Communication base station storage battery management method and device

Abstract

The invention provides a management method and device for a storage battery of a communication base station. The method comprises the steps that multi-dimensional operation parameters of the current storage battery are collected in real time; based on the impedance spectrum sequence, calculating an internal resistance covariance matrix, performing characteristic decomposition on the internal resistance covariance matrix, and extracting a characteristic value and a characteristic vector representing the degradation state of the battery; matching the extracted feature value and the feature vector with a preset degradation mode template library, and determining a dominant degradation mode of the current storage battery; and determining a corresponding charging strategy from a preset strategy library according to the dominant degradation mode and the currently calculated storage battery capacity retention ratio. According to the method, accurate diagnosis of the health state of the storage battery, dynamic optimization of the charging process and active prevention and control of safety risks are realized through multi-dimensional feature fusion and dynamic strategy linkage, so that the service life of the battery is remarkably prolonged, the system efficiency is improved, and the operation safety is guaranteed.

Description

Technical Field

[0001] This application relates to the field of energy management technology for communication networks, and more specifically, to a method and apparatus for managing batteries in communication base stations. Background Technology

[0002] In the energy systems of mobile communication base stations and equipment rooms, batteries need to be in a float charging state for extended periods to ensure continuous power supply. Existing technologies for battery charging strategy management mainly include: the "single threshold method for internal resistance judgment," which determines the degree of capacity decay by measuring the battery's DC internal resistance and setting a fixed threshold; the "discharge curve fitting calibration method," which collects the terminal voltage sequence during the constant current discharge process of the battery and calculates the capacity retention rate by comparing it with historical benchmark curves; and the "linear compensation model for cycle count," which uses the number of battery charge-discharge cycles as input and linearly corrects the charging parameters according to a preset decay coefficient.

[0003] The aforementioned existing technical solutions mainly rely on static modeling and control based on a single parameter (such as internal resistance, voltage, or cycle count), which has significant limitations. Specifically, these methods cannot comprehensively consider the impact of temperature gradient distribution on the battery polarization process, nor can they identify the nonlinear effects of different degradation modes (such as sulfation and drying) on ​​capacity decay. Because the charging current adjustment rules are fixed, dynamic correction based on real-time covariance changes in battery internal resistance is not possible, and temperature rise protection relies solely on a single-point temperature threshold, ignoring the dynamic characteristics of the battery pack's three-dimensional thermal field, resulting in poor strategy adaptability. Furthermore, overcharge / over-discharge control is based on post-event voltage thresholds, failing to predict failure risks through impedance spectrum characteristics, lacking a graded repair mechanism for different degradation modes, and exhibiting a lag in safety protection. This leads to distorted battery capacity estimation, low repair efficiency, and the accumulation of safety hazards. Summary of the Invention

[0004] The purpose of this application is to provide a management method and device for communication base station batteries, which achieves accurate diagnosis of battery health status, dynamic optimization of charging process and proactive prevention and control of safety risks through multi-dimensional feature fusion and dynamic strategy linkage, thereby significantly extending battery life, improving system efficiency and ensuring operational safety.

[0005] Firstly, a method for managing the storage battery of a communication base station is provided, which may include: The system collects multi-dimensional operating parameters of the current battery in real time. These multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by the temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. Based on the impedance spectrum sequence, the internal resistance covariance matrix is ​​calculated, and the internal resistance covariance matrix is ​​subjected to eigenvalue decomposition to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state. The extracted feature values ​​and feature vectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery. Based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a preset strategy library. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

[0006] In one possible implementation, the impedance spectrum sequence is obtained through frequency-converted AC excitation, including: An AC excitation signal with a preset frequency range is applied to the battery, and the excitation is continued for a preset duration at each frequency point; The impedance amplitude and phase angle at each frequency point are collected to form the impedance data at each frequency point; The impedance data collected at multiple frequency points are arranged in sequence to form the impedance spectrum sequence.

[0007] In one possible implementation, the internal resistance covariance matrix is ​​calculated based on the impedance spectrum sequence, including: The impedance spectrum sequence of N consecutively acquired periods is determined as N multidimensional feature vectors, where N is an integer greater than 1; The corresponding covariance matrix is ​​calculated based on the N multidimensional feature vectors.

[0008] In one possible implementation, the internal resistance covariance matrix is ​​subjected to eigenvalue decomposition to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state, including: Eigenvalue decomposition is performed on the covariance matrix to obtain the eigenvalue matrix and eigenvector matrix; Extract the largest and second largest eigenvalues ​​from the eigenvalue matrix.

