T-s fuzzy modeling method and system for solid oxide fuel cell

Abstract

The application discloses a T-S fuzzy modeling method and system of a solid oxide fuel cell, and the method comprises the following steps: analyzing the model structure of the solid oxide fuel cell system; based on the model structure analysis result of the solid oxide fuel cell system, the solid oxide fuel cell system is decomposed into five linear sub-models by using a grid-based and balanced decomposition algorithm based on an included angle; the five linear sub-models are converted into five segmented radiation models; hydrogen flow is taken as a premise variable, and the five segmented radiation models are converted into five fuzzy rules according to the modeling principle of the T-S fuzzy model; the corresponding fuzzy membership functions are constructed according to the five segmented radiation models, and the T-S fuzzy model of the solid oxide fuel cell is obtained by weighted summation in combination with the five fuzzy rules; the T-S (Takagi-Sugeno) fuzzy model of the solid oxide fuel cell system can be established, and the SOFC system can be analyzed and optimized by using a linear analysis method subsequently.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of modeling of human solid oxide fuel cells, and particularly relates to a T-S fuzzy modeling method and system of a solid oxide fuel cell. BACKGROUND

[0002] Fuel cell technology is the fourth generation of power generation technology after water, fire and nuclear power, with a series of advantages such as high power generation efficiency, fast response speed, low pollution, low noise and high reliability, bringing revolutionary changes to the energy industry. Among them, the solid oxide fuel cell (SOFC) has the advantages of wide fuel application range, high-temperature waste heat recovery, all-solid-state structure, and no use of noble metals. Therefore, it has broad application prospects in large-scale power generation, distributed power generation, combined heat and power, transportation and peak load storage, and is known as the most promising new energy power generation technology in the 21st century. Vigorously developing solid oxide fuel cell technology will help promote the structural reform of China's energy supply side and promote the energy technology revolution. It lays a technical foundation for achieving the carbon peak and carbon neutralization targets.

[0003] However, the solid oxide fuel cell system is a very complex nonlinear system, and it is difficult to accurately establish its model, and it is also difficult to analyze and control the subsequent complex nonlinear model, so it is very important to study a modeling method that is convenient for subsequent analysis and control.

[0004] The information disclosed in this BACKGROUND section is only intended to increase an understanding of the general context in which the present application can be practiced. It is not admitted that any of the information provided in this section constitutes prior art against the present application. SUMMARY

[0005] The purpose of the present application is to provide a T-S fuzzy modeling method and system of a solid oxide fuel cell, which can accurately establish a T-S (Takagi-Sugeno) fuzzy model of a solid oxide fuel cell system and facilitate subsequent linear analysis of the SOFC system.

[0006] To achieve the above purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a T-S fuzzy modeling method of a solid oxide fuel cell, comprising:

[0008] analyzing the model structure of the solid oxide fuel cell system;

[0009] based on the model structure analysis results of the solid oxide fuel cell system, using a griding and balanced decomposition algorithm based on the included angle to decompose the solid oxide fuel cell system into five linear sub-models;

[0010] convert the five linear sub-models into five piecewise radiation models;

[0011] take the hydrogen flow as the premise variable, and convert the five piecewise radiation models into five fuzzy rules according to the modeling principle of the T-S fuzzy model;

[0012] construct the corresponding fuzzy membership functions according to the five piecewise radiation models, combine the five fuzzy rules, and obtain the T-S fuzzy model of the solid oxide fuel cell through weighted summation.

[0013] Optionally, the output voltage is taken as the scheduling variable of the solid oxide fuel cell system, and the model structure of the solid oxide fuel cell system is analyzed, and the formula is as follows:

[0014]

[0015] wherein, represents the hydrogen input flow; represents the hydrogen partial pressure, the oxygen partial pressure, and the water vapor partial pressure, respectively; represents the derivative of the hydrogen partial pressure, the oxygen partial pressure, and the water vapor partial pressure, respectively;I fc is the current of the fuel cell system;V fc is the output voltage of the fuel cell system; represents the time constant of the hydrogen flow; represents the time constant of the water flow; represents the time constant of the oxygen flow; represents the hydrogen molar constant;K r represents the set constant; represents the water molar constant; represents the oxygen molar constant; r H-O represents the hydrogen-oxygen ratio; represents the number of cells in the stack; E0 represents the standard cell electromotive force; R represents the universal gas constant; T represents the absolute temperature; F represents the Faraday constant; and r represents the ohmic loss.

