Energy storage system optimization method and apparatus, and device, storage medium, and program product

Abstract

Provided in the present application are an energy storage system optimization method and apparatus, and a device, a storage medium, and a program product. The energy storage system optimization method comprises: in response to an inspection instruction, acquiring configuration information of each battery cell within a target station corresponding to the inspection instruction; calculating actual state information of each battery cell on the basis of the configuration information; on the basis of the actual state information of each battery cell, determining a state to be optimized of a group where each battery cell is located; and in response to an optimization instruction, determining a target optimization group from among groups and optimizing battery cells contained in the target optimization group. The present application allows for less granularity of a determination criterion for finally determining a state to be optimized, and a more accurate determination result, thereby timely and effectively detecting problems occurring in an energy storage system and achieving a better optimization effect.

Description

Energy storage system optimization method, device, equipment, storage medium and program product

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 22, 2024, with application number 202410338349.X and application name “Energy Storage System Optimization Method, Device, Equipment, Storage Medium and Program Product”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of energy storage control technology, and in particular to an energy storage system optimization method, device, equipment, storage medium and program product. Background Art

[0003] With the development of energy storage technology, energy storage systems, as an important component of smart grid and microgrid systems, are playing an increasingly important role.

[0004] Typically, personnel monitor the operating status of each container, subsystem, or battery cluster in the energy storage system by obtaining parameters such as the rated power, rated current, charging efficiency, and discharge efficiency. Based on the monitoring results, they optimize the energy storage system by performing energy replenishment or hardware replacement. However, this monitoring method cannot accurately determine the actual status of the batteries in the energy storage system, and therefore cannot promptly and effectively detect problems with the battery system, resulting in poor optimization results.

[0005] Summary of the Invention

[0006] The present application provides an energy storage system optimization method, device, equipment, storage medium and program product to solve the problem of poor optimization effect of energy storage systems in the prior art.

[0007] In a first aspect, the present application provides an energy storage system optimization method, comprising:

[0008] In response to a detection instruction, obtaining configuration information of each battery cell in a target station corresponding to the detection instruction;

[0009] Calculating actual status information of each of the battery cells according to the configuration information;

[0010] Determining the state to be optimized of the group to which each battery cell belongs based on the actual state information of each battery cell;

[0011] In response to the optimization instruction, a target optimization group is determined from each of the groups, and the battery cells included in the target optimization group are optimized.

[0012] In one embodiment, before calculating the actual status information of each battery cell according to the configuration information, the method further includes:

[0013] According to a preset information threshold, abnormal data is detected from the configuration information;

[0014] Cleaning the abnormal data and sorting the remaining configuration information to obtain processed configuration information;

[0015] Calculating the actual status information of each battery cell according to the configuration information includes:

[0016] The actual status information of each battery cell is calculated according to the processed configuration information.

[0017] In one embodiment, the detection instruction carries a time period tag;

[0018] The obtaining of configuration information of each battery cell in the target station corresponding to the detection instruction includes:

[0019] Determine the target time period according to the time period label;

[0020] Obtain configuration information of each of the battery cells within the target time period.

[0021] In one embodiment, the configuration information carries a generation time tag;

[0022] The cleaning of the abnormal data and sorting of the remaining configuration information to obtain processed configuration information includes:

[0023] Cleaning and filling the abnormal data to obtain cleaned configuration information;

[0024] The generation time of the cleaned configuration information is determined according to the generation time tag carried by the cleaned configuration information, and the cleaned configuration information is sorted according to the generation time to obtain the processed configuration information.

[0025] In one embodiment, before calculating the actual status information of each battery cell according to the configuration information, the method further includes:

[0026] Determining, based on the configuration information, actual power information of each battery cell at the beginning of the target time period and actual power information at the end of the target time period;

[0027] Calculating the actual status information of each battery cell according to the configuration information includes:

[0028] The actual state information of each battery cell is calculated according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information at the end of the target time period.

[0029] In one embodiment, the configuration information includes current information generated by each of the battery cells within the target time period;

[0030] Calculating the actual state information of each battery cell according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information of each battery cell at the end of the target time period includes:

[0031] Determine a first reference value according to a difference between actual power information of each battery cell at the beginning of the target time period and actual power information at the end of the target time period;

[0032] determining a second reference value according to current information generated by each of the battery cells within the target time period;

[0033] The actual state information of each of the battery cells is calculated according to the first reference value and the second reference value.

[0034] In one embodiment, determining the state to be optimized of the group to which each battery cell belongs based on the actual state information of each battery cell includes:

[0035] Determining group power information of the group according to actual power information of each battery cell included in the group;

[0036] Determining group status information of the group according to actual status information of each battery cell included in the group;

[0037] The state to be optimized of the group is determined according to the group power information, the group state information, and a preset group power threshold interval and a preset group state threshold interval.