[0009] In one possible implementation, the extracted feature values ​​and feature vectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery, including: Calculate the ratio of the largest eigenvalue to the second largest eigenvalue; If the ratio is greater than the first preset threshold, the battery is determined to have a sulfation fault mode. Alternatively, calculate the cosine similarity between the feature vector corresponding to the largest eigenvalue and the feature vector of the dendrite growth template in the degradation mode template library; If the cosine similarity is greater than the second preset threshold, it is determined that the battery has a dendrite growth fault mode. Alternatively, if the temperature difference corresponding to the temperature field data is greater than the third threshold and the battery capacity retention rate is less than the fourth threshold, then the battery is determined to have a dry-out fault mode.

[0010] In one possible implementation, based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a pre-set strategy library, including: Based on the type of the dominant degradation mode and the preset range of the battery capacity retention rate, the corresponding charging strategy combination is mapped and queried from the preset strategy library; the strategy library pre-stores charging strategy combinations mapped to different degradation mode types and different capacity retention rate ranges. Furthermore, for the same degradation mode, the parameters of the charging strategy are further adjusted according to the different ranges in which the battery capacity retention rate falls: When the battery capacity retention rate is within a first preset range, a first type of strategy is determined, wherein the first type of strategy is to lower the float charge voltage based on the benchmark value. When the battery capacity retention rate is in a second preset range that is lower than the first preset range, a second type of strategy is determined, which is to enable trapezoidal wave pulse charging with adjustable duty cycle.

[0011] In one possible implementation, the duty cycle adjustable rule includes: The initial phase uses a 1:1 duty cycle; The pulse off-time is gradually increased at fixed time intervals; If the rate of decrease in battery terminal voltage exceeds the set voltage threshold during the adjustment process, the duty cycle will be restored to the parameters of the previous stage.

[0012] Secondly, a management device for a communication base station battery is provided, the device including: The acquisition unit is used to acquire the multi-dimensional operating parameters of the current battery in real time. The multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. The calculation unit is used to calculate the internal resistance covariance matrix based on the impedance spectrum sequence. The extraction unit is used to perform eigenvalue decomposition on the internal resistance covariance matrix and extract eigenvalues ​​and eigenvectors that characterize the battery degradation state. The matching unit is used to match the extracted feature values ​​and feature vectors with a preset degradation mode template library to determine the dominant degradation mode of the current battery. The determining unit is used to determine a corresponding charging strategy from a preset strategy library based on the dominant degradation mode and the currently calculated battery capacity retention rate. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

[0013] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0014] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0015] This application provides a method and apparatus for managing a communication base station battery. The method collects multi-dimensional operating parameters of the battery in real time, including an impedance spectrum sequence obtained through frequency conversion AC excitation, temperature field data obtained through a temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. Based on the impedance spectrum sequence, an internal resistance covariance matrix is ​​calculated, and eigenvalues ​​and eigenvectors characterizing the battery degradation state are extracted through eigenvalue decomposition. The extracted eigenvalues ​​and eigenvectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery. Based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a pre-set strategy library. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy. This method, through multi-dimensional feature fusion and dynamic strategy linkage, achieves accurate diagnosis of battery health status, dynamic optimization of the charging process, and proactive prevention and control of safety risks, thereby significantly extending battery life, improving system efficiency, and ensuring operational safety. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for managing a communication base station battery, provided in an embodiment of this application; Figure 2 A schematic diagram of a management device for a communication base station battery provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] The battery systems of mobile communication base stations and equipment rooms need to be in a float charging state for extended periods. Existing charging strategies suffer from accelerated capacity decay and increased safety risks due to battery aging processes (such as plate sulfation and active material shedding). Traditional methods rely solely on voltage and current threshold control, failing to consider the covariance characteristics of battery internal resistance distribution and the non-uniformity of the temperature field, leading to the following problems: 1. Capacity estimation distortion: The coupling effect of polarization and cyclic aging affects capacity calibration error by more than 30%; 2. Lack of repair strategy: There is a lack of dynamically activated pulse repair mechanism when sulfide crystallization leads to a decrease in charging efficiency; 3. Accumulated safety hazards: The sudden change in internal resistance covariance did not trigger the protection strategy in real time, resulting in a significant risk of thermal runaway.

