A method for optimizing the life of an energy storage system with topology reconfiguration and electro-thermal degradation

Abstract

The application discloses a kind of topological reorganization and electric heat degradation energy storage system life optimization control method, comprising: first, construct modular energy storage topological reorganization model and basic aging degradation model;Then according to the modular energy storage topological reorganization model and basic aging degradation model, construct the battery module compound aging degradation model considering topological reorganization SOC imbalance and electric-thermal coupling effect;Finally, the processing operation of the battery module compound aging degradation model is executed, the optimal topological configuration result of modular energy storage system and full-time operation control strategy are obtained, to realize the optimization control of modular energy storage system.It can solve the SOC imbalance problem existing in the topology switching of traditional modular energy storage system and the accelerated degradation problem of battery module life caused by ignoring electric-thermal coupling effect, while ensuring the operation performance of energy storage system, can greatly realize the collaborative optimization of life loss, improve the overall service life and reliability of energy storage system.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation optimization technology, specifically to a method for optimizing the lifetime of an energy storage system that addresses topology reconfiguration and electrothermal degradation. Background Technology

[0002] With the rapid advancement of new power systems and the gradual empowerment of flexible energy storage services across multiple scenarios, new requirements have been placed on the flexible reconfiguration capabilities and long-life operation paradigms of modern energy storage systems. Traditional energy storage systems are limited by fixed series and parallel topologies and generally adopt an overall charging and discharging and static balancing architecture, which makes it difficult to avoid the bottleneck effect and localized excessive aging problems caused by differences in individual battery cells.

[0003] In recent years, modular energy storage systems have achieved advantages such as on-demand dynamic response and flexible management by establishing dynamic connections between multiple battery modules to achieve topology reconfiguration. However, this operating mode often suffers from severe imbalances in the State of Charge (SOC) among modules due to frequent topology switching, and is prone to significant electrothermal coupling degradation during complex discharge and reconfiguration processes. Existing conventional control methods mostly focus only on meeting transient power distribution requirements, failing to fully consider the SOC dispersion caused by dynamic topology reconfiguration and the accelerated degradation mechanism of battery life due to temperature changes. This makes the system highly susceptible to severe unbalanced aging among modules during long-term operation due to localized heat accumulation and increased sensitivity to current surges, reducing the operational reliability of the energy storage system and significantly shortening its overall lifespan. Against this backdrop, how to comprehensively consider the impact of SOC imbalance during topology reconfiguration and the electrothermal coupling degradation mechanism, construct an accurate aging and degradation model, and thereby achieve synergistic optimization control of the modular energy storage system's operating performance and lifespan loss, reducing the degree of unbalanced aging among modules to improve the overall lifespan of the energy storage system, has become a pressing technical problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the lifespan control of energy storage systems by addressing topology reconfiguration and electrothermal degradation, thereby overcoming the shortcomings of traditional technologies. It solves the problem of unbalanced State of Charge (SOC) during topology switching in traditional modular energy storage systems, as well as the accelerated lifespan degradation of battery modules due to neglecting electrothermal coupling effects. While ensuring the operational performance of the energy storage system, it can significantly optimize lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by localized overheating, and improving the overall lifespan and operational reliability of the energy storage system.

[0005] One embodiment of this application provides a method for optimizing the lifetime control of an energy storage system by addressing topology reconfiguration and electrothermal degradation. This method is applied to a modular energy storage system, which includes multiple independently switchable battery modules. Each battery module is dynamically connected to a common node via a power electronic switch array to achieve topology reconfiguration. The method includes: Construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; Based on the modular energy storage topology reconfiguration model and the basic aging degradation model, a composite aging degradation model for battery modules considering topology reconfiguration SOC imbalance and electrothermal coupling effects is constructed. The battery module composite aging degradation model is processed to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to achieve the optimized control of the modular energy storage system.

[0006] Optionally, the construction of the modular energy storage topology reconfiguration model and the basic aging and degradation model includes: Based on the preset node set, battery module set, operating cycle time set, equipment protection parameter topology switching dead zone, and maximum number of switching times per cycle, the connection status variables, charging / discharging flag variables, connection / disconnection action variables, and corresponding topology switching constraints of the battery modules and nodes during the preset time period are obtained. Based on the connection state variables, the charge / discharge flag variables, the connection / disconnection action variables, and the corresponding topology switching constraints, combined with the upper limit of charge / discharge power, the upper limit of reactive power, the charge / discharge efficiency, the upper and lower limits of capacity, and the time step system parameters, the battery module charge / discharge power, reactive power, battery module energy state, and state of charge are obtained. Based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss are obtained to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

[0007] Optionally, the modular energy storage topology reconfiguration model includes a charge and discharge model for modular energy storage.

