Energy management method applied to energy storage system, energy storage control unit, control equipment and program product

Abstract

The invention is suitable for the technical field of energy storage, and provides an energy management method applied to an energy storage system, an energy storage control unit, control equipment and a program product, and the energy storage system comprises a plurality of energy storage subunits. The method comprises the following steps: acquiring an energy management demand for the energy storage system; acquiring multi-dimensional state information of the plurality of energy storage subunits; wherein the multi-dimensional state information at least comprises power conversion efficiency related information and auxiliary component related information; the power conversion efficiency related information comprises at least one of the following information: state information of each energy storage subunit, operation capability information of each energy storage subunit and related information of a power device; the related information of the auxiliary component comprises at least one of the following information: start-stop demand evaluation information of the auxiliary component, operation energy consumption information of the auxiliary component and hardware service life state information of the auxiliary component; grading and screening a plurality of energy storage subunits based on the energy management demand and the multi-dimensional state information, and determining a target energy storage subunit set; and controlling the energy storage sub-units in the target energy storage sub-unit set to form a sub-working system so as to respond to and execute the energy management demand. The energy storage system can adaptively and dynamically reconstruct the working unit combination according to the real-time requirement and the comprehensive state of each unit, so that the system operation is more intelligent, flexible and reliable.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage, and particularly relates to an energy management method applied to an energy storage system, an energy storage control unit, a control device and a program product. BACKGROUND

[0002] The energy storage system is a key component in the modern energy system. In the prior art, there are mainly two common methods to improve the performance of the energy storage system. The first method is to select high-efficiency power conversion devices, such as direct-current-direct-current converters or direct-current-alternating-current converters, in the system design stage. The second method is to use a relatively simple control strategy in the system operation stage, such as shutting down part of the equipment during long-time standby, or mainly determining which energy storage units to put into operation or cut off according to a few parameters such as the state of charge, voltage and the like of the energy storage units.

[0003] However, the above prior art solutions have obvious deficiencies. First, they ignore the dynamic nature of the efficiency of the power conversion device, that is, the efficiency will dynamically change with the load rate, temperature and the like working conditions, resulting in that the existing strategy cannot ensure that the power device always works in the highest efficiency interval, causing unnecessary energy conversion loss. Second, the scheduling is only based on a few states such as the state of charge or voltage of the battery, which cannot comprehensively reflect the overall health status of the energy storage unit, and may cause frequent use of some energy storage units with acceptable battery state but low power conversion efficiency, thereby reducing the overall performance of the entire system. SUMMARY

[0004] The embodiments of the application provide an energy management method applied to an energy storage system, an energy storage control unit, a control device and a program product, which can solve the technical problems in the prior art that the existing technology only schedules based on a few states such as the state of charge or voltage of the battery, does not consider the power conversion efficiency, causes unnecessary energy conversion loss, and the overall energy utilization efficiency of the system is low.

[0005] In a first aspect, the embodiments of the application provide an energy management method applied to an energy storage system, the energy storage system comprising a plurality of energy storage sub-units; the method comprising:

[0006] obtaining an energy management requirement for the energy storage system;

[0007] obtaining multi-dimensional state information of the plurality of energy storage sub-units; wherein the multi-dimensional state information at least comprises power conversion efficiency related information and auxiliary component related information; the power conversion efficiency related information comprises at least one of the following: state information of each energy storage sub-unit, operation capability information of each energy storage sub-unit, and related information of a power device; the auxiliary component related information comprises at least one of the following: start-stop requirement evaluation information of an auxiliary component, operation energy consumption information of the auxiliary component, and hardware life state information of the auxiliary component;

[0008] based on the energy management requirement and the multi-dimensional state information, performing hierarchical screening on a plurality of energy storage sub-units to determine a target energy storage sub-unit set;

[0009] controlling the energy storage sub-units in the target energy storage sub-unit set to form a sub-working system to respond to and execute the energy management requirement.

[0010] Optionally, the related information of the power device includes temperature of the power device, temperature derating information of the power device, and efficiency optimal working interval of the power device.

[0011] The method further includes:

[0012] based on the state information of each energy storage sub-unit, the operating capacity information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device, determining the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different working conditions;

[0013] from the plurality of energy storage sub-units, screening out energy storage sub-units whose energy conversion efficiency of the power conversion link is in the efficiency optimal working interval when the energy management requirement is met, to form a first candidate set;

[0014] performing start-stop requirement evaluation of the auxiliary components on each energy storage sub-unit in the first candidate set to determine start-stop requirement evaluation information of the auxiliary components;

[0015] based on the start-stop requirement evaluation information of the auxiliary components, the operating energy consumption information of the auxiliary components, and the hardware life state information of the auxiliary components, screening out energy storage sub-units that meet the requirements of the auxiliary components from the first candidate set to form a second candidate set, and determining the second candidate set as the target energy storage sub-unit set.

[0016] Optionally, the method further includes:

[0017] inputting the state information of each energy storage sub-unit, the operating capacity information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device into a pre-trained power conversion efficiency prediction model to output the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different working conditions;

[0018] from the plurality of energy storage sub-units, screening out energy storage sub-units whose energy conversion efficiency of the power conversion link is in the efficiency optimal working interval when the energy management requirement is met, to form a first candidate set;

[0019] performing start-stop requirement evaluation of the auxiliary component on each energy storage subunit in the first candidate set based on the preset logistic regression model to determine start-stop requirement evaluation information of the auxiliary component; and determining operation energy consumption information of the auxiliary component and hardware life status information of the auxiliary component based on a battery aging model;

[0020] filtering out energy storage subunits meeting auxiliary component requirements from the first candidate set based on the start-stop requirement evaluation information of the auxiliary component, the operation energy consumption information of the auxiliary component, and the hardware life status information of the auxiliary component to form a second candidate set, and determining the second candidate set as the target energy storage subunit set.

[0021] Optionally, the multi-dimensional state information further includes electrochemical state information; the electrochemical state information includes at least one of the following: health degree, state of charge, internal resistance, temperature, efficiency optimal working interval, and power characteristics of a battery body of each energy storage subunit; and the related information of the power device includes temperature, temperature derating information, and efficiency optimal working interval of the power device.

[0022] The multi-dimensional state information includes at least one of the following: health degree, state of charge, internal resistance, temperature, efficiency optimal working interval, and power characteristics of a battery body of each energy storage subunit; and the related information of the power device includes temperature, temperature derating information, and efficiency optimal working interval of the power device.

[0023] determining energy conversion efficiency of a power conversion link of each energy storage subunit under different working conditions based on the state information of each energy storage subunit, the operation capability information of each energy storage subunit, the temperature of the power device, and the temperature derating information of the power device.

[0024] filtering out, from the plurality of energy storage subunits, an energy storage subunit whose energy conversion efficiency of a power conversion link is in an efficiency optimal working interval when the energy management requirement is met to form a first candidate set;

[0025] performing start-stop requirement evaluation of the auxiliary component on each energy storage subunit in the first candidate set to determine start-stop requirement evaluation information of the auxiliary component;

[0026] filtering out, from the first candidate set, an energy storage subunit meeting auxiliary component requirements based on the start-stop requirement evaluation information of the auxiliary component, the operation energy consumption information of the auxiliary component, and the hardware life status information of the auxiliary component to form a second candidate set;

[0027] filtering out, from the second candidate set, an energy storage subunit meeting electrochemical requirements based on the electrochemical state information to form a third candidate set, and determining the third candidate set as the target energy storage subunit set.

[0028] Optionally, the determining of the target energy storage subunit set includes:

[0029] inputting the state information of each energy storage subunit, the operation capability information of each energy storage subunit, the temperature of the power device, the derating information of the temperature of the power device into a pre-trained power conversion efficiency prediction model, and outputting the energy conversion efficiency of the power conversion link of each energy storage subunit under different working conditions; and selecting, from the plurality of energy storage subunits, an energy storage subunit whose energy conversion efficiency of the power conversion link is in an efficiency optimal working interval when the energy management demand is met, to form a first candidate set; wherein each energy storage subunit in the first candidate set corresponds to a power device efficiency evaluation result;

[0030] based on a preset logistic regression model, performing auxiliary component start-stop demand evaluation on each energy storage subunit in the first candidate set to determine auxiliary component start-stop demand evaluation information; based on a battery aging model, determining the operation energy consumption information of the auxiliary component and the hardware life state information of the auxiliary component; and based on the auxiliary component start-stop demand evaluation information, the operation energy consumption information of the auxiliary component and the hardware life state information of the auxiliary component, selecting an energy storage subunit meeting the auxiliary component demand from the first candidate set to form a second candidate set; wherein each energy storage subunit in the second candidate set corresponds to a power device efficiency evaluation result and an auxiliary system loss evaluation result;

[0031] based on the electrochemical state information, selecting an energy storage subunit meeting the electrochemical demand from the second candidate set to form a third candidate set; wherein each energy storage subunit in the third candidate set corresponds to a power device efficiency evaluation result, an auxiliary system loss evaluation result and a battery body state evaluation result;

[0032] performing weighted calculation on the power device efficiency evaluation result, the auxiliary system loss evaluation result and the battery body state evaluation result of each energy storage subunit in the third candidate set by using a weighted scoring method to obtain a comprehensive score of each energy storage subunit;

[0033] determining the target energy storage subunit set based on the comprehensive score of each energy storage subunit.

