A method for estimating soc of series-connected vrfb battery pack based on inconsistency

Abstract

The application discloses a SOC estimation method for series VRFB battery pack based on inconsistency, relates to the technical field of energy storage battery system management, and comprises the following steps: performing parameter initialization on a double-layer filter; selecting a characteristic monomer based on the series VRFB battery pack; constructing a battery inconsistency model between the characteristic monomer and a first remaining monomer; respectively estimating the characteristic monomer and the battery inconsistency model through the double-layer filter; screening a VRFB monomer with the smallest charging capacity according to the characteristic monomer estimation result and the battery inconsistency model estimation result; calculating the SOC of the series VRFB battery pack i ; reconstructing the battery inconsistency model, and repeating steps S3 to S6 until the operation ends; the method effectively reduces the calculation complexity, simultaneously realizes effective monitoring on the remaining monomers, provides guarantee for the operation of the series VRFB battery pack, and realizes accurate estimation on the SOC of the series VRFB battery pack. pack ​

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage battery system management, and particularly relates to a SOC estimation method for a series VRFB battery pack based on inconsistency. BACKGROUND

[0002] Under ideal working conditions, if all VRFB monomers can maintain high consistency in parameters and performance, the series VRFB pack can be equivalent to a large-capacity VRFB monomer, and the SOC estimation method for the VRFB monomer can be used to estimate the SOC of the series VRFB pack.

[0003] However, in actual operation, the inconsistency of the VRFB monomers generally exists in the series VRFB pack due to the comprehensive influence of the parameters of the VRFB monomers and the running environment, which seriously challenges the accurate estimation of the SOC of the battery pack.

[0004] In the prior art, the series VRFB pack is equivalent to a large-capacity VRFB monomer, and the inconsistency of the VRFB monomers is not considered. Firstly, the initial parameters of the VRFB monomers are inconsistent. In the production process of the VRFB monomers, the inherent differences exist in the key parameters such as the internal resistance, capacity and coulomb efficiency of the VRFB monomers due to factors such as product process, raw material parameters and production batch. Secondly, the running environment of the VRFB monomers is inconsistent. In the running process of the VRFB monomers, the external conditions such as uneven temperature distribution, fluctuation of connection resistance and difference in charge and discharge rate cause some batteries to be in the overcharged and overdischarged state for a long time, accelerate the aging rate of the batteries and further aggravate the inconsistency of the series VRFB pack, and further cause the SOC estimation to deviate from the true value.

[0005] Therefore, the present application provides a SOC estimation method for a series VRFB battery pack based on inconsistency to solve the above problems. SUMMARY

[0006] The present application aims to provide a SOC estimation method for a series VRFB battery pack based on inconsistency, which characterizes the SOC of the series VRFB pack by the characteristic monomer parameters, effectively reduces the complexity of calculation, simultaneously realizes effective monitoring of the remaining monomers, provides guarantee for the operation of the series VRFB pack and realizes accurate estimation of the SOC of the series VRFB pack.

[0007] To achieve the above object, the present application provides a SOC estimation method for a series VRFB battery pack based on inconsistency, comprising the following steps:

[0008] S1: performing parameter initialization on a double-layer MTDKF filter;

[0009] S2: Selecting a characteristic cell based on the series VRFB battery pack;

[0010] S3: Constructing a battery inconsistency model between the characteristic cell and the first i remaining cell;

[0011] S4: Estimating the characteristic cell and the battery inconsistency model by a double-layer MTDKF filter respectively;

[0012] S5: Selecting the VRFB cell with the smallest charging capacity according to the estimation results of the characteristic cell and the battery inconsistency model;

[0013] S6: Calculating the SOC pack of the series VRFB battery pack;

[0014] S7: Reconstructing the battery inconsistency model and repeating steps S3-S6 until the end of the operation.