[0038] In one embodiment, the group includes one of a container, a subsystem, and a battery cluster; wherein the container includes at least one subsystem, the subsystem includes at least one battery cluster, and the battery cluster includes at least one battery cell;

[0039] The determining the group power information of the group according to the actual power information of each battery cell included in the group includes:

[0040] Determining group power information of the battery cluster according to actual power information of the battery cells included in the battery cluster;

[0041] Determining group power information of the subsystem according to group power information of the battery clusters included in the subsystem;

[0042] Determining group power information of the container according to group power information of the subsystems included in the container;

[0043] The determining the group status information of the group according to the actual status information of each battery cell included in the group includes:

[0044] determining group status information of the battery cluster according to actual status information of the battery cells included in the battery cluster;

[0045] determining group status information of the subsystem according to group status information of battery clusters included in the subsystem;

[0046] The group status information of the container is determined according to the group status information of the subsystems included in the container.

[0047] In one embodiment, the state to be optimized includes a power to be optimized class and a state to be optimized class;

[0048] The group power threshold interval includes at least one of a container power threshold interval, a subsystem power threshold interval, and a battery cluster power threshold interval;

[0049] The group status threshold interval includes at least one of a container status threshold interval, a subsystem status threshold interval, and a battery cluster status threshold interval;

[0050] The determining the state of the group to be optimized according to the group power information, the group state information, and a preset group power threshold interval and a preset group state threshold interval includes:

[0051] When the group power information of the battery cluster does not meet the battery cluster power threshold interval, the group power information of the subsystem does not meet the subsystem power threshold interval, and the group power information of the container does not meet the container power threshold interval, the battery cluster, the subsystem, and the container are determined to be in the power to be optimized category;

[0052] When the group status information of the battery cluster does not meet the battery cluster status threshold interval, the group status information of the subsystem does not meet the subsystem status threshold interval, and the group status information of the container does not meet the container status threshold interval, the battery cluster, the subsystem, and the container are determined to be in a state to be optimized category.

[0053] In one embodiment, the optimization instruction carries at least one of a power optimization tag and a state optimization tag;

[0054] The step of determining a target optimization group from each of the groups in response to the optimization instruction and optimizing the cells included in the target optimization group includes:

[0055] When the optimization instruction carries a power optimization tag, the group of cells to be optimized is used as the target optimization group according to the power optimization tag, and the cells in the target optimization group are charged.

[0056] When the optimization instruction carries a state optimization tag, the group of the state to be optimized is used as the target optimization group according to the state optimization tag, and the battery cells in the target optimization group are replaced;

[0057] When the optimization instruction carries a power optimization tag and a state optimization tag, according to the power optimization tag, the group of power to be optimized is used as the target optimization group, and the battery cells in the target optimization group are recharged; and according to the state optimization tag, the group of state to be optimized is used as the target optimization group, and the battery cells in the target optimization group are replaced.

[0058] In a second aspect, the present application further provides an energy storage system optimization device, comprising:

[0059] An acquisition module, configured to, in response to a detection instruction, acquire configuration information of each battery cell in a target station corresponding to the detection instruction;

[0060] A calculation module, configured to calculate actual status information of each of the battery cells based on the configuration information;

[0061] A determination module, configured to determine the state to be optimized of the group to which each of the battery cells belongs based on the actual state information of each of the battery cells;

[0062] The optimization module is configured to determine a target optimization group from each of the groups in response to the optimization instruction, and optimize the battery cells included in the target optimization group.

[0063] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the energy storage system optimization method described in any of the above embodiments when executing the computer program.

[0064] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy storage system optimization method described in any of the above embodiments.

[0065] In a fifth aspect, the present application further provides a computer program product, which includes a computer program and, when executed by a processor, implements the energy storage system optimization method described in any of the above embodiments.

[0066] The above-described energy storage system optimization method, device, equipment, storage medium, and program product can accurately determine the state to be optimized down to the cell level, enabling accurate judgment of the actual state of the batteries in the energy storage system. This allows for more precise determination of the basis for determining the state to be optimized, resulting in more accurate judgment results. This allows for timely and effective detection of problems with the energy storage system, resulting in better optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0068] FIG1 is a diagram illustrating an application environment of an energy storage system optimization method according to an embodiment;

[0069] FIG2 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0070] FIG3 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0071] FIG4 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0072] FIG5 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0073] FIG6 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0074] FIG7 is a schematic flow chart of an energy storage system optimization method according to an embodiment;

[0075] FIG8 is a schematic flow chart of an energy storage system optimization method according to one embodiment;

[0076] FIG9 is a schematic structural diagram of an energy storage system optimization device according to one embodiment;

[0077] FIG10 is a diagram showing the internal structure of a computer device in one embodiment.

[0078] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0079] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0080] The energy storage system optimization method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. In this case, the terminal 102 communicates with the server 104 via a network.