[0034] Optionally, the control of the energy storage subunits in the target energy storage subunit set to form a sub-working system to respond to and execute the energy management demand comprises:

[0035] by controlling the on-off devices of each energy storage subunit in the target energy storage subunit set, connecting each energy storage subunit to a working loop and isolating the energy storage subunits not selected to form the sub-working system;

[0036] The on-off device is an on-off device connected in series with the energy storage subunit or a bypass switch connected in parallel with the energy storage subunit.

[0037] Optionally, the method further comprises:

[0038] During the operation of the sub-working system, multi-dimensional state information of the energy storage subunits constituting the sub-working system is continuously monitored.

[0039] When it is detected that the change in the multi-dimensional state information meets a preset triggering condition, the step of performing the hierarchical screening of the plurality of energy storage subunits based on the energy management demand and the multi-dimensional state information to determine the target energy storage subunit set is returned to.

[0040] In a second aspect, an energy storage control unit applied to an energy storage system is provided, and the energy storage control unit comprises:

[0041] A demand acquisition module is configured to acquire an energy management demand for the energy storage system.

[0042] A state information acquisition module is configured to acquire multi-dimensional state information of a plurality of energy storage subunits, the multi-dimensional state information at least comprising power conversion efficiency related information and auxiliary component related information.

[0043] A hierarchical screening module is configured to perform hierarchical screening of the plurality of energy storage subunits based on the energy management demand and the multi-dimensional state information to determine a target energy storage subunit set.

[0044] A construction module is configured to control the energy storage subunits in the target energy storage subunit set to constitute a sub-working system to respond to and execute the energy management demand.

[0045] In a third aspect, an control device is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the energy management method applied to an energy storage system according to any one of the first aspect when executing the computer program.

[0046] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executable by a processor to implement the energy management method applied to an energy storage system according to any one of the first aspect.

[0047] In a fifth aspect, a computer program product is provided, which, when executed on a control device, causes the control device to perform the energy management method applied to an energy storage system according to any one of the first aspect.

[0048] The embodiments of the present application ensure that the main power device works in the high-efficiency area through hierarchical screening based on the power conversion efficiency related information and the auxiliary component related information, reduce the energy conversion loss, and reduce the unnecessary operation of the auxiliary component, thereby reducing the system loss. The energy storage sub-units in the target energy storage sub-unit set form a sub-working system, so that the energy storage system can dynamically reconfigure the working unit combination according to the real-time demand and the comprehensive state of each unit, so that the system operation is more intelligent, flexible and reliable.

[0049] It can be understood that the beneficial effects of the above-mentioned second aspect to the fifth aspect can be referred to the related description in the above-mentioned first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0051] Figure 1 is a schematic diagram of the overall architecture of the energy storage system provided by the embodiments of the present application;

[0052] Figure 2 is a schematic flow chart of the energy management method applied to the energy storage system provided by an embodiment of the present application;

[0053] Figure 3 is a parallel and series topology structure diagram of the energy storage sub-unit provided by an embodiment of the present application;

[0054] Figure 4 is a schematic flow chart of the energy management method applied to the energy storage system provided by an embodiment of the present application;

[0055] Figure 5 is an execution framework diagram of the energy management method applied to the energy storage system provided by an embodiment of the present application;

[0056] Figure 6 is a schematic flow chart of the energy management method applied to the energy storage system provided by the embodiments of the present application;

[0057] Figure 7 is a structural schematic diagram of the energy storage control unit applied to the energy storage system provided by the embodiments of the present application;

[0058] Figure 8 is a structural schematic diagram of the control device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0059] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0060] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0061] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0062] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0063] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0065] This embodiment provides an energy management method applied to an energy storage system. As an optional implementation, this method is applied to systems such as... Figure 1The energy storage system shown. (Refer to...) Figure 1 The overall architecture of this energy storage system includes an energy storage control unit (not shown in the figure) as the control core, multiple energy storage sub-units (energy storage sub-units 1-1 and 1-2 in the figure), and an energy management and inverter unit. The energy storage control unit, which can be implemented by an industrial computer or programmable logic controller, acts as the brain of the entire energy storage system, interacting bidirectionally with each energy storage sub-unit via a communication link. This communication link can be an industrial standard bus such as CAN bus, Ethernet, or RS485, used to acquire the real-time status of each energy storage sub-unit and issue control commands to it. Each energy storage sub-unit can collect its electrical energy to the energy management and inverter unit (often also called the Power Conversion System, PCS) via a power link (such as a high-voltage DC bus). This unit is responsible for realizing energy exchange and power conversion between the energy storage system and the power grid, various loads, or photovoltaic power generation systems, such as DC / AC conversion and voltage matching.

[0066] Figure 2 A schematic flowchart of an energy management method for an energy storage system provided in an embodiment of this application is shown.

[0067] S201, Obtain the energy management requirements for the energy storage system.

[0068] Energy management requirements are a set of task instructions that guide the energy storage system to perform energy dispatch. These requirements typically include information such as power magnitude, direction of action (charging / discharging), and duration. For example, the requirement might be: Discharge the grid at a constant power of 30 kilowatts over the next hour.

[0069] In this embodiment, the energy storage control unit can receive energy management requests to be executed from the upper-level energy management system (EMS) or directly from the energy management and inverter unit.

[0070] S202, obtain multi-dimensional status information of multiple energy storage sub-units.

[0071] Among them, the energy storage subunit refers to the smallest schedulable module that integrates battery clusters, battery management systems (BMS), thermal management systems, and safety components, and can independently realize energy storage, status monitoring, and energy interaction. Its function is to decompose the energy storage system into standardized modules, which facilitates hierarchical screening, dynamic combination (such as forming sub-working systems), and maintenance. It is the direct carrier for multi-dimensional status information acquisition and energy management command execution.

[0072] Among them, multi-dimensional status information refers to key information that reflects the operational capabilities and health status of energy storage sub-units. Multi-dimensional status information includes at least power conversion efficiency-related information and auxiliary component-related information; the power conversion efficiency-related information includes at least one of the following: status information of each energy storage sub-unit, operational capability information of each energy storage sub-unit, and power device-related information; the auxiliary component-related information includes at least one of the following: auxiliary component start-up and shutdown requirement assessment information, auxiliary component operating energy consumption information, and auxiliary component hardware lifespan status information.

[0073] Optionally, power conversion efficiency-related information focuses on the performance of the energy conversion stage of the energy storage sub-unit, and is used to determine the power loss level of the energy storage sub-unit when performing energy management requirements, including at least one or more of the following:

[0074] Status information of energy storage sub-units: refers to the current basic operating status data of energy storage sub-units, such as the current working mode (charging / discharging / standby) and connection status (grid connected / not connected).

[0075] Operating capacity information of energy storage sub-units: refers to the range of power and capacity that the energy storage sub-unit can stably output / receive, such as the current maximum discharge power and remaining available capacity of the energy storage sub-unit, which is used to match the power and duration parameters in energy management requirements (e.g., if the requirement is 30kW discharge, energy storage sub-units with an operating capacity ≥30kW need to be selected).

[0076] Information related to power devices: refers to the key parameters of the devices responsible for energy conversion within the energy storage sub-unit (such as DC / DC converters and IGBTs), which may include: the temperature of the power devices, the temperature derating information of the power devices, and the optimal operating range of the power devices.

[0077] Information related to auxiliary components focuses on the operating status and losses of auxiliary components (such as equalization circuits, heating devices, and heat dissipation devices) within the energy storage subunit, including at least one or more of the following:

[0078] Auxiliary component start / stop requirement assessment information: refers to the decision results of whether auxiliary components such as equalization circuit, heating device, and heat dissipation device need to be started or stopped based on the current state of the energy storage sub-unit (such as battery temperature and cell consistency). For example, the heating device needs to be started when the battery temperature is 5℃, and the equalization circuit needs to be started when the cell voltage difference exceeds 50mV.

[0079] Energy consumption information for auxiliary components: refers to the electrical energy consumed after the auxiliary component starts up.

[0080] Hardware lifespan status information of auxiliary components: refers to the remaining lifespan or aging degree of auxiliary components.

[0081] Optionally, the energy storage control unit can broadcast status query requests to all energy storage sub-units under its management (assuming the energy storage system contains three energy storage sub-units, labeled as sub-unit A, sub-unit B, and sub-unit C) via a communication link. Upon receiving the request, each energy storage sub-unit's internal battery management system (BMS) collects and evaluates multi-dimensional status information, packages this information into data frames, and reports it to the energy storage control unit.

[0082] Each sub-unit can be assigned a unique identifier (such as ABC, 123, etc.), and the energy storage control unit 10 uses this identifier to build and control the system.

[0083] S203, based on the energy management requirements and the multi-dimensional status information, multiple energy storage sub-units are classified and screened to determine the target energy storage sub-unit set.

[0084] In this embodiment, the energy storage control unit combines energy management requirements (such as power and duration) with multi-dimensional status information to gradually eliminate energy storage sub-units that do not meet the conditions, and finally selects a cluster of sub-units that can meet the requirements and take into account energy efficiency (i.e., the target energy storage sub-unit set).