[0008] Optionally, the step of constructing a composite aging and degradation model for battery modules, considering topology reconfiguration SOC imbalance and electrothermal coupling effects, based on the modular energy storage topology reconfiguration model and the basic aging and degradation model, includes: The average state of charge of the node is obtained based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module. Based on the average state of charge of the nodes, combined with the real-time battery temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature, the temperature dynamic aging coefficient is obtained. Based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, and combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, the aging increment of state of charge deviation, additional capacity loss, and updated cumulative capacity loss are obtained to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effects.

[0009] Optionally, the step of performing the processing operation on the composite aging degradation model of the battery module includes: The objective function of the composite aging degradation model of the battery module is determined, and the constraints are transformed into second-order cone programming constraints through linearization and cone relaxation.

[0010] Another embodiment of this application provides a topology reconfiguration and electrothermal degradation lifespan optimization control system for an energy storage system, applied to a modular energy storage system. The modular energy storage system includes multiple independently switchable battery modules, each battery module being dynamically connected to a common node via a power electronic switch array to achieve topology reconfiguration. The system includes: The first building module is used to construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; The second construction module is used to construct a composite aging and degradation model of battery modules that considers the SOC imbalance and electrothermal coupling effect of topology reorganization based on the modular energy storage topology reorganization model and the basic aging and degradation model. The execution module is used to perform processing operations on the composite aging and degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

[0011] Optionally, the first building module includes: The first obtaining unit is used to obtain the connection status variables, charging and discharging flag variables, connection and disconnection action variables and corresponding topology switching constraints of the battery module and the node during a preset time period based on the preset node set, battery module set, operating cycle time set, equipment protection parameter topology switching dead zone and maximum number of switching times per cycle. The second obtaining unit is used to obtain the battery module's charging and discharging power, reactive power, battery module energy state and state of charge based on the connection state variable, the charging and discharging flag variable, the connection and disconnection action variable and the corresponding topology switching constraints, combined with the upper limit of charging and discharging power, the upper limit of reactive power, charging and discharging efficiency, the upper and lower limits of capacity, and the time step system parameters. The first generation unit is used to obtain the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, so as to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

[0012] Optionally, the second building module includes: The third obtaining unit is used to obtain the average state of charge of the node based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module. The fourth obtaining unit is used to obtain the temperature dynamic aging coefficient based on the node's average state of charge, combined with the battery's real-time temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature. The second generation unit is used to obtain the state of charge deviation aging increment, additional capacity loss, and updated cumulative capacity loss based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, so as to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effect.

[0013] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to implement the method described in any of the above-described embodiments when running.

[0014] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the method described in any of the above embodiments.

[0015] Compared with existing technologies, this invention first constructs a modular energy storage topology reconfiguration model and a basic aging degradation model; then, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, it constructs a battery module composite aging degradation model that considers topology reconfiguration SOC imbalance and electrothermal coupling effects; finally, it performs processing operations on the battery module composite aging degradation model to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, thereby achieving optimized control of the modular energy storage system. It can solve the SOC imbalance problem existing in traditional modular energy storage systems during topology switching and the problem of accelerated battery module lifespan degradation caused by neglecting electrothermal coupling effects. While ensuring the operating performance of the energy storage system, it can greatly achieve synergistic optimization of lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by local overheating, and improving the overall service life and operational reliability of the energy storage system. Attached Figure Description

[0016] Figure 1 Hardware structure block diagram of a computer terminal for a method for optimizing the lifetime of an energy storage system by topology reorganization and electrothermal degradation, provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a method for optimizing the lifetime of an energy storage system by addressing topology reconfiguration and electrothermal degradation, as provided in an embodiment of the present invention. Figure 3 A schematic diagram of the charging and discharging power of various battery modules provided for an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the relative degradation trajectory of the health status of a battery module during a 24-hour operating cycle, provided by an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of a lifespan optimization control system for an energy storage system that combines topology reorganization and electrothermal degradation, provided as an embodiment of the present invention. Detailed Implementation

[0017] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] The present invention first provides a method for optimizing the lifetime control of energy storage systems that incorporate topology reorganization and electrothermal degradation. This method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, tablets, etc.

[0019] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware block diagram of a computer terminal for a method of optimizing the lifetime of an energy storage system based on topology reorganization and electrothermal degradation, provided in an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0020] The non-volatile storage medium can store an operating system and a computer program. This computer program includes program instructions that, when executed, cause the processor to perform any method for optimizing the lifetime of the energy storage system by addressing topology reconfiguration and electrothermal degradation.

[0021] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0022] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to perform any topology reconfiguration and electrothermal degradation energy storage system lifetime optimization control method.

[0023] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0024] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also 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. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0025] See Figure 2 , Figure 2 This is a flowchart illustrating a method for optimizing the lifetime of an energy storage system by addressing topology reconfiguration and electrothermal degradation, provided in an embodiment of the present invention. The method is applied to a modular energy storage system, which includes multiple independently switchable battery modules. Each battery module is dynamically connected to a common node via a power electronic switch array to achieve topology reconfiguration. The method may include the following steps: S201: Construct a modular energy storage topology reconfiguration model and a basic aging and degradation model.

