Solid oxide fuel cell variable load control method and device

Abstract

The invention discloses a variable load control method and device for a solid oxide fuel cell, and relates to the field of power load control, and the method comprises the steps: building a nonlinear state space model of the solid oxide fuel cell according to a heat flow model of the solid oxide fuel cell; converting the nonlinear state space model into a standard state space model by using a Jacobian matrix linearization method; and according to the power output objective function, the constraint condition and the standard state space model, performing variable load control by using a model prediction control algorithm. According to the method, a cross-scale heat flow model is established based on the overall heat exchange process of the solid oxide fuel cell, proper state variables and input variables are selected, and a nonlinear state space model of the solid oxide fuel cell is established by taking variable loads as guidance. A standard state space model is obtained by utilizing Jacobian matrix linearization, and power is adjusted under a multi-constraint condition by utilizing a model predictive control algorithm so as to meet an expected load change.

Description

Technical Field

[0001] The present application relates to the field of power load control, and in particular to a method and device for variable load control of a solid oxide fuel cell. Background Art

[0002] Achieving scaled renewable energy is an important way to build a new power system. The high penetration of renewable energy, such as wind and solar power plants replacing traditional thermal energy, significantly changes the frequency stability and load balancing characteristics of the power system. In addition, the intermittent and random nature of wind and solar energy poses a major challenge to the stability of the power system, leading to increased grid frequency disturbances and load imbalances between the generation and consumption sides.

[0003] The stable power supply and load balance of the power system are crucial to the sustainable development of modern society. At present, there is a lack of corresponding power generation flexibility regulation resources to maintain the balance of power system load supply and demand. Therefore, finding suitable flexibility regulation resources to ensure that the power system can stably supply the required electricity and meet the load demand on the power consumption side has become the focus of current research. Solid oxide fuel cell is an advanced energy conversion technology that can directly convert hydrogen energy into electrical energy. It has the characteristics of high energy conversion efficiency and great flexibility in fuel selection. Its high operating temperature not only helps to directly utilize various fuels, but also can significantly improve power efficiency, usually exceeding 60%. It is considered to be a feasible solution to supplement flexibility regulation resources and can effectively cope with load fluctuations in the power system and the uncertainty of new energy generation. Solid oxide fuel cells involved in integrated energy systems are a technology with great potential to meet the needs of faster load response.

[0004] Solid oxide fuel cells are complex nonlinear systems with multiple dimensions and scales. Most existing studies focus on the dynamic simulation of fuel cells, which can obtain relatively detailed characteristics such as temperature, concentration, velocity and stress distribution. However, at present, the control of solid oxide fuel cells mainly focuses on temperature-related thermal management strategies and output voltage optimization control. Dynamic control of variable loads within the safety constraints is of far-reaching significance for supplementing the flexibility resources of the power grid at this stage. Summary of the invention

[0005] The purpose of the present application is to provide a solid oxide fuel cell variable load control method and device, which can realize variable load control of solid oxide fuel cells and more flexibly supplement grid resources.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a solid oxide fuel cell variable load control method, comprising:

[0008] A nonlinear state space model of the solid oxide fuel cell is established according to the heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power;

[0009] The nonlinear state space model is converted into a standard state space model by using a Jacobian matrix linearization method;

[0010] According to the power output objective function, constraints and the standard state space model, the model predictive control algorithm is used to solve the optimal input quantity to achieve variable load control; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include the upper and lower limit constraints of the state variables, the upper and lower limit constraints of the input quantity and the upper and lower limit constraints of the output quantity.

[0011] In a second aspect, the present application provides a solid oxide fuel cell variable load control device, comprising:

[0012] A nonlinear state space model building module is used to establish a nonlinear state space model of a solid oxide fuel cell according to a heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power;

[0013] A model linearization processing module, used for converting the nonlinear state space model into a standard state space model by using a Jacobian matrix linearization method;

[0014] The variable load control module is used to solve the optimal input quantity to achieve variable load control based on the power output objective function, constraints and the standard state space model using a model predictive control algorithm; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include upper and lower limit constraints of the state variables, upper and lower limit constraints of the input quantity and upper and lower limit constraints of the output quantity.

[0015] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned solid oxide fuel cell variable load control method.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned variable load control method for solid oxide fuel cells.