[0020] To address the aforementioned issues, this application provides a method for managing batteries in communication base stations. This method achieves precise management of battery health and extends battery life through multi-dimensional data perception, intelligent degradation diagnosis, dynamic strategy loading, and safety linkage control.

[0021] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0022] Figure 1 This is a flowchart illustrating a method for managing a communication base station battery, provided as an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Real-time acquisition of multi-dimensional operating parameters of the current battery.

[0023] Among them, the multi-dimensional operating parameters may include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by the temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system.

[0024] The system in this application deploys a multi-source sensor network on the base station battery pack. In specific implementation: The internal resistance covariance detection unit starts working: This unit sweeps frequencies from 0.1Hz to 10kHz at a frequency of once per second. For example, it sequentially applies AC excitation signals of 0.1Hz, 1Hz, 10Hz, 100Hz, 1kHz, and 10kHz to the battery, with each signal duration being 10ms. After each excitation, the unit synchronously measures the impedance amplitude |Z| and phase angle θ of the battery at that frequency. Specifically: an AC excitation signal within a preset frequency range is applied to the battery, and the excitation is sustained for a preset duration at each frequency; the impedance amplitude |Z| and phase angle θ at each frequency are collected to form impedance data for each frequency; the impedance data collected at multiple frequency points are arranged sequentially to form the impedance spectrum sequence. For example, after one complete sweep cycle, a vector containing impedance data for 6 frequency points is obtained. The system continuously performs N=60 cycles of acquisition, thereby obtaining 60 such 6-dimensional impedance vectors, forming the impedance spectrum sequence.

[0025] The temperature field monitoring module works synchronously: a 5x6 matrix infrared temperature sensor installed on the battery casing collects the temperature values ​​of all 30 measuring points every 10 seconds. Based on this data, the maximum temperature difference ΔT of the current battery pack is calculated, which is the difference between the highest and lowest temperatures.

[0026] Battery Management System (BMS) data acquisition: Real-time reading of battery capacity retention rate estimated by coulomb counting or voltage curve fitting from the existing BMS at the base station. The calculation method is an existing technical means, and will not be described in detail here.

[0027] Step S120: Based on the impedance spectrum sequence, calculate the internal resistance covariance matrix, and perform eigenvalue decomposition on the internal resistance covariance matrix to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state.

[0028] The collected multidimensional operational parameters are sent to the decision-making layer for the following calculations and analyses: (1) Calculation of the internal resistance covariance matrix: The impedance spectrum sequence of N consecutive periods is determined as N multidimensional eigenvectors, where N is an integer greater than 1; the corresponding covariance matrix Σ is calculated based on the N multidimensional eigenvectors.

[0029] Specifically, the 60 continuously acquired impedance vectors are treated as a sample set, and their 6x6 covariance matrix Σ is calculated. The calculation method is a conventional statistical approach. ,in The average of 60 vectors. Let i be the i-th sample.

[0030] (2) Perform eigenvalue decomposition on the internal resistance covariance matrix to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state. Specifically, perform eigenvalue decomposition on the covariance matrix Σ. ), to obtain the eigenvalue matrix Λ (with eigenvalues ​​on the diagonal) and the eigenvector matrix U; extract the largest eigenvalue λ_max and the second largest eigenvalue λ_sub from the eigenvalue matrix Λ.

[0031] Step S130: Match the extracted feature values ​​and feature vectors with the preset degradation mode template library to determine the dominant degradation mode of the current battery.

[0032] The system initiates multi-path parallel matching logic to compare the above features with a pre-stored degradation mode template library. The degradation mode template library contains feature templates for six typical fault modes: sulfidation, dendrite formation, drying, expansion, short circuit, and micro-open circuit.

[0033] (1) Path 1 (Ratio Determination): Calculate the ratio of the largest eigenvalue λ_max to the second largest eigenvalue λ_sub. If the ratio If the value exceeds a first preset threshold, the battery is determined to have a sulfation fault mode; for example, if It was determined to be a vulcanization mode.

[0034] (2) Path 2 (Dynamic Time Warping (DTW): To further confirm, the system also calculates the DTW distance between the feature vector corresponding to λ_max and the feature template of the sulfur mode in the template library. The calculated minimum DTW distance D_min is less than the preset matching threshold, thus confirming that the current dominant degradation mode is sulfur.

[0035] For example, when calculated (> If the distance D_min from the DTW of the sulfation template is 0.7 (<0.85) and ΔT = 3℃ (<5℃), then the current dominant degradation mode of the battery is sulfation.