[0015] Preferably, step S2 specifically includes the following steps:

[0016] S21: Real-time acquisition of the terminal voltage and the terminal current of each VRFB cell in the series VRFB battery pack by a sensor, setting the VRFB cell with the minimum terminal voltage as the initial characteristic cell X ;

[0017] S22: Estimating the SOC value of the first remaining cell and the capacity value of the first remaining cell based on the terminal voltage i and the terminal current of each VRFB cell by the MTDKF method; i

[0018] S23: Calculating the of each VRFB cell;

[0019] S24: Comparing the X of the initial characteristic cell with the of each VRFB cell, if the n of the initial characteristic cell X are all the minimum values within continuous time, the initial characteristic cell X is the characteristic cell, and the selection is ended, otherwise, repeating steps S21-S23.

[0020] Preferably, step S3 includes the following steps: ​​

[0021] S31: Constructing a SOC inconsistency model to obtain the SOC inconsistency between the characteristic monomer and the first residual monomer i Specifically, the SOC inconsistency is set as:

[0022]

[0023] wherein, represents the inconsistency between the characteristic monomer and the first residual monomer at the discrete time moment t, represents the inconsistency between the characteristic monomer and the first residual monomer at the discrete time moment t, represents the SOC value of the characteristic monomer at the discrete time moment t, represents the SOC value of the characteristic monomer at the discrete time moment t, represents the SOC value of the first residual monomer at the discrete time moment t, represents the SOC value of the first residual monomer at the discrete time moment t, represents the current value at the discrete time moment t, represents the sampling period, and represents the capacity value of the characteristic monomer at the discrete time moment t. i i i i i

[0024] S32: Constructing a capacity inconsistency model to obtain the capacity inconsistency between the characteristic monomer and the first residual monomer i Specifically, the capacity inconsistency is set as:

[0025]

[0026] wherein, represents the capacity inconsistency between the characteristic monomer and the first residual monomer at the discrete time moment t, represents the capacity inconsistency between the characteristic monomer and the first residual monomer at the discrete time moment t, represents the capacity value of the characteristic monomer at the discrete time moment t, represents the capacity value of the characteristic monomer at the discrete time moment t, represents the capacity value of the first residual monomer at the discrete time moment t, and represents the capacity value of the first residual monomer at the discrete time moment t. i i ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​represents state noise;

[0027] S33: constructing an internal resistance equivalent circuit model representing the working mechanism of the VRFB monomer.

[0028] Preferably, step S33 specifically comprises the following steps:

[0029] Step 1: obtaining , Specifically set as:

[0030]

[0031] wherein, represents the number of batteries, represents the gas constant, represents the temperature, represents the Faraday constant, represents the discrete time SOC value of the characteristic monomer at the moment, represents the discrete time SOC inconsistency between the characteristic monomer and the remaining monomers at the moment;

[0032] Step 2: obtaining the voltage inconsistency between the terminal voltage of the characteristic monomer and the terminal voltage of the i th remaining monomer based on the Kirchhoff's law , the voltage inconsistency is specifically set as:

[0033]

[0034] wherein, represents the voltage inconsistency between the terminal voltage of the characteristic monomer and the terminal voltage of the th remaining monomer at the discrete time , the terminal voltage of the characteristic monomer at the discrete time i represents the terminal voltage of the th remaining monomer at the discrete time represents the open-circuit voltage of the characteristic monomer at the discrete time , the current at the discrete time represents the terminal voltage of the th remaining monomer at the discrete time i , the internal resistance of the characteristic monomer at the discrete time represents the current at the discrete time , the open-circuit voltage of the characteristic monomer at the discrete time represents the current at the discrete time , the internal resistance of the characteristic monomer at the discrete time represents the internal resistance of the characteristic monomer at the discrete time , the internal resistance of the characteristic monomer at the discrete time represents the internal resistance of the characteristic monomer at the discrete time , the internal resistance of the characteristic monomer at the discrete time i ​The open-circuit voltage of the remaining individual cells, Representing discrete time Time of the first i The internal resistance of the remaining monomers, Representing discrete time Time-feature single entity and the first i The resistance of the remaining individual cells is inconsistent;

[0035] Step 3: Based on For voltage inconsistency Simplification yields inconsistent voltage values. The simplified voltages are inconsistent. Specifically set as follows:

[0036] ;

[0037] Step 4: Constructing inconsistencies in internal resistance Inconsistency of internal resistance Specifically set as follows:

[0038]

[0039] in, Representing discrete time Time-feature single entity and the first i The resistance of the remaining individual cells is inconsistent.

