Method and system for optimizing service life of high-power UPS (Uninterrupted Power Supply) battery and medium

Abstract

The invention provides a service life optimization method and system for a high-power UPS battery and a medium. The method comprises the following steps: acquiring and processing operation state parameter data of a sub-battery to obtain a sub-battery performance evaluation index, processing the sub-battery performance evaluation index with a preset sub-battery performance reference index to obtain a sub-battery performance deviation rate, comparing the sub-battery performance deviation rate with a preset battery performance grade evaluation threshold value, and determining a battery performance grade corresponding to the sub-battery. The method comprises the following steps: acquiring discharge instruction information and energy consumption demand evaluation data of a load, processing according to the energy consumption demand evaluation data, acquiring energy consumption demand prediction data of the load, acquiring real-time discharge capability parameter data of a sub-battery for processing, acquiring effective capacity data, and processing in combination with the energy consumption demand prediction data. Obtaining a discharge distribution strategy of the high-power UPS battery; according to the invention, by evaluating the battery performance grade of the sub-battery and calculating the energy consumption demand prediction data of the load, reasonable distribution of the discharge strategy is realized, and the service life of the sub-battery is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of uninterruptible power supply, and in particular to a life optimization method, system and medium for a high-power UPS battery. Background Art

[0002] With the rapid development of information technology, key facilities such as data centers and communication base stations have increasingly higher requirements for the stability and reliability of power supply. As a key device to ensure uninterrupted power supply, the service life of the battery of high-power UPS directly affects the operating cost and reliability of the entire system. At present, UPS batteries face many challenges during use, such as changes in ambient temperature and improper charging and discharging management, which will lead to battery performance degradation and shortened life. Therefore, there is an urgent need for a method that can effectively optimize and improve the life of high-power UPS batteries to improve battery reliability and service life and reduce costs.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a life optimization method, system and medium for a high-power UPS battery, which can evaluate the battery performance level of the sub-battery, calculate the energy demand forecast data of the load, realize the reasonable allocation of the discharge strategy, and help to improve the service life of the sub-battery.

[0005] The present application also provides a method for optimizing the life of a high-power UPS battery, comprising the following steps: Obtaining the operating status parameter data of the high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining the sub-battery performance evaluation index; Processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; Obtain the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, process the energy demand evaluation data, and obtain the energy demand forecast data of the load; Acquire real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain effective capacity data; The effective capacity data is processed in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery.

[0006] Optionally, in the life optimization method of a high-power UPS battery described in the present application, the step of obtaining operating status parameter data of a sub-battery of a high-power UPS battery and processing the operating status parameter data to obtain a sub-battery performance evaluation index includes: Obtaining operating status parameter data of high-power UPS battery sub-batteries, the operating status parameter data including remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data and self-discharge rate; Comparing the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate, and comparing the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate; The sub-battery performance evaluation index is obtained by processing the capacity decay rate, internal resistance increase rate, charging characteristic data, discharge characteristic data and self-discharge rate.

[0007] Optionally, in the life optimization method of a high-power UPS battery described in the present application, the sub-battery performance evaluation index is processed with a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate, the sub-battery performance deviation rate is compared with a preset battery performance level assessment threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including: Processing the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate; Comparing the sub-battery performance deviation rate with a preset battery performance level assessment threshold, and determining a battery performance level corresponding to the sub-battery, including primary performance or secondary performance; If the sub-battery performance deviation rate is less than or equal to the preset battery performance level assessment threshold, the sub-battery performance level is determined to be level one performance; If the sub-battery performance deviation rate is greater than a preset battery performance level assessment threshold, the sub-battery performance level is determined to be level 2 performance.

[0008] Optionally, in the life optimization method of a high-power UPS battery described in the present application, the obtaining of discharge instruction information of the high-power UPS battery and energy demand evaluation data of the power system load, and processing according to the energy demand evaluation data to obtain energy demand forecast data of the load include: Obtaining high-power UPS battery discharge instruction information and power system load energy demand evaluation data, including load power, expected energy supply duration data and power characteristic category data; According to the power characteristic category data, a preset power supply demand evaluation database is queried to obtain a power characteristic weight coefficient; The load power, expected energy supply duration data and power characteristic weight coefficient are processed to obtain load energy demand forecast data.

[0009] Optionally, in the life optimization method of a high-power UPS battery described in the present application, the step of acquiring real-time discharge capacity parameter data of a high-power UPS battery sub-battery according to the discharge instruction information, and performing processing according to the real-time discharge capacity parameter data to obtain effective capacity data includes: Acquire real-time discharge capability parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, including real-time capacity data and corresponding conversion rate and minimum inventory requirement data; The real-time capacity data and the corresponding conversion rate and minimum inventory requirement data are processed to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-battery and the second-level effective capacity data of the second-level performance sub-battery.

[0010] Optionally, in the life optimization method of a high-power UPS battery described in the present application, the process of processing the effective capacity data in combination with the energy demand forecast data to obtain a discharge allocation strategy for the high-power UPS battery includes: Comparing the effective capacity data with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery, including balanced discharge, primary performance sub-battery discharge, or primary and secondary performance sub-battery discharge; If the effective capacity data is less than the energy demand forecast data, the power is discharged evenly, and an early warning response is output to start the preset backup power supply plan; If the effective capacity data is greater than or equal to the energy demand forecast data, then comparing the primary effective capacity data with the energy demand forecast data; If the first-level effective capacity data is greater than or equal to the energy demand forecast data, the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge; If the first-level effective capacity data is less than the energy demand forecast data, the energy demand forecast data is allocated to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio.

[0011] Optionally, the life optimization method of the high-power UPS battery described in the present application further includes: Obtain the rated capacity and battery category characteristic data of high-power UPS batteries; According to the rated capacity and battery type characteristic data, a preset charging parameter database is searched to obtain a charging current corresponding to a constant current charging stage; Controlling the high-power UPS battery to perform constant current charging according to the charging current, and obtaining the real-time charging monitoring voltage of the sub-battery; Comparing the real-time charging monitoring voltage with the preset equalizing charging voltage; If it is less than the preset equalization charging voltage, it is determined that the charging is in the constant current charging stage; If it is greater than or equal to the preset equalizing charging voltage, the preset charging parameter database is queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage, and the high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage.

[0012] Optionally, the life optimization method of the high-power UPS battery described in the present application further includes: Get the real-time ambient temperature of the location where the high-power UPS battery is located; Comparing the real-time ambient temperature with the preset standard ambient temperature to obtain an ambient temperature difference value, and querying a preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage; The temperature compensation voltage and the charging requirement voltage are summed to obtain a charging correction voltage; The high-power UPS battery is controlled to perform constant voltage charging according to the charging correction voltage.