[0016] Optionally, based on the model structure analysis result of the solid oxide fuel cell system, the solid oxide fuel cell system is decomposed into five linear sub-models by using the grid-based and balanced decomposition algorithm based on the included angle, including:

[0017] based on the model structure analysis result of the solid oxide fuel cell system, i.e. formula (1) and formula (2), let y = V fc ;

[0018] The five linear sub-models are obtained by using the grid-based and balanced decomposition algorithm based on the included angle, and the formula (1) and formula (2) are discretized as follows:

[0019]

[0020] Wherein, k represents time, that is, the kth moment; A i ,B i ,C i ,D i is the correlation matrix of the linear sub-model, that is, the state space matrix of the discretized linear sub-model of the solid oxide fuel cell system at the equilibrium point (x 0i ,u 0i ,y 0i ), x 0i ,u 0i ,y 0i respectively represent the value of the state variable, input and output at the ith equilibrium point, and i represents the sequence number.

[0021] Optionally, the five linear sub-models are converted into five piecewise radial models, wherein the ith piecewise radial model is represented as follows:

[0022]

[0023] Wherein, b i =-A i x 0i -B i u 0i , d i =-C i x 0i -D i u 0i .

[0024] Optionally, the hydrogen flow rate is taken as the premise variable, and the five piecewise radial models are converted into five fuzzy rules according to the modeling principle of the T-S fuzzy model, wherein the ith fuzzy rule R i is represented as follows:

[0025]

[0026] Wherein, is the premise variable, also known as the antecedent variable; F i is the fuzzy domain of , that is, the ith sub-region corresponding to the ith piecewise radial model.

[0027] Optionally, the corresponding fuzzy membership functions are constructed according to the five piecewise radial models, comprising:

[0028] Calculate the slope angle θ of the static input-output curve of the solid oxide fuel cell system at time k. k =actan(K) k ); where K k This represents the slope of the curve.

[0029] Based on slope angle θ k The normalized angle between the solid oxide fuel cell system at time k and the i-th segmented radiation model is calculated. It is expressed as follows:

[0030]

[0031] Where, θ max The maximum angle is the absolute value of the difference in slope angles between any two points on the static input-output curve.

[0032] According to the normalized angle Calculate the membership function φ of the i-th segmented radiation model at time k. i (k) is represented as follows:

[0033]

[0034] Where, k e >0 indicates a tuning parameter; and φ i (k) satisfies j represents the sequence number.

[0035] Optionally, the TS fuzzy model of the solid oxide fuel cell is obtained by combining the five fuzzy rules and performing a weighted summation, as follows:

[0036]

[0037] Secondly, the present invention provides a TS fuzzy modeling system for solid oxide fuel cells, comprising:

[0038] The first processing module analyzes the model structure of the solid oxide fuel cell system.

[0039] The second processing module is used to decompose the solid oxide fuel cell system into five linear sub-models based on the model structure analysis results of the solid oxide fuel cell system using angle-based meshing and equalization decomposition algorithms.

[0040] The third processing module is used to convert the five linear sub-models into five segmented radial models;

[0041] The fourth processing module is used to convert the five segmented radiation models into five fuzzy rules based on the hydrogen flow rate as a prerequisite variable and the modeling principles of the TS fuzzy model.

[0042] The fifth processing module is used to construct the corresponding fuzzy membership functions based on the five segmented radial models, and combine them with the five fuzzy rules to obtain the TS fuzzy model of the solid oxide fuel cell through weighted summation.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0044] This invention provides a fuzzy modeling method and system for the TS (Transmission Transmission) model of solid oxide fuel cells (SOFCs). The method targets nonlinear SOFC systems. First, it decomposes the SOFC into five linear sub-models using an angle-based equilibrium decomposition method. These five linear sub-models are then transformed into five piecewise radial (PWA) models. Next, hydrogen flow rate is selected as the premise variable, and five fuzzy rules are established for the five PWA models. Then, a fuzzy membership function is constructed for each PWA model using the angle, and the TS fuzzy model of the SOFC system is obtained through weighted summation. The SOFC system modeling method provided by this invention not only produces a highly accurate TS fuzzy model but also facilitates subsequent analysis and optimization control. Attached Figure Description

[0045] Figure 1 The diagram shown is a schematic flowchart of the TS fuzzy modeling method in one embodiment of the present invention;

[0046] Figure 2 The figure shown is a schematic diagram of five fuzzy membership curves based on the included angle in one embodiment of the present invention;

[0047] Figure 3 The diagram shown is a comparison of the system output and the TS model output in an embodiment of the present invention for the open-loop model verification of the SOFC system.

