Extension method and system of sodium ion battery uninterruptible power supply

Abstract

The invention provides a sodium ion battery uninterruptible power supply extension method and system, and the method comprises the steps: obtaining the attribute parameters of different batches of sodium ion battery modules in history, building a sodium ion battery module attribute database, and obtaining a sodium ion battery module attribute database according to the batch and attribute parameter difference of the sodium ion battery modules; determining adjustment coefficients of the initial charging current and the charging time, wherein the attribute parameters comprise capacity, internal resistance and voltage; if there is energy imbalance, querying a sodium ion battery module attribute database, obtaining a target charging current in the current state of charge, calculating the required charging time based on the battery capacity and the target charging current of the sodium ion battery module, and controlling the output of the charger in real time; after charging is completed, the capacity, the voltage and the internal resistance of different batches of sodium ion battery modules in the UPS system are estimated again, and the performance difference and the attenuation law between the different batches of sodium ion battery modules are analyzed to update the sodium ion battery module attribute database.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an expansion method and system for a sodium ion battery uninterruptible power supply. Background Art

[0002] In the Uninterruptible Power System (UPS) system, there are deviations in the capacity, internal resistance and voltage of different batches of battery modules, which will cause an imbalance in the energy distribution of the system. This imbalance will be more obvious when adding or removing battery modules. How to dynamically adjust the charging current and charging time according to the characteristics of different batches of battery modules to achieve energy balance of the UPS system after adding or removing battery modules is a technical problem that needs to be solved urgently. First, the capacity, internal resistance and voltage deviations of different batches of battery modules will cause them to exhibit different characteristics during the charging and discharging process. This difference makes the charging state and health status of each battery module in the UPS system inconsistent, causing an imbalance in the energy distribution of the system. Secondly, when adding or removing battery modules, the difference between the newly added or removed battery modules and the original battery modules will further aggravate the energy imbalance of the system. This imbalance not only affects the performance and reliability of the UPS system, but also may accelerate the aging and loss of the battery modules. Therefore, how to monitor and dynamically adjust the charging current and charging time according to the characteristics of different batches of battery modules in real time to achieve energy balance of the UPS system after adding or removing battery modules is a complex technical challenge. Summary of the invention

[0003] The present invention provides a method for expanding a sodium ion battery uninterruptible power supply, which mainly comprises: Obtain the property parameters of different batches of sodium-ion battery modules in history, establish a sodium-ion battery module property database, and determine the adjustment coefficients of the initial charging current and charging time based on the differences in batches and property parameters of the sodium-ion battery modules. The property parameters include capacity, internal resistance and voltage. Real-time monitoring of the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system. When the addition or removal of the sodium-ion battery module is detected, it is determined whether there is energy imbalance in the UPS system based on the batch and property parameters of the added or removed sodium-ion battery module, as well as the determined adjustment coefficients of the initial charging current and charging time. The energy imbalance includes capacity deviation, internal resistance deviation and voltage deviation. If there is an energy imbalance, the sodium-ion battery module attribute database is queried to obtain the target charging current at the current state of charge. Based on the battery capacity and target charging current of the sodium-ion battery module, the required charging time is calculated to control the charger output in real time. During the charging process, the charging status and system energy changes of different batches of sodium-ion battery modules in the UPS system are continuously monitored, and based on the differences in property parameters between different batches of sodium-ion battery modules, several different property parameters of the sodium-ion battery modules are compared with corresponding preset target values. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results; After charging is completed, the capacity, voltage and internal resistance of different batches of sodium-ion battery modules in the UPS system are re-estimated, and the performance differences and attenuation laws between different batches of sodium-ion battery modules are analyzed to update the sodium-ion battery module property database; Based on the updated sodium-ion battery module property database, the UPS system load is monitored in real time, the load characteristics including load type, load nature and load change law are analyzed, and the load peak within the preset time is recorded to predict the load growth trend of the UPS system. Based on the current load, peak load and predicted load growth trend, the system backup time and capacity redundancy are calculated to obtain the extended capacity of the sodium-ion battery module of the UPS system.

[0004] Optionally, the property parameters of different batches of sodium ion battery modules in history are obtained, a sodium ion battery module property database is established, and the adjustment coefficients of the initial charging current and charging time are determined according to the differences in batches and property parameters of the sodium ion battery modules, wherein the property parameters include capacity, internal resistance and voltage, including: The sodium ion battery production batch identification, production time identification and battery parameters are obtained through a data collector, and a multi-dimensional attribute data structure is established according to a recursive partitioning algorithm; A random forest regression algorithm is used to analyze the battery capacity parameter, the internal resistance value and the voltage parameter in the multi-dimensional attribute data structure to obtain a mapping relationship between the charging current value and the charging time length of the battery module; Read the battery module power percentage, discharge duration and discharge power from the multi-dimensional attribute data structure according to the mapping relationship, and calculate a charging time reference value; The charging time correction factor is calculated based on the battery's internal resistance and voltage parameters.

[0005] Optionally, the real-time monitoring of the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system, when it is detected that a sodium ion battery module is added or removed, determines whether the UPS system has energy imbalance according to the added or removed sodium ion battery module batch and property parameters, and the determined initial charging current and charging time adjustment coefficient. The energy imbalance includes the existence of capacity deviation, internal resistance deviation and voltage deviation, including: The voltage value, internal resistance value, capacity value, output power value and remaining operating time of the battery pack are obtained through the voltage data collector, and the recursive neural network is used to obtain the time series feature vector of the battery pack parameters; Generate a battery pack state feature matrix using a convolutional neural network according to the battery pack parameter time series feature vector and the battery module batch number, charging current parameter and charging time coefficient read from the sodium ion battery manager; Calculating a capacity deviation value, an internal resistance deviation value, and a voltage deviation value for the battery pack state characteristic matrix, and determining whether the capacity deviation value, the internal resistance deviation value, and the voltage deviation value exceed a pre-stored corresponding threshold interval to obtain an imbalance state identification code; The imbalance state identification code is graded by a recursive binary division method to generate three-level imbalance alarm signals, namely, high, medium and low levels. A compensation coefficient is obtained from a charging parameter database according to the level of the imbalance alarm signal. The battery energy imbalance compensation value is calculated by combining the charging current parameter and the charging time coefficient.