[0085] In an optional embodiment, S203 performs hierarchical screening of multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine a target set of energy storage sub-units, including:

[0086] Step a1: Based on the status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device, determine the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions.

[0087] Among them, the power conversion link refers to the path within the energy storage sub-unit where energy conversion and transmission are realized.

[0088] Energy conversion efficiency refers to the ratio of output energy to input energy of the link (efficiency = output energy / input energy × 100%), which directly reflects the level of loss in the energy conversion process.

[0089] Different operating conditions refer to the operating conditions of the coverage subunit under different power (e.g., 10kW, 20kW, 30kW) and different temperature environments (e.g., -10℃, 25℃, 40℃).

[0090] Step a2: Select energy storage sub-units from multiple energy storage sub-units whose power conversion efficiency is in the optimal operating range when meeting the energy management requirements, and form a first candidate set.

[0091] Among them, meeting energy management requirements means that the operating capabilities of the energy storage sub-unit (such as power and charging / discharging direction) match the parameters of the energy management requirements (such as required power, direction, and duration), that is, the sub-unit will not be unable to complete the task due to its own capacity limitations (such as insufficient power) when performing the requirement.

[0092] The optimal operating range refers to the specific range in which the energy conversion efficiency of the power conversion link is the highest (such as the power and temperature range with an efficiency of ≥97%). This range is determined by the factory calibration of the power devices and the operating characteristics of the sub-units, and is a key threshold for measuring the energy conversion loss of the sub-units.

[0093] The first candidate set refers to the energy storage sub-unit cluster that, after screening in this step, retains the capacity that matches the demand and has the best conversion efficiency.

[0094] Optionally, the status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device can be input into a pre-trained power conversion efficiency prediction model to output the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions; from multiple energy storage sub-units, energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal operating range when meeting the energy management requirements are selected to form a first candidate set.

[0095] In this embodiment, the energy storage control unit can store a pre-trained power conversion efficiency prediction model, which can be a machine learning regression model based on gradient boosting decision trees. The energy storage control unit takes energy management requirements and the state parameters of each sub-unit (such as the operating capacity information of each sub-unit, the temperature of power devices, etc.) as input, calls the model, and quickly predicts the power conversion efficiency of each energy storage sub-unit under that operating condition. This stage aims to quickly eliminate energy storage sub-units with excessively low efficiency through coarse screening, thereby reducing the complexity of subsequent calculations.

[0096] Step a3: Assess the start-up and shutdown requirements of auxiliary components for each energy storage sub-unit in the first candidate set, and determine the start-up and shutdown requirements assessment information of the auxiliary components.

[0097] Among them, the start-stop requirement assessment of auxiliary components refers to the analysis process of determining whether the equalization circuit, heating device, and heat dissipation device need to be started or stopped for the energy storage sub-units in the first candidate set based on their current real-time status (such as battery temperature, cell voltage difference, power device temperature, etc.) according to preset assessment rules.

[0098] The start-stop requirement assessment information of auxiliary components is the output of this assessment process. Specifically, it includes the balance requirement assessment results (start-up required / no start-up required), heating requirement assessment results (start-up required / no start-up required), and heat dissipation requirement assessment results (start-up required / no start-up required). Its core function is to provide a decision-making basis for subsequent calculation of the operating energy consumption of auxiliary components and selection of low-energy-consumption sub-units.

[0099] For ease of understanding, an example is provided here. Real-time status parameters of each sub-unit within the first candidate set are collected, primarily the current battery temperature, inter-cell voltage difference, and real-time power device temperature, ensuring a one-to-one correspondence between data and sub-units and synchronized acquisition time. Then, evaluation logic is executed according to component type: For the equalization circuit, the maximum inter-cell voltage difference is compared to a preset threshold (e.g., 50mV); if the difference is ≥ the threshold, it is evaluated as needing activation; otherwise, activation is not required. For the heating device, the current battery temperature is compared to the minimum allowable charging / discharging temperature (e.g., 0℃); if the temperature is < the threshold, it is evaluated as needing activation; otherwise, activation is not required. For the heat dissipation device, the power device temperature is compared to the safety warning temperature (e.g., 80℃); if the temperature is ≥ the threshold, it is evaluated as needing activation; otherwise, activation is not required.

[0100] Step a4: Based on the start-stop requirement assessment information of the auxiliary component, the operating energy consumption information of the auxiliary component, and the hardware life status information of the auxiliary component, select energy storage sub-units that meet the requirements of the auxiliary component from the first candidate set to form a second candidate set, and determine the second candidate set as the target energy storage sub-unit set.

[0101] Among them, meeting the requirements of auxiliary components means that the auxiliary components (balancing circuit, heating device, heat dissipation device) of the energy storage sub-unit have their operating energy consumption within a preset low energy consumption threshold and their hardware lifespan status meets the safe operation standards (such as remaining lifespan ≥ 50%), provided that the start-stop requirements are met.

[0102] The second candidate set is a cluster of sub-units retained from the first candidate set after being filtered by the auxiliary component dimension. It is ultimately used as the target energy storage sub-unit set to ensure that the sub-working system built subsequently is efficient in energy conversion while taking into account auxiliary energy consumption control and long-term hardware stability.

[0103] In this embodiment, the auxiliary component screening criteria are first defined, such as setting an auxiliary component operating energy consumption threshold (e.g., total energy consumption ≤ 0.5kW) and a hardware lifespan status threshold (e.g., remaining lifespan ≥ 50%), and associating it with the start / stop requirement assessment information output in step a3 (prioritizing sub-units that do not require starting high-energy-consuming components). Secondly, each sub-unit in the first candidate set is evaluated: for each sub-unit, the start / stop requirements of its auxiliary components (e.g., whether the heating device needs to be started) and operating energy consumption information (e.g., heating device energy consumption 1kW) are considered to determine if the actual energy consumption is ≤ the threshold. Simultaneously, the hardware lifespan status information of the auxiliary components (e.g., cooling fan remaining lifespan 40%) is checked to determine if it is ≥ the lifespan threshold. Then, sub-units meeting the criteria are selected: sub-units with energy consumption ≤ the threshold and lifespan ≥ the threshold are retained and included in the second candidate set. Finally, optionally, verify and determine the target set: check whether the total power and total capacity of the second candidate set meet the energy management requirements. If they do, directly determine it as the target energy storage sub-unit set; if they do not, appropriately relax the energy consumption or lifetime threshold (e.g., adjust the energy consumption threshold from 0.5kW to 0.8kW) and re-screen until the set capacity meets the requirements.

[0104] Optionally, the start-up and shutdown requirements of auxiliary components for each energy storage sub-unit in the first candidate set can be evaluated based on a preset logistic regression model to determine the start-up and shutdown requirement evaluation information of the auxiliary components; and the operating energy consumption information and hardware life status information of the auxiliary components can be determined based on a battery aging model; based on the start-up and shutdown requirement evaluation information, the operating energy consumption information, and the hardware life status information of the auxiliary components, energy storage sub-units that meet the requirements of the auxiliary components can be selected from the first candidate set to form a second candidate set, and the second candidate set can be determined as the target energy storage sub-unit set.

[0105] In this embodiment of the application, this stage performs a more comprehensive quantitative evaluation of the sub-units in the first candidate set, including start-stop requirement evaluation, energy consumption evaluation, and lifespan evaluation.

[0106] S204, control the energy storage sub-units within the target energy storage sub-unit set to form a sub-working system to respond to and execute the energy management requirements.

[0107] Specifically, by controlling the switching devices of each energy storage sub-unit within the target energy storage sub-unit set, each energy storage sub-unit is connected to the working circuit, while those not selected are isolated to form a sub-working system. The switching devices are either connected in series with the energy storage sub-units or bypass switches connected in parallel with the energy storage sub-units.

[0108] In this embodiment, each energy storage sub-unit is connected to a common power link via an independent controllable switching device. The hardware implementation can be found in [reference needed]. Figure 3The parallel section shown in the diagram has each energy storage sub-unit connected in parallel to the DC bus via a controllable on / off device 31. Alternatively, its hardware implementation can be found in [reference needed]. Figure 3 The series connection shown in the diagram involves each energy storage sub-unit connected in series to the DC bus via a controllable on / off device 32, thus forming a sub-operating system. Energy management requirements can then be responded to and executed based on this sub-operating system.

[0109] Optional, refer to Figure 3 The series connection shown on the right represents a system consisting of four energy storage sub-units connected in series to meet the requirements of high-voltage applications. Unlike parallel structures, each energy storage sub-unit here is electrically connected in parallel with a controllable switching device 32. In this embodiment, the controllable switching device 32 is specifically a controllable bypass switch, such as an anti-parallel IGBT or mechanical relay. When a sub-unit needs to be isolated (i.e., not participating in operation), its parallel bypass switch is closed, providing a low-impedance path for the main power current, allowing the current to bypass the sub-unit; conversely, when a sub-unit needs to be put into operation, its bypass switch is opened, forcing the main power current to flow through the sub-unit.

