A battery system control method, device and storage medium

Abstract

The application discloses a battery system control method, equipment and storage medium, and belongs to the technical field of fuel cells. The method comprises the following steps: a solid oxide fuel cell system corresponding model structure is subjected to multi-model decomposition processing by using an angle-based balanced multi-model decomposition method, and a plurality of linear sub-models are obtained; an angle-based fuzzy weighting function corresponding to each linear sub-model is constructed based on a static input-output curve corresponding to the solid oxide fuel cell system; each sub-predictive controller is subjected to weighting and synthesis processing according to the angle-based fuzzy weighting function corresponding to each linear sub-model, and a global predictive controller is obtained; each sub-predictive controller is designed based on each linear sub-model; and the solid oxide fuel cell system is subjected to optimal control based on the global predictive controller. The technical scheme provided by the application can effectively inhibit the influence of an interference signal on an output voltage, and ensure that fuel utilization rate is within a safe range.

Description

Technical Field

[0001] This invention relates to the field of solid oxide fuel cell technology, and in particular to a battery system control method, device and storage medium. Background Technology

[0002] Fuel cell technology is the fourth generation of power generation technology after hydropower, thermal power, and nuclear power. It boasts a series of advantages, including high power generation efficiency, fast response speed, low pollution, low noise, and high reliability, bringing revolutionary changes to the energy industry. Solid oxide fuel cells (SOFCs), in addition to the above advantages, also offer benefits such as a wide range of fuel applications and the ability to recover high-temperature waste heat. Therefore, SOFC technology has broad application prospects in large-scale power generation, distributed power generation, combined heat and power (CHP), transportation, and peak-shaving energy storage, and is hailed as the most promising new energy power generation technology of the 21st century.

[0003] However, solid oxide fuel cell systems are highly complex nonlinear systems. Over the past decade, various control techniques have been proposed for fuel cell systems, such as traditional PID control (Proportional Integral Derivative control), fuzzy control, neural networks, robust control, and predictive control. In recent years, multi-model control (MMM) methods have achieved good control results. Based on the decomposition-synthesis principle, MMM methods can effectively transform complex nonlinear control problems into a combination of several simple linear control problems; by solving the linear control problems, the nonlinear control problem is solved. The simplification characteristic of MMM methods makes them widely used in the field of nonlinear control. However, in the control of solid oxide fuel cell systems, it is necessary to ensure that the output voltage stabilizes quickly and accurately at the desired value, and to ensure that the fuel utilization rate is within the desired range. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a battery system control method, device and storage medium that can effectively suppress the influence of interference signals on the output voltage and ensure that the fuel utilization rate is within a safe range, thereby improving the effectiveness of solid oxide fuel cell system control and enhancing the reliability of solid oxide fuel cells.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] On one hand, the present invention provides a battery system control method, the method comprising:

[0007] Determine the model structure corresponding to the solid oxide fuel cell system;

[0008] Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a predetermined number of linear sub-models.

[0009] Based on the static input-output curves corresponding to the solid oxide fuel cell system, a fuzzy weighting function based on the included angle is constructed for each linear sub-model.

[0010] Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, each sub-predictive controller is weighted and synthesized to obtain a global predictive controller; each sub-predictive controller is designed based on each linear sub-model.

[0011] The solid oxide fuel cell system is optimized and controlled based on the global predictive controller.

[0012] In some possible implementations, the output voltage is used as the scheduling variable for the solid oxide fuel cell system; the determination of the model structure corresponding to the solid oxide fuel cell system includes:

[0013] ; (1)

[0014] ; (2)

[0015] The model structure corresponding to the solid oxide fuel cell system is determined based on equations (1) and (2), wherein, , , These represent the derivatives corresponding to the partial pressures of hydrogen, oxygen, and water vapor, respectively. These represent the partial pressures of hydrogen, oxygen, and water vapor, respectively. Indicates the hydrogen input flow rate; The time constant representing the hydrogen gas flow; This represents the time constant of the water flow; The time constant representing the oxygen flow; This represents the molar constant of hydrogen. Represents the water molar constant; This represents the molar constant of oxygen; Indicates a constant value; Indicates the hydrogen-to-oxygen ratio; This represents the current of the solid oxide fuel cell system; This indicates the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the fuel cell stack; This represents the standard battery electromotive force; Represents the universal gas constant; Indicates absolute temperature; Denotes Faraday's constant; This indicates an ohmic loss.