[0026] Specifically, the construction of the modular energy storage topology reconfiguration model and the basic aging and degradation model may include: Step 1: Based on the preset node set, battery module set, operating cycle time set, device protection parameter topology switching dead zone, and maximum number of switching times per cycle, obtain the connection status variables, charging / discharging flag variables, connection / disconnection action variables, and corresponding topology switching constraints of the battery modules and nodes during the preset time period.

[0027] Step 2: Based on the connection state variable, the charge / discharge flag variable, the connection / disconnection action variable, and the corresponding topology switching constraints, combined with the upper limit of charge / discharge power, the upper limit of reactive power, the charge / discharge efficiency, the upper and lower limits of capacity, and the time step system parameters, obtain the battery module's charge / discharge power, reactive power, battery module energy state, and state of charge.

[0028] Step 3: Based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, obtain the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

[0029] For example, a modular energy storage topology reconfiguration model can be constructed as follows: in, They represent Modular mobile energy storage battery modules The charging and discharging indicators, Represents a set of nodes. express Modular mobile energy storage battery modules With nodes The connection status, express Modular mobile energy storage battery modules With nodes The connection status, Represents the time set of a running cycle. express Modular mobile energy storage battery modules Charging symbol, express Modular mobile energy storage battery modules With nodes The connection status, express Modular mobile energy storage battery modules The discharge indicator, This refers to a collection of battery modules in modular mobile energy storage. A 0-1 variable, used to represent in Time Battery Module With nodes Did a connection action occur? A 0-1 variable, used to represent in Time Battery Module With nodes Did a disconnection occur? Used to indicate in Time Battery Module With nodes Did a connection action occur? Used to indicate in Time Battery Module With nodes Did a disconnection occur? Indicates the dead zone for battery module topology switching. Indicates a complete running cycle Within this range, the maximum frequency of topology switching operations allowed for this module.

[0030] It should be noted that, if This indicates the battery module. Charging, if This indicates the battery module. Discharge; if ,express Time Node Charging, if ,express Time Node Discharge; for Modular mobile energy storage battery modules With nodes The connection status, if the two are connected, then If the two are disconnected, then ; A variable that is 0-1 indicates that in At any time, battery module With nodes Whether a connection action has occurred: 1 indicates that a connection action has occurred, and 0 indicates that a connection action has not occurred. A variable that is 0-1 indicates that in At any time, battery module With nodes Whether a disconnection action occurred: 1 indicates a disconnection action occurred, and 0 indicates no disconnection action occurred.

[0031] Furthermore, constructing a modular energy storage topology reconfiguration model and a basic aging degradation model forms the foundation for all subsequent modeling and optimization. This model itself comprises three progressively advancing sub-steps. Sub-step 1 first establishes topology connectivity and switching constraints. Sub-step 2, after determining the connectivity state, establishes the charging / discharging and energy evolution relationships. Sub-step 3, finally, establishes the basic aging degradation relationships after determining the discharge power. A strict variable transfer chain exists between these three sub-steps. Specifically, the connectivity state variables and charging / discharging flag variables output from sub-step 1 directly serve as input conditions for the power and energy calculations in sub-step 2; the discharge power variable output from sub-step 2 then serves as the input for the basic aging calculations in sub-step 3.

[0032] The specific operational logic of sub-step 1 is as follows: The input to this sub-step is the set of nodes given during the system initialization phase. Battery module collection , runtime set And the dead zone for topology switching of equipment protection parameters Maximum number of switching cycles per cycle Based on these known conditions, the following decision and state variables are introduced and solved: This represents the connection status between battery module u and node j at time t, where 1 indicates connection and 0 indicates disconnection. and These represent the charging and discharging flags of module u at time t, respectively. and These represent whether module u and node j have connected and disconnected at time t, respectively. Operational constraints require that module u cannot be charged and discharged simultaneously, and charging and discharging are only permitted when the module remains connected to a node. and The sum of and the receiving and The product constraint; the charging and discharging behavior of module u must be related to the charging and discharging state of its node i. , Maintain consistency and be subject to connection state. Gating; changes in connection state are controlled by and The difference is equal to the difference between adjacent times. The difference is used to depict it; in addition, in the dead zone Repeated switching is not allowed within the time limit, and the total number of switching times within the entire time period T shall not exceed [a certain limit]. After completing the steps in this book, 、 , , , , as well as , Once the variables are determined or retained as optimization decision variables, they are all passed on to sub-step 2 and subsequent model use.

