An energy storage system and method based on intelligent energy management

Abstract

The application discloses an energy storage system and method based on intelligent energy management, relates to the technical field of electric energy storage systems, and solves the technical problem that the existing energy storage equipment operation management system is difficult to timely adjust the state of the energy storage battery when facing large-scale energy storage battery state supervision, thereby causing the overall energy storage efficiency to decrease; the application provides an energy storage method based on intelligent energy management, which comprises the following steps: collecting the operation data and temperature data of each energy storage battery in the energy storage equipment based on collected analysis signals; generating a plurality of detection groups and the detection priority corresponding to each energy storage battery based on the temperature data; sequentially analyzing the energy storage battery based on the operation data and the temperature data in the order of the detection priority, so as to obtain the battery state corresponding to the energy storage battery; and generating a control signal for controlling the operation of the energy storage battery based on the battery state; and the energy storage efficiency of the energy storage equipment is improved.

Description

Technical Field

[0001] This application belongs to the field of energy storage system technology, specifically an energy storage system and method based on intelligent energy management. Background Technology

[0002] Energy storage power stations are critical infrastructure in power systems, used to store electrical energy for future use. The most common existing energy storage power stations store electrical energy in battery arrays, converting it back into electricity when needed to balance power supply and demand, thereby improving the reliability, stability, and efficiency of the power system. Energy storage power stations play a vital role in renewable energy integration, power load balancing, power quality improvement, and responding to emergencies.

[0003] The prior art (invention patent announcement number CN119154513B) discloses a large-scale energy storage power station operation monitoring and management system and management method, which relates to the field of energy storage system technology. It includes: a data acquisition module responsible for real-time collection of battery pack status and external environmental parameters; a model building module using historical data to train a battery health status model to obtain the current health coefficient; a data processing module analyzing the operating temperature of each individual cell and calculating the thermal equilibrium coefficient; a coefficient correction module combining the environmental impact coefficient to evaluate the comprehensive health coefficient of the energy storage power station; and an evaluation module calculating a revenue index based on the comprehensive health coefficient, electricity price, and load demand to comprehensively evaluate the economic benefits of the energy storage power station.

[0004] The aforementioned energy storage power station operation and management system uses historical data to train models to assess the health status of batteries, thereby achieving a comprehensive evaluation of the energy storage power station's effectiveness. However, in large-scale energy storage power stations, the number of energy storage batteries is enormous. If the status of each energy storage battery is monitored using the above method, the amount of data that needs to be processed is huge. Furthermore, analyzing the status of each battery individually through a single process may lead to the failure to detect abnormal states of some energy storage batteries, making it impossible to adjust the corresponding energy storage strategies in a timely manner, resulting in a decline in the overall energy storage efficiency. Therefore, an energy storage system and method based on intelligent energy management is needed. Summary of the Invention

[0005] This application provides an energy storage system, method, and device based on intelligent energy management, which solves the technical problem that existing energy storage equipment operation and management systems are unable to adjust the state of energy storage batteries in a timely manner when facing large-scale energy storage battery status monitoring, resulting in a decline in overall energy storage efficiency.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, an energy storage method based on intelligent energy management is provided, including:

[0008] The system collects and analyzes the operating data and temperature data of each energy storage battery in the energy storage device. The operating data includes several electrical parameters of the corresponding energy storage battery, including current and voltage.

[0009] Several detection groups are generated based on the temperature data, and the detection priority of each energy storage battery in the detection group is determined.

[0010] According to the order of detection priority, the energy storage battery is analyzed based on the operation data and temperature data in turn to obtain the corresponding battery status.

[0011] Based on the battery state, control signals are generated to control the operation of the energy storage battery.

[0012] Based on the above technical solution, in the energy storage method based on intelligent energy management provided in this application, the operating data and temperature data of each energy storage battery in the energy storage device are collected based on the collected and analyzed signals; several detection groups and detection priorities corresponding to each energy storage battery are generated based on the temperature data; in the detection groups, the energy storage batteries are analyzed sequentially based on the operating data and temperature data according to the detection priority to obtain the battery state corresponding to the energy storage battery; control signals for controlling the operation of the energy storage battery are generated based on the battery state; by prioritizing the analysis of the temperature of each energy storage battery, several detection groups and priorities of each energy storage battery are generated, and energy storage batteries with higher risks are analyzed synchronously and preferentially; at the same time, the relevant parameters of the energy storage battery are analyzed in a timely manner to generate corresponding control signals; and the state of the corresponding energy storage battery is adjusted in a timely manner, thereby improving the overall energy storage efficiency of the energy storage device.