[0036] (3) Path 3 (cosine similarity method): Calculate the cosine similarity between the feature vector corresponding to the maximum feature value λ_max and the feature vector of the dendrite growth template in the degradation mode template library; if the cosine similarity is greater than the second preset threshold, it is determined that the battery has a dendrite growth fault mode. (4) Path Four (Temperature and Capacity Auxiliary Judgment): Based on the temperature field data, the temperature difference is greater than the third threshold, and the battery capacity retention rate ( If the temperature is less than the fourth threshold, the battery is determined to have a dry-out fault mode. For example, if ΔT > 5℃ and <80%, classified as dry mode.

[0037] Step S140: Determine the corresponding charging strategy from the preset strategy library based on the dominant degradation mode and the currently calculated battery capacity retention rate.

[0038] The charging strategy may include at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

[0039] Based on the type of the dominant degradation mode and the preset range of the battery capacity retention rate, the corresponding charging strategy combination is mapped and queried from the preset strategy library; the strategy library pre-stores charging strategy combinations mapped to different degradation mode types and different capacity retention rate ranges. Among them, when the dominant degradation mode is the sulfidation mode, the determined charging strategy includes the pulse repair strategy. When the dominant degradation mode is dendrite growth, the determined charging strategy includes a strategy for precisely controlling the charging termination voltage. When the dominant degradation mode is the dry-out mode, the determined charging strategy includes a strategy of forcibly derating the charging current. Furthermore, for the same degradation mode, the parameters of the charging strategy are further adjusted according to the different ranges in which the battery capacity retention rate is located: When the battery capacity retention rate is within the first preset range, the first type of strategy is determined, which is to lower the float charge voltage based on the benchmark value. When the battery capacity retention rate is in a second preset range that is lower than the first preset range, a second type of strategy is determined, which is to enable trapezoidal wave pulse charging with adjustable duty cycle.

[0040] The duty cycle adjustable rules can include the following: The initial phase uses a 1:1 duty cycle; The pulse off-time is gradually increased at fixed time intervals; If the rate of decrease in battery terminal voltage exceeds the set voltage threshold during the adjustment process, the duty cycle will be restored to the parameters of the previous stage.

[0041] Specifically: (1) Two dimensions need to be considered: Dimension 1: Dominant Degradation Mode Sulfation mode: The strategic goal is to decompose sulfide crystals and activate active substances. The corresponding strategy is based on pulse charging, using the peak voltage of the pulse to break the crystals and using the interval to allow the electrolyte to diffuse.

[0042] Dendrite growth mode: The key strategy is to avoid overcharging and high temperatures to inhibit further dendrite growth. The corresponding strategy is to precisely control the charging termination voltage to prevent overcharging, and to strengthen temperature monitoring to avoid using aggressive pulse charging.

[0043] Dry-out mode: The core strategy is to prevent thermal runaway and slow down water loss. The corresponding strategy is to forcibly reduce the charging current and the float charge voltage to reduce heat generation at the source.

[0044] Dimension Two: Capacity Retention Rate Range 80%-100% (Healthy and Mild Decline Period): The strategic goal is preventative maintenance to slow down aging. Actions are gentle, such as slightly reducing the float voltage.

[0045] 60%-80% (Significant Decline and Repairable Period): The strategic goal is proactive repair and capacity restoration. More aggressive measures are taken, such as pulse repair.

[0046] <60% (Severe Decline Period): The strategic objective is to ensure safety, and a replacement plan is in place. The strategy primarily involves alerts and usage restrictions.

[0047] (2) Real-time matching and strategy invocation Once the current dominant degradation mode (e.g., "sulfidation") is identified in real time and the current capacity retention (e.g., 78%) is obtained, perform the following operations: Pattern matching: Locates all subsets of policies in the policy library that correspond to the sulfur pattern.

[0048] Interval matching: In the above subset, based on the interval [60%, 80%) where the capacity retention rate of 78% falls, specific strategy entries are further identified.

[0049] Strategy Loading: The strategy instruction ultimately retrieved by the engine might be: Enable trapezoidal pulse charging with an on / off ratio of 1:3, and adjust the float charge voltage by 0.15V above or below the reference value. This instruction includes the specific charging mode (pulse charging) and voltage parameter adjustment (lowering the float charge voltage).

