Fast calculation method and system of reconfigurable battery network state quantity under time-varying working condition

Abstract

The application discloses a fast calculation method and system of a reconfigurable battery network state quantity under time-varying working conditions, and the method comprises the following steps: modeling a reconfigurable battery network topology based on a directed graph to generate a node correlation matrix; determining the voltage-current relationship of a preset working condition battery branch based on a preset battery model and a Norton equivalent circuit; determining the voltage-current relationship of a switch branch according to a preset working condition; obtaining a preset working condition node admittance matrix according to the node correlation matrix, the voltage-current relationship of the preset working condition battery branch and the switch branch, and establishing a network equation to solve the state quantity of the reconfigurable battery network under the preset working condition; judging whether the node admittance matrix needs to be updated according to the current working condition change based on a preset reconfiguration period; if the current working condition changes, updating the node admittance matrix to obtain a corresponding modified network equation, and solving the equation to obtain the state quantity of the reconfigurable battery network under the current working condition. The application has strong universality, considers the accuracy and the calculation cost, and meets the real-time requirement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery state estimation, in particular to a fast calculation method and system for state variables of a reconfigurable battery network under time-varying conditions. BACKGROUND

[0002] Electrochemical energy storage has been widely used in various industrial, commercial and residential scenarios, such as renewable energy, electric vehicles and uninterruptible power supplies, due to its fast response speed, high energy density and other advantages. The core of the electrochemical energy storage system is a battery network composed of battery monomers and battery modules. However, the traditional battery network has a "short board effect" due to the use of fixed series-parallel mode, i.e. the performance of the battery system depends on the weakest battery monomer, which leads to problems such as low safety, poor consistency and low energy efficiency of the battery pack, restricting the application of the battery network in energy storage.

[0003] In order to overcome the shortcomings of the traditional battery network, dynamically reconfigurable battery network (DRBN) has attracted widespread attention. DRBN realizes dynamic reconfiguration of the battery network by deeply coupling low-power power semiconductor devices with batteries, thereby having the ability to finely control and manage, being able to connect or disconnect any battery monomer from the system, and being able to realize the conversion of network topology between series and parallel, which provides a basis for precise isolation of faulty monomers and balancing of battery states. However, there are various types of existing DRBN systems, which can produce various topological connection modes according to the number of switches each battery is connected to. Since each DRBN is a nonlinear time-varying system, the measurement and estimation of its state variables, such as monomer current and state of charge (SOC), face great challenges.

[0004] There are two main problems with existing methods for calculating the state of DRBN: first, the existing calculation methods have poor universality and can only be applied to certain specific battery network topologies, cannot describe complex networks with multiple switches connected to a single monomer, and do not have the ability to solve the state of any reconfigurable battery network topology; second, the existing methods have high computational complexity and long solution time for battery state, thus failing to meet the real-time requirements of engineering applications. SUMMARY

[0005] Therefore, the application provides a fast calculation method and system for state variables of a reconfigurable battery network under time-varying conditions, which can efficiently and quickly solve state variables of any reconfigurable battery network topology, has strong universality, is suitable for occasions with dynamic changes of the network, and also takes into account accuracy and calculation cost, reduces calculation time on the premise of ensuring the calculation accuracy of the state variables of the battery network, and can meet the real-time requirements in engineering applications, so as to solve the technical problems proposed in the above background.

[0006] To achieve the above object, the application provides the following technical scheme.

[0007] In a first aspect, the application embodiment provides a fast calculation method for state variables of a reconfigurable battery network under time-varying conditions, comprising:

[0008] modeling the topology of the reconfigurable battery network based on a directed graph mode to generate a node correlation matrix;

[0009] determining the voltage-current relationship of the battery branch under the preset condition based on a preset battery model and a Norton equivalent circuit;

[0010] determining the voltage-current relationship of the switch branch according to the preset condition;

[0011] obtaining the node admittance matrix of the reconfigurable battery network under the preset condition according to the node correlation matrix, the voltage-current relationship of the battery branch under the preset condition, and the voltage-current relationship of the switch branch, establishing a network equation based on the node admittance matrix, and solving to obtain the state variables of the reconfigurable battery network under the preset condition;

[0012] based on the preset reconfiguration period of the reconfigurable battery network, judging whether to update the node admittance matrix according to the current condition change; if the current condition changes, the corresponding node admittance matrix is updated to obtain a corrected network equation, and the corrected network equation is solved to obtain the state variables of the reconfigurable battery network under the current condition.

