Energy storage system battery topology generation method and system based on reconfigurable battery network

Abstract

The invention discloses an energy storage system battery topology generation method and system based on a reconfigurable battery network, and relates to the field of energy storage systems, and the method comprises the steps: obtaining a reconfigurable battery network of an energy storage system battery; establishing a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network; the Thevenin equivalent circuit model is utilized to determine an equivalent circuit of the single batteries in the reconfigurable battery network; key parameters of the battery state are determined based on the equivalent circuit and the reconfigurable battery network topology optimization control framework; and according to the key parameters, dynamic balance control optimization modeling is carried out on the continuous time of the reconfigurable battery network, and a battery network topology optimization generation strategy based on the key state parameters is realized. According to the invention, an efficient energy storage system collaborative optimization operation strategy can be generated, differentiated management and control of the battery are realized, the system'short plate effect 'is avoided, and safe and reliable operation of the battery energy storage system is ensured.

Description

Technical Field

[0001] The present application relates to the field of energy storage systems, and in particular, to a method and system for generating a battery topology of an energy storage system based on a reconfigurable battery network. Background Art

[0002] With the increasing demand for large-scale applications of large-capacity battery energy storage systems, battery cells with low voltage and capacity levels are usually fixed in series and parallel in the order of "millions". The common practice is to first connect battery cells in series and parallel to form a battery module, the battery modules are connected in series and parallel to form a battery cluster, and then connected in series and parallel on the AC side through an energy storage power conversion system to increase the voltage and current levels of the energy storage system. To improve the consistency level of battery modules in the energy storage system, the energy storage system usually requires additional hardware circuits to manage the charge and discharge balance of the batteries, and a large number of balancing hardware circuits and control methods have been proposed.

[0003] In traditional active or passive balancing methods, the balancing circuit is directly welded to the battery module, which makes the system have too large a granularity of battery control, unable to effectively balance and isolate the faulty module, resulting in low efficiency and the risk of accident spread. When domestic and foreign scholars engage in research on the optimal operation strategy of the energy storage system, they usually start from the perspectives of economy and the overall output of the energy storage system, and achieve the economic and reliable operation of the energy storage system in the power grid through capacity configuration optimization, multi-class resource collaborative optimization, etc. The research on the internal operation control of the battery energy storage system mainly focuses on the research of the PCS control strategy, aiming to achieve safe and stable bidirectional energy transfer between the battery energy storage system and the AC power grid: among them, there are literatures that have deeply studied the commutation suppression strategy when the energy storage system is in parallel / grid-isolated operation, and literatures that have studied the battery energy storage constant voltage / frequency control strategy by designing a reasonable controller. However, due to the fixed welding connection method of battery modules in traditional battery energy storage systems, the above research strategies rarely involve the collaborative optimization of battery cells inside the battery module.

[0004] The technology of dynamic reconfigurable battery network (DRBN) effectively makes up for the theoretical lack of optimizing control of the internal connection topology in battery modules of large-scale battery energy storage systems, providing a new technical idea for the safe and efficient operation of large-scale battery energy storage systems. Based on the flexible adjustment of the battery network topology at the battery module level, constraint conditions such as the reliability and safety of battery cells can be incorporated into the research of operation strategies. The precise control and safety isolation at the battery module level can effectively reduce the operation safety risks of the energy storage system in case of emergencies and provide active safety prevention measures for the real-time operation of the battery energy storage system. The dynamic reconfigurable battery network technology can also achieve differential management at the battery module level, reasonably distribute the charge and discharge current and voltage among the batteries, so as to achieve fast and efficient battery equalization, and further improve the service life of the energy storage system.

[0005] The design and operation of large-scale reconfigurable battery networks are essentially a dynamic optimization and control problem of complex networks. The dynamic generation of the optimal topology of the battery network is an important factor affecting the characteristics of large-scale battery networks. The dynamic reconfigurable battery network can form control strategies for different application requirements by changing the series-parallel topological connections of the batteries. Based on the graph-based dynamic reconfigurable battery network topology architecture, N battery modules form a parallel control structure through controllable switches, M parallel units are connected in series to form a battery cluster, and K battery clusters form a battery energy storage system. By controlling the states of the switches, the connection topology structure between the battery units can be dynamically reconfigured to form the optimal topology connection strategy under different control objectives.