[0033] The operation logic for sub-step 2 is as follows: The input variables for this sub-step come directly from sub-step 1: Determine whether module u is connected to the power grid node at time t. and This determines whether module u is in charging or discharging mode. Additionally, this sub-step requires system parameters. , (Upper limit of charging and discharging power) , (Upper limit of reactive power) , (charge and discharge efficiency) , (Capacity upper and lower limits) and time step Under the constraints of the connection and mode flags output from sub-step 1, this sub-step calculates: and Let be the charging active power and discharging active power of module u at time t, respectively, and their values ​​range from . and Gating means that the charging power can only be greater than 0 when the charging flag is 1, and the same applies to discharging; and This corresponds to reactive power. Energy state. From the energy of the previous moment The result is obtained by recursively calculating the charging and discharging power at this moment, and the recursive formula is as follows: equal Plus the incremental benefit of charging · · Subtract the reduction caused by discharge. / · ,and Always maintain and Between. By energy With capacity limit The module's state of charge can be further obtained. Therefore, sub-step 2 calculates the specific power based on the assumptions given in sub-step 1 regarding whether the module is connected and whether charging / discharging is permitted. , and energy ,in, This will be passed to sub-step 3 for basic aging calculations. and All power and energy variables will be passed to subsequent models for SOC imbalance analysis and used to construct objective functions and constraints.

[0034] The operation logic of sub-step 3 is as follows: The input variables for this sub-step come from sub-step 2 above, where, This serves as the direct driver for basic aging calculations. Additionally, a module capacity limit is required. and piecewise linear aging coefficient and First, the discharge power transferred from sub-step 2. Calculate normalized discharge power ,Right now equal Divide by Its physical meaning is to convert absolute discharge power into a relative rate, making the aging of modules with different capacities comparable. Then, by Using piecewise linear inequalities ≥ · + Determine the basic capacity loss for this period. The lower bound. Finally, the cumulative capacity loss. Accumulated value from the previous time step Compared with the increase in this period Adding them together gives us a temporal recursive relationship. The output of this sub-step is... This information is passed to subsequent models for composite aging calculations. and This is passed to subsequent models, where the basic aging amount in the battery module composite aging degradation model that considers topological reorganization SOC imbalance and electrothermal coupling effects will be further corrected by the SOC imbalance term and the temperature term.

[0035] In one optional implementation, the modular energy storage topology reconfiguration model includes a charging and discharging model for modular energy storage, which can be constructed in the following manner: in, express Time Battery Module The charging active power, express Modular mobile energy storage battery modules The charging icon This indicates the upper limit of the active power for charging the battery module. express Time Battery Module The active power of discharge, express Modular mobile energy storage battery modules The discharge indicator, This indicates the upper limit of the active power of the battery module's discharge. express Time Battery Module The charging reactive power, Indicates battery module The upper limit of the charging reactive power. Indicates battery module The upper limit of the reactive power of discharge. express Time Battery Module The reactive power of discharge, Represented as Time Battery Module capacity, Represented as Time Battery Module capacity, Indicates battery module Charging efficiency, Indicates time interval, Indicates battery module The discharge efficiency, Indicates battery module The lower limit of capacity, Indicates battery module The maximum capacity.

[0036] Specifically, the construction of the modular energy storage topology reconfiguration model and the basic aging and degradation model may include: The basic aging and degradation model is constructed as follows: in, express Time Battery Module Normalized discharge power, Indicates the battery module at time t The active power of discharge, Indicates battery module Maximum capacity, Representation module Deadline Capacity loss during the period Indicates the first The slope coefficient of each segment, Indicates the first The intercept coefficient of each segment, Indicates battery module Deadline Cumulative capacity loss at any given moment Indicates battery module Deadline Cumulative capacity loss at any given moment.

[0037] S202: Based on the modular energy storage topology reconfiguration model and the basic aging degradation model, construct a battery module composite aging degradation model that considers the topology reconfiguration SOC imbalance and electrothermal coupling effect.

[0038] Specifically, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, a composite aging degradation model for battery modules considering topology reconfiguration SOC imbalance and electrothermal coupling effects is constructed, including: 1. Based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module, obtain the average state of charge of the node.

[0039] 2. Based on the average state of charge of the nodes, combined with the real-time battery temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature, the temperature dynamic aging coefficient is obtained.

[0040] 3. Based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, and combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, the aging increment of state of charge deviation, additional capacity loss, and updated cumulative capacity loss are obtained to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effect.

[0041] For example, a combined aging and degradation model for a battery module that considers topology remodeling, SOC imbalance, and electrothermal coupling effects can be constructed as follows: in, express Time Node The average SOC of all assembled battery modules Indicate Modular mobile energy storage battery modules With nodes The connection status, express Modular mobile energy storage battery modules SOC, Represents the time set of a running cycle. This refers to a collection of battery modules in modular mobile energy storage. Represents a set of nodes. The dynamic aging factor represents the aging rate at the current temperature. The sensitivity of the battery to current surges. This indicates the Kelvin temperature of the battery module at the current moment. Indicates the activation energy of an electrochemical reaction. Represents the ideal gas constant. Indicates reference temperature. express Time Battery Module The amplitude of charging and discharging power; This indicates that due to the battery module SOC and average value Inconsistencies in aging-up levels after connection Indicates battery module Deadline Cumulative capacity loss at any given moment Indicates battery module Deadline Cumulative capacity loss at any given moment.