[0081] For example, the energy storage system optimization method is applied to terminal 102. Upon receiving a detection instruction, terminal 102 obtains the configuration information of each battery cell in the target site corresponding to the optimization instruction from the data storage system of server 104. Then, based on the configuration information, terminal 102 calculates the actual status information of each battery cell. Furthermore, based on the actual status information of each battery cell, terminal 102 determines the status of the group to be optimized for each battery cell. Finally, upon receiving the optimization instruction, terminal 102 determines a target optimization group from each group and optimizes the batteries included in the target optimization group. Terminal 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart car devices, and the like. Portable wearable devices may include smart watches, smart bracelets, head-mounted devices, and the like. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers. Terminal 102 and server 104 may be directly or indirectly connected via wired or wireless communication, such as via a network connection.

[0082] For another example, the energy storage system optimization method is applied to server 104. When terminal 102 receives a detection instruction, it sends the detection instruction to server 104. Subsequently, server 104 obtains the configuration information of each battery cell in the target site corresponding to the detection instruction from the data storage system; and calculates the actual status information of each battery cell based on the configuration information; and determines the state to be optimized of the group to which each battery cell belongs based on the actual status information of each battery cell; finally, when server 104 receives the optimization instruction, it determines the target optimization group from each group and optimizes the battery cells included in the target optimization group. It is understandable that the data storage system can be an independent storage device, or the data storage system can be located on server 104, or the data storage system can be located on another terminal.

[0083] It should be noted that the network communication between the terminal 102 and the server 104 is applicable to different network standards, for example, it can be applicable to Global System of Mobile communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE) system and future 5G network standards. Optionally, the above-mentioned communication system can be a system in the scenario of Ultra-Reliable and Low Latency Communications (URLLC) transmission in the 5G communication system.

[0084] Therefore, optionally, the above-mentioned base station can be a base station (Base Transceiver Station, referred to as BTS) and / or a base station controller in GSM or CDMA, or a base station (NodeB, referred to as NB) and / or a radio network controller (Radio Network Controller, referred to as RNC) in WCDMA, or an evolved base station (Evolutional Node B, referred to as eNB or eNodeB) in LTE, or a relay station or access point, or a base station (gNB) in a future 5G network, etc., and this application is not limited here.

[0085] The terminal 102 can be either a wireless terminal or a wired terminal. A wireless terminal can be a device that provides voice and / or other service data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. A wireless terminal can communicate with one or more core network devices via a Radio Access Network (RAN). A wireless terminal can be a mobile terminal, such as a mobile phone (also known as a "cellular" phone) or a computer with a mobile terminal. For example, a wireless terminal can be a portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network. For another example, a wireless terminal can be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), or other devices. A wireless terminal may also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, remote terminal, access terminal, user terminal, user agent, or user device or user equipment, without limitation herein. Optionally, the terminal device may also be a smartwatch, tablet computer, or other device.

[0086] In one embodiment, a method for optimizing an energy storage system is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. As shown in FIG2 , the method for optimizing an energy storage system includes:

[0087] Step 202: In response to the detection instruction, obtain configuration information of each battery cell in the target station corresponding to the detection instruction.

[0088] A test instruction is an instruction to check the actual status of a battery cell at a station. For example, a test instruction can be issued by a station staff member through a fixed component on the terminal's human-computer interface, or it can be automatically generated by the terminal or server at a pre-set generation frequency. The fixed component can be a pre-built page or mini-program.

[0089] The energy storage system optimization method in this embodiment is applied to the energy storage system. The energy storage system refers to a system that can convert electrical energy into other forms of energy and then convert it back into electrical energy when needed. This system can be used to store electrical energy to balance supply and demand imbalances, cope with power grid fluctuations, and improve energy utilization efficiency.

[0090] The energy storage system can include multiple stations, which can be distributed in different locations and achieve coordinated operation through interconnection to improve the overall energy storage capacity and flexibility.

[0091] The detection instruction can carry an object tag. Based on the object tag carried in the detection instruction, the terminal can determine the corresponding station in the energy storage system as the target station and further obtain the configuration information of all battery cells contained in the target station. The object tag can be composed of at least one of letters, characters, or numbers, such as the name or number of the station. The object tag is used to uniquely identify the station. The server in this embodiment pre-stores the mapping relationship between object tags and stations, as well as the mapping relationship between stations and all contained battery cells.

[0092] Configuration information is used to describe the actual status of the battery cell. Configuration information may include, for example, the battery cell model, capacity, operating voltage, operating current, operating power, operating temperature, remaining power (State of Charge, SOC), health status (State of Health, SOH), etc.

[0093] The configuration information may be stored in a data storage system of the server, and the server may collect the actual status of each battery cell according to a preset collection frequency and overwrite the stored information.

[0094] Step 204: Calculate the actual status information of each battery cell according to the configuration information.

[0095] The actual status information may include, for example, health status information of the battery cells.

