Distributed energy storage lithium battery state optimization management method based on intelligent algorithm

Abstract

The invention provides a distributed energy storage lithium battery state optimization management method based on an intelligent algorithm, and relates to the technical field of an intelligent power distribution system of new energy power transmission and distribution equipment, IPC classification numbers belong to H02J7 / 00 and G01R31 / 382, the intelligent algorithm is introduced, health state data and discharge efficiency indexes of a lithium battery module are collected and analyzed in real time, and the lithium battery module state optimization management method based on the intelligent algorithm is achieved. A battery state evaluation coefficient is dynamically generated, and priority ranking and intelligent strategy adjustment are carried out; therefore, the response speed and efficiency of the system are improved, and accurate adjustment under different load conditions is realized through simulation and calibration strategy optimization, so that the stability and reliability of the whole energy storage system are enhanced while the use efficiency and the service life of the battery are remarkably improved and prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power distribution systems for new energy power transmission and distribution equipment, and the IPC classification number belongs to H02J7 / 00 and G01R31 / 382, and specifically to a distributed energy storage lithium battery state optimization management method based on an intelligent algorithm. Background Art

[0002] Distributed energy storage technology plays a key role in modern energy systems, especially in improving the flexibility and stability of power grid and renewable energy integration. At the same time, in the field of new energy vehicles and smart power distribution systems, lithium batteries are widely used in distributed energy storage systems in smart grids or new energy vehicle charging pile areas due to their high energy density, long life, and low self-discharge rate.

[0003] How to effectively manage and optimize the performance of lithium batteries has become an urgent problem to be solved. Traditional lithium battery management systems mainly focus on power balance and simple health status monitoring, but there are still many deficiencies in optimizing the overall energy storage system performance. Especially in heterogeneous energy storage systems, the discharge efficiency and health status of different lithium battery modules vary significantly due to manufacturing differences, usage conditions and aging rates, which requires more intelligent and precise management methods to maximize the overall efficiency of the system.

[0004] In the prior art, the publication number is CN116154900B, and the name is a three-level active safety prevention and control method and system for a battery energy storage power station, which relates to the field of safety prevention and control technology of energy storage power stations to overcome the inherent problems in the battery management system BMS and regular maintenance in the prior art; the method includes: real-time analysis based on battery operation data to identify the risk source of battery failure online; early warning of battery micro-short circuit failure and battery abnormal exhaustion failure; early warning of battery thermal runaway failure; the three-level active safety prevention and control system of the battery energy storage power station is applied to the three-level active safety prevention and control method of the battery energy storage power station.

[0005] Existing technologies mainly rely on static battery management strategies, which usually use simple voltage and current monitoring when evaluating the battery health status, and control the battery charging and discharging through fixed threshold settings; this management method lacks in-depth analysis and dynamic response capabilities for real-time battery status; in multi-module distributed energy storage systems, this static management strategy is difficult to fully tap the maximum potential of each module, resulting in the battery failing to achieve optimal conditions in terms of load regulation and energy efficiency; further, the priority sorting in traditional systems is based only on the current state of the battery, ignoring the interactive impact and trend analysis of long-term operating data; therefore, existing systems are prone to response lags and efficiency losses when faced with rapidly fluctuating load environments;

[0006] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a distributed energy storage lithium battery state optimization management method based on an intelligent algorithm to solve the problems raised in the above background technology.

[0008] The object of the present invention is achieved by: a distributed energy storage lithium battery state optimization management method based on an intelligent algorithm, the specific steps comprising:

[0009] Step S1: Determine that the number of lithium battery modules in the distributed energy storage lithium battery device is M, and collect the power transmission and distribution strategy, operation health status data and discharge efficiency index of each lithium battery module in the current monitoring cycle;

[0010] Step S2: analyzing the running health status data of each lithium battery module in the current monitoring cycle, and generating a battery status evaluation coefficient for evaluating the health of the current lithium battery module;

[0011] Step S3: Prioritizing the M lithium battery modules according to the evaluation results corresponding to the battery status evaluation coefficients of each lithium battery module, and providing a first adjustment strategy for the power transmission and distribution strategy of each lithium battery module based on the priority ranking results;

[0012] Step S4: according to the priority ranking results of the M lithium battery modules, set the load adjustment range of each lithium battery module, and divide each load adjustment range into N load simulation values;

[0013] For each lithium battery module, perform N load simulations within the corresponding load adjustment range, and calculate the discharge efficiency index corresponding to each load simulation value;

[0014] Calculating a first variation trend coefficient of a discharge efficiency index of each lithium battery module within a corresponding load regulation range;

[0015] Determine the load regulation range of each lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, combine the correlation trend coefficient and the first change trend coefficient for analysis, and generate a calibration strategy for optimizing the first adjustment strategy and the load regulation range.

[0016] Furthermore, the acquisition of transmission and distribution strategies, operational health status data and discharge efficiency indicators includes:

[0017] Through the main control unit, the unique identifier of each lithium battery module is read by using RFID technology, and i∈{1,2,…,M} is set, where i represents the identifier index of the i-th lithium battery module;

[0018] The monitoring period of the distributed monitoring node in the current data collection is set to time T, and the operation health status data includes the internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude;

[0019] Transmission and distribution strategies include charging mode and discharging strategy;

[0020] 1) Charging modes include fast charging mode and slow charging mode, as follows:

[0021] 1.1) Description of the power transmission and distribution strategy for fast charging mode, including:

[0022] In the monitoring period T, the power transmission and distribution strategy of the fast charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module;

[0023] 1.2) Description of the power transmission and distribution strategy for slow charging mode, including:

[0024] In the monitoring period T, the power transmission and distribution strategy of the slow charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module;

[0025] 2) The discharge strategy includes continuous discharge and intermittent discharge, as follows:

[0026] 2.1) Description of the transmission and distribution strategy for continuous discharge, including:

