SOC balance adjusting system for distributed energy storage unit

Abstract

The invention provides an SOC equalization adjustment system for a distributed energy storage unit, and relates to the technical field of SOC equalization, and the system comprises an SOH prediction module which is used for generating a distributed SOH prediction value set; the SOH clustering module is used for generating multiple groups of energy storage units; the SOC prediction module is used for generating a plurality of distributed SOC prediction values; the equalization analysis module is used for generating an SOC equalization index; the energy transfer strategy analysis module is used for generating an optimal energy transfer strategy; and the energy transfer control module is used for performing energy transfer control. Through the method and the device, the technical problem that the balancing effect is poor due to the fact that the SOH difference of the energy storage units is not fully considered because most of the energy storage units are based on a fixed balancing threshold value in the prior art can be solved, energy transfer control over the distributed energy storage units is realized, the SOC consistency of the distributed energy storage units is improved, and the energy transfer efficiency of the distributed energy storage units is improved. And the performance difference between the units is reduced.

Description

Technical Field

[0001] The present application relates to the field of SOC balancing technology, and in particular to a SOC balancing adjustment system for a distributed energy storage unit. Background Art

[0002] With the continuous growth of energy demand and the widespread application of renewable energy, distributed energy storage systems play an increasingly important role in power systems. As the basic component of the energy storage system, the operating status of the distributed energy storage unit directly affects the performance and life of the entire energy storage system. However, in actual applications, due to differences in the use environment, charge and discharge times and other factors of the distributed energy storage unit, the SOC of each energy storage unit varies greatly, which may lead to unstable operation of the energy storage system and even shorten the service life of the energy storage unit.

[0003] Currently, most of the existing SOC balancing technologies are based on fixed balancing thresholds and fail to fully consider the SOH differences of energy storage units, resulting in poor balancing effects. Summary of the invention

[0004] The purpose of this application is to provide a SOC balancing and adjustment system for distributed energy storage units, so as to solve the technical problem in the prior art that most of them are based on fixed balancing thresholds and fail to fully consider the SOC differences of energy storage units, resulting in poor balancing effects.

[0005] In view of the above problems, the present application provides a SOC balancing and regulation system for distributed energy storage units, including: an SOH prediction module, which is used to collect service records of multiple distributed energy storage units, perform SOH prediction, and generate a distributed SOH prediction value set; an SOH clustering module, which is used to cluster the distributed SOH prediction value set according to a preset SOH consistency deviation, perform energy storage unit mapping on the clustering results, and generate multiple groups of energy storage units, wherein the multiple groups of energy storage units have SOH tag values; an SOC prediction module, which is used to monitor the current in and out of the multiple distributed energy storage units, generate multiple total charges, perform SOC prediction, and generate multiple Distributed SOC prediction values; a balance analysis module, used to perform distribution difference analysis on the multiple distributed SOC prediction values ​​to generate an SOC balance index, wherein the SOC balance index represents the consistency of the SOCs of the multiple distributed energy storage units; an energy transfer strategy analysis module, used to perform energy transfer strategy analysis in combination with the SOH mark values ​​of the multiple groups of energy storage units and the multiple distributed SOC prediction values ​​when the SOC balance index is less than a preset balance index to generate an optimal energy transfer strategy; an energy transfer control module, used to control the energy transferor to perform energy transfer control on the multiple distributed energy storage units with the optimal energy transfer strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] An SOH prediction module is used to collect service records of multiple distributed energy storage units, perform SOH prediction, and generate a distributed SOH prediction value set; an SOH clustering module is used to cluster the distributed SOH prediction value set according to a preset SOH consistency deviation, perform energy storage unit mapping on the clustering results, and generate multiple groups of energy storage units, wherein the multiple groups of energy storage units have SOH marking values; an SOC prediction module is used to monitor the current in and out of the multiple distributed energy storage units, generate multiple total charges, perform SOC prediction, and generate multiple distributed SOC prediction values; a balance analysis module is used to perform distribution difference analysis on the multiple distributed SOC prediction values ​​and generate an SOC balance index, wherein the SOC balance index represents the consistency of the SOC of multiple distributed energy storage units; an energy transfer strategy analysis module is used to perform energy transfer strategy analysis in combination with the SOH marking values ​​of the multiple groups of energy storage units and the multiple distributed SOC prediction values ​​when the SOC balance index is less than the preset balance index, and generate an optimal energy transfer strategy; an energy transfer control module is used to control the energy transferor to perform energy transfer control on the multiple distributed energy storage units with the optimal energy transfer strategy. Through SOH prediction and clustering, as well as SOC prediction and balance analysis, combined with SOH for SOC balance adjustment, energy transfer control of distributed energy storage units is achieved, thereby improving the SOC consistency of distributed energy storage units, reducing performance differences between units, and thus improving the overall efficiency and reliability.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a structural schematic diagram of the SOC balancing and regulating system for distributed energy storage units in this application;