[0040] Preferably, step S4 specifically includes the following steps:

[0041] S41: After each sampling cycle The SOC of the feature unit is estimated by using the MIUKF filter in the first layer of the dual-layer MTDKF filter.

[0042] S42: After each sampling cycle The SOC inconsistency is addressed by using the MIUKF filter in the second layer of the dual-layer MTDKF filter. Make an estimate;

[0043] S43: Every time passing through n Each sampling period The capacity of a feature cell is estimated by using the EKF filter in the first layer of the dual-layer MTDKF filter.

[0044] S44: Every time passing through n Each sampling period Capacity inconsistency is addressed by using the EKF filter in the second layer of the dual-layer MTDKF filter. Make an estimate.

[0045] Preferably, in step S41, the SOC of the characteristic monomer is estimated, and the specific estimation method is set as:

[0046]

[0047] wherein, represents the voltage of the electrode capacitor at the discrete time represents the voltage of the electrode capacitor at the discrete time represents the voltage of the electrode capacitor at the discrete time represents the voltage of the electrode capacitor at the discrete time R 1 represents the VRFB electrolyte impedance, R 2 represents the electrode impedance, R 3 represents the exchange membrane impedance, C 1 represents the electrode capacitor, represents the VRFB terminal current at the discrete time represents the VRFB terminal current at the discrete time represents the pump loss current at the discrete time represents the pump loss current at the discrete time represents the standard electrode potential of the electrode, R represents the energy loss in the VRFB due to parasitic parameters, represents the VRFB state of charge at the discrete time represents the VRFB state of charge at the discrete time represents the VRFB state of charge at the discrete time represents the VRFB state of charge at the discrete time represents the maximum capacity of the battery, represents the VRFB terminal voltage at the discrete time represents the VRFB terminal voltage at the discrete time represents the pump loss current at the discrete time represents the pump loss current at the discrete time

[0048] Preferably, in step S43, the capacity of the characteristic monomer is estimated, and the specific estimation method is set as:

[0049]

[0050] wherein, represents the VRFB capacity at the discrete time represents the VRFB capacity at the discrete time represents the VRFB capacity at the discrete time represents the VRFB capacity at the discrete time represents the state noise, represents the change of the SOC in a state estimation period at the discrete time represents the change of the SOC in a state estimation period at the discrete time represents the VRFB terminal current value at the discrete time represents the VRFB terminal current value at the discrete time represents the observation noise.

[0051] Preferably, in step S6, the SOC of the series VRFB battery pack is calculated pack , the SOC of the series VRFB battery pack pack Specifically set to:

[0052]

[0053] Among them, represents the dischargeable capacity of the first i monomer VRFB, represents the chargeable capacity of the first i monomer VRFB.

[0054] Therefore, the present application adopts the above-mentioned SOC estimation method for series VRFB battery pack based on inconsistency, which has the following beneficial effects:

[0055] (1) The present scheme uses characteristic monomer parameters to represent the SOC of the series VRFB battery pack, which can better estimate the difference between the SOC and the capacity of the characteristic monomer and the remaining monomers, effectively reducing the complexity of the calculation, compared to equating the series VRFB battery pack to a large-capacity VRFB monomer without considering the inconsistency of the VRFB monomer.

[0056] (2) The present scheme constructs a differentiated model to effectively monitor the remaining monomers, thereby better protecting the VRFB monomers and making the estimation of the SOC of the series VRFB battery pack more accurate, providing a guarantee for the operation of the series VRFB battery pack.

[0057] (3) The present scheme accurately estimates the SOC of the series VRFB battery pack through a double-layer MTDKF filter, which not only ensures the real-time performance of the SOC estimation, but also takes into account the long-term stability of the capacity change.