[0027] During the monitoring period T, the power transmission and distribution strategy for continuous discharge is to manage the discharge current, cut-off voltage, discharge completion time and discharge temperature changes of the i-th lithium battery module;

[0028] 2.2) Description of the transmission and distribution strategy for intermittent discharge, including:

[0029] In the monitoring period T, the transmission and distribution strategy of intermittent discharge is to manage the discharge current, discharge cycle, cumulative discharge time and discharge temperature change of the i-th lithium battery module;

[0030] On the main control unit, the discharge efficiency index calculated for the i-th lithium battery module in the current monitoring period T is marked as , the calculation formula is:

[0031] ; in, is the actual discharge energy measured by the i-th lithium battery module in the current monitoring period T, sj is the abbreviation of “actual”; is the theoretical discharge energy measured by the i-th lithium battery module in the current monitoring period T, Label the abbreviation for "theory";

[0032] The actual discharge energy is calculated by the integration method, and the theoretical discharge energy is the rated capacity multiplied by the nominal voltage.

[0033] Furthermore, the internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude of the i-th lithium battery module are recorded as , , , ;

[0034] For the internal resistance change rate , in the current monitoring period T, by measuring the average internal resistance of the i-th lithium battery module during the charging and discharging process, and calculating the change rate of the average internal resistance in the adjacent monitoring periods T and T-1;

[0035] For the capacity decay rate , obtained by measuring the change rate of the actual capacity and the rated capacity of the i-th lithium battery module during the discharge process within the current monitoring period T;

[0036] For thermal stability coefficient , obtained by calculating the temperature change rate of the i-th lithium battery module during the charge and discharge cycle in the current monitoring period T;

[0037] For voltage fluctuation , obtained by measuring the standard deviation of the voltage of the i-th lithium battery module during discharge in the current monitoring period T;

[0038] Define the battery state evaluation coefficient of the i-th lithium battery module as , the calculation formula is as follows:

[0039] ; Among them, a, b, c, d are weight coefficients, and ; The specific values ​​of a, b, c, and d are set based on experiments and historical data;

[0040] Set the battery status evaluation coefficient The valid value range of is (0,1);

[0041] like , indicating that the health status of the i-th lithium battery module is not good and requires planned maintenance;

[0042] like , indicating that the health status of the i-th lithium battery module is normal and needs to be monitored regularly;

[0043] like , indicating that the health status of the i-th lithium battery module is good and can continue to be used.

[0044] Further, the prioritization and first adjustment strategies include:

[0045] according to The M lithium battery modules are sorted by the value of . The larger the value, the higher the priority. The priority sorting result is represented by {1, 2, ..., R}, and j∈{1, 2, ..., R}, j represents the index of the lithium battery module sorted with the jth priority, R is the index of the last lithium battery module sorted among the M lithium battery modules, and j∈{1, 2, ..., M};

[0046] The first adjustment strategy includes: specifying a fast charging strategy, specifying a slow charging strategy, continuous discharge management, and intermittent discharge management;

[0047] For the specified fast charging strategy, including:

[0048] Will comply with The lithium battery modules are classified as high priority, and the fast charging strategy is implemented within the current monitoring period T, the charging current is adjusted to 150% of the rated capacity, the target voltage is 100% of the nominal voltage, and the monitoring charging temperature change does not exceed 5%; and the fast charging completion time is 35% of the rated charging time;

[0049] For specifying slow charging strategies, including:

[0050] Will comply with The lithium battery module is classified as medium priority, and a slow charging strategy is implemented in the current monitoring period T. The charging current is adjusted to 48% of the rated capacity, the target voltage is 75% of the nominal voltage, the monitoring charging temperature change does not exceed 3%, and the slow charging completion time is 150% of the rated charging time;

[0051] For continuous discharge management, including:

[0052] Will comply with The lithium battery module is classified as low priority, and continuous discharge is implemented within the current monitoring period T. The discharge current is adjusted to 20% of the rated capacity, discharged to 60% of the nominal cut-off voltage, the discharge completion time is 60% of the total time, and the discharge temperature change does not exceed 3%;

[0053] For intermittent discharge management, including:

[0054] Will comply with The lithium battery modules are classified as high priority and intermittent discharge management is implemented within the current monitoring period T. The discharge current is 10% to 90% of the rated capacity; the ratio of discharge to rest in the discharge cycle is 65%; the cumulative discharge time is 70% of the total time; and the discharge temperature change is less than 8%.

[0055] Furthermore, N load simulations are performed within the corresponding load regulation range, and the discharge efficiency index corresponding to each load simulation value is calculated, specifically including:

[0056] 2.1) According to the priority ranking result {1, 2, ..., R} of each lithium battery module, a load adjustment range is set for each lithium battery module;

[0057] For high-priority lithium battery modules, the load regulation range is set to [70%, 100%] of the rated load capacity;

[0058] For medium-priority lithium battery modules, the load regulation range is set to [50%, 70%] of the rated load capacity;

[0059] For low-priority lithium battery modules, the load regulation range is set to [10%, 50%] of the rated load capacity;

[0060] 2.2) Divide the load regulation range of each lithium battery module into N equally spaced load simulation values;

[0061] Each load simulation corresponds to the divided load simulation value one by one, and the N load simulations are represented by the number set as {1, 2, ..., N}, and e∈{1, 2, ..., N}, e represents the index of the e-th load simulation;

[0062] 2.3) In the current monitoring cycle T, the discharge efficiency index of the jth priority-ranked lithium battery module in the eth load simulation is marked as , the calculation formula is as follows:

[0063] ; in, is the actual discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation;

[0064] is the theoretical discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation;

[0065] 2.4) Calculate the first change trend coefficient of the discharge efficiency index of each lithium battery module within the corresponding load adjustment range; use the linear regression analysis method, take the load simulation value as the independent variable and the discharge efficiency index as the dependent variable, and calculate the trend slope represented by the first change trend coefficient;

[0066] The jth lithium battery module is subjected to N load simulations within the corresponding load regulation range, and the obtained load simulation values ​​and corresponding discharge efficiency indicators are ,in is the load simulation value corresponding to the e-th load simulation, and is the corresponding discharge efficiency;

[0067] The first change trend coefficient of the discharge efficiency index of the jth lithium battery module within the corresponding load regulation range is recorded as , the calculation formula is as follows:

[0068] ; If the first change trend coefficient A positive number indicates that the overall discharge efficiency index improves when the load increases;

[0069] If the first change trend coefficient A negative number indicates that the overall discharge efficiency index decreases when the load increases.