[0006] Optionally, if there is an energy imbalance, query the sodium ion battery module attribute database, obtain the target charging current under the current state of charge, calculate the required charging time based on the battery capacity and target charging current of the sodium ion battery module, and control the charger output in real time, including: Acquire a battery surface temperature value from a temperature sensor and a battery voltage value from a voltage collector, and obtain a target charging current parameter according to the battery surface temperature value and the battery voltage value; Processing the battery surface temperature value and the battery voltage value using a gradient descent algorithm to obtain a charging control feature vector including a temperature change rate and a voltage change rate; Performing a recursive least squares calculation on the remaining battery power value according to the charging control characteristic vector to obtain a segmented charging current curve; If the output power of the segmented charging current curve exceeds a preset threshold interval, the charging current output value is reduced until the battery voltage value reaches a charging cut-off condition.

[0007] Optionally, during the charging process, the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system are continuously monitored, and based on the differences in property parameters between different batches of sodium ion battery modules, several different property parameters of the sodium ion battery modules are compared with corresponding preset target values. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results, including: Obtaining the charging state value, charging power value and remaining power value of the sodium ion battery module through a monitoring unit, wherein the monitoring unit outputs a voltage change value, a temperature change value and an internal resistance change value according to an attribute data collector; Using a recursive neural network to process data based on the voltage change value, temperature change value and internal resistance change value, to obtain battery state characteristic parameters; Read the battery batch number and the corresponding charging current value, charging time length and discharge depth value from the charging controller, and divide the battery modules of different batches into parameter groups using the battery state characteristic parameters; A charging parameter reference interval is calculated for the parameter group, the charging parameter reference interval is compared with a preset target value in a preset threshold database to obtain a deviation value, and a charging current adjustment amount and a charging time adjustment amount are generated according to the deviation value.

[0008] Optionally, after the charging is completed, the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system are re-estimated, and the performance differences and attenuation laws between different batches of sodium ion battery modules are analyzed to update the sodium ion battery module property database, including: Obtain the capacity measurement value, voltage measurement value and internal resistance measurement value of the battery module collected by the data collector in the charging completion state, and filter the measured values ​​through a sliding average filter to obtain smooth measurement data; The capacity attenuation rate, voltage attenuation rate and internal resistance growth rate of the battery module are calculated according to the smoothed measurement data, and the performance deviation data is obtained by comparing with the preset benchmark performance value; Processing the performance deviation data using a long short-term memory neural network to obtain a battery performance characteristic matrix, calculating a comprehensive performance score based on the battery performance characteristic matrix and dividing the performance level identification code; The data update time interval value is determined according to the performance level identification code, and the smoothed measurement data is processed by recursive least square method to obtain the predicted values ​​of capacity, voltage and internal resistance parameters.

[0009] Optionally, the updated sodium ion battery module attribute database is used to monitor the UPS system load in real time, analyze the load characteristics including the load type, load properties and load change law, and record the load peak within a preset time, predict the load growth trend of the UPS system, and calculate the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend to obtain the expanded capacity of the sodium ion battery module of the UPS system, including: When acquiring the electric load data through the load monitoring unit, the load monitoring unit classifies the electric load data according to resistive load, inductive load and capacitive load to obtain load classification data; Calculate a load characteristic vector according to the load classification data, extract a peak load amount and a peak-to-valley ratio value from the load characteristic vector, and obtain a load power ratio and a device utilization rate; Using a support vector regression method to process the load power ratio and the equipment utilization rate to obtain a load growth curve; The backup power supply time is calculated according to the load growth curve, the standard capacity specification is obtained from the battery attribute database, and the battery capacity expansion value is determined.

[0010] The embodiment of the present invention further provides an expansion system of a sodium ion battery uninterruptible power supply, comprising: A database establishment module is used to obtain the property parameters of different batches of sodium-ion battery modules in history, establish a sodium-ion battery module property database, and determine the adjustment coefficients of the initial charging current and charging time according to the differences in batches and property parameters of the sodium-ion battery modules. The property parameters include capacity, internal resistance and voltage. A monitoring and judgment module is used to monitor the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system in real time. When it is detected that a sodium-ion battery module is added or removed, it is judged whether there is an energy imbalance in the UPS system according to the batch and property parameters of the added or removed sodium-ion battery module, and the determined adjustment coefficient of the initial charging current and charging time. The energy imbalance includes the existence of capacity deviation, internal resistance deviation and voltage deviation; a processing and calculation module, configured to query a sodium-ion battery module attribute database to obtain a target charging current under a current state of charge when the monitoring and judgment module determines that the UPS system has an energy imbalance, calculate a required charging time based on a battery capacity and a target charging current of the sodium-ion battery module, and control a charger output in real time; The monitoring and judging module is also used to continuously monitor the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system during the charging process, and compare several different property parameters of the sodium ion battery modules with corresponding preset target values ​​based on the property parameter differences between different batches of sodium ion battery modules. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results; The monitoring and judgment module is also used to re-estimate the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system after charging is completed, and analyze the performance differences and attenuation laws between different batches of sodium ion battery modules to update the sodium ion battery module attribute database; The monitoring and judgment module is also used to monitor the UPS system load in real time based on an updated sodium ion battery module attribute database, analyze load characteristics including load type, load properties and load change rules, and record the load peak within a preset time, predict the load growth trend of the UPS system, calculate the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend, and obtain the extended capacity of the sodium ion battery module of the UPS system.

[0011] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor on an electronic device.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a method for expanding a sodium-ion battery uninterruptible power supply. The method establishes a sodium-ion battery module attribute database, monitors battery pack parameters and system output in real time, and determines the energy balance state when a battery module change is detected. If there is an energy imbalance, the charging strategy is adjusted according to the database information, and the charging process is controlled in real time. After charging is completed, the battery performance is re-evaluated and the database is updated. The present invention also predicts the system load growth trend, calculates the backup time and capacity redundancy, and determines the battery module expansion capacity based on the updated database and load analysis. This method can effectively manage the performance differences of different batches of sodium-ion battery modules, optimize the charging strategy, improve the reliability and efficiency of the UPS system, and provide a basis for system capacity planning, thereby realizing intelligent battery management and system optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a method for expanding a sodium ion battery uninterruptible power supply.

[0014] Figure 2 The present invention is a schematic diagram of a method for expanding a sodium ion battery uninterruptible power supply.

[0015] Figure 3 It is another schematic diagram of a method for expanding a sodium ion battery uninterruptible power supply according to the present invention.