[0110] In this scenario, it is assumed that the energy management requirement received by the system necessitates the operation of two energy storage sub-units connected in series to achieve the target voltage level and power requirements of the grid interface. The system currently offers four energy storage sub-units for selection.

[0111] Traditional methods may employ simple strategies, such as selecting the two sub-units with the highest SOH (State of Health) or choosing the first two sub-units to operate in a fixed physical order, while bypassing the remaining two. This approach ignores the comprehensive performance of each sub-unit under the current operating conditions, such as temperature and efficiency, and may not be the optimal choice. It may lead to some sub-units overheating or requiring additional energy to maintain their operation.

[0112] Using the method of this application, the above-mentioned intelligent decision-making can be performed, and a weighted scoring formula is used to calculate the comprehensive score Scori for each sub-unit. After the calculation is completed, the energy storage control unit sorts the four sub-units according to their comprehensive scores from high to low, and selects the two sub-units with the highest scores from the sorted list as the target energy storage sub-unit set for this task. The two sub-units with the lowest scores are regarded as non-target sub-units. Next, the energy storage control unit generates and sends specific control commands for the series topology based on the screening results. The specific operation is as follows: For the two selected target energy storage sub-units, the energy storage control unit sends a disconnect command to their respective switching devices 32, forcing the main circuit current to flow through these two sub-units, thereby effectively connecting them in series to the working circuit. For the two non-target energy storage sub-units that are not selected, the energy storage control unit sends a closing command to their corresponding switching devices 32, forming a low-impedance path, bypassing the main circuit current from these two sub-units, thereby effectively isolating them from the series circuit and putting them in a standby state. Through the above control, a sub-working system consisting of two energy storage sub-units with optimal overall performance is precisely constructed and begins to perform energy management tasks.

[0113] This embodiment demonstrates that the multi-dimensional evaluation-based screening method proposed in this application has good universality and is not dependent on a specific hardware connection topology. The evaluation and screening logic is universal, with different control signals generated only in the final control execution stage based on the hardware topology (parallel or series). This method is effectively applicable whether parallel connection for access and isolation is achieved through series switching devices or series connection for connection and bypass is achieved through parallel bypass switches.

[0114] In this embodiment, based on power conversion efficiency and auxiliary component information, hierarchical screening ensures that the main power devices operate in the high-efficiency region, reducing energy conversion losses and unnecessary operation of auxiliary components, thus lowering system losses. By controlling the energy storage sub-units within the target energy storage sub-unit set to form a sub-working system, the energy storage system can adaptively and dynamically reconfigure its working unit combination according to real-time demand and the overall state of each sub-unit, making the system operation more intelligent, flexible, and reliable.

[0115] In one optional example, the multi-dimensional state information further includes: electrochemical state information; the electrochemical state information includes at least one of the following: the battery health, state of charge, internal resistance, temperature, optimal operating range of efficiency, and power characteristics of each energy storage sub-unit.

[0116] Electrochemical state information (ESI) is a key dataset focusing on the electrochemical characteristics of the battery itself within energy storage sub-units. It supplements the deficiencies in power conversion efficiency and auxiliary component information regarding core battery performance evaluation. ESI primarily reflects the battery's health, remaining capacity, energy output capability, and safety margins, providing a basis for determining battery suitability for tiered selection. Its parameters are directly related to the battery's charge / discharge safety, lifespan degradation rate, and energy output stability, serving as crucial support for ensuring that the target energy storage sub-unit assembly balances short-term energy efficiency with long-term reliability.

[0117] The electrochemical state information includes at least one of the following:

[0118] Battery health status (SOH): This refers to the ratio of the current maximum usable capacity of the energy storage sub-unit battery to its factory rated capacity. It reflects the degree of aging of the battery. The higher the SOH, the longer the remaining battery life and the stronger the charge-discharge cycle capability. In the grading and screening process, sub-units with SOH ≥ a preset threshold (e.g., 85%) are usually given priority to avoid problems such as insufficient capacity and increased internal resistance caused by battery aging, while also taking into account the long-term economic efficiency of the system.

[0119] The State of Charge (SOC) of the battery refers to the ratio of the battery's current remaining charge to its current maximum available capacity. It is used to determine whether the battery has the necessary charge level to perform energy management tasks. For example, if the requirement is to discharge at 30kW for 1 hour (requiring 30kWh of charge), and the sub-cell's SOC is only 20% (corresponding to 19kWh), then the requirement cannot be met. During the selection process, it is necessary to ensure that the sub-cell's SOC meets the requirement of charge level ÷ current maximum available capacity ≤ SOC, to avoid the battery running out of power mid-task.

[0120] Internal resistance of the battery cell refers to the equivalent resistance within the battery cell, including ohmic internal resistance (electrode and electrolyte resistance) and polarization internal resistance (generated by electrochemical polarization). It is typically calculated based on voltage and current changes during charging and discharging. It is used to correlate battery charging and discharging efficiency with heat generation risk; higher internal resistance results in greater energy loss and more severe heat generation during charging and discharging. During screening, priority should be given to sub-cells with internal resistance ≤ a preset threshold (e.g., ≤ 120% of initial internal resistance) to reduce energy waste and lower the risk of thermal runaway.

[0121] Battery cell temperature: refers to the real-time temperature of the battery cell (not ambient temperature, collected by the NTC temperature sensor built into the cell). It is used to determine the safe operating boundaries of the battery. For example, the optimal operating temperature for lithium iron phosphate batteries is 15℃~35℃. Temperatures below 0℃ can easily lead to lithium dendrite growth (damaging the battery), while temperatures above 55℃ can easily trigger thermal runaway. During screening, it is necessary to ensure that the battery temperature is within the required safe range, and at the same time, provide basic data for the start-up and shutdown evaluation of auxiliary components (heating / heat dissipation devices).

[0122] The optimal operating range for battery efficiency refers to the range within which the battery achieves the highest energy conversion efficiency (output energy / input energy × 100%) during charging and discharging. This range is used to match energy management requirements, ensuring the battery operates within its high-efficiency range to minimize energy loss.

[0123] Battery power characteristics: This refers to the maximum power that the battery can stably output / receive within a safe range, including continuous power and peak power. It ensures the battery can meet energy management requirements. For example, if the requirement is 30kW continuous discharge, a sub-cell with a continuous power ≥30kW should be selected to avoid insufficient power causing the requirement to be unfulfilled or battery overload damage.

[0124] Optionally, S203 performs hierarchical screening of multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine the target energy storage sub-unit set, including:

[0125] Step b1: Based on the status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device, determine the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions.

[0126] Step b2 involves selecting energy storage sub-units from multiple energy storage sub-units whose power conversion efficiency is within the optimal operating range when meeting the energy management requirements, forming a first candidate set. Each energy storage sub-unit in the first candidate set corresponds to a power device efficiency evaluation result.

[0127] Step b3: Assess the start-up and shutdown requirements of auxiliary components for each energy storage sub-unit in the first candidate set, and determine the start-up and shutdown requirements assessment information of the auxiliary components.

[0128] Step b4: Based on the start-stop requirement assessment information of the auxiliary components, the operating energy consumption information of the auxiliary components, and the hardware lifetime status information of the auxiliary components, energy storage sub-units that meet the requirements of the auxiliary components are selected from the first candidate set to form a second candidate set. Each energy storage sub-unit in the second candidate set corresponds to a power device efficiency assessment result and an auxiliary system loss assessment result.

[0129] In this embodiment, the relevant descriptions of steps b1 to b4 are the same as those of steps a1 to a4 in the previous embodiment, and will not be repeated here.

[0130] Step b5: Based on the electrochemical state information, select energy storage sub-units that meet the electrochemical requirements from the second candidate set to form a third candidate set. Each energy storage sub-unit in the third candidate set corresponds to a power device efficiency evaluation result, an auxiliary system loss evaluation result, and a battery body state evaluation result.

[0131] Among them, electrochemical requirements refer to the threshold values ​​of electrochemical state parameters set based on energy management requirements and battery safety operation standards. Specifically, these include the lower limit of battery health (SOH), the range of state of charge (SOC) adaptation, the upper limit of internal resistance, the safe temperature range, the coverage of the optimal efficiency operating range, and the power characteristic matching degree, which are used to measure whether the battery itself has the electrochemical performance basis to perform the requirements.

[0132] The third candidate set is a cluster of sub-units retained from the second candidate set after electrochemical screening. This cluster of sub-units can ensure that the sub-working system optimizes energy efficiency and auxiliary energy consumption while taking into account battery safety, lifespan and capacity reliability.

[0133] In this embodiment, electrochemical screening indicators and thresholds are first defined, such as SOH ≥ 85% (ensuring sufficient remaining lifetime), SOC ≥ required power / current maximum available capacity (e.g., if the required power is 30kWh and the current maximum capacity of the sub-unit is 95kWh, then SOC ≥ 32%), internal resistance ≤ initial internal resistance 120% (controlling energy loss), temperature within the safe range of 15℃~35℃ (avoiding extreme temperatures affecting performance), optimal efficiency operating range covering the required operating conditions (e.g., if 0.3C discharge is required, then this rate must be included), and continuous power ≥ required power (e.g., 30kW). Next, the electrochemical state information of sub-units in the second candidate set is verified one by one, checking each of the above indicators. If sub-unit A has an SOH of 90%, an SOC of 40%, and internal resistance meeting the thresholds, it is included in the candidate list; if the internal resistance of sub-unit B exceeds the initial value by 130%, it is excluded. Then, a third candidate set is formed, and all sub-units meeting the electrochemical requirements are included in this set. Finally, verify the total capacity of the collection, and calculate whether the total power and total capacity of the third candidate collection meet the energy management requirements. If they do, it is determined as the target energy storage sub-unit collection; if they do not, some relaxed indicator thresholds are appropriately relaxed (such as adjusting the lower limit of SOH from 85% to 80%), and the collection is re-screened until the capacity meets the requirements.

