Battery energy storage management method and system based on data center

Abstract

The application is suitable for the technical field of energy storage management, and particularly relates to a battery energy storage management method and system based on a data center.The method comprises the following steps: obtaining the power information of each energy storage module in a battery pack, dividing the energy storage modules into basic energy storage modules and dynamic energy storage modules; charging the basic energy storage modules, obtaining charging parameter information, and predicting the charging duration of the basic energy storage modules; scheduling the basic energy storage modules with short charging duration based on the charging duration, and dynamically replacing the basic energy storage modules through the dynamic energy storage modules; counting the replacement times of each energy storage module, identifying the abnormality, and determining the abnormal energy storage module.The application divides the energy storage modules, dynamically replaces the energy storage modules being charged in the charging process, ensures that the charging speed of the whole battery pack remains consistent, avoids the situation that the power of the energy storage modules in the battery pack is inconsistent, and improves the performance uniformity of the energy storage modules.

Description

Technical Field

[0001] This invention belongs to the field of energy storage management technology, and particularly relates to a battery energy storage management method and system based on data centers. Background Technology

[0002] A battery energy storage management system is a technological framework for the effective monitoring, control, and maintenance of battery energy storage systems. It ensures that the battery operates within safe limits by monitoring battery status parameters in real time, such as voltage, current, temperature, and state of charge. Simultaneously, it optimizes the charging and discharging process to extend battery life and improve energy efficiency. This system is of great significance for improving the efficiency of renewable energy utilization, balancing grid load, and providing emergency backup power.

[0003] In the current battery energy storage management process, due to the differences in energy storage performance of different energy storage units, the energy storage time of energy storage units is prone to vary, resulting in some energy storage modules being overcharged and others being undercharged. Summary of the Invention

[0004] The purpose of this invention is to provide a battery energy storage management method based on data centers, which aims to solve the problem that due to the differences in energy storage performance of different energy storage units, the energy storage time of energy storage units is easily different, resulting in some energy storage modules being overcharged and others being undercharged.

[0005] This invention is implemented as follows: a battery energy storage management method based on a data center, the method comprising:

[0006] Obtain the power information of each energy storage module in the battery pack, and divide the energy storage modules into basic energy storage modules and dynamic energy storage modules. The ratio of the number of dynamic energy storage modules to basic energy storage modules is a preset value.

[0007] The basic energy storage module is charged, charging parameter information is obtained, and the charging time of the basic energy storage module is predicted based on the charging parameter information.

[0008] Based on the charging time, basic energy storage modules with short charging times are scheduled, and dynamic replacement is performed through dynamic energy storage modules.

[0009] The number of replacements for each energy storage module is counted, and energy storage modules with a replacement count lower than a preset value are selected for anomaly identification to determine the energy storage modules that have anomalies.

[0010] Preferably, the step of charging the basic energy storage module, obtaining charging parameter information, and predicting the charging time of the basic energy storage module based on the charging parameter information specifically includes:

[0011] Charge all basic energy storage modules, monitor the charging process of each basic energy storage module in real time, and record the corresponding charging parameter information, which includes at least charging time, charging temperature and charging current.

[0012] Based on the charging parameter information, time-temperature curves and temperature-current curves are constructed, and function simulations are performed to obtain time-temperature functions and temperature-current functions.

[0013] The charging amount is predicted based on the time-temperature function and the temperature-current function, and the charging time is predicted based on the remaining capacity of the basic energy storage module.

[0014] Preferably, the step of scheduling basic energy storage modules with short charging times based on charging duration and dynamically replacing them through dynamic energy storage modules includes:

[0015] The charging time of each basic energy storage module is queried according to a preset time interval, and the maximum difference in charging time is calculated.

[0016] The maximum difference in charging time is compared with the preset time value. If it is greater than the preset time value, the basic energy storage module with the shortest charging time is replaced with a set of dynamic energy storage modules.

[0017] The query time interval is redefined based on the maximum difference in charging time, and the new query time interval takes effect immediately.

[0018] Preferably, the step of counting the number of replacements for each energy storage module, selecting energy storage modules with a replacement count lower than a preset value for anomaly identification, and determining the energy storage modules that have anomalies specifically includes:

[0019] The number of replacements for each energy storage module is counted. If the number of replacements for a single energy storage module exceeds the preset value within the preset charging time, an anomaly is identified.

[0020] Select energy storage modules that have been replaced less than the set number of times, determine abnormal heating based on the relative position of the energy storage modules, and mark the energy storage modules that have abnormalities.