[0048] Figure 4 The diagram shown is a residual diagram of the open-loop model verification of the SOFC system in one embodiment of the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0050] Example 1

[0051] like Figure 1 As shown in the figure, this embodiment of the invention introduces a TS fuzzy modeling method for solid oxide fuel cells, including the following steps:

[0052] S1: analyzing the model structure of the solid oxide fuel cell system;

[0053] S2: based on the model structure analysis result of the solid oxide fuel cell system, using the griding and balanced decomposition algorithm based on the included angle to decompose the solid oxide fuel cell system into five linear sub-models;

[0054] S3: converting the five linear sub-models into five piecewise radiation models;

[0055] S4: taking the hydrogen flow as the premise variable, and converting the five piecewise radiation models into five fuzzy rules according to the modeling principle of the T-S fuzzy model;

[0056] S5: constructing the corresponding fuzzy membership functions according to the five piecewise radiation models, combining the five fuzzy rules, and obtaining the T-S fuzzy model of the solid oxide fuel cell through weighted summation.

[0057] The embodiment provides a T-S fuzzy modeling method of a solid oxide fuel cell. The method is aimed at a nonlinear solid oxide fuel cell (SOFC) system. Firstly, the SOFC system is decomposed into five linear sub-models by using a balanced decomposition method based on an included angle, and then the five linear sub-models are converted into five piecewise radiation (PWA) models. Secondly, hydrogen flow is selected as a premise variable, and five fuzzy rules for the five PWA models are established. Thirdly, a fuzzy membership function is constructed for each PWA model by using the included angle, and a T-S fuzzy model of the SOFC system is obtained through weighted summation. The SOFC system modeling method provided by the embodiment has high precision of the finally established T-S fuzzy model, and is convenient for subsequent analysis and optimization control.

[0058] In the embodiment, the model structure of the solid oxide fuel cell system is analyzed in step S1 by taking the output voltage as the scheduling variable of the solid oxide fuel cell system, and the formula is as follows:

[0059]

[0060] wherein, represents the hydrogen input flow; respectively represent the hydrogen partial pressure, the oxygen partial pressure and the water vapor partial pressure; respectively represent the derivatives of the hydrogen partial pressure, the oxygen partial pressure and the water vapor partial pressure; fc (A) is the current of the fuel cell system; fc (V) is the output voltage of the fuel cell system; represents the time constant of the hydrogen flow; represents the time constant of the water flow; represents the time constant of the oxygen flow; Expressed as a molar constant for hydrogen gas; K r Expressed as a set constant; Expressed as a molar constant for water; Expressed as a molar constant for oxygen gas; r H-O Expressed as a ratio of hydrogen to oxygen; Expressed as the number of cells in the stack; E0expressed as a standard cell electromotive force; R expressed as a universal gas constant; T expressed as an absolute temperature; F expressed as a Faraday constant; r expressed as an ohmic loss.

[0061] In this embodiment, step S2 decomposes the solid oxide fuel cell system into five linear sub-models based on the model structure analysis result of the solid oxide fuel cell system using the grid-based and balanced decomposition algorithm based on the included angle, including:

[0062] Based on the model structure analysis result of the solid oxide fuel cell system, i.e., formula (1) and formula (2), let y = V fc ;

[0063] The grid-based and balanced decomposition algorithm based on the included angle is used to perform multi-model decomposition and discretization on formula (1) and formula (2) to obtain the following five linear sub-models in the state space form:

[0064]

[0065] Wherein, k represents time, i.e., the kth moment; A i ,B i ,C i ,D i are the related matrices of the linear sub-models, i.e., the state space matrices of the discretized linear sub-models of the solid oxide fuel cell system at the equilibrium point (x 0i ,u 0i ,y 0i ), x 0i ,u 0i ,y 0i represent the values of the state variable, input, and output at the ith equilibrium point, respectively, and i represents the sequence number.

