A method, system and medium for optimizing the lifespan of a high-power UPS battery

Abstract

The present application provides a method, a system and a medium for optimizing the lifespan of a high-power UPS battery. The method includes: obtaining and processing the operation status parameter data of the sub-batteries to obtain the sub-battery performance evaluation index, then processing it with the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, comparing it with the preset battery performance level assessment threshold to determine the battery performance level corresponding to the sub-battery, obtaining the discharge instruction information and the energy consumption demand evaluation data of the load, processing according to the energy consumption demand evaluation data to obtain the energy consumption demand prediction data of the load, obtaining and processing the real-time discharge capacity parameter data of the sub-batteries to obtain the effective capacity data, and processing in combination with the energy consumption demand prediction data to obtain the discharge distribution strategy of the high-power UPS battery; the present application realizes the reasonable distribution of the discharge strategy by evaluating the battery performance level of the sub-batteries and calculating the energy consumption demand prediction data of the load, and improves the service life of the sub-batteries.

Description

Technical Field

[0001] The present application relates to the technical field of uninterruptible power supplies, and specifically, to a method, a system, and a medium for optimizing the lifespan of high-power UPS batteries. 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 for ensuring uninterrupted power supply, the service life of the batteries of high-power UPS directly affects the operating cost and reliability of the entire system. Currently, during the use of UPS batteries, many challenges are faced. For example, factors such as environmental temperature changes and improper charge and discharge management will all lead to a decline in battery performance and a shortening of the lifespan. Therefore, there is an urgent need for a method that can effectively optimize and extend the lifespan of high-power UPS batteries to improve the reliability and service life of the batteries and reduce costs.

[0003] In view of the above problems, there is an urgent need for effective technical solutions. Summary of the Invention

[0004] The purpose of the present application is to provide a method, a system, and a medium for optimizing the lifespan of high-power UPS batteries, which can evaluate the battery performance level of sub-batteries, calculate the predicted data of the energy consumption demand of the load, realize the reasonable allocation of the discharge strategy, and contribute to extending the service life of the sub-batteries.

[0005] The present application also provides a method for optimizing the lifespan of high-power UPS batteries, including the following steps:

[0006] Obtain the operation status parameter data of the sub-batteries of the high-power UPS battery, and process the operation status parameter data to obtain the sub-battery performance evaluation index;

[0007] Process the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, compare the sub-battery performance deviation rate with a preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range to which it belongs;

[0008] Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the electrical system load, and process the energy consumption demand evaluation data to obtain the predicted data of the energy consumption demand of the load;

[0009] Obtain the real-time discharge capacity parameter data of the sub-batteries of the high-power UPS battery according to the discharge instruction information, and process the real-time discharge capacity parameter data to obtain the effective capacity data;

[0010] Process according to the effective capacity data in combination with the predicted energy consumption data to obtain a discharge allocation strategy for the high-power UPS battery.

[0011] Optionally, in the high-power UPS battery life optimization method described in this application, the operation state parameter data of the sub-batteries of the high-power UPS battery is obtained, and processed according to the operation state parameter data to obtain a sub-battery performance evaluation index, including:

[0012] Obtain the operation state parameter data of the sub-batteries of the high-power UPS battery. The operation state parameter data includes remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data, and self-discharge rate;

[0013] Compare the remaining available capacity data with the preset rated capacity data to obtain a capacity attenuation rate, and compare the real-time internal resistance value with the preset initial internal resistance value to obtain an internal resistance increase rate;

[0014] Process according to the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate to obtain a sub-battery performance evaluation index.

[0015] Optionally, in the high-power UPS battery life optimization method described in this application, the sub-battery performance deviation rate is obtained by processing the sub-battery performance evaluation index and the preset sub-battery performance reference index, and the sub-battery performance deviation rate is compared with the preset battery performance level evaluation threshold. According to the threshold range to which it belongs, determine the corresponding battery performance level of the sub-battery, including:

[0016] Process the sub-battery performance evaluation index and the preset sub-battery performance reference index to obtain a sub-battery performance deviation rate;

[0017] Compare the sub-battery performance deviation rate with the preset battery performance level evaluation threshold to determine the corresponding battery performance level of the sub-battery, including first-level performance or second-level performance;

[0018] If the sub-battery performance deviation rate is less than or equal to the preset battery performance level evaluation threshold, determine that the sub-battery performance level is first-level performance;

[0019] If the sub-battery performance deviation rate is greater than the preset battery performance level evaluation threshold, determine that the sub-battery performance level is second-level performance.

[0020] Optionally, in the high-power UPS battery life optimization method described in this application, the discharge instruction information of the high-power UPS battery and the energy consumption evaluation data of the electrical system load are obtained, and processed according to the energy consumption evaluation data to obtain the predicted energy consumption data of the load, including:

[0021] Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the power consumption system load. The energy consumption demand evaluation data includes load power, expected energy supply duration data, and power characteristic category data;

[0022] Query the preset power supply demand evaluation database according to the power characteristic category data to obtain the power characteristic weight coefficient;

[0023] Process according to the load power, expected energy supply duration data, and power characteristic weight coefficient to obtain the energy consumption demand prediction data of the load.

[0024] Optionally, in the high-power UPS battery life optimization method described in this application, 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 effective capacity data is obtained by processing the real-time discharge capacity parameter data, including:

[0025] Obtain the 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 the corresponding conversion rate and minimum stock demand data;

[0026] Process according to the real-time capacity data and the corresponding conversion rate and minimum stock demand data to obtain the 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.

[0027] Optionally, in the high-power UPS battery life optimization method described in this application, the discharge allocation strategy of the high-power UPS battery is obtained by processing the effective capacity data in combination with the energy consumption demand prediction data, including:

[0028] Compare the effective capacity data with the energy consumption demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery, including balanced discharge, discharge of the first-level performance sub-battery, or discharge of the first-level and second-level performance sub-batteries;

[0029] If the effective capacity data is less than the energy consumption demand prediction data, perform balanced discharge, output a warning response, and activate the pre-equipment backup power supply plan;

[0030] If the effective capacity data is greater than or equal to the energy consumption demand prediction data, compare the first-level effective capacity data with the energy consumption demand prediction data;

[0031] If the first-level effective capacity data is greater than or equal to the energy consumption demand prediction data, evenly distribute the energy consumption demand prediction data to the first-level performance sub-batteries for discharge;

[0032] If the first-level effective capacity data is less than the predicted energy consumption data, the predicted energy consumption data is allocated to the first-level performance and second-level performance sub-batteries for discharging according to a preset energy supply allocation ratio.