[0021] Real-time monitoring is performed during the charging process to record the charging data of each energy storage module and store it in the data center. The data center then analyzes the charging distribution of the energy storage modules.

[0022] Preferably, in the step of determining abnormal heating based on the relative position of the energy storage modules, a two-dimensional coordinate system is constructed based on the position of the energy storage modules, and a module matrix is ​​constructed in the two-dimensional coordinate system. Each element in the module matrix represents an energy storage module. Elements of energy storage modules that have been replaced less than a set number of times are extracted. Each time, an energy storage module that has been replaced less than a set number of times is selected, and the energy storage module is turned off by electronic fuse. Normal charging continues. The charging parameters of its adjacent elements are monitored. If the charging speed of the energy storage module corresponding to the adjacent element increases, it is determined that the turned-off energy storage module is in an abnormal state.

[0023] Another object of the present invention is to provide a data center-based battery energy storage management system, the system comprising:

[0024] The module division module is used to obtain the power information of each energy storage module in the battery pack, and divide the energy storage modules into basic energy storage modules and dynamic energy storage modules. The ratio of the number of dynamic energy storage modules to basic energy storage modules is a preset value.

[0025] The charging parameter recording module is used to charge the basic energy storage module, acquire charging parameter information, and predict the charging time of the basic energy storage module based on the charging parameter information.

[0026] The dynamic replacement module is used to schedule basic energy storage modules with short charging times based on charging duration, and to dynamically replace the energy storage modules.

[0027] The anomaly identification module is used to count the number of replacements for each energy storage module, select energy storage modules with a replacement count lower than a preset value for anomaly identification, and determine the energy storage modules that have anomalies.

[0028] Preferably, the charging parameter recording module includes:

[0029] The data recording unit is used to charge all basic energy storage modules, monitor the charging process of each basic energy storage module in real time, and record the corresponding charging parameter information. The charging parameter information includes at least charging time, charging temperature and charging current.

[0030] The curve construction unit is used to construct time-temperature curves and temperature-current curves based on charging parameter information, and to perform function simulation to obtain time-temperature functions and temperature-current functions.

[0031] The charging prediction unit is used to predict the charging amount based on the time-temperature function and the temperature-current function, and to predict the charging time based on the remaining capacity of the basic energy storage module.

[0032] Preferably, the dynamic replacement module includes:

[0033] The charging time statistics unit is used to query the charging time of each basic energy storage module at preset time intervals and calculate the maximum difference in charging time.

[0034] The module replacement unit is used to compare the maximum difference in charging time with the preset time value. If it is greater than the preset time value, the basic energy storage module with the shortest charging time is replaced with a set of dynamic energy storage modules.

[0035] The time interval adjustment unit is used to redetermine the query time interval based on the maximum difference in charging time, and the new query time interval takes effect immediately.

[0036] Preferably, the anomaly detection module includes:

[0037] The replacement count unit is used to count the number of replacements for each energy storage module. If the number of replacements for a single energy storage module exceeds the preset value within the preset charging time, an anomaly is identified.

[0038] An anomaly marking unit is used to select energy storage modules that have been replaced less than a set number of times, determine abnormal heating based on the relative position of the energy storage modules, and mark the energy storage modules that have anomalies.

[0039] The data recording unit is used to monitor the charging process in real time, record the charging records of a single energy storage module, store them in the data center, and analyze the charging distribution of the energy storage module through the data center.

[0040] Preferably, in the step of determining abnormal heating based on the relative position of the energy storage modules, a two-dimensional coordinate system is constructed based on the position of the energy storage modules, and a module matrix is ​​constructed in the two-dimensional coordinate system. Each element in the module matrix represents an energy storage module. Elements of energy storage modules that have been replaced less than a set number of times are extracted. Each time, an energy storage module that has been replaced less than a set number of times is selected, and the energy storage module is turned off by electronic fuse. Normal charging continues. The charging parameters of its adjacent elements are monitored. If the charging speed of the energy storage module corresponding to the adjacent element increases, it is determined that the turned-off energy storage module is in an abnormal state.

[0041] The battery energy storage management method based on data centers provided by this invention divides the energy storage modules and dynamically replaces the charging energy storage modules during the charging process to ensure that the overall charging speed of the battery pack remains consistent. This avoids inconsistent power levels among the energy storage modules within the battery pack and improves the performance uniformity of the energy storage modules. Attached Figure Description

[0042] Figure 1 A flowchart illustrating a data center-based battery energy storage management method provided in an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps for charging a basic energy storage module, obtaining charging parameter information, and predicting the charging time of the basic energy storage module based on the charging parameter information, provided in an embodiment of the present invention.