[0013] In a second aspect, the present application provides a life optimization system for a high-power UPS battery, the system comprising: a memory and a processor, the memory comprising a program of a high-power UPS battery life optimization method, the program of the high-power UPS battery life optimization method being executed by the processor to implement the following steps: Obtaining the operating status parameter data of the high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining the sub-battery performance evaluation index; Processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; Obtain the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, process the energy demand evaluation data, and obtain the energy demand forecast data of the load; Acquire real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain effective capacity data; The effective capacity data is processed in combination with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a life optimization method program for a high-power UPS battery. When the life optimization method program for a high-power UPS battery is executed by a processor, the steps of a life optimization method for a high-power UPS battery as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the present application provides a method, system and medium for optimizing the life of a high-power UPS battery. By evaluating the battery performance level of the sub-battery and calculating the energy demand forecast data of the load, a reasonable allocation of the discharge strategy is achieved, which helps to improve the service life of the sub-battery.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for optimizing the life of a high-power UPS battery provided in an embodiment of the present application; Figure 2 A flow chart of obtaining a sub-battery performance evaluation index in a method for optimizing the life of a high-power UPS battery provided in an embodiment of the present application; Figure 3 A flowchart of obtaining load energy demand forecast data for a method for optimizing the life of a high-power UPS battery provided in an embodiment of the present application; Figure 4 A flowchart of obtaining effective capacity data of a method for optimizing the life of a high-power UPS battery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 The flowchart of a method for optimizing the life of a high-power UPS battery in some embodiments of the present application. The method for optimizing the life of a high-power UPS battery is used in terminal devices, such as computers, mobile phone terminals, etc. The method for optimizing the life of a high-power UPS battery includes the following steps: S11, obtaining operating status parameter data of a high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining a sub-battery performance evaluation index; S12, processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; S13, obtaining the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, and processing the energy demand evaluation data to obtain the energy demand forecast data of the load; S14, acquiring real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and processing the real-time discharge capacity parameter data to obtain effective capacity data; S15. Process the effective capacity data in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery.

[0022] It should be noted that in order to optimize the life of a high-power UPS battery, firstly, the operating status parameter data of the sub-battery is obtained, and the operating status parameter data is processed to obtain a sub-battery performance evaluation index for evaluating the performance level of the sub-battery, and then the sub-battery performance deviation rate is obtained by processing the sub-battery performance deviation rate with a preset battery performance level evaluation threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including primary performance or secondary performance, and then the energy demand evaluation data of the power system load is obtained and processed to obtain the energy demand forecast data of the load, and the power demand of the power system load is analyzed, and finally the real-time capacity data of the sub-battery and the corresponding conversion rate and minimum stock demand data are obtained, and the effective capacity data is obtained after processing, and the effective capacity data is processed in combination with the energy demand forecast data to obtain the discharge allocation strategy of the high-power UPS battery, and by reasonably allocating the discharge strategy, over-discharge or excessive use of some batteries is avoided, thereby optimizing the life of the high-power UPS battery.

[0023] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of obtaining a sub-battery performance evaluation index in a method for optimizing the life of a high-power UPS battery in some embodiments of the present invention. According to an embodiment of the present invention, the operation status parameter data of the sub-battery of the high-power UPS battery is obtained, and the sub-battery performance evaluation index is obtained by processing the operation status parameter data, including: S21, obtaining operating status parameter data of the high-power UPS battery sub-battery, the operating status parameter data including remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data and self-discharge rate; S22, comparing the remaining available capacity data with the preset rated capacity data to obtain a capacity attenuation rate, and comparing the real-time internal resistance value with the preset initial internal resistance value to obtain an internal resistance increase rate; S23, performing processing according to the capacity decay rate, internal resistance increase rate, charging characteristic data, discharging characteristic data and self-discharge rate to obtain a sub-battery performance evaluation index.

[0024] It should be noted that in order to evaluate the performance of the sub-battery and analyze the power supply potential of the sub-battery, the operating status parameter data including the remaining available capacity data, the real-time internal resistance value, the charging characteristic data, the discharging characteristic data and the self-discharge rate are first obtained, wherein the remaining available capacity data refers to the amount of electricity that the sub-battery can discharge under certain discharge conditions, the charging characteristic data refers to the time data of the sub-battery reaching the equalizing charging voltage during the charging process, the discharging characteristic data refers to the rate of decrease of the battery voltage with the discharge time during the discharge process, and the self-discharge rate refers to the amount of electricity lost by the sub-battery itself due to the internal chemical reaction within the preset time, and then the remaining available capacity data is compared with the preset rated The capacity data is compared to obtain the capacity attenuation rate, wherein the capacity attenuation rate refers to the ratio of the difference between the preset rated capacity data and the remaining available capacity data to the preset rated capacity data; the real-time internal resistance value is compared with the preset initial internal resistance value to obtain the internal resistance increase rate, wherein the internal resistance increase rate refers to the ratio of the difference between the real-time internal resistance value and the preset initial internal resistance value to the preset initial internal resistance value; the preset rated capacity data and the preset initial internal resistance value are obtained by querying the preset high-power UPS battery life optimization management platform; finally, the sub-battery performance evaluation index is obtained by processing according to the capacity attenuation rate, the internal resistance increase rate, the charging characteristic data, the discharging characteristic data and the self-discharge rate; The battery performance evaluation index calculation formula is: ; in, It is the battery performance evaluation index. , , , , They are capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharge characteristic data and self-discharge rate. , It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0025] According to an embodiment of the present invention, the sub-battery performance evaluation index is processed with a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate, the sub-battery performance deviation rate is compared with a preset battery performance level assessment threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including: Processing the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate; Comparing the sub-battery performance deviation rate with a preset battery performance level assessment threshold, and determining a battery performance level corresponding to the sub-battery, including primary performance or secondary performance; If the sub-battery performance deviation rate is less than or equal to the preset battery performance level assessment threshold, the sub-battery performance level is determined to be level one performance; If the sub-battery performance deviation rate is greater than a preset battery performance level assessment threshold, the sub-battery performance level is determined to be level 2 performance.

[0026] It should be noted that in order to classify and manage sub-batteries according to their performance potential, the obtained sub-battery performance evaluation index is first processed with the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, wherein the sub-battery performance deviation rate refers to the ratio of the absolute value of the difference between the battery performance evaluation index and the preset battery performance benchmark index to the preset battery performance benchmark index, and then the obtained sub-battery performance deviation rate is compared with the preset battery performance level assessment threshold to determine the battery performance level corresponding to the sub-battery, including primary performance or secondary performance, wherein the primary performance is better than the secondary performance. In this embodiment, the battery performance level assessment threshold is set to (0, 0.2], (0.2, 1], corresponding to the primary performance and the secondary performance, respectively. If the obtained sub-battery performance deviation rate is 0.1, which is less than the preset battery performance level assessment threshold, the sub-battery performance level is determined to be primary performance. If the obtained sub-battery performance deviation rate is 0.3, which is greater than the preset battery performance level assessment threshold, the sub-battery performance level is determined to be secondary performance.

[0027] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of obtaining load energy demand forecast data in a method for optimizing the life of a high-power UPS battery in some embodiments of the present application. According to an embodiment of the present invention, obtaining discharge instruction information of a high-power UPS battery and energy demand evaluation data of a power system load, and processing the energy demand evaluation data to obtain load energy demand forecast data includes: S31, obtaining discharge instruction information of a high-power UPS battery and energy demand evaluation data of a power system load, wherein the energy demand evaluation data includes load power, expected energy supply duration data, and power characteristic category data; S32, querying a preset power supply demand evaluation database according to the power characteristic category data to obtain a power characteristic weight coefficient; S33: Processing is performed according to the load power, expected energy supply duration data and power characteristic weight coefficient to obtain load energy demand forecast data.

