Battery cell balance evaluation method, device and equipment of energy storage system and medium

Abstract

The invention discloses an energy storage system cell balance evaluation method, device and equipment and a medium. The evaluation method comprises the following steps: acquiring historical operation data of at least one energy storage system; according to the historical operation data, generating a first relation curve of the energy storage system in a charging state and a second relation curve of the energy storage system in a discharging state; according to the voltage data of each battery cell in the energy storage system, based on the first relation curve and the second relation curve, predicting in the running day to obtain the liftable capacity of the energy storage system after the battery cells are balanced; and determining the cell equalization priority of the plurality of energy storage systems according to the liftable capacity and the cell distribution condition of the energy storage systems. Through the evaluation method provided by the invention, the liftable capacity and the balance priority of the energy storage system can be quantitatively evaluated, so that the technical effects of assisting technicians to complete the operation and maintenance work of battery cell balance, reducing the equipment shutdown time, improving the operation efficiency and furthest improving the available capacity of the energy storage system are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of battery cell balancing in energy storage systems, and in particular to a battery cell balancing evaluation method, device, equipment and medium for energy storage systems. Background Art

[0002] Cell balancing refers to the process of using specific technical means to balance the SOC (State of Charge) of all single cells in the battery pack. The high efficiency of balancing is crucial to ensure the stable operation of the equipment, so it has attracted much attention from the industry. As a key maintenance link in the energy storage system, cell balancing plays a decisive role in ensuring system performance and extending service life. Even for cells in the same production batch, there may be subtle differences in key parameters such as capacity, internal resistance, and self-discharge rate. These differences will gradually accumulate during the use of the battery pack, eventually leading to an imbalance in the working state between the cells. This imbalance not only reduces the overall efficiency of the energy storage system, but may also cause premature aging or damage to some cells.

[0003] However, due to the late start of large-scale application of energy storage systems, most energy storage systems have a short operating time, most of the cells are in a healthy state, and the imbalance problem has not yet fully manifested. The demand for balanced evaluation of energy storage system cells has not yet been fully met. Existing balanced evaluation methods often lack in-depth analysis of the historical operating data of energy storage systems, resulting in the inability of operation and maintenance personnel to quantitatively evaluate the increased capacity of the system after balanced operation, and thus unable to scientifically formulate balanced order and plan arrangements. This makes the priority sorting of balanced operations less accurate, affecting the overall performance and life of the energy storage system. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a method, device, equipment and medium for evaluating the balancing of battery cells in an energy storage system, so as to quantitatively evaluate the expandable capacity of the energy storage system, determine the balancing priority of multiple energy storage systems, and then assist technical personnel to complete the operation and maintenance of battery cell balancing, reduce equipment downtime, improve operating efficiency, and maximize the technical effect of increasing the available capacity of the energy storage system.

[0005] According to the first aspect of the present application, a method for evaluating cell balancing of an energy storage system is proposed, the method comprising:

[0006] Obtaining historical operating data of at least one energy storage system;

[0007] Generate a first relationship curve of the energy storage system under a charging state and a second relationship curve of the energy storage system under a discharging state according to the historical operation data;

[0008] According to the voltage data of each battery cell in the energy storage system, and based on the first relationship curve and the second relationship curve, predicting the increaseable capacity of the energy storage system after the battery cells are balanced within the operation day;

[0009] According to the expandable capacity and the distribution of the battery cells of the energy storage system, the battery cell balancing priority of the plurality of energy storage systems is determined.

[0010] Optionally, the first relationship curve includes a mapping relationship curve between a charging voltage value and an SOC value of the energy storage system; and the second relationship curve includes a mapping relationship curve between a discharging voltage value and an SOC value of the energy storage system.

[0011] Optionally, the historical operation data includes at least one of the following: the operation state, voltage value, and SOC value of the energy storage system at different sampling times before the operation date;

[0012] The step of generating a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system according to the historical operation data comprises:

[0013] generating a multi-dimensional information matrix according to the historical operation data of the at least one energy storage system;

[0014] According to the multi-dimensional information matrix, a charging information matrix in a charging state and a discharging information matrix in a discharging state are obtained respectively;

[0015] The charging information matrix and the discharging information matrix are fitted to obtain the first relationship curve and the second relationship curve respectively.