[0044] Figure 3 A flowchart illustrating the steps of scheduling basic energy storage modules with short charging times based on charging duration and dynamically replacing them through dynamic energy storage modules, as provided in an embodiment of the present invention.

[0045] Figure 4 The flowchart below shows the steps for identifying abnormal energy storage modules by counting the number of replacements for each energy storage module and selecting energy storage modules with a replacement count lower than a preset value.

[0046] Figure 5 An architecture diagram of a data center-based battery energy storage management system provided in an embodiment of the present invention;

[0047] Figure 6 This is an architecture diagram of the charging parameter recording module provided in an embodiment of the present invention;

[0048] Figure 7 This is an architecture diagram of the dynamic replacement module provided in an embodiment of the present invention;

[0049] Figure 8 This is an architecture diagram of the anomaly recognition module provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] like Figure 1 The diagram shows a flowchart of a data center-based battery energy storage management method provided in an embodiment of the present invention. The method includes:

[0052] S100: Obtain the power information of each energy storage module in the battery pack, divide the energy storage modules into basic energy storage modules and dynamic energy storage modules, and set the ratio of the number of dynamic energy storage modules to basic energy storage modules as a preset value.

[0053] In this step, the power information of each energy storage module in the battery pack is obtained. During charging, all energy storage modules in the battery pack are numbered, and the remaining power in each energy storage module is read. A portion of the energy storage modules are selected as dynamic energy storage modules. The ratio of the number of dynamic energy storage modules to the total number of energy storage modules is a preset value, such as 3%. When the battery pack contains 100 energy storage modules, three energy storage modules are selected as dynamic energy storage modules, and the remaining energy storage modules are used as basic energy storage modules. During charging, the basic energy storage modules are charged first, and the dynamic energy storage modules are used for charging regulation.

[0054] The S200 charges the basic energy storage module, acquires charging parameter information, and predicts the charging time of the basic energy storage module based on the charging parameter information.

[0055] In this step, the basic energy storage modules are charged. Data generated during the charging process is recorded to obtain charging parameter information. The energy storage modules are connected in parallel and have the same charging voltage. However, due to different losses in different energy storage modules, their internal resistances also differ, resulting in different charging currents. This information is recorded, and the charging time required to fully charge each basic energy storage module is estimated based on its real-time charging amount. Because of differences in losses and remaining capacity, the charging time for different basic energy storage modules will vary. Modules with more remaining capacity and faster charging speeds will have shorter charging times, and vice versa. If charging is performed simultaneously, some energy storage modules will be fully charged, while the remaining modules will still be undercharged.

[0056] The S300 schedules basic energy storage modules with short charging times based on charging duration and dynamically replaces them through dynamic energy storage modules.

[0057] In this step, basic energy storage modules with shorter charging times are scheduled based on their charging duration. Each energy storage module is equipped with an electronic fuse, which controls the on / off state of each module. The electronic fuse can control whether the energy storage module is connected to or disconnected from the charging system. During the charging process, if the charging time of different energy storage modules differs too much, the energy storage module with the shortest charging time is disconnected. At the same time, a corresponding number of dynamic energy storage modules are connected. During this process, since the number of energy storage modules in the charging state is the same, the charging current of each energy storage module remains stable while the charging voltage remains constant. After a certain charging time, the dynamic energy storage modules replace the disconnected energy storage modules to reduce the difference in charging time between different energy storage modules, so that different energy storage modules can be fully charged at the same time.

[0058] S400 counts the number of replacements for each energy storage module, selects energy storage modules with fewer than the preset number of replacements for anomaly identification, and determines the energy storage module that has an anomaly.

[0059] In this step, the number of replacements for each energy storage module is counted. When some energy storage modules malfunction, their charging speed will slow down. For example, if an energy storage module ages, its internal resistance will increase, causing it to heat up during charging and affecting charging efficiency. In this case, the charging time of the energy storage module will increase. In order to maintain the balance of charging, the energy storage modules that are charging normally will be temporarily replaced. Therefore, the energy storage modules that are replaced more often will have a faster charging speed, and conversely, the energy storage modules that are replaced less often will have a slower charging speed. An anomaly judgment is then made to identify the energy storage modules that are malfunctioning.

[0060] like Figure 2 As shown, in a preferred embodiment of the present invention, the steps of charging the basic energy storage module, obtaining charging parameter information, and predicting the charging time of the basic energy storage module based on the charging parameter information specifically include:

[0061] S201 charges all basic energy storage modules, monitors the charging process of each basic energy storage module in real time, and records the corresponding charging parameter information, which includes at least charging time, charging temperature, and charging current.