[0096] Step 206: Determine the state to be optimized of the group to which each battery cell belongs based on the actual state information of each battery cell.

[0097] A group's pending optimization status refers to the optimization projects that need to be performed on the current group. To ensure the normal operation of the energy storage system, the group must not only ensure that the battery cells have no equipment failures but also ensure that the remaining battery charge is sufficient. Therefore, the group's pending optimization status can include the status where the battery cells within the group need to be replaced or the status where the battery cells within the group need to be charged and replenished.

[0098] Step 208 : In response to the optimization instruction, determine a target optimization group from each group, and optimize the cells included in the target optimization group.

[0099] Optimization instructions are instructions for optimizing the battery cells in a station. For example, optimization instructions can be issued by station staff through a fixed component on the human-computer interface of the terminal, or they can be automatically generated by the terminal or server according to a pre-set generation frequency.

[0100] The optimization instruction may carry an optimization tag. Based on the optimization tag carried in the optimization instruction, the terminal may determine the corresponding group as the target optimization group and further optimize all cells included in the target optimization group. The optimization tag may be composed of at least one of letters, characters, or numbers, such as the station name, number, or group status to be optimized. The optimization tag uniquely identifies the group. The server in this embodiment pre-stores a mapping between optimization tags and groups.

[0101] As an example, after determining the target optimization group, the terminal can optimize the battery cells included in the target optimization group according to the target optimization group's state to be optimized. When the target optimization group's state to be optimized indicates that the battery cells in the current group need to be replaced, the terminal can generate a prompt message to prompt the staff to replace the battery cells, or the terminal can automatically generate a control signal and send it to a robotic arm pre-set in the station and serving the target optimization group to control the robotic arm to replace the battery cells in the target optimization group; when the target optimization group's state to be optimized indicates that the battery cells in the current group need to be charged and replenished, the terminal can generate another prompt message to prompt the staff to charge and replenish the battery cells, or the terminal can automatically generate another control signal and send it to the backup power supply in the target station to control the backup power supply to replenish and charge the battery cells in the target optimization group.

[0102] In the above energy storage system optimization method, after receiving the detection command, the terminal can obtain the configuration information of each battery cell in the target site, use the configuration information to calculate the actual status information, and determine the status to be optimized of the battery cell group, thereby making accurate judgments on the status to be optimized down to the battery cell level, and achieving accurate judgments on the actual status of the batteries in the energy storage system. This makes the granularity of the judgment basis for the final determination of the status to be optimized smaller, and the judgment results more accurate, thereby timely and effectively discovering problems with the energy storage system and achieving better optimization effects.

[0103] As shown in FIG3 , in some optional embodiments, before step 204, the following steps are further included:

[0104] Step 203a: Detect abnormal data from the configuration information according to a preset information threshold;

[0105] Step 203b: clean abnormal data and sort the remaining configuration information to obtain processed configuration information;

[0106] Step 204 includes:

[0107] Step 204a: Calculate the actual status information of each battery cell according to the processed configuration information.

[0108] The information threshold refers to at least one threshold or threshold range corresponding to the configuration information. As an example, when the configuration information includes operating voltage, operating current, operating power, and operating temperature, the information threshold may correspond to the operating voltage threshold range, the operating current threshold range, the operating power threshold range, and the operating temperature threshold range.

[0109] The terminal can match the configuration data with the corresponding threshold range, and clean out the configuration data that exceeds the corresponding threshold range as abnormal data. In step 204, the actual status information of each battery cell is calculated based on the remaining configuration information after the abnormal data is eliminated and sorted.

[0110] This embodiment achieves rapid screening and processing of abnormal data in configuration information by presetting information thresholds, improves data processing efficiency, and ensures the accuracy of configuration information.

[0111] As shown in FIG4 , in some optional embodiments, the detection instruction carries a time period tag;

[0112] Step 202 includes:

[0113] Step 2022: Determine the target time period based on the time period tag;

[0114] Step 2024: Obtain configuration information of each battery cell within the target time period.

[0115] The time period tag may be composed of at least one of letters, characters or numbers. The time period tag is used to uniquely refer to a time period. The server of this embodiment pre-stores a mapping relationship between the time period tag and the time period.

[0116] The terminal can determine the target time period based on the time period tag carried by the detection instruction, and obtain the configuration information generated by the battery cells in the target station during the target time period, thereby realizing the status judgment and optimization processing of the battery cells within a fixed time period.

[0117] As shown in FIG5 , in some optional embodiments, the configuration information carries a generation time tag;

[0118] Step 203b includes:

[0119] Step 2032: Clean and fill the abnormal data to obtain cleaned configuration information;

[0120] Step 2034: Determine the generation time of the cleaned configuration information according to the generation time tag carried by the cleaned configuration information, and sort the cleaned configuration information according to the generation time to obtain processed configuration information.