[0013] In conjunction with the first aspect above, in one possible implementation, generating the detection priority corresponding to each energy storage battery based on various temperature data includes:

[0014] Extract the temperature value from the temperature data corresponding to each energy storage battery; obtain the position coordinates corresponding to each energy storage battery; the position coordinates are the coordinates of the center point of each energy storage battery after constructing a three-dimensional Cartesian coordinate system based on the energy storage device;

[0015] The feature vector of the energy storage battery is constructed based on its location coordinates and corresponding temperature value. The feature vectors of each energy storage battery are obtained in sequence, and cluster analysis is performed on each feature vector to obtain several clusters.

[0016] Obtain the central feature vector of the cluster center corresponding to the cluster, and several feature vectors belonging to the cluster; and integrate the central feature vector and several feature vectors into a detection group;

[0017] Calculate the Euclidean distance between each feature vector and its corresponding central feature vector; number the Euclidean distances from smallest to largest, and use the numbers as the detection priority for the feature vectors;

[0018] The detection priority of each feature vector in each detection group is obtained sequentially;

[0019] In conjunction with the first aspect above, in one possible implementation, the battery state includes a temperature state, and the temperature state of the energy storage battery is obtained by analyzing temperature data, including:

[0020] Acquire the temperature value and the set safe temperature threshold from the temperature data; determine whether the temperature value is greater than its corresponding safe temperature threshold.

[0021] If yes, then the temperature state of the energy storage battery will be set to an abnormal state.

[0022] No, then the temperature state of the energy storage battery is set to the normal state;

[0023] The temperature status includes abnormal status and normal status.

[0024] In conjunction with the first aspect above, in one possible implementation, the battery state includes an operating state, obtained by analyzing operating data to determine the corresponding operating state of the energy storage battery, including:

[0025] Extract several electrical parameters from the operating data and obtain the set safety threshold corresponding to each electrical parameter; sequentially determine whether each electrical parameter is greater than the set safety threshold; if yes, mark the parameter status corresponding to the electrical parameter as abnormal; if no, mark the parameter status corresponding to the electrical parameter as normal.

[0026] If any of the electrical parameters are in an abnormal state, the operating state of the energy storage battery is set to abnormal; otherwise, the operating state is set to normal.

[0027] In conjunction with the first aspect above, in one possible implementation, generating the control signal based on the battery state includes:

[0028] Extract the operating status and temperature status from the battery status;

[0029] When both the operating status and temperature status are abnormal, a stop operation signal is generated;

[0030] When the operating status is abnormal and the temperature status is normal, a fault detection signal is generated.

[0031] When the operating state is normal, parameter control signals for each electrical parameter are generated based on the temperature value and the safe temperature threshold.

[0032] The control signals include stop operation signals, fault detection signals, and parameter control signals for various electrical parameters.

[0033] In conjunction with the first aspect above, in one possible implementation, the generation of parameter control signals for each electrical parameter based on the temperature value and the safe temperature threshold includes:

[0034] The difference between the temperature value and the safe temperature threshold is calculated as the temperature correction difference; the operating data and temperature values ​​are integrated into a parameter data set; the parameter data set and the temperature correction difference are substituted into the correction parameter generation model to obtain the parameter correction ratio corresponding to each electrical parameter.

[0035] The parameter correction ratios corresponding to each electrical parameter are obtained sequentially; based on the parameter correction ratios, the target values ​​of the corresponding electrical parameters after correction are generated, and the corresponding parameter control signals are generated.

[0036] In conjunction with the first aspect above, in one possible implementation, the step of generating the target value after electrical parameter correction based on the parameter correction ratio and generating the corresponding parameter control signal includes:

[0037] Acquire several temperature data points within a set time period; extract the temperature values ​​from each temperature data point; fit the temperature values ​​into a temperature change curve according to their corresponding acquisition time; predict the temperature value at the next acquisition time using the temperature change curve; calculate the predicted temperature change slope based on the predicted temperature value and the current temperature value.