[0028] It should be noted that before the high-power UPS battery needs to be discharged, energy demand evaluation data including load power, expected energy supply duration data and power characteristic category data are first obtained, and the power characteristic category data refers to constant power load category data, pulse power load category data or variable power load category data. Then, according to the power characteristic category data, a preset power supply demand evaluation database is queried to obtain a power characteristic weight coefficient, wherein the preset power supply demand evaluation database is obtained by querying a preset high-power UPS battery life optimization management platform. Finally, according to the load power, expected energy supply duration data and power characteristic weight coefficient, the load energy demand forecast data is obtained. The energy demand forecast data calculation formula is: ; in, For energy demand forecast data, , , They are load power, expected energy supply duration data and power characteristic weight coefficient, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0029] Please refer to Figure 4 , Figure 4 The flowchart of obtaining effective capacity data of a high-power UPS battery life optimization method in some embodiments of the present application. According to the embodiment of the present invention, the real-time discharge capacity parameter data of the high-power UPS battery sub-battery is obtained according to the discharge instruction information, and the real-time discharge capacity parameter data is processed to obtain the effective capacity data, including: S41, acquiring real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, including real-time capacity data and corresponding conversion rate and minimum inventory requirement data; S42. Process the real-time capacity data and the corresponding conversion rate and minimum inventory demand data to obtain effective capacity data, including the primary effective capacity data of the primary performance sub-battery and the secondary effective capacity data of the secondary performance sub-battery.

[0030] It should be noted that before the high-power UPS battery executes the discharge instruction, the real-time discharge capability parameter data of the sub-battery is first obtained, including the real-time capacity data and the corresponding conversion rate and minimum inventory demand data, wherein the conversion rate refers to the ratio of the effective electric energy output by the battery during the discharge process to the real-time capacity data, and the minimum inventory demand data refers to the minimum amount of electricity set to ensure the battery performance. The real-time capacity data and the corresponding conversion rate and minimum inventory demand data are processed to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-battery and the second-level effective capacity data of the second-level performance sub-battery; The calculation formula for the primary effective capacity data is: ; in, It is the first-level effective capacity data. , , They are the real-time capacity data, conversion rate and minimum inventory requirement data of the i-th sub-battery of the first-level performance sub-battery; The calculation formula for the secondary effective capacity data is: ; in, It is the secondary effective capacity data. , , They are the real-time capacity data, conversion rate and minimum inventory requirement data of the jth sub-battery of the secondary performance sub-battery.

[0031] According to an embodiment of the present invention, the process of processing the effective capacity data in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery includes: Comparing the effective capacity data with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery, including balanced discharge, primary performance sub-battery discharge, or primary and secondary performance sub-battery discharge; If the effective capacity data is less than the energy demand forecast data, the power is discharged evenly, and an early warning response is output to start the preset backup power supply plan; If the effective capacity data is greater than or equal to the energy demand forecast data, then comparing the primary effective capacity data with the energy demand forecast data; If the first-level effective capacity data is greater than or equal to the energy demand forecast data, the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge; If the first-level effective capacity data is less than the energy demand forecast data, the energy demand forecast data is allocated to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio.

[0032] It should be noted that in order to determine the discharge distribution strategy of the sub-battery, the effective capacity data is first compared with the energy demand forecast data. If the effective capacity data is less than the energy demand forecast data, balanced discharge is performed first, that is, the energy demand forecast data is evenly distributed to each sub-battery, and an early warning response is output, and a preset backup power supply plan is started at the same time; if the effective capacity data is greater than or equal to the energy demand forecast data, the first-level effective capacity data is further compared with the energy demand forecast data. If the first-level effective capacity data is greater than or equal to the energy demand forecast data, it means that the first-level performance sub-battery has sufficient power, and the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge, that is, evenly distributed to the first-level performance sub-battery for discharge, and the second-level performance sub-battery does not participate in the discharge, reducing the number of cycles and preventing further performance degradation; if the first-level effective capacity data is less than the energy demand forecast data, it means that only the first-level performance sub-battery is powered and the power is insufficient. The energy demand forecast data is further distributed to the first-level and second-level performance sub-batteries for discharge according to the preset energy supply distribution ratio. In this embodiment, the first-level performance sub-battery allocation ratio is the ratio of the first-level effective capacity data to the energy demand forecast data, and the rest is discharged by the second-level performance sub-battery.

[0033] According to an embodiment of the present invention, it also includes: Obtain the rated capacity and battery category characteristic data of high-power UPS batteries; According to the rated capacity and battery type characteristic data, a preset charging parameter database is searched to obtain a charging current corresponding to a constant current charging stage; Controlling the high-power UPS battery to perform constant current charging according to the charging current, and obtaining the real-time charging monitoring voltage of the sub-battery; Comparing the real-time charging monitoring voltage with the preset equalizing charging voltage; If it is less than the preset equalization charging voltage, it is determined that the charging is in the constant current charging stage; If it is greater than or equal to the preset equalizing charging voltage, the preset charging parameter database is queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage, and the high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage.

[0034] It should be noted that in order to optimize the life of high-power UPS batteries, while optimizing the discharge distribution, it is necessary to analyze and optimize the charging method, further improve the service life of high-power UPS batteries, and optimize the charging process into two stages: constant current charging and constant voltage charging. In the constant current charging stage, first obtain the rated capacity and battery category characteristic data of the high-power UPS battery, the battery category characteristic data refers to the lead-acid battery category characteristic data or the lithium battery category characteristic data, and then query the preset charging parameter database according to the rated capacity and the battery category characteristic data to obtain the charging current corresponding to the constant current charging stage, wherein the preset charging parameter database is obtained by querying the preset high-power UPS battery life optimization management platform. For example, during the constant current charging stage of a 100Ah lead-acid battery, the charging device will output a stable 0.1C current, that is, 0.1x100=10A, to gradually increase the battery voltage. At the same time, the real-time charging monitoring voltage of the single battery is obtained. When it is greater than or equal to the preset equalizing charging voltage, it means that the constant current charging stage is over and the constant voltage charging stage begins. The preset equalizing charging voltage is obtained by querying the preset high-power UPS battery life optimization management platform. The preset charging parameter database is further queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage. The high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage to reduce the impact of improper charging on battery life.

[0035] According to an embodiment of the present invention, it also includes: Get the real-time ambient temperature of the location where the high-power UPS battery is located; Comparing the real-time ambient temperature with the preset standard ambient temperature to obtain an ambient temperature difference value, and querying a preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage; The temperature compensation voltage and the charging requirement voltage are summed to obtain a charging correction voltage; The high-power UPS battery is controlled to perform constant voltage charging according to the charging correction voltage.

[0036] It should be noted that the ambient temperature has a significant impact on the charging performance of the battery, especially for lead-acid batteries. Therefore, in order to more accurately determine the charging demand voltage in the constant voltage charging stage, first obtain the real-time ambient temperature of the location of the high-power UPS battery, and compare it with the preset standard ambient temperature to obtain the ambient temperature difference. The ambient temperature difference refers to the difference between the real-time ambient temperature and the preset standard ambient temperature. For example, if the real-time ambient temperature is 26°C and the preset standard ambient temperature is 25°C, then 26-25=1°C is the ambient temperature difference. If the real-time ambient temperature is 24°C, then 24-25=-1°C is the ambient temperature difference. According to the ambient temperature difference, query the preset charging voltage compensation adjustment database to obtain the temperature compensation voltage, wherein the temperature compensation voltage can be positive or negative. The preset charging voltage compensation adjustment database is obtained by querying the preset high-power UPS battery life optimization management platform, and then the temperature compensation voltage is summed with the charging demand voltage to obtain the charging correction voltage. The charging correction voltage calculation formula is: ; in, Correction voltage for charging, , They are temperature compensation voltage and charging requirement voltage respectively.