[0070] Furthermore, the correlation trend coefficient and the first change trend coefficient are analyzed to generate a calibration strategy for optimizing the first adjustment strategy and the load adjustment range, specifically including:

[0071] Determine the load regulation range of the jth lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, and express the correlation trend coefficient as ;

[0072] 3.1) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are greater than 1, the jth lithium battery module is positively correlated with the discharge efficiency index within the load regulation range, and a first-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, while the upper limit of the load regulation range of the current lithium battery module is expanded by 5%;

[0073] The first-level forward optimization calibration strategy includes: positively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%; the positive adjustment is intended to adjust in a direction that is beneficial to the current goal or strategy;

[0074] 3.2) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last load simulation of >1 is set as the upper limit of the load adjustment range of the current lithium battery module, and a second-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first-level forward optimization calibration strategy is 30% larger than that of the second-level forward optimization calibration strategy;

[0075] 3.3) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are less than 1, the jth lithium battery module is negatively correlated with the discharge efficiency index within the load regulation range. A first-level negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the upper limit of the load regulation range of the current lithium battery module is reduced by 5%;

[0076] The first-level negative optimization calibration strategy includes: negatively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%; the negative adjustment is intended to adjust in a direction that is not conducive to the current goal or strategy;

[0077] 3.4) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last load simulation of >1 is set as the upper limit of the load regulation range of the current lithium battery module, and a secondary negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first negative optimization calibration strategy is 15% smaller than that of the second negative optimization calibration strategy.

[0078] Compared with the prior art, the beneficial effects of the present invention are: by introducing intelligent algorithms, real-time collection and analysis of health status data and discharge efficiency indicators of lithium battery modules, dynamic generation of battery status evaluation coefficients, priority sorting and intelligent adjustment strategies; this not only improves the response speed and efficiency of the system, but also achieves precise adjustment under different load conditions through simulation and calibration strategy optimization, thereby significantly improving battery efficiency and life while enhancing the stability and reliability of the entire energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0080] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] Embodiment 1:

[0083] See also Figure 1 , the present invention provides a technical solution:

[0084] The distributed energy storage lithium battery state optimization management method based on intelligent algorithm includes the following specific steps:

[0085] Step S1: Determine that the number of lithium battery modules in the distributed energy storage lithium battery device is M, and collect the power transmission and distribution strategy, operation health status data and discharge efficiency index of each lithium battery module in the current monitoring cycle;

[0086] Step S2: analyzing the running health status data of each lithium battery module in the current monitoring cycle, and generating a battery status evaluation coefficient for evaluating the health of the current lithium battery module;

[0087] Step S3: Prioritizing the M lithium battery modules according to the evaluation results corresponding to the battery status evaluation coefficients of each lithium battery module, and providing a first adjustment strategy for the power transmission and distribution strategy of each lithium battery module based on the priority ranking results;

[0088] Step S4: according to the priority ranking results of the M lithium battery modules, set the load adjustment range of each lithium battery module, and divide each load adjustment range into N load simulation values;

[0089] For each lithium battery module, perform N load simulations within the corresponding load adjustment range, and calculate the discharge efficiency index corresponding to each load simulation value;

[0090] Calculating a first variation trend coefficient of a discharge efficiency index of each lithium battery module within a corresponding load regulation range;

[0091] Determine the load regulation range of each lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, combine the correlation trend coefficient and the first change trend coefficient for analysis, and generate a calibration strategy for optimizing the first adjustment strategy and the load regulation range.

[0092] Further explanation: the acquisition of transmission and distribution strategy, operation health status data and discharge efficiency indicators includes:

[0093] Start the main control unit of the distributed energy storage system, and use RFID technology to read the unique identifier of each lithium battery module through the main control unit. Each lithium battery module is affixed with an RFID tag that complies with the ISO15693 standard. The number of lithium battery modules is M, and i∈{1,2,…,M} is set, where i represents the i-th lithium battery module;

[0094] Initialize the communication protocol between lithium battery modules, select the CAN bus protocol, set the baud rate to 500kbps, and achieve a data transmission rate of 100 times per second to ensure normal data transmission between modules;

[0095] The monitoring period of the distributed monitoring node in the current data collection is set to time T. In this embodiment, the monitoring period of the current data collection is set to 1 minute, and each node is based on an STM32 microcontroller, using relevant sensors such as Hall effect sensors and thermocouple sensors, and the operating health status data includes internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude;

[0096] The power transmission and distribution strategy includes charging mode and discharging strategy, which are recorded in the main control unit;

[0097] 1) Charging modes include fast charging mode and slow charging mode, as follows:

[0098] 1.1) Description of the power transmission and distribution strategy for fast charging mode, including:

[0099] In the monitoring period T, the power transmission and distribution strategy of the fast charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module;

[0100] In the monitoring period T under the current fast charging mode, the charging current of the i-th lithium battery module is recorded as , and the charging current Need to reach 100% to 300% of rated capacity;

[0101] In the monitoring period T under the current fast charging mode, the target voltage of the i-th lithium battery module is recorded as , the charging strategy is to charge to 100% of the nominal voltage;

[0102] In the monitoring period T under the current fast charging mode, the charging completion time of the i-th lithium battery module is recorded as , fast charging completion time 0% to 50% of the rated charging time;

[0103] In the monitoring period T under the current fast charging mode, the charging temperature change of the i-th lithium battery module is recorded as , the charging temperature change needs to be controlled Below 20%.