[0017] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned solid oxide fuel cell variable load control method when executed by a processor.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] The present application provides a variable load control method and device for a solid oxide fuel cell. According to the heat flow model of the solid oxide fuel cell, a nonlinear state space model of the solid oxide fuel cell is established; wherein the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power; the nonlinear state space model is converted into a standard state space model by using the Jacobian matrix linearization method; variable load control is performed using a model predictive control algorithm according to the power output objective function, constraints and the standard state space model. The present invention is based on a cross-scale heat flow model established in the overall heat exchange process of a solid oxide fuel cell, selects appropriate state variables and input quantities, and establishes a nonlinear state space model of a solid oxide fuel cell with variable load as the guide. The standard state space model is obtained by Jacobian matrix linearization, and the power is adjusted under multiple constraints by using a model predictive control algorithm to meet the expected load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is an application environment diagram of a variable load control method for a solid oxide fuel cell in one embodiment of the present application;

[0022] Figure 2 A schematic flow chart of a variable load control method for a solid oxide fuel cell provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram of a physical model of a solid oxide fuel cell provided in one embodiment of the present application;

[0024] Figure 4 A schematic diagram of a heat flow model of a solid oxide fuel cell provided in one embodiment of the present application;

[0025] Figure 5 A schematic diagram of the Jacobian matrix linearization process provided in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of functional modules of a solid oxide fuel cell variable load control device provided in another embodiment of the present application.

[0027] Figure numerals: 1-first preheater; 2-second preheater; 3-fuel cell stack; 4-exhaust gas combustion module; 5-electronic control module. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] The solid oxide fuel cell variable load control method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send a variable load control request instruction to the server. After the server receives the variable load control request instruction, it establishes a nonlinear state space model of the solid oxide fuel cell according to the heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel pipe wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power. The nonlinear state space model is converted into a standard state space model using the Jacobian matrix linearization method. According to the power output objective function, the constraints and the standard state space model, the model predictive control algorithm is used to perform variable load control. The server can feed back the output power of the solid oxide fuel cell calculated based on the model predictive control algorithm to the terminal.

[0031] In an exemplary embodiment, Figure 2As shown, a solid oxide fuel cell variable load control method is provided, which is executed by a computer device. In the embodiment of the present application, the method is applied to Figure 1 The server in is taken as an example to illustrate, including the following steps 101 to 103.

[0032] Step 101, based on the heat flow model of the solid oxide fuel cell, a nonlinear state space model of the solid oxide fuel cell is established; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power.

[0033] For the process of establishing the heat flow model of the solid oxide fuel cell in step 101, the solid oxide fuel cell heat exchange module is first segmented, and the physical model of the solid oxide fuel cell is established by combining the heat exchange links between the heat exchange surface and the air and fuel, such as Figure 3 As shown, a physical model of a solid oxide fuel cell is shown, including two preheaters (a first preheater 1 and a second preheater 2 ), a fuel cell stack 3 , an exhaust gas combustion module 4 and an electronic control module 5 .

[0034] Then, a heat flow model is established for the physical model. Specifically, the physical models of each heat exchange part of the segment are simplified into a linear dynamic heat flow model. When evaluating the heat storage performance of solid oxide fuel cells and the heat exchange capacity of air and fuel, the energy stored in the heat exchange fluid in the battery and on the key heat exchange wall plays a vital role. Therefore, the following assumptions are made before the heat flow model:

[0035] 1) The gas content, flow rate and temperature of the solid oxide fuel cell remain unchanged on a short time scale.

[0036] 2) The energy stored in solid oxide fuel cells is mainly in the stack and preheater components, and other components can be ignored.

[0037] 3) Assume that the outside world continuously provides air and fuel with a certain pressure, temperature and flow rate.

[0038] Based on the above assumptions, the heat flow model is established, and the heat released by the chemical reaction inside the solid oxide fuel cell can be expressed as:

[0039] Q gen =Q chem -Q elec (1)

[0040] Among them, Q chem is the available work released by the chemical reaction and is calculated as:

[0041] Q chem=nΔH (2)

[0042] Where ΔH is the change in enthalpy of the chemical reaction inside the solid oxide fuel cell; n represents the number of moles of the reactant. elec is the electrical output power of the solid oxide fuel cell and is defined as:

[0043] Q elec =I·V out (3)

[0044] Where, I represents the output current; V out Indicates the output voltage.