[0013] Preferably, the state variables of the reconfigurable battery network include the node voltage and the branch current of the reconfigurable battery network.

[0014] Preferably, the process of modeling the topology of the reconfigurable battery network based on the directed graph mode to generate the node correlation matrix comprises:

[0015] modeling the topology of the reconfigurable battery network based on the directed graph mode to obtain the structure of the corresponding directed graph, comprising: defining each battery monomer and switch in the reconfigurable battery network as a branch, and defining the connection point as a node of the directed graph; all nodes are numbered in sequence, and the direction of the branch is defined as the small node pointing to the large node; the negative electrode of the output end of the reconfigurable battery network is set as a ground node;

[0016] The structure of the directed graph is described to obtain a corresponding node correlation matrix, the rows of the node correlation matrix correspond to nodes, and the columns correspond to branches; when a branch is emitted from a node, the corresponding element in the node correlation matrix is set to 1, when the branch points to a node, the corresponding element is set to -1, and the other elements are zero, and the ground node does not appear in the node correlation matrix.

[0017] Preferably, based on the preset battery model and the Norton equivalent circuit, the process of determining the voltage-current relationship of the battery branch under the preset working condition comprises:

[0018] Based on the preset battery model and the battery state of charge relationship, the voltage source and the internal resistance of the battery are obtained;

[0019] According to the Norton equivalent circuit, the voltage source is equivalent to a branch current source, and a corresponding branch voltage-current relationship is obtained. Based on the relationship, the branch current source is equivalent to a node injection current according to the Norton equivalent circuit by using the node correlation matrix, and a node-branch current relationship corresponding to the preset working condition is obtained. The working condition includes the load current size and the on-off state of the switch, and the preset working condition is that the load current size and the on-off state of the switch have been set.

[0020] Preferably, the process of determining the voltage-current relationship of the switch branch according to the preset working condition comprises:

[0021] The switch in the reconfigurable battery network is defined as a variable resistance branch, and the on-off of the switch is equivalent to the change of the branch resistance. The voltage-current relationship of the switch branch is determined according to the preset working condition.

[0022] Preferably, the process of obtaining the node admittance matrix of the reconfigurable battery network under the preset working condition according to the node correlation matrix, the voltage-current relationship of the battery branch under the preset working condition, and the voltage-current relationship of the switch branch comprises:

[0023] Based on the voltage-current relationship of the battery branch under the preset working condition and the voltage-current relationship of the switch branch, an original admittance matrix of the reconfigurable battery network is obtained;

[0024] Based on the node correlation matrix, a node admittance matrix of the reconfigurable battery network under the preset working condition is obtained.

[0025] Preferably, the process of updating the corresponding node admittance matrix to obtain the corresponding modified network equation comprises:

[0026] The node admittance matrix is updated by using the matrix inversion auxiliary theorem, and the corresponding modified network equation is obtained based on the updated node admittance matrix.

[0027] In a second aspect, the embodiments of the present application provide a fast calculation system for the state quantity of a reconfigurable battery network under time-varying working conditions, comprising:

[0028] The node correlation matrix establishing module is configured to model the reconfigurable battery network topology in a directed graph mode and generate a node correlation matrix.

[0029] The battery branch establishing module is configured to determine the voltage-current relationship of the battery branch under the preset working condition based on the preset battery model and the Norton equivalent circuit.

[0030] The switch branch establishing module is configured to determine the voltage-current relationship of the switch branch according to the preset working condition.

[0031] The network equation establishing module is configured to obtain a node admittance matrix of the reconfigurable battery network under the preset working condition according to the node correlation matrix, the voltage-current relationship of the battery branch under the preset working condition, and the voltage-current relationship of the switch branch, establish a network equation based on the node admittance matrix, and solve to obtain the state quantity of the reconfigurable battery network under the preset working condition.

[0032] The network equation correcting module is configured to determine whether to update the node admittance matrix according to a current working condition change based on a preset reconfiguration period of the reconfigurable battery network; if the current working condition changes, the corresponding node admittance matrix is updated to obtain a corrected network equation, and the corrected network equation is solved to obtain the state quantity of the reconfigurable battery network under the current working condition.