[0016] Figure 4 The present invention is a schematic diagram of the expansion system structure of a sodium ion battery uninterruptible power supply. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the expansion method of a sodium ion battery uninterruptible power supply in this embodiment may specifically include: Step S101, obtaining the property parameters of different batches of sodium ion battery modules in history, establishing a sodium ion battery module property database, and determining the adjustment coefficients of the initial charging current and charging time according to the differences in batches and property parameters of the sodium ion battery modules. The property parameters include capacity, internal resistance and voltage.

[0019] The sodium ion battery production batch identification, production time identification and battery parameters are obtained through a data collector, and a multi-dimensional attribute data structure is established according to a recursive partitioning algorithm; the battery capacity parameters, internal resistance values ​​and voltage parameters in the multi-dimensional attribute data structure are analyzed by a random forest regression algorithm to obtain a mapping relationship between the charging current value and the charging time length of the battery module; the battery module power percentage, discharge time and discharge power are read from the multi-dimensional attribute data structure according to the mapping relationship to calculate a charging time reference value; and the charging time correction coefficient is calculated based on the internal resistance value and voltage parameters of the battery.

[0020] Specifically, the production batch, production time, battery capacity parameters, internal resistance value, voltage parameters, and module production line identification are obtained from the sodium ion battery production line through a data collector. After the collected parameters are standardized, a multi-dimensional attribute data structure is established according to the recursive partitioning algorithm. For the battery capacity parameters, internal resistance value, and voltage parameters in the multi-dimensional attribute data structure, the random forest regression algorithm is used to calculate the mapping relationship between the charging current value and the charging time length of each batch of battery modules, and a charging parameter prediction model is constructed. The battery module power percentage, discharge time, and discharge power are read from the multi-dimensional attribute data structure, and the charging time reference value is calculated according to the charging parameter prediction model, and the charging current interval is divided according to the battery capacity. The charging time correction coefficient is calculated based on the internal resistance value and voltage parameters of the battery, and the actual charging time is obtained by combining the charging time reference value. According to the battery module batch number, the corresponding charging current upper limit value is queried in the multi-dimensional attribute data structure, and the actual charging current parameter is obtained by multiplying the charging time correction coefficient with the charging current reference value. A charging control instruction sequence is established according to the actual charging time and the actual charging current parameter, and the charging process segmentation point is set according to the battery power percentage. The production process of sodium-ion battery modules includes multiple key parameter collection points. By arranging high-precision collection equipment on the production line to obtain production batch and production time information, the core parameters such as battery capacity, internal resistance, and voltage are normalized by standardized processing, and the value range is controlled between 0 and 1. The sampling frequency of the data collector is set to 200 samples per second. The sampled data is first denoised and then stored in a multi-dimensional attribute data structure. In the recursive partitioning algorithm, the battery module parameters are clustered according to the production batch, and the capacity parameter is used as the first dimension feature, the internal resistance parameter is used as the second dimension feature, and the voltage parameter is used as the third dimension feature to construct a feature space. Each battery module forms a unique feature vector in the feature space. For a battery module with a capacity of 180 ampere hours, an internal resistance of 2.5 milliohms, and a voltage of 3.2 volts, its feature vector is normalized and expressed as (0.8, 0.6, 0.7). The random forest regression algorithm uses 50 decision trees to construct a charging parameter prediction model. The input features include three parameters: battery capacity, internal resistance, and voltage. The output results are the charging current value and the length of the charging time. When the battery capacity is 180 amperes, the charging current reference value is set to 90 amperes, and the charging time reference value is 120 minutes. Taking into account the differences in internal resistance of battery modules in different batches, the charging current range is divided according to the internal resistance value. When the internal resistance is 2.5 milliohms, the upper limit of the charging current is 100 amperes. The charging time correction factor is related to the battery voltage parameters. When the voltage is 3.2 volts, the correction factor is 1.2, and the actual charging time is 144 minutes. The battery module batch number is associated with the upper limit of the charging current. The limit data is obtained by looking up the table. The actual charging current parameter is obtained by multiplying the correction factor by the reference value to obtain 108 amperes.During the charging control process, segmentation points are set according to the battery power, and different charging strategies are adopted when the power reaches 30%, 60%, and 90%. In the 0-30% stage, the constant current charging method is adopted, and the charging current is 108 amperes. In the 30-60% stage, the charging current is reduced to 80 amperes, and in the 60-90% stage, the charging current is reduced to 50 amperes. In the 90-100% stage, the constant voltage charging method is adopted.

[0021] Step S102, real-time monitoring of the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system. When it is detected that a sodium-ion battery module is added or removed, it is determined whether there is an energy imbalance in the UPS system based on the batch and property parameters of the added or removed sodium-ion battery module, and the determined adjustment coefficients of the initial charging current and charging time. The energy imbalance includes the existence of capacity deviation, internal resistance deviation and voltage deviation.

[0022] The voltage value, internal resistance value, capacity value, output power value and remaining operating time of the battery pack are obtained through a voltage data collector, and a recursive neural network is used to process the battery pack parameter time series feature vector; a convolutional neural network is used to generate a battery pack state feature matrix according to the battery pack parameter time series feature vector and the battery module batch number, charging current parameter and charging time coefficient read from the sodium ion battery manager; the capacity deviation value, internal resistance deviation value and voltage deviation value are calculated for the battery pack state feature matrix, and it is determined whether the capacity deviation value, internal resistance deviation value and voltage deviation value exceed the corresponding threshold interval stored in advance to obtain an imbalance state identification code; a recursive binary division method is used to classify the imbalance state identification code to generate three levels of imbalance alarm signals, namely, high, medium and low levels; the compensation coefficient is obtained from the charging parameter database according to the level of the imbalance alarm signal, and the battery energy imbalance compensation value is calculated in combination with the charging current parameter and the charging time coefficient.