[0045] The radiation heat transfer between the battery stack and the air supply pipe AST is expressed as:

[0046]

[0047] In the formula, Q rad is the radiation heat transfer between the battery stack and the air supply pipe AST, ε AST represents the emissivity of the air supply duct; σ represents the Stefan-Boltzmann constant.

[0048] According to the above heat transfer, the heat flow model parameters are calculated. Among them, the stack is the core structure of the solid oxide fuel cell, which is crucial for adjusting the load change of the solid oxide fuel cell system. The simplified energy conservation equation of the stack is:

[0049]

[0050] In the formula, C AST Indicates the heat capacity of the stack air supply pipe, in J / Kg·K; T AST Indicates the temperature of the stack air supply pipe, in K; Q rad Indicates the radiation heat transfer between the battery stack and the air supply pipe, in J; T aircell,o Indicates the air outlet temperature of the stack air supply pipe, in K; R con,AST,outer It indicates the heat transfer resistance of air outside the stack air supply pipe, in K·s / J; T air,i Indicates the transient temperature of the air in the stack air supply duct, in K; R con,AST,inner It indicates the heat transfer resistance of air in the stack air supply pipe, in K·s / J; C cell Indicates the heat capacity of the battery stack, in J / Kg·K; T cell Indicates the battery stack temperature, in K; Q chem Indicates the available work released by the chemical reaction, in J; Q elec Indicates the output power of the solid oxide fuel cell, in J; Q radIndicates the radiation heat transfer between the battery stack and the air supply pipe, in J; T aircell,i Indicates the transient temperature of the air in the battery stack, in K; T fuel,i Indicates the transient temperature of the fuel, in K; R con,air It represents the heat exchange resistance between air and battery stack, in K·s / J; R con,fuel It represents the heat transfer resistance between the fuel and the battery stack, with the unit of K·s / J.

[0051] Heat flows through thermal resistance, and the thermal resistance in the battery stack is expressed as follows:

[0052]

[0053]

[0054] For the preheater of solid oxide fuel cell, the simplified energy conservation equation of the preheater is:

[0055]

[0056] In the formula, C airtube Indicates the heat capacity of the air duct, in J / Kg·K; T airtube Indicates the wall temperature of the air duct, in K; T bur Indicates the preheater flue gas inlet temperature, in K; T air1 Indicates the air temperature at the inlet of the battery stack, in K; R conv,bur,airtube It indicates the heat transfer resistance between flue gas and air duct, in K·s / J; R conv,air,tube Indicates the heat transfer resistance between air and air duct, in K·s / J; C fueltube Indicates the heat capacity of the fuel pipeline of the preheater, in J / Kg·K; T fueltube Indicates the wall temperature of the fuel pipeline of the preheater, in K; T bur Indicates the flue gas inlet temperature of the preheater, in K; T fuel1 Indicates the fuel temperature at the inlet of the stack, in K; R conv,bur,fueltube It represents the heat transfer resistance between flue gas and preheater fuel pipeline, in K·s / J; R conv,fuel,tube It represents the heat transfer resistance between the fuel and the preheater fuel pipeline, with the unit of K·s / J.

[0057] The thermal resistance in the preheater is expressed as follows:

[0058]

[0059]

[0060] The heat flow model is essentially determined by the energy conservation equations of formula (4), formula (5), formula (10) and formula (11), so the heat flow model of the solid oxide fuel cell is obtained as follows: Figure 4 shown.

[0061] Therefore, step 101, based on the heat flow model of the solid oxide fuel cell, establishes a nonlinear state space model of the solid oxide fuel cell, specifically including:

[0062] (1) The heat exchange module of the solid oxide fuel cell is segmented, and a physical model of the solid oxide fuel cell is established by combining the heat exchange links between the heat exchange surface and air and fuel.

[0063] (2) The physical model of each heat exchange part in the physical model of the solid oxide fuel cell is simplified into a linear heat flow model.

[0064] (3) Based on the heat flow model of solid oxide fuel cells, a nonlinear state space model of solid oxide fuel cells is established.