[0033] Optionally, in the method for optimizing the lifespan of a high-power UPS battery described in this application, it further includes:

[0034] Obtain the rated capacity and battery category characteristic data of the high-power UPS battery;

[0035] Query a preset charging parameter database according to the rated capacity and battery category characteristic data to obtain the charging current corresponding to the constant current charging stage;

[0036] Control the high-power UPS battery to perform constant current charging according to the charging current, and obtain the real-time charging monitoring voltage of the sub-battery;

[0037] Compare the real-time charging monitoring voltage with a preset equalizing charge voltage;

[0038] If it is less than the preset equalizing charge voltage, it is determined that the charging is in the constant current charging stage;

[0039] If it is greater than or equal to the preset equalizing charge voltage, query a preset charging parameter database according to the battery category characteristic data to obtain the charging demand voltage corresponding to the constant voltage charging stage, and control the high-power UPS battery to perform constant voltage charging according to the charging demand voltage.

[0040] Optionally, in the method for optimizing the lifespan of a high-power UPS battery described in this application, it further includes:

[0041] Obtain the real-time ambient temperature at the location where the high-power UPS battery is located;

[0042] Compare the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature difference value, and query a preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage;

[0043] Perform a summation calculation on the temperature compensation voltage and the charging demand voltage to obtain a charging correction voltage;

[0044] Control the high-power UPS battery to perform constant voltage charging according to the charging correction voltage.

[0045] In a second aspect, this application provides a system for optimizing the lifespan of a high-power UPS battery. The system includes: a memory and a processor. The memory includes a program for a method for optimizing the lifespan of a high-power UPS battery. When the program for the method for optimizing the lifespan of a high-power UPS battery is executed by the processor, the following steps are implemented:

[0046] Obtain the operation status parameter data of the sub-batteries of the high-power UPS battery, process the operation status parameter data, and obtain the sub-battery performance evaluation index;

[0047] Process the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate. Compare the sub-battery performance deviation rate with the preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range it belongs to;

[0048] Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the power consumption system load, process the energy consumption demand evaluation data, and obtain the energy consumption demand prediction data of the load;

[0049] Obtain the real-time discharge capacity parameter data of the sub-batteries of the high-power UPS battery according to the discharge instruction information, process the real-time discharge capacity parameter data, and obtain the effective capacity data;

[0050] Process the effective capacity data in combination with the energy consumption demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery.

[0051] In a third aspect, the present application also provides a computer-readable storage medium. A life optimization method program for a high-power UPS battery is stored in the computer-readable storage medium. 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 are implemented.

[0052] As can be seen from the above, a life optimization method, system and medium for a high-power UPS battery provided by the present application realizes the reasonable allocation of the discharge strategy by evaluating the battery performance level of the sub-batteries and calculating the energy consumption demand prediction data of the load, which helps to improve the service life of the sub-batteries.

[0053] Other features and advantages of the present application will be described in the subsequent description, and part of them will become obvious from the description, or be understood by implementing the embodiments of the present application. The objectives 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

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 Flow chart of a method for optimizing the lifespan of a high-power UPS battery provided by an embodiment of the present application;

[0056] Figure 2 Flow chart of obtaining a sub-battery performance evaluation index for a method for optimizing the lifespan of a high-power UPS battery provided by an embodiment of the present application;

[0057] Figure 3 Flow chart of obtaining predicted data on the energy consumption requirements of a load for a method for optimizing the lifespan of a high-power UPS battery provided by an embodiment of the present application;

[0058] Figure 4 Flow chart of obtaining effective capacity data for a method for optimizing the lifespan of a high-power UPS battery provided by an embodiment of the present application. Detailed implementation manners

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and illustrated herein generally may be arranged and designed in a variety of different configurations. Therefore, the detailed description of the embodiments of the present application provided herein is not intended to limit the scope of the claimed present application, but is merely representative of selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0060] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, 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 the present application, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0061] Please refer to Figure 1 , Figure 1 which is a flow chart of a method for optimizing the lifespan of a high-power UPS battery in some embodiments of the present application. This method for optimizing the lifespan of a high-power UPS battery is used in terminal devices such as computers, mobile phone terminals, etc. This method for optimizing the lifespan of a high-power UPS battery includes the following steps:

[0062] S11. Obtain the operating state parameter data of the sub-batteries of the high-power UPS battery, and process the operating state parameter data to obtain a sub-battery performance evaluation index;

[0063] S12. Process according to the sub-battery performance evaluation index and the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate. Compare the sub-battery performance deviation rate with the preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range it belongs to;

[0064] S13. Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the power consumption system load. Process according to the energy consumption demand evaluation data to obtain the energy consumption demand prediction data of the load;

[0065] S14. Obtain the real-time discharge capacity parameter data of the sub-battery of the high-power UPS battery according to the discharge instruction information. Process according to the real-time discharge capacity parameter data to obtain the effective capacity data;

[0066] S15. Process according to the effective capacity data in combination with the energy consumption demand prediction data to obtain the discharge distribution strategy of the high-power UPS battery.

[0067] It should be noted that in order to optimize the life of the high-power UPS battery, first obtain the operation state parameter data of the sub-battery, process according to the operation state parameter data to obtain the sub-battery performance evaluation index for evaluating the performance level of the sub-battery, then process it with the preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate. Compare the sub-battery performance deviation rate with the preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range it belongs to, including the first-level performance or the second-level performance. Then, obtain the energy consumption demand evaluation data of the power consumption system load and process it to obtain the energy consumption demand prediction data of the load, and analyze the power consumption demand of the power consumption system load. Finally, obtain the real-time capacity data of the sub-battery and the corresponding conversion rate and the minimum stock demand data, process it to obtain the effective capacity data, and process according to the effective capacity data in combination with the energy consumption demand prediction data to obtain the discharge distribution strategy of the high-power UPS battery. By reasonably distributing the discharge strategy, over-discharge or over-use of some batteries can be avoided, thereby realizing the life optimization of the high-power UPS battery.

[0068] Please refer to Figure 2 , Figure 2 is a flowchart of obtaining the sub-battery performance evaluation index of 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, the operation state parameter data of the sub-battery of the high-power UPS battery is obtained, and processed according to the operation state parameter data to obtain the sub-battery performance evaluation index, including:

[0069] S21. Obtain the operating status parameter data of the sub-batteries of the high-power UPS battery. The operating status parameter data includes remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data, and self-discharge rate.

[0070] S22. Compare the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate, and compare the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate.

[0071] S23. Process according to the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate to obtain the sub-battery performance evaluation index.

[0072] It should be noted that in order to evaluate the performance of the sub-batteries and analyze the power supply potential of the sub-batteries, first obtain 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. Among them, the remaining available capacity data refers to the amount of electricity that the sub-battery can discharge under certain discharging conditions. The charging characteristic data refers to the data of the time taken for the sub-battery to reach the equalizing charge voltage during the charging process. The discharging characteristic data refers to the rate of decrease of the battery voltage with the discharging time during the discharging process. The self-discharge rate refers to the amount of electricity loss caused by internal chemical reactions of the sub-battery itself within a preset time. Then compare the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate. Among them, the capacity attenuation rate is the ratio of the difference between the preset rated capacity data and the remaining available capacity data to the preset rated capacity data. Compare the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate. Among them, the internal resistance increase rate is 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 through the preset high-power UPS battery life optimization management platform. Finally, process according to the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate to obtain the sub-battery performance evaluation index.