[0037] It is worth mentioning that according to an embodiment of the present invention, it also includes: Get the real-time battery temperature of the sub-battery during the high-power UPS battery discharge phase; Performing a threshold comparison of the real-time battery temperature with a preset temperature threshold; Extracting a first temperature threshold and a second temperature threshold according to the preset temperature threshold, wherein the first temperature threshold is less than the second temperature threshold; If it is less than or equal to the first temperature threshold, it is determined that the discharge battery temperature is normal; If it is greater than the first temperature threshold and less than or equal to the second temperature threshold, it is determined that the discharge battery temperature is abnormal, and a cooling response is activated; If it is greater than the second temperature threshold, it is determined that the discharge battery temperature is abnormal, and the load adjustment response is activated.

[0038] It should be noted that when a high-power UPS battery executes a discharge instruction, the battery temperature will rise accordingly, and the battery life will also be greatly affected. Therefore, it is necessary to obtain the real-time battery temperature of the sub-battery during the discharge phase and compare it with the preset temperature threshold. The temperature threshold includes a first temperature threshold and a second temperature threshold. In this embodiment, the first temperature threshold is set to 40°C and the second temperature threshold is set to 45°C. If the real-time temperature obtained is 35°C, the discharge battery temperature is determined to be normal; if the real-time temperature obtained is 41°C, the discharge battery temperature is determined to be abnormal, and the refrigeration and cooling response is activated to reduce the battery temperature; if the real-time temperature obtained is 46°C, it means that the cooling effect is insufficient, then the discharge battery temperature is determined to be abnormal, and the load adjustment response is activated, such as allocating the load to the backup battery for power supply.

[0039] It is worth mentioning that, according to an embodiment of the present invention, the real-time capacity data and the corresponding conversion rate of the high-power UPS battery sub-battery are obtained according to the discharge instruction information, and the real-time capacity data and the corresponding conversion rate are processed in combination with the energy demand forecast data to obtain the discharge allocation strategy of the high-power UPS battery, and then it also includes: Obtain the discharge effectiveness parameter data of high-power UPS batteries, including real-time remaining capacity deviation value, sub-battery voltage range and sub-battery voltage standard deviation; The real-time remaining capacity deviation value, the voltage extreme difference between sub-batteries and the sub-battery voltage standard deviation are input into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index.

[0040] It should be noted that after the high-power UPS battery performs the discharge action, it is necessary to evaluate the discharge effectiveness of each sub-battery. First, the discharge effectiveness parameter data including the real-time remaining capacity deviation value, the voltage extreme difference between the sub-batteries and the sub-battery voltage standard deviation are obtained, wherein the real-time remaining capacity deviation value refers to the absolute value of the difference between the real-time remaining capacity of the sub-battery and the preset expected remaining capacity, and the preset expected remaining capacity is obtained by querying the preset high-power UPS battery life optimization management platform. The voltage extreme difference between the sub-batteries refers to the difference between the highest voltage and the lowest voltage of the sub-battery. The real-time remaining capacity deviation value, the voltage extreme difference between the sub-batteries and the sub-battery voltage standard deviation are input into the preset discharge effectiveness evaluation model for processing to obtain the discharge effectiveness evaluation index; The calculation formula of the discharge effectiveness evaluation index in the discharge effectiveness evaluation model is: ; in, It is the discharge effectiveness evaluation index. , , They are the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the sub-battery voltage standard deviation. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0041] It is worth mentioning that, according to an embodiment of the present invention, the real-time remaining capacity deviation value, the voltage extreme difference between batteries and the voltage standard deviation between batteries are input into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index, and then the method further includes: Comparing the discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate; Performing a threshold comparison between the discharge effectiveness deviation rate and a preset discharge effectiveness deviation rate threshold; If it is less than or equal to the preset discharge effectiveness deviation rate threshold, the discharge allocation strategy is determined to be normal; If it is greater than the preset discharge effectiveness deviation rate threshold, the discharge allocation strategy is determined to be abnormal.

[0042] It should be noted that the obtained discharge effectiveness evaluation index is compared with the preset discharge reference index to obtain the discharge effectiveness deviation rate, which is the ratio of the absolute value of the difference between the discharge effectiveness evaluation index and the preset discharge reference index to the discharge reference index. The obtained discharge effectiveness deviation rate is compared with the preset discharge effectiveness deviation rate threshold. In this embodiment, the discharge effectiveness deviation rate threshold is set to (0, 0.25], (0.25, 1], which respectively correspond to the normal discharge allocation strategy and the abnormal discharge allocation strategy. For example, if the obtained discharge effectiveness deviation rate is 0.2, the discharge allocation strategy is determined to be normal. If the obtained discharge effectiveness deviation rate is 0.3, the discharge allocation strategy is determined to be abnormal.

[0043] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain the real-time voltage corresponding to the high-power UPS battery sub-battery, and extract the real-time maximum voltage value based on the real-time voltage; Comparing the real-time voltage with the real-time maximum voltage value to obtain a corresponding voltage difference; Performing a threshold comparison between the voltage difference and a preset voltage balancing threshold; If it is less than or equal to the preset voltage balancing threshold, the battery voltage is determined to be balanced; If it is greater than the preset voltage balancing threshold, the battery voltage is determined to be unbalanced, and the corresponding sub-battery is charged.

[0044] It should be noted that during long-term use, the batteries in the battery pack may be charged unevenly. For UPS battery packs used in series, it is very important to perform balanced charging regularly. First, the real-time voltage corresponding to the sub-battery of the high-power UPS battery is obtained, and the real-time maximum voltage value is extracted according to the real-time voltage. Then, the real-time voltage is compared with the real-time maximum voltage value to obtain the corresponding voltage difference. The voltage difference refers to the difference between the real-time maximum voltage value and the real-time voltage. Finally, the voltage difference is compared with the preset voltage balance threshold. In this embodiment, the voltage balance threshold is set to 0.2V. For example, if the obtained voltage difference is 0.1V, which is less than the preset voltage balance threshold, the battery voltage is determined to be balanced. If the obtained voltage difference is 0.3V, which is greater than the preset voltage balance threshold, the battery voltage is determined to be unbalanced, and the corresponding sub-batteries are charged to keep the voltages of each sub-battery balanced.

[0045] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain the average voltage fluctuation, frequency fluctuation, harmonic distortion and power outage times of the mains power within a preset time period; Processing is performed according to the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value and power outage number value to obtain a power supply stability evaluation index of the mains power within a preset time period; Compare the power supply stability evaluation index with a preset power supply stability evaluation threshold to obtain the number of times in a preset time period that the power supply stability evaluation index is continuously greater than the preset power supply stability evaluation threshold; Compare the number of times with a preset monitoring number threshold; If it is less than the preset monitoring times threshold, the high-power UPS battery is controlled not to enter battery sleep mode; If it is greater than or equal to the preset monitoring times threshold, the high-power UPS battery is controlled to enter battery hibernation.