[0066] Specifically, the related matrices A i ,B i ,C i ,D i of the linear sub-models represent the following, respectively:

[0067] For the first linear sub-model, i.e., i = 1 in the above linear sub-model formula:

[0068] Ci = [148.9, -8.5, 72.82], Di = 0;

[0069] For the second linear submodel, i.e. i = 2 in the above linear submodel formula:

[0070] C2 = [121.1 -8.5 68.3], D2 = 0;

[0071] For the third linear submodel, i.e. i = 3 in the above linear submodel formula:

[0072] C3 = [99.9 -8.5 63.9], D3 = 0;

[0073] For the fourth linear submodel, i.e. i = 4 in the above linear submodel formula:

[0074] C4 = [81.9 -8.5 59.0], D4 = 0;

[0075] For the fifth linear submodel, i.e. i = 5 in the above linear submodel formula:

[0076] C5 = [65.4-8.5 53.3], D5 = 0;

[0077] Specifically, the values of the state variables, input and output at the i-th equilibrium point are respectively represented as:

[0078] First equilibrium point: x 01 = [0.1414, 2.4811, 0.1446]', u 01 = 0.816, y 01 = 328.3

[0079] Second equilibrium point: x 02 = [0.1738, 2.4811, 0.1541]', u 02 = 0.844, y 02 = 333.3

[0080] Third equilibrium point: x 03 = [0.2109, 2.4811, 0.1649]', u 03 = 0.875, y 03 = 338.1

[0081] Fourth equilibrium point: x 04 = [0.2573, 2.4811, 0.1785]', u 04 = 0.914, y 04 = 340.3= 343.1

[0082] Fifth equilibrium point: x 05 = [0.3221, 2.4811, 0.1974]', u 05 = 0.969, y 05 = 348.9

[0083] In this embodiment, step S3 converts the five linear sub-models into five piecewise radial models, where the ith piecewise radial model is represented as follows:

[0084]

[0085] where, b i = -A i x 0i -B i u 0i , d i = -C i x 0i -D i u 0i .

[0086] In this embodiment, step S4 takes the hydrogen flow rate as the premise variable and converts the five piecewise radial models into five fuzzy rules according to the modeling principle of T-S fuzzy models, where the ith fuzzy rule R i is represented as follows:

[0087]

[0088] where, is the premise variable, also known as the antecedent variable; F i is the fuzzy domain of , i.e., the ith sub-region corresponding to the ith piecewise radial model;

[0089] Specifically, the first sub-region corresponding to the first piecewise radial model: 0.75 ≤ u < 0.828;

[0090] The second sub-region corresponding to the second piecewise radial model: 0.828 ≤ u < 0.856;

[0091] The third sub-region corresponding to the third piecewise radial model: 0.856 ≤ u < 0.891;

[0092] The fourth sub-region corresponding to the fourth piecewise radial model: 0.891 ≤ u < 0.938;

[0093] The fifth sub-region corresponding to the fifth piecewise radial model: 0.938 ≤ u < 1.

[0094] In this embodiment, step S5 constructs the corresponding fuzzy membership function based on the five segmented radial models, including:

[0095] Considering that the SOFC system described by equations (1) and (2) has a Hammerstein-Wiener model structure, its nonlinear characteristics can be measured by the included angle. Therefore, n grid points are distributed on its static input-output curve. In this embodiment, n = 70 is set.

[0096] Therefore, the slope angle θ at any grid point on the static input-output curve of the solid oxide fuel cell (SOFC) system can be calculated. z =actan(K) z ),in That is, the slope at the z-th grid point on the static input-output curve of the SOFC system.

[0097] Therefore, the included angle between any two points can be calculated using the following formula:

[0098] θ zj =|θ z -θ j |,1≤z,j≤n

[0099] And find the largest included angle:

[0100]

[0101] Based on the above theory, we can first calculate the slope angle θ of the static input-output curve of the solid oxide fuel cell system at time k. k =actan(K) k ); where K k This represents the slope of the curve.

[0102] Then based on the slope angle θ k The normalized angle between the solid oxide fuel cell system at time k and the i-th segmented radiation model is calculated. It is expressed as follows:

[0103]

[0104] Where, θ max The maximum angle is the absolute value of the difference in slope angles between any two points on the static input-output curve.