[0073] The calculation formula for the battery performance evaluation index is:

[0074] ;

[0075] Among them, is the battery performance evaluation index, 、 、 、 、 are the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate respectively, 、 It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0076] 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:

[0077] Processing the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate;

[0078] 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;

[0079] 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;

[0080] 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.

[0081] 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.

[0082] Please refer to Figure 3 , Figure 3It is a flowchart for obtaining predicted data of energy consumption requirements of a load in a method for optimizing the lifespan 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 the high-power UPS battery and evaluation data of the energy consumption requirements of the electrical system load, and processing the evaluation data of the energy consumption requirements to obtain predicted data of the energy consumption requirements of the load, including:

[0083] S31. Obtain discharge instruction information of the high-power UPS battery and evaluation data of the energy consumption requirements of the electrical system load, where the evaluation data of the energy consumption requirements includes load power, expected energy supply duration data, and power characteristic category data;

[0084] S32. Query a preset power supply demand evaluation database according to the power characteristic category data to obtain a power characteristic weight coefficient;

[0085] S33. Process according to the load power, expected energy supply duration data, and power characteristic weight coefficient to obtain predicted data of the energy consumption requirements of the load.

[0086] It should be noted that before the high-power UPS battery needs to discharge, first obtain evaluation data of the energy consumption requirements including load power, expected energy supply duration data, and power characteristic category data. The power characteristic category data refers to constant power load category data, pulse power load category data, or variable power load category data. Then query a preset power supply demand evaluation database according to the power characteristic category data to obtain a power characteristic weight coefficient, where the preset power supply demand evaluation database is obtained by querying a preset high-power UPS battery lifespan optimization management platform. Finally, process according to the load power, expected energy supply duration data, and power characteristic weight coefficient to obtain predicted data of the energy consumption requirements of the load;

[0087] The calculation formula for the predicted data of the energy consumption requirements is:

[0088] ;

[0089] Wherein, is the predicted data of the energy consumption requirements, 、 、 are the load power, expected energy supply duration data, and power characteristic weight coefficient respectively, is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset high-power UPS battery lifespan optimization management platform).

[0090] Please refer to Figure 4 , Figure 4It is a flowchart for obtaining effective capacity data of a high - power UPS battery life optimization method in some embodiments of the present application. According to the embodiments of the present invention, obtaining real - time discharge capacity parameter data of sub - batteries of a high - power UPS battery according to the discharge instruction information, and processing the real - time discharge capacity parameter data to obtain effective capacity data, including:

[0091] S41. Obtain real - time discharge capacity parameter data of sub - batteries of a high - power UPS battery according to the discharge instruction information, including real - time capacity data and corresponding conversion rate and minimum stock requirement data;

[0092] S42. Process the real - time capacity data and corresponding conversion rate and minimum stock requirement data to obtain effective capacity data, including primary effective capacity data of primary - performance sub - batteries and secondary effective capacity data of secondary - performance sub - batteries.

[0093] It should be noted that before the high - power UPS battery executes the discharge instruction, first obtain the real - time discharge capacity parameter data of the sub - batteries, including real - time capacity data and corresponding conversion rate and minimum stock requirement data. Among them, the conversion rate refers to the ratio of the effective electric energy output by the battery during discharge to the real - time capacity data, and the minimum stock requirement data refers to the minimum power set to ensure the battery performance. Process the real - time capacity data and corresponding conversion rate and minimum stock requirement data to obtain effective capacity data, including primary effective capacity data of primary - performance sub - batteries and secondary effective capacity data of secondary - performance sub - batteries;

[0094] The calculation formula for the primary effective capacity data is:

[0095] ;

[0096] Wherein, is the primary effective capacity data, , , are respectively the real - time capacity data, conversion rate and minimum stock requirement data of the i - th sub - battery of the primary - performance sub - battery;

[0097] The calculation formula for the secondary effective capacity data is:

[0098] ;

[0099] Wherein, is the secondary effective capacity data, , , are respectively the real - time capacity data, conversion rate and minimum stock requirement data of the j - th sub - battery of the secondary - performance sub - battery.

[0100] According to an embodiment of the present invention, processing the effective capacity data in combination with the energy consumption demand prediction data to obtain a discharge allocation strategy for a high-power UPS battery includes:

[0101] Comparing the effective capacity data with the energy consumption demand prediction data to obtain a discharge allocation strategy for a high-power UPS battery, including balanced discharge, discharge of first-level performance sub-batteries, or discharge of first-level performance and second-level performance sub-batteries;

[0102] If the effective capacity data is less than the energy consumption demand prediction data, perform balanced discharge, output a warning response, and activate a pre-set backup power supply plan;

[0103] If the effective capacity data is greater than or equal to the energy consumption demand prediction data, compare the first-level effective capacity data with the energy consumption demand prediction data;

[0104] If the first-level effective capacity data is greater than or equal to the energy consumption demand prediction data, evenly allocate the energy consumption demand prediction data to the first-level performance sub-batteries for discharge;

[0105] If the first-level effective capacity data is less than the energy consumption demand prediction data, allocate the energy consumption demand prediction data to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio.

[0106] It should be noted that in order to determine the discharge allocation strategy of the sub-batteries, first compare the effective capacity data with the energy consumption demand prediction data. If the effective capacity data is less than the energy consumption demand prediction data, perform balanced discharge first, that is, evenly distribute the energy consumption demand prediction data to each sub-battery, output a warning response, and at the same time activate a pre-set backup power supply plan; if the effective capacity data is greater than or equal to the energy consumption demand prediction data, further compare the first-level effective capacity data with the energy consumption demand prediction data. If the first-level effective capacity data is greater than or equal to the energy consumption demand prediction data, it means that the first-level performance sub-batteries have sufficient power, then evenly allocate the energy consumption demand prediction data to the first-level performance sub-batteries for discharge, that is, evenly distribute it to the first-level performance sub-batteries for discharge, and the second-level performance sub-batteries do 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 consumption demand prediction data, it means that only the first-level performance sub-batteries supply power and the power is insufficient. Further allocate the energy consumption demand prediction data to the first-level performance and second-level performance sub-batteries for discharge according to a preset energy supply allocation ratio. In this embodiment, the allocation ratio of the first-level performance sub-batteries is the ratio of the first-level effective capacity data to the energy consumption demand prediction data, and the rest is discharged by the second-level performance sub-batteries.

[0107] According to an embodiment of the present invention, it further includes:

[0108] Obtain the rated capacity and battery category characteristic data of the high-power UPS battery;

[0109] Query the preset charging parameter database according to the rated capacity and battery category characteristic data to obtain the charging current corresponding to the constant current charging stage;

[0110] Control the high-power UPS battery to perform constant current charging according to the charging current, and obtain the real-time charging monitoring voltage of the sub-battery;

[0111] Compare the real-time charging monitoring voltage with the preset equalizing charge voltage;

[0112] If it is less than the preset equalizing charge voltage, it is determined that the charging is in the constant current charging stage;

[0113] If it is greater than or equal to the preset equalizing charge voltage, query the preset charging parameter database according to the battery category characteristic data to obtain the charging demand voltage corresponding to the constant voltage charging stage, and control the high-power UPS battery to perform constant voltage charging according to the charging demand voltage.