[0134] The energy storage sub-units within the target energy storage sub-unit set are those that meet the requirements and can constitute a sub-working system. However, if there is a requirement for the number of sub-units constituting the sub-working system, further scoring and screening of the energy storage sub-units within the target energy storage sub-unit set is necessary. For example, if there are 10 energy storage sub-units in the target energy storage sub-unit set, and the requirement is for 8 sub-units to constitute the sub-working system, then 8 sub-units need to be further screened from the 10. A weighted scoring method can be used to weight and calculate the power device efficiency evaluation results, auxiliary system loss evaluation results, and battery status evaluation results of each energy storage sub-unit in the third candidate set (also known as the initial target energy storage sub-unit set) to obtain a comprehensive score for each energy storage sub-unit; based on the comprehensive scores of each energy storage sub-unit, the final target energy storage sub-unit set is determined.

[0135] As mentioned above, the comprehensive evaluation strategy is not based on a single parameter. In one optional embodiment, a comprehensive evaluation is conducted from three dimensions: power device efficiency evaluation (first type of evaluation) 110, auxiliary system loss evaluation (second type of evaluation) 120, and battery body state evaluation (third type of evaluation) 130.

[0136] Understandably, in order to uniformly quantify and compare the three dimensions with different properties, this embodiment introduces a weighted scoring model. For the i-th energy storage sub-unit, its comprehensive score, Scorei, is calculated using the following formula: Score i =w1·f1(P i )+w i ·f2(A i )+w3·f3(B i ), where w1, w2, and w3 are the weighting coefficients for the three evaluation dimensions of power device efficiency, auxiliary system loss, and battery status, respectively.

[0137] These weights can be preset or dynamically adjusted according to the overall operational goals of the system. For example, in winter when temperature requirements are stringent, the weight w2 for auxiliary system losses can be increased. To balance the aging of each sub-unit at the end of system operation, w3 can be increased. In this scenario, to emphasize the focus on the overall system energy efficiency, the weights are set as follows: w1 = 0.3, w2 = 0.5, w3 = 0.2, and the sum of the three is 1. i A i B i These represent the raw data or evaluation results of the i-th sub-unit under the current operating conditions regarding power conversion efficiency, auxiliary system losses, and battery status, respectively. f1, f2, and f3 are normalization functions responsible for mapping raw data of different dimensions (such as percentage of efficiency, wattage of power consumption) to a unified, dimensionless scoring interval (such as 0 to 1) for weighted summation.

[0138] The energy storage control unit will perform the following detailed evaluation on each sub-unit:

[0139] 1) Power Device Efficiency Assessment (Category 1 Assessment) 110: This assessment aims to determine the energy conversion efficiency of the internal power conversion link (such as the DC / DC converter) of each energy storage sub-unit when performing a 30kW discharge task. The energy storage control unit calculates its real-time operating efficiency based on pre-stored efficiency characteristic curves of each unit, combined with information such as current power demand and sub-unit temperature. In this scenario, the assessment results are as follows: Sub-unit A has a power conversion efficiency of 97% at 30kW. Sub-unit B has a power conversion efficiency of 97.5% at 30kW. Sub-unit C has a power conversion efficiency of 97.2% at 30kW. Based on the efficiency value, a normalization function f1 is used for scoring. The sub-unit B with the highest efficiency receives 1.0 point, the second highest sub-unit C receives 0.9 points, and the lowest sub-unit A receives 0.8 points. This assessment aims to select energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal operating range when meeting energy management requirements, thereby reducing energy loss during power conversion.

[0140] 2) Auxiliary System Loss Assessment (Second Type Assessment) 120: This assessment focuses on the additional energy cost incurred to bring the energy storage sub-unit to suitable operating conditions, i.e., the power consumption of its internal thermal management system (heaters, fans, air conditioning, etc.). The energy storage control unit assesses whether auxiliary components need to be activated and their power consumption based on the current state of each sub-unit. For sub-unit A, its current temperature is 5℃, far below the optimal operating temperature range of lithium batteries (typically 15℃-35℃). To ensure discharge performance and avoid irreversible damage such as low-temperature lithium plating, its built-in 1kW heater must be activated, therefore its auxiliary system loss is 1kW. For sub-unit B, its current temperature is 25℃, within the ideal operating temperature range, and no temperature control auxiliary equipment needs to be activated, therefore its auxiliary system loss is 0kW. For sub-unit C, its current temperature is 45℃, slightly above the upper limit of the optimal operating temperature. To prevent the risk of thermal runaway and accelerated aging, its built-in 0.5kW cooling fan needs to be activated, therefore its auxiliary system loss is 0.5kW. The normalization function f2 scores the auxiliary power consumption, with lower losses resulting in higher scores. Therefore, sub-unit B with zero loss receives 1.0 point, sub-unit C with 0.5kW loss receives 0.5 points, and sub-unit A with a loss as high as 1kW receives 0 points. This evaluation aims to select energy storage sub-units that require no start-up auxiliary components or have the lowest energy consumption and lifespan loss after start-up, thereby improving the system's net output energy.

[0141] 3) Battery Cell State Assessment (Category III Assessment) 130: This assessment focuses on the electrochemical state of the battery cell itself, primarily based on the State of Health (SOH). A higher SOH indicates lower battery aging, generally lower internal resistance, less heat generation at the same current, higher charge / discharge efficiency, and longer remaining lifespan. Sub-cell A has an SOH of 98%. Sub-cell B has an SOH of 92%. Sub-cell C has an SOH of 95%. SOH is scored based on the normalization function f3. Sub-cell A, with the highest SOH, receives 1.0 point, followed by sub-cell C with 0.8 points, and sub-cell B, with the lowest, receives 0.6 points. This assessment aims to screen energy storage sub-cells that meet electrochemical requirements and can be used to implement long-term strategies for balancing the aging rates of each sub-cell.

[0142] Comprehensive Decision-Making: The energy storage control unit calculates the final comprehensive score for each sub-unit by weighting and summing the normalized scores from the three dimensions mentioned above with preset weights. A The score is 0.44. B The score is 0.92. C It is 0.68.

[0143] The calculation results show that Score B Score C Score A Although sub-unit A has the best battery health (highest state of equilibrium, SOH), its extremely low operating temperature leads to very high auxiliary system losses, resulting in the lowest overall score. Sub-unit B, while having a slightly lower SOH, performs best overall because it requires no auxiliary power consumption and has the highest power conversion efficiency. Therefore, the energy storage control unit has determined sub-unit B as the target energy storage sub-unit for this energy management requirement.

[0144] Optionally, the energy storage control unit then generates and sends control commands. It sends an activation command to sub-unit B, and simultaneously sends standby or disconnect commands to sub-units A and C. Specifically... Figure 3 In the parallel section, the energy storage control unit controls the controllable switching device 31, which is electrically connected to sub-unit B, to close, safely integrating it into the power link, while keeping the contactors connected to sub-units A and C in the open state. In this way, a sub-working system consisting only of sub-unit B is dynamically constructed and begins to perform a 30kW discharge task.

[0145] Therefore, the method in this embodiment avoids the 1kW extra energy loss that might result from traditional methods that prioritize SOH and select sub-unit A, significantly improving the system's net energy output efficiency and overall economy. The entire decision-making process is based on a quantitative, multi-dimensional evaluation model, enabling refined and intelligent management of energy storage resources.

[0146] The following examples illustrate the role of auxiliary system loss assessment dimensions in specific scenarios and demonstrate how the proposed solution addresses the decision ambiguity problem of traditional methods in such scenarios. The system architecture and basic methodology are the same as those in the embodiments described above.

[0147] In this scenario, the energy management requirement changes to charging the energy storage system at a power of 10 kilowatts. The system has two available energy storage sub-units, sub-unit A and sub-unit B, whose state parameters are very similar, enough to stump traditional decision-making methods.

[0148] First, steps S201 and S202 are executed. The energy storage control unit obtains the demand for 10kW charging and acquires the following status information from sub-unit A and sub-unit B: Sub-unit A: Health state SOH is 95%, current temperature is 25℃, and the estimated power conversion efficiency under 10kW charging condition is 98%. Sub-unit B: Health state SOH is 95%, current temperature is 0℃, and the estimated power conversion efficiency under 10kW charging condition is 98%.

[0149] Traditional evaluation methods based solely on the battery's state of health (SOH) or power conversion efficiency present a decision-making dilemma. Since the SOH and estimated power conversion efficiency of sub-cell A and sub-cell B are identical, traditional methods cannot effectively distinguish their relative merits, potentially leading to random selection or selection based on a default order. Randomly selecting sub-cell B would result in unnecessary energy waste and potential battery damage.