[0033] In a third aspect, an embodiment of the present application provides a computer device, which comprises at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the fast calculation method of the state quantity of the reconfigurable battery network under the time-varying working condition according to the first aspect of the present application.

[0034] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions for enabling a computer to perform the fast calculation method of the state quantity of the reconfigurable battery network under the time-varying working condition according to the first aspect of the present application.

[0035] The technical scheme of the present application has the following advantages:

[0036] The application provides a fast calculation method and system of a reconfigurable battery network state quantity under time-varying working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0038] Figure 1 The flowchart of the fast calculation method of the reconfigurable battery network state quantity under time-varying working conditions provided in the embodiments of the present application is shown in the figure.

[0039] Figure 2 (a)-(b) of the figure are a specific schematic diagram of the topological structure and the corresponding directed graph of the DRBN provided in the embodiments of the present application.

[0040] Figure 3 (a)-(b) of the figure are another specific schematic diagram of the topological structure and the corresponding directed graph of the DRBN provided in the embodiments of the present application.

[0041] Figure 4 The specific flowchart of the fast calculation method of the reconfigurable battery network state quantity under time-varying working conditions provided in the embodiments of the present application is shown in the figure.

[0042] Figure 5 The module composition diagram of the fast calculation system of the reconfigurable battery network state quantity under time-varying working conditions provided in the embodiments of the present application is shown in the figure.

[0043] Figure 6 The composition diagram of one specific example of the computer device provided in the embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments, and are not intended to limit the scope of the present application. In addition, in the following description, the description of the known structures and technologies is omitted to avoid unnecessary confusion of the concepts disclosed in the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of the present application.

[0045] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0046] Embodiment 1

[0047] The embodiment of the present application provides a fast calculation method of reconfigurable battery network state quantity under time-varying working conditions, as shown in the formula (1), the method comprises the steps of: Figure 1

[0048] Step S1: modeling the topology of the reconfigurable battery network based on the directed graph mode, and generating a node association matrix.

[0049] In the embodiment, each battery monomer and switch in the reconfigurable battery network is defined as a branch, and the connection point is defined as the node of the directed graph; all nodes are numbered in sequence, and the direction of the branch is defined as the small node pointing to the large node; the negative electrode of the output end of the reconfigurable battery network is set as the ground node. It should be noted that in this step, the switch state in the reconfigurable battery network is regarded as an independent branch whether it is on or off, and the node merging caused by the switch on is not considered.

[0050] ​In the embodiment, the structure of the directed graph is described to obtain a corresponding node incidence matrix, the rows of the node incidence matrix correspond to nodes, and the columns correspond to branches; when a branch is emitted from a node, the corresponding element in the node incidence matrix is 1, when the branch points to a node, the corresponding element is -1, and the other elements are 0, and the ground node does not appear in the node incidence matrix. It should be noted that the node incidence matrix is formed once and will not change in the subsequent solving process; each reconfigurable battery network has a unique corresponding node incidence matrix, and different reconfigurable battery networks correspond to different node incidence matrices. The above analysis method based on the directed graph is suitable for various types of reconfigurable battery network topologies and has strong universality. In a specific embodiment, the node incidence matrix is denoted as A, as shown below:

[0051]

[0052] wherein the node incidence matrix is denoted as A, the structure of the corresponding directed graph has N nodes and l branches; n1, n2, …, nN correspond to the nodes, and b1, b2, …, bl correspond to the branches. N Each row corresponds to a node, and b1, b2, …, bl correspond to the branches. l Each column corresponds to a branch, which is only used as an example and is not limited thereto.

[0053] Step S2: determining the voltage-current relationship of the battery branch under the preset working condition based on the preset battery model and the Norton equivalent circuit.

[0054] In the embodiment, based on the preset battery model and the battery state of charge relationship, the voltage source and the internal resistance of the battery are obtained; according to the Norton equivalent circuit, the voltage source is equivalent to a branch current source, and the corresponding branch voltage-current relationship is obtained; based on the relationship, the node incidence matrix is used, and the branch current source is equivalent to a node injection current according to the Norton equivalent circuit, to obtain the node-branch current relationship corresponding to the preset working condition, wherein the working condition includes the load current size and the on-off state of the switch, and the preset working condition is that the load current size and the on-off state of the switch have been set. It should be noted that the preset battery model includes Rint model, Thevenin model, second-order RC model, PNGV model and GNL model, which are only used as examples and are determined according to actual application requirements.