[0016] Optionally, the method further comprises:

[0017] Responding to the preset operating conditions of the energy storage system during the operating day; wherein,

[0018] Under the first operating condition, a first battery cell list is generated according to the voltage data of each battery cell obtained in real time, and a first battery cell list to be balanced whose actual voltage value at the moment of charging cutoff is higher than a preset voltage threshold is obtained by screening;

[0019] Under the second operating condition, a second battery cell list is generated according to the voltage data of each battery cell acquired in real time, and a second battery cell list to be balanced whose actual voltage value at the moment of discharge cutoff is lower than a preset voltage threshold is obtained by screening.

[0020] Optionally, the predicting, based on the voltage data of each battery cell of the energy storage system and based on the first relationship curve and the second relationship curve, the increaseable capacity of the energy storage system after battery cell balancing during the operation day includes:

[0021] In the first operating condition:

[0022] According to the first battery cell list, obtaining the highest voltage value in each battery cell;

[0023] Determine a first cell not to be balanced according to the first cell list and the first cell to be balanced list, and obtain a maximum voltage value of the first cell not to be balanced;

[0024] Based on the first relationship curve, according to the maximum voltage value in each of the battery cells and the maximum voltage value of the first non-battery cell to be balanced, predicting the increaseable capacity of the energy storage system after battery cell balancing within the operation day;

[0025] In the second operating condition:

[0026] According to the second battery cell list, obtaining the lowest voltage value in each battery cell;

[0027] Determine a second cell not to be balanced according to the second cell list and the second cell to be balanced list, and obtain a minimum voltage value of the second cell not to be balanced;

[0028] Based on the second relationship curve, according to the lowest voltage value in each of the battery cells and the lowest voltage value of the second battery cell not to be balanced, the increaseable capacity of the energy storage system after battery cell balancing is predicted within an operation day.

[0029] Optionally, determining the cell balancing priority of a plurality of the energy storage systems according to the expandable capacity and the cell distribution of the energy storage system includes:

[0030] Obtaining the sum of the upgradable capacities of each energy storage system according to the upgradable capacity calculated at the most recent charging cut-off moment and the upgradable capacity calculated at the most recent discharging cut-off moment;

[0031] According to the sum of the expandable capacities of each of the energy storage systems and the number of battery packs corresponding to the cells to be balanced in the energy storage system, respectively obtain a balancing score value for each of the energy storage systems;

[0032] The balancing score values ​​of the plurality of energy storage systems are sorted, and the cell balancing priorities of the plurality of energy storage systems are determined.

[0033] Optionally, the method further comprises:

[0034] Performing data preprocessing on the acquired historical operation data;

[0035] The data preprocessing includes at least one of the following operations: performing missing value processing, duplicate value processing, abnormal value processing, and point filling and alignment processing on the historical operation data.

[0036] According to a second aspect of the present application, a device for evaluating cell balancing of an energy storage system is provided, the device comprising:

[0037] An acquisition unit, used to acquire historical operation data of at least one energy storage system;

[0038] A curve generating unit, configured to generate a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system according to the historical operation data;

[0039] A capacity calculation unit, configured to predict, according to voltage data of each battery cell in the energy storage system and based on the first relationship curve and the second relationship curve, the increaseable capacity of the energy storage system after battery cell balancing within an operating day;

[0040] A priority determination unit is used to determine the cell balancing priority of multiple energy storage systems according to the expandable capacity and the cell distribution of the energy storage system.

[0041] According to a third aspect of the present application, an electronic device is provided, comprising: a processor; and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the evaluation method as described in any one of the first aspects above.

[0042] According to a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the evaluation method as described in any one of the first aspects above is implemented.

[0043] As can be seen from the above, at least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: a method, device, equipment and medium for evaluating battery cell balancing of an energy storage system are provided. First, historical operation data of at least one energy storage system is obtained; second, a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system are generated based on the historical operation data; third, according to the voltage data of each battery cell in the energy storage system, and based on the first relationship curve and the second relationship curve, the increaseable capacity of the energy storage system after battery cell balancing is predicted within the operating day; finally, according to the increaseable capacity and the battery cell distribution of the energy storage system, the battery cell balancing priority of multiple energy storage systems is determined. Through the evaluation method of the embodiment of the present application, on the one hand, by utilizing big data real-time computing technology, it is possible to screen the balanced battery cell list and quantitatively evaluate the expandable capacity of the energy storage system without requiring the energy storage system equipment to be stationary, shut down, or unpacked, thereby providing an effective basis for the battery cell balancing operation of the energy storage system; on the other hand, by utilizing the historical operation data of the energy storage system, the mapping relationship between the voltage value and the SOC value is dynamically updated, and the evaluation is performed based on the latest mapping relationship, thereby achieving a more accurate evaluation effect; on the other hand, it is possible to determine the balancing priority of multiple energy storage systems, and assist relevant operators in performing battery cell balancing, thereby achieving the technical effect of reducing the equipment downtime and maximizing the available capacity of the energy storage system.