[0104] 1.2) Description of the power transmission and distribution strategy for slow charging mode, including:

[0105] In the monitoring period T, the power transmission and distribution strategy of the slow charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module;

[0106] In the monitoring period T under the current slow charging mode, the charging current of the i-th lithium battery module is recorded as , when slow charging, the charging current 10% to 50% of the rated capacity;

[0107] In the current monitoring period T in the slow charging mode, the target voltage of the i-th lithium battery module is recorded as , the charging strategy is to charge to 80% of the nominal voltage;

[0108] In the monitoring period T under the current slow charging mode, the charging completion time of the i-th lithium battery module is recorded as , slow charging completion time 100% to 200% of the rated charging time;

[0109] In the monitoring period T of the current slow charging mode, the charging temperature change of the i-th lithium battery module is recorded as , the charging temperature change needs to be controlled Below 10%.

[0110] 2) The discharge strategy includes continuous discharge and intermittent discharge, as follows:

[0111] 2.1) Description of the transmission and distribution strategy for continuous discharge, including:

[0112] During the monitoring period T, the power transmission and distribution strategy for continuous discharge is to manage the discharge current, cut-off voltage, discharge completion time and discharge temperature changes of the i-th lithium battery module;

[0113] In the monitoring period T under the current continuous discharge, the discharge current of the i-th lithium battery module is recorded as , when continuously discharging, the discharge current 10% to 100% of rated capacity;

[0114] In the monitoring period T under the current continuous discharge, the cut-off voltage of the i-th lithium battery module is recorded as , when continuously discharging, the cut-off voltage Reach 50% to 70% of the nominal cut-off voltage;

[0115] In the monitoring period T under the current continuous discharge, the discharge completion time of the i-th lithium battery module is recorded as , when discharging continuously, the discharge completion time 50% to 100% of the total time;

[0116] In the monitoring period T under the current continuous discharge, the discharge temperature change of the i-th lithium battery module is recorded as , when discharging continuously, it is necessary to control the discharge temperature change Below 15%.

[0117] 2.2) Description of the transmission and distribution strategy for intermittent discharge, including:

[0118] In the monitoring period T, the transmission and distribution strategy of intermittent discharge is to manage the discharge current, discharge cycle, cumulative discharge time and discharge temperature change of the i-th lithium battery module;

[0119] In the monitoring period T under the current intermittent discharge, the discharge current of the i-th lithium battery module is recorded as , during intermittent discharge, the discharge current 10% to 90% of rated capacity;

[0120] In the monitoring period T under the current intermittent discharge, the discharge period of the i-th lithium battery module is recorded as , during intermittent discharge, the discharge cycle The ratio of discharge to rest is 50% to 100%;

[0121] In the monitoring period T under the current intermittent discharge, the cumulative discharge time of the i-th lithium battery module is recorded as , when discharging intermittently, the cumulative discharge time 50% to 85% of the total time;

[0122] In the monitoring period T under the current intermittent discharge, the discharge temperature change of the i-th lithium battery module is recorded as , when discharging intermittently, it is necessary to control the change of discharge temperature Below 10%.

[0123] The above ranges include the endpoints.

[0124] On the main control unit, the discharge efficiency index calculated for the i-th lithium battery module in the current monitoring period T is marked as , the calculation formula is:

[0125] ; in, is the actual discharge energy measured by the i-th lithium battery module in the current monitoring period T; is the theoretical discharge energy measured by the i-th lithium battery module in the current monitoring period T;

[0126] The actual discharge energy is calculated by the integration method, and the theoretical discharge energy is the rated capacity multiplied by the nominal voltage.

[0127] The integral method for calculating the actual discharge energy includes:

[0128] The calculation formula is as follows:

[0129] ; in, is the actual discharge energy measured by the i-th lithium battery module in the current monitoring period T;

[0130] is the instantaneous voltage at time t; is the instantaneous current at time t;

[0131] and It is the start and end time of the discharge process corresponding to the current monitoring period T;

[0132] The calculation of theoretical discharge energy includes:

[0133] The calculation formula is as follows:

[0134] ; in, is the theoretical discharge energy measured by the i-th lithium battery module in the current monitoring period T;

[0135] is the rated capacity of the i-th lithium battery module; is the nominal voltage of the ith lithium battery module;

[0136] The ratio of actual discharge energy to theoretical discharge energy reflects the energy conversion efficiency of the battery.

[0137] If the actual discharge energy is significantly lower than 60% of the theoretical value, it means that the battery has energy loss, aging or damage;

[0138] All collected and analyzed data are stored in a centralized database using a MySQL database and a 32GB industrial-grade SD card. Data cleaning and labeling are completed through automatic Python scripts.

[0139] To further illustrate, the internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude of the i-th lithium battery module are respectively recorded as , , , ;

[0140] For the internal resistance change rate , in the current monitoring period T, by measuring the average internal resistance of the i-th lithium battery module during the charging and discharging process, and calculating the change rate of the average internal resistance in the adjacent monitoring periods T and T-1;

[0141] For the capacity decay rate , obtained by measuring the change rate of the actual capacity and the rated capacity of the i-th lithium battery module during the discharge process within the current monitoring period T;

[0142] For thermal stability coefficient , obtained by calculating the temperature change rate of the i-th lithium battery module during the charge and discharge cycle in the current monitoring period T;

[0143] For voltage fluctuation , obtained by measuring the standard deviation of the voltage of the i-th lithium battery module during discharge in the current monitoring period T;

[0144] For the internal resistance change rate , the calculation formula is as follows:

[0145] ; in, is the average internal resistance of the i-th lithium battery module measured in the current monitoring cycle T; the time interval between adjacent monitoring cycles T is set to , is the time interval from the last monitoring period T The average internal resistance measured under is the time interval in hours;

[0146] For the capacity decay rate , the calculation formula is as follows:

[0147] ; in, is the rated capacity of the i-th lithium battery module; is the actual capacity measured by the i-th lithium battery module during the discharge process of the current monitoring period T;

[0148] For thermal stability coefficient , the calculation formula is as follows:

[0149] ; in, is the highest temperature measured by the i-th lithium battery module in the current monitoring period T; is the lowest temperature measured by the i-th lithium battery module in the current monitoring period T;

[0150] For voltage fluctuation , the calculation formula is as follows:

[0151] ; in, is the voltage standard deviation of the i-th lithium battery module during the discharge process in the current monitoring period T;

[0152] is the average voltage of the i-th lithium battery module during the discharge process in the current monitoring period T;

[0153] Define the battery state evaluation coefficient of the i-th lithium battery module as , the calculation formula is as follows:

[0154] ; Among them, a, b, c, d are weight coefficients, and ; The specific values ​​of a, b, c, and d are set based on experiments and historical data; in this embodiment, the values ​​of a, b, c, and d are 0.2, 0.2, 0.3, and 0.3 respectively;

[0155] Set the battery status evaluation coefficient The valid value range of is (0,1);

[0156] like , indicating that the health status of the i-th lithium battery module is not good and requires planned maintenance;

[0157] like , indicating that the health status of the i-th lithium battery module is normal and needs to be monitored regularly;

[0158] like , indicating that the health status of the i-th lithium battery module is good and can continue to be used.

[0159] Further explanation, prioritization, and first adjustment strategies include:

[0160] according to The M lithium battery modules are sorted by the value of . The larger the value, the higher the priority. The priority sorting result is represented by {1, 2, ..., R}, and j∈{1, 2, ..., R}, j represents the index of the lithium battery module sorted with the jth priority, R is the index of the last lithium battery module sorted among the M lithium battery modules, and j∈{1, 2, ..., M};

[0161] The purpose of prioritization is to ensure that battery modules in good health are maintained and managed first when resources are limited;

[0162] The first adjustment strategy includes: specifying a fast charging strategy, specifying a slow charging strategy, continuous discharge management, and intermittent discharge management;

[0163] According to the priority sorting results, a personalized adjustment strategy is formulated for each lithium battery module, and the lithium battery modules with high priority will receive priority resources;

[0164] This ensures that the overall performance of the system is maximized while extending the service life of unhealthy battery modules;

[0165] For the specified fast charging strategy, including:

[0166] Will comply with The lithium battery modules are classified as high priority, and the fast charging strategy is implemented within the current monitoring period T, the charging current is adjusted to 150% of the rated capacity, the target voltage is 100% of the nominal voltage, and the monitoring charging temperature change does not exceed 5%; and the fast charging completion time is 35% of the rated charging time;

[0167] Through fast charging, batteries in good health can be quickly replenished to improve overall efficiency;

[0168] For specifying slow charging strategies, including:

[0169] Will comply with The lithium battery module is classified as medium priority, and a slow charging strategy is implemented in the current monitoring period T. The charging current is adjusted to 48% of the rated capacity, the target voltage is 75% of the nominal voltage, the monitoring charging temperature change does not exceed 3%, and the slow charging completion time is 150% of the rated charging time;

[0170] Slow charging can steadily restore the battery's power while reducing the stress on the battery in medium condition;

[0171] For continuous discharge management, including:

[0172] Will comply with The lithium battery module is classified as low priority, and continuous discharge is implemented within the current monitoring period T. The discharge current is adjusted to 20% of the rated capacity, discharged to 60% of the nominal cut-off voltage, the discharge completion time is 60% of the total time, and the discharge temperature change does not exceed 3%;

[0173] Continuous discharge reduces risk and allows for planned maintenance;

[0174] For intermittent discharge management, including:

[0175] Will comply with The lithium battery modules are classified as high priority and intermittent discharge management is implemented within the current monitoring period T. The discharge current is 10% to 90% of the rated capacity; the ratio of discharge to rest in the discharge cycle is 65%; the cumulative discharge time is 70% of the total time; and the discharge temperature change is less than 8%.

[0176] A battery module in good health is more stable in performance and can withstand frequent charge and discharge cycles caused by intermittent discharge; such a battery can better maintain capacity and output capacity, thereby providing a more reliable power supply in intermittent discharge mode.

[0177] Nominal Voltage: This is the typical voltage value exhibited by the battery under standard operating conditions. It is an approximate value used to describe the voltage level of the battery during most of the discharge process;

[0178] Nominal Cut-off Voltage: This refers to the lowest allowable voltage value during the battery discharge process. Discharging should be stopped when this voltage is reached to avoid over-discharge and damage to the battery. Continuing to discharge beyond this voltage may damage the battery and affect its life and performance.

[0179] Further explanation: N load simulations are performed within the corresponding load regulation range, and the discharge efficiency index corresponding to each load simulation value is calculated, specifically including:

[0180] 2.1) According to the priority ranking result {1, 2, ..., R} of each lithium battery module, a load adjustment range is set for each lithium battery module;

[0181] For high-priority lithium battery modules, the load regulation range is set to [70%, 100%] of the rated load capacity;

[0182] For medium-priority lithium battery modules, the load regulation range is set to [50%, 70%] of the rated load capacity;

[0183] For low-priority lithium battery modules, the load regulation range is set to [10%, 50%] of the rated load capacity;

[0184] This partitioning takes into account battery status and safety, ensuring that batteries with high status are subjected to higher loads, while batteries with poor health are operated at lower loads to reduce losses;

[0185] 2.2) Divide the load regulation range of each lithium battery module into N equally spaced load simulation values; in this embodiment, the equally spaced interval is set to 5%; and each load simulation corresponds to the divided load simulation value one by one, and the N load simulations are represented by a number set as {1,2,…,N}, and e∈{1,2,…,N}, where e represents the index of the e-th load simulation;

[0186] For each load simulation value, a load test is performed and the corresponding discharge efficiency index is calculated;