[0065] Specifically, according to the heat flow model of solid oxide fuel cells, the temperature of key points in the stack (stack cell temperature T cell , Stack air supply pipe temperature T AST ) and the key point temperature in the preheater (the air duct wall temperature of the first preheater T airtube1 , the air duct wall temperature of the second preheater T airtube2 , the fuel pipeline wall temperature of the first preheater T fueltube1 , the fuel pipe wall temperature of the second preheater T fueltube2 ), and determine the initial mass flow rate D of air and fuel air ,D fuel .

[0066] The input quantities of the solid oxide fuel cell control system include: the input mass flow, pressure and inlet temperature of the air required by the air supply subsystem to maintain the system operation, and the mass flow, pressure, humidity and inlet temperature of the fuel required by the fuel supply subsystem to maintain the system operation.

[0067] Since the initial values ​​of the input u and state x of the control system use the given values ​​of the standard working condition, that is, the data description model is based on the stable working condition reached after the battery is cold started, and the research faces a wide range of loads and many working conditions. In order to accurately present the changes in parameters under different working conditions, by selecting multiple state variables and finding the correlation function between them and the operating parameters, the target nonlinear state space equation is obtained to ensure the accuracy of the model. Figure 4 As shown in the heat flow model established, the fuel mass flow rate and the air mass flow rate are selected as the input u of the variable load control of the present invention: u = [Dair ,D fuel ] T . Figure 4 In, T air,in Indicates the initial temperature of the air when it enters the preheater; T fuel,in Indicates the initial temperature of the fuel when it enters the preheater. C dl Represents the equivalent capacitance of the double layer charging effect, R act , R ohm , R conc Represents the equivalent resistance of activation voltage drop, ohmic voltage drop, and concentration voltage drop. V cell represents the output voltage of SOFC, Q elec Represents the electrical output power. E cell Represents the electromotive force between the cathode and the anode.

[0068] Select the battery stack temperature T cell , Stack air supply pipe temperature T AST , Preheater air duct temperature T airtube1 ,T airtube2 , preheater fuel pipeline temperature T fueltube1 ,T fueltube2 The temperature of a total of 6 heat exchange surfaces is the state variable x: x = [T AST ,T cell ,T airtube1 ,T airtube2 ,T fueltube1 ,T fueltube2 ] T .

[0069] At the same time, power P is selected as the output y to explore the variable load regulation flexibility of the solid oxide fuel cell under different working conditions.

[0070] According to the energy conservation equations of formula (4), formula (5), formula (10) and formula (11), the nonlinear state space model is preliminarily obtained:

[0071]

[0072] in, Used to evaluate the heat storage capacity of heat exchange medium and battery heat exchange surface, are the state matrix and the input matrix, and Describes the changes in output caused by changes in input and state variables. The matrix is ​​usually a zero matrix. Due to the complexity of the parameters, the above matrix parameters are currently in nonlinear form.

[0073] According to the heat flow model of solid oxide fuel cells, combined with equations (4) to (15), the detailed expression of its nonlinear state transfer equation is obtained from the functional relationship of the temperature state variable x:

[0074]

[0075] The B matrix and the input u in formula (17) are mixed together and cannot be separated, which is why a linearization step is required later.

[0076] At the same time, the selected output y and input u are placed in the output equation to obtain the detailed expression of its nonlinear output equation:

[0077]