[0150] However, the method provided in this application, through the multi-dimensional evaluation and screening in step S203, can make a clear and optimal selection. The energy storage control unit still adopts the aforementioned weighted scoring model and uses the same weighting coefficients.

[0151] The evaluation process is as follows: 1) Power device efficiency evaluation (first type of evaluation) 110: Sub-unit A and sub-unit B have an estimated efficiency of 98% under the current operating conditions, so they perform the same in this dimension and the normalized score is 1.0.

[0152] 2) Battery Body State Assessment (Category 3 Assessment) 130: The State of Health (SOH) of both sub-unit A and sub-unit B is 95%, and they also perform the same in this dimension, with a normalized score of 1.0.

[0153] 3) Auxiliary System Loss Assessment (Category II Assessment) 120: For sub-unit A, its current temperature is 25℃, within the ideal charging temperature range, requiring no heating or cooling equipment to be activated; its auxiliary system loss is 0kW. For sub-unit B, its current temperature is 0℃, within the low-temperature prohibited or severely restricted area for lithium battery charging. To charge safely and efficiently and protect the battery from low-temperature lithium plating, its built-in 1kW heater must be activated to preheat the cell to the allowable temperature; therefore, its auxiliary system loss is 1kW. In the normalized score for auxiliary power consumption, sub-unit A, with a loss of 0, receives the full score of 1.0, while sub-unit B, with a loss of 1kW, receives the lowest score of 0. Comprehensive Decision: The energy storage control unit calculates the comprehensive score for the two sub-units: Score A Scores 1.0, Scpre B It is 0.5.

[0154] The calculation results show that, when the evaluation results in the other two dimensions are exactly the same, the auxiliary system loss assessment becomes the decisive criterion. Therefore, the energy storage control unit explicitly and unambiguously selects sub-unit A as the target energy storage sub-unit.

[0155] Subsequently, the energy storage control unit controls the on / off device connected to sub-unit A to close, while keeping the on / off device connected to sub-unit B open, and the sub-working system composed of sub-unit A begins to perform a 10kW charging task.

[0156] This embodiment demonstrates that by taking the energy consumption information of auxiliary components as an essential evaluation dimension, the method provided in this application can still make correct energy-saving decisions through precise quantitative calculations when other key parameters are similar and traditional methods are difficult to choose.

[0157] In an optional embodiment, the method further includes: continuously monitoring the multi-dimensional state information of the energy storage sub-units constituting the sub-working system during operation of the sub-working system; when a change in the multi-dimensional state information is detected to meet a preset triggering condition, returning to the step of performing hierarchical screening of multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine the target energy storage sub-unit set.

[0158] In an optional embodiment, the solution of this application is not a one-time static decision, but an intelligent closed-loop control strategy that can adapt to changes in system state. Assume the system architecture adopts a parallel structure as described above. Assume that in the initial state, the system is performing a long-term, stable-power discharge task. At the start of the task, the energy storage control unit has performed a complete evaluation and selection, selecting a sub-working system composed of sub-unit A and sub-unit B to perform the task. At this time, the switching devices connected to sub-unit A and sub-unit B are all closed.

[0159] During the operation of the sub-working system, the method enters the dynamic monitoring step. The energy storage control unit continuously polls the multi-dimensional status information of all energy storage sub-units (including units in operation and units in standby) through the communication link at a preset period (e.g., once per second or once per minute, which is configurable), so as to realize the real-time perception of the global status of the system.

[0160] In this scenario, after the sub-system operated smoothly for 30 minutes, its status changed. During a routine status check, the energy storage control unit detected that the internal temperature parameter 24 reported by the operating sub-unit A had risen from the initial 30°C to 50°C. This temperature value triggered an over-temperature protection warning from its internal BMS, causing its cooling fan to start automatically. The operation of the cooling fan resulted in an additional energy consumption of 0.6kW, meaning that the auxiliary system losses of sub-unit A increased from 0kW to 0.6kW.

[0161] It should be noted that the energy storage control unit has several internal trigger conditions for initiating the reassessment mechanism. These conditions are designed to capture any changes that may affect the optimal operating state of the system. Trigger conditions include, but are not limited to: receiving a new energy management request; detecting a significant degradation in the performance parameters (such as internal resistance and efficiency) of the working unit; the working unit reporting a serious alarm or fault; or, as shown in this scenario, detecting an unexpected change in the auxiliary system loss status of the working unit.

[0162] When the trigger condition of the auxiliary power consumption of sub-unit A changing from 0 to 0.6kW is detected, the process will automatically jump back to step S203 and immediately re-execute the multi-dimensional evaluation and screening. The energy storage control unit uses the latest real-time acquired multi-dimensional status information to perform a new multi-dimensional evaluation and quantitative scoring of all available energy storage sub-units (including sub-units A, B, and all standby sub-units). The multi-dimensional evaluation and quantitative scoring process is as described above. Then, a switching operation is performed, sending a closing command to the switching device 31 corresponding to the sub-unit that needs to be closed, putting it into the working circuit. After confirming that it has successfully connected to the grid and the current is stable, a disconnection command is sent to the switching device corresponding to the new sub-unit that needs to be added, cutting it out of the working circuit. Through this method of putting on and then cutting off, the continuity and stability of the system's total output power can be guaranteed, avoiding impact on the grid or load.

[0163] This embodiment continuously monitors and triggers reassessment and dynamic adjustment when necessary, enabling it to respond in real time to changes in the internal state of the system, proactively avoid sub-units with degraded performance or increased losses, and replace them with the currently optimal sub-units. This ensures that the entire energy storage system is continuously maintained within the optimal operating range, achieving dynamic optimization management.

[0164] In an optional embodiment, see Figure 4 The diagram shown is a schematic flowchart of an energy management method applied to energy storage systems.

[0165] Class 1: Prioritize demand while also considering the efficiency of main power devices.

[0166] Input information: the status and operational capabilities of each energy storage unit (i.e., the energy storage sub-unit status information and operational capability information in the power conversion efficiency related information above), system operational capability requirements (i.e., energy management requirements, including power, charging and discharging direction, etc.), temperature and temperature derating information of power components (power devices), and the optimal efficiency operating range of power devices, etc.

[0167] Evaluation dimensions: Improve the conversion efficiency of power components such as DC-DC and DC-AC (energy conversion efficiency of power conversion links under different operating conditions) and ensure that the main power devices operate in the high-efficiency range with low loss.

[0168] Output results: All energy storage sub-units that can participate in energy dispatch requirements are selected, and sub-energy storage units that can operate within the optimal efficiency range (sub-units whose efficiency is in the optimal operating range) are identified, forming the first candidate set.

[0169] Class 2: Meeting requirements while also improving the lifespan and energy efficiency of auxiliary hardware.

[0170] Input information: Balanced demand assessment, heating demand assessment, heat dissipation demand assessment.

[0171] Evaluation dimensions: From the perspective of improving the lifespan and energy-saving capabilities of auxiliary working circuits (i.e., auxiliary components), it is required to meet the auxiliary function requirements while reducing energy waste and protecting auxiliary hardware.

[0172] Output results include balanced demand response energy consumption and balanced link hardware lifespan status, i.e., outputting auxiliary component operating energy consumption information and hardware lifespan status information. These outputs collectively serve as the basis for selecting sub-units that meet the auxiliary component requirements from the first candidate set, forming the second candidate set (e.g., excluding sub-units with excessively high auxiliary energy consumption or insufficient hardware lifespan).

[0173] Class 3: Maximizes battery efficiency and promotes battery-friendly operation to extend battery life.

[0174] Input information:

[0175] Information such as battery resistance, current battery state, battery health, and optimal operating range for battery efficiency (i.e., electrochemical state information).

[0176] Evaluation dimensions: From the perspective of improving battery charging and discharging efficiency and energy storage module lifespan, to ensure that the battery operates in a safe and efficient state and avoid excessive wear and tear that leads to a shortened lifespan.

[0177] Output results: differences in battery health, etc. These outputs collectively serve as the basis for selecting sub-units from the second candidate set that meet electrochemical requirements, forming the third candidate set, ultimately ensuring that the target energy storage sub-unit set balances battery efficiency and long-term lifespan.

[0178] Figure 5 This is a control framework diagram of an energy management method applied to an energy storage system according to an embodiment of this application. It includes:

[0179] 01 Energy Management & Inverter Unit, corresponding to the Energy Management and Inverter Unit mentioned above, refers to the core execution unit responsible for realizing energy exchange and power conversion between the energy storage system and the grid / load. It is the energy outlet for the sub-working system to perform energy management requirements, such as the sub-working system composed of the target energy storage sub-units.

[0180] 02. Based on self-learning technology and statistical models, the flexibility and accuracy of judgment methods are improved. This corresponds to power conversion efficiency prediction model, logistic regression model, and battery aging model. The self-learning technology is reflected in the model's optimization of prediction accuracy through historical operating data (such as the power conversion efficiency prediction model being trained based on experimental data). The statistical model corresponds to the gradient boosting decision tree regression model, which quantifies key indicators such as efficiency, auxiliary component start-up and shutdown, and battery aging through statistical regularities, thus solving the problem of traditional decision-making relying on fixed thresholds and lacking flexibility.