[0187] 2.3) In the current monitoring cycle T, the discharge efficiency index of the jth priority-ranked lithium battery module in the eth load simulation is marked as , the calculation formula is as follows:

[0188] ; in, is the actual discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation;

[0189] is the theoretical discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation;

[0190] 2.4) Calculate the first change trend coefficient of the discharge efficiency index of each lithium battery module within the corresponding load adjustment range; use the linear regression analysis method, take the load simulation value as the independent variable and the discharge efficiency index as the dependent variable, and calculate the trend slope represented by the first change trend coefficient;

[0191] The jth lithium battery module is subjected to N load simulations within the corresponding load regulation range, and the obtained load simulation values ​​and corresponding discharge efficiency indicators are ,in is the load simulation value corresponding to the e-th load simulation, and is the corresponding discharge efficiency;

[0192] The first change trend coefficient of the discharge efficiency index of the jth lithium battery module within the corresponding load regulation range is recorded as , the calculation formula is as follows:

[0193] ; in, Indicates the linear variation trend of load simulation value to discharge efficiency;

[0194] If the first change trend coefficient A positive number indicates that the overall discharge efficiency index improves when the load increases;

[0195] If the first change trend coefficient A negative number indicates that the overall discharge efficiency index decreases when the load increases.

[0196] Further description, combining the relevant trend coefficient and the first change trend coefficient for analysis to generate a calibration strategy for optimizing the first adjustment strategy and the load adjustment range specifically includes:

[0197] Determine the load regulation range of the jth lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, and express the correlation trend coefficient as ;

[0198] 3.1) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are greater than 1, the jth lithium battery module is positively correlated with the discharge efficiency index within the load regulation range, and a first-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, while the upper limit of the load regulation range of the current lithium battery module is expanded by 5%;

[0199] The first-level forward optimization calibration strategy includes: positively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%;

[0200] The concept of positive adjustment is to benefit the positive direction of the current adjustment strategy, aiming to adjust specific parameters in a direction that is beneficial to the current goal or strategy. For example, for a specified fast charging strategy, "adjust the charging current to 150% of the rated capacity, the target voltage to 100% of the nominal voltage, monitor the charging temperature change to no more than 5%; and the fast charging completion time is 35% of the rated charging time";

[0201] Increase the charge current and target voltage by 3% of the current value, while the charge temperature change and charge completion time need to be reduced by 3% of the current value;

[0202] 3.2) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last simulation with >1 is set as the upper limit of the load regulation range of the current lithium battery module, and a second-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first-level forward optimization calibration strategy is 30% larger than that of the second-level forward optimization calibration strategy;

[0203] For example, among N load simulations {1, 2, ..., N}, the correlation trend coefficients of the first e load simulations are >1, e+1 and subsequent load simulations correspond to <1; then the load simulation value corresponding to the e-th load simulation is set as the upper limit of the load adjustment range of the current lithium battery module;

[0204] 3.3) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are less than 1, the jth lithium battery module is negatively correlated with the discharge efficiency index within the load regulation range. A first-level negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the upper limit of the load regulation range of the current lithium battery module is reduced by 5%;

[0205] The first-level negative optimization calibration strategy includes: negatively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%;

[0206] The concept of negative adjustment is the negative direction that is beneficial to the current adjustment strategy, aiming to adjust specific parameters in a direction that is not beneficial to the current goal or strategy. For example, for continuous discharge management, "adjust the discharge current to 20% of the rated capacity, discharge to 60% of the nominal cut-off voltage, discharge completion time to 60% of the total time, and the discharge temperature change does not exceed 3%";

[0207] Reduce the discharge current and cut-off voltage by 3% of the current value, while the discharge completion time and discharge temperature change need to be increased by 3% of the current value;

[0208] The description of the remaining strategies is omitted here;

[0209] 3.4) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last simulation with >1 is set as the upper limit of the load regulation range of the current lithium battery module, and a secondary negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first negative optimization calibration strategy is 15% smaller than that of the second negative optimization calibration strategy.

[0210] Embodiment 2:

[0211] In this embodiment, the influence of different charging and discharging strategies on the overall performance of lithium battery modules in a distributed energy storage system is studied; the system includes M lithium battery modules, each module is prioritized from high to low according to the battery status evaluation coefficient, and the ranking result is represented by {1, 2, ..., R}, where R is the ranking index of the last lithium battery module;

[0212] The experimental data are shown in Table 1 below:

[0213] Module Name Load simulation value Discharge efficiency index The first trend coefficient Module 1 80 88 0.85 Module 1 95 90 Module 2 55 76 0.45 Module 2 60 78 Module 3 20 58 -0.3 Module 3 30 60 Module 4 85 92 0.9 Module 4 100 95

[0214] Table 1

[0215] For high priority analysis of modules 1 and 4:

[0216] The discharge efficiency index of module 1 increased from 88% to 90%, and the efficiency of module 4 increased from 92% to 95%. is positive, indicating that the discharge efficiency index improves as the load increases;

[0217] Effect of calibration strategy: The first trend coefficient of positive correlation indicates that the performance is better under high load range. The calibration strategy will recommend expanding the load adjustment range. In this embodiment, the upper limit is expanded by 5%, and the charging parameters are optimized to continue to improve the efficiency under high load conditions, with an expected efficiency improvement of 3%.

[0218] For medium priority analysis in Module 2:

[0219] As the load increases from 55W to 60W, the efficiency of module 2 increases from 76% to 78%. A positive value indicates that the efficiency increases as the load increases.

[0220] Effect of calibration strategy: The calibration strategy will recommend slightly expanding the load range and slightly adjusting parameters through secondary forward optimization; this will improve the technical effect by 1%-2% and ensure the stability of efficiency in the medium load range.

[0221] For low priority analysis of module 3:

[0222] The efficiency of module 3 increased from 58% to 60%, although is negative, indicating that the overall efficiency increases with the increase of load.