[0006] The literature proposed an optimization framework for DRBN and verified the equalization effect of DRBN on battery modules; the literature proposed a fast equalization method for lithium battery packs based on the DRBN structure, but the network topology is complex and the reliability is not high; the literature proposed a strategy to accelerate the charging equalization of a series battery system by using dynamic reconfiguration; the literature proposed a reconfiguration topology composed of single batteries in series, which is simple and easy to implement, but lacks the modeling and quantitative analysis of the network, and when the branch capacity requirement is very large, it causes too much switch redundancy. To sum up, how to generate an efficient cooperative optimization operation strategy for the energy storage system through the coupled cooperative control of battery modules and power electronic topologies, realize the differential management of batteries and avoid the "short board effect" of the system is an urgent problem to be solved for the safe and reliable operation of the battery energy storage system. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for generating the battery topology of an energy storage system based on a reconfigurable battery network, which can generate an efficient cooperative optimization operation strategy for the energy storage system, realize the differential management of batteries and avoid the "short board effect" of the system, and ensure the safe and reliable operation of the battery energy storage system.

[0008] To achieve the above object, the present application provides the following solutions:

[0009] A method for generating a battery topology of an energy storage system based on a reconfigurable battery network, comprising:

[0010] Obtaining the reconfigurable battery network of the energy storage system battery;

[0011] Establishing a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network;

[0012] Using the Thevenin equivalent circuit model to determine the equivalent circuit of the battery cells in the reconfigurable battery network;

[0013] Determining the key parameters of the battery state based on the equivalent circuit and the reconfigurable battery network topology optimization control framework;

[0014] According to the key parameters, performing dynamic equalization control optimization modeling on the continuous time of the reconfigurable battery network to realize a battery network topology optimization generation strategy based on the key state parameters.

[0015] Optionally, the reconfigurable battery network topology optimization control framework satisfies the load current I of the reconfigurable battery network L and the output voltage U o On the basis of, the topology connection control variable Z of the reconfigurable battery network is optimized and controlled.

[0016] Optionally, the using the Thevenin equivalent circuit model to determine the equivalent circuit of the battery cells in the reconfigurable battery network specifically includes:

[0017] Determining the equation of the key parameters of the battery state in continuous time according to the equivalent circuit;

[0018] Using the extended Kalman filter to determine the discrete state equation and the observation equation corresponding to the equation in continuous time;

[0019] Based on the joint estimation of the key parameters of the battery state corresponding to the extended Kalman filter and the SOC, the battery SOC is determined.

[0020] Optionally, the based on the joint estimation of the key parameters of the battery state corresponding to the extended Kalman filter and the SOC to determine the battery SOC specifically includes the following formula:

[0021]

[0022] where P k is the estimation error covariance matrix, E is the identity matrix, is the estimation error covariance matrix before the start of the k-th moment, is the estimation error covariance matrix after the start of the k-th moment, Gk-1 is the feature matrix decomposed at time k-1, is the estimated error covariance matrix starting from time k-1, is the transpose matrix of the feature matrix decomposed at time k-1, Q is the error matrix, is the error transfer function for the observed values after time k-1, are the observed values after time k-1, are the observed values before time k, K k is the feature matrix decomposed at time k, H k is a positive definite Hamiltonian matrix, is the transpose of the positive definite Hamiltonian matrix, R is the error matrix, are the observed values after time k, y k is the estimated value after time k.

[0023] Optionally, the dynamic equilibrium control optimization modeling of the reconfigurable battery network in continuous time is performed according to the key parameters to implement the battery network topology optimization generation strategy based on the key state parameters, specifically including:

[0024] Establish an objective function that minimizes the sum of the product of the absolute value of the remaining SOC and voltage deviation and the scaling factor during charging, and minimizes the sum of the product of the negative remaining SOC during discharging.