[0044] 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 in accordance with the contents of the specification. 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 listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1 A schematic diagram of a flow chart of a method for evaluating battery cell balancing in an energy storage system in one embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of a first relationship curve (charging voltage value-SOC value mapping relationship curve) under a charging state in one embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of a second relationship curve (a mapping relationship curve of discharge voltage value-SOC value) under a discharge state in one embodiment of the present application;

[0049] Figure 4 It is a structural schematic diagram of a battery cell balancing evaluation device for an energy storage system in one embodiment of the present application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0052] In actual operation, cell balancing is divided into two strategies: passive balancing and active balancing. Whether passive balancing or active balancing is adopted, when large-scale or deep balancing adjustments are made, it is usually necessary to suspend the operation of the energy storage system for safety reasons or to achieve the best balancing effect. Therefore, giving priority to balancing the energy storage system with the greatest capacity improvement potential within the limited downtime has become the key to reducing downtime.

[0053] Based on this, the technical concept of the present application is to take advantage of the massive data of the energy storage cloud platform, form a comprehensive voltage-SOC curve of the energy storage system based on the second-level operation data of the battery cells, identify the list of battery cells to be balanced in the energy storage system, and quantitatively evaluate the increaseable capacity of the energy storage system after battery cell balancing based on the comprehensive voltage-SOC curve, thereby calculating the balancing priority of each energy storage system according to the list of battery cells to be balanced and the increaseable capacity.

[0054] It should be noted that the embodiments of the present application are preferentially applicable to the passive balancing scenario of the battery cells, such as completing the balancing operation through equipment such as an intelligent balancing power replenisher. That is, the embodiments of the present application can predict the increaseable capacity of the energy storage system after completing the corresponding battery cell balancing operation, and arrange relevant technical personnel to perform the operation and maintenance work of battery cell balancing in order of priority, so that within the limited downtime, the battery cell balancing can be prioritized for the energy storage system with greater capacity increase potential, thereby improving the operation efficiency of the overall energy storage system.

[0055] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present application, Figure 1 As shown, a flow chart of a method for evaluating the cell balancing of an energy storage system is provided. The evaluation method of the embodiment of the present application at least includes the following steps S110 to S140:

[0057] Step S110, obtaining historical operation data of at least one energy storage system.

[0058] The energy storage system in the embodiment of the present application refers to the minimum device combination that can independently realize the storage, conversion and release of electric energy. The historical operation data refers to the operation data before the operation day of the energy storage system. Preferably, the historical operation data of the energy storage system in the past 30 days can be obtained, and the historical operation data includes but is not limited to the operation status, voltage value, SOC value, etc. of the energy storage system; the operation status includes charging, discharging, standby, etc., and the SOC value indicates the remaining power of the energy storage system at the current moment, usually expressed as a percentage.

[0059] Step S120: generating a first relationship curve of the energy storage system in a charging state and a second relationship curve of the energy storage system in a discharging state according to the historical operation data.

[0060] In this embodiment, the massive data advantage of the energy storage cloud platform is used to classify the operating status of the energy storage system. For example, the energy storage system is divided into charging state, discharging state, standby state, etc., and a big data calculation method is used to form a first relationship curve of the energy storage in the charging state and a second relationship curve in the discharging state based on the second-level operation data of the battery cell. For example, a comprehensive voltage-SOC curve of the energy storage system in different operating states is formed respectively.

[0061] Step S130 , predicting the increaseable capacity of the energy storage system after cell balancing within an operation day according to the voltage data of each cell in the energy storage system and based on the first relationship curve and the second relationship curve.

[0062] After generating the first relationship curve and the second relationship curve, it is necessary to identify the voltage data of all cells in the energy storage system and generate a list of cells to be balanced in the energy storage system on the operation day. This allows the capacity of the energy storage system to be quantitatively evaluated after cell balancing based on the relationship between the list of cells to be balanced and the first relationship curve and the second relationship curve.

[0063] Step S140: determining a cell balancing priority of a plurality of the energy storage systems according to the expandable capacity and the distribution of the cells of the energy storage system.