[0078] Where, T cell Indicates the battery stack temperature, in K; T AST Indicates the temperature of the stack air supply pipe, in K; T airtube1 Indicates the air duct wall temperature of the first preheater, in K; T airtube2 Indicates the air duct wall temperature of the second preheater, in K; T fueltube1 Indicates the fuel pipe wall temperature of the first preheater, in K; T fueltube2 Indicates the fuel pipe wall temperature of the second preheater, in K; T air,i Indicates the transient temperature of the air in the stack air supply duct, in K; T air,o Indicates the initial temperature of the air in the stack air supply duct, in K; T aircell,i Indicates the transient temperature of the air in the battery stack, in K; T fuel,i Indicates the transient temperature of the fuel in K; T fuel,o Indicates the initial temperature of the fuel, in K; T air1 Indicates the air temperature at the inlet of the battery stack, in K; T fuel1 Indicates the fuel temperature at the inlet of the fuel stack, in K; T bur1 Indicates the flue gas inlet temperature of the first preheater, in K; T bur2 Indicates the flue gas inlet temperature of the second preheater, in K; R con,AST,inner It indicates the heat transfer resistance of air in the stack air supply pipe, in K·s / J; R con,AST,outer It represents the heat transfer resistance of air outside the stack air supply pipe, in K·s / J; R conv,bur,airtube1 It represents the heat exchange resistance between the flue gas and the air duct of the first preheater, in K·s / J; R conv,air,tube1 It represents the heat exchange resistance between air and the first preheater air duct, in K·s / J; R conv,bur,airtube2It represents the heat exchange resistance between the flue gas and the air duct of the second preheater, in K·s / J; R conv,air,tube2 It represents the heat exchange resistance between air and the air duct of the second preheater, in K·s / J; R conv,bur,fueltube1 It represents the heat exchange resistance between the flue gas and the first preheater fuel pipeline, in K·s / J; R conv,fuel,tube1 It represents the heat transfer resistance between the fuel and the first preheater fuel pipeline, in K·s / J; R conv,bur,fueltube2 It represents the heat exchange resistance between the flue gas and the second preheater fuel pipeline, in K·s / J; R conv,fuel,tube2 It represents the heat transfer resistance between the fuel and the second preheater fuel pipeline, in K·s / J; R con,air It represents the heat exchange resistance between air and battery stack, in K·s / J; R con,fuel Indicates the thermal resistance of heat exchange between fuel and battery, in K·s / J; C AST Indicates the heat capacity of the air supply pipe, the unit is J / Kg·K; C cell Indicates the heat capacity of the battery, in J / Kg·K; C airtube1 Indicates the heat capacity of the air duct of the first preheater, in J / Kg·K; C airtube2 Indicates the heat capacity of the air duct of the second preheater, in J / Kg·K; C fueltube1 Indicates the heat capacity of the fuel pipeline of the first preheater, in J / Kg·K; C fueltube2 Indicates the heat capacity of the fuel pipeline of the second preheater, in J / Kg·K; Q gen Indicates the heat released by the chemical reaction in the battery, in J; Q chem Indicates the available work released by the chemical reaction, in J; Q rad Indicates the radiation heat transfer between the battery stack and the air supply pipe, in J; D air Indicates the air input mass flow rate; D fuel Represents the fuel input mass flow rate.

[0079] An important reason for establishing the state space expression is that it can accurately characterize the heat storage capacity of the key components of the solid oxide fuel cell at each stage, and make corrections based on the model data to obtain accurate heat transfer in real time, thereby establishing a control system model for the solid oxide fuel cell that can accurately evaluate the heat transfer capacity. The state transfer equation and output equation of the above heat transfer system can be simplified to:

[0080]

[0081] Among them, f(x,u,t) is the nonlinear state equation describing the dynamic behavior of the solid oxide fuel cell, and g(x,u,t) is the nonlinear output equation.

[0082] Step 102: using a Jacobian matrix linearization method, convert the nonlinear state space model into a standard state space model.

[0083] According to the nonlinear state space model of the solid oxide fuel cell established in the previous step, the stable point after startup is selected as the standard operating point, and the standard state space model is obtained using the Jacobian matrix linearization method. Specifically, the stable point reached when the battery is cold started is selected as the standard operating point, and the Jacobian matrix is ​​used for linearization at the standard operating point. When characterizing the state equation under different load conditions, due to the real-time influence of the value of the state variable on the properties of the solid oxide fuel cell control system, the Jacobian matrix is ​​used to The key parameter matrix linearization is as follows: Figure 5 As shown, the properties of the solid oxide fuel cell control system under different load conditions are characterized. Figure 5 In the equation, G is the heat capacity, which is a nonlinear quantity in the energy flow model.

[0084]

[0085] In formula (20), A and B are the Jacobian matrices of the state equation for the state variables and the control variables (input quantities), C and D are the Jacobian matrices of the output equation for the state variables and the control variables, (x0, u0) are the state variables and input quantities of the selected standard operating point, so as to obtain the linearized standard state space model, as shown in formula (21).

[0086]

[0087] Step 103, based on the power output objective function, constraints and the standard state space model, use the model predictive control algorithm to solve the optimal input to achieve variable load control; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include upper and lower limit constraints of the state variables, upper and lower limit constraints of the input, and upper and lower limit constraints of the output.