[0025] Optionally, the objective function is:

[0026]

[0027] The constraint conditions are:

[0028]

[0029] Among them, S ocij (τ m-1 ) is the change in the SOC of a single cell within the time interval τ m-1 , z ij (τ m ) is the switching state of the battery module in the i-th row and j-th column within the time interval τ m , ρ is the discharge rate, τ m is the time interval τ m , λ is the absolute value of the remaining SOC and voltage deviation and the scaling factor during charging, U re is the cluster target output voltage, e rr is the maximum allowable deviation, u o is the terminal voltage value, z i，j (τ m ) is within the time interval τ mThe switching state of the battery module in the i-th row and j-th column during the time interval, L(i, j) is the lower limit of the state of charge (SOC) of the battery module in the i-th row and j-th column, U(i, j) is the upper limit of the SOC of the battery module in the i-th row and j-th column, S ocij (τ m ) is the SOC of the battery module during τ m time interval, U o (τ m ) is the terminal voltage value of the battery module during τ m time interval, γ ij is the coordinate of the battery module in the i-th row and j-th column, O cvij is the open-circuit voltage of the battery module in the i-th row and j-th column, U o is the output voltage, D(i, j) is the loss of charge capacity of the battery module in the i-th row and j-th column during τ m time interval.

[0030] A battery topology generation system for an energy storage system based on a reconfigurable battery network, comprising: a memory for storing a software control program; the software control program is used to implement the battery topology generation method for an energy storage system based on a reconfigurable battery network;

[0031] A processor, connected to the memory, for retrieving and executing the software control program.

[0032] Optionally, the memory includes:

[0033] An acquisition module for acquiring the reconfigurable battery network of the energy storage system battery;

[0034] A control framework establishment module for establishing a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network;

[0035] An equivalent circuit determination module for determining the equivalent circuit of the battery cells in the reconfigurable battery network using the Thevenin equivalent circuit model;

[0036] A key parameter determination module for determining the key parameters of the battery state based on the equivalent circuit and the reconfigurable battery network topology optimization control framework;

[0037] A battery network topology optimization generation module for dynamically balancing and controlling and optimizing the modeling of the continuous time of the reconfigurable battery network according to the key parameters, and realizing a battery network topology optimization generation strategy based on the key state parameters.

[0038] Optionally, the memory is a computer-readable storage medium.

[0039] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0040] A method and system for generating a battery topology of an energy storage system based on a reconfigurable battery network. Based on the advantage that the open-circuit voltage of the battery can be measured for the reconfigurable battery network, a battery network topology optimization control architecture and a battery equivalent circuit model based on the reconfigurable battery network are constructed, and key parameters of the battery state are obtained, so as to realize a battery network topology optimization generation strategy based on the key state parameters. The proposed method for generating a battery topology of an energy storage system based on a reconfigurable battery network aims to be able to online observe the state parameters during battery operation and estimate the real-time operating state of the battery, and formulate a generation strategy for the battery topology through an optimization decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 Schematic diagram of the process of a method for generating a battery topology of an energy storage system based on a reconfigurable battery network provided by the present application;

[0043] Figure 2 Schematic diagram of a reconfigurable battery network;

[0044] Figure 3 Schematic diagram of the topology optimization control framework of a reconfigurable battery network;

[0045] Figure 4 Schematic diagram of the equivalent circuit of a single battery cell;

[0046] Figure 5 Schematic diagram of the numbering of series-connected unit batteries and switches in a cluster;

[0047] Figure 6 For U re Schematic diagram of the relationship curve between the battery SOC and the number of reconstructions (discharge) when = 85V;

[0048] Figure 7 For U re Schematic diagram of the relationship curve between the battery SOC and the number of reconstructions (discharge) when = 90V;

[0049] Figure 8 Schematic diagram of the relationship curve between the output voltage and the number of reconstructions (discharge) when σ1 = 0.05;

[0050] Figure 9 Schematic diagram of the relationship curve between the output voltage and the number of reconstructions (discharge) when σ1 = 0.2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0052] The purpose of the present application is to provide a method and system for generating a battery topology of an energy storage system based on a reconfigurable battery network, which can generate an efficient collaborative optimization operation strategy for the energy storage system, realize the differential control of the battery, avoid the "short board effect" of the system, and ensure the safe and reliable operation of the battery energy storage system.

[0053] To make the above objects, features, and advantages of the present application more obvious and understandable, the following further describes the present application in detail with reference to the accompanying drawings and specific embodiments.