[0062] In this step, all basic energy storage modules are charged. At the beginning of charging, only the remaining power of each energy storage module can be obtained, and its charging speed cannot be calculated. In order to determine its charging speed and estimate its charging time, the charging process of each basic energy storage module is monitored and the charging parameter information is recorded. The charging parameter information includes at least the charging time, charging temperature and charging current. During the charging process, the energy storage module will generate heat. The higher its internal resistance, the greater the heat generation, and the lower the charging speed.

[0063] S202, based on the charging parameter information, constructs time-temperature curves and temperature-current curves, and performs function simulation to obtain time-temperature functions and temperature-current functions.

[0064] In this step, a time-temperature curve and a temperature-current curve are constructed based on the charging parameter information. Two sets of two-dimensional coordinate systems are constructed. In one set of two-dimensional coordinate systems, the time-temperature curve is constructed with time as the abscissa and temperature as the ordinate. In the other set of two-dimensional coordinate systems, temperature is constructed with temperature as the abscissa and current value as the ordinate, thus obtaining a set of time-temperature curves and a set of temperature-current curves. The two sets of curves are fitted to the corresponding functions using a function fitting tool to obtain the time-temperature function T(t) and the temperature-current curve I(T).

[0065] S203 predicts the charging amount based on the time-temperature function and the temperature-current function, and predicts the charging time based on the remaining power of the basic energy storage module.

[0066] In this step, the charging amount is predicted based on the time-temperature function and the temperature-current function. When making the prediction, the time-temperature function T(t) is predicted according to the preset time gradient. For example, if the time gradient is set to Δt, the time is increased by Δt each time and substituted into the time-temperature function T(t) to predict the temperature at the corresponding time. Then, the temperature is substituted into the temperature-current curve I(T) to predict the charging current at the corresponding time. The charging amount is calculated based on the current and the time Δt, and the charging time required to fully charge each basic energy storage module is calculated accordingly.

[0067] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of scheduling basic energy storage modules with short charging times based on charging duration and dynamically replacing them through dynamic energy storage modules includes:

[0068] S301 queries the charging time of each basic energy storage module according to a preset time interval and calculates the maximum difference in charging time.

[0069] In this step, the charging time of each basic energy storage module is queried at preset time intervals. The basic energy storage modules are sorted based on the charging time. The basic energy storage module with the longest charging time has the slowest charging speed. The difference between the longest charging time and the shortest charging time is calculated to obtain the maximum difference.

[0070] S302 compares the maximum difference in charging time with the preset time value. If it is greater than the preset time value, the basic energy storage module with the shortest charging time is replaced with a set of dynamic energy storage modules.

[0071] In this step, the maximum difference in charging time is compared with the preset time value. If the maximum difference in charging time is greater than the preset time value, it indicates that there is an uneven charging speed among the energy storage modules during this charging process. In order to maintain the consistency of charging speed, the basic energy storage module with the shortest charging time is selected and disconnected from the charging system. At the same time, a group of dynamic energy storage modules is connected to the charging system to ensure that the total number of energy storage modules in the charging state remains unchanged. This process is repeated.

[0072] S303, redetermine the query time interval based on the maximum difference in charging time, and the new query time interval takes effect immediately.

[0073] In this step, the query time interval is redefined based on the maximum difference in charging time. The larger the maximum difference, the greater the gap in charging time during the previous round of dynamic adjustment, and the query time interval needs to be shortened to achieve a faster adjustment frequency. Conversely, the larger the maximum difference, the stronger the charging consistency of the energy storage module, and a larger query time interval can be used. The new query time interval takes effect immediately.

[0074] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of counting the number of replacements for each energy storage module, selecting energy storage modules with a replacement count lower than a preset value for anomaly identification, and determining the energy storage module that has an anomaly specifically includes:

[0075] S401: The number of replacements for each energy storage module is counted. If the number of replacements for a single energy storage module exceeds the preset value within the preset charging time, an anomaly is identified.

[0076] In this step, the number of replacements for each energy storage module is counted. If the number of replacements for a certain energy storage module exceeds the preset value within the preset charging time, it indicates that the charging speed is extremely uneven during this charging process, and there may be an abnormal energy storage module. For example, if a certain energy storage module overheats abnormally and its charging speed decreases significantly, then frequent dynamic adjustments will be made, and anomaly identification will be performed at this time.