[0023] Specifically, the voltage value, internal resistance value, capacity value, output power value, and remaining operating time are obtained from the battery pack through the voltage data collector, and the abnormal values ​​are removed according to the maximum and minimum value limiting method of the collected signal, and the recursive neural network is used to construct the battery pack parameter time series feature vector. The battery module batch number, charging current parameter, and charging time coefficient are read from the sodium ion battery manager, and the battery pack state feature matrix is ​​generated by the convolutional neural network in combination with the battery pack parameter time series feature vector. The voltage threshold, internal resistance threshold, and capacity threshold pre-stored in the battery manager are read, and the capacity deviation value, internal resistance deviation value, and voltage deviation value are calculated according to the battery pack state feature matrix. The capacity deviation value, internal resistance deviation value, and voltage deviation value are compared with the corresponding threshold value, and the energy imbalance state is determined to be exceeded when the threshold interval is exceeded, and the corresponding imbalance state identification code is generated. The imbalance state identification code is classified by recursive binary division, and three-level imbalance alarm signals of high, medium, and low are generated accordingly. The compensation coefficient is extracted from the charging parameter database according to the imbalance alarm signal level, and the battery energy imbalance compensation value is calculated in combination with the charging current parameter and the charging time coefficient. The battery pack parameter monitoring in the UPS system involves multiple key indicators. The battery pack is sampled in real time through a high-precision voltage data collector. The sampling frequency is set to 500 times per second to collect voltage values, internal resistance values, capacity values, output power values ​​and remaining operating time. For the collected original signal, the voltage limit range is set between 2.8 volts and 3.6 volts, and the internal resistance limit range is between 2 milliohms and 5 milliohms. Data outside the range is determined as an abnormal value and removed. After the sampled data is input into the recursive neural network, a time series feature vector is generated. The feature vector reflects the dynamic change trend of the battery pack parameters. The sodium ion battery manager stores basic information such as the production batch and material code of the battery module, and records the charging current parameters and charging time coefficient. When the battery pack is in the charging state, the charging current parameter range is between 20 amperes and 100 amperes, and the charging time coefficient is between 0.8 and 1.5. The convolutional neural network receives the time series feature vector and charging parameter data, and outputs a 16×16 dimensional battery pack state feature matrix, where each element of the matrix corresponds to a battery state feature. The preset voltage threshold of the battery manager is plus or minus 0.2 volts, the internal resistance threshold is plus or minus 0.5 milliohms, and the capacity threshold is plus or minus 5%. Taking a group of sodium-ion batteries as an example, if the measured voltage value is 3.4 volts and the nominal value is 3.2 volts, the calculated voltage deviation value is 0.2 volts, which reaches the upper limit of the preset threshold. The system determines that there is a voltage imbalance in this group of batteries. Similarly, the measured internal resistance value is 4.2 milliohms, the nominal value is 3 milliohms, and the internal resistance deviation value is 1.2 milliohms, which exceeds the threshold range and determines that there is an internal resistance imbalance. For the determined imbalance state, the system uses recursive binary division to classify it, setting the voltage deviation exceeding the threshold by more than 50% as a high-level alarm, exceeding 20% ​​to 50% as a medium-level alarm, and less than 20% as a low-level alarm.When a high-level alarm occurs, the compensation coefficient 0.7 is extracted from the charging parameter database and multiplied by the original charging current 80 amperes to obtain a compensated charging current of 56 amperes. The intermediate alarm corresponds to a compensation coefficient of 0.8, and the low alarm corresponds to a compensation coefficient of 0.9, realizing adaptive adjustment of charging parameters.

[0024] Step S103, if there is an energy imbalance, query the sodium ion battery module attribute database to obtain the target charging current under the current state of charge, calculate the required charging time based on the battery capacity and target charging current of the sodium ion battery module, and control the charger output in real time.

[0025] The battery surface temperature value is obtained from the temperature sensor and the battery voltage value is obtained from the voltage collector, and the target charging current parameter is obtained according to the battery surface temperature value and the battery voltage value; the battery surface temperature value and the battery voltage value are processed by a gradient descent algorithm to obtain a charging control feature vector including a temperature change rate and a voltage change rate; a recursive least squares calculation is performed on the battery remaining power value according to the charging control feature vector to obtain a segmented charging current curve; if the output power of the segmented charging current curve exceeds a preset threshold interval, the charging current output value is reduced until the battery voltage value reaches a charging cut-off condition.

[0026] Specifically, the battery imbalance mark of the sodium-ion battery module is read through the real-time data collector, the battery surface temperature is obtained from the temperature sensor, and the target charging current parameter of the corresponding working range is obtained from the attribute database according to the state of charge value and the battery capacity value. The temperature change rate is calculated based on the temperature sensor data, and the voltage change rate is calculated by obtaining the voltage data from the voltage collector. The charging control feature vector containing the target charging current, temperature change rate, and voltage change rate is constructed by the gradient descent algorithm. The initial charging time is calculated by the remaining power percentage and the capacity decay rate, and the initial charging time is corrected based on the charging control feature vector. The recursive least squares method is used to generate a segmented charging current curve. The charger output power of each time period is calculated according to the segmented charging current curve, and a charging control instruction containing the current size and duration is generated. The actual output current value is read from the charger current detection unit and compared with the charging control instruction. When the deviation exceeds the preset range, the charging current correction is triggered. Based on the charger power detection unit data, it is judged whether the output power exceeds the preset threshold interval, and the charging current output value is reduced when it exceeds. Through the battery voltage detection unit and temperature detection unit data, it is judged whether the battery reaches the charging cut-off condition, and a charging stop instruction is generated when the cut-off condition is reached. Sodium-ion battery charging control involves real-time monitoring of multiple key parameters. The temperature data is obtained by sampling 10 times per second through the temperature sensor arranged on the surface of the battery. The real-time data collector synchronously obtains parameters such as imbalance identification, state of charge value, and battery capacity value. When the battery pack is in an energy imbalance state, the attribute database returns the corresponding target charging current parameter according to the state of charge. For example, the corresponding target charging current is 20 amperes at 80% state of charge. The temperature change rate reflects the heating condition of the battery charging process. It is calculated by continuous 60 seconds of temperature sampling data. The temperature change rate is within 0.1 degrees Celsius per minute during normal operation. The sampling frequency of voltage data is 100 times per second. The voltage change rate characterizes the voltage response characteristics of the charging process. When the voltage change rate exceeds 0.1 volts per minute, it indicates that the charging current is too large. The charging control feature vector contains three parameters: target charging current, temperature change rate, and voltage change rate. The feature vector is optimized by the gradient descent algorithm. Taking a sodium-ion battery with a capacity of 180 ampere hours as an example, when the initial state of charge is 30%, the initial charging time is calculated to be 4 hours based on the remaining power. After correction based on the temperature and voltage change rate, the actual charging time is 4.5 hours. The segmented charging current curve uses a 60 ampere constant current charging in the 0-30% range, a 40 ampere constant current charging in the 30-80% range, and a 20 ampere constant current charging in the 80-100% range. The charger current detection unit monitors the output current in real time with a sampling frequency of 1000 times per second. When the deviation between the actual current value and the control instruction exceeds 2 amperes, the current correction is triggered. The charger power detection unit monitors the output power, and the preset power threshold is 3000 watts. When the threshold is exceeded, the charging current is gradually reduced in steps of 100 watts.When the battery voltage reaches 3.8 volts or the temperature exceeds 45 degrees Celsius, the charging cut-off condition is determined to be met, and a charging stop instruction is generated to cut off the charger output. During the entire charging process, the charging controller continues to perform closed-loop control, and keeps the battery temperature and voltage within a safe range by adjusting the charging current in real time. The dynamic adjustment of the charging current takes into account both the battery's state of charge and the changing trends of temperature and voltage, thereby achieving precise control of the battery charging process. When the battery has energy imbalance, the charging controller can correct the imbalance by adjusting the charging parameters to ensure the charging balance of the battery pack.