[0054] In the present application, the terminal voltage U and terminal current I of the batteries in the DRBN are measured, that is, M = {U, I} is measured, and the joint estimation of the parameters and states of the batteries in the network is performed, including the state of charge (SOC) of the batteries and the resistance and capacitance parameters, that is, the state and parameters S = {R0, R1, C1, SOC}. On the basis of satisfying the load current I L and the output voltage Uo, the topology connection control variable Z of the reconfigurable battery network is optimized and controlled to generate a battery network topology generation scheme that meets the optimization objectives and constraint conditions.

[0055] Based on this optimization control framework, first, a method for estimating the battery state and extracting key parameters based on the reconfigurable battery network and the equivalent circuit model is studied. Then, the balance between the battery modules in the energy storage system is taken as the optimization objective, so that the state S = {R0, R1, C1, SOC} of the energy storage system satisfies the physical constraints, and the battery topology structure of the energy storage system is found from the optimization feasible region. Finally, the balance effect of the algorithm is verified through a hardware-in-the-loop simulation platform.

[0056] As Figure 1 shown, a method for generating a battery topology of an energy storage system based on a reconfigurable battery network provided by the present application includes:

[0057] S101, obtaining a reconfigurable battery network of the energy storage system battery, as Figure 2 shown; in the reconfigurable battery network, the battery modules can be disconnected from the energy storage system, and the electrical parameters such as the terminal voltage and current of the battery modules can be actually measured.

[0058] S102, establishing a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network, asFigure 3 as shown; the reconfigurable battery network topology optimization control framework satisfies the load current I of the reconfigurable battery network L and the output voltage U o On this basis, the topology connection control variable Z of the reconfigurable battery network is optimized and controlled.

[0059] S103. Use the Thevenin equivalent circuit model to determine the equivalent circuit of the battery cells in the reconfigurable battery network, and as Figure 4 shown;

[0060] S103 specifically includes:

[0061] Determine the equations of the key parameters of the battery state over continuous time according to the equivalent circuit:

[0062]

[0063] Among them, the open-circuit voltage U of the battery ocv is a variable related to the initial state of charge SOC0 and the dynamic state of charge SOC(t) of the battery, and can be expressed by the ampere-hour integration method at the same time. The current i(t) of the battery cell is affected by the battery polarization capacitance C1. C1 is the equivalent capacitance value of the battery module, R1 is the equivalent resistance value of the battery module, η is the energy conversion efficiency of the battery module, and u c (t) is the voltage value of the equivalent capacitance of the battery module.

[0064] Use the extended Kalman filter to determine the discrete state equation and observation equation corresponding to the equation over continuous time:

[0065]

[0066] Among them, S N is the rated capacity of the battery, I1 is the polarization resistance current, η is the charge and discharge efficiency of the battery, and β k = exp(-1 / τ(k)), τ = R1C1 is the battery time constant. Usually, the OCV of the battery is the terminal voltage under the condition of long-term battery rest, and it has a relatively fixed functional relationship with the battery SOC, denoted as OCV = f(SOC). q = N(0, Q), r = N(0, R) are Gaussian white noises.

[0067] Based on the joint estimation of the key parameters of the battery state corresponding to the extended Kalman filter and the SOC, determine the battery SOC.

[0068] Record the first five lines of the discrete state equation and the observation equation as: x k = g k-1 (x k-1 )+q k-1 ; Record the sixth line as: y k = hk (x k ) + r k 。x = [I1 S oc R0 C1 R1], y = U, G k = g k ′(x)|x = x k , H k = h k ′(x)|x = x k 。

[0069] Specifically, it includes the following formulas:

[0070]

[0071] Among them, P k is the estimated error covariance matrix, E is the identity matrix, is the estimated error covariance matrix before the start of time k, is the estimated error covariance matrix after the start of time k, G k-1 is the eigenmatrix decomposed at time k - 1, is the estimated error covariance matrix after the start of time k - 1, is the transpose matrix of the eigenmatrix decomposed at time k - 1, Q is the error matrix, is the error transfer function for the observed values after time k - 1, is the observed value after time k - 1, is the observed value before the start of time k, K k is the eigenmatrix decomposed at time k, H k is a positive definite Hamiltonian matrix, is the transpose of the positive definite Hamiltonian matrix, R is the error matrix, is the observed value after the start of time k, y k is the estimated value after the start of time k.