[0105] Finally, based on the normalized angle Calculate the membership function φ of the i-th segmented radiation model at time k. i (k) is represented as follows:

[0106]

[0107] Where, k e >0 is the tuning parameter, which is equal to 5 here; and φ i (k) satisfies j represents the sequence number.

[0108] Specifically, such as Figure 2 The diagram shows the membership function curves of the five segmented radiation models, where MF1, MF2, MF3, MF4, and MF5 represent the membership curves of the first, second, third, fourth, and fifth segmented radiation models, respectively.

[0109] In this embodiment, the TS fuzzy model of the solid oxide fuel cell, obtained by combining five fuzzy rules and weighted summation, is expressed as follows:

[0110]

[0111] Example 2

[0112] Based on the TS fuzzy modeling method for solid oxide fuel cells provided in Example 1, this example provides a computational example, which is described below:

[0113] This embodiment uses a pseudo-random binary signal between [0.75, 1] ​​to perform open-loop simulation of the TS fuzzy model and the SOFC system, such as... Figure 3 As shown, y represents the output voltage of the SOFC system under the action of a pseudo-random binary signal. m This represents the output voltage of the TS fuzzy model under the action of a pseudo-random binary signal; Figure 3 The x-coordinate is t(s);

[0114] exist Figure 3 Based on this, we can obtain the following: Figure 4 The diagram shown illustrates the model validation residuals of the SOFC system under pseudo-random binary signaling, where error represents y and y'. m The deviation between them is calculated; based on this, the overall root mean square error RMSE = 0.0012 is calculated, indicating a very high degree of fit. Therefore, the TS fuzzy model finally obtained based on the modeling method proposed in this invention has high accuracy, can well approximate the dynamic characteristics of SOFC, accurately express the SOFC system, and facilitates subsequent analysis and optimization control.

[0115] Example 3

[0116] This embodiment provides a TS fuzzy modeling system for solid oxide fuel cells, including:

[0117] The first processing module analyzes the model structure of the solid oxide fuel cell system.

[0118] The second processing module is configured to, based on a model structure analysis result of the solid oxide fuel cell system, decompose the solid oxide fuel cell system into five linear sub-models by using a grid-based and balanced decomposition algorithm based on an included angle;

[0119] The third processing module is configured to convert the five linear sub-models into five piecewise radiation models.

[0120] The fourth processing module is configured to take a hydrogen flow as a premise variable, and convert the five piecewise radiation models into five fuzzy rules according to a modeling principle of a T-S fuzzy model.

[0121] The fifth processing module is configured to construct corresponding fuzzy membership functions according to the five piecewise radiation models, and obtain a T-S fuzzy model of the solid oxide fuel cell by weighted summation in combination with the five fuzzy rules.

[0122] Embodiment 4

[0123] The embodiment provides a computer readable storage medium storing a computer program, the computer program being executed to implement the T-S fuzzy modeling method of the solid oxide fuel cell according to the embodiment 1.

[0124] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0125] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0126] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0127] The embodiments of the present application described above are merely intended to illustrate the present application, and the above-described embodiments are merely illustrative but not restrictive, and those skilled in the art can make many forms under the guidance of the present application without departing from the purpose of the present application and the scope of the claims, which are all within the protection scope of the present application.

Claims

1. A TS fuzzy modeling method for solid oxide fuel cells, characterized in that, include: Analyze the model structure of the solid oxide fuel cell system; Based on the model structure analysis results of the solid oxide fuel cell system, the solid oxide fuel cell system is decomposed into five linear sub-models using an angle-based gridding and equalization decomposition algorithm. The five linear sub-models are converted into five piecewise radial models; Using hydrogen flow rate as the premise variable, and based on the modeling principles of the TS fuzzy model, the five segmented radiation models are transformed into five fuzzy rules; Based on the five segmented radial models, corresponding fuzzy membership functions are constructed, and combined with the five fuzzy rules, the TS fuzzy model of solid oxide fuel cells is obtained by weighted summation.