[0055] In a specific embodiment, the Rint battery model is used to equivalent the battery as a structure of an ideal voltage source and an equivalent internal resistance in series, and the values of the voltage source and the internal resistance are not fixed, but satisfy the function of the state of charge of the battery, and the relationship is as follows:

[0056]

[0057] wherein the voltage V oc is the open circuit voltage of the battery, and R Sis the internal resistance of the battery, SOC represents the charge capacity of the battery, defined as the ratio of the current available capacity of the battery to the rated capacity, the coefficients a0, a1,..., a5 and b0, b1,..., b5 in the formula are determined by the electrochemical characteristics of the battery itself, and can be obtained through the charge and discharge experiments of the battery monomer.

[0058] The open-circuit voltage V oc of the battery corresponding to the battery can be obtained by the above battery state of charge relationship. S ; according to the Norton equivalent circuit, the open-circuit voltage V oc of the battery is equivalent to the form of node injection current, that is, for any branch, its electromotive force source e k (e k = V oc ) and the current source i sk are converted through the following relationship, and the branch voltage and current relationship is:

[0059] y k e k = -i sk (2)

[0060] Wherein, y k represents the branch admittance,

[0061] According to the Norton equivalent circuit, the current source of the branch can be equivalent to the injection current of the nodes at both ends of the branch, and the relationship between the equivalent node injection current i n (i s .e. the injection current of the node by the outside world) and the branch current source i s ′ (i s ′ = i sk ) and the equivalent branch current source i n of the voltage source can be calculated by using the node incidence matrix A, that is, the node-branch current relationship corresponding to the preset working condition is:

[0062] i s = A(i s +i on ′) (3).

[0063] Step S3: determining the voltage and current relationship of the switch branch according to the preset working condition.

[0064] In this embodiment, the switch in the reconfigurable battery network is defined as a variable resistance branch, and the on-off of the switch is equivalent to the change of the branch resistance, and the voltage and current relationship of the switch branch is determined according to the preset working condition.

[0065] In a specific embodiment, it is assumed that the on-resistance of the switch is R off, the state of the switch is represented by s(t), s(t) is equal to 1 when the switch is in the on state, and is equal to 0 when the switch is in the off state, then the resistance of each switch is:

[0066]

[0067] The above-mentioned way of determining the voltage-current relationship of the switch branch is equivalent to the change of the branch resistance through the on-off of the switch, avoids the merging and splitting of the nodes, and keeps the dimension of the reconfigurable battery network matrix unchanged.

[0068] Step S4: obtaining the node admittance matrix of the reconfigurable battery network in the preset working condition according to the node association matrix, the voltage-current relationship of the battery branch in the preset working condition and the voltage-current relationship of the switch branch, establishing a network equation based on the node admittance matrix, and solving to obtain the state quantity of the reconfigurable battery network in the preset working condition.

[0069] In this embodiment, the state quantity of the reconfigurable battery network includes: the node voltage and the branch current of the reconfigurable battery network.

[0070] In this embodiment, based on the voltage-current relationship of the battery branch in the preset working condition and the voltage-current relationship of the switch branch, the original admittance matrix of the reconfigurable battery network is obtained; based on the node association matrix, the node admittance matrix of the reconfigurable battery network in the preset working condition is obtained.

[0071] Specifically, on the basis of modeling the battery monomer and the switch, the original admittance matrix y b of the reconfigurable battery network DRBN can be obtained. b Based on the related theory in the field, when there is no mutual inductance in the network, the original admittance matrix y b is a diagonal matrix, and the elements on the diagonal correspond to the admittance of the branch. Assuming that the original admittance matrix y b obtained under a given set of switch states is:

[0072]

[0073] Combined with the node association matrix A, it can be deduced that the node admittance matrix Y under this specific network connection mode is:

[0074] Y=Ay b A T (6)

[0075] Based on the node admittance matrix Y, the network equation is established as:

[0076] Yu n =i n (7)

[0077] By solving the above network equation, the node voltage u nwherein the node injects a current i n The load current i s after injection can be obtained on the basis of formula (3).