[0072] S104. Determine the key parameters of the battery state based on the equivalent circuit and the reconfigurable battery network topology optimization control framework;

[0073] S105. According to the key parameters, perform dynamic equilibrium control optimization modeling on the continuous time of the reconfigurable battery network to realize the battery network topology optimization generation strategy based on the key state parameters.

[0074] To facilitate the labeling of the battery module numbers in the dynamic reconfigurable battery network energy storage system, the Figure 5 batteries and switches in each series unit of the reconfigurable battery network cluster in

[0075] When the battery cluster is in operation, it needs to follow the continuous-time control constraints. Without loss of generality, assume that the switch is an ideal switch when establishing the continuous-time model of the cluster under this operation strategy. If switch s j is closed, then switch s ij , i is disconnected from 1 to m, and the output voltage of the j-th column of batteries is 0. If switch s j is disconnected, then switch s ij , and there is exactly one closed i from 1 to m. In actual operation, the fluctuation of the port output voltage of the dynamically reconfigurable battery network needs to be controlled within a certain range, that is:

[0076]

[0077] Among them, U re is the target output voltage of the cluster, and err is the maximum value of the allowable deviation. The selection of U re is related to the properties of the cluster, such as the number of reconfigurable batteries in the cluster, the external characteristics of the batteries, etc. U re should be an optimal estimation problem that satisfies a certain utilization rate of the batteries in the cluster. However, in order to reduce the complexity of the problem, its selection can adopt an engineering simplification method: U re =βU max . U max is the maximum value of the voltage that the cluster can output under the rated state, and U re can be estimated through an algorithm, and the simplification method will be given in the subsequent model solution.

[0078] In order to make each battery module in the battery cluster achieve the purpose of SOC balance on the premise of maintaining the output voltage basically unchanged, so that it can be charged and discharged simultaneously, the objective function can be the sum of the SOC variance of the battery and the voltage deviation multiplied by the proportionality factor λ. From the above four control principles and assumptions, the dynamic optimization model of the battery cluster can be obtained:

[0079]

[0080]

[0081] Among them, z ij is the switch state, taking values {0,1}; L(i,j), U(i,j) are the charge / discharge cut-off SOCs of the battery module in the i-th row and j-th column of the m-series and n-parallel battery cluster. The above problem is a large-scale non-linear dynamic optimization problem, f ij is a non-linear function and contains a large number of 0, 1 variables. Therefore, it is difficult to obtain an analytical solution to the above problem.

[0082] In the given dynamically reconfigurable battery network, the method given can calibrate the numerical values of the available capacity, SOC, and resistance-capacitance parameters of the battery, which are denoted as SN ij , SOCij (0), R ij (0) and C ij (0). During a relatively short time interval, the change in SOC is relatively small. There are many factors affecting the available capacity and the resistance-capacitance parameters of the battery, such as the number of battery cycles, operating temperature, state of charge, etc. The available capacity SN of the battery ij is a constant within a fixed time interval. The resistance-capacitance parameters R and C can be considered as functions of SOC and temperature T. The modeling of the battery charge amount can be:

[0083] O cvij (τ m+1 ) ≈ O cvij (τ m ) - k ij (τ m )ΔS ocij (τ m );

[0084] Among them, Q represents the charge amount of the battery, and τ is the discrete time interval.

[0085] Due to the differences in the battery capacities in the battery cluster, when the same charge amount flows through, there are differences in the battery SOC changes. A battery capacity difference matrix is constructed:

[0086]

[0087] Among them, S norm is the standard capacity of a single battery cell, taken as the average value of the sum of the capacities of all battery cells in the selected cluster. The discharge rate ρ is defined as:

[0088]

[0089] In the case where the same amount of electricity Q flows through each unit in the network, the SOC change of each single cell can be expressed as:

[0090] S ocij (τ m+1 ) = S ocij (τ m ) - ρD(i, j);