[0064] The energy storage system can be a single set or multiple sets. When a single energy storage system is included, the increaseable capacity of the single energy storage system can be used as a reference indicator for the battery cell balancing operation and maintenance work, to assist relevant technical personnel in completing the operation and maintenance work of energy storage battery cell balancing; when multiple energy storage systems are included, the list of batteries to be balanced and the increaseable capacity of each energy storage system can be combined to calculate the balancing priority of multiple energy storage systems. The higher the balancing priority, the more urgent the battery cell balancing demand of the corresponding energy storage system. In this way, the operation and maintenance personnel can scientifically plan the priority of battery cell balancing, thereby effectively reducing the equipment downtime and maximizing the available capacity of the energy storage system. Of course, the embodiment of the present application does not limit the number of energy storage systems.

[0065] It can be seen that, unlike the existing technical solutions that cannot quantitatively evaluate the available capacity of the energy storage system after cell balancing, the energy storage system cell balancing evaluation method of the embodiment of the present application, on the one hand, by using big data real-time computing technology, it is possible to screen the balanced cell list and quantitatively evaluate the increaseable capacity of the energy storage system after balancing without the need to keep the energy storage system equipment stationary, shut down, or unpack, thereby providing an effective basis and guidance for the cell balancing operation of the energy storage system; on the other hand, by using the historical operation data of the energy storage system, the voltage-SOC mapping relationship is dynamically updated, and the evaluation is performed based on the latest mapping relationship, so as to achieve a more accurate evaluation effect; on the other hand, it is possible to determine the balancing priority of multiple energy storage systems, assist relevant technical personnel in performing cell balancing operations, and achieve the technical effect of reducing the equipment downtime, improving the operating efficiency, and maximizing the available capacity of the energy storage system.

[0066] It should be understood that the energy storage system cell balancing evaluation method of the embodiment of the present application may include additional steps not shown and / or may omit some of the steps shown, and the scope of the present disclosure is not limited to this.

[0067] In some embodiments, the first relationship curve includes a mapping relationship curve between a charging voltage value and an SOC value of the energy storage system; and the second relationship curve includes a mapping relationship curve between a discharging voltage value and an SOC value of the energy storage system.

[0068] In some preferred embodiments, the historical operation data includes at least one of the following: the operation status, voltage value, and SOC value of the energy storage system at different sampling times before the operation date.

[0069] Preferably, the method further includes: performing data preprocessing on the acquired historical operation data; the data preprocessing includes at least one of the following operations: performing missing value processing, duplicate value processing, outlier processing, and point filling and alignment processing on the historical operation data.

[0070] In one example, the format of each piece of data in the historical operation data includes the following:

[0071] message=[pid,value,time];

[0072] Among them, message represents a single piece of data information; pid represents the point name, which can be used to represent the operating status (charging, discharging, standby), voltage, SOC, etc.; value represents the value corresponding to pid; time represents the sampling time of the data information.

[0073] For example, the missing value processing of historical operation data includes: when one of the items [pid, value, time] of the message data information has a null value, the message data information is discarded;

[0074] The processing of duplicate values ​​of historical operation data includes: when there are two or more message data information that are completely consistent, only one of the message data information is retained;

[0075] The abnormal value processing of historical operation data includes: when one of the [pid, value, time] items of the message data information is abnormal, the message data is discarded;

[0076] The historical operation data point alignment process includes: for a certain battery cell in the energy storage system, all non-repeated time values ​​whose pid in the message is the voltage value or SOC value are obtained to form a time series T = [time1, time2…time n ], and according to each moment in the time series T, query the corresponding value when the corresponding pid is the operating status, voltage, and SOC. If there is no data at that moment, the value of the previous corresponding pid with data will be used.

[0077] In some embodiments, generating a first relationship curve under the charging state and a second relationship curve under the discharging state of the energy storage system according to the historical operation data includes: generating a multi-dimensional information matrix according to the historical operation data of at least one energy storage system; obtaining a charging information matrix under the charging state and a discharging information matrix under the discharging state according to the multi-dimensional information matrix; fitting the charging information matrix and the discharging information matrix to obtain the first relationship curve and the second relationship curve, respectively.

[0078] In one example, the multi-dimensional information matrix includes the following form:

[0079]

[0080] Among them, data represents the processed multi-dimensional information matrix, state represents the operating state of the energy storage system, and u represents the voltage value;

[0081] Fitting the charging information matrix and the discharging information matrix to obtain the first relationship curve and the second relationship curve respectively includes the following process:

[0082] When the running state is charging, extract the data of state as charging state from data to form the charging information matrix data 充 ;

[0083] When the running state is discharge, the data of state as discharge state is extracted from data to form the discharge information matrix data 放 ;

[0084] For data 充 With data 放 , using the least squares fitting method, we can get the mapping relationship between the charging voltage value and the SOC value , that is, u=f 充 (soc); and the mapping relationship between the discharge voltage value and the SOC value , that is, u=f 放 (soc);

[0085] Thereby, a comprehensive voltage-SOC curve of the energy storage system in the charging state, that is, a first relationship curve, and a comprehensive voltage-SOC curve in the discharging state, that is, a second relationship curve, are generated.

