Balanced control method and system for cascaded power conversion system of energy storage system

Abstract

The invention belongs to the field of electric power system energy storage, and provides an energy storage system cascade type power conversion system balance control method and system, and the method comprises the steps: dividing an energy storage system into a multi-battery module system, representing the output of the energy storage system through the output of a battery module, and building a battery monomer-battery module state-of-charge consistency model; analyzing the health state characteristics of the battery module, and determining a health state consistency model of the battery module; constructing an adaptive equalization objective function by taking equal output power of the energy storage module before and after equalization as a constraint condition and taking the minimum sum of the charge state consistency of the battery monomer-battery module and the health state consistency of the battery module as an objective; and taking the consistency of the charge state and the health state of each energy storage module as an equalization change, solving the adaptive equalization objective function by adopting an adaptive algorithm to obtain an optimal solution, and performing equalization management and control on the cascaded power conversion system by using the optimal solution. According to the scheme of the invention, the dynamic adjustment capability and the equalization efficiency of equalization are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy storage, and in particular relates to a method and system for balancing control of a cascade power conversion system of an energy storage system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the vigorous development of electrochemical energy storage power stations, their energy conversion efficiency and safety issues have become the focus of increasing attention. Electrochemical energy storage power stations are based on battery modules. The inconsistency of each battery cell in the module leads to problems such as unsatisfactory energy conversion efficiency, reduced battery cycle life, and even frequent safety accidents. It is of great significance to slow down the aging of batteries, improve the charging and discharging efficiency of multi-module energy storage systems, and reduce system operation and maintenance costs by analyzing the output correlation between electrochemical energy storage power stations and each battery module, establishing a correlation model between battery module SOC and SOH, and performing consistency and balance control on them.

[0004] Battery module balancing plays a key role in improving module energy conversion efficiency, extending battery life, and reducing system operating costs. The key to battery balancing is to select balancing indicators, establish balancing thresholds, and establish balancing methods. Currently, the selection of balancing indicators is mostly focused on battery cell voltage, current, SOC and other parameters, and fails to reflect the dynamic changes of battery module indicator parameters. The reliability and flexibility of the establishment of balancing topology are not high, and the conversion efficiency is low. The balancing method lacks dynamic adjustment capabilities and environmental adaptability, and fails to consider the dynamic consistency changes of the energy storage system and balance it in a timely and efficient manner. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a balancing control method and system for a cascade power conversion system of an energy storage system. The present invention establishes a dynamic consistency model of SOC and SOH of a battery cell-module, and uses the SOC and SOH dynamic consistency model of the battery module as a balancing variable. A cascade power conversion system control architecture is designed as a balancing topology and an adaptive algorithm is adopted as a balancing control method, which provides a theoretical basis and a practical method for improving the dynamic consistency of SOC and SOH of an electrochemical energy storage power station.

[0006] According to some embodiments, a first solution of the present invention provides a method for balancing control of a cascade power conversion system of an energy storage system, which adopts the following technical solution:

[0007] A balanced control method for a cascade power conversion system of an energy storage system, comprising:

[0008] The energy storage system is divided into a multi-battery module system, the output of the energy storage system is represented by the output of the battery module, and a battery cell-battery module state of charge consistency model is established;

[0009] Analyze the health status characteristics of the battery module, determine the correlation model between the state of charge and the health status, and the consistency model of the health status of the battery module;

[0010] With the equal output power of the energy storage module before and after balancing as the constraint condition, and the minimum sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module as the goal, an adaptive balancing objective function is constructed;

[0011] Taking the consistency of the state of charge and health state of each energy storage module as the balancing variable, an adaptive algorithm is used to solve the adaptive balancing objective function to obtain the optimal solution, and the cascade power conversion system is balanced and controlled with the optimal solution.