[0114] It should be noted that in order to optimize the life of the high-power UPS battery, while optimizing the discharge distribution, it is necessary to analyze and optimize the charging method to further improve the service life of the high-power UPS battery. The charging process is optimized 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 lithium battery category characteristic data. Then query the preset charging parameter database according to the rated capacity and battery category characteristic data to obtain the charging current corresponding to the constant current charging stage. Among them, the preset charging parameter database is obtained by querying the preset high-power UPS battery life optimization management platform. For example, for a 100Ah lead-acid battery in the constant current charging stage, 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, obtain the real-time charging monitoring voltage of the single battery. When it is greater than or equal to the preset equalizing charge voltage, it means that the constant current charging stage ends and the constant voltage charging stage begins. Among them, the preset equalizing charge voltage is obtained by querying the preset high-power UPS battery life optimization management platform. Further query the preset charging parameter database according to the battery category characteristic data to obtain the charging demand voltage corresponding to the constant voltage charging stage, and control the high-power UPS battery to perform constant voltage charging according to the charging demand voltage to reduce the impact of improper charging on the battery life.

[0115] According to the embodiment of the present invention, it further includes:

[0116] Obtain the real-time ambient temperature at the location where the high-power UPS battery is located;

[0117] Compare the real-time ambient temperature with the preset standard ambient temperature to obtain an ambient temperature difference value, and query the preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage;

[0118] Sum the temperature compensation voltage and the charging demand voltage to obtain a charging correction voltage;

[0119] Control the high-power UPS battery to perform constant-voltage charging according to the charging correction voltage.

[0120] 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 at the location of the high-power UPS battery, compare it with the preset standard ambient temperature to obtain an ambient temperature difference value. The ambient temperature difference value 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 value. If the real-time ambient temperature is 24°C, then 24 - 25 = -1°C is the ambient temperature difference value. Query the preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain a temperature compensation voltage. Among them, the temperature compensation voltage can be positive or negative, and the preset charging voltage compensation adjustment database is obtained by querying the preset high-power UPS battery life optimization management platform. Then sum the temperature compensation voltage and the charging demand voltage to obtain a charging correction voltage;

[0121] The calculation formula for the charging correction voltage is:

[0122] ;

[0123] Among them, is the charging correction voltage, 、 are the temperature compensation voltage and the charging demand voltage respectively.

[0124] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0125] Obtain the real-time battery temperature of the sub-battery during the discharge stage of the high-power UPS battery;

[0126] Compare the real-time battery temperature with a preset temperature threshold;

[0127] Extract a first temperature threshold and a second temperature threshold according to the preset temperature threshold, and the first temperature threshold is less than the second temperature threshold;

[0128] If it is less than or equal to the first temperature threshold, it is determined that the discharge battery temperature is normal;

[0129] If it is greater than the first temperature threshold and less than or equal to the second temperature threshold, it is determined that the temperature of the discharging battery is abnormal, and the refrigeration and cooling response is activated;

[0130] If it is greater than the second temperature threshold, it is determined that the temperature of the discharging battery is abnormal, and the load adjustment response is activated.

[0131] It should be noted that when the high-power UPS battery executes the discharging instruction, the battery temperature will increase 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 discharging stage and compare it with the preset temperature threshold. The temperature threshold includes the first temperature threshold and the 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 obtained real-time temperature is 35 °C, it is determined that the temperature of the discharging battery is normal; if the obtained real-time temperature is 41 °C, it is determined that the temperature of the discharging battery is abnormal, and the refrigeration and cooling response is activated to reduce the battery temperature; if the obtained real-time temperature is 46 °C, indicating that the cooling effect is insufficient, it is determined that the temperature of the discharging battery is abnormal, and the load adjustment response is activated, such as allocating the load to the backup battery for power supply.

[0132] It is worth mentioning that according to the embodiment of the present invention, the real-time capacity data and the corresponding conversion rate of the sub-battery of the high-power UPS battery are obtained according to the discharging instruction information, and the obtained real-time capacity data and the corresponding conversion rate are processed in combination with the predicted energy demand data to obtain the discharging distribution strategy of the high-power UPS battery. After that, it further includes:

[0133] Obtain the discharging effectiveness parameter data of the high-power UPS battery, including the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the voltage standard deviation of sub-batteries;

[0134] Input the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the voltage standard deviation of sub-batteries into a preset discharging effectiveness evaluation model for processing to obtain a discharging effectiveness evaluation index.

[0135] It should be noted that after the high-power UPS battery executes the discharging action, it is necessary to evaluate the discharging effectiveness of each sub-battery. First, obtain the discharging effectiveness parameter data including the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the voltage standard deviation of sub-batteries. Among them, 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 through a preset high-power UPS battery life optimization management platform. The voltage range between sub-batteries refers to the difference between the highest voltage and the lowest voltage of the sub-batteries. Input the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the voltage standard deviation of sub-batteries into a preset discharging effectiveness evaluation model for processing to obtain a discharging effectiveness evaluation index;

[0136] In the discharge effectiveness evaluation model, the calculation formula for the discharge effectiveness evaluation index is as follows:

[0137] ;

[0138] Wherein, is the discharge effectiveness evaluation index, 、 、 are the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the voltage standard deviation of sub-batteries respectively, is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset high-power UPS battery life optimization management platform).

[0139] It should be noted that according to the embodiment of the present invention, after inputting the real-time remaining capacity deviation value, the voltage range between batteries, and the voltage standard deviation between batteries into the preset discharge effectiveness evaluation model for processing to obtain the discharge effectiveness evaluation index, it further includes:

[0140] Comparing the discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate;

[0141] Comparing the discharge effectiveness deviation rate with a preset discharge effectiveness deviation rate threshold;

[0142] If it is less than or equal to the preset discharge effectiveness deviation rate threshold, it is determined that the discharge distribution strategy is normal;

[0143] If it is greater than the preset discharge effectiveness deviation rate threshold, it is determined that the discharge distribution strategy is abnormal.

[0144] It should be noted that by comparing the obtained discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate, the discharge effectiveness deviation rate 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. By comparing the obtained discharge effectiveness deviation rate with a preset discharge effectiveness deviation rate threshold, in this embodiment, the discharge effectiveness deviation rate threshold is set to (0, 0.25] and (0.25, 1], corresponding to a normal discharge distribution strategy and an abnormal discharge distribution strategy respectively. For example, if the obtained discharge effectiveness deviation rate is 0.2, it is determined that the discharge distribution strategy is normal; if the obtained discharge effectiveness deviation rate is 0.3, it is determined that the discharge distribution strategy is abnormal.