[0091] Usually, during the actual operation process, the current flowing through the battery network i(t) changes continuously, and there are no sudden changes in the change of i(t). Therefore, when Q is selected reasonably, the reconstruction time interval of the battery network is in the order of seconds or minutes. Assume that the j-th column of series cells is not bypassed, then the output voltage of the j-th column of series cells is u j (t), and within time, the value of u j (t) is basically unchanged, that is, u j(t)≈u j (τ m ). Through the system hardware design, the transient process of the capacitor and the voltage change caused by the transient process of the capacitor can be ignored. Therefore, in the τ time slot, the output voltage of the battery is approximately:

[0092]

[0093] The objective function is established to minimize the sum of the product of the absolute value of the remaining SOC and the voltage deviation and the proportional factor during charging, and minimize the sum of the product of the negative remaining SOC and the voltage deviation during discharging. By converting the complex nonlinear optimization model of continuous time into a linear {0,1} programming model of discrete time (the absolute value objective function can be converted to linear), when the battery network is running, the objective function, constraints and recursive relationship of the optimization problem are summarized as follows:

[0094]

[0095] The constraints are:

[0096]

[0097] Among them, S ocij (τ m-1 ) is τ m-1 The SOC change of the monomer within the time interval, z ij (τ m ) is τ m The switch state of the battery module in the i-th row and j-th column in the time interval, ρ· is the discharge rate, τ m is τ m time gap, λ is the absolute value and proportional factor of the remaining SOC and voltage deviation during charging, U re is the cluster target output voltage, e rr is the maximum allowable deviation, u o is the terminal voltage value, z i，j (τ m ) is τ m The switch state of the battery module in the i-th row and j-th column in the time interval, L(i, j) is the SOC lower limit value of the battery module in the i-th row and j-th column, U(i, j) is the SOC upper limit value of the battery module in the i-th row and j-th column, S ocij (τ m ) is τ m Battery module SOC in the time interval, U o (τ m ) is τ m The voltage value of the battery module terminal during the time interval, γ ij is the coordinate of the battery module in the i-th row and j-th column, O cvij is the open circuit voltage of the battery module in the i-th row and j-th column, Uo is the output voltage, and D(i, j) is the charge capacity loss of the battery module in the i-th row and j-th column during the τ m time interval.

[0098] An experimental verification environment was built using the retired power battery modules of Yangtze River pure electric vehicles. The battery modules used single-cell lithium batteries with a voltage of 3.2V and a capacity of 13.5Ah. The smallest electromagnetic module was formed by 5 series and 6 parallel connections, and the nominal voltage of each battery module was 16V. The system consisted of 69 battery modules connected in 23 series and 3 parallel, with a total of 2070 single-cell lithium batteries.

[0099] During operation, the SOC-OCV fitting function was segmented and linearized, divided into 80 segments according to the SOC from 0.1 to 0.9. During the experiment, the battery modules were tested, and the ambient temperature was controlled at 25 °C. During the experiment, an Arbin device was used to load current on the battery and record parameters such as voltage, current, and temperature. The average data recording interval was 1 s.

[0100] Without loss of generality, in this experiment, the SOC upper limit U = 0.9 and the SOC lower limit L = 0.2 of all battery modules were set; the maximum deviation err of the battery network output voltage from the load voltage Ure was taken as 0.05, and the scaling factor λ was flexibly selected as 1. Taking the equalization effect of all battery modules during the discharge process of the battery energy storage system as the main research object, the effects of different load demands Ure and the difference σ of the initial battery modules on the equalization effect of the energy storage system were verified respectively. The discharge rate ρ = 0.01 was set, and a battery topology structure of 1P40S (n = 40, m = 1) was selected. The statistical average value μ of the SOC of the battery modules was measured to be 0.65, and the statistical standard deviation σ was 0.05. Two different load demands were set, that is, Ure was set to 90V and 85V respectively, and the equalization effect of the battery modules in the energy storage system was verified by measuring the reconfigurable battery network topology under different load voltage demand conditions.

[0101] The experimental results of the curve of the battery module SOC versus the number of reconstructions are as Figure 6 、 Figure 7 shown. The experimental results show that on the premise of keeping the system output voltage unchanged, the equalization time of the battery cluster experienced about 50 reconstructions, and the SOC between the battery modules achieved good equalization; at the same time, it was verified that the system had good adaptability to the 85V load voltage and could achieve the equalization of the battery modules during 30 reconstructions.