[0046] It should be noted that, when the mains power is stable for a long time, the battery hibernation program is started to put the battery in a low power consumption state, reduce self-discharge and unnecessary charge and discharge cycles, and extend the service life of the high-power UPS battery. First, the stability of the mains power is evaluated by obtaining the voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number values ​​of the mains power within a preset time period, wherein the voltage fluctuation mean refers to the mean of the difference between multiple real-time voltages collected and a preset reference voltage, the frequency fluctuation mean refers to the mean of the difference between multiple real-time frequencies collected and a preset reference frequency, and the harmonic distortion mean can be calculated by a technician in this field through the effective values ​​of multiple harmonic voltages collected and the effective value of a preset fundamental voltage. In this embodiment, the preset time period is set to 1 day, and the power supply stability evaluation index of the mains power within the preset time period is obtained according to the obtained voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number values; The power supply stability evaluation index calculation formula is: ; in, It is the power supply stability evaluation index. , , , They are the voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number value, , The preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform); The obtained power supply stability evaluation index is compared with the preset power supply stability evaluation threshold. In this embodiment, the power supply stability evaluation threshold is set to (0, 0.8], (0.8, 1]. For example, if the obtained power supply stability evaluation index is 0.7, which is less than the preset power supply stability evaluation threshold, the number of times is not counted. If the obtained power supply stability evaluation index is 0.85, which is greater than the preset power supply stability evaluation threshold, it is recorded as 1 time. Further obtain the number of times in the preset time period that is continuously greater than the preset power supply stability evaluation threshold, and then compare the number of times with the preset monitoring number threshold. In this embodiment, the preset monitoring number threshold is set to 7 times. If the obtained number value is 5 times, which is less than 7 times, the high-power UPS battery does not enter sleep mode. If the obtained number value is 8 times, which is greater than 7 times, it means that the AC power stability is good, then the high-power UPS battery is controlled to enter battery sleep mode, thereby reducing self-discharge and unnecessary charge and discharge cycles, thereby extending the service life.

[0047] The present invention also discloses a life optimization system for a high-power UPS battery, comprising a memory and a processor, wherein the memory comprises a life optimization method program for a high-power UPS battery, and when the life optimization method program for a high-power UPS battery is executed by the processor, the following steps are implemented: Obtaining the operating status parameter data of the high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining the sub-battery performance evaluation index; Processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; Obtain the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, process the energy demand evaluation data, and obtain the energy demand forecast data of the load; Acquire real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain effective capacity data; The effective capacity data is processed in combination with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery.

[0048] It should be noted that in order to optimize the life of a high-power UPS battery, firstly, the operating status parameter data of the sub-battery is obtained, and the operating status parameter data is processed to obtain a sub-battery performance evaluation index for evaluating the performance level of the sub-battery, and then the sub-battery performance deviation rate is obtained by processing the sub-battery performance deviation rate with a preset battery performance level evaluation threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including primary performance or secondary performance, and then the energy demand evaluation data of the power system load is obtained and processed to obtain the energy demand forecast data of the load, and the power demand of the power system load is analyzed, and finally the real-time capacity data of the sub-battery and the corresponding conversion rate and minimum stock demand data are obtained, and the effective capacity data is obtained after processing, and the effective capacity data is processed in combination with the energy demand forecast data to obtain the discharge allocation strategy of the high-power UPS battery, and by reasonably allocating the discharge strategy, over-discharge or excessive use of some batteries is avoided, thereby optimizing the life of the high-power UPS battery.

[0049] According to an embodiment of the present invention, the step of obtaining the operating status parameter data of the sub-battery of the high-power UPS battery and processing the operating status parameter data to obtain the sub-battery performance evaluation index includes: Obtaining operating status parameter data of high-power UPS battery sub-batteries, the operating status parameter data including remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data and self-discharge rate; Comparing the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate, and comparing the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate; The sub-battery performance evaluation index is obtained by processing the capacity decay rate, internal resistance increase rate, charging characteristic data, discharge characteristic data and self-discharge rate.

[0050] It should be noted that in order to evaluate the performance of the sub-battery and analyze the power supply potential of the sub-battery, the operating status parameter data including the remaining available capacity data, the real-time internal resistance value, the charging characteristic data, the discharging characteristic data and the self-discharge rate are first obtained, wherein the remaining available capacity data refers to the amount of electricity that the sub-battery can discharge under certain discharge conditions, the charging characteristic data refers to the time data of the sub-battery reaching the equalizing charging voltage during the charging process, the discharging characteristic data refers to the rate of decrease of the battery voltage with the discharge time during the discharge process, and the self-discharge rate refers to the amount of electricity lost by the sub-battery itself due to the internal chemical reaction within the preset time, and then the remaining available capacity data is compared with the preset rated The capacity data is compared to obtain the capacity attenuation rate, wherein the capacity attenuation rate refers to the ratio of the difference between the preset rated capacity data and the remaining available capacity data to the preset rated capacity data; the real-time internal resistance value is compared with the preset initial internal resistance value to obtain the internal resistance increase rate, wherein the internal resistance increase rate refers to the ratio of the difference between the real-time internal resistance value and the preset initial internal resistance value to the preset initial internal resistance value; the preset rated capacity data and the preset initial internal resistance value are obtained by querying the preset high-power UPS battery life optimization management platform; finally, the sub-battery performance evaluation index is obtained by processing according to the capacity attenuation rate, the internal resistance increase rate, the charging characteristic data, the discharging characteristic data and the self-discharge rate; The battery performance evaluation index calculation formula is: ; in, It is the battery performance evaluation index. , , , , They are capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharge characteristic data and self-discharge rate. , It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0051] According to an embodiment of the present invention, the sub-battery performance evaluation index is processed with a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate, the sub-battery performance deviation rate is compared with a preset battery performance level assessment threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including: Processing the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate; Comparing the sub-battery performance deviation rate with a preset battery performance level assessment threshold, and determining a battery performance level corresponding to the sub-battery, including primary performance or secondary performance; If the sub-battery performance deviation rate is less than or equal to the preset battery performance level assessment threshold, the sub-battery performance level is determined to be level one performance; If the sub-battery performance deviation rate is greater than a preset battery performance level assessment threshold, the sub-battery performance level is determined to be level 2 performance.

[0052] It should be noted that in order to classify and manage sub-batteries according to their performance potential, the obtained sub-battery performance evaluation index is first processed with the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, wherein the sub-battery performance deviation rate refers to the ratio of the absolute value of the difference between the battery performance evaluation index and the preset battery performance benchmark index to the preset battery performance benchmark index, and then the obtained sub-battery performance deviation rate is compared with the preset battery performance level assessment threshold to determine the battery performance level corresponding to the sub-battery, including primary performance or secondary performance, wherein the primary performance is better than the secondary performance. In this embodiment, the battery performance level assessment threshold is set to (0, 0.2], (0.2, 1], corresponding to the primary performance and the secondary performance, respectively. If the obtained sub-battery performance deviation rate is 0.1, which is less than the preset battery performance level assessment threshold, the sub-battery performance level is determined to be primary performance. If the obtained sub-battery performance deviation rate is 0.3, which is greater than the preset battery performance level assessment threshold, the sub-battery performance level is determined to be secondary performance.

[0053] According to an embodiment of the present invention, the step of obtaining discharge instruction information of a high-power UPS battery and energy demand evaluation data of a power system load, and processing the energy demand evaluation data to obtain energy demand forecast data of the load includes: Obtaining high-power UPS battery discharge instruction information and power system load energy demand evaluation data, including load power, expected energy supply duration data and power characteristic category data; According to the power characteristic category data, a preset power supply demand evaluation database is queried to obtain a power characteristic weight coefficient; The load power, expected energy supply duration data and power characteristic weight coefficient are processed to obtain load energy demand forecast data.

[0054] It should be noted that before the high-power UPS battery needs to be discharged, energy demand evaluation data including load power, expected energy supply duration data and power characteristic category data are first obtained, and the power characteristic category data refers to constant power load category data, pulse power load category data or variable power load category data. Then, according to the power characteristic category data, a preset power supply demand evaluation database is queried to obtain a power characteristic weight coefficient, wherein the preset power supply demand evaluation database is obtained by querying a preset high-power UPS battery life optimization management platform. Finally, according to the load power, expected energy supply duration data and power characteristic weight coefficient, the load energy demand forecast data is obtained. The energy demand forecast data calculation formula is: ; in, For energy demand forecast data, , , They are load power, expected energy supply duration data and power characteristic weight coefficient, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0055] According to an embodiment of the present invention, the real-time discharge capacity parameter data of the high-power UPS battery sub-battery is obtained according to the discharge instruction information, and the real-time discharge capacity parameter data is processed to obtain the effective capacity data, including: Acquire real-time discharge capability parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, including real-time capacity data and corresponding conversion rate and minimum inventory requirement data; The real-time capacity data and the corresponding conversion rate and minimum inventory requirement data are processed to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-battery and the second-level effective capacity data of the second-level performance sub-battery.