[0145] It should be noted that according to the embodiment of the present invention, it further includes:

[0146] Obtaining the real-time voltage corresponding to the sub-battery of the high-power UPS battery and extracting the real-time highest voltage value according to the real-time voltage;

[0147] Comparing the real-time voltage with the real-time highest voltage value respectively to obtain the corresponding voltage difference;

[0148] Compare the voltage difference with a preset voltage equalization threshold value;

[0149] If it is less than or equal to the preset voltage equalization threshold value, it is determined that the battery voltages are balanced;

[0150] If it is greater than the preset voltage equalization threshold value, it is determined that the battery voltages are unbalanced, and the corresponding sub-battery is charged.

[0151] It should be noted that during long-term use, the batteries in the battery pack may have uneven charging. For a series-connected UPS battery pack, it is very important to perform equalization charging regularly. First, obtain the real-time voltage corresponding to the sub-battery of the high-power UPS battery, extract the real-time highest voltage value according to the real-time voltage, then compare the real-time voltages with the real-time highest voltage value respectively to obtain the corresponding voltage differences. The voltage difference refers to the difference between the real-time highest voltage value and the real-time voltage. Finally, compare the voltage difference with the preset voltage equalization threshold value. In this embodiment, the voltage equalization threshold value is set to 0.2V. For example, if the obtained voltage difference is 0.1V, which is less than the preset voltage equalization threshold value, it is determined that the battery voltages are balanced. If the obtained voltage difference is 0.3V, which is greater than the preset voltage equalization threshold value, it is determined that the battery voltages are unbalanced, and the corresponding sub-battery is charged to keep the voltages of all sub-batteries balanced.

[0152] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0153] Obtain the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value, and power outage times value of the commercial power within a preset time period;

[0154] Process according to the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value, and power outage times value to obtain the power supply stability evaluation index of the commercial power within a preset time period;

[0155] Compare the power supply stability evaluation index with a preset power supply stability evaluation threshold value to obtain the number value of the preset time periods that are continuously greater than the preset power supply stability evaluation threshold value;

[0156] Compare the number value with a preset monitoring number threshold value;

[0157] If it is less than the preset monitoring number threshold value, control the high-power UPS battery not to enter battery sleep;

[0158] If it is greater than or equal to the preset monitoring number threshold value, control the high-power UPS battery to enter battery sleep.

[0159] It should be noted that in the case of stable mains power supply for a long time, the battery sleep program is started to place the battery in a low-power state, reducing self-discharge and unnecessary charge-discharge cycles, which can extend the service life of the high-power UPS battery. First, the stability of the mains power supply is evaluated by obtaining the average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages within a preset time period. Among them, the average voltage fluctuation refers to the average value of the differences between multiple collected real-time voltages and the preset reference voltage, the average frequency fluctuation refers to the average value of the differences between multiple collected real-time frequencies and the preset reference frequency, and the average harmonic distortion can be obtained by those skilled in the art through calculating the effective values of multiple collected harmonic voltages and the effective value of the preset fundamental voltage. In this embodiment, the preset time period is set to 1 day. Based on the obtained average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages, processing is performed to obtain the power supply stability evaluation index within the preset time period;

[0160] The calculation formula for the power supply stability evaluation index is as follows:

[0161] ;

[0162] Wherein, is the power supply stability evaluation index, , , , are respectively the average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages, , are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset high-power UPS battery life optimization management platform);

[0163] 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, the number of times within the preset time period when the index is continuously greater than the preset power supply stability evaluation threshold is obtained, and then the number of times is compared with the preset monitoring number threshold. In this embodiment, the preset monitoring number threshold is set to 7 times. If the obtained number of times is 5 times, which is less than 7 times, the high-power UPS battery does not enter the sleep state. If the obtained number of times is 8 times, which is greater than 7 times, it indicates that the mains power supply is stable, and then the high-power UPS battery is controlled to enter the battery sleep state, reducing self-discharge and the number of unnecessary charge-discharge cycles, achieving the purpose of extending the service life.

[0164] The present invention also discloses a lifespan optimization system for high-power UPS batteries, including a memory and a processor. The memory includes a program for a lifespan optimization method for high-power UPS batteries. When the program for the lifespan optimization method for high-power UPS batteries is executed by the processor, the following steps are implemented:

[0165] Obtain the operation status parameter data of the sub-batteries of the high-power UPS battery, process the operation status parameter data to obtain the sub-battery performance evaluation index;

[0166] Process the sub-battery performance evaluation index and a preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate. Compare the sub-battery performance deviation rate with a preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range it belongs to;

[0167] Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the power consumption system load, process the energy consumption demand evaluation data to obtain the energy consumption demand prediction data of the load;

[0168] Obtain the real-time discharge capacity parameter data of the sub-batteries of the high-power UPS battery according to the discharge instruction information, process the real-time discharge capacity parameter data to obtain the effective capacity data;

[0169] Process the effective capacity data in combination with the energy consumption demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery.

[0170] It should be noted that in order to optimize the lifespan of the high-power UPS battery, first obtain the operation status parameter data of the sub-batteries, process the operation status parameter data to obtain the sub-battery performance evaluation index for evaluating the performance level of the sub-batteries, then process it with a preset sub-battery performance benchmark index to obtain the sub-battery performance deviation rate, compare the sub-battery performance deviation rate with a preset battery performance level evaluation threshold, and determine the corresponding battery performance level of the sub-battery according to the threshold range it belongs to, including first-level performance or second-level performance. Then, by obtaining the energy consumption demand evaluation data of the power consumption system load and processing it to obtain the energy consumption demand prediction data of the load, analyze the power consumption demand of the power consumption system load. Finally, obtain the real-time capacity data of the sub-batteries and the corresponding conversion rate and minimum stock demand data, process them to obtain the effective capacity data, process the effective capacity data in combination with the energy consumption demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery, and avoid over-discharge or over-use of some batteries by reasonably allocating the discharge strategy, thereby achieving the lifespan optimization of the high-power UPS battery.

[0171] According to an embodiment of the present invention, obtaining the operating state parameter data of the sub-battery of the high-power UPS battery, and processing the operating state parameter data to obtain the sub-battery performance evaluation index, including:

[0172] Obtaining the operating state parameter data of the sub-battery of the high-power UPS battery, where the operating state parameter data includes remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data, and self-discharge rate;

[0173] 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;

[0174] Processing according to the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate to obtain the sub-battery performance evaluation index.

[0175] 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, first, obtain the operating state parameter data including remaining available capacity data, real-time internal resistance value, charging characteristic data, discharging characteristic data, and self-discharge rate. Among them, 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 data of the time when the sub-battery reaches the equalizing charge voltage during the charging process, the discharging characteristic data refers to the rate of decrease of the battery voltage with the discharging time during the discharging process, and the self-discharge rate refers to the amount of electricity loss caused by internal chemical reactions of the sub-battery itself within a preset time. Then, compare the remaining available capacity data with the preset rated capacity data to obtain the capacity attenuation rate, where the capacity attenuation rate is the ratio of the difference between the preset rated capacity data and the remaining available capacity data to the preset rated capacity data. Compare the real-time internal resistance value with the preset initial internal resistance value to obtain the internal resistance increase rate, where the internal resistance increase rate is 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 through a preset high-power UPS battery life optimization management platform. Finally, process according to the capacity attenuation rate, internal resistance increase rate, charging characteristic data, discharging characteristic data, and self-discharge rate to obtain the sub-battery performance evaluation index;

[0176] The calculation formula of the battery performance evaluation index is:

[0177] ;

[0178] Wherein, is the battery performance evaluation index, , , , , They are respectively the capacity attenuation rate, the internal resistance increase rate, the charge characteristic data, the discharge characteristic data, and the self-discharge rate. , are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset high-power UPS battery life optimization management platform).