[0011] Figure 2 A schematic diagram of a flow chart for generating an optimal energy transfer strategy in a SOC balancing regulation system of a distributed energy storage unit used in the present application.

[0012] Description of reference numerals:

[0013] SOH prediction module 11, SOH clustering module 12, SOC prediction module 13, balance analysis module 14, energy transfer strategy analysis module 15, energy transfer control module 16. DETAILED DESCRIPTION

[0014] This application solves the technical problem in the prior art that most of the existing technologies are based on fixed balancing thresholds and fail to fully consider the SOH differences of energy storage units, resulting in poor balancing effects by providing an SOC balancing adjustment system for distributed energy storage units. Through SOH prediction and clustering, as well as SOC prediction and balancing analysis, combined with SOH for SOC balancing adjustment, energy transfer control of distributed energy storage units is achieved, thereby improving the SOC consistency of distributed energy storage units, reducing performance differences between units, and thus improving the overall efficiency and reliability.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] Please see attached Figure 1 , the present application provides a SOC balancing and regulating system for a distributed energy storage unit, comprising:

[0017] The SOH prediction module 11 is used to collect service records of multiple distributed energy storage units, perform SOH prediction, and generate a distributed SOH prediction value set.

[0018] Specifically, the distributed energy storage unit refers to a plurality of energy storage devices dispersedly arranged in the power grid, which may be batteries at different locations, and its main function is to store electrical energy so as to release energy during the peak load period of the power grid and balance the load of the power grid. The service record includes the usage history data of the distributed energy storage unit, such as the number of charge and discharge times, charge and discharge depth, temperature changes, voltage fluctuations, etc. SOH (State of Health) is a key indicator to measure the health status of the energy storage unit, reflecting the maximum capacity retention ability of the energy storage unit relative to the new state. The prediction of SOH is to predict the future health status of the energy storage unit by analyzing its service record.

[0019] The process of generating a set of distributed SOH prediction values ​​includes: First, it is necessary to collect service record data from each distributed energy storage unit. This data can be obtained in real time through the existing intelligent monitoring system or extracted from the historical database. Since the actual collected data may contain noise, missing values ​​or outliers, the data needs to be cleaned and preprocessed to ensure the quality and accuracy of the data. Features useful for SOH prediction are extracted from the preprocessed data, such as the number of charge and discharge times, charge and discharge voltage, current, temperature changes, etc. These features will be used as model input to predict SOH.

[0020] Before predicting SOH, it is necessary to train a SOH prediction model. Professional technicians in this field can collect data samples including charge and discharge voltage, current, temperature changes, etc., and collect the corresponding actual SOH feature values, and train a SOH prediction model based on machine learning models such as random forests, support vector machines or neural networks. The trained SOH prediction model is used to analyze the charge and discharge voltage, current, temperature changes, etc. in the service records of distributed energy storage units to generate the SOH prediction value of each energy storage unit.

[0021] The SOH prediction values ​​of all distributed energy storage units are integrated to form a distributed SOH prediction value set to provide support for subsequent SOC balancing.

[0022] The SOH clustering module 12 is used to cluster the distributed SOH prediction value set according to a preset SOH consistent deviation, perform energy storage unit mapping on the clustering result, and generate multiple groups of energy storage units, wherein the multiple groups of energy storage units have SOH marking values.

[0023] Specifically, the distributed SOH prediction value set is clustered according to the preset SOH consistent deviation in order to group energy storage units with similar health status into the same group, so that these energy storage units can be better managed and maintained, because energy storage units in the same group may face similar aging or performance degradation problems. Clustering is an unsupervised learning method that divides data points into multiple categories based on similarity. Here, each data point is the SOH prediction value of an energy storage unit. The preset SOH consistent deviation is a threshold used to determine which SOH prediction values ​​are considered similar, and is specifically set by professional and technical personnel in this field based on actual conditions. If the difference between two SOH prediction values ​​is less than or equal to this threshold, they are considered to be similar and can be classified into the same category.