[0102] To verify the compatibility of the energy storage system with the differences of battery modules, the discharge rate ρ is set to 0.01, the load demand voltage Ure is taken as 90V, and the battery topology structure of 1P40S (n = 40, m = 1) is selected. The statistical average value μ of the SOC of the same battery module is 0.65, and the different statistical standard deviations σ1 is 0.05 and σ2 is 0.2 are respectively tested for their influence on the equalization effect of the energy storage system. As the number of reconstructions increases, the SOC statistics of the battery modules are as Figure 8 、 Figure 9 shown. The experimental results show that as the differences of the battery modules increase, under the equalization control effect of the system on the battery modules, the equalization of all battery modules can be achieved. And through reasoning, if the statistical standard deviation σ of the battery modules increases and better equalization effect is desired at the same time, the output voltage of the cluster needs to be reduced.

[0103] In response to the trend of energy integration in the digital and information-based power supply system of 5G information energy, this application proposes a method for co-frequency processing of energy flow and information flow based on a reconfigurable battery network, a method for physically discretizing and digitizing analog / continuous energy flow, and a modeling method for components including batteries, power electronics, loads, etc. At the same time, this application takes the 5G information energy system as the research object, conducts digital research on battery cells, battery modules, and the coupling of batteries and power electronics, and analyzes the characteristics of batteries, battery modules, and the coupling of batteries and power electronics. On this basis, this application studies the abstract modeling and electrical modeling methods of reconfigurable battery networks, analyzes the information attributes contained in the energy flow, and realizes the resource management and control of the energy network, studies the information management and control method of energy slices, and realizes the deep integration of energy information.

[0104] To execute the method corresponding to the above embodiments to achieve the corresponding functions and technical effects, a battery topology generation system for an energy storage system based on a reconfigurable battery network provided by this application includes: a memory for storing a software control program; the software control program is used to implement the method for generating the battery topology of an energy storage system based on a reconfigurable battery network;

[0105] a processor connected to the memory for retrieving and executing the software control program.

[0106] The memory includes:

[0107] an acquisition module for acquiring the reconfigurable battery network of the energy storage system battery;

[0108] a control framework establishment module for establishing a topology optimization control framework for the reconfigurable battery network based on the reconfigurable battery network;

[0109] an equivalent circuit determination module for determining the equivalent circuit of the battery cells in the reconfigurable battery network using the Thevenin equivalent circuit model;

[0110] A key parameter determination module, configured to determine key parameters of the battery state based on an equivalent circuit and a reconfigurable battery network topology optimization control framework;

[0111] A battery network topology optimization generation module, configured to perform dynamic equilibrium control optimization modeling on the continuous time of the reconfigurable battery network according to the key parameters, so as to implement a battery network topology optimization generation strategy based on key state parameters.

[0112] The memory is a computer-readable storage medium.

[0113] Based on the above description, the technical solution of the present application, in essence or the part that contributes to the prior art or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The foregoing computer storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks or optical discs that can store program codes.

[0114] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0115] Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for generating battery topology of an energy storage system based on a reconfigurable battery network, characterized in that: include: Obtain a reconfigurable battery network of energy storage system batteries; Establish a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network; The Thevenin equivalent circuit model is used to determine the equivalent circuit of battery cells in the reconfigurable battery network; Determine the key parameters of battery status based on equivalent circuit and reconfigurable battery network topology optimization control framework; According to the key parameters, the dynamic balancing control optimization modeling of the reconfigurable battery network is carried out continuously, and the battery network topology optimization generation strategy based on the key state parameters is realized.

2. According to claim 1, a method for generating battery topology of an energy storage system based on a reconfigurable battery network is characterized in that: The reconfigurable battery network topology optimization control framework satisfies the reconfigurable battery network load current I L And the output voltage U o Based on this, the topological connection control variable Z of the reconfigurable battery network is optimized and controlled.

3. According to claim 1, a method for generating battery topology of an energy storage system based on a reconfigurable battery network is characterized in that: The method of determining the equivalent circuit of a battery cell in a reconfigurable battery network by using the Thevenin equivalent circuit model specifically includes: Determine the equations for the key parameters of the battery state in continuous time based on the equivalent circuit; Use extended Kalman filtering to determine the discrete state equation and observation equation corresponding to the equation in continuous time; The battery SOC is determined by jointly estimating the key parameters of the battery state corresponding to the extended Kalman filter and the SOC.