[0058] The method scheme of the present application will be further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 is a flow chart of the SOC estimation method for series VRFB battery pack based on inconsistency of the present application;

[0060] Figure 2 is a flow chart of selecting a characteristic monomer of the present application;

[0061] Figure 3 is a schematic diagram of the internal resistance equivalent circuit model of the present application;

[0062] Figure 4 is a schematic diagram of the equivalent circuit model considering losses of the present application. DETAILED DESCRIPTION

[0063] The method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0064] Unless otherwise defined, the methodological or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0065] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0066] Example

[0067] like Figure 1 As shown, this invention provides a SOC estimation method for series VRFB battery packs based on inconsistency, comprising the following steps:

[0068] S1: Initialize the parameters of the two-layer MTDKF filter;

[0069] S2: Selecting characteristic cells based on series VRFB battery packs;

[0070] like Figure 2 As shown, step S2 specifically includes the following steps:

[0071] S21: Real-time acquisition of the terminal voltage of each VRFB cell in the series VRFB battery pack via sensors. and terminal current , terminal voltage The smallest VRFB monomer is set as the initial feature monomer. X ;

[0072] S22: Terminal voltage based on each VRFB cell and terminal current Estimate the th using the MTDKF method i SOC value of the remaining monomers and the capacity value of the i remaining monomers ;

[0073] S23: calculate the of each VRFB monomer ;

[0074] S24: compare the X of the initial characteristic monomer with the of each VRFB monomer, if the n of the initial characteristic monomer X are all minimum values within a continuous time, it indicates that the algorithm converges, then the initial characteristic monomer is the characteristic monomer, and the selection ends, otherwise it indicates that the algorithm has not yet converged, and steps S21-S23 are repeated. X S3: construct a battery inconsistency model between the characteristic monomer and the

[0075] remaining monomers i ;

[0076] Step S3 includes the following steps:

[0077] S31: construct an SOC inconsistency model to obtain the SOC inconsistency i between the characteristic monomer and the remaining monomers , and the SOC inconsistency is specifically set as:

[0078]

[0079]

[0080]

[0081] wherein, denotes the inconsistency between the remaining monomers i and the characteristic monomer at a discrete time denotes the inconsistency between the remaining monomers i and the characteristic monomer at a discrete time denotes the SOC value of the characteristic monomer at a discrete time denotes the SOC value of the characteristic monomer at a discrete time denotes the SOC value of the remaining monomers at a discrete time denotes the SOC value of the i remaining monomers at a discrete time denotes the SOC value of thei SOC value of the remaining monomer, represents the current value at the discrete time , represents the sampling period, represents the capacity value of the characteristic monomer at the discrete time , represents the capacity value of the remaining monomer at the discrete time , i

[0082] S32: The capacity of the VRFB is a slowly changing quantity and does not change significantly within a short time. A capacity inconsistency model is constructed to obtain the capacity inconsistency i between the characteristic monomer and the first remaining monomer. The capacity inconsistency is specifically set as:

[0083]

[0084] wherein, represents the capacity inconsistency between the characteristic monomer and the first remaining monomer at the discrete time , i represents the capacity inconsistency between the characteristic monomer and the first remaining monomer at the discrete time , i represents the state noise.

[0085] The state space equation for characterizing the capacity inconsistency is composed of a state equation and an observation equation. The state variable is , and the observation variable is . The specific representation is:

[0086] .

[0087] S33: In a series VRFB battery pack, the most intuitive difference between two VRFB monomers is the difference in terminal voltage. In order to construct the difference relationship between the terminal voltage of the characteristic monomer battery and the terminal voltage of the remaining monomer battery, the complexity of calculation is taken into account. An internal resistance equivalent circuit model representing the working mechanism of the VRFB monomer is constructed. The internal resistance equivalent circuit model only includes a resistor R 0 and an ideal voltage source V s .

[0088] As shown in Figure 3 , the internal resistance equivalent circuit model has the significant advantage of simple structure. The resistor R 0 can represent the pressure drop characteristics inside the VRFB, and the ideal voltage source V ​​​s The open-circuit voltage is represented, and is used to associate key parameters such as SOC, and the model exhibits the core advantages of simple structure and convenient calculation, and when the inconsistency of the battery is constructed based on the internal resistance equivalent circuit model, the low complexity characteristics of the internal resistance equivalent circuit model can effectively control the overall calculation complexity of the system.