[0012] Furthermore, the energy storage system is divided into a multi-battery module system, the output of the energy storage system is characterized by the output of the battery module, and a battery cell-battery module state of charge consistency model is established, specifically:

[0013] The ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the maximum capacity of the energy storage battery cell at time k is taken as the dynamic change characteristic of the charge state of the battery cell;

[0014] According to the dynamic change characteristics of the battery cell state of charge, the maximum value of the battery cell state of charge in the battery module at time k and the minimum value of the battery cell state of charge in the battery module at time k are determined as the battery cell-battery module consistency model.

[0015] Furthermore, according to the dynamic change characteristics of the state of charge of the battery cell, the state of charge of the battery cell at time k and the maximum capacity of the energy storage battery cell at time k determine the state of charge of the battery cell at the next moment.

[0016] Furthermore, the analysis of the battery module health status characteristics, determination of the charge status and health status correlation model and the battery module health status consistency model are specifically:

[0017] The health status of the battery cell is determined by the ratio of the maximum capacity of the energy storage battery cell at time k to the rated capacity of the battery when it leaves the factory;

[0018] Determine a correlation model between the state of charge and the health state according to the state of charge of the battery cell at time k, the ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the product of the health state and the rated capacity of the energy storage battery cell at time k;

[0019] Based on the difference between the maximum value of the health state of the battery cells in the battery module at time k and the minimum value of the health state of the battery cells in the battery module at time k, a consistency model of the health state of the battery module is determined.

[0020] Furthermore, the control architecture of the cascade power conversion system is composed of an energy storage module, an inverter module and a filter reactor, and the inverter module is composed of an IGBT inverter circuit;

[0021] The inverter module, energy storage module and filter capacitor are connected in parallel to realize DC-AC conversion. Each energy storage module and inverter module are connected in cascade and connected to the AC bus through the filter reactor.

[0022] The control architecture of the cascade power conversion system also includes a master controller and slave controllers corresponding to each energy storage module.

[0023] Furthermore, the consistency of the state of charge and health state of each energy storage module is used as a balancing variable, and an adaptive algorithm is used to solve the adaptive balancing objective function to obtain an optimal solution, and the cascade power conversion system is balanced and controlled by the optimal solution, specifically:

[0024] Real-time monitoring of the charge state and health status of each energy storage module;

[0025] When the battery cell-battery module charge state consistency model is greater than or equal to the charge state balance threshold, and the battery module health state consistency model is greater than or equal to the health state balance threshold, start to balance the cascade power conversion system;

[0026] The consistency of state of charge and health status is used as the balancing variable. When the two are inconsistent, an adaptive algorithm is used to dynamically adjust the control parameters of each energy storage module, and the adaptive balancing objective function is solved to obtain the optimal solution. The optimal solution is used as the control parameter of each energy storage module to balance the cascade power conversion system.

[0027] When the battery cell-battery module charge state consistency model is less than the charge state balance threshold, and the battery module health state consistency model is less than the health state balance threshold, the balancing control of the cascade power conversion system is stopped.

[0028] Furthermore, the battery module output is a unit of battery module output power to represent the energy storage system output, specifically:

[0029] Battery module output power model:

[0030]

[0031] In the formula, Q max,m (k) is the maximum capacity of the energy storage battery module at time k; SOC m(k+Δk) and SOC m (k) are the SOC of the battery module at time k+Δk and time k respectively; Δk is the sampling time interval;

[0032] Energy storage system output power model:

[0033]

[0034] Where N is the number of battery modules in the energy storage system; P m,i (k) is the output power of the i-th battery module in the energy storage system at time k.

[0035] According to some embodiments, a second solution of the present invention provides a cascade power conversion system balancing control system for an energy storage system, which adopts the following technical solution:

[0036] A cascade power conversion system balance control system for an energy storage system, comprising:

[0037] The battery cell-battery module charge state consistency module is configured to divide the energy storage system into a multi-battery module system, characterize the output of the energy storage system with the battery module output, and establish a battery cell-battery module charge state consistency model;

[0038] A battery module health state consistency module is configured to analyze the battery module health state characteristics, determine the association model between the state of charge and the health state and the battery module health state consistency model;

[0039] The adaptive balancing objective function construction module is configured to construct an adaptive balancing objective function with the output power of the energy storage module before and after balancing being equal as a constraint condition and the sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module being minimized as a goal;

[0040] The adaptive balancing control module is configured to use the consistency of the charge state and health state of each energy storage module as the balancing variable, adopt an adaptive algorithm to solve the adaptive balancing objective function to obtain the optimal solution, and use the optimal solution to balance and control the cascade power conversion system.