[0038] When the temperature condition is abnormal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the negative correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value; the negative correction function is:

[0039]

[0040] Where i is the number of the electrical parameter, MCI is the target value of the electrical parameter with number i, DCi is the value of the electrical parameter with number i, that is, the value of the corresponding electrical parameter in the running data, CBi is the parameter correction ratio of the electrical parameter with number i, k is the temperature change slope, and ε is the set adjustment coefficient used to adjust the degree of influence of the temperature change slope on the adjustment range of the electrical parameter, 0<ε<1, and ε=0.1 in this embodiment;

[0041] When the temperature is normal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the positive correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value; the positive correction function is:

[0042]

[0043] Where i is the number of the electrical parameter, MCI is the target value of the electrical parameter with number i, DCi is the value of the electrical parameter with number i, that is, the value of the corresponding electrical parameter in the running data, CBi is the parameter correction ratio of the electrical parameter with number i, k is the temperature change slope, and ε is the set adjustment coefficient used to adjust the degree of influence of the temperature change slope on the adjustment range of the electrical parameter, 0<ε<1, and ε=0.1 in this embodiment.

[0044] In conjunction with the first aspect above, in one possible implementation, the modified parameter generation model has the following training method:

[0045] Acquire several operational data and temperature values ​​of the energy storage battery; generate training data and test data based on the operational data and temperature values; including: recording the operational data and its corresponding temperature values ​​as parameter data groups; numbering each electrical parameter and temperature value in the operational data within each parameter data group, denoted as DCij and WDj; where i is the number of the electrical parameter and j is the number of the parameter data group;

[0046] The temperature correction difference is calculated using the formula WDC=WDj1-WDj2;

[0047] The parameter correction ratio for electrical parameter i, from the temperature value corresponding to parameter data group j1 to the temperature value corresponding to parameter data group j2, is calculated using the formula CBi=(DCij1-DCij2) / DCij1.

[0048] The parameter data set, temperature correction difference, and the parameter correction ratio of each electrical parameter corresponding to the temperature correction difference are integrated into training data and test data.

[0049] The artificial intelligence model is trained using training data; the trained artificial intelligence model is tested using test data; the final result is a model for generating correction parameters with parameter data sets and temperature correction differences as inputs and parameter correction ratios corresponding to each electrical parameter as outputs; the artificial intelligence model includes BP neural network model and RBF neural network model, etc.

[0050] Secondly, this application provides an energy storage device based on intelligent energy management, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method described in the first aspect and any possible implementation thereof. This energy storage device based on intelligent energy management can be an electronic device or a chip within an electronic device.

[0051] Thirdly, this application provides an energy storage system based on intelligent energy management, comprising: a data acquisition module, a data analysis module, an analysis signal generation module, an early warning module, a parameter control module, and a database; wherein,

[0052] The data acquisition module is used to collect the operating data and temperature data of each energy storage battery in the energy storage device based on the collected and analyzed signals. The operating data includes several electrical parameters of the corresponding energy storage battery.

[0053] The data analysis module analyzes the temperature data collected by the data acquisition module to obtain the detection priority of each energy storage battery, and analyzes the energy storage battery based on the operating data and temperature data in the order of detection priority to obtain the battery status of the energy storage battery; and generates control signals for controlling the operation of the energy storage battery based on the battery status.

[0054] The analysis signal generation module is used to generate the acquired and analyzed signals;

[0055] The parameter control module is used to adjust the corresponding parameters of the energy storage battery according to the control signal;

[0056] The early warning module is used to perform corresponding early warning operations based on control signals;

[0057] The database is used to store the data generated by the above modules.

[0058] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on an energy storage device based on intelligent energy management, cause the energy storage device based on intelligent energy management to perform the methods described in the first aspect and any possible implementation thereof.

[0059] Fifthly, this application provides a computer program product containing instructions that, when the computer program product is run on an energy storage device based on intelligent energy management, causes the energy storage device based on intelligent energy management to perform the methods described in the first aspect and any possible implementation thereof.

[0060] This application provides an energy storage system and method based on intelligent energy management. It can collect operational and temperature data of each energy storage battery in an energy storage device based on acquired and analyzed signals; generate several detection groups and corresponding detection priorities for each energy storage battery based on the temperature data; analyze the energy storage batteries sequentially based on operational and temperature data according to the detection priority within each detection group to obtain the battery state; generate control signals for controlling the operation of the energy storage batteries based on the battery state; by prioritizing the temperature analysis of each energy storage battery to generate several detection groups and priorities for each energy storage battery, synchronously prioritizing the analysis of energy storage batteries with higher risks; simultaneously analyze relevant parameters of the energy storage batteries in a timely manner to generate corresponding control signals; and adjust the state of the corresponding energy storage batteries in a timely manner, thereby improving the overall energy storage efficiency of the energy storage device.