[0121] The generation time label can be composed of at least one of letters, characters or numbers. The time period label is used to uniquely refer to the generation time corresponding to the configuration information of the battery cell. The server of this embodiment pre-stores a mapping relationship between the generation time label and the specific time.

[0122] Data filling refers to the method of replacing missing values ​​or null values ​​with other values. Common data filling methods include: constant filling: replacing missing values ​​with fixed values ​​(such as 0, mean, median, etc.); forward filling: filling missing values ​​with the value before the missing value; backward filling: filling missing values ​​with the value after the missing value; interpolation filling: filling missing values ​​based on the values ​​of known data points through interpolation methods (such as linear interpolation, polynomial interpolation, etc.); random filling: filling missing values ​​with randomly generated values; model filling: using machine learning models (such as regression, random forest, etc.) to predict missing values ​​and fill them, etc.

[0123] As shown in FIG6 , in some optional embodiments, before step 204, the following steps are further included:

[0124] Step 203c: Determine the actual power information of each battery cell at the beginning and end of the target time period according to the configuration information;

[0125] Step 204 includes:

[0126] Step 204b: Calculate the actual state information of each battery cell according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information at the end of the target time period.

[0127] The actual power information may refer to the remaining power information SOC.

[0128] As shown in FIG7 , in some optional embodiments, the configuration information includes current information generated by each battery cell within a target time period;

[0129] Step 204b includes:

[0130] Step 2042: Determine a first reference value based on the difference between the actual power information of each battery cell at the beginning of the target time period and the actual power information at the end of the target time period;

[0131] Step 2044: Determine a second reference value based on the current information generated by each battery cell within the target time period;

[0132] Step 2046: Calculate the actual status information of each battery cell according to the first reference value and the second reference value.

[0133] As an example, the quotient of the second reference value and the first reference value may be used as the actual state information of the battery cell.

[0134] In step 204b, the actual status information can be calculated using the following formula:

[0135] Among them, SOC n1 Indicates the actual power information at the beginning of the target time period, SOC n2 Indicates the actual power information at the end of the target time period, SOC n1 -SOC n2 represents the first reference value, I represents the current information generated by the cell within the target time period, and t diff It represents the first-order difference of time within the target time period, and SOH represents the actual state information.

[0136] As shown in FIG8 , in some optional embodiments, step 206 includes:

[0137] Step 2062: Determine the group power information of the group based on the actual power information of each battery cell included in the group;

[0138] Step 2064: Determine group status information of the group based on actual status information of each battery cell included in the group;

[0139] Step 2066: Determine the state of the group to be optimized based on the group power information, the group status information, and the preset group power threshold interval and group status threshold interval.

[0140] In this embodiment, the terminal can determine the group power information of the group according to the actual power information of each battery cell included in the group, and determine the group status information of the group according to the actual status information of each battery cell included in the group.

[0141] As an example, the group includes one of a container, a subsystem, and a battery cluster; wherein the container includes at least one subsystem, the subsystem includes at least one battery cluster, and the battery cluster includes at least one battery cell;

[0142] In step 2062, the group power information of the battery cluster is determined based on the actual power information of the battery cells included in the battery cluster; the group power information of the subsystem is determined based on the group power information of the battery cluster included in the subsystem; and the group power information of the container is determined based on the group power information of the subsystem included in the container.

[0143] In step 2064, the group status information of the battery cluster is determined based on the actual status information of the battery cells included in the battery cluster; the group status information of the subsystem is determined based on the group status information of the battery cluster included in the subsystem; and the group status information of the container is determined based on the group status information of the subsystem included in the container.

[0144] Specifically, the state to be optimized includes the power to be optimized class and the state to be optimized class; the group power threshold interval includes at least one of the container power threshold interval, the subsystem power threshold interval, and the battery cluster power threshold interval; the group state threshold interval includes at least one of the container state threshold interval, the subsystem state threshold interval, and the battery cluster state threshold interval; then step 2066 includes: when the group power information of the battery cluster does not conform to the battery cluster power threshold interval, the group power information of the subsystem does not conform to the subsystem power threshold interval, and the group power information of the container does not conform to the container power threshold interval, the battery cluster, subsystem, and container are determined to be the power to be optimized class; when the group state information of the battery cluster does not conform to the battery cluster state threshold interval, the group state information of the subsystem does not conform to the subsystem state threshold interval, and the group state information of the container does not conform to the container state threshold interval, the battery cluster, subsystem, and container are determined to be the state to be optimized class.

[0145] In some optional embodiments, the optimization instruction carries at least one of a power optimization tag and a state optimization tag;

[0146] Step 208 includes:

[0147] When the optimization instruction carries a power optimization tag, the group of cells to be optimized is selected as the target optimization group based on the power optimization tag, and the cells in the target optimization group are charged.