[0077] S402, Select energy storage modules that have been replaced less than the set number of times, determine abnormal heating based on the relative position of the energy storage modules, and mark the energy storage modules that have abnormalities.

[0078] In this step, energy storage modules that have been replaced less than a set number of times are selected. A two-dimensional coordinate system is constructed based on the location of the energy storage modules, and a module matrix is ​​constructed in the two-dimensional coordinate system. Each element in the module matrix represents an energy storage module. Elements of energy storage modules that have been replaced less than a set number of times are extracted. Each time, an energy storage module that has been replaced less than a set number of times is selected, and the energy storage module is turned off by electronic fuse. Normal charging continues. The charging parameters of its adjacent elements are monitored. If the charging speed of the energy storage module corresponding to the adjacent element increases, it is determined that the turned-off energy storage module is in an abnormal state.

[0079] The S403 performs real-time monitoring during the charging process, records the charging data of a single energy storage module, stores it in the data center, and analyzes the charging distribution of the energy storage module through the data center.

[0080] In this step, real-time monitoring is performed during the charging process. In order to analyze the working status of each energy storage module, all charging records are recorded in the data center. The data in the data center is updated regularly. The charging amount of each group of energy storage modules is analyzed each time to construct a charging amount prediction function. The charging amount prediction function is used to estimate the lifespan and provide maintenance suggestions for users.

[0081] like Figure 5 As shown, this is a data center-based battery energy storage management system provided in an embodiment of the present invention. The system includes:

[0082] The module division module 100 is used to obtain the power information of each energy storage module in the battery pack, and divide the energy storage modules into basic energy storage modules and dynamic energy storage modules. The ratio of the number of dynamic energy storage modules to basic energy storage modules is a preset value.

[0083] In this system, the module division module 100 obtains the power information of each energy storage module in the battery pack. During charging, all energy storage modules in the battery pack are numbered, and the remaining power in each energy storage module is read. A portion of the energy storage modules are selected as dynamic energy storage modules. The ratio of the number of dynamic energy storage modules to the total number of energy storage modules is a preset value, such as 3%. When the battery pack contains 100 energy storage modules, three energy storage modules are selected as dynamic energy storage modules, and the remaining energy storage modules are used as basic energy storage modules. During charging, the basic energy storage modules are charged first, and the dynamic energy storage modules are used for charging regulation.

[0084] The charging parameter recording module 200 is used to charge the basic energy storage module, acquire charging parameter information, and predict the charging time of the basic energy storage module based on the charging parameter information.

[0085] In this system, the charging parameter recording module 200 charges the basic energy storage modules. By charging the basic energy storage modules, the data generated during the charging process is recorded, thus obtaining the charging parameter information. The energy storage modules are connected in parallel and have the same charging voltage during charging. However, due to the different losses of different energy storage modules, their internal resistances are also different, resulting in different charging currents. The above information is recorded, and the charging time required to fully charge each basic energy storage module is estimated based on the real-time charging amount of the basic energy storage modules. Due to the different losses of the basic energy storage modules and the differences in the remaining power, the charging time of different basic energy storage modules will vary. Energy storage modules with more remaining power and faster charging speeds will have shorter charging times, and vice versa. If charging is performed synchronously, some energy storage modules will be fully charged, while the remaining energy storage modules will still be in an undercharged state.

[0086] The dynamic replacement module 300 is used to schedule basic energy storage modules with short charging times based on charging duration, and to dynamically replace the energy storage modules.

[0087] In this system, the dynamic replacement module 300 schedules basic energy storage modules with short charging times based on charging duration. Each energy storage module is equipped with an electronic fuse, which controls the on / off state of each module. The electronic fuse can control whether the energy storage module is connected to or disconnected from the charging system. During charging, if the charging time of different energy storage modules differs too much, the module with the shortest charging time is disconnected, and a corresponding number of dynamic energy storage modules are connected. During this process, since the number of energy storage modules in the charging state is the same, the charging current of each module remains stable while the charging voltage remains constant. After a certain charging time, the dynamic energy storage modules replace the disconnected modules to reduce the difference in charging time between different energy storage modules, allowing different energy storage modules to be fully charged simultaneously.

[0088] The anomaly identification module 400 is used to count the number of replacements for each energy storage module, select energy storage modules with a replacement count lower than a preset value for anomaly identification, and determine the energy storage modules that have anomalies.