[0042] It should be noted that the construction of a battery module composite aging and degradation model considering topology reorganization SOC imbalance and electrothermal coupling effects is based on the topology connection state, module SOC, normalized power, and cumulative aging amount output from the modular energy storage topology reorganization model and the basic aging and degradation model. It further introduces node-level SOC aggregation, temperature sensitivity coefficient, and SOC deviation penalty to construct a battery module composite aging and degradation model considering topology reorganization SOC imbalance and electrothermal coupling effects. The construction of this model involves three sequentially executed sub-steps, with the variable transfer relationship as follows: First, using... and Calculate the node average Temperature measured externally Calculate the dynamic aging coefficient Finally, all the above-mentioned relevant variables are combined to calculate the final composite aging amount.

[0043] First, input variables. Used to determine which modules connected to node j at time t; input variables This represents the current state of charge of each module. The calculation method is as follows: for node j, all modules that satisfy... module u Calculate the weighted average, with the denominator being the number of modules connected to that node, to obtain... That is, the average SOC of all combined battery modules at node j at time t. The physical meaning is that when multiple modules are connected to the same node through topology reconfiguration, the node is equivalent to a combined entity. This represents the overall charge level of this assembly. With a single module Compare and calculate the aging increment caused by SOC deviation.

[0044] Based on the node average state of charge, combined with the real-time battery temperature, electrochemical activation energy, ideal gas constant, and reference temperature, the calculation logic for obtaining the temperature dynamic aging coefficient is as follows: The input is the current Kelvin temperature of the battery given by external measurement or thermal model. And battery parameters, electrochemical reaction activation energy Ideal gas constant R and reference temperature The calculation formula is: The dynamic aging coefficient is obtained. The underlying physical logic is that the higher the temperature, the more sensitive the battery is to current surges, and the faster it ages at the same power. This will be passed on to subsequent steps and used as a temperature amplification factor in the calculation of the composite aging amount.

[0045] Finally, all relevant variables from the preceding steps are aggregated: This determines whether module u is connected to node j, thereby preventing SOC imbalance aging. The square of the difference between the two ) The deviation between the module's SOC and the node's average SOC is characterized. The greater the deviation, the greater the SOC impact the module experiences when it is first connected. It amplifies the temperature. Reflects the charging and discharging power intensity; The accumulated aging results from the previous period. Only when module u accesses node j through topology reorganization ( The SOC deviation item only takes effect after the SOC deviation is reached; the larger the SOC deviation, the higher the temperature, and the higher the power, the faster the aging will be in this period. The updated cumulative capacity loss is Furthermore, the additional capacity loss caused by SOC deviation and electrothermal coupling is denoted as... Output , and All of these are passed to the subsequent objective function, driving the optimizer to weigh operating costs against lifespan loss.

[0046] S203: Perform processing operations on the composite aging degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

[0047] Specifically, the processing operation of the composite aging degradation model of the battery module may include: The objective function of the battery module composite aging degradation model is determined, and the constraints are transformed into second-order cone programming constraints through linearization and cone relaxation; wherein, the constraints are determined by transforming the bilinear terms in the modular energy storage topology reconfiguration model into linear constraints in the following manner: in, for Modular mobile energy storage battery modules With nodes The connection status, Indicates battery module With nodes During the period The connection status within.

[0048] That is, by solving the constructed composite aging and degradation model of battery modules, the optimal topology configuration scheme and operation control strategy of modular energy storage system are obtained, so as to achieve synergistic optimization of the operating performance and life loss of modular energy storage system and reduce the degree of uneven aging between modules.

[0049] For example, by transforming the nonlinear model into a linear model in the above manner and then solving it using the Gurobi solver, the optimal solution strategy of the modular energy storage lifetime optimization control method considering topology reorganization and electrothermal degradation of the present invention can be obtained.

[0050] In one optional implementation, determining the objective function of the battery module composite aging degradation model includes: The objective function is determined in the following way: in, Let represent the objective function of the battery module composite aging and degradation model. This represents the unit cost of line loss. Indicates battery module Charging efficiency, express Time Battery Module The charging active power, Indicates battery module The discharge efficiency, express Time Battery Module The active power of discharge, Indicates time interval, Indicates battery module Deadline Capacity loss during the period Indicates time Battery Module Because its SOC is inconsistent with the average SOC of the battery modules already assembled at the access node, and due to the additional capacity loss caused by the electrothermal coupling effect, For battery module The unit capacity loss coefficient.

[0051] As can be seen, this invention first constructs a modular energy storage topology reconfiguration model and a basic aging degradation model; then, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, it constructs a battery module composite aging degradation model considering topology reconfiguration SOC imbalance and electrothermal coupling effects; finally, it performs processing operations on the battery module composite aging degradation model to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, thereby achieving optimized control of the modular energy storage system. It can solve the SOC imbalance problem existing in traditional modular energy storage systems during topology switching and the problem of accelerated battery module lifespan degradation caused by neglecting electrothermal coupling effects. While ensuring the operating performance of the energy storage system, it can greatly achieve synergistic optimization of lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by local overheating, and improving the overall service life and operational reliability of the energy storage system.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention comprehensively considers the impact of SOC imbalance during topology reconfiguration and the electrothermal coupling degradation mechanism, breaking through the limitations of traditional energy storage systems that only focus on macroscopic power distribution and static equilibrium. By constructing a multi-dimensional aging model covering dynamic connectivity, electrothermal coupling effects, and battery discharge degradation, it accurately depicts the actual degradation law of individual battery cells under complex topology switching conditions, effectively making up for the shortcomings of existing technologies in refined lifetime assessment and multi-dimensional coupling mechanism analysis.