[0056] It should be noted that before the high-power UPS battery executes the discharge instruction, the real-time discharge capability parameter data of the sub-battery is first obtained, including the real-time capacity data and the corresponding conversion rate and minimum inventory demand data, wherein the conversion rate refers to the ratio of the effective electric energy output by the battery during the discharge process to the real-time capacity data, and the minimum inventory demand data refers to the minimum amount of electricity set to ensure the battery performance. The real-time capacity data and the corresponding conversion rate and minimum inventory demand data are processed to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-battery and the second-level effective capacity data of the second-level performance sub-battery; The calculation formula for the primary effective capacity data is: ; in, It is the first-level effective capacity data. , , They are the real-time capacity data, conversion rate and minimum inventory requirement data of the i-th sub-battery of the first-level performance sub-battery; The calculation formula for the secondary effective capacity data is: ; in, It is the secondary effective capacity data. , , They are the real-time capacity data, conversion rate and minimum inventory requirement data of the jth sub-battery of the secondary performance sub-battery.

[0057] According to an embodiment of the present invention, the process of processing the effective capacity data in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery includes: Comparing the effective capacity data with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery, including balanced discharge, primary performance sub-battery discharge, or primary and secondary performance sub-battery discharge; If the effective capacity data is less than the energy demand forecast data, the power is discharged evenly, and an early warning response is output to start the preset backup power supply plan; If the effective capacity data is greater than or equal to the energy demand forecast data, then comparing the primary effective capacity data with the energy demand forecast data; If the first-level effective capacity data is greater than or equal to the energy demand forecast data, the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge; If the first-level effective capacity data is less than the energy demand forecast data, the energy demand forecast data is allocated to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio.

[0058] It should be noted that in order to determine the discharge distribution strategy of the sub-battery, the effective capacity data is first compared with the energy demand forecast data. If the effective capacity data is less than the energy demand forecast data, balanced discharge is performed first, that is, the energy demand forecast data is evenly distributed to each sub-battery, and an early warning response is output, and a preset backup power supply plan is started at the same time; if the effective capacity data is greater than or equal to the energy demand forecast data, the first-level effective capacity data is further compared with the energy demand forecast data. If the first-level effective capacity data is greater than or equal to the energy demand forecast data, it means that the first-level performance sub-battery has sufficient power, and the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge, that is, evenly distributed to the first-level performance sub-battery for discharge, and the second-level performance sub-battery does not participate in the discharge, reducing the number of cycles and preventing further performance degradation; if the first-level effective capacity data is less than the energy demand forecast data, it means that only the first-level performance sub-battery is powered and the power is insufficient. The energy demand forecast data is further distributed to the first-level and second-level performance sub-batteries for discharge according to the preset energy supply distribution ratio. In this embodiment, the first-level performance sub-battery allocation ratio is the ratio of the first-level effective capacity data to the energy demand forecast data, and the rest is discharged by the second-level performance sub-battery.

[0059] According to an embodiment of the present invention, it also includes: Obtain the rated capacity and battery category characteristic data of high-power UPS batteries; According to the rated capacity and battery type characteristic data, a preset charging parameter database is searched to obtain a charging current corresponding to a constant current charging stage; Controlling the high-power UPS battery to perform constant current charging according to the charging current, and obtaining the real-time charging monitoring voltage of the sub-battery; Comparing the real-time charging monitoring voltage with the preset equalizing charging voltage; If it is less than the preset equalization charging voltage, it is determined that the charging is in the constant current charging stage; If it is greater than or equal to the preset equalizing charging voltage, the preset charging parameter database is queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage, and the high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage.

[0060] It should be noted that in order to optimize the life of high-power UPS batteries, while optimizing the discharge distribution, it is necessary to analyze and optimize the charging method, further improve the service life of high-power UPS batteries, and optimize the charging process into two stages: constant current charging and constant voltage charging. In the constant current charging stage, first obtain the rated capacity and battery category characteristic data of the high-power UPS battery, the battery category characteristic data refers to the lead-acid battery category characteristic data or the lithium battery category characteristic data, and then query the preset charging parameter database according to the rated capacity and the battery category characteristic data to obtain the charging current corresponding to the constant current charging stage, wherein the preset charging parameter database is obtained by querying the preset high-power UPS battery life optimization management platform. For example, during the constant current charging stage of a 100Ah lead-acid battery, the charging device will output a stable 0.1C current, that is, 0.1x100=10A, to gradually increase the battery voltage. At the same time, the real-time charging monitoring voltage of the single battery is obtained. When it is greater than or equal to the preset equalizing charging voltage, it means that the constant current charging stage is over and the constant voltage charging stage begins. The preset equalizing charging voltage is obtained by querying the preset high-power UPS battery life optimization management platform. The preset charging parameter database is further queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage. The high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage to reduce the impact of improper charging on battery life.

[0061] According to an embodiment of the present invention, it also includes: Get the real-time ambient temperature of the location where the high-power UPS battery is located; Comparing the real-time ambient temperature with the preset standard ambient temperature to obtain an ambient temperature difference value, and querying a preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage; The temperature compensation voltage and the charging requirement voltage are summed to obtain a charging correction voltage; The high-power UPS battery is controlled to perform constant voltage charging according to the charging correction voltage.

[0062] It should be noted that the ambient temperature has a significant impact on the charging performance of the battery, especially for lead-acid batteries. Therefore, in order to more accurately determine the charging demand voltage in the constant voltage charging stage, first obtain the real-time ambient temperature of the location of the high-power UPS battery, and compare it with the preset standard ambient temperature to obtain the ambient temperature difference. The ambient temperature difference refers to the difference between the real-time ambient temperature and the preset standard ambient temperature. For example, if the real-time ambient temperature is 26°C and the preset standard ambient temperature is 25°C, then 26-25=1°C is the ambient temperature difference. If the real-time ambient temperature is 24°C, then 24-25=-1°C is the ambient temperature difference. According to the ambient temperature difference, query the preset charging voltage compensation adjustment database to obtain the temperature compensation voltage, wherein the temperature compensation voltage can be positive or negative. The preset charging voltage compensation adjustment database is obtained by querying the preset high-power UPS battery life optimization management platform, and then the temperature compensation voltage is summed with the charging demand voltage to obtain the charging correction voltage. The charging correction voltage calculation formula is: ; in, Correction voltage for charging, , They are temperature compensation voltage and charging requirement voltage respectively.

[0063] It is worth mentioning that according to an embodiment of the present invention, it also includes: Get the real-time battery temperature of the sub-battery during the high-power UPS battery discharge phase; Performing a threshold comparison of the real-time battery temperature with a preset temperature threshold; Extracting a first temperature threshold and a second temperature threshold according to the preset temperature threshold, wherein the first temperature threshold is less than the second temperature threshold; If it is less than or equal to the first temperature threshold, it is determined that the discharge battery temperature is normal; If it is greater than the first temperature threshold and less than or equal to the second temperature threshold, it is determined that the discharge battery temperature is abnormal, and a cooling response is activated; If it is greater than the second temperature threshold, it is determined that the discharge battery temperature is abnormal, and the load adjustment response is activated.