[0086] In some embodiments, the method further includes: responding to a preset operating condition of the energy storage system during an operating day; wherein, under a first operating condition, a first battery cell list is generated based on the voltage data of each battery cell obtained in real time, and a first battery cell list to be balanced whose actual voltage value at the moment of charging cutoff is higher than a preset voltage threshold is obtained by screening; under a second operating condition, a second battery cell list is generated based on the voltage data of each battery cell obtained in real time, and a second battery cell list to be balanced whose actual voltage value at the moment of discharging cutoff is lower than the preset voltage threshold is obtained by screening.

[0087] In one example, the preset operating conditions of the energy storage system include at least a first operating condition and a second operating condition. The first operating condition includes: during the operating day, the energy storage system is continuously in a charging condition (for example, more than 10 minutes), and the charging is cut off at an instant due to the excessive voltage of the battery cell; at the instant of charging cut off, the battery cells whose battery cell voltage is higher than the average (for example, a millivolt) are selected as the first battery cells to be balanced and a list of battery cells to be balanced is formed.

[0088] The second operating condition includes: during the operating day, the energy storage system is continuously in the discharge condition (more than 10 minutes), and the discharge is cut off at the moment of discharge due to the low voltage of the battery cell; at the moment of discharge cut off, the battery cells with a voltage lower than the average (for example, a millivolt) are selected as the second battery cells to be balanced and a list of batteries to be balanced is formed. Of course, the energy storage system also includes normal charging and discharging conditions, standby, shutdown conditions, etc., which will not be elaborated here.

[0089] It should be noted that in this example, when calculating the list of battery cells to be balanced in the energy storage system, big data technology can be used to complete the voltage data of all battery cells in the energy storage system at that time section (the moment when charging or discharging is terminated), and sort and average them. It can be understood that average calculation means excluding 10% of the battery cells with the highest and lowest voltages, and then calculating the average of the remaining battery cell voltages. Taking lithium-ion batteries as an example, the value of a millivolt is 200, and for other batteries, the value of a can be determined based on the characteristics of the battery materials.

[0090] In some embodiments, the predicting the increaseable capacity of the energy storage system after cell balancing according to the voltage data of each cell of the energy storage system and based on the first relationship curve and the second relationship curve during the operation day includes: under a first operation condition: according to the first cell list, obtaining the highest voltage value in each cell; according to the first cell list and the first cell list to be balanced, determining a first non-to-be-balanced cell, and obtaining the highest voltage value of the first non-to-be-balanced cell; based on the first relationship curve, according to the highest voltage value in each cell and the highest voltage value of the first non-to-be-balanced cell, predicting the increaseable capacity of the energy storage system after cell balancing during the operation day; under a second operation condition: according to the second cell list, obtaining the lowest voltage value in each cell; according to the second cell list and the second cell list to be balanced, determining a second non-to-be-balanced cell, and obtaining the lowest voltage value of the second non-to-be-balanced cell; based on the second relationship curve, according to the lowest voltage value in each cell and the lowest voltage value of the second non-to-be-balanced cell, predicting the increaseable capacity of the energy storage system after cell balancing during the operation day.

[0091] In one example, under the first operating condition, for the charging cut-off instant:

[0092] The maximum voltage of all cells in the energy storage system is recorded as u max ; Filter out the cells that do not belong to the first list of cells to be balanced, and the highest voltage value of the first cell not to be balanced is recorded as u max-无需均衡 ;

[0093] Under charging conditions, since the cells to be balanced that are higher than the average value need to be discharged during cell balancing, the increase in capacity of the system after balancing is the difference between the maximum value of the cell voltage and the maximum value of the cell voltage that is not to be balanced. The calculation formula for the increase in SOC is:

[0094] ;

[0095] in, for The inverse function of a mapping function representing a first relationship curve;

[0096] Therefore, the calculation formula for the increaseable capacity of the energy storage system is:

[0097] ;

[0098] Under the second operating condition, at the moment of discharge cut-off:

[0099] The lowest voltage value of all cells in the energy storage system is recorded as u min ; Filter out the cells that do not belong to the second list of cells to be balanced, and the lowest voltage value of the second unbalanced cell is recorded as u min-无需均衡 ;