[0148] When the optimization instruction carries a state optimization tag, the group of the state to be optimized is used as the target optimization group according to the state optimization tag, and the battery cells in the target optimization group are replaced;

[0149] When the optimization instruction carries a power optimization tag and a state optimization tag, the group whose power is to be optimized is used as the target optimization group according to the power optimization tag, and the battery cells in the target optimization group are recharged. According to the state optimization tag, the group whose state is to be optimized is used as the target optimization group, and the battery cells in the target optimization group are replaced.

[0150] The power optimization label can be composed of at least one of letters, characters or numbers. The power optimization label is used to uniquely refer to the group power information of the class to be optimized. The state optimization label can be composed of at least one of letters, characters or numbers. The state optimization label is used to uniquely refer to the group state information of the class to be optimized.

[0151] The terminal can determine, according to the optimization type corresponding to the optimization instruction, a group whose state to be optimized matches the optimization instruction from among the multiple groups as a target optimization group.

[0152] As an example, the terminal can also pre-set priorities from high to low for battery clusters, subsystems, and containers. When the optimization instruction is matched to the to-be-optimized status of a battery cluster, a subsystem, and a container at the same time, and the battery cluster is contained in the subsystem, and the subsystem is contained in the container, the terminal will use the battery cluster as the target optimization group.

[0153] In one embodiment, the optimization instruction may also carry a level label, which may be composed of at least one of letters, characters or numbers. The level label is used to uniquely refer to the level of the group, that is, one of the battery cluster, subsystem, and container. When the terminal receives the optimization instruction, it can first filter out the groups that do not match the level label based on the level label, and then further use the power optimization label and / or status optimization label carried by the optimization instruction to match the group corresponding to the optimization instruction in the remaining groups as the target optimization group.

[0154] The above-mentioned energy storage system optimization method can use the configuration information at the cell level to calculate the actual status information and actual power information of the cell, and further determine the state to be optimized of the battery cluster where the cell is located, the subsystem where the battery cluster is located, and the container where the subsystem is located. This can make accurate judgments on the state to be optimized down to the cell level, and achieve accurate judgments on the actual state of the battery in the energy storage system. This makes the granularity of the judgment basis for the final determination of the state to be optimized smaller, and the judgment results more accurate, thereby timely and effectively discovering problems in the energy storage system and achieving better optimization effects.

[0155] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0156] Based on the same inventive concept, embodiments of the present application also provide an energy storage system optimization device for implementing the aforementioned energy storage system optimization method. The solution provided by this energy storage system optimization device is similar to the solution described in the aforementioned energy storage system optimization method. Therefore, the specific limitations in one or more device embodiments provided below can be found in the aforementioned limitations of the energy storage system optimization method and will not be further elaborated here.

[0157] In one embodiment, as shown in FIG9 , an energy storage system optimization device 900 is provided, comprising:

[0158] An acquisition module 902 is configured to, in response to a detection instruction, acquire configuration information of each battery cell in a target station corresponding to the detection instruction;

[0159] A calculation module 904 is used to calculate the actual status information of each battery cell according to the configuration information;

[0160] A determination module 906 is configured to determine the state to be optimized of the group to which each battery cell belongs based on the actual state information of each battery cell;

[0161] The optimization module 908 is configured to determine a target optimization group from each group in response to the optimization instruction, and optimize the cells included in the target optimization group.

[0162] In some optional embodiments, the calculation module 904 is further configured to:

[0163] Detect abnormal data from configuration information according to pre-set information thresholds;

[0164] Clean abnormal data and sort the remaining configuration information to obtain processed configuration information;

[0165] Based on the processed configuration information, the actual status information of each battery cell is calculated.

[0166] In some optional embodiments, the detection instruction carries a time period tag;

[0167] The acquisition module 902 is further configured to:

[0168] Determine the target time period based on the time period label;

[0169] Get the configuration information of each battery cell within the target time period.

[0170] In some optional embodiments, the configuration information carries a generation time tag;

[0171] The calculation module 904 is further configured to:

[0172] Clean and fill abnormal data to obtain cleaned configuration information;

[0173] The generation time of the cleaned configuration information is determined according to the generation time tag carried by the cleaned configuration information, and the cleaned configuration information is sorted according to the generation time to obtain the processed configuration information.

[0174] In some optional embodiments, the calculation module 904 is further configured to:

[0175] Determine the actual power information of each battery cell at the beginning and end of the target time period based on the configuration information;

[0176] The actual state information of each battery cell is calculated according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information at the end of the target time period.

[0177] In some optional embodiments, the configuration information includes current information generated by each battery cell within a target time period;

[0178] The calculation module 904 is further configured to:

[0179] Determining a first reference value based on a difference between actual power information of each battery cell at the beginning of the target time period and actual power information at the end of the target time period;

[0180] Determining a second reference value based on current information generated by each battery cell within a target time period;

[0181] The actual state information of each battery cell is calculated according to the first reference value and the second reference value.