[0181] 03 Energy Storage Control Unit, also known as the Energy Storage Control Unit, undertakes the core functions of acquiring specific needs, acquiring status information, hierarchical screening, and constructing sub-working systems, such as broadcasting status query commands through communication links, calling models to calculate efficiency, and controlling the connection / isolation of on / off devices to sub-units.

[0182] The 04 user-friendly system and the method for confirming the highest energy efficiency are the core basis for graded screening and weighted scoring. Among them, the user-friendly system corresponds to the screening logic of prioritizing sub-units that do not trigger high-energy-consuming auxiliary components, have good battery consistency, and have long remaining life; the highest energy efficiency corresponds to the comprehensive screening criteria of optimal power conversion efficiency + lowest auxiliary energy consumption + qualified electrochemical state, such as determining the optimal sub-unit by comparing power device efficiency, auxiliary energy consumption, and SOH.

[0183] 05. The system control strategy to improve system energy efficiency and service life, namely the strategy of hierarchical screening to exclude inefficient / aging sub-units, dynamic monitoring to trigger re-screening, and avoiding low / over-temperature operation of batteries, all serve this goal. For example, energy loss is reduced by screening sub-units with efficiency ≥96%, and the overall system life is extended by setting the SOH threshold to ≥85%.

[0184] Specifically, the right side of the attached diagram includes the data input and storage section, which forms the data foundation of the control framework. This section involves the collection and pre-storage of multi-dimensional state information, providing raw data for subsequent predictions and judgments.

[0185] Input and store the efficiency capabilities of each module, corresponding to power conversion efficiency and electrochemical state information; input and store factors affecting efficiency and fitting curves or formulas, corresponding to the temperature derating information of power devices and the optimal operating range curve of power devices; input and store the power consumption information of auxiliary components such as thermal management units, corresponding to the operating energy consumption information of auxiliary components; input and store the heat generation capacity of each power module and unit, corresponding to the self-generated heat data related to the temperature of power devices and the temperature of the battery body; input and store the working capacity of the battery in various states, corresponding to electrochemical state information.

[0186] The data prediction and evaluation section is the core analysis component of the control framework. It involves determining power conversion efficiency, assessing auxiliary component requirements, and judging battery status, transforming stored data into evaluation results usable for decision-making.

[0187] This system acquires, predicts, and records the operating range and parameters of power modules such as DC-DC and DC-AC during charging and discharging at high efficiency, thereby determining the energy conversion efficiency of each energy storage sub-unit's power conversion link under different operating conditions. It also acquires, predicts, and records the battery's capabilities and efficiency under different rates, temperatures, and state of equilibrium (SOH), corresponding to the optimal operating range and power characteristics of the battery in the electrochemical state information. Furthermore, it acquires, predicts, and records the energy consumption for heat dissipation or heating, corresponding to the energy consumption information of auxiliary components. It acquires, predicts, and records the self-heating capability under different operating conditions, corresponding to the prediction of power device and battery temperatures. Finally, it acquires, predicts, and records the battery's efficiency and capabilities under different operating conditions, corresponding to the battery's energy conversion efficiency and real-time operating capabilities.

[0188] The decision-making criteria section, which outlines the screening logic of the control framework, corresponds to the core judgment criteria for hierarchical screening, progressing from feasibility to user-friendliness to efficiency in selecting sub-units.

[0189] 1) Identify energy storage units that can meet the requirements for performing current energy management or scheduling, and conduct basic feasibility screening accordingly;

[0190] 2) User-friendliness evaluation principle: Prioritize those that do not trigger heating or heat dissipation requirements (if triggering is required, select the one closest to the low energy consumption state); prioritize those with short service life, those with good overall energy storage unit charge and discharge capacity characteristics, and those with good consistency between individual energy storage unit batteries, corresponding to user-friendly screening logic;

[0191] 3) Principle of highest energy utilization efficiency: Based on energy management or scheduling needs, combined with the efficiency of power modules, the working requirements of auxiliary circuits, and the charging and discharging efficiency of batteries, a comprehensive efficiency screening is conducted.

[0192] The execution and implementation process is the implementation phase of the control framework, corresponding to the complete process of defining the target set, building sub-work systems, and executing requirements. It also implicitly includes dynamic adjustment logic.

[0193] 1) Based on the method for confirming the most user-friendly system and the highest energy utilization efficiency, a sequence is compiled for the energy storage modules of the sub-working system that can participate in adaptive flexible configuration, and the candidate set is sorted after corresponding hierarchical screening.

[0194] 2) Under the management of the energy management & inverter unit and the energy storage control unit, an adaptive and flexibly configurable sub-working system is built, and the corresponding set of control objectives constitutes the sub-working system;

[0195] 3) Perform charging and discharging tasks according to energy management requirements, corresponding to the sub-work systems.

[0196] Optional, please refer to Figure 6 This is a schematic flowchart illustrating the energy management method applied to energy storage systems provided in this application embodiment. The flowchart presents the entire process logic of an adaptive and flexible configuration sub-working system, with demand acquisition, status acquisition, hierarchical sorting, system construction, and dynamic response as its core. It achieves optimal selection of energy storage sub-units and construction of the sub-working system based on energy demand. The specific steps are as follows:

[0197] Step 1: Start the process and enter the Energy Management & Inverter Unit, which is the energy interaction hub between the energy storage system and the grid / load, responsible for power conversion and energy dispatch execution.

[0198] Step 2: Based on the working mode and changes of the energy management unit, obtain the energy management needs for the present and a period of time to come, and provide a target basis for subsequent screening.

[0199] Step 3: Enter the energy storage system control unit (i.e., the energy storage control unit), which undertakes functions such as demand response, status acquisition, hierarchical screening, and system construction.

[0200] Step 4: Obtain the status parameters of each energy storage unit. The energy storage control unit broadcasts / polls status query commands to all available energy storage sub-units through the communication link to obtain the status parameters of each unit (such as state of health (SOH), state of charge (SOC), temperature, internal resistance, etc.) as the basis data for hierarchical screening.

[0201] Step 5: Based on the principle of maximizing system energy utilization efficiency and being most user-friendly to the system, assign a sequence number to each energy storage unit to participate in the energy management process. Combine the dimensions of power conversion efficiency, auxiliary energy consumption, battery health, etc., comprehensively score and rank the energy storage sub-units to generate a sequence number for participating in energy management (e.g., prioritize the sub-units with the best efficiency and lowest auxiliary energy consumption).

[0202] Step 6: Output the set of energy storage modules and their joining sequence that can participate in the adaptive and flexible configuration of sub-working systems under different energy management requirements. Determine the target set of energy storage sub-units and construct the sub-working system accordingly. That is, select the optimal energy storage sub-units to form the sub-working system according to the sequence number and clarify the joining order of each unit to ensure optimal system energy efficiency.

[0203] Step 7: Respond to the energy management system's needs and make dynamic adjustments. If the system is operating normally, the status information is continuously and dynamically refreshed (right-hand flow), i.e., dynamic monitoring logic, updating the energy storage unit status parameters in real time to provide the latest data for subsequent decisions. If a capacity warning or limitation occurs (such as insufficient sub-unit health or abnormal temperature), feedback is sent to the preceding steps for re-selection to ensure the system always responds to needs in the optimal state. The process ends after completing the demand response, or it can be repeated to adapt to new energy management needs. This achieves the goal of improving the energy utilization efficiency and extending the service life of the energy storage system.

[0204] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0205] Corresponding to the energy management method applied to energy storage systems described in the above embodiments, Figure 2 This paper shows a structural block diagram of an energy storage control unit applied to an energy storage system according to an embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0206] Reference Figure 7 The energy storage control unit applied to the energy storage system includes:

[0207] The demand acquisition module is used to acquire the energy management requirements for the energy storage system.

[0208] The status information acquisition module is used to acquire multi-dimensional status information of multiple energy storage sub-units. The multi-dimensional status information includes at least power conversion efficiency related information and auxiliary component related information.

[0209] The hierarchical screening module is used to perform hierarchical screening of multiple energy storage sub-units based on the energy management requirements and the multi-dimensional status information, and to determine the target set of energy storage sub-units.

[0210] The module is used to control the energy storage sub-units within the target energy storage sub-unit set to form a sub-working system in order to respond to and execute the energy management requirements.

[0211] The specific functions of the relevant modules will not be elaborated here.

[0212] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0213] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional sub-units and modules is used as an example. In practical applications, the above functions can be assigned to different functional sub-units and modules as needed, that is, the internal structure of the device can be divided into different functional sub-units or modules to complete all or part of the functions described above. The functional sub-units and modules in the embodiments can be integrated into one processing sub-unit, or each sub-unit can exist physically separately, or two or more sub-units can be integrated into one sub-unit. The integrated sub-units can be implemented in hardware or as software functional sub-units. Furthermore, the specific names of the functional sub-units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the sub-units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0214] This application also provides a control device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0215] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0216] This application provides a computer program product that, when run on a control device, enables the control device to perform the steps described in the above-described method embodiments.