[0088] According to the standard state space model established in the previous step, the model predictive control algorithm is used to design a variable load control system that can be flexibly adjusted within a safe range. The objective function is constructed as follows:

[0089]

[0090] In formula (22), h represents the controlled object (both nonlinear equations and standard state space equations are acceptable); r is the sample target. The value of target r is different in different stages of variable load and needs to be determined according to the demand on the power consumption side. It has the randomness of the sample; N is the prediction time domain and m is the control time domain. The load response weight matrix and the fuel input control weight matrix are defined as EP,i and E U,j . E represents the weight matrix factor, including E P,i and E U,j ;y(k+i|k) represents the system output of the future i-th step predicted at the current time step k, y represents the predicted output, |k represents that the predicted output is based on the information of time step k. r(k+i) represents the reference trajectory or set value of time step k+i, which is the goal that the system expects to achieve. Δu(k+j-1) represents the change of control input between time steps k+j-1 and k+j. This change is calculated by the difference between the current predicted control input u(k+j) and the predicted control input u(k+j-1) of the previous time step, that is, Δu(k+j-1)=u(k+j)-u(k+j-1).

[0091] After the objective function is established, certain constraints need to be set as safety boundaries to ensure that the power changes within the expected safe range. Solid oxide fuel cells need to change load within the rated power range, the temperature cannot change frequently or fluctuate violently, and the air and fuel inputs must be within safety limits, so the constraints set are as follows:

[0092] u min,e ≤u e ≤u max,e

[0093] y min ≤y≤y max

[0094] T min,e ≤x e ≤T max,e (twenty three)

[0095] In formula (23), the input quantity, output quantity and state variable are constrained respectively. min,e and u max,e Respectively represent the input quantity u e The minimum and maximum values ​​of y min and max Respectively represent the minimum and maximum values ​​of the output y; T min,e and T max,e Represent the state variable x e The minimum and maximum temperature values.

[0096] The present invention establishes a complete solid oxide fuel cell control system including an objective function and constraints, and then uses a model predictive control algorithm to adjust the power within a safety boundary, so that the output power is close to the sample target while satisfying the constraints, minimizes the objective function, and solves the optimal input quantity (i.e., the optimal air input mass flow rate and fuel input mass flow rate). Based on the optimal input quantity, the air input mass flow rate and fuel input mass flow rate of the solid oxide fuel cell are regulated, so that the output power is close to the sample target and meets the expected load change. For the principle of the model predictive control algorithm, a standard state space model equation is required, which is brought into the algorithm, and the objective function and constraints are used to optimize and solve the optimal input quantity to achieve the desired variable load control (power control).

[0097] By implementing the above steps 101 to 103, the present invention establishes a control system that comprehensively considers key structures such as the solid oxide fuel cell stack and preheater. Based on the cross-scale heat flow model established in the overall heat exchange process of the solid oxide fuel cell, appropriate state variables and input quantities are selected, and a nonlinear state space model is established with variable load as the guide. The standard state space model is obtained by linearization of the Jacobian matrix, the controller parameters are designed, and the model predictive control algorithm is used to adjust the power under multiple constraints to meet the expected changes. Among them, cross-scale means that it can effectively handle the complex characteristics of multi-parameter, multi-level and multi-process of internal electrochemistry and external heat exchange of SOFC (solid oxide fuel cell), and integrate the external heat transfer and mass transfer processes in the SOFC system.

[0098] Based on the same inventive concept, the embodiment of the present application also provides a solid oxide fuel cell variable load control device for implementing the solid oxide fuel cell variable load control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more solid oxide fuel cell variable load control device embodiments provided below can refer to the limitations of the solid oxide fuel cell variable load control method above, and will not be repeated here.

[0099] In an exemplary embodiment, Figure 6 As shown, a solid oxide fuel cell variable load control device is provided, comprising:

[0100] The nonlinear state space model building module M1 is used to establish a nonlinear state space model of the solid oxide fuel cell based on the heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel pipe wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is power.

[0101] The model linearization processing module M2 is used to transform the nonlinear state space model into a standard state space model by using the Jacobian matrix linearization method.

[0102] The variable load control module M3 is used to solve the optimal input quantity to achieve variable load control based on the power output objective function, constraints and the standard state space model using the model predictive control algorithm; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include the upper and lower limit constraints of the state variables, the upper and lower limit constraints of the input quantity and the upper and lower limit constraints of the output quantity.