[0179] 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, and the sub-battery performance deviation rate is compared with a preset battery performance level evaluation threshold for threshold comparison. According to the belonging threshold range, the corresponding battery performance level of the sub-battery is determined, including:

[0180] The sub-battery performance evaluation index is processed with a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate;

[0181] The sub-battery performance deviation rate is compared with a preset battery performance level evaluation threshold to determine the corresponding battery performance level of the sub-battery, including first-level performance or second-level performance;

[0182] If the sub-battery performance deviation rate is less than or equal to the preset battery performance level evaluation threshold, the sub-battery performance level is determined to be first-level performance;

[0183] If the sub-battery performance deviation rate is greater than the preset battery performance level evaluation threshold, the sub-battery performance level is determined to be second-level performance.

[0184] It should be noted that in order to classify and manage sub-batteries according to performance potential, first, the obtained sub-battery performance evaluation index is processed with a preset sub-battery performance benchmark index to obtain a sub-battery performance deviation rate. Among them, 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. Then, the obtained sub-battery performance deviation rate is compared with a preset battery performance level evaluation threshold to determine the corresponding battery performance level of the sub-battery, including first-level performance or second-level performance. Among them, first-level performance is better than second-level performance. In this embodiment, the battery performance level evaluation threshold is set to (0, 0.2] and (0.2, 1], corresponding to first-level performance and second-level performance respectively. If the obtained sub-battery performance deviation rate is 0.1, which is less than the preset battery performance level evaluation threshold, the sub-battery performance level is determined to be first-level performance. If the obtained sub-battery performance deviation rate is 0.3, which is greater than the preset battery performance level evaluation threshold, the sub-battery performance level is determined to be second-level performance.

[0185] According to an embodiment of the present invention, obtaining the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the electrical system load, and processing according to the energy consumption demand evaluation data to obtain the energy consumption demand prediction data of the load, including:

[0186] Obtain the discharge instruction information of the high-power UPS battery and the energy consumption demand evaluation data of the power consumption system load. The energy consumption demand evaluation data includes load power, expected energy supply duration data, and power characteristic category data;

[0187] Query the preset power supply demand evaluation database according to the power characteristic category data to obtain the power characteristic weight coefficient;

[0188] Process according to the load power, expected energy supply duration data, and power characteristic weight coefficient to obtain the energy consumption demand prediction data of the load.

[0189] It should be noted that before the high-power UPS battery needs to discharge, first obtain the energy consumption demand evaluation data including load power, expected energy supply duration data, and power characteristic category data. The power characteristic category data refers to constant power load category data, pulse power load category data, or variable power load category data. Then query the preset power supply demand evaluation database according to the power characteristic category data to obtain the power characteristic weight coefficient. Among them, the preset power supply demand evaluation database is obtained by querying the preset high-power UPS battery life optimization management platform. Finally, process according to the load power, expected energy supply duration data, and power characteristic weight coefficient to obtain the energy consumption demand prediction data of the load;

[0190] The calculation formula of the energy consumption demand prediction data is:

[0191] ;

[0192] Among them, is the energy consumption demand prediction data, , , are the load power, expected energy supply duration data, and power characteristic weight coefficient respectively, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-power UPS battery life optimization management platform).

[0193] According to the embodiment of the present invention, obtaining the real-time discharge capacity parameter data of the sub-battery of the high-power UPS battery according to the discharge instruction information, and processing according to the real-time discharge capacity parameter data to obtain the effective capacity data, including:

[0194] Obtain the real-time discharge capacity parameter data of the sub-battery of the high-power UPS battery according to the discharge instruction information, including real-time capacity data and the corresponding conversion rate and minimum stock demand data;

[0195] Process according to the real-time capacity data, the corresponding conversion rate, and the minimum stock demand data to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-batteries and the second-level effective capacity data of the second-level performance sub-batteries.

[0196] It should be noted that before the high-power UPS battery executes the discharge instruction, first obtain the real-time discharge capacity parameter data of the sub-batteries, including the real-time capacity data, the corresponding conversion rate, and the minimum stock demand data. Among them, the conversion rate refers to the ratio of the effective electric energy output by the battery during discharge to the real-time capacity data, and the minimum stock demand data refers to the minimum power set to ensure the battery performance. Process the real-time capacity data, the corresponding conversion rate, and the minimum stock demand data to obtain effective capacity data, including the first-level effective capacity data of the first-level performance sub-batteries and the second-level effective capacity data of the second-level performance sub-batteries;

[0197] The calculation formula for the first-level effective capacity data is:

[0198] ;

[0199] Wherein, is the first-level effective capacity data, , , are respectively the real-time capacity data, the conversion rate, and the minimum stock demand data of the i-th sub-battery of the first-level performance sub-batteries;

[0200] The calculation formula for the second-level effective capacity data is:

[0201] ;

[0202] Wherein, is the second-level effective capacity data, , , are respectively the real-time capacity data, the conversion rate, and the minimum stock demand data of the j-th sub-battery of the second-level performance sub-batteries.

[0203] According to the embodiments of the present invention, process according to the effective capacity data in combination with the energy demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery, including:

[0204] Compare the effective capacity data with the energy demand prediction data to obtain the discharge allocation strategy of the high-power UPS battery, including balanced discharge, discharge of the first-level performance sub-batteries, or discharge of the first-level and second-level performance sub-batteries;

[0205] If the effective capacity data is less than the energy demand prediction data, perform balanced discharge, output a warning response, and activate the pre-equipment standby power supply plan;

[0206] If the effective capacity data is greater than or equal to the predicted energy consumption data, compare the primary effective capacity data with the predicted energy consumption data;

[0207] If the primary effective capacity data is greater than or equal to the predicted energy consumption data, evenly distribute the predicted energy consumption data to the primary performance sub-batteries for discharging;

[0208] If the primary effective capacity data is less than the predicted energy consumption data, distribute the predicted energy consumption data to the primary performance and secondary performance sub-batteries for discharging according to a preset energy supply distribution ratio.