[0100] Under discharge conditions, since the cells to be balanced that are lower than the average value need to be charged during cell balancing, the increase in capacity of the system after balancing is the difference between the lowest cell voltage and the lowest discharge voltage of the non-balanced cells. The calculation formula for the increase in SOC is:

[0101]

[0102] in, f 放 The inverse function of (), f 放 () represents the mapping function of the second relationship curve; therefore, the upgradeable capacity of the energy storage system is:

[0103] ;

[0104] Therefore, during the operation day, the increaseable capacity of the energy storage system under different operating conditions after cell balancing can be predicted, thereby providing an effective basis for the cell balancing operation of the energy storage system.

[0105] In some embodiments, the determining the cell balancing priority of the plurality of energy storage systems according to the expandable capacity and the cell distribution of the energy storage system includes: obtaining the sum of the expandable capacity of each energy storage system according to the expandable capacity calculated at the most recent charging cut-off moment and the expandable capacity calculated at the most recent discharging cut-off moment; obtaining the balancing score value of each energy storage system according to the sum of the expandable capacity of each energy storage system and the number of battery packs corresponding to the cells to be balanced in the energy storage system; sorting the balancing score values ​​of the plurality of energy storage systems, and determining the cell balancing priority of the plurality of energy storage systems.

[0106] In one example, for each energy storage system: when its most recent charging is terminated (i.e., the most recent first operating condition) and the most recent discharging is terminated (i.e., the most recent second operating condition), the battery packs (i.e., energy storage battery packs) to which the corresponding cells in the cell list to be balanced belong can be located according to the first cell list to be balanced and the second cell list to be balanced calculated in the above steps, and the number of battery packs distributed in each energy storage system is counted respectively, which is recorded as n, such as n1, n2, and n3; for example, in an energy storage system, there are 100 cells to be balanced in its cell list to be balanced, and the above cells to be balanced are distributed in 20 battery packs, then the number of battery packs corresponding to the cells to be balanced of the energy storage system is n=20, which will not be elaborated here.

[0107] When the last charge and discharge of each energy storage system are terminated, the sum of the accumulative capacity of each energy storage system is calculated based on the accumulative capacity calculated at the last charge and the accumulative capacity calculated at the last discharge, and is recorded as m, such as m1, m2, and m3; therefore, the balanced cost-effectiveness score of each energy storage system, that is, the balanced score value, is recorded as score=m / n.

[0108] Furthermore, when multiple energy storage systems are involved, the balancing score of each system can be ranked. The higher the score, the higher the balancing priority. This can guide on-site operation and maintenance personnel to perform cell balancing operations on energy storage systems with high priority first, thereby improving the overall operating efficiency of the energy storage system and extending its service life.

[0109] To facilitate understanding of the technical solution of this application, a specific application example is provided below:

[0110] A 200MW / 400MWh independent energy storage power station consists of 10 sets of 35kV 20MW / 40MWh energy storage systems, each of which can be charged and discharged independently.

[0111] Taking the No. 1 energy storage system as an example, its operating data is used as the data of this embodiment. The No. 1 energy storage system uses the data from September 1, 2024 to September 30, 2024 as the historical operating data of the past 30 days. After processing, the data can be formed in the following form:

[0112]

[0113] According to the above data, the voltage value-SOC value mapping relationship f of the No. 1 energy storage system under the charging and discharging state is obtained by fitting. 充 () and f 放 (), please refer to Figure 2 The first relationship curve and Figure 3 The second relationship curve is shown.

[0114] On the operation date of October 1, 2024, under the condition that specific operating conditions are met, the list of cells to be balanced for the No. 1 energy storage system will be calculated and the increased capacity after balancing will be evaluated.

[0115] For example, at 11:38:12 on October 1, 2024, the energy storage system reaches the cut-off voltage because the voltage of the single cell is too high, resulting in charging cut-off. At this time, the highest voltage value of all cells in the energy storage system is recorded as u max =3.6v, the voltage average is 3.35V, and the cells with a voltage between 3.45V and 3.6V are included in the list of cells to be balanced. The highest voltage value of the cells not to be balanced is recorded as u max-无需均衡 =3.34v. Figure 2 Curve, when charging is terminated, we can get:

[0116]

[0117] Therefore, after the cells are balanced, the SOC can be increased by:

[0118] ;

[0119] The energy storage system can increase the capacity to:

[0120] ;

[0121] Similarly, when the discharge is terminated, the cells with too low power can be balanced, and the increased capacity of the energy storage system can also be obtained.