[0024] The clustering results are mapped to energy storage units, that is, each clustering category corresponds to a group of energy storage units. The energy storage units in each clustering category are considered to have similar SOH status, so a SOH tag value can be assigned to each clustering category. This SOH tag value can be the average of the SOH prediction values ​​of all energy storage units in the category, which is used to describe the overall health status of the energy storage units in the category.

[0025] The SOC prediction module 13 is used to monitor the current in and out of the multiple distributed energy storage units, generate multiple total charge amounts, perform SOC prediction, and generate multiple distributed SOC prediction values.

[0026] Specifically, the current in and out monitoring of multiple distributed energy storage units is to track the charging and discharging status of each energy storage unit. Current in and out monitoring is usually achieved by current sensors installed at the entrance and exit of the energy storage unit, which can measure the magnitude and direction of the current flowing through the energy storage unit in real time. Current data is key information for evaluating the state of charge (SOC) of the energy storage unit, because SOC refers to the ratio of the electric energy currently stored in the energy storage unit to its rated capacity. The monitored current data is integrated to generate multiple total charges. Since current is the rate of change of charge over time, the total charge can be calculated by integrating the change of current over time. The total charge is the total amount of energy storage unit charged or discharged over a period of time. SOC prediction is based on historical current data and total charge, and uses a prediction model to estimate the charge state of the energy storage unit at a certain moment in the future. The SOC prediction model can be a prediction method based on a physical model, or it can be a data-driven machine learning model, such as time series analysis, regression analysis, or neural network, to generate multiple distributed SOC prediction values, which is convenient for judging the SOC equilibrium state and provides a basis for SOC equilibrium control.

[0027] The balance analysis module 14 is used to perform distribution difference analysis on the multiple distributed SOC prediction values ​​to generate an SOC balance index, wherein the SOC balance index represents the consistency of the SOCs of the multiple distributed energy storage units.

[0028] Specifically, the distribution difference analysis of multiple distributed SOC prediction values ​​is to evaluate the consistency of the charging state between each distributed energy storage unit. The SOC balance index is a quantitative index that reflects the distribution of the SOC values ​​of multiple distributed energy storage units, that is, the degree of SOC difference between these energy storage units. Based on the results of the difference analysis, one or more SOC balance indicators are generated, thereby realizing the evaluation of the SOC balance of multiple distributed energy storage units and providing necessary support for SOC balance adjustment.

[0029] The energy transfer strategy analysis module 15 is used to perform energy transfer strategy analysis in combination with the SOH mark values ​​of the multiple groups of energy storage units and the multiple distributed SOC prediction values ​​to generate an optimal energy transfer strategy when the SOC balance index is less than a preset balance index.

[0030] Specifically, when the SOC balance index is less than the preset balance index, it means that the SOC differences between multiple distributed energy storage units are large, and some energy storage units may be overcharged or over-discharged. This situation may reduce the efficiency of the energy storage system and may even shorten the service life of the energy storage unit. Therefore, it is necessary to combine the SOH mark values ​​of multiple groups of energy storage units and multiple distributed SOC prediction values ​​to analyze the energy transfer strategy in order to achieve energy balance between energy storage units and improve the overall performance of the system. The energy transfer strategy refers to how to transfer energy between energy storage units to achieve SOC balance. This may include transferring energy from energy storage units with higher SOC to energy storage units with lower SOC, generating the optimal energy transfer strategy to achieve energy balance between energy storage units and improve the efficiency and life of the energy storage system.

[0031] The energy transfer control module 16 is used to control the energy transfer device to perform energy transfer control on the multiple distributed energy storage units according to the optimal energy transfer strategy.

[0032] Specifically, the energy transfer device is controlled by the optimal energy transfer strategy to control the energy transfer of multiple distributed energy storage units, that is, according to the best solution obtained by analysis, the energy transfer device is used to achieve real-time adjustment of energy between energy storage units. The energy transfer device can be a bidirectional DC-DC converter that can adjust the energy flow between energy storage units according to control instructions. Energy balance between energy storage units is achieved, and the efficiency and reliability of the energy storage system are improved.