[0061] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a schematic diagram illustrating the principle of the smart energy storage management method in this application;

[0064] Figure 2 This is a schematic diagram of the module connections of the energy storage system for intelligent energy management in this application;

[0065] Figure 3 This is a schematic diagram of the hardware structure of the energy storage device for intelligent energy management in this application. Detailed Implementation

[0066] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0067] Please see Figure 1 The first aspect of this application provides an energy storage method based on intelligent energy management, including: collecting operating data and temperature data of each energy storage battery in the energy storage device based on the collected and analyzed signal; the operating data includes several electrical parameters of the corresponding energy storage battery, including current and voltage, etc.; the collected and analyzed signal is a signal for extracting and analyzing the operating data and temperature data of each energy storage battery.

[0068] The detection priority for each energy storage battery is generated based on the temperature data; the detection priority is the priority for data analysis of each energy storage battery.

[0069] Following the order of detection priority, the energy storage batteries are analyzed sequentially based on operational data and temperature data to obtain the corresponding battery status. It can be understood that the analysis here is a description of a process, that is, following the order of detection priority means following the detection priority order of each energy storage battery in the detection group.

[0070] Based on the battery state, control signals are generated to control the operation of the energy storage battery.

[0071] It should be noted that, in accordance with the order of detection priority, the energy storage battery is analyzed based on the operating data and temperature data in sequence to obtain the corresponding battery state; based on the battery state, a control signal for controlling the operation of the energy storage battery is generated; the above process is carried out simultaneously in multiple processes.

[0072] Based on the above technical solution, in the energy storage method based on intelligent energy management provided in this application, the operating data and temperature data of each energy storage battery in the energy storage device are collected based on the collected and analyzed signals; several detection groups and detection priorities corresponding to each energy storage battery are generated based on the temperature data; in the detection groups, the energy storage batteries are analyzed sequentially based on the operating data and temperature data according to the detection priority to obtain the battery state corresponding to the energy storage battery; control signals for controlling the operation of the energy storage battery are generated based on the battery state; by prioritizing the analysis of the temperature of each energy storage battery, several detection groups and priorities of each energy storage battery are generated, and energy storage batteries with higher risks are analyzed synchronously and preferentially; at the same time, the relevant parameters of the energy storage battery are analyzed in a timely manner to generate corresponding control signals; and the state of the corresponding energy storage battery is adjusted in a timely manner, thereby improving the overall energy storage efficiency of the energy storage device.

[0073] In one possible implementation, the detection priority for each energy storage battery is generated based on various temperature data, including:

[0074] Extract the temperature value from the temperature data corresponding to each energy storage battery; obtain the position coordinates corresponding to each energy storage battery; the position coordinates are the coordinates of the center point of each energy storage battery after constructing a three-dimensional Cartesian coordinate system based on the energy storage device;

[0075] The feature vector of the energy storage battery is constructed based on the location coordinates of the energy storage battery and its corresponding temperature value. For example, the coordinates of the center point of a certain energy storage battery are (x, y, z), and its corresponding temperature value is T. Then the feature vector constructed is {x, y, z, T}. The feature vectors corresponding to each energy storage battery are obtained in sequence, and cluster analysis is performed on each feature vector to obtain several clusters.

[0076] Obtain the central feature vector of the cluster center corresponding to the cluster, and several feature vectors belonging to the cluster; and integrate the central feature vector and several feature vectors into a detection group;

[0077] Calculate the Euclidean distance between each feature vector and its corresponding central feature vector; number the Euclidean distances from smallest to largest, and use the numbers as the detection priority for the feature vectors; if two or more feature vectors have the same Euclidean distance to the central feature vector, then sort the ones with higher temperature values ​​first.

[0078] The detection priority of each feature vector in each detection group is obtained sequentially; it can be understood that the detection priority of a feature vector is the detection priority of the corresponding energy storage battery.

[0079] It is worth noting that the testing priority of each energy storage battery in each testing group is independent, and the subsequent data analysis of each testing group is carried out independently in its corresponding process, that is, the analysis of each testing group is carried out simultaneously.

[0080] Since the distance between the energy storage batteries in an energy storage device is basically equal, the temperature difference between different energy storage batteries is the main reason for the clustering results. If an energy storage battery has an abnormal temperature, such as excessively high temperature, the temperature of its neighboring batteries will also rise. Through the above clustering analysis, the affected batteries and the abnormal batteries can be classified into the same cluster, and the higher the temperature, the closer to the cluster center. This facilitates the subsequent analysis of the abnormal batteries and the batteries affected by them. In this embodiment, the above method is used to develop multiple processes according to the detection group and analyze multiple energy storage batteries that may have abnormalities at the same time, which greatly improves the analysis efficiency.