[0078] Step S5: Based on the preset reconfiguration period of the reconfigurable battery network, it is judged whether to update the node admittance matrix according to the current working condition change; if the current working condition changes, the corresponding node admittance matrix is updated, the corresponding modified network equation is obtained, and the state quantity of the reconfigurable battery network under the current working condition is obtained by solving the modified network equation.

[0079] In this embodiment, based on the preset reconfiguration period of the reconfigurable battery network, it is judged whether to update the node admittance matrix according to the current working condition change; if the current working condition does not change, the corresponding node admittance matrix is not updated, the network equation is unchanged, and the state quantity of the reconfigurable battery network under the current working condition is obtained by solving the network equation; if the current working condition changes, the node admittance matrix is updated by using the matrix inverse auxiliary theorem, and the corresponding modified network equation is obtained based on the updated node admittance matrix, and the state quantity of the reconfigurable battery network under the current working condition is obtained by solving the modified network equation.

[0080] It should be noted that in the process of reconfiguring the reconfigurable battery network DRBN, the on-off of the switch will cause the change of the topological connection mode of the network, and correspondingly the node admittance matrix Y will also change, so when solving the node voltage u n , the inverse matrix of Y needs to be recalculated. When the number of batteries in the DRBN increases, the number of nodes in the network increases, and at this time the dimension of the node admittance matrix Y is high, and frequent matrix inversion will greatly increase the calculation complexity.

[0081] In order to overcome the problem of frequent inversion of Y matrix, the matrix inversion auxiliary theorem is used to simplify the inversion operation in the embodiment of the application, and this process is equivalent to simplifying the matrix inversion operation to algebraic operation, which greatly reduces the calculation complexity. Specifically, the on and off process of the switch will change the resistance value of the switch branch, and this process is equivalent to adding a branch between two nodes. For example, for the case that the switch branch k is switched from the on state to the off state, it is equivalent to first connecting a branch with a resistance of -R on in parallel, and then connecting a branch with a resistance of R off in parallel. Let the total admittance change of the two parallel branches be Δy bk , and the associated vector of the branch k be M k , then the new node admittance matrix is:

[0082]

[0083] wherein the associated vector M kis a column vector whose values correspond to the k-th column of the node incidence matrix A, and Y is the node admittance matrix corresponding to the previous operating condition.

[0084] According to the matrix inversion auxiliary theorem, the inverse of the new node admittance matrix Y is:

[0085]

[0086] where c is an intermediate parameter, If the state of multiple branches changes, change M k and the dimension of Δy bk to solve.

[0087] In a specific embodiment, the fast calculation of state variables is performed on a four-switch reconfigurable battery network (DRBN) topology. First, the DRBN topology is abstracted into a directed graph, as shown in Figure 2 . It should be noted that the fast calculation method provided in the embodiments of the present application is also applicable to other types of DRBN architectures by abstracting the DRBN topology into a directed graph, as shown in Figure 3 .

[0088] The specific steps for solving the DRBN state variables during charging and discharging are shown in Figure 4 , including:

[0089] First step: generate the node incidence matrix of the network. Specifically, the node incidence matrix is used to describe the topological connection mode of the directed graph and does not involve the specific information of the branches, and is used to describe the constraints of Kirchhoff's current and voltage equations on the topology. First, each node and each branch in the directed graph is numbered, each row of the matrix corresponds to a node, and each column corresponds to a branch. Then determine the value of each element in the matrix. When a branch is emitted from a node, the corresponding element in the matrix is set to 1; when a branch points to a node, the corresponding element is set to -1; other elements are set to zero. Note that the ground node (node numbered 0) does not appear in the matrix. In this embodiment, the node incidence matrix (a 11x17 matrix, each row represents a node, a total of 11 nodes; each column represents a branch, a total of 17 branches) is as follows:

[0090]

[0091] Second step: determine the voltage-current relationship of each branch. In order to solve the state variables in the network, i.e. node voltage and branch current, only the node incidence matrix is not enough, the voltage-current relationship of each branch also needs to be associated to make the number of equations equal to the number of variables, so that the network equation has a unique solution. The voltage-current relationship of the branch describes the characteristics of the branch element. According to the foregoing content, the battery branch is equivalent to a series connection of an ideal voltage source and the internal resistance of the battery, and the voltage source can be equivalent to the node injection current, so the voltage-current relationship of the battery branch can be described by formula (1)-(3). For the switch branch, each switch is represented by an on-state resistance in the on state and a off-state resistance in the off state, so the voltage-current relationship of the switch branch can be described by formula (4).