[0209] It should be noted that in order to determine the discharge distribution strategy of the sub-batteries, first compare the effective capacity data with the predicted energy consumption data. If the effective capacity data is less than the predicted energy consumption data, perform balanced discharging first, that is, evenly distribute the predicted energy consumption data to each sub-battery, and output a warning response. At the same time, start the pre-equipment backup power supply plan; if the effective capacity data is greater than or equal to the predicted energy consumption data, further compare the primary effective capacity data with the predicted energy consumption data. If the primary effective capacity data is greater than or equal to the predicted energy consumption data, it means that the primary performance sub-batteries have sufficient power, then evenly distribute the predicted energy consumption data to the primary performance sub-batteries for discharging, that is, evenly distribute it to the primary performance sub-batteries for discharging, and the secondary performance sub-batteries do not participate in discharging, reducing the number of cycles and preventing further performance degradation; if the primary effective capacity data is less than the predicted energy consumption data, it means that only the primary performance sub-batteries supply power and the power is insufficient. Further distribute the predicted energy consumption data to the primary performance and secondary performance sub-batteries for discharging according to a preset energy supply distribution ratio. In this embodiment, the distribution ratio of the primary performance sub-batteries is the ratio of the primary effective capacity data to the predicted energy consumption data, and the rest is discharged by the secondary performance sub-batteries.

[0210] According to an embodiment of the present invention, it further includes:

[0211] Obtain the rated capacity and battery category characteristic data of the high-power UPS battery;

[0212] Query a preset charging parameter database according to the rated capacity and battery category characteristic data to obtain the charging current corresponding to the constant current charging stage;

[0213] Control the high-power UPS battery to perform constant current charging according to the charging current, and obtain the real-time charging monitoring voltage of the sub-battery;

[0214] Compare the real-time charging monitoring voltage with a preset equalizing charge voltage;

[0215] If it is less than the preset equalizing charge voltage, it is determined that the charging is in the constant current charging stage;

[0216] If it is greater than or equal to the preset equalizing charge voltage, query the preset charging parameter database according to the battery category characteristic data to obtain the charging required voltage corresponding to the constant voltage charging stage, and control the high-power UPS battery to perform constant voltage charging according to the charging required voltage.

[0217] It should be noted that in order to optimize the life of the high-power UPS battery, while optimizing the discharge distribution, it is necessary to analyze and optimize the charging method to further improve the service life of the high-power UPS battery. The charging process is optimized into two stages: constant current charging and constant voltage charging. In the constant current charging stage, first obtain the rated capacity of the high-power UPS battery and the battery category characteristic data. The battery category characteristic data refers to the lead-acid battery category characteristic data or the lithium battery category characteristic data. 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. Among them, the preset charging parameter database is obtained by querying the preset high-power UPS battery life optimization management platform. For example, for a lead-acid battery with a capacity of 100Ah in the constant current charging stage, the charging device will output a stable 0.1C current, that is, 0.1×100 = 10A, to gradually increase the battery voltage. At the same time, obtain the real-time charging monitoring voltage of the single battery. When it is greater than or equal to the preset equalizing charge voltage, it indicates that the constant current charging stage ends and the constant voltage charging stage begins. Among them, the preset equalizing charge voltage is obtained by querying the preset high-power UPS battery life optimization management platform. Further query the preset charging parameter database according to the battery category characteristic data to obtain the charging required voltage corresponding to the constant voltage charging stage, and control the high-power UPS battery to perform constant voltage charging according to the charging required voltage to reduce the impact of improper charging on the battery life.

[0218] According to the embodiment of the present invention, it further includes:

[0219] Obtain the real-time ambient temperature at the location where the high-power UPS battery is located;

[0220] Compare the real-time ambient temperature with the preset standard ambient temperature to obtain the ambient temperature difference value, and query the preset charging voltage compensation and adjustment database according to the ambient temperature difference value to obtain the temperature compensation voltage;

[0221] Perform a summation calculation on the temperature compensation voltage and the charging required voltage to obtain the charging correction voltage;

[0222] Control the high-power UPS battery to perform constant voltage charging according to the charging correction voltage.

[0223] 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 required voltage in the constant voltage charging stage, first obtain the real-time ambient temperature at the location of the high-power UPS battery, compare it with the preset standard ambient temperature, and obtain the ambient temperature difference value. The ambient temperature difference value 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 value. If the real-time ambient temperature is 24°C, then 24 - 25 = -1°C is the ambient temperature difference value. Query the preset charging voltage compensation adjustment database according to the ambient temperature difference value to obtain the temperature compensation voltage. Among them, 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 sum the temperature compensation voltage and the charging required voltage to obtain the charging corrected voltage;

[0224] The formula for the charging corrected voltage is:

[0225] ;

[0226] Among them, is the charging corrected voltage, 、 are the temperature compensation voltage and the charging required voltage respectively.

[0227] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0228] Obtain the real-time battery temperature of the sub-battery in the discharge stage of the high-power UPS battery;

[0229] Compare the real-time battery temperature with a preset temperature threshold;

[0230] Extract a first temperature threshold and a second temperature threshold according to the preset temperature threshold, and the first temperature threshold is less than the second temperature threshold;

[0231] If it is less than or equal to the first temperature threshold, it is determined that the discharge battery temperature is normal;

[0232] 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 activate the refrigeration and cooling response;

[0233] If it is greater than the second temperature threshold, it is determined that the discharge battery temperature is abnormal and activate the load adjustment response.

[0234] It should be noted that when the high-power UPS battery executes the discharge command, the battery temperature will increase 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 stage and compare it with the preset temperature threshold. The temperature threshold includes the first temperature threshold and the 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 obtained real-time temperature is 35 °C, it is determined that the discharge battery temperature is normal; if the obtained real-time temperature is 41 °C, it is determined that the discharge battery temperature is abnormal, and the refrigeration and cooling response is activated to reduce the battery temperature; if the obtained real-time temperature is 46 °C, indicating that the cooling effect is insufficient, it is determined that the discharge battery temperature is abnormal, and the load adjustment response is activated, such as allocating the load to the backup battery for power supply.

[0235] It is worth mentioning that according to the embodiment of the present invention, the real-time capacity data and the corresponding conversion rate of the sub-battery of the high-power UPS battery are obtained according to the discharge instruction information, and the discharge distribution strategy of the high-power UPS battery is obtained by processing the real-time capacity data and the corresponding conversion rate in combination with the energy demand prediction data. After that, it further includes:

[0236] Obtain the discharge effectiveness parameter data of the high-power UPS battery, including the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the standard deviation of the sub-battery voltage;

[0237] Input the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the standard deviation of the sub-battery voltage into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index.

[0238] It should be noted that after the high-power UPS battery executes the discharge action, it is necessary to evaluate the discharge effectiveness of each sub-battery. First, obtain the discharge effectiveness parameter data including the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the standard deviation of the sub-battery voltage. Among them, 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. The preset expected remaining capacity is obtained by querying the preset high-power UPS battery life optimization management platform. The voltage range between sub-batteries refers to the difference between the highest voltage and the lowest voltage of the sub-battery. Input the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the standard deviation of the sub-battery voltage into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index;

[0239] The calculation formula of the discharge effectiveness evaluation index in the discharge effectiveness evaluation model is:

[0240] ;

[0241] Among them, is the discharge effectiveness evaluation index, 、 、 They are the real-time remaining capacity deviation value, the voltage range between sub-batteries, and the standard deviation of sub-battery voltages, which are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset high-power UPS battery life optimization management platform).