[0041] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in a method for balancing control of a cascade power conversion system of an energy storage system as described in the first aspect above.

[0043] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0044] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method for balancing control of a cascade power conversion system of an energy storage system as described in the first aspect above are implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention divides the electrochemical energy storage system into a multi-module system, and uses the battery module output to characterize the output of the energy storage system. The connection between the battery cells and the modules is analyzed, and a cell-module dynamic consistency model of SOC is established. The correlation characteristics of SOC and SOH are analyzed, and a cell-module dynamic consistency model of SOH is established. Using SOC and SOH as balancing indicators, a cascade power conversion architecture is designed as a balancing structure, and an adaptive algorithm is used as a balancing method to improve the dynamic consistency of SOC and SOH of the energy storage power station, providing a method for balancing the energy storage system.

[0047] The balancing of electrochemical energy storage power stations is somewhat complex. The present invention divides the electrochemical energy storage power station into a multi-module system, performs balancing control on the battery module, constructs a correlation model from the monomer to the module, and thus establishes a SOC dynamic consistency model. According to the correlation characteristic model of SOC-SOH, the SOH dynamic consistency model of the battery module is determined; a cascade power conversion system control architecture is designed to improve the reliability and flexibility of balancing control, an adaptive algorithm is used as the balancing control method, and the dynamic consistency of SOC and SOH is used as the balancing variable for balancing control, thereby improving the dynamic consistency of SOC and SOC in the electrochemical energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0049] Figure 1 It is a flow chart of a balanced control method of a cascade power conversion system of an energy storage system in an embodiment of the present invention;

[0050] Figure 2 It is a framework diagram of a balanced control method for a cascade power conversion system of an energy storage system in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0054] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0055] Embodiment 1

[0056] The present embodiment provides a balanced control method for a cascade power conversion system of an energy storage system. The present embodiment takes the application of the method to a server as an example. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In the present embodiment, the method includes the following steps:

[0057] The energy storage system is divided into a multi-battery module system, the output of the energy storage system is represented by the output of the battery module, and a battery cell-battery module state of charge consistency model is established;

[0058] Analyze the health status characteristics of the battery module, determine the correlation model between the state of charge and the health status, and the consistency model of the health status of the battery module;

[0059] With the equal output power of the energy storage module before and after balancing as the constraint condition, and the minimum sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module as the goal, an adaptive balancing objective function is constructed;

[0060] Taking the consistency of the state of charge and health state of each energy storage module as the balancing variable, an adaptive algorithm is used to solve the adaptive balancing objective function to obtain the optimal solution, and the cascade power conversion system is balanced and controlled with the optimal solution.

[0061] The basic idea of ​​the cascaded PCS balancing control method for energy storage systems considering the dynamic consistency of SOC and SOH proposed in this embodiment is: in order to solve the problems of low energy conversion efficiency and fast aging of energy storage power stations, the electrochemical energy storage power station is divided into a multi-module system, and the output of the energy storage power station is characterized by the output of the battery module; the connection between the battery cell and the battery module is analyzed, and a dynamic consistency model of the SOC cell-module is established; the characteristics of SOC and SOH are analyzed, and a correlation model of SOC-SOH is established, thereby establishing a dynamic consistency model of the SOH of the battery cell-module; in order to improve the reliability and flexibility of the balancing topology, a cascaded power conversion system control architecture is designed, and in order to improve the dynamic adjustment capability and balancing efficiency of the balancing, an adaptive algorithm is used as a balancing method to improve the dynamic consistency of the energy storage system.