[0182] In some optional embodiments, the determination module 906 is further configured to:

[0183] Determine the group power information of the group based on the actual power information of each battery cell included in the group;

[0184] Determine group status information of the group based on actual status information of each battery cell included in the group;

[0185] The state of the group to be optimized is determined according to the group power information, the group state information, and the preset group power threshold interval and group state threshold interval.

[0186] In some optional embodiments, the group includes one of a container, a subsystem, and a battery cluster; wherein the container includes at least one subsystem, the subsystem includes at least one battery cluster, and the battery cluster includes at least one battery cell;

[0187] The determination module 906 is further configured to:

[0188] Determining group power information of the battery cluster based on actual power information of the battery cells included in the battery cluster;

[0189] Determining the group power information of the subsystem according to the group power information of the battery cluster included in the subsystem;

[0190] Determine the group power information of the container according to the group power information of the subsystems included in the container;

[0191] Determining group status information of the battery cluster according to actual status information of the battery cells included in the battery cluster;

[0192] Determining group status information of the subsystem according to group status information of battery clusters included in the subsystem;

[0193] The group status information of the container is determined according to the group status information of the subsystems included in the container.

[0194] In some optional embodiments, the state to be optimized includes a power to be optimized class and a state to be optimized class;

[0195] The group power threshold interval includes at least one of a container power threshold interval, a subsystem power threshold interval, and a battery cluster power threshold interval;

[0196] The group status threshold interval includes at least one of a container status threshold interval, a subsystem status threshold interval, and a battery cluster status threshold interval;

[0197] The determination module 906 is further configured to:

[0198] When the battery cluster group power information does not meet the battery cluster power threshold range, the subsystem group power information does not meet the subsystem power threshold range, and the container group power information does not meet the container power threshold range, the battery cluster, subsystem, and container are determined to be in the power optimization category;

[0199] When the group status information of the battery cluster does not meet the battery cluster status threshold range, the group status information of the subsystem does not meet the subsystem status threshold range, and the group status information of the container does not meet the container status threshold range, the battery cluster, subsystem, and container are determined to be in the status to be optimized category.

[0200] In some optional embodiments, the optimization instruction carries at least one of a power optimization tag and a state optimization tag;

[0201] The optimization module 908 is further configured to:

[0202] When the optimization instruction carries a power optimization tag, the group of cells to be optimized is selected as the target optimization group based on the power optimization tag, and the cells in the target optimization group are charged.

[0203] When the optimization instruction carries a state optimization tag, the group of the state to be optimized is used as the target optimization group according to the state optimization tag, and the battery cells in the target optimization group are replaced;

[0204] When the optimization instruction carries a power optimization tag and a state optimization tag, the group whose power is to be optimized is used as the target optimization group according to the power optimization tag, and the battery cells in the target optimization group are recharged. According to the state optimization tag, the group whose state is to be optimized is used as the target optimization group, and the battery cells in the target optimization group are replaced.

[0205] Each module in the above-mentioned apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0206] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in Figure 10. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WiFi, a mobile cellular network, NFC (near field communication), or other technologies. When the computer program is executed by the processor, a method for optimizing an energy storage system is implemented. The display unit of the computer device is used to form a visually visible image, and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0207] Those skilled in the art will understand that the structure shown in FIG10 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0208] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, each step of the above-mentioned energy storage system optimization method is implemented.

[0209] In one embodiment, a computer program product is provided, including a computer program. When the computer program product is executed by a processor, the computer program product implements the various steps of the above-mentioned energy storage system optimization method.

[0210] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0211] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for optimizing an energy storage system, characterized in that: include: In response to a detection instruction, obtaining configuration information of each battery cell in a target station corresponding to the detection instruction; Calculating actual status information of each of the battery cells according to the configuration information; Determining the state to be optimized of the group to which each battery cell belongs based on the actual state information of each battery cell; In response to the optimization instruction, a target optimization group is determined from each of the groups, and the battery cells included in the target optimization group are optimized.

2. The method according to claim 1, wherein Before calculating the actual status information of each battery cell according to the configuration information, the method further includes: According to a preset information threshold, abnormal data is detected from the configuration information; Cleaning the abnormal data and sorting the remaining configuration information to obtain processed configuration information; Calculating the actual status information of each battery cell according to the configuration information includes: The actual status information of each battery cell is calculated according to the processed configuration information.

3. The method according to claim 2, characterized in that The detection instruction carries a time period tag; The obtaining of configuration information of each battery cell in the target station corresponding to the detection instruction includes: Determine the target time period according to the time period label; Obtain configuration information of each of the battery cells within the target time period.