[0053] 2. This invention achieves deep synergistic optimization of the operational performance and lifespan degradation of modular energy storage systems. By solving a multi-objective optimization model that encompasses network operating costs and battery aging degradation, the optimal topology configuration and operational control strategy of the system can be adaptively obtained. This method, while meeting the system's power dispatch requirements, significantly reduces the uneven aging between modules caused by frequent topology switching and temperature differences, effectively overcoming the bottleneck effect caused by individual cell differences in traditional battery packs.

[0054] 3. This invention performs global time-series control of the topology reconfiguration process and dynamic electrothermal evolution, enabling the energy storage system to possess stronger proactive health management capabilities. Through refined collaborative control of each battery module, it not only avoids accelerated aging caused by local overheating or current surge sensitivity, but also greatly reduces unreasonable operational losses, ultimately significantly improving the overall service life, operational reliability, and long-term economic benefits of the modular energy storage system.

[0055] The present invention will now be described with reference to a specific embodiment.

[0056] In this embodiment, the optimized control method of the present invention is a modular energy storage lifetime optimization control method that considers topology reconfiguration and electrothermal degradation. It creates a new energy storage health management model that takes into account both dynamic electrical topology and underlying physical degradation mechanisms. Through dynamic connection reconfiguration between battery modules and network nodes, it proposes a multi-dimensional lifetime collaborative optimization architecture that integrates SOC balance, electrothermal coupling effect, and unbalanced aging. If the modular energy storage system adopts a traditional fixed series-parallel topology, its energy dispatch can only be carried out at the overall system level. Local battery modules in high-temperature environments or with low initial SOC are prone to accelerated aging due to local heat accumulation and continuous charge-discharge impact, thus becoming the bottleneck limiting the lifetime of the entire energy storage system. However, under the topology reconfiguration mechanism constructed in this invention, there is a dynamic connection relationship between battery modules. Each battery module is an independent and flexible energy carrier, which can realize equivalent transfer and dynamic allocation of cross-module power through topology switching. By combining the electrothermal coupling degradation mechanism with SOC balanced scheduling, the system can adaptively avoid damage to high-temperature sensitive modules caused by current, and fully balance thermal stress and state of charge in time and space. This not only expands the distribution channels of electrical energy among different modules, but also extends the coverage of system health management to the physical level of individual devices.

[0057] To enable those skilled in the art to better understand the present invention, the numerical analysis in the present invention includes the following components: Numerical simulation tests were conducted on a coupled system of an improved IEEE 33-bus system with a rated voltage of 12.66 kV and a transportation network containing 14 nodes. The system's safe voltage range was set to [0.95, 1.05] pu, and nodes 9, 13, 15, 25, and 32 were designated as modular mobile energy storage access nodes. Line loss coefficient... Set to 0.5; Battery module Unit capacity loss coefficient The system is set to 150; a total of 15 batteries and 4 cars are set up, with each car carrying a maximum of 4 batteries. Each battery has a capacity of 20 kWh and a maximum charging and discharging power of 10 kW. The ideal speed of the car is 60 km / h.

[0058] See Figure 3 , Figure 3 This is a schematic diagram of the charging and discharging power of each battery module provided in an embodiment of the present invention, wherein (a) is the charging power of each battery module and (b) is the discharging power of each battery module, and it can be seen that the charging and discharging power are all within the safe range.

[0059] See Figure 4 , Figure 4 This diagram illustrates the relative degradation trajectory of a battery module's health status over a 24-hour operating cycle, as provided in an embodiment of the present invention. All battery modules are initialized to 100% health and exhibit a clear non-linear, step-like decline over operating time, with daily SOH (State of Health) decreases ranging from 0.0016% to 0.0061%. Given that 80% SOH is generally considered the critical threshold for safe and reliable battery operation, based on the evaluation results of the model according to the present invention, the average usable lifespan of the battery module is approximately 14.23 years.

[0060] The results of the numerical examples show that the modular energy storage lifetime optimization control method proposed in this invention, which considers topology reconfiguration and electrothermal degradation, not only achieves the optimal topology configuration and dynamic energy distribution of the modular energy storage system under complex operating conditions, but also realizes the refined consideration and synergistic optimization of the SOC imbalance effect and electrothermal coupling degradation mechanism during the topology reconfiguration process. This effectively reduces the degree of uneven aging between battery modules, thereby greatly improving the overall lifetime and operational reliability of the energy storage system.