[0223] Effect of calibration strategy: The negative correlation trend requires negative optimization calibration, shrinking the load adjustment range and optimizing parameters to mitigate the negative effects of load increase. It is expected that the efficiency loss can be reduced by 3%.

[0224] It can more accurately reveal the impact of load changes on efficiency and provide a more sophisticated optimization adjustment strategy. This strategy is based on positive and negative trend changes and effectively improves the overall efficiency of the system in different load ranges.

[0225] By detecting the change of discharge efficiency under different load conditions, the system establishes a dynamic optimization framework, which enables the system to operate efficiently under different loads and states. This dynamic capability not only increases flexibility, but also enhances the overall reliability of the system in practical applications.

[0226] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0227] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0228] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0229] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A distributed energy storage lithium battery state optimization management method based on intelligent algorithm, characterized in that: The specific steps include: Step S1: Determine that the number of lithium battery modules in the distributed energy storage lithium battery device is M, and collect the power transmission and distribution strategy, operation health status data and discharge efficiency index of each lithium battery module in the current monitoring cycle; Step S2: analyzing the running health status data of each lithium battery module in the current monitoring cycle, and generating a battery status evaluation coefficient for evaluating the health of the current lithium battery module; Step S3: Prioritizing the M lithium battery modules according to the evaluation results corresponding to the battery status evaluation coefficients of each lithium battery module, and providing a first adjustment strategy for the power transmission and distribution strategy of each lithium battery module based on the priority ranking results; Step S4: according to the priority ranking results of the M lithium battery modules, setting the load adjustment range of each lithium battery module, and dividing each load adjustment range into N load simulation values; For each lithium battery module, perform N load simulations within the corresponding load adjustment range, and calculate the discharge efficiency index corresponding to each load simulation value; Calculating a first variation trend coefficient of a discharge efficiency index of each lithium battery module within a corresponding load regulation range; Determine the load regulation range of each lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, combine the correlation trend coefficient and the first change trend coefficient for analysis, and generate a calibration strategy for optimizing the first adjustment strategy and the load regulation range.

2. The distributed energy storage lithium battery state optimization management method based on intelligent algorithm according to claim 1 is characterized in that: The acquisition of transmission and distribution strategies, operational health status data and discharge efficiency indicators includes: Through the main control unit, the unique identifier of each lithium battery module is read by using RFID technology, and i∈{1,2,…,M} is set, where i represents the identifier index of the i-th lithium battery module; The monitoring period of the distributed monitoring node in the current data collection is set to time T, and the operation health status data includes the internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude; Transmission and distribution strategies include charging mode and discharging strategy; 1) Charging modes include fast charging mode and slow charging mode, as follows: 1.1) Description of the power transmission and distribution strategy for fast charging mode, including: In the monitoring period T, the power transmission and distribution strategy of the fast charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module; 1. 2) Description of the power transmission and distribution strategy for slow charging mode, including: In the monitoring period T, the power transmission and distribution strategy of the slow charging mode is to manage the charging current, target voltage, charging completion time and charging temperature changes of the i-th lithium battery module; 2) The discharge strategy includes continuous discharge and intermittent discharge, as follows:

2. 1) Description of the transmission and distribution strategy for continuous discharge, including: During the monitoring period T, the power transmission and distribution strategy for continuous discharge is to manage the discharge current, cut-off voltage, discharge completion time and discharge temperature changes of the i-th lithium battery module; 2.2) Description of the transmission and distribution strategy for intermittent discharge, including: In the monitoring period T, the transmission and distribution strategy of intermittent discharge is to manage the discharge current, discharge cycle, cumulative discharge time and discharge temperature change of the i-th lithium battery module; On the main control unit, the discharge efficiency index calculated for the i-th lithium battery module in the current monitoring period T is marked as , the calculation formula is: ； in, is the actual discharge energy measured by the ith lithium battery module in the current monitoring period T, sj is the abbreviation of "actual"; is the theoretical discharge energy measured by the i-th lithium battery module in the current monitoring period T, Abbreviation for "theory"; The actual discharge energy is calculated by the integration method, and the theoretical discharge energy is the rated capacity multiplied by the nominal voltage.

3. The distributed energy storage lithium battery state optimization management method based on intelligent algorithm according to claim 2 is characterized in that: The internal resistance change rate, capacity decay rate, thermal stability coefficient and voltage fluctuation amplitude of the i-th lithium battery module are respectively recorded as , , , ; For the internal resistance change rate , in the current monitoring period T, by measuring the average internal resistance of the i-th lithium battery module during the charging and discharging process, and calculating the change rate of the average internal resistance in the adjacent monitoring periods T and T-1; For the capacity decay rate , obtained by measuring the change rate of the actual capacity and the rated capacity of the i-th lithium battery module during the discharge process within the current monitoring period T; For thermal stability coefficient , obtained by calculating the temperature change rate of the i-th lithium battery module during the charge and discharge cycle in the current monitoring period T; For voltage fluctuation , obtained by measuring the standard deviation of the voltage of the i-th lithium battery module during discharge in the current monitoring period T; Define the battery state evaluation coefficient of the i-th lithium battery module as , the calculation formula is as follows: ； Among them, a, b, c, d are weight coefficients, and ; The specific values ​​of a, b, c, and d are set based on experiments and historical data; Set the battery status evaluation coefficient The valid value range of is (0,1); like , indicating that the health status of the i-th lithium battery module is not good and requires planned maintenance; like , indicating that the health status of the i-th lithium battery module is normal and needs to be monitored regularly; like , indicating that the health status of the i-th lithium battery module is good and can continue to be used.