[0089] In this system, the anomaly identification module 400 counts the number of replacements for each energy storage module. When some energy storage modules malfunction, their charging speed will slow down. For example, if an energy storage module ages, its internal resistance will increase, causing it to heat up during charging and affecting charging efficiency. In this case, the charging time of the energy storage module will increase. In order to maintain the balance of charging, the energy storage modules that are charging normally will be temporarily replaced. Therefore, the energy storage modules that are replaced more often will have a faster charging speed, and conversely, the energy storage modules that are replaced less often will have a slower charging speed. Anomaly determination will be made to identify the energy storage modules that are malfunctioning.

[0090] like Figure 6 As shown, in a preferred embodiment of the present invention, the charging parameter recording module 200 includes:

[0091] The data recording unit 201 is used to charge all basic energy storage modules, monitor the charging process of each basic energy storage module in real time, and record the corresponding charging parameter information. The charging parameter information includes at least the charging time, charging temperature, and charging current.

[0092] In this module, the data recording unit 201 charges all basic energy storage modules. At the beginning of charging, it can only obtain the remaining power of each energy storage module and cannot calculate its charging speed. In order to determine its charging speed and estimate its charging time, the charging process of each basic energy storage module is monitored and the charging parameter information is recorded. The charging parameter information includes at least the charging time, charging temperature and charging current. During the charging process, the energy storage module will generate heat. The greater its internal resistance, the greater the heat generation, and the lower the charging speed.

[0093] Curve construction unit 202 is used to construct time-temperature curves and temperature-current curves based on charging parameter information, and to perform function simulation to obtain time-temperature functions and temperature-current functions.

[0094] In this module, the curve construction unit 202 constructs a time-temperature curve and a temperature-current curve based on the charging parameter information, and constructs two sets of two-dimensional coordinate systems. In one set of two-dimensional coordinate systems, the time-temperature curve is constructed with time as the abscissa and temperature as the ordinate. In the other set of two-dimensional coordinate systems, temperature is used as the abscissa and current value is used as the ordinate, thus obtaining a set of time-temperature curves and a set of temperature-current curves. The two sets of curves are fitted to the corresponding functions by the function fitting tool to obtain the time-temperature function T(t) and the temperature-current curve I(T).

[0095] The charging prediction unit 203 is used to predict the charging amount based on the time-temperature function and the temperature-current function, and to predict the charging time based on the remaining power of the basic energy storage module.

[0096] In this module, the charging prediction unit 203 predicts the charging amount based on the time-temperature function and the temperature-current function. When making the prediction, the time-temperature function T(t) is predicted according to the preset time gradient. For example, if the time gradient is set to Δt, the time Δt is increased each time and substituted into the time-temperature function T(t) to predict the temperature at the corresponding time. Then, the temperature is substituted into the temperature-current curve I(T) to predict the charging current at the corresponding time. The charging amount is calculated based on the current and the time Δt, and the charging time required to fully charge each basic energy storage module is calculated accordingly.

[0097] like Figure 7 As shown, in a preferred embodiment of the present invention, the dynamic replacement module 300 includes:

[0098] The charging time statistics unit 301 is used to query the charging time of each basic energy storage module according to a preset time interval and calculate the maximum difference in charging time.

[0099] In this module, the charging time statistics unit 301 queries the charging time of each basic energy storage module according to a preset time interval, sorts the basic energy storage modules based on the charging time, and the basic energy storage module with the longest charging time has the slowest charging speed. The difference between the longest charging time and the shortest charging time is calculated to obtain the maximum difference.

[0100] The module replacement unit 302 is used to compare the maximum difference in charging time with the preset time value. If it is greater than the preset time value, the basic energy storage module with the shortest charging time is replaced with a set of dynamic energy storage modules.

[0101] In this module, the module replacement unit 302 compares the maximum difference in charging time with the preset time value. When the maximum difference in charging time is greater than the preset time value, it indicates that there is an imbalance in the charging speed of the energy storage modules during the current charging process. In order to maintain the consistency of charging speed, the basic energy storage module with the shortest charging time is selected and disconnected from the charging system. At the same time, a group of dynamic energy storage modules are connected to the charging system to ensure that the total number of energy storage modules in the charging state remains unchanged, and this process is repeated.

[0102] The time interval adjustment unit 303 is used to redetermine the query time interval based on the maximum difference in charging time, and the new query time interval takes effect immediately.

[0103] In this module, the time interval adjustment unit 303 redetermines the query time interval based on the maximum difference in charging time. The larger the maximum difference, the greater the gap in charging time during the previous round of dynamic adjustment, and the shorter the query time interval needs to be to achieve a faster adjustment frequency. Conversely, the larger the maximum difference, the stronger the charging consistency of the energy storage module, and a larger query time interval can be used. The new query time interval takes effect immediately.