[0033] Furthermore, the SOC prediction module 13 in the SOC balancing and regulating system for distributed energy storage units is also used for:

[0034] A current sensing component is configured to monitor the charge and discharge currents of the multiple distributed energy storage units in real time to generate multiple charge and discharge currents; the multiple charge and discharge currents are integrated to generate the multiple total charge amounts; the multiple rated charge amounts of the multiple distributed energy storage units are obtained, and the SOC is estimated in combination with the multiple total charge amounts to generate the multiple distributed SOC prediction values.

[0035] Specifically, the purpose of configuring a current sensing component to monitor the charging and discharging currents of multiple distributed energy storage units in real time is to accurately track the flow of electric energy in each energy storage unit. The current sensing component is usually installed at the charging and discharging interface of the energy storage unit, and can measure the magnitude and direction of the current flowing through the interface in real time to generate multiple charging and discharging currents. The purpose of integrating multiple charging and discharging currents is to calculate the total charge of each energy storage unit over a period of time. Since current is the rate of change of charge over time, the total charge can be obtained by integrating the change of current over time. The total charge is the total amount of charge or discharge of the energy storage unit over a period of time, which is directly related to the SOC of the energy storage unit. The purpose of obtaining multiple rated charges of multiple distributed energy storage units is to serve as a reference value for SOC estimation. The rated charge is the maximum charge capacity specified when the energy storage unit is designed, which can be obtained by querying the user manual of the energy storage unit.

[0036] By combining multiple total charges and multiple rated charges for SOC estimation, multiple distributed SOC prediction values ​​can be generated. The SOC estimation process is to calculate the ratio of the total charge to the rated charge, and the SOC percentage of each distributed energy storage unit can be calculated as its current state of charge to obtain the multiple distributed SOC prediction values. Through the above steps, the SOC prediction of multiple distributed energy storage units can be achieved, providing data support for SOC balancing adjustment.

[0037] Furthermore, the SOC prediction module 13 in the SOC balancing and regulating system for distributed energy storage units is also used for:

[0038] Performing charge estimation error analysis to generate a charge estimation error; configuring an SOC feedback network with the charge estimation error to optimize the multiple distributed SOC prediction values.

[0039] Specifically, the charge estimation error analysis is performed to evaluate the accuracy of SOC prediction. The charge estimation error refers to the difference between the actual charge and the estimated charge. This error may be caused by a variety of factors, including measurement errors of the current sensor, numerical errors in the integration process, aging effects of the energy storage unit, etc. First, sample data of the actual charge and the estimated charge need to be collected. The actual charge can be obtained by fully discharging the energy storage unit and measuring the discharged charge, while the estimated charge is obtained through the previous integration process. Compare the actual charge and the estimated charge, and calculate the difference, that is, the charge estimation error. This can be achieved by calculating statistics such as absolute error, relative error, or root mean square error, and evaluating the accuracy of SOC prediction based on the calculated charge estimation error.

[0040] Specifically, according to the charge estimation error, the SOC feedback network is configured. The SOC feedback network is a control system that can adjust the SOC prediction value according to the difference between the actual charge and the estimated charge to improve the accuracy of the prediction. Using the configured SOC feedback network, multiple distributed SOC prediction values ​​are optimized to reduce the estimation error and improve the accuracy of the SOC prediction.

[0041] Furthermore, the balance analysis module 14 in the SOC balance regulation system for distributed energy storage units is also used for:

[0042] The multiple distributed SOC prediction values ​​are normalized and then averaged to generate a SOC normalized center value; the distances from the normalized multiple distributed SOC prediction values ​​to the SOC normalized center value are calculated to generate multiple distributed distances; based on the multiple distributed distances, the ratio between the minimum distance and the maximum distance is calculated to generate the SOC balance index.

[0043] Specifically, the purpose of normalizing multiple distributed SOC prediction values ​​is to eliminate the impact of different energy storage unit capacities or characteristics on the SOC prediction values, so that these values ​​can be compared and analyzed at the same scale. Normalization is a commonly used data preprocessing method that scales data to a specific range, usually between 0 and 1, which will not be expanded here. The normalized SOC prediction values ​​are averaged to generate the SOC normalized center value.