[0081] It should be noted that each detection group corresponds to a processing process, and the relevant parameters of the energy storage batteries in multiple detection groups are analyzed simultaneously, which greatly improves the efficiency of energy storage battery status analysis. At the same time, by analyzing the temperature values ​​of each energy storage battery, the analysis of energy storage batteries with abnormal temperatures is prioritized, thereby enabling the control and adjustment of abnormal energy storage batteries to be prioritized.

[0082] In one possible implementation, the battery state includes a temperature state, obtained by analyzing temperature data to determine the corresponding temperature state of the energy storage battery, including:

[0083] Acquire the temperature value and the set safe temperature threshold from the temperature data; the safe temperature threshold is set by experts and is the upper limit of the temperature during normal charging and discharging of the corresponding energy storage battery; determine whether the temperature value is greater than its corresponding safe temperature threshold;

[0084] If yes, then the temperature state of the energy storage battery will be set to an abnormal state.

[0085] No, then the temperature state of the energy storage battery is set to the normal state;

[0086] Temperature conditions include abnormal conditions and normal conditions.

[0087] In one possible implementation, the battery state includes the operating state, which is obtained by analyzing operating data, including:

[0088] Extract several electrical parameters from the operating data and obtain the set safety thresholds for each electrical parameter. The safety thresholds are the upper limits of each electrical parameter that the energy storage battery has at the factory, such as the upper limit of voltage and the upper limit of current. Sequentially determine whether each electrical parameter is greater than the set safety threshold. If yes, mark the parameter status corresponding to the electrical parameter as abnormal; otherwise, mark the parameter status corresponding to the electrical parameter as normal.

[0089] If any of the electrical parameters are in an abnormal state, the operating state of the energy storage battery is set to abnormal; otherwise, the operating state is set to normal.

[0090] In one possible implementation, generating the control signal based on the battery state includes: extracting the operating state and temperature state from the battery state;

[0091] When both the operating status and temperature status are abnormal, a stop operation signal is generated; the stop operation signal is a signal that controls the corresponding energy storage battery to disconnect from charging or discharging, that is, to stop the operation of the corresponding energy storage battery.

[0092] When the operating status is abnormal but the temperature status is normal, a fault detection signal is generated. The fault detection signal is a type of early warning signal. When this signal is generated, the corresponding energy storage battery's operating data and temperature data will be displayed simultaneously, and an alarm will be issued to remind staff to carry out timely maintenance.

[0093] When the operating state is normal, parameter control signals for each electrical parameter are generated based on the temperature value and the safe temperature threshold. The parameter control signal is a signal that adjusts the corresponding electrical parameter to the set value; for example, adjusting the current 60V output voltage to 40V.

[0094] Control signals include stop signals, fault detection signals, and parameter control signals for various electrical parameters.

[0095] In one possible implementation, generating parameter control signals for each electrical parameter based on the temperature value and the safe temperature threshold includes: calculating the difference between the temperature value and the safe temperature threshold as a temperature correction difference; integrating the operating data and the temperature value into a parameter data set; and substituting the parameter data set and the temperature correction difference into the correction parameter generation model to obtain the parameter correction ratio corresponding to each electrical parameter.

[0096] The parameter correction ratios corresponding to each electrical parameter are obtained sequentially; based on the parameter correction ratios, the target values ​​of the corresponding electrical parameters after correction are generated, and the corresponding parameter control signals are generated.

[0097] In one possible implementation, generating the target value after electrical parameter correction based on the parameter correction ratio and generating the corresponding parameter control signal includes:

[0098] Acquire several temperature data points within a set time period; the set time is manually set, such as 10 minutes, 1 hour, etc.; extract the temperature values ​​from each temperature data point; fit the temperature values ​​into a temperature change curve according to their corresponding acquisition time sequence; specifically, use time as the x-axis and temperature value as the y-axis, and fit the curve using interpolation; other fitting methods can also be used; predict the temperature value at the next acquisition time using the temperature change curve. It can be understood that both temperature and other electrical parameters are acquired in real time, i.e., acquired at specific time intervals, such as 1 second; and the analysis of each data point is controlled by the acquired and analyzed signals; calculate the predicted temperature change slope based on the predicted temperature value and the current temperature value.