[0061] See Figure 5 , Figure 5 A schematic diagram of a lifespan optimization control system for an energy storage system that addresses topology reorganization and electrothermal degradation, provided as an embodiment of the present invention, specifically includes: The first building module 501 is used to build a modular energy storage topology reconfiguration model and a basic aging and degradation model; The second construction module 502 is used to construct a battery module composite aging and degradation model that considers the topology reorganization SOC imbalance and electrothermal coupling effect based on the modular energy storage topology reorganization model and the basic aging and degradation model. The execution module 503 is used to perform processing operations on the composite aging degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

[0062] Specifically, the first construction module includes: The first obtaining unit is used to obtain the connection status variables, charging and discharging flag variables, connection and disconnection action variables and corresponding topology switching constraints of the battery module and the node during a preset time period based on the preset node set, battery module set, operating cycle time set, equipment protection parameter topology switching dead zone and maximum number of switching times per cycle. The second obtaining unit is used to obtain the battery module's charging and discharging power, reactive power, battery module energy state and state of charge based on the connection state variable, the charging and discharging flag variable, the connection and disconnection action variable and the corresponding topology switching constraints, combined with the upper limit of charging and discharging power, the upper limit of reactive power, charging and discharging efficiency, the upper and lower limits of capacity, and the time step system parameters. The first generation unit is used to obtain the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, so as to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

[0063] Specifically, the second building module includes: The third obtaining unit is used to obtain the average state of charge of the node based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module. The fourth obtaining unit is used to obtain the temperature dynamic aging coefficient based on the node's average state of charge, combined with the battery's real-time temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature. The second generation unit is used to obtain the state of charge deviation aging increment, additional capacity loss, and updated cumulative capacity loss based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, so as to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effect.

[0064] Compared with existing technologies, this invention first constructs a modular energy storage topology reconfiguration model and a basic aging degradation model; then, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, it constructs a battery module composite aging degradation model that considers topology reconfiguration SOC imbalance and electrothermal coupling effects; finally, it performs processing operations on the battery module composite aging degradation model to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, thereby achieving optimized control of the modular energy storage system. It can solve the SOC imbalance problem existing in traditional modular energy storage systems during topology switching and the problem of accelerated battery module lifespan degradation caused by neglecting electrothermal coupling effects. While ensuring the operating performance of the energy storage system, it can greatly achieve synergistic optimization of lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by local overheating, and improving the overall service life and operational reliability of the energy storage system.

[0065] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to implement the steps in the above method embodiments when running.

[0066] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps: S201: Construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; S202: Based on the modular energy storage topology reconfiguration model and the basic aging degradation model, construct a battery module composite aging degradation model that considers the topology reconfiguration SOC imbalance and electrothermal coupling effect. S203: Perform processing operations on the composite aging degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

[0067] Specifically, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0068] Compared with existing technologies, this invention first constructs a modular energy storage topology reconfiguration model and a basic aging degradation model; then, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, it constructs a battery module composite aging degradation model that considers topology reconfiguration SOC imbalance and electrothermal coupling effects; finally, it performs processing operations on the battery module composite aging degradation model to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, thereby achieving optimized control of the modular energy storage system. It can solve the SOC imbalance problem existing in traditional modular energy storage systems during topology switching and the problem of accelerated battery module lifespan degradation caused by neglecting electrothermal coupling effects. While ensuring the operating performance of the energy storage system, it can greatly achieve synergistic optimization of lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by local overheating, and improving the overall service life and operational reliability of the energy storage system.

[0069] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps described in the method embodiments above.

[0070] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0071] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program: S201: Construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; S202: Based on the modular energy storage topology reconfiguration model and the basic aging degradation model, construct a battery module composite aging degradation model that considers the topology reconfiguration SOC imbalance and electrothermal coupling effect. S203: Perform processing operations on the composite aging degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

[0072] Compared with existing technologies, this invention first constructs a modular energy storage topology reconfiguration model and a basic aging degradation model; then, based on the modular energy storage topology reconfiguration model and the basic aging degradation model, it constructs a battery module composite aging degradation model that considers topology reconfiguration SOC imbalance and electrothermal coupling effects; finally, it performs processing operations on the battery module composite aging degradation model to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, thereby achieving optimized control of the modular energy storage system. It can solve the SOC imbalance problem existing in traditional modular energy storage systems during topology switching and the problem of accelerated battery module lifespan degradation caused by neglecting electrothermal coupling effects. While ensuring the operating performance of the energy storage system, it can greatly achieve synergistic optimization of lifespan loss, thereby reducing the degree of uneven aging between modules and the possibility of failures caused by local overheating, and improving the overall service life and operational reliability of the energy storage system.

[0073] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0074] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple 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 units may be electrical or other forms.