[0092] Third step: establish network equations to solve the state variables of the battery network under given conditions. The network topology information (characterized by the node incidence matrix) and the branch voltage-current relationship (including battery branches and switch branches, characterized by formula (1)-(4)) are associated to obtain the node admittance matrix containing the entire network information, as shown in formula (5)-(6). The node admittance matrix contains not only the topological connection information of the network, but also the admittance information of each branch. For a system consisting of four battery monomers, when the four monomers are connected in parallel, an example of the node admittance matrix (11x11 matrix, each row and each column represents a node, a total of 11 nodes, and the 0 element represents that there is no electrical connection between two nodes, unit: Siemens (S)) can be obtained, as follows:

[0093]

[0094] The node admittance matrix is combined with the node voltage vector and the injection current vector to obtain the network equation, as shown in formula (7). By solving the matrix equation, the voltage value of each node under the given conditions (given load current, given on-off mode of the switch) can be obtained. According to the node voltage vector and the branch voltage-current relationship, the current value of each branch can be calculated. For example, in the case of 4 monomers in parallel, the node voltage vector of the battery network (11x1 column vector, representing the voltage of 11 nodes, unit: volt (V)) and the battery branch current vector (4x1 column vector, representing the current value of 4 battery monomers, unit: ampere (A)) can be calculated, as follows:

[0095]

[0096]

[0097] Fourth step: considering the dynamic reconstruction process of the battery network, the network model is quickly corrected. The dynamic reconstruction of the battery network refers to that in the process of charging and discharging, the on-off state of each switch will also change with the change of the load and the battery state. The charging and discharging process of the battery network is divided into a small time scale of reconstruction period, and the reconstruction period of the battery network is Ts. At the beginning of each reconstruction period (t=t0), the state of the switch is updated according to the load condition and the battery state, at this time (t=t+Ts), the node admittance matrix Y may change. If the state of the switch does not change in the new reconstruction period (t>t _end ), the node admittance matrix Y is unchanged, the network equation is unchanged, and the equation is solved to obtain the node voltage and branch current of the battery network at the current time; if the Y matrix changes at the beginning of the reconstruction period, the original network equation needs to be corrected. In order to improve the calculation efficiency, the matrix inversion auxiliary theorem is used to correct the node admittance matrix Y, as shown in formulas (8)-(9). In the process of correcting the equation, only the branch whose state changes needs to be considered, the dimension of the matrix inversion is reduced, and the inverse operation of the whole Y matrix is avoided, so that the calculation efficiency is improved. After completing the correction of the equation, the network equation mentioned in the third step is solved again, so as to obtain the node voltage and branch current of the battery network at the current time.

[0098] The fast calculation method of the state quantity of the reconfigurable battery network under time-varying working conditions provided by the embodiment of the application can be applied to the reconfigurable battery network with any topological structure, has strong generalization, is suitable for occasions with dynamic network changes, and balances the precision and the calculation cost, so that the calculation time is reduced under the premise of ensuring the calculation precision of the state quantity of the battery network, and the real-time requirement in engineering application can be met.

[0099] Embodiment 2

[0100] The embodiment of the application provides a fast calculation system of a state quantity of a reconfigurable battery network under time-varying working conditions, as shown in Figure 5 , comprising:

[0101] The node association matrix establishing module is used for modeling the topology of the reconfigurable battery network based on a directed graph mode, and generating a node association matrix. This module executes the method described in step S1 in embodiment 1, and will not be repeated here.

[0102] The battery branch establishing module is used for determining the voltage-current relationship of the battery branch under the preset working condition based on a preset battery model and a Norton equivalent circuit. This module executes the method described in step S2 in embodiment 1, and will not be repeated here.

[0103] The switch branch establishment module is used to determine the voltage and current relationship of the switch branch according to the preset operating conditions; this module performs the method described in step S3 of embodiment 1, which will not be repeated here.

[0104] The network equation establishment module is used to obtain the node admittance matrix of the reconfigurable battery network under the preset operating conditions based on the node association matrix, the voltage and current relationship of the battery branch under the preset operating conditions, and the voltage and current relationship of the switch branch. Based on the node admittance matrix, the network equation is established and the state variables of the reconfigurable battery network under the preset operating conditions are solved. This module performs the method described in step S4 of embodiment 1, which will not be repeated here.