[0099] When the temperature condition is abnormal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the negative correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value; the negative correction function is:

[0100]

[0101] Where i is the number of the electrical parameter, MCI is the target value of the electrical parameter with number i, DCi is the value of the electrical parameter with number i, that is, the value of the corresponding electrical parameter in the running data, CBi is the parameter correction ratio of the electrical parameter with number i, k is the temperature change slope, and ε is the set adjustment coefficient used to adjust the degree of influence of the temperature change slope on the adjustment range of the electrical parameter, 0<ε<1, and ε=0.1 in this embodiment;

[0102] When the temperature is normal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the positive correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value; the positive correction function is:

[0103]

[0104] Where i is the number of the electrical parameter, MCI is the target value of the electrical parameter with number i, DCi is the value of the electrical parameter with number i, that is, the value of the corresponding electrical parameter in the running data, CBi is the parameter correction ratio of the electrical parameter with number i, k is the temperature change slope, and ε is the set adjustment coefficient used to adjust the degree of influence of the temperature change slope on the adjustment range of the electrical parameter, 0<ε<1, and ε=0.1 in this embodiment.

[0105] This embodiment generates target values ​​for corresponding parameters using negative and true correction functions. When the temperature is abnormal, it may be due to the excessively high temperature of nearby batteries, causing the battery's temperature to rise. Therefore, the operating state of the energy storage battery needs to be lowered to reduce its own heat generation and thus its operating temperature, preventing it from operating at high temperatures and extending its lifespan. Simultaneously, by predicting future temperature changes, the adjustment range of the operating state is increased or decreased. If the temperature trend is upward, the downward adjustment is increased to ensure operational safety; if the temperature trend is downward, the downward adjustment is decreased to ensure operational efficiency. When the temperature is outside the safe range, the operating state of the energy storage battery may be increased to enhance its energy storage efficiency. Again, by predicting future temperature changes, the adjustment range of the operating state is increased or decreased. If the temperature trend is upward, the upward adjustment is decreased to ensure operational safety; if the temperature trend is downward, the upward adjustment is increased to ensure operational efficiency.

[0106] In one possible implementation, the modified parameter generation model has the following training method:

[0107] Acquire several operational data and temperature values ​​of the energy storage battery; generate training data and test data based on the operational data and temperature values. In this embodiment, the operational data and their corresponding temperature values ​​are collected from energy storage batteries of the same model in the energy storage device under normal operating conditions. It can be understood that in this embodiment, when both the temperature state and the electrical state are normal, the corresponding data is stored for supplementary training of the operating temperature prediction model. This includes: recording the operational data and their corresponding temperature values ​​as parameter data groups; numbering each electrical parameter and temperature value in the operational data within each parameter data group, denoted as DCij and WDj; where i is the number of the electrical parameter and j is the number of the parameter data group.

[0108] The temperature correction difference is calculated using the formula WDC=WDj1-WDj2;

[0109] The parameter correction ratio for electrical parameter i, from the temperature value corresponding to parameter data group j1 to the temperature value corresponding to parameter data group j2, is calculated using the formula CBi=(DCij1-DCij2) / DCij1.

[0110] The parameter data set, temperature correction difference, and the parameter correction ratio of each electrical parameter corresponding to the temperature correction difference are integrated into training data and test data.

[0111] The artificial intelligence model is trained using training data; the trained artificial intelligence model is tested using test data; the final result is a model for generating correction parameters with parameter data sets and temperature correction differences as inputs and parameter correction ratios corresponding to each electrical parameter as outputs; the artificial intelligence model includes BP neural network model and RBF neural network model, etc.

[0112] In one possible implementation, the acquisition and analysis signal is generated by: obtaining the maximum value of the ratio of the target value to the current value of each electrical parameter in each energy storage battery, and recording it as TB; calculating the waiting time DT using the formula DT=BT-TB×KT; where BT is the standard time and KT is the adjustable time; both the standard time and the adjustable time are set manually; and generating the acquisition and analysis signal after the waiting time has elapsed.

[0113] This embodiment controls the time interval of the next analysis by adjusting the state of the energy storage battery. When the adjustment range is larger, the change in the state of the energy storage battery is greater, and it is necessary to verify the adjustment process and results in a timely manner. Therefore, the waiting time is set to be shorter in this case to facilitate the timely detection of abnormalities in the adjustment process.