[0217] Figure 8 This is a schematic diagram of the structure of a control device provided in one embodiment of this application. Figure 8 As shown, the control device of this embodiment includes: at least one processor 80 ( Figure 8 (Only one is shown in the diagram), memory 81, and computer program 82 stored in said memory 81 and executable on said at least one processor 80, wherein said processor 80 executes said computer program 82 to implement the steps in any of the above embodiments of the energy management method applied to the energy storage system.

[0218] The control device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of a control device and does not constitute a limitation on the control device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0219] The processor 80 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0220] In some embodiments, the memory 81 may be an internal storage subunit of the control device, such as a hard disk or memory of the control device. In other embodiments, the memory 81 may be an external storage device of the control device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the control device. Furthermore, the memory 81 may include both internal storage subunits and external storage devices of the control device. The memory 81 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0221] If the integrated subunit is implemented as a software functional subunit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / control device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. Computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0222] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0223] Those skilled in the art will recognize that the sub-units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0224] In the embodiments provided in this application, it should be understood that the disclosed devices / control equipment and methods can be implemented in other ways. For example, the device / control equipment embodiments described above are merely illustrative. For instance, the division of modules or sub-units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or sub-units may be electrical, mechanical, or other forms.

[0225] The sub-units described as separate components may or may not be physically separate. The components shown as sub-units may or may not be physical sub-units; that is, they may be located in one place or distributed across multiple network sub-units. Some or all of the sub-units can be selected to achieve the purpose of this embodiment according to actual needs.

[0226] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An energy management method applied to an energy storage system, characterized in that, The energy storage system includes multiple energy storage sub-units; the method includes: Obtain the energy management requirements for the energy storage system; Acquire multi-dimensional status information of multiple energy storage sub-units; wherein, the multi-dimensional status information includes at least power conversion efficiency related information and auxiliary component related information; the power conversion efficiency related information includes at least one of the following: status information of each energy storage sub-unit, operating capability information of each energy storage sub-unit, and power device related information; the auxiliary component related information includes at least one of the following: start-up and shutdown requirement assessment information of auxiliary components, operating energy consumption information of auxiliary components, and hardware lifespan status information of auxiliary components; Based on the energy management requirements and the multi-dimensional status information, multiple energy storage sub-units are classified and screened to determine the target set of energy storage sub-units. The energy storage sub-units within the target energy storage sub-unit set are controlled to form a sub-working system to respond to and execute the energy management requirements.

2. The energy management method applied to an energy storage system as described in claim 1, characterized in that, The relevant information of the power device includes the power device temperature, the power device temperature derating information, and the power device's optimal operating range. The step of classifying and screening multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine the target set of energy storage sub-units includes: Based on the status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device, the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions is determined. From multiple energy storage sub-units, energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal operating range when meeting the energy management requirements are selected to form a first candidate set; For each energy storage sub-unit in the first candidate set, the start-up and shutdown requirements of the auxiliary components are assessed to determine the start-up and shutdown requirements assessment information of the auxiliary components; Based on the start-stop requirement assessment information of the auxiliary components, the operating energy consumption information of the auxiliary components, and the hardware life status information of the auxiliary components, energy storage sub-units that meet the requirements of the auxiliary components are selected from the first candidate set to form a second candidate set, and the second candidate set is determined as the target energy storage sub-unit set.

3. The energy management method applied to an energy storage system as described in claim 2, characterized in that, The determination of the target energy storage sub-unit set includes: The status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device are input into the pre-trained power conversion efficiency prediction model, and the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions is output. From multiple energy storage sub-units, energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal operating range when meeting the energy management requirements are selected to form a first candidate set; Based on a preset logistic regression model, the start-up and shutdown requirements of auxiliary components for each energy storage sub-unit in the first candidate set are evaluated to determine the start-up and shutdown requirements evaluation information of the auxiliary components; and based on a battery aging model, the operating energy consumption information and hardware life status information of the auxiliary components are determined. Based on the start-stop requirement assessment information of the auxiliary component, the operating energy consumption information of the auxiliary component, and the hardware life status information of the auxiliary component, energy storage sub-units that meet the requirements of the auxiliary component are selected from the first candidate set to form a second candidate set, and the second candidate set is determined as the target energy storage sub-unit set.

4. The energy management method applied to an energy storage system as described in claim 1, characterized in that, The multi-dimensional state information also includes: electrochemical state information; the electrochemical state information includes at least one of the following: the battery health, state of charge, internal resistance, temperature, optimal efficiency operating range, and power characteristics of each energy storage sub-unit; the relevant information of the power device includes the temperature of the power device, the temperature derating information of the power device, and the optimal efficiency operating range of the power device. The step of classifying and screening multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine the target energy storage sub-unit set includes: Based on the status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device, the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions is determined. From multiple energy storage sub-units, energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal operating range when meeting the energy management requirements are selected to form a first candidate set; For each energy storage sub-unit in the first candidate set, the start-up and shutdown requirements of the auxiliary components are assessed to determine the start-up and shutdown requirements assessment information of the auxiliary components; Based on the start-stop requirement assessment information of the auxiliary components, the operating energy consumption information of the auxiliary components, and the hardware life status information of the auxiliary components, energy storage sub-units that meet the requirements of the auxiliary components are selected from the first candidate set to form a second candidate set. Based on the electrochemical state information, energy storage sub-units that meet the electrochemical requirements are selected from the second candidate set to form a third candidate set, and the third candidate set is determined as the target energy storage sub-unit set.

5. The energy management method applied to an energy storage system as described in claim 4, characterized in that, The determination of the target energy storage sub-unit set includes: The status information of each energy storage sub-unit, the operating capability information of each energy storage sub-unit, the temperature of the power device, and the temperature derating information of the power device are input into a pre-trained power conversion efficiency prediction model to output the energy conversion efficiency of the power conversion link of each energy storage sub-unit under different operating conditions; and, from multiple energy storage sub-units, energy storage sub-units whose power conversion link energy conversion efficiency is in the optimal efficiency operating range when meeting the energy management requirements are selected to form a first candidate set; wherein, each energy storage sub-unit in the first candidate set corresponds to a power device efficiency evaluation result; Based on a preset logistic regression model, the start-up and shutdown requirements of auxiliary components are assessed for each energy storage sub-unit in the first candidate set to determine the start-up and shutdown requirement assessment information of the auxiliary components; based on a battery aging model, the operating energy consumption information and hardware lifetime status information of the auxiliary components are determined; and based on the start-up and shutdown requirement assessment information, the operating energy consumption information, and the hardware lifetime status information of the auxiliary components, energy storage sub-units that meet the requirements of the auxiliary components are selected from the first candidate set to form a second candidate set; wherein, each energy storage sub-unit in the second candidate set corresponds to a power device efficiency assessment result and an auxiliary system loss assessment result; Based on the electrochemical state information, energy storage sub-units that meet the electrochemical requirements are selected from the second candidate set to form a third candidate set; wherein, each energy storage sub-unit in the third candidate set corresponds to a power device efficiency evaluation result, an auxiliary system loss evaluation result, and a battery body state evaluation result; The power device efficiency evaluation results, auxiliary system loss evaluation results, and battery status evaluation results of each energy storage sub-unit in the third candidate set are weighted and calculated using a weighted scoring method to obtain the comprehensive score of each energy storage sub-unit. The target set of energy storage sub-units is determined based on the comprehensive score of each energy storage sub-unit.

6. The energy management method applied to an energy storage system as described in claim 1, characterized in that, The control of the energy storage sub-units within the target energy storage sub-unit set constitutes a sub-working system to respond to and execute the energy management requirements, including: By controlling the on / off devices of each energy storage sub-unit within the target energy storage sub-unit set, each energy storage sub-unit is connected to the working circuit, and the energy storage sub-units that are not selected are isolated to form the sub-working system. The switching device is either a switching device connected in series with the energy storage sub-unit or a bypass switch connected in parallel with the energy storage sub-unit.

7. The energy management method applied to an energy storage system as described in claim 1, characterized in that, The method further includes: During the operation of the sub-working system, the multi-dimensional status information of the energy storage sub-units constituting the sub-working system is continuously monitored; When the change in the multi-dimensional state information is detected to meet the preset triggering conditions, the process returns to the step of classifying and screening multiple energy storage sub-units based on the energy management requirements and the multi-dimensional state information to determine the target energy storage sub-unit set.

8. An energy storage control unit applied to an energy storage system, characterized in that, include: The demand acquisition module is used to acquire the energy management requirements for the energy storage system. The status information acquisition module is used to acquire multi-dimensional status information of multiple energy storage sub-units. The multi-dimensional status information includes at least power conversion efficiency related information and auxiliary component related information. The hierarchical screening module is used to perform hierarchical screening of multiple energy storage sub-units based on the energy management requirements and the multi-dimensional status information, and to determine the target set of energy storage sub-units. The module is used to control the energy storage sub-units within the target energy storage sub-unit set to form a sub-working system in order to respond to and execute the energy management requirements.

9. A control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the energy management method for an energy storage system as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, It includes a computer program that, when run, implements the energy management method for an energy storage system as described in any one of claims 1 to 7.

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