[0089] Step S33 specifically includes the following steps:

[0090] Step 1: Obtain , Specifically set to:

[0091]

[0092] Wherein, represents the number of batteries, represents the gas constant, represents the temperature, represents the Faraday constant, represents the discrete time SOC value of the characteristic monomer at the moment, represents the SOC inconsistency between the characteristic monomer and the remaining monomers at the discrete time ;

[0093] Step 2: Based on Kirchhoff's law, obtain the voltage inconsistency between the terminal voltage of the characteristic monomer and the terminal voltage of the first i remaining monomer , the voltage inconsistency is specifically set to:

[0094]

[0095] Wherein, represents the voltage inconsistency between the terminal voltage of the characteristic monomer and the terminal voltage of the first remaining monomer at the discrete time , i represents the terminal voltage of the characteristic monomer at the discrete time , represents the terminal voltage of the first remaining monomer at the discrete time , i represents the open-circuit voltage of the characteristic monomer at the discrete time , represents the current at the discrete time , represents the current at the discrete time , ​​The internal resistance of the characteristic monomer at the moment, The discrete time The open circuit voltage of the characteristic monomer at the moment, i The discrete time The internal resistance of the characteristic monomer at the moment, The discrete time i The resistance inconsistency between the characteristic monomer and the first remaining monomer at the moment; The discrete time The resistance inconsistency between the characteristic monomer and the first remaining monomer at the moment; i

[0096] Step 3: Based on , the voltage inconsistency is simplified to obtain the simplified voltage inconsistency , and the simplified voltage inconsistency is specifically set as:

[0097] ;

[0098] Step 4: Constructing the inconsistency of the internal resistance , the inconsistency of the internal resistance is specifically set as:

[0099]

[0100] , wherein The resistance inconsistency between the characteristic monomer and the first remaining monomer at the moment. i

[0101] S4: The characteristic monomer and the battery inconsistency model are estimated by a double-layer MTDKF filter, and the architecture realizes high-precision estimation of the SOC of the series VRFB battery pack through a double-time scale cooperative mechanism: dynamically tracking the SOC of the characteristic monomer and the inconsistency between the SOC of the characteristic monomer and the SOC of the remaining monomers in the fast time scale, and realizing estimation of the capacity of the characteristic monomer and the inconsistency between the capacity of the characteristic monomer and the capacity of the remaining monomers in the slow time scale, and finally realizing high-precision estimation of the SOC of the series VRFB battery pack, and simultaneously monitoring the capacity and SOC of all the remaining monomers to ensure the safe and stable operation of the VRFB energy storage system;

[0102] Step S4 specifically includes the following steps:

[0103] S41: Every time a sampling period passes, the SOC of the characteristic monomer is estimated by the MIUKF filter in the first layer of the double-layer MTDKF filter;

[0104] As shown in Figure 4 , in step S41, the SOC of the characteristic monomer is estimated, and the specific estimation method is set as: ​​​

[0105]

[0106] in, Representing discrete time The voltage across the electrode capacitor at any given time. Representing discrete time The voltage across the electrode capacitor at any given time. R 1 represents the electrolyte resistance of VRFB. R 2 represents electrode impedance. R 3 represents the exchange membrane impedance. C 1 represents electrode capacitance. Representing discrete time VRFB terminal current at any time Representing discrete time Constant pump loss current, Indicates the standard electrode potential. R This indicates the energy loss in VRFB due to parasitic parameters. Representing discrete time VRFB state of charge at any time Representing discrete time VRFB state of charge at any time Indicates the maximum battery capacity. Representing discrete time The terminal voltage of VRFB at any given time. Representing discrete time Constant pump loss current.

[0107] S42: After each sampling cycle The SOC inconsistency is addressed by using the MIUKF filter in the second layer of the dual-layer MTDKF filter. Make an estimate;

[0108] S43: Every time passing through n Each sampling period The capacity of a feature cell is estimated by using the EKF filter in the first layer of the dual-layer MTDKF filter.