[0062] The cascaded PCS balancing control method for energy storage systems considering the dynamic consistency of SOC and SOH improves the energy conversion efficiency of the energy storage system, slows down its aging rate and reduces its operating cost. First, the energy storage system is divided into a multi-module system, and the output of the energy storage system is characterized by the output of the battery module. Secondly, the relationship between the battery cell and the module is analyzed, and the SOC dynamic consistency model of the battery cell-module is established. In addition, the correlation characteristics of SOC and SOH are analyzed, and the dynamic consistency model of the battery module SOH is established. Finally, a cascaded power conversion architecture is designed as the balancing topology, an adaptive algorithm is used as the balancing control method, and the dynamic consistency of SOC and SOH is used as the balancing variable for balancing control, which improves the flexibility and reliability of balancing.

[0063] like Figure 1 and Figure 2 As shown, this embodiment specifically includes:

[0064] Step 1: Establish a battery cell-battery module state of charge consistency model.

[0065] The energy storage system is divided into a multi-battery module system, the output of the energy storage system is characterized by the output of the battery module, and a battery cell-battery module state of charge consistency model is established, specifically:

[0066] The ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the maximum capacity of the energy storage battery cell at time k is taken as the dynamic change characteristic of the charge state of the battery cell;

[0067] According to the dynamic change characteristics of the battery cell state of charge, the maximum value of the battery cell state of charge in the battery module at time k and the minimum value of the battery cell state of charge in the battery module at time k are determined as the battery cell-battery module consistency model.

[0068] (1) Construct an energy storage system output model based on multiple battery modules.

[0069] The energy storage system is divided into a multi-battery group model, and the output model of the energy storage system is characterized by the output power of the battery module.

[0070] Battery module output power model:

[0071]

[0072] In the formula, Q max,m (k) is the maximum capacity of the energy storage battery module at time k; SOC m (k+Δk) and SOC m (k) are the SOC of the battery module at time k+Δk and time k respectively; Δk is the sampling time interval.

[0073] Energy storage system output power model:

[0074]

[0075] Where N is the number of battery modules in the energy storage system; P m,i (k) is the output power of the i-th battery module in the energy storage system at time k.

[0076] (2) Construct a battery cell-battery module SOC consistency model.

[0077] Analysis of the dynamic change characteristics of battery cell SOC:

[0078]

[0079] In the formula, Q max (k) is the maximum capacity of the energy storage battery cell at time k; P(k) is the output power of the energy storage battery cell at time k; SOC(k+Δk) and SOC(k) are the SOC of the battery cell at time k+Δk and time k respectively.

[0080] It can be seen from formula (3) that the SOC and Q of the battery cell at time k determine the SOC at the next time k+Δk, so the difference in initial SOC and Q between battery cells leads to an increase in their SOC difference after charging and discharging behavior.

[0081] Battery cell-module SOC consistency model:

[0082] C SOC (k) = SOCmax (k)-SOC min (k) (4);

[0083] In the formula, SOC max (k) is the maximum SOC of the battery cell in the battery module at time k; SOC min (k) is the minimum SOC value of the battery cell in the battery module at time k.

[0084] Step 2: Analyze the SOH characteristics of the battery module, build a SOC characterization SOH characteristic model, and establish a battery module health status SOH consistency model.

[0085] Analyze the health status characteristics of the battery module, determine the correlation model between the state of charge and the health status, and the consistency model of the health status of the battery module, specifically:

[0086] The health status of the battery cell is determined by the ratio of the maximum capacity of the energy storage battery cell at time k to the rated capacity of the battery when it leaves the factory;

[0087] Determine a correlation model between the state of charge and the health state according to the state of charge of the battery cell at time k, the ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the product of the health state and the rated capacity of the energy storage battery cell at time k;

[0088] Based on the difference between the maximum value of the health state of the battery cells in the battery module at time k and the minimum value of the health state of the battery cells in the battery module at time k, a consistency model of the health state of the battery module is determined.