[0027] Step S104, during the charging process, continuously monitor the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system, and based on the differences in property parameters between different batches of sodium ion battery modules, compare several different property parameters of the sodium ion battery modules with corresponding preset target values. If the property parameters of the battery reach the preset comparison results, adjust the charging time and charging current accordingly according to the comparison results.

[0028] The charging state value, charging power value and remaining power value of the sodium ion battery module are obtained through a monitoring unit, and the monitoring unit outputs a voltage change value, a temperature change value and an internal resistance change value according to an attribute data collector; a recursive neural network is used to perform data processing according to the voltage change value, the temperature change value and the internal resistance change value to obtain a battery state characteristic parameter; the battery batch number and the corresponding charging current value, the charging time length and the discharge depth value are read from a charging controller, and the battery state characteristic parameters are used to divide different batches of battery modules into parameter groups; a charging parameter reference interval is calculated for the parameter group, and the charging parameter reference interval is compared with a preset target value in a preset threshold database to obtain a deviation value, and a charging current adjustment amount and a charging time adjustment amount are generated according to the deviation value.

[0029] Specifically, the charging state, charging power value, and remaining power value of the sodium-ion battery module are collected in batches through the monitoring unit, and the voltage change value, temperature change value, and internal resistance change value are obtained based on the attribute data collector, and the collected data is denoised and smoothed. The recursive neural network is used to process the denoised data, and the input features include the voltage change value, temperature change value, and internal resistance change value, and the battery state characteristic parameters are output. The battery batch number and the corresponding charging current value, charging time length, and discharge depth value are obtained from the charging controller, and the parameters of different batches of battery modules are grouped according to the battery state characteristic parameters. Based on the parameter grouping results, the mean and variance of the charging parameters of each group of batteries are calculated to generate the charging parameter reference interval. The preset target values ​​of each batch of batteries are read from the preset threshold database, compared with the charging parameter reference interval, and the deviation value is generated. The correction level is divided according to the size of the deviation value, and the charging current adjustment amount or charging time adjustment amount is generated according to the correction level. The adjusted charging parameters are input into the charging controller, and the actual output parameters are read from the controller for comparison with the target parameters. When the deviation exceeds the threshold, the adjustment amount is recalculated. The data collection of sodium-ion battery monitoring involves the real-time acquisition of multiple key indicators. The monitoring unit samples data for each battery module, and the sampling frequency is set to 1,000 times per second. Basic parameters including charging status, charging power value, and remaining power value are collected. During the acquisition process, the original signal is denoised by sliding average filtering, and the sliding window length is set to 20 sampling points. The smoothed data can reflect the real change trend of battery parameters. The recursive neural network receives the processed voltage change value, temperature change value, and internal resistance change value as input features. The network contains 10 hidden layer nodes and outputs battery state characteristic parameters. Taking a batch of sodium-ion batteries as an example, when the voltage change value is 0.5 millivolts per second, the temperature change value is 0.2 degrees Celsius per minute, and the internal resistance change value is 0.1 milliohms per hour, the state characteristic parameters output by the recursive neural network show that the battery is in a normal charging state. For different batches of battery modules, the charging controller records parameters such as charging current value, charging time length, and discharge depth value. For a battery pack with a capacity of 180 ampere hours, the charging current value is between 20 amperes and 100 amperes, the charging time is between 2 hours and 8 hours, and the depth of discharge value is between 10% and 90%. These charging parameters are grouped based on the battery state characteristic parameters, and batteries of the same capacity level are divided into the same group. The mean and variance of the charging parameters are calculated within each group of batteries to generate a parameter reference interval. When the mean charging current of a group of batteries is 60 amperes and the variance is 5 amperes, the charging current reference interval is set to 55 amperes to 65 amperes. The preset threshold database stores the preset target values ​​of different batches of batteries, and the deviation value can be obtained by comparing with the reference interval. The deviation value is divided into three correction levels according to 0-10%, 10%-20%, and above 20%, corresponding to 5%, 10%, and 15% adjustment amounts respectively.Taking the charging current adjustment as an example, when the deviation value is 15%, it belongs to the second level correction, and the charging current needs to be adjusted by 10%. If the original charging current is 60 amps, the adjusted charging current becomes 54 amps. The charging controller reads the actual output current every 10 seconds and compares it with the target current of 54 amps. When the deviation exceeds 2 amps, a new round of adjustment calculation is triggered.

[0030] Step S105, after charging is completed, re-estimate the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system, and analyze the performance differences and attenuation laws between different batches of sodium ion battery modules to update the sodium ion battery module property database.

[0031] The capacity measurement value, voltage measurement value and internal resistance measurement value of the battery module in the charging completion state collected by the data collector are obtained, and the measured values ​​are filtered by a sliding average filter to obtain smoothed measurement data; the capacity attenuation rate, voltage attenuation rate and internal resistance growth rate of the battery module are calculated according to the smoothed measurement data, and performance deviation data are obtained by comparing with preset benchmark performance values; the performance deviation data are processed by a long short-term memory neural network to obtain a battery performance characteristic matrix, and the comprehensive performance score is calculated according to the battery performance characteristic matrix and the performance level identification code is divided; the data update time interval value is determined according to the performance level identification code, and the smoothed measurement data is processed by a recursive least squares method to obtain predicted values ​​of capacity, voltage and internal resistance parameters.