[0109] In step S43, the capacity of the characteristic monomer is estimated, and the specific estimation method is set as follows:

[0110]

[0111] in, Representing discrete time VRFB capacity at any time Representing discrete time VRFB capacity at any time Indicates state noise. Representing discrete time The change in SOC at time point within a state estimation period. Representing discrete time VRFB terminal current value at any time This indicates observation noise.

[0112] S44: Every time passing through n Each sampling period Capacity inconsistency is addressed by using the EKF filter in the second layer of the dual-layer MTDKF filter. Make an estimate.

[0113] S5: Based on the estimation results of the characteristic cells and the estimation results of the battery inconsistency model, select the VRFB cells with the smallest charging capacity.

[0114] S6: Calculate the SOC of the series VRFB battery pack pack ;

[0115] In step S6, the SOC of the series VRFB battery pack is calculated. pack SOC of VRFB battery pack in series pack Specifically set as follows:

[0116]

[0117] in, Indicates the first i The storage capacity of a single VRFB unit Indicates the first i The rechargeable capacity of a single VRFB unit.

[0118] S7: Reconstruct the battery inconsistency model, repeating steps S3-S6 until the end of the run.

[0119] Therefore, the present invention adopts the above-mentioned SOC estimation method for series VRFB battery packs based on inconsistency, which characterizes the SOC of series VRFB battery packs through characteristic cell parameters, effectively reducing the computational complexity, while realizing effective monitoring of the remaining cells, providing a guarantee for the operation of series VRFB battery packs, and realizing accurate estimation of the SOC of series VRFB battery packs.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the method of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the method of the present invention, and these modifications or equivalent substitutions should not cause the modified method to deviate from the spirit and scope of the method of the present invention.

Claims

1. A method for SOC estimation of series connected VRFB battery bank based on inconsistency, characterized in that, The method comprises the following steps: S1: parameter initialization of the double-layer MTDKF filter; S2: selecting a characteristic monomer based on a series VRFB battery pack; Step S2 specifically comprises the following steps: S21: Real-time acquisition of terminal voltage of each VRFB monomer in series VRFB battery pack through sensor and terminal current , the terminal voltage The smallest VRFB monomer is set as the initial characteristic monomer X ; S22: estimating the SOC value of the first remaining monomer and the capacity value of the first remaining monomer based on the terminal voltage and the terminal current of each VRFB monomer i i ;​​​​​ S23: calculating the state of charge of each VRFB monomer ; S24: Compare initial characteristic monomers X of With each VRFB unit If in consecutive n Within each time period, the initial feature single entity X of If all values ​​are minimum, then the initial characteristic monomer... X If the selected entity is a characteristic single entity, the selection process ends; otherwise, repeat steps S21-S23. S3: build a battery inconsistency model between the feature monomer and the first i remaining monomer; S4: estimating the characteristic monomer and the battery inconsistency model respectively through the double-layer MTDKF filter; S5: screening out the VRFB monomer with the smallest charging capacity according to the characteristic monomer estimation result and the battery inconsistency model estimation result; S6: Calculate the SOC of the series VRFB battery pack pack ; S7: re-establishing the battery inconsistency model and repeating steps S3-S6 until the end of operation.

2. The SOC estimation method of series-connected VRFB battery bank based on inconsistency according to claim 1, wherein, Step S3 comprises the following steps: S31: build a SOC inconsistency model to obtain SOC inconsistency between the characteristic monomer and the first residual monomer i Specifically, the SOC inconsistency is set as:​​ in, Representing discrete time Time of the first i Inconsistency between the remaining monomers and the characteristic monomers Representing discrete time Time of the first i Inconsistency between the remaining monomers and the characteristic monomers Representing discrete time The SOC value of the time-varying feature unit Representing discrete time The SOC value of the time-varying feature unit Representing discrete time Time of the first i The SOC value of the remaining monomers, Representing discrete time Time of the first i The SOC value of the remaining monomers, Representing discrete time The current value at time [time]. Indicates the sampling period. Representing discrete time The capacity value of a single unit at a given time. Representing discrete time Time of the first i The capacity value of the remaining monomers; S32: Constructing a capacity inconsistency model to obtain the capacity inconsistency between the feature monomer and the first remaining monomer i is specifically configured as:​​ wherein, represents the capacity inconsistency between the characteristic monomer and the first remaining monomer at the discrete time i instance, represents the capacity inconsistency between the characteristic monomer and the first remaining monomer at the discrete time i instance, represents the state noise; S33: constructing an internal resistance equivalent circuit model representing the working mechanism of the VRFB monomer.