[0105] The network equation correction module is used to determine whether to update the node admittance matrix based on the preset reconstruction cycle of the reconfigurable battery network and the changes in the current operating conditions. If the current operating conditions change, the corresponding node admittance matrix is ​​updated to obtain the corresponding corrected network equation. The corrected network equation is solved to obtain the state variables of the reconfigurable battery network under the current operating conditions. This module executes the method described in step S5 of embodiment 1, which will not be repeated here.

[0106] The fast calculation system for reconfigurable battery network state variables under time-varying conditions provided by this invention can efficiently and quickly solve the state variables of any reconfigurable battery network topology; it balances accuracy and computational cost, reducing computation time while ensuring the accuracy of battery network state variable calculation, and can meet the real-time requirements of engineering applications.

[0107] Example 3

[0108] This invention provides a computer device, such as... Figure 6 As shown, the system includes: at least one processor 601, at least one communication interface 603, a memory 604, and at least one communication bus 602. The communication bus 602 is used to enable communication between these components. The communication interface 603 may include a display screen and a keyboard; optionally, the communication interface 603 may also include a standard wired interface or a wireless interface. The memory 604 may be a high-speed volatile random access memory, an unstable memory, or at least one storage device located remotely from the processor 601. The processor 601 can execute the fast calculation method for reconfigurable battery network state variables under time-varying operating conditions according to Embodiment 1. The memory 604 stores a set of program code, and the processor 601 calls the program code stored in the memory 604 to execute the fast calculation method for reconfigurable battery network state variables under time-varying operating conditions according to Embodiment 1.

[0109] The communication bus 602 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 602 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one line is used in the figure to represent the bus, but it is understood that there can be more than one bus or more than one type of bus.

[0110] The memory 604 can include a volatile memory (Volatile Memory), such as a Random Access Memory (RAM); the memory can also include a non-volatile memory (Non-volatile Memory), such as a Flash Memory, a Hard Disk Drive (HDD) or a Solid-state Drive (SSD); the memory 604 can also include a combination of the above types of memories.

[0111] The processor 601 can be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of the CPU and the NP.

[0112] The processor 601 can further include a hardware chip. The hardware chip can be an Application-Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD) or a combination thereof. The PLD can be a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA), a Generic Array Logic (GAL) or any combination thereof.

[0113] Optionally, the memory 604 is further configured to store program instructions. The processor 601 can invoke the program instructions to implement the fast calculation method of the reconfigurable battery network state quantity under time-varying working conditions as described in Embodiment 1.

[0114] The embodiment of the present application further provides a computer readable storage medium, and computer executable instructions are stored on the computer readable storage medium, the computer executable instructions being executable to implement the fast calculation method of the reconfigurable battery network state quantity under time-varying working conditions of the embodiment 1. The storage medium can be a disk, an optical disc, a read only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0115] Obviously, the above-mentioned embodiments are only examples for clearly illustrating, but not limitation to the embodiments. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above-mentioned description. Here, all the embodiments need not and cannot be exhausted. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A fast calculation method of reconfigurable battery network state variables under time-varying conditions, characterized in that, The method comprises the following steps: modeling the topology of the reconfigurable battery network based on a directed graph to generate a node incidence matrix; determining the voltage-current relationship of the battery branch under a preset working condition based on a preset battery model and a Norton equivalent circuit, wherein the working condition comprises a load current size and an on-off state of a switch, and the preset working condition is that the load current size and the on-off state of the switch have been set; determining the voltage-current relationship of the switch branch according to the preset working condition; obtaining a node admittance matrix of the reconfigurable battery network under the preset working condition according to the node incidence matrix, the voltage-current relationship of the battery branch under the preset working condition, and the voltage-current relationship of the switch branch, establishing a network equation based on the node admittance matrix, and solving to obtain the state quantity of the reconfigurable battery network under the preset working condition; based on a preset reconfiguration period of the reconfigurable battery network, determining whether to update the node admittance matrix according to the current working condition; if the current working condition changes, the corresponding node admittance matrix is updated to obtain a modified network equation, and the state quantity of the reconfigurable battery network under the current working condition is obtained by solving the modified network equation.