[0050] (3) Parameterized execution and fine-tuning of the strategy The defined strategy is translated into specific control parameters and sent to the power devices in the execution layer. For example, for the pulse charging strategy mentioned above: Dynamic adjustment: Initially, a 1:1 duty cycle is used, and then the shutdown duration is gradually increased at fixed time intervals (e.g., every 5 minutes) to perform dynamic scanning in order to find the parameter point with the best repair effect under the current battery state.

[0051] Safety boundary constraints: The entire execution process is strictly constrained by a multi-objective optimization model and safety linkage rules. For example, if a rapid drop in terminal voltage is detected during the adjustment process ( ) or the rate of temperature rise exceeds the standard ( Regardless of whether the current strategy has been completed, it will immediately trigger protection actions (such as rolling back parameters or cutting off the circuit) to ensure that safety is always the top priority.

[0052] In some embodiments, the method may further include an active thermal runaway protection mechanism, including: The temperature monitoring module continuously calculates the battery pack's temperature rise rate dT / dt and the battery pack's maximum temperature difference ΔT.

[0053] If the detected temperature at a certain point is greater than the rate threshold in continuous sampling (e.g., dT / dt), then... If the maximum temperature difference ΔT exceeds the temperature difference threshold, a safety protection action is triggered. This safety protection action includes, but is not limited to: reducing the charging current; disconnecting the main charging circuit; activating the associated auxiliary cooling equipment; and resuming charging after the temperature rise rate returns to normal and the temperature drops back to a safe range.

[0054] In some embodiments, the method may further include: Construct a multi-objective optimization function with the goals of extending battery life and improving charging efficiency; Based on Bayesian networks, the weight coefficients (α,β) of the multi-objective optimization function are updated online according to historical operating data, thereby achieving adaptive and continuous fine-tuning of the charging strategy parameters, enabling the system to maintain its optimal operating state for a long time.

[0055] This application has the following beneficial effects: Precise Diagnosis and Extended Lifespan: By integrating multi-dimensional features such as internal resistance covariance and temperature field, accurate identification of battery health status and degradation modes is achieved, avoiding misjudgments based on a single parameter. Personalized charging strategies implemented accordingly effectively slow down battery aging.

[0056] Dynamic optimization and efficiency improvement: Based on real-time data, dynamic strategy adjustment overcomes the problem of poor adaptability of traditional fixed strategies under extreme temperature and other operating conditions.

[0057] Active safety and risk control: An early warning mechanism based on covariance mutation and temperature rise rate has been established, which can identify potential thermal runaway risks in advance (such as providing early warning more than 2 hours in advance), and achieve millisecond-level safety intervention through thermoelectric linkage control, significantly reducing the false alarm rate.

[0058] Corresponding to the above method, embodiments of this application also provide a management device for a communication base station battery, such as... Figure 2 As shown, the device includes: The acquisition unit 210 is used to acquire the multi-dimensional operating parameters of the current battery in real time. The multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. Calculation unit 220 is used to calculate the internal resistance covariance matrix based on the impedance spectrum sequence; Extraction unit 230 is used to perform eigenvalue decomposition on the internal resistance covariance matrix and extract eigenvalues ​​and eigenvectors characterizing the battery degradation state. The matching unit 240 is used to match the extracted feature values ​​and feature vectors with a preset degradation mode template library to determine the dominant degradation mode of the current battery. The determining unit 250 is used to determine a corresponding charging strategy from a preset strategy library based on the dominant degradation mode and the currently calculated battery capacity retention rate. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

[0059] The functions of each functional unit of the communication base station battery management device provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the communication base station battery management device provided in the embodiments of this application will not be repeated here.

[0060] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0061] Memory 330 is used to store computer programs; When the processor 310 executes the program stored in the memory 330, it performs the following steps: The system collects multi-dimensional operating parameters of the current battery in real time. These multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by the temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. Based on the impedance spectrum sequence, the internal resistance covariance matrix is ​​calculated, and the internal resistance covariance matrix is ​​subjected to eigenvalue decomposition to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state. The extracted feature values ​​and feature vectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery. Based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a preset strategy library. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

[0062] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0063] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0064] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0065] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0067] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the communication base station battery management method described in any of the above embodiments.

[0068] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the communication base station battery management method described in any of the above embodiments.