2. The TS fuzzy modeling method for solid oxide fuel cells according to claim 1, characterized in that, The model structure of the analyzed solid oxide fuel cell system is expressed by the following formula: in, This is expressed as the hydrogen input flow rate; These are the partial pressures of hydrogen, oxygen, and water vapor, respectively. Let I represent the derivatives of the partial pressures of hydrogen, oxygen, and water vapor, respectively. fc V represents the current of the fuel cell system. fc This refers to the output voltage of the fuel cell system. Expressed as the time constant of the hydrogen gas flow; Expressed as the time constant of the water flow; Expressed as the time constant of the oxygen flow; Expressed as the molar constant of hydrogen; K r Represented as a set constant; Expressed as the water molar constant; Represented as the oxygen molar constant; r H-O Expressed as the hydrogen-to-oxygen ratio; E0 represents the number of cells in the stack; R represents the standard cell electromotive force; T represents the absolute temperature; F represents the Faraday constant; and r represents the ohmic loss.

3. The TS fuzzy modeling method for solid oxide fuel cells according to claim 2, characterized in that, The model structure analysis results of the solid oxide fuel cell system, using an angle-based meshing and equalization decomposition algorithm, decompose the solid oxide fuel cell system into five linear sub-models, including: Based on the model structure analysis results of the solid oxide fuel cell system, namely equations (1) and (2), let Using an angle-based gridding and equalization decomposition algorithm, equations (1) and (2) are decomposed into multiple models and discretized to obtain five linear sub-models in the following state-space form: Where k represents time, i.e., the kth moment; A i B i C i D i The correlation matrix of the linear sub-model, i.e., the solid oxide fuel cell system at the equilibrium point (x 0i ,u 0i ,y 0i The state space matrix of the discretized linear submodel at position x 0i ,u 0i ,y 0i Let i represent the values ​​of the state variable, input, and output at the i-th equilibrium point, respectively, where i represents the sequence number.

4. The TS fuzzy modeling method for solid oxide fuel cells according to claim 3, characterized in that, The five linear sub-models are converted into five piecewise radial models, where the i-th piecewise radial model is represented as follows: Among them, b i =-A i x 0i -B i in 0i ,d i =-C i x 0i -D i in 0i 。 5. The TS fuzzy modeling method for solid oxide fuel cells according to claim 4, characterized in that, The process uses hydrogen flow rate as a prerequisite variable and, based on the modeling principles of the TS fuzzy model, transforms the five segmented radiation models into five fuzzy rules, where the i-th fuzzy rule R... i It is expressed as follows: in, Premise variables, also known as antecedent variables; F i for The fuzzy domain is the i-th sub-region corresponding to the i-th segmented radial model.

6. The TS fuzzy modeling method for solid oxide fuel cells according to claim 5, characterized in that, The construction of the corresponding fuzzy membership function based on the five segmented radial models includes: Calculate the slope angle θ of the static input-output curve of the solid oxide fuel cell system at time k. k =actan(K) k ); where K k This represents the slope of the curve. Based on slope angle θ k The normalized angle between the solid oxide fuel cell system at time k and the i-th segmented radiation model is calculated. It is expressed as follows: Where, θ max The maximum angle is the absolute value of the difference in slope angles between any two points on the static input-output curve. According to the normalized angle Calculate the membership function φ of the i-th segmented radiation model at time k. i (k) is represented as follows: Where, k e >0 indicates a tuning parameter; and φ i (k) satisfies j represents the sequence number.

7. The TS fuzzy modeling method for solid oxide fuel cells according to claim 6, characterized in that, The TS fuzzy model of a solid oxide fuel cell, obtained by combining five fuzzy rules and using weighted summation, is expressed as follows:

8. A TS fuzzy modeling system for solid oxide fuel cells, characterized in that, include: The first processing module analyzes the model structure of the solid oxide fuel cell system. The second processing module is used to decompose the solid oxide fuel cell system into five linear sub-models based on the model structure analysis results of the solid oxide fuel cell system using angle-based meshing and equalization decomposition algorithms. The third processing module is used to convert the five linear sub-models into five segmented radial models; The fourth processing module is used to convert the five segmented radiation models into five fuzzy rules based on the hydrogen flow rate as a prerequisite variable and the modeling principles of the TS fuzzy model. The fifth processing module is used to construct the corresponding fuzzy membership functions based on the five segmented radial models, and combine them with the five fuzzy rules to obtain the TS fuzzy model of the solid oxide fuel cell through weighted summation.

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