[0122] By using big data technology to perform real-time online calculations on 10 energy storage systems, it is possible to effectively screen out the number of cells to be balanced, the battery packs to which they belong, and the sum of the capacities that can be increased after balancing when charging and discharging are cut off for each energy storage system.

[0123] Finally, the balance scores of the 10 energy storage systems were calculated, which were 0.9288, 0.9134, 0.6413, 0.7157, 0.4713, 0.2741, 0.4693, 0.4931, 0.4341, and 0.8133, respectively. Therefore, the balance priorities are system 1, system 2, system 10, system 4, system 3, system 8, system 5, system 7, system 9, and system 6. This can assist relevant technicians to balance the cells of each energy storage system in accordance with the above balance priority, so that they can prioritize their limited time and equipment resources on the most cost-effective battery pack, thereby reducing the downtime of the equipment and maximizing the available capacity of the energy storage system.

[0124] In another embodiment of the present application, Figure 4 As shown, a device for evaluating the balance of cells in an energy storage system is provided. The device 400 for evaluating the balance of cells in an energy storage system includes:

[0125] An acquisition unit 410, configured to acquire historical operation data of at least one energy storage system;

[0126] A curve generating unit 420, configured to generate a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system according to the historical operation data;

[0127] A capacity calculation unit 430, configured to predict, according to the voltage data of each battery cell in the energy storage system and based on the first relationship curve and the second relationship curve, the increaseable capacity of the energy storage system after the battery cells are balanced within an operating day;

[0128] The priority determination unit 440 is used to determine the cell balancing priority of the multiple energy storage systems according to the expandable capacity and the cell distribution of the energy storage system.

[0129] It can be understood that each step of the aforementioned energy storage system cell balancing evaluation method can be performed by the energy storage system cell balancing evaluation device provided in this embodiment. Therefore, the relevant explanations about the energy storage system cell balancing evaluation method are applicable to the energy storage system cell balancing evaluation device and will not be repeated here.

[0130] In summary, the present application has achieved at least the following technical effects: on the one hand, by utilizing big data real-time computing technology, it is possible to screen the balanced battery cell list and quantitatively evaluate the expandable capacity of the energy storage system without the need to keep the energy storage system equipment stationary, shut down, or unpack it, thereby providing an effective basis and guidance for the battery cell balancing operation of the energy storage system; on the other hand, by utilizing the historical operation data of the energy storage system, the voltage-SOC mapping relationship is dynamically updated, and an evaluation is performed based on the latest mapping relationship, thereby achieving a more accurate evaluation effect; on the other hand, it is possible to determine the balancing priority of multiple energy storage systems, assist relevant technical personnel in performing the operation and maintenance of battery cell balancing, thereby reducing the equipment downtime and maximizing the available capacity of the energy storage system.

[0131] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0132] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0133] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0134] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an energy storage system cell balancing evaluation device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0135] Obtaining historical operating data of at least one energy storage system;

[0136] Generate a first relationship curve of the energy storage system under a charging state and a second relationship curve of the energy storage system under a discharging state according to the historical operation data;

[0137] According to the voltage data of each battery cell in the energy storage system, and based on the first relationship curve and the second relationship curve, predicting the increaseable capacity of the energy storage system after the battery cells are balanced within the operation day;

[0138] According to the expandable capacity and the distribution of the battery cells of the energy storage system, the battery cell balancing priority of the plurality of energy storage systems is determined.

[0139] The above application Figure 1 The energy storage system cell balancing evaluation method disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0140] The present application also provides a computer program product, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 1The energy storage system cell balancing evaluation method in the illustrated embodiment is specifically used to perform:

[0141] Obtaining historical operating data of at least one energy storage system;

[0142] Generate a first relationship curve of the energy storage system under a charging state and a second relationship curve of the energy storage system under a discharging state according to the historical operation data;

[0143] According to the voltage data of each battery cell in the energy storage system, and based on the first relationship curve and the second relationship curve, predicting the increaseable capacity of the energy storage system after the battery cells are balanced within the operation day;

[0144] According to the expandable capacity and the distribution of the battery cells of the energy storage system, the battery cell balancing priority of the plurality of energy storage systems is determined.

[0145] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0151] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0154] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating the balance of battery cells in an energy storage system, characterized in that: The method comprises: Obtaining historical operating data of at least one energy storage system; Generating a first relationship curve of the energy storage system under a charging state and a second relationship curve of the energy storage system under a discharging state according to the historical operation data; According to the voltage data of each battery cell in the energy storage system, and based on the first relationship curve and the second relationship curve, predicting the increaseable capacity of the energy storage system after the battery cells are balanced within the operation day; According to the expandable capacity and the distribution of the battery cells of the energy storage system, the battery cell balancing priority of the plurality of energy storage systems is determined.