[0089] (1) The relationship between SOH and the maximum available capacity of the battery is:

[0090]

[0091] In the formula, Q init is the rated capacity of the battery when it leaves the factory, Q max (k) is the maximum capacity of the energy storage battery cell at time k. The maximum available capacity of the battery decreases with the cycle of the battery, so the SOH decreases with the increase of the number of cycles.

[0092] (2) Based on the calculation formula of SOC and the definition formula of SOH, a correlation model between SOC and SOH is constructed:

[0093]

[0094] (3) Battery module SOH consistency model:

[0095] C SOH (k) = SOH max (k)-SOH min (k) (7);

[0096] In the formula, SOH max (k) is the maximum SOH value of the battery cell in the battery module at time k; SOH min (k) is the minimum SOH value of the battery cell in the battery module at time k. Subsequent balancing control is used to ensure that the SOH of all batteries is as consistent as possible.

[0097] Step 3: Design a cascade power conversion system control architecture to balance the SOC and SOH of the battery module.

[0098] (1) Design of cascade power conversion system control architecture:

[0099] The control architecture of the cascade power conversion system is composed of an energy storage module, an inverter module and a filter reactor. The inverter module is composed of an IGBT inverter circuit; the energy storage module is composed of multiple energy storage batteries, and the energy storage battery refers to a battery cell;

[0100] The inverter module, energy storage module and filter capacitor are connected in parallel to form and realize DC-AC conversion. The energy storage modules and inverter modules are connected in a cascade manner and connected to the AC bus through the filter reactor.

[0101] An inverter module consisting of energy storage batteries and IGBTs is selected, and a filter module is composed of filter capacitors and filter reactors. The inverter modules are cascaded to form a power conversion system control architecture and are connected to the AC bus through filter reactors.

[0102] It also includes a main controller and slave controllers corresponding to each energy storage module; the main controller monitors the parameters of each energy storage module, and uses an adaptive algorithm to adjust the working parameters of the slave controller under the condition of satisfying the constraints to solve the adaptive balancing objective function, and uses the optimal solution as the working parameters of the slave controller, that is, the control parameters of each energy storage module to achieve the balance of SOC and SOH between each module.

[0103] (2) Constraints that the cascaded power conversion system must meet to achieve dynamic consistency balance between SOC and SOH:

[0104]

[0105] Where P bm,i is the output power of the i-th energy storage module before balancing; P lm,i is the output power of the i-th energy storage module after balancing.

[0106] (3) The adaptive algorithm equalization start index should meet the following requirements:

[0107]

[0108] In the formula, δSOC is the SOC equilibrium threshold; δ SOH is the SOH balance threshold.

[0109] The balanced stopping principle is:

[0110]

[0111] (4) Adaptive algorithm balance objective function:

[0112] f=min{C SOC +C SOH} (11).

[0113] The consistency of the state of charge and health status of each energy storage module is used as the balancing variable, and an adaptive algorithm is used to solve the adaptive balancing objective function to obtain the optimal solution. The cascade power conversion system is balanced and controlled with the optimal solution. The specific control process is as follows:

[0114] (1) Use the main controller to monitor the charge state and health status of each energy storage module in real time;

[0115] (2) When the battery cell-battery module state of charge consistency model is greater than or equal to the state of charge balancing threshold, and the battery module health state consistency model is greater than or equal to the health state balancing threshold, start balancing control of the cascade power conversion system;

[0116] (3) The consistency of the state of charge and the health state is used as the balancing variable. When the two are inconsistent, an adaptive algorithm is used to dynamically adjust the control parameters of each energy storage module, and the adaptive balancing objective function is solved to obtain the optimal solution. The optimal solution is used as the control parameter of each energy storage module to balance the cascade power conversion system.

[0117] It can be understood that the control parameters of each energy storage module are the operating parameters of the slave controller;

[0118] (4) When the battery cell-battery module charge state consistency model is less than the charge state balance threshold, and the battery module health state consistency model is less than the health state balance threshold, stop the balancing control of the cascade power conversion system.