2. The method of claim 1, wherein, The state quantity of the reconfigurable battery network comprises the node voltage and the branch current of the reconfigurable battery network.

3. The method of claim 1, wherein, The process of modeling the topology of the reconfigurable battery network based on a directed graph to generate a node incidence matrix comprises the following steps: modeling the topology of the reconfigurable battery network based on a directed graph to obtain the structure of the corresponding directed graph, which comprises defining each battery monomer and switch in the reconfigurable battery network as a branch and defining the connection point as a node of the directed graph; numbering all nodes in sequence, and defining the direction of the branch as the small node pointing to the large node; and setting the negative electrode of the output end of the reconfigurable battery network as a ground node; describing the structure of the directed graph to obtain a corresponding node incidence matrix, wherein the rows of the node incidence matrix correspond to the nodes, and the columns correspond to the branches; when a branch is emitted from a node, the corresponding element in the node incidence matrix is 1; when a branch points to a node, the corresponding element is-1; and other elements are zero, and the ground node does not appear in the node incidence matrix.

4. The method of claim 1, wherein, The process of determining the voltage-current relationship of the battery branch under a preset working condition based on a preset battery model and a Norton equivalent circuit comprises the following steps: based on a preset battery model and a battery state of charge relationship, obtaining a voltage source and an internal resistance of the battery; according to the Norton equivalent circuit, equivalent the voltage source to a branch current source to obtain a corresponding branch voltage-current relationship, based on the relationship, using the node incidence matrix, and according to the Norton equivalent circuit, equivalent the branch current source to a node injection current to obtain the node-branch current relationship corresponding to the preset working condition.

5. The method of claim 4, wherein, The process of determining the voltage-current relationship of the switch branch according to the preset working condition comprises the following steps: defining the switch in the reconfigurable battery network as a variable resistance branch, and equivalent the on-off of the switch to the change of the branch resistance, and determining the voltage-current relationship of the switch branch according to the preset working condition.

6. The method of claim 5, wherein, The process of obtaining the node admittance matrix of the reconfigurable battery network under the preset working condition according to the node incidence matrix, the voltage-current relationship of the battery branch under the preset working condition, and the voltage-current relationship of the switch branch comprises the following steps: The original admittance matrix of the reconfigurable battery network is obtained based on the voltage-current relationship of the battery branch and the voltage-current relationship of the switch branch under the preset working condition; The node admittance matrix of the reconfigurable battery network under the preset working condition is obtained based on the node association matrix.

7. The method of claim 6, wherein, The process of updating the corresponding node admittance matrix to obtain the corresponding modified network equation comprises: The node admittance matrix is updated by using the matrix inversion auxiliary theorem, and the corresponding modified network equation is obtained based on the updated node admittance matrix. 8.A system for fast calculation of reconfigurable battery network state variables under time-varying operating conditions, characterized in that, It comprises: The node association matrix establishment module is used to model the topology of the reconfigurable battery network in a directed graph manner to generate a node association matrix; The battery branch establishment module is used to determine the voltage-current relationship of the battery branch under the preset working condition based on a preset battery model and a Norton equivalent circuit, wherein the working condition includes a load current size and a switch on-off state, and the preset working condition is that the load current size and the switch on-off state have been set; The switch branch establishment module is used to determine the voltage-current relationship of the switch branch according to the preset working condition; The network equation establishment module is used to obtain the node admittance matrix of the reconfigurable battery network under the preset working condition according to the node association matrix, the voltage-current relationship of the battery branch under the preset working condition, and the voltage-current relationship of the switch branch, establish a network equation based on the node admittance matrix, and solve to obtain the state quantity of the reconfigurable battery network under the preset working condition. The network equation correction module is used to determine whether to update the node admittance matrix according to the current working condition change based on the preset reconfiguration period of the reconfigurable battery network; if the current working condition changes, the corresponding node admittance matrix is updated to obtain the corresponding modified network equation, and the state quantity of the reconfigurable battery network under the current working condition is obtained by solving the modified network equation.

9. A computer device, comprising: It comprises: At least one processor and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to make the at least one processor execute the fast calculation method of the state quantity of the reconfigurable battery network under the time-varying working condition in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for making the computer execute the fast calculation method of the state quantity of the reconfigurable battery network under the time-varying working condition in any one of claims 1-7.

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