[0114] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0115] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as an energy storage device based on intelligent energy management, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] This application embodiment can divide an energy storage device based on intelligent energy management into functional units according to the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0117] Please see Figure 2 This application provides an energy storage system based on intelligent energy management, including: a data acquisition module, a data analysis module, an analysis signal generation module, an early warning module, a parameter control module, and a database; wherein,

[0118] The data acquisition module is used to collect the operating data and temperature data of each energy storage battery in the energy storage device based on the collected and analyzed signals. The operating data includes several electrical parameters of the corresponding energy storage battery.

[0119] The data analysis module analyzes the temperature data collected by the data acquisition module to obtain the detection priority of each energy storage battery, and analyzes the energy storage battery based on the operating data and temperature data in the order of detection priority to obtain the battery status of the energy storage battery; and generates control signals for controlling the operation of the energy storage battery based on the battery status.

[0120] The analysis signal generation module is used to generate the acquired and analyzed signals;

[0121] The parameter control module is used to adjust the corresponding parameters of the energy storage battery according to the control signal;

[0122] The early warning module is used to perform corresponding early warning operations based on control signals;

[0123] The database is used to store the data generated by the above modules.

[0124] This application also provides a hardware structure diagram of an energy storage device based on intelligent energy management, see [link / reference]. Figure 3 The energy storage device 10 based on intelligent energy management includes a processor 101, and optionally, a memory 102 connected to the processor 101.

[0125] In the first possible implementation, see Figure 3An energy storage device 10 based on intelligent energy management further includes a transceiver 103. The processor 101, memory 102, and transceiver 103 are connected via a bus. The transceiver 103 is used to communicate with other devices or communication networks. Optionally, the transceiver 103 may include a transmitter and a receiver. The device in the transceiver 103 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 103 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0126] Based on the first possible implementation method Figure 3 The structural diagram shown can be used to illustrate the structure of an energy storage device based on intelligent energy management involved in the above embodiments.

[0127] in, Figure 3 This can also be illustrated as a system chip in an energy storage device based on intelligent energy management. In this case, the actions performed by the aforementioned energy storage device based on intelligent energy management can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0128] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0129] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0130] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0131] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0132] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0133] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0134] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0135] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0136] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0137] The above embodiments are only used to illustrate the technical methods of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of this application without departing from the spirit and scope of the technical methods of this application.

Claims

1. An energy storage method based on intelligent energy management, characterized in that, include: The operating data and temperature data of each energy storage battery in the energy storage device are collected; The operating data includes several electrical parameters of the corresponding energy storage battery; the electrical parameters include current and voltage. Several detection groups are generated based on the temperature data, and the detection priority of each energy storage battery in the detection group is determined. In the testing group, the energy storage battery is analyzed sequentially based on the operating data and temperature data according to the testing priority to obtain the corresponding battery state. Based on the battery state, a control signal is generated to control the operation of the energy storage battery; The control signal is generated based on the battery state, including: Extract the operating status and temperature status from the battery status; When both the operating status and temperature status are abnormal, a stop operation signal is generated; When the operating status is abnormal and the temperature status is normal, a fault detection signal is generated. When the operating state is normal, parameter control signals for various electrical parameters are generated based on the temperature value and the safe temperature threshold; including: The difference between the temperature value and the safe temperature threshold is calculated as the temperature correction difference; the operating data and temperature values ​​are integrated into a parameter data set; the parameter data set and the temperature correction difference are substituted into the correction parameter generation model to obtain the parameter correction ratio corresponding to each electrical parameter. Sequentially obtain the parameter correction ratio corresponding to each electrical parameter; generate the target value of the corresponding electrical parameter after correction based on the parameter correction ratio, and generate the corresponding parameter control signal; including: Acquire several temperature data points within a set time period; extract the temperature values ​​from each temperature data point; fit the temperature values ​​into a temperature change curve according to their corresponding acquisition time; predict the temperature value at the next acquisition time using the temperature change curve; calculate the predicted temperature change slope based on the predicted temperature value and the current temperature value. When the temperature is abnormal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the negative correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value. When the temperature is normal, acquire various electrical parameters from the operating data; substitute the electrical parameters, temperature change slope, and parameter correction ratio into the positive correction function to obtain the target value corresponding to the electrical parameters; and generate a parameter control signal to adjust the electrical parameters to the target value. The negative correction function is: The positive correction function is: Where i is the number of the electrical parameter, MCI is the target value of the electrical parameter numbered i, DCi is the value of the electrical parameter numbered i, CBi is the parameter correction ratio of the electrical parameter numbered i, k is the temperature change slope, and ε is the set adjustment coefficient, where 0 < ε < 1. The control signals include stop operation signals, fault detection signals, and parameter control signals for various electrical parameters.