[0119] This embodiment relates to a cascaded power conversion system (PCS) balancing control method that considers the dynamic consistency of the state of charge (SOC) and the state of health (SOH). The output model of the energy storage system is constructed based on multiple battery modules, the dynamic change characteristics of the SOC of the battery cells in each module are analyzed, and a characterization model of the influence of the SOC consistency of the battery cell-battery module is constructed. According to the SOH and SOC characteristics of the battery module of the energy storage system, a SOC-SOH correlation model is constructed and a SOH consistency index model is established; the cascaded power conversion system control architecture is designed, and the main controller is used to monitor the SOC and SOH of each module in real time, and the adaptive algorithm is used to dynamically adjust the working parameters of the slave controller to ensure that the SOC and SOH of each module gradually tend to be consistent and perform battery module balancing control. This balancing control method can reduce the aging rate of the battery, further improve the charging and discharging efficiency of the multi-module energy storage system, and reduce the maintenance and replacement costs of the system.

[0120] Embodiment 2

[0121] This embodiment provides a cascade power conversion system balance control system for an energy storage system, including:

[0122] The battery cell-battery module charge state consistency module is configured to divide the energy storage system into a multi-battery module system, characterize the output of the energy storage system with the battery module output, and establish a battery cell-battery module charge state consistency model;

[0123] A battery module health state consistency module is configured to analyze the battery module health state characteristics, determine the association model between the state of charge and the health state and the battery module health state consistency model;

[0124] The adaptive balancing objective function construction module is configured to construct an adaptive balancing objective function with the output power of the energy storage module before and after balancing being equal as a constraint condition and the sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module being minimized as a goal;

[0125] The adaptive balancing control module is configured to use the consistency of the charge state and health state of each energy storage module as the balancing variable, adopt an adaptive algorithm to solve the adaptive balancing objective function to obtain the optimal solution, and use the optimal solution to balance and control the cascade power conversion system.

[0126] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0127] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0129] Embodiment 3

[0130] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the balanced control method of a cascade power conversion system of an energy storage system as described in the first embodiment above are implemented.

[0131] Embodiment 4

[0132] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for balancing control of a cascade power conversion system of an energy storage system as described in the first embodiment above are implemented.

[0133] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0134] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0137] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0138] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for balancing control of a cascade power conversion system of an energy storage system, characterized in that: include: The energy storage system is divided into a multi-battery module system, the output of the energy storage system is represented by the output of the battery module, and a battery cell-battery module state of charge consistency model is established; Analyze the health status characteristics of the battery module, determine the correlation model between the state of charge and the health status, and the consistency model of the health status of the battery module; With the equal output power of the energy storage module before and after balancing as the constraint condition, and the minimum sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module as the goal, an adaptive balancing objective function is constructed; Taking the consistency of the state of charge and health state of each energy storage module as the balancing variable, an adaptive algorithm is used to solve the adaptive balancing objective function to obtain the optimal solution, and the cascade power conversion system is balanced and controlled with the optimal solution.

2. A method for controlling a cascaded power conversion system of an energy storage system as claimed in claim 1, characterized in that: The energy storage system is divided into a multi-battery module system, the output of the energy storage system is characterized by the output of the battery module, and a battery cell-battery module charge state consistency model is established, specifically: The ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the maximum capacity of the energy storage battery cell at time k is taken as the dynamic change characteristic of the charge state of the battery cell; According to the dynamic change characteristics of the battery cell state of charge, the maximum value of the battery cell state of charge in the battery module at time k and the minimum value of the battery cell state of charge in the battery module at time k are determined as the battery cell-battery module consistency model.

3. A method for controlling a cascaded power conversion system of an energy storage system as claimed in claim 2, characterized in that: According to the dynamic change characteristics of the state of charge of the battery cell, the state of charge of the battery cell at time k and the maximum capacity of the energy storage battery cell at time k determine the state of charge of the battery cell at the next moment.