4. The distributed energy storage lithium battery state optimization management method based on intelligent algorithm according to claim 3 is characterized in that: Prioritization, and first adjustment strategies, include: according to The M lithium battery modules are sorted by the value of . The larger the value, the higher the priority. The priority sorting result is represented by {1, 2, ..., R}, and j∈{1, 2, ..., R}, j represents the index of the lithium battery module sorted with the jth priority, R is the index of the last lithium battery module sorted among the M lithium battery modules, and j∈{1, 2, ..., M}; The first adjustment strategy includes: specifying a fast charging strategy, specifying a slow charging strategy, continuous discharge management, and intermittent discharge management; For the specified fast charging strategy, including: Will comply with The lithium battery modules are classified as high priority, and the fast charging strategy is implemented within the current monitoring period T, the charging current is adjusted to 150% of the rated capacity, the target voltage is 100% of the nominal voltage, and the monitoring charging temperature change does not exceed 5%; and the fast charging completion time is 35% of the rated charging time; For specifying slow charging strategies, including: Will comply with The lithium battery module is classified as medium priority, and a slow charging strategy is implemented in the current monitoring period T. The charging current is adjusted to 48% of the rated capacity, the target voltage is 75% of the nominal voltage, the monitoring charging temperature change does not exceed 3%, and the slow charging completion time is 150% of the rated charging time; For continuous discharge management, including: Will comply with The lithium battery module is classified as low priority, and continuous discharge is implemented within the current monitoring period T. The discharge current is adjusted to 20% of the rated capacity, discharged to 60% of the nominal cut-off voltage, the discharge completion time is 60% of the total time, and the discharge temperature change does not exceed 3%; For intermittent discharge management, including: Will comply with The lithium battery modules are classified as high priority and intermittent discharge management is implemented within the current monitoring period T. The discharge current is 10% to 90% of the rated capacity; the ratio of discharge to rest in the discharge cycle is 65%; the cumulative discharge time is 70% of the total time; and the discharge temperature change is less than 8%.

5. The distributed energy storage lithium battery state optimization management method based on intelligent algorithm according to claim 4 is characterized in that: Perform N load simulations within the corresponding load regulation range, and calculate the discharge efficiency index corresponding to each load simulation value, including: 2.1) According to the priority ranking result {1, 2, ..., R} of each lithium battery module, a load adjustment range is set for each lithium battery module; For high-priority lithium battery modules, the load regulation range is set to [70%, 100%] of the rated load capacity; For medium-priority lithium battery modules, the load regulation range is set to [50%, 70%] of the rated load capacity; For low-priority lithium battery modules, the load regulation range is set to [10%, 50%] of the rated load capacity; 2.2) Divide the load regulation range of each lithium battery module into N equally spaced load simulation values; Each load simulation corresponds to the divided load simulation value one by one, and the N load simulations are represented by the number set as {1, 2, ..., N}, and e∈{1, 2, ..., N}, e represents the index of the e-th load simulation; 2.3) In the current monitoring cycle T, the discharge efficiency index of the jth priority-ranked lithium battery module in the eth load simulation is marked as , the calculation formula is as follows: ； in, is the actual discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation; is the theoretical discharge energy of the jth priority-ranked lithium battery module measured in the eth load simulation; 2.4) Calculate the first change trend coefficient of the discharge efficiency index of each lithium battery module within the corresponding load adjustment range; use the linear regression analysis method, take the load simulation value as the independent variable and the discharge efficiency index as the dependent variable, and calculate the trend slope represented by the first change trend coefficient; The jth lithium battery module is subjected to N load simulations within the corresponding load regulation range, and the obtained load simulation values ​​and corresponding discharge efficiency indicators are ,in is the load simulation value corresponding to the e-th load simulation, and is the corresponding discharge efficiency; The first change trend coefficient of the discharge efficiency index of the jth lithium battery module within the corresponding load regulation range is recorded as , the calculation formula is as follows: ； If the first change trend coefficient A positive number indicates that the overall discharge efficiency index improves when the load increases; If the first change trend coefficient A negative number indicates that the overall discharge efficiency index decreases when the load increases.

6. The distributed energy storage lithium battery state optimization management method based on intelligent algorithm according to claim 5 is characterized in that: The relevant trend coefficient and the first change trend coefficient are analyzed to generate a calibration strategy for optimizing the first adjustment strategy and the load adjustment range, specifically including: Determine the load regulation range of the jth lithium battery module, and calculate the correlation trend coefficient of the discharge efficiency index corresponding to all adjacent load simulation values ​​within the load regulation range, and express the correlation trend coefficient as ; 3.1) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are greater than 1, the jth lithium battery module is positively correlated with the discharge efficiency index within the load regulation range, and a first-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, while the upper limit of the load regulation range of the current lithium battery module is expanded by 5%; The first-level forward optimization calibration strategy includes: positively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%; the positive adjustment is intended to adjust in a direction that is beneficial to the current goal or strategy; 3.2) If the first change trend coefficient is a positive number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last load simulation of >1 is set as the upper limit of the load adjustment range of the current lithium battery module, and a second-level forward optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first-level forward optimization calibration strategy is 30% larger than that of the second-level forward optimization calibration strategy; 3.3) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range If all are less than 1, the jth lithium battery module is negatively correlated with the discharge efficiency index within the load regulation range. A first-level negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the upper limit of the load regulation range of the current lithium battery module is reduced by 5%; The first-level negative optimization calibration strategy includes: negatively adjusting the values ​​set in the specified fast charging strategy, specified slow charging strategy, continuous discharge management, and intermittent discharge management by 3%; the negative adjustment is intended to adjust in a direction that is not conducive to the current goal or strategy; 3.4) If the first change trend coefficient is a negative number, and the correlation trend coefficients corresponding to all adjacent load simulation values ​​within the load regulation range exist >1, and <1, the load simulation will be the relevant trend coefficient in {1,2,…,N} The load simulation value corresponding to the last load simulation of >1 is set as the upper limit of the load regulation range of the current lithium battery module, and a secondary negative optimization calibration strategy is provided for the first adjustment strategy of the power transmission and distribution strategy, and the amplitude of the first negative optimization calibration strategy is 15% smaller than that of the second negative optimization calibration strategy.

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