3. The SOC estimation method of series-connected VRFB battery bank based on inconsistency according to claim 2, characterized in that, Step S33 specifically comprises the following steps: Step 1 : To a solution of , Specifically provided are: wherein, represents the number of cells, represents the gas constant, represents the temperature, represents the Faraday constant, represents the discrete time characteristic monomer at the time point, represents the discrete time the SOC inconsistency between the characteristic monomer and the remaining monomers at the time point; Step 2: Based on Kirchhoff's laws, obtain the characteristic cell terminal voltage. With the i The remaining single-cell terminal voltage Voltage inconsistency Voltage inconsistency Specifically set as follows: wherein represents the discrete time instantaneous characteristic cell terminal voltage represents the voltage inconsistency between the i first remaining cell terminal voltage represents the discrete time instantaneous characteristic cell terminal voltage represents the discrete time instantaneous first remaining cell terminal voltage i represents the discrete time instantaneous characteristic cell open circuit voltage represents the discrete time instantaneous current represents the discrete time instantaneous characteristic cell internal resistance represents the discrete time instantaneous first remaining cell open circuit voltage i represents the discrete time instantaneous first remaining cell internal resistance i represents the discrete time instantaneous characteristic cell and first remaining cell resistance inconsistency i ​​​​​ Step 3: based on , voltage inconsistency , voltage inconsistency , voltage inconsistency is specifically set as: ； Step 4: Constructing the inconsistency of internal resistance , the inconsistency of internal resistance Specifically set to: wherein, represents the resistance inconsistency of the discrete time characteristic monomer with the first i residual monomer.

4. The SOC estimation method of series-connected VRFB battery bank based on inconsistency according to claim 3, characterized in that, Step S4 specifically comprises the following steps: S41: every time a sampling period passes estimate the SOC of the feature monomer through the MIUKF filter in the first layer of the double-layer MTDKF filter; S42: every time a sampling period passes , estimate the SOC inconsistency by the MIUKF filter in the second layer of the double-layer MTDKF filter ; S43: every time a sampling period passes n , estimate the capacity of the feature monomer by the EKF filter in the first layer of the double-layer MTDKF filter​ S44: Every time passing through n Each sampling period Capacity inconsistency is addressed by using the EKF filter in the second layer of the dual-layer MTDKF filter. Make an estimate.

5. The SOC estimation method of series-connected VRFB battery bank based on inconsistency according to claim 4, characterized in that, In step S41, the SOC of the characteristic monomer is estimated, and the specific estimation method is set as: wherein, V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time R 1 represents the VRFB electrolyte impedance, R 2 represents the electrode impedance, R 3 represents the exchange membrane impedance, C 1 represents the electrode capacitance, V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the VRFB terminal current at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the pump loss current at the discrete time E(t) represents the standard electrode potential of the electrode, R W(t) represents the energy loss in the VRFB due to parasitic parameters, V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the VRFB state of charge at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the VRFB state of charge at the discrete time Cmax represents the maximum capacity of the battery, V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the VRFB terminal voltage at the discrete time V(t) represents the voltage of the electrode capacitor at the discrete time V(t) represents the voltage of the pump loss current at the discrete time 6. The SOC estimation method of series-connected VRFB battery bank based on inconsistency according to claim 4, wherein, In step S43, the capacity of the characteristic monomer is estimated, and the specific estimation method is set as: wherein, denotes a discrete time denotes the VRFB capacity at time denotes a discrete time denotes the VRFB capacity at time denotes a state noise, denotes a discrete time denotes the change of SOC within one state estimation period, denotes a discrete time denotes the VRFB terminal current value at time denotes an observation noise.

7. The SOC estimation method of series VRFB battery bank based on inconsistency according to claim 4, in step S6, the SOC of series VRFB battery bank is calculated pack , the SOC of series VRFB battery bank pack Specifically set as: 。

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