4. A method for controlling a cascade power conversion system of an energy storage system as claimed in claim 1, characterized in that: The analysis of the battery module health status characteristics, determination of the charge status and health status correlation model and the battery module health status consistency model, is specifically: The health status of the battery cell is determined by the ratio of the maximum capacity of the energy storage battery cell at time k to the rated capacity of the battery when it leaves the factory; Determine a correlation model between the state of charge and the health state according to the state of charge of the battery cell at time k, the ratio of the product of the output power of the energy storage battery cell at time k and the sampling time interval to the product of the health state and the rated capacity of the energy storage battery cell at time k; Based on the difference between the maximum value of the health state of the battery cells in the battery module at time k and the minimum value of the health state of the battery cells in the battery module at time k, a consistency model of the health state of the battery module is determined.

5. A method for controlling a cascaded power conversion system of an energy storage system as claimed in claim 1, characterized in that: The control architecture of the cascade power conversion system is composed of an energy storage module, an inverter module and a filter reactor; the inverter module is composed of an IGBT inverter circuit; The inverter module, energy storage module and filter capacitor are connected in parallel to realize DC-AC conversion. Each energy storage module and inverter module are connected in cascade and connected to the AC bus through the filter reactor. The control architecture of the cascade power conversion system also includes a master controller and slave controllers corresponding to each energy storage module.

6. A method for controlling a cascaded power conversion system of an energy storage system as claimed in claim 1, characterized in that: The consistency of the state of charge and health state of each energy storage module is used as the balancing variable, and an adaptive algorithm is used to solve the adaptive balancing objective function to obtain the optimal solution, and the cascade power conversion system is balanced and controlled by the optimal solution, specifically: Real-time monitoring of the charge state and health status of each energy storage module; When the battery cell-battery module charge state consistency model is greater than or equal to the charge state balance threshold, and the battery module health state consistency model is greater than or equal to the health state balance threshold, start to balance the cascade power conversion system; The consistency of state of charge and health status is used as the balancing variable. When the two are inconsistent, an adaptive algorithm is used to dynamically adjust the control parameters of each energy storage module, and the adaptive balancing objective function is solved to obtain the optimal solution. The optimal solution is used as the control parameter of each energy storage module to balance the cascade power conversion system. When the battery cell-battery module charge state consistency model is less than the charge state balance threshold, and the battery module health state consistency model is less than the health state balance threshold, the balancing control of the cascade power conversion system is stopped.

7. A method for controlling a cascade power conversion system of an energy storage system as claimed in claim 1, characterized in that: The battery module output is expressed in terms of the battery module output power as a unit to represent the energy storage system output, specifically: Battery module output power model: In the formula, Q max,m (k) is the maximum capacity of the energy storage battery module at time k; SOC m (k+Δk) and SOC m (k) are the SOC of the battery module at time k+Δk and time k respectively; Δk is the sampling time interval; Energy storage system output power model: Where N is the number of battery modules in the energy storage system; P m,i (k) is the output power of the i-th battery module in the energy storage system at time k.

8. A cascade power conversion system balance control system for an energy storage system, characterized in that: include: The battery cell-battery module charge state consistency module is configured to divide the energy storage system into a multi-battery module system, characterize the output of the energy storage system with the battery module output, and establish a battery cell-battery module charge state consistency model; A battery module health state consistency module is configured to analyze the battery module health state characteristics, determine the association model between the state of charge and the health state and the battery module health state consistency model; The adaptive balancing objective function construction module is configured to construct an adaptive balancing objective function with the output power of the energy storage module before and after balancing being equal as a constraint condition and the sum of the consistency of the state of charge of the battery cell-battery module and the consistency of the health state of the battery module being minimized as a goal; The adaptive balancing control module is configured to use the consistency of the charge state and health state of each energy storage module as the balancing variable, adopt an adaptive algorithm to solve the adaptive balancing objective function to obtain the optimal solution, and use the optimal solution to balance and control the cascade power conversion system.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a balanced control method for a cascade power conversion system of an energy storage system as described in any one of claims 1-7 are implemented.

10. A computer 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 program, the steps in the balanced control method of a cascade power conversion system of an energy storage system as described in any one of claims 1-7 are implemented.

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