[0064] It should be noted that when a high-power UPS battery executes a discharge instruction, the battery temperature will rise accordingly, and the battery life will also be greatly affected. Therefore, it is necessary to obtain the real-time battery temperature of the sub-battery during the discharge phase and compare it with the preset temperature threshold. The temperature threshold includes a first temperature threshold and a second temperature threshold. In this embodiment, the first temperature threshold is set to 40°C and the second temperature threshold is set to 45°C. If the real-time temperature obtained is 35°C, the discharge battery temperature is determined to be normal; if the real-time temperature obtained is 41°C, the discharge battery temperature is determined to be abnormal, and the refrigeration and cooling response is activated to reduce the battery temperature; if the real-time temperature obtained is 46°C, it means that the cooling effect is insufficient, then the discharge battery temperature is determined to be abnormal, and the load adjustment response is activated, such as allocating the load to the backup battery for power supply.

[0065] It is worth mentioning that, according to an embodiment of the present invention, the real-time capacity data and the corresponding conversion rate of the high-power UPS battery sub-battery are obtained according to the discharge instruction information, and the real-time capacity data and the corresponding conversion rate are processed in combination with the energy demand forecast data to obtain the discharge allocation strategy of the high-power UPS battery, and then it also includes: Obtain the discharge effectiveness parameter data of high-power UPS batteries, including real-time remaining capacity deviation value, sub-battery voltage range and sub-battery voltage standard deviation; The real-time remaining capacity deviation value, the voltage extreme difference between sub-batteries and the sub-battery voltage standard deviation are input into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index.

[0066] It should be noted that after the high-power UPS battery performs the discharge action, it is necessary to evaluate the discharge effectiveness of each sub-battery. First, the discharge effectiveness parameter data including the real-time remaining capacity deviation value, the voltage extreme difference between the sub-batteries and the sub-battery voltage standard deviation are obtained, wherein the real-time remaining capacity deviation value refers to the absolute value of the difference between the real-time remaining capacity of the sub-battery and the preset expected remaining capacity, and the preset expected remaining capacity is obtained by querying the preset high-power UPS battery life optimization management platform. The voltage extreme difference between the sub-batteries refers to the difference between the highest voltage and the lowest voltage of the sub-battery. The real-time remaining capacity deviation value, the voltage extreme difference between the sub-batteries and the sub-battery voltage standard deviation are input into the preset discharge effectiveness evaluation model for processing to obtain the discharge effectiveness evaluation index; The calculation formula of the discharge effectiveness evaluation index in the discharge effectiveness evaluation model is: ; in, It is the discharge effectiveness evaluation index. , , They are the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the sub-battery voltage standard deviation. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0067] It is worth mentioning that, according to an embodiment of the present invention, the real-time remaining capacity deviation value, the voltage extreme difference between batteries and the voltage standard deviation between batteries are input into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index, and then the method further includes: Comparing the discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate; Performing a threshold comparison between the discharge effectiveness deviation rate and a preset discharge effectiveness deviation rate threshold; If it is less than or equal to the preset discharge effectiveness deviation rate threshold, the discharge allocation strategy is determined to be normal; If it is greater than the preset discharge effectiveness deviation rate threshold, the discharge allocation strategy is determined to be abnormal.

[0068] It should be noted that the obtained discharge effectiveness evaluation index is compared with the preset discharge reference index to obtain the discharge effectiveness deviation rate, which is the ratio of the absolute value of the difference between the discharge effectiveness evaluation index and the preset discharge reference index to the discharge reference index. The obtained discharge effectiveness deviation rate is compared with the preset discharge effectiveness deviation rate threshold. In this embodiment, the discharge effectiveness deviation rate threshold is set to (0, 0.25], (0.25, 1], which respectively correspond to the normal discharge allocation strategy and the abnormal discharge allocation strategy. For example, if the obtained discharge effectiveness deviation rate is 0.2, the discharge allocation strategy is determined to be normal. If the obtained discharge effectiveness deviation rate is 0.3, the discharge allocation strategy is determined to be abnormal.

[0069] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain the real-time voltage corresponding to the high-power UPS battery sub-battery, and extract the real-time maximum voltage value based on the real-time voltage; Comparing the real-time voltage with the real-time maximum voltage value to obtain a corresponding voltage difference; Performing a threshold comparison between the voltage difference and a preset voltage balancing threshold; If it is less than or equal to the preset voltage balancing threshold, the battery voltage is determined to be balanced; If it is greater than the preset voltage balancing threshold, the battery voltage is determined to be unbalanced, and the corresponding sub-battery is charged.

[0070] It should be noted that during long-term use, the batteries in the battery pack may be charged unevenly. For UPS battery packs used in series, it is very important to perform balanced charging regularly. First, the real-time voltage corresponding to the sub-battery of the high-power UPS battery is obtained, and the real-time maximum voltage value is extracted according to the real-time voltage. Then, the real-time voltage is compared with the real-time maximum voltage value to obtain the corresponding voltage difference. The voltage difference refers to the difference between the real-time maximum voltage value and the real-time voltage. Finally, the voltage difference is compared with the preset voltage balance threshold. In this embodiment, the voltage balance threshold is set to 0.2V. For example, if the obtained voltage difference is 0.1V, which is less than the preset voltage balance threshold, the battery voltage is determined to be balanced. If the obtained voltage difference is 0.3V, which is greater than the preset voltage balance threshold, the battery voltage is determined to be unbalanced, and the corresponding sub-batteries are charged to keep the voltages of each sub-battery balanced.

[0071] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain the average voltage fluctuation, frequency fluctuation, harmonic distortion and power outage times of the mains power within a preset time period; Processing is performed according to the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value and power outage number value to obtain a power supply stability evaluation index of the mains power within a preset time period; Compare the power supply stability evaluation index with a preset power supply stability evaluation threshold to obtain the number of times in a preset time period that the power supply stability evaluation index is continuously greater than the preset power supply stability evaluation threshold; Compare the number of times with a preset monitoring number threshold; If it is less than the preset monitoring times threshold, the high-power UPS battery is controlled not to enter battery sleep mode; If it is greater than or equal to the preset monitoring times threshold, the high-power UPS battery is controlled to enter battery hibernation.

[0072] It should be noted that, when the mains power is stable for a long time, the battery hibernation program is started to put the battery in a low power consumption state, reduce self-discharge and unnecessary charge and discharge cycles, and extend the service life of the high-power UPS battery. First, the stability of the mains power is evaluated by obtaining the voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number values ​​of the mains power within a preset time period, wherein the voltage fluctuation mean refers to the mean of the difference between multiple real-time voltages collected and a preset reference voltage, the frequency fluctuation mean refers to the mean of the difference between multiple real-time frequencies collected and a preset reference frequency, and the harmonic distortion mean can be calculated by a technician in this field through the effective values ​​of multiple harmonic voltages collected and the effective value of a preset fundamental voltage. In this embodiment, the preset time period is set to 1 day, and the power supply stability evaluation index of the mains power within the preset time period is obtained according to the obtained voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number values; The power supply stability evaluation index calculation formula is: ; in, It is the power supply stability evaluation index. , , , They are the voltage fluctuation mean, frequency fluctuation mean, harmonic distortion mean and power outage number value, , The preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform); The obtained power supply stability evaluation index is compared with the preset power supply stability evaluation threshold. In this embodiment, the power supply stability evaluation threshold is set to (0, 0.8], (0.8, 1]. For example, if the obtained power supply stability evaluation index is 0.7, which is less than the preset power supply stability evaluation threshold, the number of times is not counted. If the obtained power supply stability evaluation index is 0.85, which is greater than the preset power supply stability evaluation threshold, it is recorded as 1 time. Further obtain the number of times in the preset time period that is continuously greater than the preset power supply stability evaluation threshold, and then compare the number of times with the preset monitoring number threshold. In this embodiment, the preset monitoring number threshold is set to 7 times. If the obtained number value is 5 times, which is less than 7 times, the high-power UPS battery does not enter sleep mode. If the obtained number value is 8 times, which is greater than 7 times, it means that the AC power stability is good, then the high-power UPS battery is controlled to enter battery sleep mode, thereby reducing self-discharge and unnecessary charge and discharge cycles, thereby extending the service life.