[0032] Specifically, the capacity measurement value, voltage measurement value, and internal resistance measurement value of the sodium-ion battery module in the charging state are collected in batches through the voltage and current data collector, and the historical measurement values ​​are read from the attribute database. The measurement data is subjected to sliding average filtering to generate smooth measurement data. Based on the smooth measurement data, the capacity attenuation rate, voltage attenuation rate, and internal resistance growth rate of each batch of battery modules are calculated, and compared with the benchmark performance value in the attribute database to generate performance deviation data. The performance deviation threshold is obtained from the attribute database, the performance deviation data is normalized, and the battery performance characteristic matrix is ​​constructed using a long short-term memory neural network. The comprehensive performance score is calculated based on the battery performance characteristic matrix, and the performance level is divided according to the score interval to generate a performance grading identification code. The data update time interval is determined based on the performance grading identification code, and the high performance level interval is set long and the low performance level interval is set short. The recursive least squares method is used to calculate the change trend of the measurement data and generate the predicted values ​​of the capacity, voltage, and internal resistance parameters. The measurement data, performance level, and parameter prediction value are written into the corresponding batch record of the attribute database to complete the dynamic update of the battery attribute data. The performance evaluation of sodium-ion battery modules involves the collection and analysis of multiple key parameters. The battery in the charging state is sampled by a high-precision voltage and current collector. The sampling frequency is set to 500 times per second to record the three key parameters of capacity, voltage, and internal resistance. The original sampled data is processed by a 20-point sliding average filter, and the filtered data can reflect the real change trend of the battery parameters. Taking the sodium-ion battery with a capacity of 180 ampere hours as an example, the actual capacity measured when charging is completed is 162 ampere hours, the voltage is 3.2 volts, and the internal resistance is 3.5 milliohms. The benchmark data of this batch of batteries when put into use is read from the attribute database, with a capacity of 180 ampere hours, a voltage of 3.6 volts, and an internal resistance of 2.5 milliohms. By comparison and calculation, the capacity attenuation rate is 10%, the voltage attenuation rate is 11%, and the internal resistance growth rate is 40%. These performance deviation data are normalized and input into the long short-term memory neural network to construct a 16×16 dimensional performance characteristic matrix. Each element in the performance characteristic matrix corresponds to a performance characteristic, and the weighted calculation results in a comprehensive performance score of 85 points. The performance level is divided according to the score range, with 90 points or more as level one, 80-90 points as level two, 70-80 points as level three, and below 70 points as level four. The battery scored 85 points and was judged to be level two performance, generating a performance grading identification code of 0010. The data update time interval varies with the performance level. Level one performance is set to be updated every 30 days, level two 20 days, level three 10 days, and level four 5 days. The recursive least squares method is used to perform trend analysis on the recent measurement data of this batch of batteries, and it is predicted that the capacity will decay to 160 ampere hours in the next cycle, the voltage will drop to 3.15 volts, and the internal resistance will rise to 3.6 milliohms. These predicted data are written into the attribute database together with the current measurement value and the level two performance identification to update the performance record of this batch of batteries. Through the dynamic update mechanism, the attribute database always keeps the latest battery performance data, providing data support for the optimization of the charging control strategy.The performance grading mechanism dynamically adjusts the update frequency based on the measured data, so that batteries with poor performance are monitored more frequently and potential problems are discovered in a timely manner. Predictive analysis quantifies the trend of battery performance changes and provides a basis for maintenance decisions.

[0033] Step S106, based on the updated sodium ion battery module attribute database, monitor the UPS system load in real time, analyze the load characteristics including load type, load nature and load change law, and record the load peak within the preset time, predict the load growth trend of the UPS system, and calculate the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend to obtain the extended capacity of the sodium ion battery module of the UPS system.

[0034] When the power load data is obtained through the load monitoring unit, the load monitoring unit classifies the power load data according to resistive load, inductive load, and capacitive load to obtain load classification data; calculates a load characteristic vector according to the load classification data, extracts the peak load and the peak-to-valley ratio value from the load characteristic vector, and obtains the load power ratio and the equipment utilization rate; uses a support vector regression method to process the load power ratio and the equipment utilization rate to obtain a load growth curve; calculates the backup power supply time according to the load growth curve, obtains the standard capacity specifications from the battery attribute database, and determines the battery capacity expansion value.

[0035] Specifically, the load monitoring unit collects power load data, records the load power value and load change rate, classifies the load according to resistive load, inductive load, and capacitive load, and uses a recursive neural network to generate a load feature vector. Read recent load records from the load data storage, extract the peak load and peak-to-valley ratio based on the load feature vector, and calculate the load power ratio and equipment utilization rate in different periods. The support vector regression method is used to process the load data, and the input features include load power ratio, equipment utilization rate, and peak-to-valley ratio value to predict the future load growth curve. The load value at the key time point is extracted from the load growth curve, and the load growth rate is calculated in combination with the current load power value to generate a load forecast report. The rated capacity and actual capacity of the battery pack are read from the sodium-ion battery attribute database, and the backup power supply time is calculated based on the load forecast report. According to the comparison result of the backup power supply time and the preset time threshold, the required capacity redundancy coefficient is calculated to generate the battery capacity expansion recommendation value. According to the difference between the capacity expansion recommendation value and the actual capacity, the standard capacity specification is obtained from the battery attribute database to determine the final expansion capacity value. UPS system load monitoring involves the identification and analysis of multiple load types. The power load is sampled in real time through a high-precision load monitoring unit. The sampling frequency is set to 1,000 times per second, and the load power value and load change rate are recorded. Load classification is based on power factor and phase angle characteristics. A power factor greater than 0.95 is determined as a resistive load, a power factor between 0.7 and 0.95 and the current lags the voltage is determined as an inductive load, and a power factor between 0.7 and 0.95 and the current leads the voltage is determined as a capacitive load. The recursive neural network receives load sampling data and outputs a 16-dimensional load feature vector. For data center load scenarios, the load data storage records the load data for the past month. The peak load occurs between 14:00 and 16:00 on weekdays, with a value of 800 kilowatts, and the valley load occurs between 2:00 and 4:00 in the morning, with a value of 200 kilowatts, and the peak-to-valley ratio value is 4. The equipment utilization rate remains above 75% during working hours and drops to around 30% at night. The load power ratio reflects the load distribution characteristics in different time periods, with peak periods accounting for 40%, flat periods accounting for 45%, and valley periods accounting for 15%. The load forecast uses the support vector regression method. The training data contains load records for the past 6 months. Each record contains three features: load power ratio, equipment utilization, and peak-to-valley ratio. The forecast results show that the peak load will increase to 900 kilowatts in the next month, with a monthly average load growth rate of 12%. The load forecast values ​​at key time points of 9:00, 14:00, and 20:00 are 600 kilowatts, 850 kilowatts, and 450 kilowatts, respectively. The battery attribute database records show that the rated capacity of the sodium ion battery pack currently configured for the UPS is 1,000 kWh, and the actual capacity is 900 kWh. The backup power supply time is calculated based on the load forecast results. When the load is at a peak of 900 kWh, the backup power supply time is 1 hour, which is different from the preset 2-hour threshold.The calculated capacity redundancy coefficient is 1.8, indicating that the battery capacity needs to be expanded. The standard capacity specification in the battery attribute database is 500 kWh. Based on the capacity difference of 800 kWh, it is determined that two sets of standard capacity battery modules need to be added. Load feature identification provides a basis for capacity planning, and combines historical data to predict future load growth trends, ultimately obtaining an accurate capacity expansion plan.