[0104] like Figure 8 As shown, in a preferred embodiment of the present invention, the anomaly identification module 400 includes:

[0105] The replacement count statistics unit 401 is used to count the replacement count of each energy storage module. If the replacement count of a single energy storage module exceeds the preset value within the preset charging time, an anomaly is identified.

[0106] In this module, the replacement count unit 401 counts the replacement count of each energy storage module. If the replacement count of a certain energy storage module exceeds the preset value within the preset charging time, it indicates that the charging speed is extremely uneven during the charging process, and there may be an abnormal energy storage module. For example, if a certain energy storage module heats up abnormally and its charging speed decreases significantly, then it will be dynamically adjusted frequently, and anomaly identification will be performed at this time.

[0107] The abnormality marking unit 402 is used to select energy storage modules that have been replaced less than a set number of times, determine abnormal heating based on the relative position of the energy storage modules, and mark the energy storage modules that have abnormalities.

[0108] In this module, the anomaly marking unit 402 selects energy storage modules that have been replaced less than a set number of times. It constructs a two-dimensional coordinate system based on the location of the energy storage modules and builds a module matrix in the two-dimensional coordinate system. Each element in the module matrix represents an energy storage module. It extracts the elements of the energy storage modules that have been replaced less than a set number of times. Each time, it selects an energy storage module that has been replaced less than a set number of times and shuts it down through an electronic fuse. It then continues to charge normally. It monitors the charging parameters of its adjacent elements. If the charging speed of the energy storage module corresponding to the adjacent element increases, it determines that the shut-down energy storage module is in an abnormal state.

[0109] The data recording unit 403 is used to monitor in real time during the charging process, record the charging record of a single energy storage module, store it in the data center, and analyze the charging distribution of the energy storage module through the data center.

[0110] In this module, the data recording unit 403 monitors the charging process in real time. In order to analyze the working status of each energy storage module, all charging records are recorded in the data center. The data in the data center is updated regularly. The charging amount of each group of energy storage modules is analyzed each time to construct a charging amount prediction function. The charging amount prediction function is used to estimate the lifespan and provide maintenance suggestions for users.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for battery energy storage management based on data center, characterized in that, The method comprises: Obtaining the power information of each energy storage module in the battery pack, dividing the energy storage modules into basic energy storage modules and dynamic energy storage modules, selecting a part of the energy storage modules as the dynamic energy storage modules, and the remaining energy storage modules as the basic energy storage modules, and the number ratio of the dynamic energy storage modules to the basic energy storage modules is a preset value; Charging the basic energy storage modules, obtaining charging parameter information, and predicting the charging duration of the basic energy storage modules based on the charging parameter information; Scheduling the basic energy storage modules with short charging time based on the charging duration, and dynamically replacing the basic energy storage modules with the dynamic energy storage modules; Counting the replacement times of each energy storage module, selecting the energy storage modules with replacement times lower than a preset value for abnormal identification, and determining the abnormal energy storage modules; The step of scheduling the basic energy storage modules with short charging time based on the charging duration, and dynamically replacing the basic energy storage modules with the dynamic energy storage modules, comprises: Querying the charging duration of each basic energy storage module at a preset time interval, and calculating the maximum difference value of the charging duration; Comparing the maximum difference value of the charging duration with a duration preset value, if the maximum difference value is greater than the duration preset value, replacing the basic energy storage module with the shortest charging duration with a group of dynamic energy storage modules; Re-determining the query time interval based on the maximum difference value of the charging duration, and the new query time interval takes effect immediately.

2. The data center based battery energy storage management method of claim 1, wherein, The step of charging the basic energy storage modules, obtaining the charging parameter information, and predicting the charging duration of the basic energy storage modules based on the charging parameter information, specifically comprises: Charging all basic energy storage modules, monitoring the charging process of each basic energy storage module in real time, and recording the corresponding charging parameter information, wherein the charging parameter information at least includes charging time, charging temperature and charging current; Based on the charging parameter information, constructing a time-temperature curve and a temperature-current curve, and performing function simulation to obtain a time-temperature function and a temperature-current function; According to the time-temperature function and the temperature-current function, predicting the charging capacity, and predicting the charging duration according to the remaining capacity of the basic energy storage modules.