4. A method for generating battery topology of an energy storage system based on a reconfigurable battery network according to claim 3, characterized in that: The key parameters of the battery state corresponding to the extended Kalman filter and the SOC are jointly estimated to determine the battery SOC, specifically including the following formula: Among them, P k is the estimated error covariance matrix, E is the identity matrix, is the estimated error covariance matrix before time k, is the estimated error covariance matrix after time k, G k-1 is the characteristic matrix decomposed at k-1 time, is the estimated error covariance matrix after k-1 time, is the transposed matrix of the characteristic matrix decomposed at time k-1, Q is the error matrix, is the error transfer function for the observations after k-1 moments, is the observed value after k-1 moments, is the observed value before time k, K k is the characteristic matrix decomposed at time k, H k is a positive definite Hamiltonian matrix, is the transpose of the positive definite Hamiltonian matrix, R is the error matrix, is the observed value after time k, y k is the estimated value after the start of time k.

5. The method for generating battery topology of an energy storage system based on a reconfigurable battery network according to claim 1, characterized in that: According to the key parameters, the dynamic balancing control optimization modeling of the reconfigurable battery network is carried out continuously for a certain period of time, and the battery network topology optimization generation strategy based on the key state parameters is realized, which specifically includes: An objective function is established to minimize the sum of the product of the absolute value of the remaining SOC and the voltage deviation and the proportional factor during charging, and to minimize the sum of the negative remaining SOC and the product during discharging.

6. A method for generating battery topology of an energy storage system based on a reconfigurable battery network according to claim 5, characterized in that: The objective function is: The constraints are: Among them, S ocij (τ m-1 ) is τ m-1 The SOC change of the monomer within the time interval, z ij (τ m ) is τ m The switch state of the battery module in the i-th row and j-th column during the time interval, ρ · is the discharge rate, τ m is τ m time gap, λ is the absolute value and proportional factor of the remaining SOC and voltage deviation during charging, U re is the cluster target output voltage, e rr is the maximum allowable deviation, u o is the terminal voltage value, z i，j (τ m ) is τ m The switch state of the battery module in the i-th row and j-th column in the time interval, L(i, j) is the SOC lower limit value of the battery module in the i-th row and j-th column, U(i, j) is the SOC upper limit value of the battery module in the i-th row and j-th column, S ocij (τ m ) is τ m Battery module SOC in the time interval, U o (τ m ) is τ m The voltage value of the battery module terminal during the time interval, γ ij is the coordinate of the battery module in the i-th row and j-th column, O cvij is the open circuit voltage of the battery module in the i-th row and j-th column, U o is the output voltage, D(i, j) is τ m The battery module in the i-th row and j-th column loses charge capacity during the time interval.

7. A battery topology generation system for an energy storage system based on a reconfigurable battery network, characterized in that: include: A memory for storing a software control program; The software control program is used to implement a method for generating a battery topology of an energy storage system based on a reconfigurable battery network as described in any one of claims 1 to 6; A processor is connected to the memory and is used to retrieve and execute the software control program.

8. The battery topology generation system for an energy storage system based on a reconfigurable battery network according to claim 7, characterized in that: The memory comprises: An acquisition module for acquiring a reconfigurable battery network of batteries of an energy storage system; Control framework establishment module, English establishes a reconfigurable battery network topology optimization control framework based on the reconfigurable battery network; An equivalent circuit determination module, used to determine the equivalent circuit of a battery cell in a reconfigurable battery network using a Thevenin equivalent circuit model; A key parameter determination module is used to determine the key parameters of the battery state based on an equivalent circuit and a reconfigurable battery network topology optimization control framework; The battery network topology optimization generation module is used to perform dynamic balancing control optimization modeling of the reconfigurable battery network in continuous time according to key parameters, and realize the battery network topology optimization generation strategy based on key state parameters.

9. The energy storage system battery topology generation system based on a reconfigurable battery network according to claim 7, characterized in that: The memory is a computer-readable storage medium.

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