[0036] The embodiment of the present invention also provides an expansion system of a sodium ion battery uninterruptible power supply, such as Figure 4 As shown, it includes the following modules: A database establishment module 401 is used to obtain the property parameters of different batches of sodium ion battery modules in history, establish a sodium ion battery module property database, and determine the adjustment coefficients of the initial charging current and charging time according to the differences in the batches and property parameters of the sodium ion battery modules. The property parameters include capacity, internal resistance and voltage. The monitoring and judging module 402 is used to monitor the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system in real time. When it is detected that a sodium-ion battery module is added or removed, it is judged whether there is an energy imbalance in the UPS system according to the batch and property parameters of the added or removed sodium-ion battery module, and the determined adjustment coefficient of the initial charging current and charging time. The energy imbalance includes the existence of capacity deviation, internal resistance deviation and voltage deviation; The processing and calculation module 403 is used to query the sodium ion battery module attribute database to obtain the target charging current under the current state of charge when the monitoring and judgment module determines that the UPS system has energy imbalance, calculate the required charging time based on the battery capacity and target charging current of the sodium ion battery module, and control the charger output in real time; The monitoring and judging module 402 is also used to continuously monitor the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system during the charging process, and compare several different property parameters of the sodium ion battery modules with corresponding preset target values ​​based on the property parameter differences between different batches of sodium ion battery modules. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results; The monitoring and judging module 402 is also used to re-estimate the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system after charging is completed, and analyze the performance differences and attenuation laws between different batches of sodium ion battery modules to update the sodium ion battery module attribute database; The monitoring and judgment module 402 is also used to monitor the UPS system load in real time based on the updated sodium ion battery module attribute database, analyze the load characteristics including load type, load nature and load change law, and record the load peak within a preset time, predict the load growth trend of the UPS system, calculate the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend, and obtain the extended capacity of the sodium ion battery module of the UPS system.

[0037] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor on an electronic device.

[0038] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for expanding a sodium ion battery uninterruptible power supply, characterized in that: The method comprises: Obtain the property parameters of different batches of sodium-ion battery modules in history, establish a sodium-ion battery module property database, and determine the adjustment coefficients of the initial charging current and charging time based on the differences in batches and property parameters of the sodium-ion battery modules. The property parameters include capacity, internal resistance and voltage. Real-time monitoring of the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system. When the addition or removal of the sodium-ion battery module is detected, it is determined whether there is energy imbalance in the UPS system based on the batch and property parameters of the added or removed sodium-ion battery module, as well as the determined adjustment coefficients of the initial charging current and charging time. The energy imbalance includes capacity deviation, internal resistance deviation and voltage deviation. If there is an energy imbalance, the sodium-ion battery module attribute database is queried to obtain the target charging current at the current state of charge. Based on the battery capacity and target charging current of the sodium-ion battery module, the required charging time is calculated to control the charger output in real time. During the charging process, the charging status and system energy changes of different batches of sodium-ion battery modules in the UPS system are continuously monitored, and based on the differences in property parameters between different batches of sodium-ion battery modules, several different property parameters of the sodium-ion battery modules are compared with corresponding preset target values. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results; After charging is completed, the capacity, voltage and internal resistance of different batches of sodium-ion battery modules in the UPS system are re-estimated, and the performance differences and attenuation laws between different batches of sodium-ion battery modules are analyzed to update the sodium-ion battery module property database; Based on the updated sodium-ion battery module property database, the UPS system load is monitored in real time, the load characteristics including load type, load nature and load change law are analyzed, and the load peak within the preset time is recorded to predict the load growth trend of the UPS system. Based on the current load, peak load and predicted load growth trend, the system backup time and capacity redundancy are calculated to obtain the extended capacity of the sodium-ion battery module of the UPS system.

2. The method according to claim 1, characterized in that The method obtains the property parameters of different batches of sodium ion battery modules in history, establishes a sodium ion battery module property database, and determines the adjustment coefficients of the initial charging current and charging time according to the differences in the batches and property parameters of the sodium ion battery modules. The property parameters include capacity, internal resistance and voltage, including: The sodium ion battery production batch identification, production time identification and battery parameters are obtained through a data collector, and a multi-dimensional attribute data structure is established according to a recursive partitioning algorithm; A random forest regression algorithm is used to analyze the battery capacity parameter, the internal resistance value and the voltage parameter in the multi-dimensional attribute data structure to obtain a mapping relationship between the charging current value and the charging time length of the battery module; Read the battery module power percentage, discharge duration and discharge power from the multi-dimensional attribute data structure according to the mapping relationship, and calculate a charging time reference value; The charging time correction factor is calculated based on the battery's internal resistance and voltage parameters.

3. The method according to claim 1, characterized in that The real-time monitoring of the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system, when detecting the addition or removal of the sodium ion battery module, judging whether the UPS system has energy imbalance according to the added or removed sodium ion battery module batch and property parameters, and the determined initial charging current and charging time adjustment coefficient, includes: The voltage value, internal resistance value, capacity value, output power value and remaining operating time of the battery pack are obtained through the voltage data collector, and the recursive neural network is used to obtain the time series feature vector of the battery pack parameters; Generate a battery pack state feature matrix using a convolutional neural network according to the battery pack parameter time series feature vector and the battery module batch number, charging current parameter and charging time coefficient read from the sodium ion battery manager; Calculating a capacity deviation value, an internal resistance deviation value, and a voltage deviation value for the battery pack state characteristic matrix, and determining whether the capacity deviation value, the internal resistance deviation value, and the voltage deviation value exceed a pre-stored corresponding threshold interval to obtain an imbalance state identification code; The imbalance state identification code is graded by a recursive binary division method to generate three-level imbalance alarm signals, namely, high, medium and low levels. A compensation coefficient is obtained from a charging parameter database according to the level of the imbalance alarm signal, and a battery energy imbalance compensation value is calculated in combination with a charging current parameter and a charging time coefficient.