[0044] The distance between the normalized multiple distributed SOC prediction values ​​and the SOC normalized center value is calculated to evaluate the difference between the SOC prediction value of each energy storage unit and the overall average level. This can be achieved by calculating the Euclidean distance between each normalized SOC prediction value and the SOC normalized center value, obtaining multiple distributed distances, and then calculating the ratio between the minimum distance and the maximum distance to generate the SOC balance index. This index reflects the uniformity of the distribution between the SOC prediction values.

[0045] If the SOC balance index is close to 1, it means that the difference between the SOC prediction values ​​is small and the SOC distribution is relatively balanced; if the SOC balance index is far away from 1, it means that the difference between the SOC prediction values ​​is large and the SOC distribution is uneven, which provides data support for SOC balance adjustment, facilitates the judgment of the current SOC balance state, and thus performs balance control to ensure the normal operation of the energy storage unit.

[0046] Further, as attached Figure 2 As shown, the energy transfer strategy analysis module 15 in the SOC balancing regulation system for distributed energy storage units is also used for:

[0047] Perform mean calculation on the multiple distributed SOC prediction values ​​to generate an average SOC characteristic value; calculate the distance from the multiple distributed SOC prediction values ​​to the average SOC characteristic value, perform SOC balancing impact analysis, and locate the energy storage unit to be balanced, wherein the energy storage unit to be balanced includes an upward balancing unit and a downward balancing unit; traverse the multiple groups of energy storage units to locate the SOH characteristic values ​​of the upward balancing unit and the downward balancing unit; perform energy balancing transfer analysis on the upward balancing unit and the downward balancing unit based on the SOH characteristic value to generate an optimal energy transfer strategy.

[0048] Specifically, the mean calculation of multiple distributed SOC prediction values ​​is to obtain the average state of charge of all energy storage units, which can be used as a reference for the overall state of charge of the system. The average SOC characteristic value is the average of all SOC prediction values. Further calculation of the distance from multiple distributed SOC prediction values ​​to the average SOC characteristic value is to evaluate the difference between the SOC prediction value of each energy storage unit and the overall average level. This can be achieved by calculating the Euclidean distance between each SOC prediction value and the average SOC characteristic value to obtain the distance between the distributed SOC prediction value of each distributed energy storage unit and the average SOC characteristic value, thereby performing SOC balance impact analysis and determining which energy storage units need to adjust the SOC to achieve SOC balance for the entire system.

[0049] By comparing the distance between the distributed SOC prediction value of each distributed energy storage unit and the average SOC characteristic value and the preset equilibrium distance, if the distance is larger and exceeds the preset equilibrium distance, it indicates that the SOC of the energy storage unit needs to be adjusted and it is used as an energy storage unit to be balanced. The energy storage units to be balanced include upward balancing units and downward balancing units. An upward balancing unit refers to a distributed energy storage unit whose SOC prediction value is higher than the average SOC characteristic value, while a downward balancing unit refers to a distributed energy storage unit whose SOC prediction value is lower than the average SOC characteristic value.

[0050] Traversing multiple groups of energy storage units and locating the SOH characteristic values ​​of the upward balancing units and the downward balancing units is to consider the health status of the energy storage units in the energy balance transfer analysis. The SOH characteristic value can be the SOH tag value of each energy storage unit. The energy balance transfer analysis based on the SOH characteristic value is to determine how to transfer energy between energy storage units in different health states to achieve SOC balance, generate the optimal energy transfer strategy to achieve energy balance between energy storage units, and improve the efficiency and reliability of distributed energy storage units.

[0051] Furthermore, the energy transfer strategy analysis module 15 in the SOC balancing regulation system for distributed energy storage units is also used for:

[0052] Calculate the distances from the multiple distributed SOC prediction values ​​to the average SOC characteristic value to generate multiple distance characteristic values, wherein the multiple distance characteristic values ​​carry a bias identifier; locate energy storage units whose distances are greater than a preset balancing distance based on the multiple distance characteristic values, and locate downward balancing units whose SOCs are higher than the average SOC characteristic value and upward balancing units whose SOCs are lower than the average SOC characteristic value in combination with the bias identifier.