[0103] In an exemplary embodiment, a computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the solid oxide fuel cell variable load control method.

[0104] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned variable load control method for solid oxide fuel cells is implemented.

[0105] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above-mentioned variable load control method for solid oxide fuel cells when executed by a processor.

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A variable load control method for a solid oxide fuel cell, characterized in that: The solid oxide fuel cell variable load control method comprises: A nonlinear state space model of the solid oxide fuel cell is established according to the heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power; The nonlinear state space model is converted into a standard state space model by using a Jacobian matrix linearization method; According to the power output objective function, constraints and the standard state space model, the model predictive control algorithm is used to solve the optimal input quantity to achieve variable load control; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include the upper and lower limit constraints of the state variables, the upper and lower limit constraints of the input quantity and the upper and lower limit constraints of the output quantity.

2. The solid oxide fuel cell variable load control method according to claim 1, characterized in that: According to the heat flow model of solid oxide fuel cell, a nonlinear state space model of solid oxide fuel cell is established, which specifically includes: The heat exchange module of the solid oxide fuel cell is segmented, and the physical model of the solid oxide fuel cell is established by combining the heat exchange links between the heat exchange surface and the air and fuel; Simplifying the physical model of each heat exchange part in the physical model of the solid oxide fuel cell into a linear heat flow model; According to the heat flow model of solid oxide fuel cell, a nonlinear state space model of solid oxide fuel cell is established.

3. The variable load control method for solid oxide fuel cell according to claim 1, characterized in that: The heat flow model of the solid oxide fuel cell includes an energy conservation equation of the stack in the solid oxide fuel cell and an energy conservation equation of the preheater; The energy conservation equation of the battery stack is: In the formula, C AST Indicates the heat capacity of the stack air supply pipe; T AST Indicates the temperature of the stack air supply pipe K; Q rad Indicates the radiation heat transfer between the battery stack and the air supply pipe; T aircell,o Indicates the air outlet temperature of the stack air supply pipe; R con,AST,outer It represents the heat transfer resistance of air outside the stack air supply pipe; T air,i Indicates the transient temperature of the air in the stack air supply duct; R con,AST,inner Indicates the heat transfer resistance of air in the stack air supply duct; C cell Indicates the heat capacity of the battery stack; T cell Indicates the battery stack temperature; Q chem Indicates the available work released by the chemical reaction; Q elec Represents the output power of the solid oxide fuel cell; Q rad Indicates the radiation heat transfer between the battery stack and the air supply pipe; T aircell,i Indicates the transient temperature of the air in the battery stack; T fuel,i Represents the transient temperature of the fuel; R con,air Represents the heat transfer resistance between air and battery stack; R con,fuel Indicates the heat transfer resistance between the fuel and the battery stack; The energy conservation equation of the preheater is: In the formula, C airtube represents the heat capacity of the air duct of the preheater; T airtube Indicates the wall temperature of the air duct of the preheater; T bur Indicates the preheater flue gas inlet temperature; T air1 Indicates the air temperature at the inlet of the battery stack; R conv,bur,airtube Represents the heat exchange resistance between flue gas and preheater air duct; R conv,air,tube Represents the heat exchange resistance between air and preheater air duct; C fueltube represents the heat capacity of the fuel pipeline of the preheater; T fueltube Indicates the fuel pipe wall temperature of the preheater; T bur Indicates the flue gas inlet temperature of the preheater; T fuel1 Indicates the fuel temperature at the inlet of the stack; R conv,bur,fueltube Represents the heat transfer resistance between flue gas and preheater fuel pipeline; R conv,fuel,tube Represents the heat transfer thermal resistance between the fuel and the preheater fuel pipeline.

