protection threshold, and the false overvoltage index is lower than the false overvoltage index threshold, it means that the current voltage anomaly has actual overvoltage risk, and the voltage protection measure is started; when the impact voltage amount exceeds the voltage protection threshold, but the false overvoltage index is higher than the false overvoltage index threshold, it means that the voltage change may be caused by non-dangerous factors such as structural stress buffering and electromagnetic disturbance, in order to prevent misjudgment from leading to unnecessary protection operation, do not start the voltage protection measure.

[0080] Real-time collection of the battery impact voltage amount, combined with the false overvoltage index to determine whether to start the overvoltage protection, when the impact voltage amount exceeds the protection threshold but the false overvoltage index is high, it means that the anomaly is not a real overvoltage, avoiding the misprotection behavior, improving the overvoltage identification accuracy, and reducing the misjudgment.

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

[0082] Multi-dimensional fusion recognition mechanism: through comprehensive modeling of battery hidden overvoltage risk and apparent overvoltage risk, objective weight allocation is realized based on entropy weight method to generate false overvoltage index, which significantly improves the recognition accuracy of battery overvoltage risk;

[0083] Dynamic judgment mechanism: high-speed sampling voltage sensor is introduced to monitor the impact voltage in real time during charging and discharging, and combined with the double conditions of false overvoltage index to judge whether to start overvoltage protection, which effectively avoids the false protection behavior caused by non-real overvoltage factors such as structural stress and electromagnetic disturbance;

[0084] Improve the accuracy of recognition: use information entropy theory to determine the index weight, reduce the interference of human experience error, combine the impact voltage change, accurately distinguish the risk attribute of abnormal voltage change, and improve the intelligence and reliability of battery management system;

[0085] Enhance the safety and stability of the system: by scientifically judging the protection trigger condition, realize the timely response to real overvoltage, effectively fault-tolerant to non-dangerous disturbance, reduce unnecessary protection action, improve the overall stability of the system, prolong the service life of the battery, and ensure the safe operation of the UPS system.

[0086] The above formulas are dimensionless to calculate their numerical values, and the formulas are obtained by software simulation of a large number of collected data to reflect the latest real situation. The preset parameters in the formula are set by the skilled person in the art according to the actual situation.

[0087] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented 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 programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. 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, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0088] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

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

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0091] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0092] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0093] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit.

[0094] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the part of the technical solutions 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 causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0095] The above description is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A battery management method for positive and negative battery systems in UPS scenarios, characterized in that: Includes the following steps: Step S1: Scan the battery surface to obtain the number of surface cracks, measure the battery frame curvature, and calculate the battery frame deformation based on the battery frame curvature. Step S2: Analyze the hidden overvoltage risk of the battery by combining the number of surface cracks 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 internal resistance temperature of the battery, and calculate the internal resistance temperature difference of the battery. In step S2, the number of surface cracks and the deformation of the battery frame are normalized. Constructing a linear function to obtain the inverse influence factor: ,in, and Here, x represents the number of surface cracks on the battery, and U is the reverse influence factor. The battery framework deformation variable is used as the reciprocal of the sum of the inverse influence factor and 1 as the battery hidden overvoltage risk score. 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 risk of battery overvoltage by combining the temperature difference of the battery internal resistance. In step S3, the electromagnetic closed area is obtained through structural modeling tools, and the electromagnetic interference is calculated by combining the total magnetic field strength with Faraday's law of electromagnetic induction. After normalizing the electromagnetic interference and the temperature difference between the battery's internal resistance, the results are summed. A logistic regression model is then constructed based on the summation to calculate the battery's apparent overvoltage risk score. The logistic regression model is then constructed using the parameters of the apparent overvoltage. , where z is the logistic regression parameter of apparent overvoltage, e is the natural base, and L is the calculation result of the corresponding logistic regression model of apparent overvoltage. The calculation result of the logistic regression model of apparent overvoltage is used as the battery apparent overvoltage risk score. Step S4: Generate a false overvoltage index for the battery by combining the hidden overvoltage risk and the apparent overvoltage risk of the battery. Detect the impact voltage during battery charging and discharging in real time. Combine the impact voltage and the false overvoltage index to determine whether to activate overvoltage protection for the battery. In step S4, the hidden overvoltage risk and the visible overvoltage risk of the battery are collected from n battery samples. The corresponding information entropy value is calculated by the entropy weight method. The weight is calculated based on the information entropy value. The hidden overvoltage risk score and the visible overvoltage risk score are weighted and summed with the corresponding weight to obtain the pseudo overvoltage index. In step S4, the voltage change during the battery charging and discharging process is monitored in real time, the maximum voltage value and the voltage value with the highest frequency are recorded, and the difference between them is used to obtain the impact voltage. The preset voltage protection threshold and the false overvoltage index threshold determine whether to activate overvoltage protection for the battery.

2. The battery management method for positive and negative battery systems in a UPS scenario 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 conjunction 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. The least squares method is used to fit the sampling points to form the target straight line.

3. The battery management method for positive and negative battery systems in a UPS scenario according to claim 2, characterized in that: In step S1, the average offset is obtained by averaging the shortest distances from each sampling point to the target straight line, which is used as the curvature of the battery frame. The deformation of the battery frame is calculated based on the ratio of the bending degree of the battery frame to the original design length of the frame.

4. The battery management method for positive and negative battery systems in a UPS scenario according to claim 1, characterized in that: In step S2, within a preset acquisition period, the magnetic field strength value and direction in the battery charging and discharging environment are detected. The magnetic field strength values ​​in each direction are combined into the magnetic field strength at the corresponding detection time by vector synthesis method, and the average value at each time is calculated as the total magnetic field strength. The internal resistance temperature of the battery is collected in the charging and discharging environment. The temperature at the start and end of the collection period is recorded, and the difference between them is calculated as the internal resistance temperature difference of the battery.

5. The battery management method for positive and negative battery systems in a UPS scenario according to claim 1, characterized in that: In step S4, if the impulse voltage is less than or equal to the voltage protection threshold, the voltage protection is not activated. If the impulse voltage is greater than the voltage protection threshold and the false overvoltage index is greater than the false overvoltage index threshold, then the voltage protection will not be activated. If the impulse voltage is greater than the voltage protection threshold and the false overvoltage index is less than or equal to the false overvoltage index threshold, then the voltage protection will be activated.

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