[0053] Specifically, for each distributed SOC prediction value, the distance between it and the average SOC characteristic value is calculated, and a bias flag is assigned according to the positive or negative sign of each distance characteristic value. If the SOC prediction value is higher than the average SOC characteristic value, an "upward" bias flag is assigned; if the SOC prediction value is lower than the average SOC characteristic value, a "downward" bias flag is assigned. The purpose of locating energy storage units whose distance is greater than the preset equilibrium distance based on multiple distance characteristic values ​​is to find energy storage units with a large difference between the SOC prediction value and the average SOC characteristic value. These units may need energy adjustment to achieve SOC equilibrium. The preset equilibrium distance refers to the maximum SOC deviation allowed while meeting the equilibrium requirements, and is set by professional and technical personnel in this field based on actual experience.

[0054] The purpose of locating downward balancing units with SOC higher than the average SOC characteristic value and upward balancing units with SOC lower than the average SOC characteristic value in combination with the bias mark is to determine which energy storage units need to be discharged (upward balancing units) and which energy storage units need to be charged (downward balancing units). Through the above steps, the energy storage units that need to adjust the SOC can be located and the direction of adjustment can be determined, providing a basis for subsequent energy balance transfer analysis.

[0055] Furthermore, the energy transfer strategy analysis module 15 in the SOC balancing regulation system for distributed energy storage units is also used for:

[0056] Taking the average SOC characteristic value and the preset balancing distance as constraints, the upward balancing unit and the downward balancing unit are configured for energy transfer balancing to generate initial energy transfer information, wherein the initial energy transfer information includes the charge transfer amount corresponding to the upward balancing unit and the charge transfer amount corresponding to the downward balancing unit, as well as the charge transfer path; the voltage and current limitation analysis of the energy storage unit is performed in combination with the SOH characteristic value to generate a limiting current and a limiting voltage; the voltage and current of the charge transfer path are calculated in combination with the limiting current, the limiting voltage and the initial energy transfer information to generate an optimal energy transfer strategy.

[0057] Specifically, the energy transfer balancing configuration of the upward balancing unit and the downward balancing unit is performed with the average SOC characteristic value and the preset balancing distance as constraints, in order to ensure that the SOC difference between the energy storage units is within the allowable range, while considering the health state (SOH) of the energy storage unit. Specifically, firstly, the amount of charge that each energy storage unit needs to receive or release is determined according to the average SOC characteristic value and the preset balancing distance. The upward balancing unit needs to receive charge to reduce its SOC, while the downward balancing unit needs to release charge to increase its SOC, and at the same time, the specific path of the charge transfer is determined, that is, which energy storage units are to be charged. Specifically, by adjusting the charge amount of the upward balancing unit and the downward balancing unit, the distance between the SOC of the upward balancing unit and the downward balancing unit and the average SOC characteristic value meets the preset balancing distance, and the charge of the downward balancing unit is transferred to the upward balancing unit, and the loss of the charge transfer process can also be identified to correct the charge transfer amount. The charge transfer path from the downward balancing unit to the upward balancing unit and the corresponding charge transfer amount and transfer amount are obtained. In layman's terms, in the aforementioned steps, the ratio of the total charge to the rated charge is calculated to obtain the SOC prediction value. Therefore, the difference SOC is obtained by calculating the difference between the SOC and the average SOC characteristic value of the upward equalizing unit and the downward equalizing unit. The rated charge is known, and the charge amount to be transferred can be inferred by calculating the SOC to obtain the charge input and output amounts, and then the charge transfer path is analyzed. Among them, a downward equalizing unit in the charge transfer path can transfer charge to multiple upward equalizing units, as long as the distance between the SOC characteristic value of the upward equalizing unit and the downward equalizing unit and the average SOC characteristic value meets the preset equalization distance.

[0058] Furthermore, according to the SOH characteristic value of each energy storage unit to be balanced, the voltage and current limit values ​​are determined to ensure that the energy transfer process does not exceed the voltage and current limits of the energy storage unit, thereby avoiding damage to the energy storage unit. Specifically, the charge and discharge current and voltage samples of the battery consistent with the SOH characteristic value and the corresponding SOH change samples can be collected, and the impact of the charge and discharge current and voltage samples on the SOH change samples can be analyzed to obtain the charge and discharge current and voltage whose impact is less than the preset threshold, and generate the limit current and limit voltage.