[0076] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0079] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the embodiments above are only for helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the lifetime control of an energy storage system by addressing topology reconfiguration and electrothermal degradation, applied to a modular energy storage system, wherein the modular energy storage system comprises multiple independently switchable battery modules, each battery module being dynamically connected to a common node via a power electronic switch array to achieve topology reconfiguration, characterized in that... The method includes: Construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; Based on the modular energy storage topology reconfiguration model and the basic aging degradation model, a composite aging degradation model for battery modules considering topology reconfiguration SOC imbalance and electrothermal coupling effects is constructed. The battery module composite aging degradation model is processed to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to achieve the optimized control of the modular energy storage system.

2. The method according to claim 1, characterized in that, The construction of the modular energy storage topology reconfiguration model and the basic aging and degradation model includes: Based on the preset node set, battery module set, operating cycle time set, equipment protection parameter topology switching dead zone, and maximum number of switching times per cycle, the connection status variables, charging / discharging flag variables, connection / disconnection action variables, and corresponding topology switching constraints of the battery modules and nodes during the preset time period are obtained. Based on the connection state variables, the charge / discharge flag variables, the connection / disconnection action variables, and the corresponding topology switching constraints, combined with the upper limit of charge / discharge power, the upper limit of reactive power, the charge / discharge efficiency, the upper and lower limits of capacity, and the time step system parameters, the battery module charge / discharge power, reactive power, battery module energy state, and state of charge are obtained. Based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss are obtained to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

3. The method according to claim 2, characterized in that, The modular energy storage topology reconfiguration model includes a charging and discharging model for modular energy storage.

4. The method according to claim 3, characterized in that, The step involves constructing a composite aging and degradation model for battery modules, considering topology reconfiguration SOC imbalance and electrothermal coupling effects, based on the modular energy storage topology reconfiguration model and the basic aging and degradation model. This includes: The average state of charge of the node is obtained based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module. Based on the average state of charge of the nodes, combined with the real-time battery temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature, the temperature dynamic aging coefficient is obtained. Based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, and combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, the aging increment of state of charge deviation, additional capacity loss, and updated cumulative capacity loss are obtained to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effects.

5. The method according to claim 4, characterized in that, The processing operation of the composite aging degradation model of the battery module includes: The objective function of the composite aging degradation model of the battery module is determined, and the constraints are transformed into second-order cone programming constraints through linearization and cone relaxation.

6. A topology reconfiguration and electrothermal degradation lifespan optimization control system for an energy storage system, applied to a modular energy storage system, wherein the modular energy storage system comprises multiple independently switchable battery modules, each battery module being dynamically connected to a common node via a power electronic switch array to achieve topology reconfiguration, characterized in that... The system includes: The first building module is used to construct a modular energy storage topology reconfiguration model and a basic aging and degradation model; The second construction module is used to construct a composite aging and degradation model of battery modules that considers the SOC imbalance and electrothermal coupling effect of topology reorganization based on the modular energy storage topology reorganization model and the basic aging and degradation model. The execution module is used to perform processing operations on the composite aging and degradation model of the battery module to obtain the optimal topology configuration result and all-time operation control strategy of the modular energy storage system, so as to realize the optimized control of the modular energy storage system.

7. The system according to claim 6, characterized in that, The first building module includes: The first obtaining unit is used to obtain the connection status variables, charging and discharging flag variables, connection and disconnection action variables and corresponding topology switching constraints of the battery module and the node during a preset time period based on the preset node set, battery module set, operating cycle time set, equipment protection parameter topology switching dead zone and maximum number of switching times per cycle. The second obtaining unit is used to obtain the battery module's charging and discharging power, reactive power, battery module energy state and state of charge based on the connection state variable, the charging and discharging flag variable, the connection and disconnection action variable and the corresponding topology switching constraints, combined with the upper limit of charging and discharging power, the upper limit of reactive power, charging and discharging efficiency, the upper and lower limits of capacity, and the time step system parameters. The first generation unit is used to obtain the normalized discharge power, the basic capacity loss over a preset period, and the cumulative capacity loss based on the battery module's charge and discharge power, reactive power, battery module energy state and state of charge, combined with the battery module's capacity limit and piecewise linear aging coefficient, so as to generate a modular energy storage topology reconfiguration model and a basic aging degradation model.

8. The system according to claim 7, characterized in that, The second building module includes: The third obtaining unit is used to obtain the average state of charge of the node based on the connection state variables of the battery module and the node during a preset time period and the state of charge of the battery module. The fourth obtaining unit is used to obtain the temperature dynamic aging coefficient based on the node's average state of charge, combined with the battery's real-time temperature, electrochemical reaction activation energy, ideal gas constant, and reference temperature. The second generation unit is used to obtain the state of charge deviation aging increment, additional capacity loss, and updated cumulative capacity loss based on the connection state variables, battery module state of charge, node average state of charge, and temperature dynamic aging coefficient, combined with the normalized discharge power of the battery module at a preset time and the cumulative capacity loss of the previous preset period, so as to generate a battery module composite aging degradation model that considers topology reorganization SOC imbalance and electrothermal coupling effect.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to implement the method of any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the method of any one of claims 1 to 5.

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