4. The variable load control method for solid oxide fuel cell according to claim 1, characterized in that: The standard state space model is: y=Cx+Du; in, In the formula, x is the state variable, x=[T AST ,T cell ,T airtube1 ,T airtube2 ,T fueltube1 ,T fueltube2 ] T ; u is the input quantity, u=[D air ,D fuel ] T ; y is the output; (x0,u0) is the state variable and input of the selected standard operating point; and Used to describe the change of output caused by the change of input and state variables; T cell Indicates the battery stack temperature; T AST Indicates the temperature of the stack air supply pipe; T airtube1 represents the air duct wall temperature of the first preheater; T airtube2 Indicates the air duct wall temperature of the second preheater; T fueltube1 represents the fuel pipeline wall temperature of the first preheater; T fueltube2 Indicates the fuel pipe wall temperature of the second preheater; T air,i Indicates the transient temperature of the air in the stack air supply duct; T air,o Indicates the initial temperature of the air in the stack air supply duct; T aircell,i Indicates the transient temperature of the air in the battery stack; T fuel,i Indicates the transient temperature of the fuel; T fuel,o Indicates the initial temperature of the fuel; T air1 Indicates the air temperature at the stack inlet; T fuel1 Indicates the fuel temperature at the inlet of the fuel stack; T bur1 represents the flue gas inlet temperature of the first preheater; T bur2 Indicates the flue gas inlet temperature of the second preheater; R con,AST,inner Represents the heat transfer resistance of air in the stack air supply duct; R con,AST,outer Represents the heat transfer resistance of air outside the stack air supply pipe; R conv,bur,airtube1 Represents the heat exchange resistance between the flue gas and the first preheater air duct; R conv,air,tube1 Represents the heat exchange resistance between air and the first preheater air duct; R conv,bur,airtube2 It represents the heat exchange resistance between flue gas and the air duct of the second preheater; R conv,air,tube2 Represents the heat exchange resistance between air and the second preheater air duct; R conv,bur,fueltube1 Represents the heat exchange resistance between the flue gas and the first preheater fuel pipeline; R conv,fuel,tube1 Represents the heat exchange resistance between the fuel and the first preheater fuel pipeline; R conv,bur,fueltube2 Represents the heat exchange resistance between the flue gas and the second preheater fuel pipeline; R conv,fuel,tube2 Represents the heat exchange resistance between the fuel and the second preheater fuel pipeline; R con,air Represents the heat transfer resistance between air and battery stack; R con,fuel Represents the thermal resistance of heat exchange between fuel and battery; C AST Indicates the heat capacity of the air supply pipe; C cell Indicates the thermal capacity of the battery; C airtube1 represents the heat capacity of the air duct of the first preheater; C airtube2 represents the heat capacity of the air duct of the second preheater; C fueltube1 represents the heat capacity of the fuel pipeline of the first preheater; C fueltube2 represents the heat capacity of the fuel pipeline of the second preheater; Q gen Indicates the heat released by the chemical reaction in the battery; Q chem Indicates the available work released by the chemical reaction; Q rad Indicates the radiation heat transfer between the battery stack and the air supply pipe; D air Indicates the air input mass flow rate; D fuel Represents the fuel input mass flow rate.

5. The variable load control method for solid oxide fuel cell according to claim 1, characterized in that: The power output objective function is: Where h represents the standard state space model; E represents the weight matrix factor, including E P,i and E U,j ; r is the sample target; N is the prediction time domain; m is the control time domain; E P,i and E U,j are the load response weight matrix and the fuel input control weight matrix respectively; y(k+i|k) represents the system output of the future i-th step predicted at the current time step k; r(k+i) represents the reference trajectory of time step k+i; Δu(k+j-1) represents the change of control input between time steps k+j-1 and k+j.

6. A variable load control device for a solid oxide fuel cell, characterized in that: The solid oxide fuel cell variable load control device comprises: A nonlinear state space model building module is used to establish a nonlinear state space model of a solid oxide fuel cell according to a heat flow model of the solid oxide fuel cell; the state variables in the nonlinear state space model are the battery temperature, the air supply pipe temperature, the air duct wall temperature and the fuel duct wall temperature, the input is the fuel mass flow rate and the air mass flow rate, and the output is the power; A model linearization processing module, used for converting the nonlinear state space model into a standard state space model by using a Jacobian matrix linearization method; The variable load control module is used to solve the optimal input quantity to achieve variable load control based on the power output objective function, constraints and the standard state space model using a model predictive control algorithm; the standard state space model is the prediction model in the model predictive control algorithm; the constraints include upper and lower limit constraints of the state variables, upper and lower limit constraints of the input quantity and upper and lower limit constraints of the output quantity.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the variable load control method for a solid oxide fuel cell according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the variable load control method for a solid oxide fuel cell according to any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the variable load control method for a solid oxide fuel cell according to any one of claims 1 to 5 is implemented.

Images