[0059] Then, combined with the limiting current, limiting voltage and initial energy transfer information, the voltage and current on each charge transfer path are calculated to ensure that all parameters are within the safe range, that is, within the range of limiting current and limiting voltage, the voltage and current on the charge transfer path, as well as the transfer time, are inferred according to the charge transfer amount and transfer amount in the initial energy transfer information, and the optimal energy transfer strategy is generated. This strategy can achieve SOC balance while ensuring the safe operation of the energy storage unit. Specifically, a large number of current samples, charge samples and voltage samples can be obtained, and a relationship network of the three can be generated through the existing mathematical model. Then, the limiting current and limiting voltage are used as constraints, and the charge transfer amount and transfer amount are input into the relationship network to obtain different voltages, currents and transfer times that meet the limiting current and limiting voltage. The voltage, current with the minimum transfer time, and the minimum transfer time are obtained as the optimal energy transfer strategy.

[0060] Through the above steps, an optimal energy transfer strategy can be generated that not only meets the SOC balancing requirements but also takes into account the health status and safety of the energy storage unit, thereby achieving effective management and optimized operation of the energy storage system.

[0061] Furthermore, the energy transfer control module 16 in the SOC balancing regulation system for distributed energy storage units is also used for:

[0062] The energy transferor includes a bidirectional DC-DC converter between the distributed energy storage units; the bidirectional DC-DC converter is triggered by the optimal energy transfer strategy to perform energy transfer control.

[0063] Specifically, the energy transferor refers to a device used to control the flow of energy between distributed energy storage units, and the bidirectional DC-DC converter is a key component to achieve this function. The bidirectional DC-DC converter can realize the step-up or step-down of DC power, as well as the bidirectional transmission of power between energy storage units. The bidirectional DC-DC converter is triggered to generate a control signal with the optimal energy transfer strategy, and the control signal is used to guide the bidirectional DC-DC converter to perform the corresponding energy transfer operation, instruct the bidirectional DC-DC converter when to start, when to stop, and how to adjust the energy transfer process, input and output voltage and current, realize bidirectional transmission of power between energy storage units, and realize energy transfer control between multiple distributed energy storage units, thereby achieving energy balance between energy storage units and improving the overall performance and efficiency of the energy storage system.

[0064] In summary, the SOC balancing and regulating system for distributed energy storage units provided in this application has the following technical effects:

[0065] An SOH prediction module is used to collect service records of multiple distributed energy storage units, perform SOH prediction, and generate a distributed SOH prediction value set; an SOH clustering module is used to cluster the distributed SOH prediction value set according to a preset SOH consistency deviation, perform energy storage unit mapping on the clustering results, and generate multiple groups of energy storage units, wherein the multiple groups of energy storage units have SOH marking values; an SOC prediction module is used to monitor the current in and out of the multiple distributed energy storage units, generate multiple total charges, perform SOC prediction, and generate multiple distributed SOC prediction values; a balance analysis module is used to perform distribution difference analysis on the multiple distributed SOC prediction values ​​and generate an SOC balance index, wherein the SOC balance index represents the consistency of the SOC of multiple distributed energy storage units; an energy transfer strategy analysis module is used to perform energy transfer strategy analysis in combination with the SOH marking values ​​of the multiple groups of energy storage units and the multiple distributed SOC prediction values ​​when the SOC balance index is less than the preset balance index, and generate an optimal energy transfer strategy; an energy transfer control module is used to control the energy transferor to perform energy transfer control on the multiple distributed energy storage units with the optimal energy transfer strategy. Through SOH prediction and clustering, as well as SOC prediction and balance analysis, combined with SOH for SOC balance adjustment, energy transfer control of distributed energy storage units is achieved, thereby improving the SOC consistency of distributed energy storage units, reducing performance differences between units, and thus improving the overall efficiency and reliability.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A SOC balancing and regulating system for distributed energy storage units, characterized in that: include: The SOH prediction module is used to collect the service records of multiple distributed energy storage units, perform SOH prediction, and generate a distributed SOH prediction value set; An SOH clustering module, used to cluster the distributed SOH prediction value set according to a preset SOH consistent deviation, perform energy storage unit mapping on the clustering result, and generate multiple groups of energy storage units, wherein the multiple groups of energy storage units have SOH tag values; An SOC prediction module is used to monitor the current in and out of the multiple distributed energy storage units, generate multiple total charge amounts, perform SOC prediction, and generate multiple distributed SOC prediction values; A balance analysis module, used to perform distribution difference analysis on the plurality of distributed SOC prediction values ​​to generate an SOC balance index, wherein the SOC balance index represents the consistency of the SOCs of the plurality of distributed energy storage units; An energy transfer strategy analysis module, configured to perform energy transfer strategy analysis in combination with the SOH mark values ​​of the multiple groups of energy storage units and the multiple distributed SOC prediction values ​​to generate an optimal energy transfer strategy when the SOC balance index is less than a preset balance index; An energy transfer control module is used to control the energy transfer of the multiple distributed energy storage units using the optimal energy transfer strategy.