2. The evaluation method according to claim 1, characterized in that: The first relationship curve includes a mapping relationship curve between a charging voltage value and an SOC value of the energy storage system; the second relationship curve includes a mapping relationship curve between a discharging voltage value and an SOC value of the energy storage system.

3. The evaluation method according to claim 1, characterized in that: The historical operation data includes at least one of the following: the operation status, voltage value, and SOC value of the energy storage system at different sampling times before the operation date; The step of generating a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system according to the historical operation data comprises: generating a multi-dimensional information matrix according to the historical operation data of the at least one energy storage system; According to the multi-dimensional information matrix, a charging information matrix in a charging state and a discharging information matrix in a discharging state are obtained respectively; The charging information matrix and the discharging information matrix are fitted to obtain the first relationship curve and the second relationship curve respectively.

4. The evaluation method according to claim 1, characterized in that: The method further comprises: Responding to the preset operating conditions of the energy storage system during the operating day; wherein, Under the first operating condition, a first battery cell list is generated according to the voltage data of each battery cell obtained in real time, and a first battery cell list to be balanced whose actual voltage value at the moment of charging cutoff is higher than a preset voltage threshold is obtained by screening; Under the second operating condition, a second battery cell list is generated according to the voltage data of each battery cell acquired in real time, and a second battery cell list to be balanced whose actual voltage value at the moment of discharge cutoff is lower than a preset voltage threshold is obtained by screening.

5. The evaluation method according to claim 4, characterized in that: The method of predicting the increaseable capacity of the energy storage system after cell balancing within an operation day according to the voltage data of each cell of the energy storage system and based on the first relationship curve and the second relationship curve includes: In the first operating condition: According to the first battery cell list, obtaining the highest voltage value in each battery cell; Determine a first cell not to be balanced according to the first cell list and the first cell to be balanced list, and obtain a maximum voltage value of the first cell not to be balanced; Based on the first relationship curve, according to the maximum voltage value in each of the battery cells and the maximum voltage value of the first non-battery cell to be balanced, predicting the increaseable capacity of the energy storage system after battery cell balancing within the operation day; In the second operating condition: According to the second battery cell list, obtaining the lowest voltage value in each battery cell; Determine a second cell not to be balanced according to the second cell list and the second cell to be balanced list, and obtain a minimum voltage value of the second cell not to be balanced; Based on the second relationship curve, according to the lowest voltage value in each of the battery cells and the lowest voltage value of the second battery cell not to be balanced, the increaseable capacity of the energy storage system after battery cell balancing is predicted within an operation day.

6. The evaluation method according to claim 1, characterized in that: The step of determining the cell balancing priority of a plurality of energy storage systems according to the expandable capacity and the cell distribution of the energy storage system comprises: Obtaining the sum of the upgradable capacities of each energy storage system according to the upgradable capacity calculated at the most recent charging cut-off moment and the upgradable capacity calculated at the most recent discharging cut-off moment; According to the sum of the expandable capacities of each of the energy storage systems and the number of battery packs corresponding to the cells to be balanced in the energy storage system, respectively obtain a balancing score value for each of the energy storage systems; The balancing score values ​​of the plurality of energy storage systems are sorted, and the cell balancing priorities of the plurality of energy storage systems are determined.

7. The evaluation method according to claim 1, characterized in that: The method further comprises: Performing data preprocessing on the acquired historical operation data; The data preprocessing includes at least one of the following operations: performing missing value processing, duplicate value processing, abnormal value processing, and point filling and alignment processing on the historical operation data.

8. A cell balance evaluation device for an energy storage system, characterized in that: The device comprises: An acquisition unit, used to acquire historical operation data of at least one energy storage system; A curve generating unit, configured to generate a first relationship curve under a charging state and a second relationship curve under a discharging state of the energy storage system according to the historical operation data; A capacity calculation unit, configured to predict, according to voltage data of each battery cell in the energy storage system and based on the first relationship curve and the second relationship curve, the increaseable capacity of the energy storage system after battery cell balancing within an operating day; A priority determination unit is used to determine the cell balancing priority of multiple energy storage systems according to the expandable capacity and the cell distribution of the energy storage system.

9. An electronic device, wherein: The electronic device comprises: a processor; and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor performs the evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the evaluation method according to any one of claims 1 to 7.

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