[0242] It is worth mentioning that according to the embodiments of the present invention, after inputting the real-time remaining capacity deviation value, the voltage range between batteries, and the standard deviation of battery voltages into a preset discharge effectiveness evaluation model for processing to obtain a discharge effectiveness evaluation index, the following steps are further included:

[0243] Comparing the discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate;

[0244] Comparing the discharge effectiveness deviation rate with a preset discharge effectiveness deviation rate threshold;

[0245] If it is less than or equal to the preset discharge effectiveness deviation rate threshold, it is determined that the discharge distribution strategy is normal;

[0246] If it is greater than the preset discharge effectiveness deviation rate threshold, it is determined that the discharge distribution strategy is abnormal.

[0247] It should be noted that by comparing the obtained discharge effectiveness evaluation index with a preset discharge reference index to obtain a discharge effectiveness deviation rate, the discharge effectiveness deviation rate 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. Comparing the obtained discharge effectiveness deviation rate with a preset discharge effectiveness deviation rate threshold. In this embodiment, the discharge effectiveness deviation rate threshold is set to (0, 0.25] and (0.25, 1], corresponding to a normal discharge distribution strategy and an abnormal discharge distribution strategy respectively. For example, if the obtained discharge effectiveness deviation rate is 0.2, it is determined that the discharge distribution strategy is normal; if the obtained discharge effectiveness deviation rate is 0.3, it is determined that the discharge distribution strategy is abnormal.

[0248] It is worth mentioning that according to the embodiments of the present invention, the following steps are further included:

[0249] Obtaining the real-time voltage corresponding to the sub-battery of the high-power UPS battery and extracting the real-time highest voltage value according to the real-time voltage;

[0250] Comparing the real-time voltage with the real-time highest voltage value respectively to obtain the corresponding voltage difference;

[0251] Comparing the voltage difference with a preset voltage equalization threshold;

[0252] If it is less than or equal to the preset voltage equalization threshold, it is determined that the battery voltages are balanced;

[0253] If it is greater than the preset voltage equalization threshold, it is determined that the battery voltage is unbalanced, and the corresponding sub-battery is charged.

[0254] It should be noted that during long-term use, the batteries in the battery pack may experience uneven charging. For a series-connected UPS battery pack, it is very important to perform equalization charging regularly. First, obtain the real-time voltage corresponding to the sub-battery of the high-power UPS battery, extract the real-time highest voltage value according to the real-time voltage, and then compare the real-time voltage with the real-time highest voltage value respectively to obtain the corresponding voltage difference. The voltage difference refers to the difference between the real-time highest voltage value and the real-time voltage. Finally, compare the voltage difference with the preset voltage equalization threshold. In this embodiment, the voltage equalization threshold is set to 0.2V. For example, if the obtained voltage difference is 0.1V, which is less than the preset voltage equalization threshold, it is determined that the battery voltage is balanced. If the obtained voltage difference is 0.3V, which is greater than the preset voltage equalization threshold, it is determined that the battery voltage is unbalanced, and the corresponding sub-battery is charged to keep the voltages of all sub-batteries balanced.

[0255] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0256] Obtain the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value, and power outage frequency value of the commercial power within a preset time period;

[0257] Process according to the voltage fluctuation mean value, frequency fluctuation mean value, harmonic distortion mean value, and power outage frequency value to obtain the power supply stability evaluation index of the commercial power within a preset time period;

[0258] Compare the power supply stability evaluation index with a preset power supply stability evaluation threshold to obtain the number value of the preset time period continuously greater than the preset power supply stability evaluation threshold;

[0259] Compare the number value with a preset monitoring number threshold;

[0260] If it is less than the preset monitoring number threshold, control the high-power UPS battery not to enter the battery sleep state;

[0261] If it is greater than or equal to the preset monitoring number threshold, control the high-power UPS battery to enter the battery sleep state.

[0262] It should be noted that in the case of stable mains power supply for a long time, the battery sleep program is started to place the battery in a low-power state, reducing self-discharge and unnecessary charge-discharge cycles, which can extend the service life of the high-power UPS battery. First, the stability of the mains power supply is evaluated by obtaining the average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages within a preset time period. Among them, the average voltage fluctuation refers to the average value of the differences between multiple collected real-time voltages and the preset reference voltage. The average frequency fluctuation refers to the average value of the differences between multiple collected real-time frequencies and the preset reference frequency. The average harmonic distortion can be obtained by those skilled in the art through calculating the effective values of multiple collected harmonic voltages and the effective value of the preset fundamental voltage. In this embodiment, the preset time period is set to 1 day. Based on the obtained average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages, processing is performed to obtain the power supply stability evaluation index within the preset time period;

[0263] The formula for calculating the power supply stability evaluation index is:

[0264] ;

[0265] Wherein, is the power supply stability evaluation index, , , , are respectively the average voltage fluctuation, average frequency fluctuation, average harmonic distortion, and number of power outages, , are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset high-power UPS battery life optimization management platform);

[0266] 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, the number of times within the preset time period when the index is continuously greater than the preset power supply stability evaluation threshold is obtained, and then the number of times is compared with the preset monitoring number threshold. In this embodiment, the preset monitoring number threshold is set to 7 times. If the obtained number of times is 5 times, which is less than 7 times, the high-power UPS battery does not enter the sleep state. If the obtained number of times is 8 times, which is greater than 7 times, it indicates that the mains power supply stability is good, then the high-power UPS battery is controlled to enter the battery sleep state, reducing self-discharge and the number of unnecessary charge-discharge cycles, achieving the purpose of extending the service life.

[0267] In a third aspect of the present invention, a readable storage medium is provided. A program for optimizing the lifespan of a high-power UPS battery is stored in the readable storage medium. When the program for optimizing the lifespan of a high-power UPS battery is executed by a processor, the steps of the method for optimizing the lifespan of a high-power UPS battery as described in any one of the above are implemented.

[0268] A method, system, and medium for optimizing the lifespan of a high-power UPS battery disclosed in the present invention achieve a reasonable allocation of the discharge strategy by evaluating the battery performance level of sub-batteries and calculating the predicted energy consumption data of the load, which helps to extend the service life of the sub-batteries.

[0269] In 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 merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0270] The units described as separate components above may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0271] In addition, each functional unit in the embodiments of the present invention can be fully integrated into one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated into one unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0272] Those of ordinary skill 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 foregoing program can be stored in a readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs.

[0273] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

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, including primary performance or secondary performance; 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, including primary effective capacity data of the primary performance sub-battery and secondary effective capacity data of the secondary performance sub-battery; 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; 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.

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: 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.

7. The method for optimizing the life of a high-power UPS battery according to claim 6, 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.

8. A life optimization system for high-power UPS batteries, 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, including primary performance or secondary performance; 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, including primary effective capacity data of the primary performance sub-battery and secondary effective capacity data of the secondary performance sub-battery; 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; 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.

9. 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 7 are implemented.

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