2. The energy storage method based on intelligent energy management according to claim 1, characterized in that, The detection priority for each energy storage battery is generated based on various temperature data, including: Extract the temperature values ​​from the temperature data corresponding to each energy storage battery; obtain the location coordinates of each energy storage battery. The feature vector of the energy storage battery is constructed based on its location coordinates and corresponding temperature value; the feature vectors corresponding to each energy storage battery are obtained in sequence, and cluster analysis is performed on each feature vector to obtain several clusters; Obtain the central feature vector of the cluster center corresponding to the cluster, and several feature vectors belonging to the cluster; and integrate the central feature vector and several feature vectors into a detection group; Calculate the Euclidean distance between each feature vector and its corresponding central feature vector; number the Euclidean distances from smallest to largest, and use the numbers as the detection priority for the feature vectors; The detection priority of each feature vector in each detection group is obtained sequentially.

3. The energy storage method based on intelligent energy management according to claim 1, characterized in that, The battery status includes temperature status, which is obtained by analyzing temperature data, including: Acquire the temperature value and the set safe temperature threshold from the temperature data; determine whether the temperature value is greater than its corresponding safe temperature threshold. If yes, then the temperature state of the energy storage battery will be set to an abnormal state. No, then the temperature state of the energy storage battery is set to the normal state; The temperature status includes abnormal status and normal status.

4. The energy storage method based on intelligent energy management according to claim 1, characterized in that, The battery status includes the operating status, which is obtained by analyzing the operating data to determine the corresponding operating status of the energy storage battery, including: Extract several electrical parameters from the operating data and obtain the set safety threshold corresponding to each electrical parameter; sequentially determine whether each electrical parameter is greater than the set safety threshold; if yes, mark the parameter status corresponding to the electrical parameter as abnormal; if no, mark the parameter status corresponding to the electrical parameter as normal. If any of the electrical parameters are in an abnormal state, the operating state of the energy storage battery is set to abnormal; otherwise, the operating state is set to normal.

5. The energy storage method based on intelligent energy management according to claim 1, characterized in that, The modified parameter generation model has the following training methods: Acquire some operational data and temperature values ​​of the energy storage battery; generate training data and test data based on the operational data and temperature values; Use training data to train artificial intelligence models; The trained artificial intelligence model was tested using test data; the final input consisted of a set of parameter data and temperature correction differences. The output is a correction parameter generation model with the parameter correction ratio corresponding to each electrical parameter.

6. The energy storage method based on intelligent energy management according to claim 5, characterized in that, The generation of training and testing data based on operational data and temperature values ​​includes: The operating data and its corresponding temperature value are recorded as parameter data groups; each electrical parameter and temperature value in the operating data within each parameter data group is numbered and recorded as DCij and WDj; where i is the number of the electrical parameter and j is the number of the parameter data group. The temperature correction difference is calculated using the formula WDC=WDj1-WDj2; The parameter correction ratio for electrical parameter i, from the temperature value corresponding to parameter data group j1 to the temperature value corresponding to parameter data group j2, is calculated using the formula CBi=(DCij1-DCij2) / DCij1. The parameter data set, temperature correction difference, and the parameter correction ratio of each electrical parameter corresponding to the temperature correction difference are integrated into training data or test data; several training data and test data are obtained in sequence.

7. An energy storage system based on intelligent energy management, operating based on the energy storage method based on intelligent energy management as described in any one of claims 1-6; characterized in that, include: The system comprises a data acquisition module, a data analysis module, an early warning module, and a parameter control module; among which, The data acquisition module is used to collect the operating data and temperature data of each energy storage battery in the energy storage device based on the collected and analyzed signals. The operating data includes several electrical parameters of the corresponding energy storage battery. The data analysis module analyzes the temperature data collected by the data acquisition module to obtain the detection priority of each energy storage battery, and analyzes the energy storage battery based on the operating data and temperature data in the order of detection priority to obtain the battery status of the energy storage battery; and generates control signals for controlling the operation of the energy storage battery based on the battery status. The parameter control module is used to adjust the corresponding parameters of the energy storage battery according to the control signal; The early warning module is used to perform corresponding early warning operations based on control signals.

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