4. The method according to claim 3, characterized in that The configuration information carries a generation time tag; The cleaning of the abnormal data and sorting of the remaining configuration information to obtain processed configuration information includes: Cleaning and filling the abnormal data to obtain cleaned configuration information; The generation time of the cleaned configuration information is determined according to the generation time tag carried by the cleaned configuration information, and the cleaned configuration information is sorted according to the generation time to obtain the processed configuration information.

5. The method according to claim 3, wherein Before calculating the actual status information of each battery cell according to the configuration information, the method further includes: Determining, based on the configuration information, actual power information of each battery cell at the beginning of the target time period and actual power information at the end of the target time period; Calculating the actual status information of each battery cell according to the configuration information includes: The actual state information of each battery cell is calculated according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information at the end of the target time period.

6. The method according to claim 5, characterized in that The configuration information includes current information generated by each of the battery cells within the target time period; Calculating the actual state information of each battery cell according to the configuration information, the actual power information of each battery cell at the beginning of the target time period, and the actual power information of each battery cell at the end of the target time period includes: Determine a first reference value according to a difference between actual power information of each battery cell at the beginning of the target time period and actual power information at the end of the target time period; determining a second reference value according to current information generated by each of the battery cells within the target time period; The actual state information of each of the battery cells is calculated according to the first reference value and the second reference value.

7. The method according to claim 5, characterized in that The step of determining the state to be optimized of the group to which each of the battery cells belongs based on the actual state information of each of the battery cells comprises: Determining group power information of the group according to actual power information of each battery cell included in the group; Determining group status information of the group according to actual status information of each battery cell included in the group; The state to be optimized of the group is determined according to the group power information, the group state information, and a preset group power threshold interval and a preset group state threshold interval.

8. The method according to claim 7, characterized in that The group includes one of a container, a subsystem, and a battery cluster; wherein the container includes at least one subsystem, the subsystem includes at least one battery cluster, and the battery cluster includes at least one battery cell; The determining the group power information of the group according to the actual power information of each battery cell included in the group includes: Determining group power information of the battery cluster according to actual power information of the battery cells included in the battery cluster; Determining group power information of the subsystem according to group power information of the battery clusters included in the subsystem; Determining group power information of the container according to group power information of the subsystems included in the container; The determining the group status information of the group according to the actual status information of each battery cell included in the group includes: determining group status information of the battery cluster according to actual status information of the battery cells included in the battery cluster; determining group status information of the subsystem according to group status information of battery clusters included in the subsystem; The group status information of the container is determined according to the group status information of the subsystems included in the container.

9. The method according to claim 8, characterized in that The states to be optimized include power to be optimized and state to be optimized; The group power threshold interval includes the container power threshold interval, the subsystem power threshold interval, the battery cluster power threshold interval, at least one of the threshold intervals; The group status threshold interval includes at least one of a container status threshold interval, a subsystem status threshold interval, and a battery cluster status threshold interval; The determining the state of the group to be optimized according to the group power information, the group state information, and a preset group power threshold interval and a preset group state threshold interval includes: When the group power information of the battery cluster does not meet the battery cluster power threshold interval, the group power information of the subsystem does not meet the subsystem power threshold interval, and the group power information of the container does not meet the container power threshold interval, the battery cluster, the subsystem, and the container are determined to be in the power to be optimized category; When the group status information of the battery cluster does not meet the battery cluster status threshold interval, the group status information of the subsystem does not meet the subsystem status threshold interval, and the group status information of the container does not meet the container status threshold interval, the battery cluster, the subsystem, and the container are determined to be in a state to be optimized category.

10. The method according to claim 9, characterized in that The optimization instruction carries at least one of a power optimization tag and a state optimization tag; The step of determining a target optimization group from each of the groups in response to the optimization instruction and optimizing the cells included in the target optimization group includes: When the optimization instruction carries a power optimization tag, the group of cells to be optimized is used as the target optimization group according to the power optimization tag, and the cells in the target optimization group are charged. When the optimization instruction carries a state optimization tag, the group of the state to be optimized is used as the target optimization group according to the state optimization tag, and the battery cells in the target optimization group are replaced; When the optimization instruction carries a power optimization tag and a state optimization tag, according to the power optimization tag, the group of power to be optimized is used as the target optimization group, and the battery cells in the target optimization group are recharged; and according to the state optimization tag, the group of state to be optimized is used as the target optimization group, and the battery cells in the target optimization group are replaced.

11. An energy storage system optimization device, characterized in that: include: An acquisition module, configured to, in response to a detection instruction, acquire configuration information of each battery cell in a target station corresponding to the detection instruction; A calculation module, configured to calculate actual status information of each of the battery cells based on the configuration information; A determination module, configured to determine the state to be optimized of the group to which each of the battery cells belongs based on the actual state information of each of the battery cells; The optimization module is used to determine the target optimization group from each group in response to the optimization instruction, and optimize the The cells included in the target optimization group.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy storage system optimization method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage system optimization method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the energy storage system optimization method according to any one of claims 1 to 10 are implemented.

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