4. The method according to claim 1, characterized in that: If there is an energy imbalance, query the sodium ion battery module attribute database, obtain the target charging current under the current state of charge, calculate the required charging time based on the battery capacity and target charging current of the sodium ion battery module, and control the charger output in real time, including: Acquire a battery surface temperature value from a temperature sensor and a battery voltage value from a voltage collector, and obtain a target charging current parameter according to the battery surface temperature value and the battery voltage value; Processing the battery surface temperature value and the battery voltage value using a gradient descent algorithm to obtain a charging control feature vector including a temperature change rate and a voltage change rate; Performing a recursive least squares calculation on the remaining battery power value according to the charging control characteristic vector to obtain a segmented charging current curve; If the output power of the segmented charging current curve exceeds a preset threshold interval, the charging current output value is reduced until the battery voltage value reaches a charging cut-off condition.

5. The method according to claim 1, characterized in that During the charging process, the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system are continuously monitored, and based on the differences in property parameters between different batches of sodium ion battery modules, several different property parameters of the sodium ion battery modules are compared with corresponding preset target values. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results, including: Obtaining the charging state value, charging power value and remaining power value of the sodium ion battery module through a monitoring unit, wherein the monitoring unit outputs a voltage change value, a temperature change value and an internal resistance change value according to an attribute data collector; Using a recursive neural network to process data based on the voltage change value, temperature change value and internal resistance change value, to obtain battery state characteristic parameters; Read the battery batch number and the corresponding charging current value, charging time length and discharge depth value from the charging controller, and divide the battery modules of different batches into parameter groups using the battery state characteristic parameters; A charging parameter reference interval is calculated for the parameter group, the charging parameter reference interval is compared with a preset target value in a preset threshold database to obtain a deviation value, and a charging current adjustment amount and a charging time adjustment amount are generated according to the deviation value.

6. The method according to claim 1, characterized in that After the charging is completed, the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system are re-estimated, and the performance differences and attenuation laws between different batches of sodium ion battery modules are analyzed to update the sodium ion battery module property database, including: Obtain the capacity measurement value, voltage measurement value and internal resistance measurement value of the battery module collected by the data collector in the charging completion state, and filter the measured values ​​through a sliding average filter to obtain smooth measurement data; The capacity attenuation rate, voltage attenuation rate and internal resistance growth rate of the battery module are calculated according to the smoothed measurement data, and the performance deviation data is obtained by comparing with the preset benchmark performance value; Processing the performance deviation data using a long short-term memory neural network to obtain a battery performance characteristic matrix, calculating a comprehensive performance score based on the battery performance characteristic matrix and dividing the performance level identification code; The data update time interval value is determined according to the performance level identification code, and the smoothed measurement data is processed by recursive least square method to obtain predicted values ​​of capacity, voltage and internal resistance parameters.

7. The method according to claim 1, characterized in that The method based on the updated sodium ion battery module attribute database monitors the UPS system load in real time, analyzes the load characteristics including the load type, load properties and load change law, records the load peak within a preset time, predicts the load growth trend of the UPS system, calculates the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend, and obtains the extended capacity of the sodium ion battery module of the UPS system, including: When acquiring the electric load data through the load monitoring unit, the load monitoring unit classifies the electric load data according to resistive load, inductive load and capacitive load to obtain load classification data; Calculate a load characteristic vector according to the load classification data, extract a peak load amount and a peak-to-valley ratio value from the load characteristic vector, and obtain a load power ratio and a device utilization rate; Using a support vector regression method to process the load power ratio and the equipment utilization rate to obtain a load growth curve; The backup power supply time is calculated according to the load growth curve, the standard capacity specification is obtained from the battery attribute database, and the battery capacity expansion value is determined.

8. An expansion system for a sodium ion battery uninterruptible power supply, characterized in that: include: A database establishment module is used to obtain the property parameters of different batches of sodium-ion battery modules in history, establish a sodium-ion battery module property database, and determine the adjustment coefficients of the initial charging current and charging time according to the differences in batches and property parameters of the sodium-ion battery modules. The property parameters include capacity, internal resistance and voltage. A monitoring and judgment module is used to monitor the property parameters of the battery pack in the UPS system and the output power and remaining time of the UPS system in real time. When it is detected that a sodium-ion battery module is added or removed, it is judged whether there is an energy imbalance in the UPS system according to the batch and property parameters of the added or removed sodium-ion battery module, and the determined adjustment coefficient of the initial charging current and charging time. The energy imbalance includes the existence of capacity deviation, internal resistance deviation and voltage deviation; a processing and calculation module, configured to query a sodium-ion battery module attribute database to obtain a target charging current under a current state of charge when the monitoring and judgment module determines that the UPS system has an energy imbalance, calculate a required charging time based on a battery capacity and a target charging current of the sodium-ion battery module, and control a charger output in real time; The monitoring and judging module is also used to continuously monitor the charging status and system energy changes of different batches of sodium ion battery modules in the UPS system during the charging process, and compare several different property parameters of the sodium ion battery modules with corresponding preset target values ​​based on the property parameter differences between different batches of sodium ion battery modules. If the property parameters of the battery reach the preset comparison results, the charging time and charging current are adjusted accordingly according to the comparison results; The monitoring and judgment module is also used to re-estimate the capacity, voltage and internal resistance of different batches of sodium ion battery modules in the UPS system after charging is completed, and analyze the performance differences and attenuation laws between different batches of sodium ion battery modules to update the sodium ion battery module attribute database; The monitoring and judgment module is also used to monitor the UPS system load in real time based on an updated sodium ion battery module attribute database, analyze load characteristics including load type, load properties and load change rules, and record the load peak within a preset time, predict the load growth trend of the UPS system, calculate the system backup time and capacity redundancy based on the current load, peak load and predicted load growth trend, and obtain the extended capacity of the sodium ion battery module of the UPS system.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor on an electronic device, the steps of the method according to any one of claims 1 to 7 are implemented.

Images