[0069] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0074] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for managing the storage battery of a communication base station, characterized in that, The method includes: The system collects multi-dimensional operating parameters of the current battery in real time. These multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by the temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. Based on the impedance spectrum sequence, the internal resistance covariance matrix is ​​calculated, and the internal resistance covariance matrix is ​​subjected to eigenvalue decomposition to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state. The extracted feature values ​​and feature vectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery. Based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a preset strategy library. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

2. The method as described in claim 1, characterized in that, Impedance spectrum sequences obtained through frequency conversion AC excitation include: An AC excitation signal with a preset frequency range is applied to the battery, and the excitation is continued for a preset duration at each frequency point; The impedance amplitude and phase angle at each frequency point are collected to form the impedance data at each frequency point; The impedance data collected at multiple frequency points are arranged in sequence to form the impedance spectrum sequence.

3. The method as described in claim 2, characterized in that, Based on the impedance spectrum sequence, the internal resistance covariance matrix is ​​calculated, including: The impedance spectrum sequence of N consecutively acquired periods is determined as N multidimensional feature vectors, where N is an integer greater than 1; The corresponding covariance matrix is ​​calculated based on the N multidimensional feature vectors.

4. The method as described in claim 1, characterized in that, The internal resistance covariance matrix is ​​subjected to eigenvalue decomposition to extract eigenvalues ​​and eigenvectors characterizing the battery degradation state, including: Eigenvalue decomposition is performed on the covariance matrix to obtain the eigenvalue matrix and eigenvector matrix; Extract the largest and second largest eigenvalues ​​from the eigenvalue matrix.

5. The method as described in claim 4, characterized in that, The extracted feature values ​​and feature vectors are matched with a pre-set degradation mode template library to determine the dominant degradation mode of the current battery, including: Calculate the ratio of the largest eigenvalue to the second largest eigenvalue; If the ratio is greater than a first preset threshold, then the battery is determined to have a sulfation fault mode; or, Calculate the cosine similarity between the eigenvector corresponding to the largest eigenvalue and the eigenvector of the dendritic growth template in the degradation mode template library; If the cosine similarity is greater than a second preset threshold, then the battery is determined to have a dendrite growth fault mode; or, If the temperature difference corresponding to the temperature field data is greater than the third threshold and the battery capacity retention rate is less than the fourth threshold, then the battery is determined to have a dry-out fault mode.

6. The method as described in claim 1, characterized in that, Based on the dominant degradation mode and the currently calculated battery capacity retention rate, a corresponding charging strategy is determined from a pre-set strategy library, including: Based on the type of the dominant degradation mode and the preset range of the battery capacity retention rate, the corresponding charging strategy combination is mapped and queried from the preset strategy library; the strategy library pre-stores charging strategy combinations mapped to different degradation mode types and different capacity retention rate ranges. Furthermore, for the same degradation mode, the parameters of the charging strategy are adjusted according to the different ranges in which the battery capacity retention rate is located: When the battery capacity retention rate is within a first preset range, a first type of strategy is determined, wherein the first type of strategy is to lower the float charge voltage based on the benchmark value. When the battery capacity retention rate is in a second preset range that is lower than the first preset range, a second type of strategy is determined, which is to enable trapezoidal wave pulse charging with adjustable duty cycle.

7. The method as described in claim 6, characterized in that, The duty cycle adjustable rules include: The initial phase uses a 1:1 duty cycle; The pulse off-time is gradually increased at fixed time intervals; If the rate of decrease in battery terminal voltage exceeds the set voltage threshold during the adjustment process, the duty cycle will be restored to the parameters of the previous stage.

8. A management device for a communication base station battery, characterized in that, The device includes: The acquisition unit is used to acquire the multi-dimensional operating parameters of the current battery in real time. The multi-dimensional operating parameters include the impedance spectrum sequence obtained by frequency conversion AC excitation, the temperature field data obtained by temperature sensor array, and the battery capacity retention rate calculated and provided in real time by the battery management system. The calculation unit is used to calculate the internal resistance covariance matrix based on the impedance spectrum sequence. The extraction unit is used to perform eigenvalue decomposition on the internal resistance covariance matrix and extract eigenvalues ​​and eigenvectors that characterize the battery degradation state. The matching unit is used to match the extracted feature values ​​and feature vectors with a preset degradation mode template library to determine the dominant degradation mode of the current battery. The determining unit is used to determine a corresponding charging strategy from a preset strategy library based on the dominant degradation mode and the currently calculated battery capacity retention rate. The charging strategy includes at least one of voltage parameter adjustment, charging mode switching, or pulse repair strategy.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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