3. The data center based battery energy storage management method of claim 1, wherein, The step of counting the replacement times of each energy storage module, selecting the energy storage modules with replacement times lower than a preset value for abnormal identification, and determining the abnormal energy storage modules, specifically comprises: Counting the replacement times of each energy storage module, if the replacement times of a single energy storage module exceed a number preset value within a preset charging duration, determining to perform abnormal identification; Selecting the energy storage modules with replacement times less than a set number, determining abnormal heating according to the relative position relationship of the energy storage modules, and marking the abnormal energy storage modules; Monitoring in real time during the charging process, recording the charging record of a single energy storage module, storing it to a data center, and analyzing the charging capacity distribution of the energy storage modules through the data center.

4. The data center based battery energy storage management method of claim 3, wherein, In the step of determining the abnormal heat generation according to the relative position relationship of the energy storage modules, a two-dimensional coordinate system is constructed based on the positions of the energy storage modules, and a module matrix is constructed in the two-dimensional coordinate system, each element in the module matrix representing an energy storage module, elements of the energy storage modules with replacement times less than a set number of times are extracted, one energy storage module with a replacement time less than the set number of times is selected each time, the energy storage module is closed through an electronic fuse, normal charging is continued, charging parameter monitoring is performed on adjacent elements, and if the charging speed of the energy storage modules corresponding to the adjacent elements is improved, it is determined that the closed energy storage module is in an abnormal state.

5. A data center based battery energy storage management system, characterized by, The system comprises: A module division module is configured to acquire power information of each energy storage module in the battery pack, divide the energy storage modules into basic energy storage modules and dynamic energy storage modules, select a part of the energy storage modules as the dynamic energy storage modules, and select the remaining energy storage modules as the basic energy storage modules, wherein the number ratio of the dynamic energy storage modules to the basic energy storage modules is a preset value. A charging parameter recording module is configured to charge the basic energy storage modules, acquire charging parameter information, and predict the charging duration of the basic energy storage modules based on the charging parameter information. A dynamic replacement module is configured to schedule the basic energy storage modules with short charging time based on the charging duration, and replace the basic energy storage modules with the dynamic energy storage modules. An abnormality identification module is configured to count the replacement times of each energy storage module, select the energy storage modules with replacement times less than a preset value for abnormality identification, and determine the energy storage modules with abnormalities. The dynamic replacement module comprises: A charging duration statistical unit is configured to query the charging duration of each basic energy storage module at preset time intervals, and calculate the maximum difference of the charging duration. A module replacement unit is configured to compare the maximum difference of the charging duration with a duration preset value, and replace the basic energy storage module with the shortest charging duration with a group of dynamic energy storage modules if the maximum difference is greater than the duration preset value. A time interval adjustment unit is configured to determine the query time interval again based on the maximum difference of the charging duration, and the new query time interval takes effect immediately.

6. The data center based battery energy storage management system of claim 5, wherein, The charging parameter recording module comprises: A data recording unit is configured to charge all the basic energy storage modules, monitor the charging process of each basic energy storage module in real time, and record corresponding charging parameter information, wherein the charging parameter information at least includes charging time, charging temperature, and charging current. A curve construction unit is configured to construct a time-temperature curve and a temperature-current curve based on the charging parameter information, perform function simulation to obtain a time-temperature function and a temperature-current function. A charging prediction unit is configured to predict the charging capacity according to the time-temperature function and the temperature-current function, and predict the charging duration according to the remaining power of the basic energy storage modules.

7. The data center based battery energy storage management system of claim 5, wherein, The abnormality identification module comprises: A replacement time statistical unit is configured to count the replacement times of each energy storage module, and determine to perform abnormality identification if the replacement times of a single energy storage module exceed a number preset value within a preset charging duration. An abnormality marking unit is configured to select the energy storage modules with replacement times less than a set number of times, determine the abnormal heat generation according to the relative position relationship of the energy storage modules, and mark the energy storage modules with abnormalities. The data recording unit is used for real-time monitoring during the charging process, recording the charging record of the single energy storage module, storing the charging record into the data center, and analyzing the charging capacity distribution of the energy storage module through the data center.

8. The data center based battery energy storage management system of claim 7, wherein, In the step of determining the abnormal heating according to the relative position relationship of the energy storage modules, a two-dimensional coordinate system is constructed based on the positions of the energy storage modules, and a module matrix is constructed in the two-dimensional coordinate system, each element in the module matrix representing an energy storage module, elements of the energy storage modules with the replacement times less than the set number of times are extracted, one energy storage module with the replacement times less than the set number of times is selected each time, the energy storage module is closed through the electronic fuse, normal charging is continuously performed, and charging parameter monitoring is performed on the adjacent elements, if the charging speed of the energy storage modules corresponding to the adjacent elements is improved, it is determined that the closed energy storage module is in an abnormal state.

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