[0073] A third aspect of the present invention provides a readable storage medium, in which a program for a method for optimizing the life of a high-power UPS battery is stored. When the program for optimizing the life of a high-power UPS battery is executed by a processor, the steps of a method for optimizing the life of a high-power UPS battery as described in any one of the above items are implemented.

[0074] The present invention discloses a life optimization method, system and medium for a high-power UPS battery, which achieves a reasonable allocation of discharge strategies by evaluating the battery performance level of sub-batteries and calculating the energy demand forecast data of loads, thereby helping to increase the service life of sub-batteries.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0076] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0077] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0078] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0079] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for optimizing the life of a high-power UPS battery, characterized in that: The following steps are involved: Obtaining the operating status parameter data of the high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining the sub-battery performance evaluation index; Processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; Obtain the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, process the energy demand evaluation data, and obtain the energy demand forecast data of the load; Acquire real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain effective capacity data; The effective capacity data is processed in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery.

2. The life optimization method of a high-power UPS battery according to claim 1, characterized in that: The step of obtaining the operating status parameter data of the sub-battery of the high-power UPS battery and processing the operating status parameter data to obtain the sub-battery performance evaluation index includes: Obtaining operating status parameter data of high-power UPS battery sub-batteries, the operating status parameter data including remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data and self-discharge rate; Comparing the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate, and comparing the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate; The sub-battery performance evaluation index is obtained by processing the capacity decay rate, internal resistance increase rate, charging characteristic data, discharge characteristic data and self-discharge rate.

3. The life optimization method of a high-power UPS battery according to claim 2, characterized in that: The sub-battery performance evaluation index and the preset sub-battery performance benchmark index are processed to obtain the sub-battery performance deviation rate, the sub-battery performance deviation rate is compared with the preset battery performance level assessment threshold, and the battery performance level corresponding to the sub-battery is determined according to the threshold range, including: Processing the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate; Comparing the sub-battery performance deviation rate with a preset battery performance level assessment threshold, and determining a battery performance level corresponding to the sub-battery, including primary performance or secondary performance; If the sub-battery performance deviation rate is less than or equal to the preset battery performance level assessment threshold, the sub-battery performance level is determined to be level one performance; If the sub-battery performance deviation rate is greater than a preset battery performance level assessment threshold, the sub-battery performance level is determined to be level 2 performance.

4. The method for optimizing the life of a high-power UPS battery according to claim 3, characterized in that: The obtaining of the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, and processing according to the energy demand evaluation data to obtain the energy demand forecast data of the load includes: Obtaining high-power UPS battery discharge instruction information and power system load energy demand evaluation data, including load power, expected energy supply duration data and power characteristic category data; According to the power characteristic category data, a preset power supply demand evaluation database is queried to obtain a power characteristic weight coefficient; The load power, expected energy supply duration data and power characteristic weight coefficient are processed to obtain load energy demand forecast data.

5. The method for optimizing the life of a high-power UPS battery according to claim 4, characterized in that: The step of acquiring the real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and performing processing according to the real-time discharge capacity parameter data to obtain effective capacity data includes: Acquire real-time discharge capability parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, including real-time capacity data and corresponding conversion rate and minimum inventory requirement data; The real-time capacity data and the corresponding conversion rate and minimum inventory requirement data are processed to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-battery and the second-level effective capacity data of the second-level performance sub-battery.

6. The method for optimizing the life of a high-power UPS battery according to claim 5, characterized in that: The process of processing the effective capacity data in combination with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery includes: Comparing the effective capacity data with the energy demand forecast data to obtain a discharge distribution strategy for a high-power UPS battery, including balanced discharge, primary performance sub-battery discharge, or primary and secondary performance sub-battery discharge; If the effective capacity data is less than the energy demand forecast data, the power is discharged evenly, and an early warning response is output to start the preset backup power supply plan; If the effective capacity data is greater than or equal to the energy demand forecast data, then comparing the primary effective capacity data with the energy demand forecast data; If the first-level effective capacity data is greater than or equal to the energy demand forecast data, the energy demand forecast data is evenly distributed to the first-level performance sub-battery for discharge; If the first-level effective capacity data is less than the energy demand forecast data, the energy demand forecast data is allocated to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio.

7. The method for optimizing the life of a high-power UPS battery according to claim 6, characterized in that: Also includes: Obtain the rated capacity and battery category characteristic data of high-power UPS batteries; According to the rated capacity and battery type characteristic data, a preset charging parameter database is searched to obtain a charging current corresponding to a constant current charging stage; Controlling the high-power UPS battery to perform constant current charging according to the charging current, and obtaining the real-time charging monitoring voltage of the sub-battery; Comparing the real-time charging monitoring voltage with a preset equalizing charging voltage; If it is less than the preset equalization charging voltage, it is determined that the charging is in the constant current charging stage; If it is greater than or equal to the preset equalizing charging voltage, the preset charging parameter database is queried according to the battery category characteristic data to obtain the charging requirement voltage corresponding to the constant voltage charging stage, and the high-power UPS battery is controlled to perform constant voltage charging according to the charging requirement voltage.

8. The method for optimizing the life of a high-power UPS battery according to claim 7, characterized in that: Also includes: Get the real-time ambient temperature of the location where the high-power UPS battery is located; Comparing the real-time ambient temperature with the preset standard ambient temperature to obtain an ambient temperature difference value, and querying a preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage; The temperature compensation voltage and the charging requirement voltage are summed to obtain a charging correction voltage; The high-power UPS battery is controlled to perform constant voltage charging according to the charging correction voltage.

9. A high power UPS battery life optimization system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of a method for optimizing the life of a high-power UPS battery, and when the program of the method for optimizing the life of a high-power UPS battery is executed by the processor, the following steps are implemented: Obtaining the operating status parameter data of the high-power UPS battery sub-battery, processing the operating status parameter data, and obtaining the sub-battery performance evaluation index; Processing the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, performing a threshold comparison between the sub-battery performance deviation rate and a preset battery performance level assessment threshold, and determining the battery performance level corresponding to the sub-battery according to the threshold range; Obtain the discharge instruction information of the high-power UPS battery and the energy demand evaluation data of the power system load, process the energy demand evaluation data, and obtain the energy demand forecast data of the load; Acquire real-time discharge capacity parameter data of the high-power UPS battery sub-battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain effective capacity data; The effective capacity data is processed in combination with the energy demand forecast data to obtain a discharge allocation strategy for a high-power UPS battery.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a life optimization method program for a high-power UPS battery. When the life optimization method program for a high-power UPS battery is executed by a processor, the steps of a life optimization method for a high-power UPS battery as described in any one of claims 1 to 8 are implemented.

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