2. The SOC balancing and regulating system for distributed energy storage units according to claim 1, characterized in that: The SOC prediction module is also used for: A current sensing component is configured to monitor the charge and discharge currents of the plurality of distributed energy storage units in real time to generate a plurality of charge and discharge currents; Integrating the plurality of charge and discharge currents to generate the plurality of total charge amounts; Acquire multiple rated charges of the multiple distributed energy storage units, perform SOC estimation in combination with the multiple total charges, and generate the multiple distributed SOC prediction values.

3. The SOC balancing and regulating system for distributed energy storage units according to claim 2, characterized in that: The SOC prediction module is also used for: Performing charge estimation error analysis to generate charge estimation error; The SOC feedback network is configured with the charge estimation error to optimize the multiple distributed SOC prediction values.

4. The SOC balancing and regulating system for distributed energy storage units according to claim 3, characterized in that: The equilibrium analysis module is also used for: After normalizing the plurality of distributed SOC prediction values, average calculation is performed to generate a SOC normalized central value; Calculating the distances from the normalized multiple distributed SOC prediction values ​​to the SOC normalized center value to generate multiple distributed distances; Based on the multiple distributed distances, a ratio between a minimum distance and a maximum distance is calculated to generate the SOC balance index.

5. The SOC balancing and regulating system for distributed energy storage units according to claim 1, characterized in that: The energy transfer strategy analysis module is also used for: Calculating the average of the plurality of distributed SOC prediction values ​​to generate an average SOC characteristic value; Calculate the distances between the multiple distributed SOC prediction values ​​and the average SOC characteristic value, perform SOC balancing impact analysis, and locate the energy storage unit to be balanced, wherein the energy storage unit to be balanced includes an upward balancing unit and a downward balancing unit; Traversing the multiple groups of energy storage units, locating the SOH characteristic values ​​of the upward equalization unit and the downward equalization unit; An energy balance transfer analysis is performed on the upward balancing unit and the downward balancing unit based on the SOH characteristic value to generate an optimal energy transfer strategy.

6. The SOC balancing and regulating system for distributed energy storage units according to claim 5, characterized in that: The energy transfer strategy analysis module is also used for: Calculating the distances from the plurality of distributed SOC prediction values ​​to the average SOC characteristic value to generate a plurality of distance characteristic values, wherein the plurality of distance characteristic values ​​carry a bias identifier; Based on the multiple distance characteristic values, the energy storage units whose distance is greater than the preset balancing distance are located, and combined with the bias mark, the downward balancing units whose SOC is higher than the average SOC characteristic value and the upward balancing units whose SOC is lower than the average SOC characteristic value are located.

7. The SOC balancing and regulating system for distributed energy storage units according to claim 6, characterized in that: The energy transfer strategy analysis module is also used for: Taking the average SOC characteristic value and the preset equalization distance as constraints, performing energy transfer equalization configuration on the upward equalization unit and the downward equalization unit to generate initial energy transfer information, wherein the initial energy transfer information includes the charge transfer amount corresponding to the upward equalization unit and the charge transfer amount corresponding to the downward equalization unit, as well as the charge transfer path; Perform voltage and current limit analysis of the energy storage unit in combination with the SOH characteristic value to generate a limit current and a limit voltage; The voltage and current of the charge transfer path are calculated in combination with the limiting current, the limiting voltage and the initial energy transfer information to generate an optimal energy transfer strategy.

8. The SOC balancing and regulating system for distributed energy storage units according to claim 1, characterized in that: The energy transfer control module is also used for: The energy transfer device includes a bidirectional DC-DC converter between the distributed energy storage units; The bidirectional DC-DC converter is triggered to perform energy transfer control using the optimal energy transfer strategy.

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