[0016] In some possible implementations, the step of using an angle-based equilibrium multi-model decomposition method to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system to obtain a predetermined number of linear sub-models includes:

[0017] make , , ;

[0018] Using the angle-based equilibrium multi-model decomposition method, the model structures corresponding to the solid oxide fuel cell system in equations (1) and (2) are subjected to multi-model decomposition processing to obtain a set of a predetermined number of linear sub-models corresponding to equation (3):

[0019] ; (3)

[0020] in, ; The solid oxide fuel cell system at its equilibrium point The state-space matrix coefficients of the linear submodel at the given point; The solid oxide fuel cell system is respectively represented by the first... The state value, input value, and output value at each equilibrium point; These represent the differences between the state variables and the state values, the differences between the input variables and the input values, and the differences between the output variables and the output values ​​of the solid oxide fuel cell system, respectively.

[0021] In some possible implementations, the preset expected range of the output voltage of the solid oxide fuel cell system is [310, 360)V, and the preset number of linear sub-models includes a first linear sub-model, a second linear sub-model, and a third linear sub-model; the preset number of linear sub-models obtained by using an angle-based equilibrium multi-model decomposition method to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system includes:

[0022] Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a first linear sub-model, a second linear sub-model, and a third linear sub-model. ;

[0023] The output voltage range of the first linear sub-model is [310, 325); the matrix coefficients of the first linear sub-model include:

[0024] ;

[0025] The output voltage range of the second linear sub-model is [325, 339); the matrix coefficients of the second linear sub-model include:

[0026] ;

[0027] The output voltage range of the third linear sub-model is [339, 360); the matrix coefficients of the third linear sub-model include:

[0028] .

[0029] In some possible implementations, constructing the angle-based fuzzy weighting function for each linear sub-model based on the static input-output curve corresponding to the solid oxide fuel cell system includes:

[0030] Multiple grid points are set for the static input-output curve, and the slope angle at each grid point is determined;

[0031] The target included angle is determined based on the slope angle at each grid point; the target included angle is the maximum absolute value of the difference between the slope angles at any two grid points;

[0032] Determine the slope angle corresponding to the target time of the solid oxide fuel cell system;

[0033] Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target included angle, a fuzzy weighting function based on the included angle is constructed for each linear sub-model.

[0034] In some possible implementations, constructing the angle-based fuzzy weighting function for each linear sub-model based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle includes:

[0035] Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model is determined, as shown in equation (4).

[0036] ; (4)

[0037] in, This represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model. This refers to the solid oxide fuel cell system. The slope angle corresponding to the time, the The time corresponds to the target time; This represents the slope angle corresponding to each linear sub-model. Indicates the included angle of the target;

[0038] Based on the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model, a fuzzy weighting function based on the angle is constructed for each linear sub-model, as shown in equation (5).

[0039] ; (5)

[0040] in, ; (6)

[0041] Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model; These are preset parameters; satisfy ; This represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

[0042] In some possible implementations, each sub-predictive controller is designed based on each linear sub-model in the following manner:

[0043] Consider the objective function of equation (7):

[0044] (7)

[0045] Subject to

[0046] , ; (8)

[0047] in, and It is the first The weight matrix of each predictive controller, It is the first The setpoint signal of the individual predictive controller; This indicates the operating time of the solid oxide fuel cell system. It is the first The control time domain of the sub-predictive controller It is the first Prediction time domain of individual predictive controllers; This indicates that the input variables of the solid oxide fuel cell system are related to the first... The difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The minimum difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The maximum difference between the input values ​​at each equilibrium point; The output variable of the solid oxide fuel cell system is related to the first... The difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The minimum difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The maximum difference between the output values ​​at each equilibrium point;

[0048] Solving the quadratic programming problem yields the optimal solution. ,

[0049] Based on the optimal solution The output of the sub-predictive controller is determined as shown in equation (9).

[0050] ; (9)

[0051] in, Indicates the first The output of the individual predictive controller The solid oxide fuel cell system represents the first The input values ​​at each equilibrium point.

[0052] In some possible implementations, the step of weighting and synthesizing each sub-predictive controller according to the angle-based fuzzy weighting function corresponding to each linear sub-model to obtain the global predictive controller includes:

[0053] Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, a weighted average is performed on each sub-predictive controller to obtain the global predictive controller, as shown in equation (10).

[0054] ; (10)

[0055] in, This represents the output of the global predictive controller. Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model. Indicates the first The output of the individual predictive controller.

[0056] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the battery system control method as described above.

[0057] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction and at least one program are stored therein, the at least one instruction and the at least one program being loaded and executed by a processor to implement the battery system control method as described above.

[0058] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0059] In this invention, by determining the model structure corresponding to the solid oxide fuel cell system, a multi-model decomposition process based on the angle-based equilibrium multi-model decomposition method is used to decompose the model structure of the solid oxide fuel cell system into a predetermined number of linear sub-models. Based on each linear sub-model, a sub-predictive controller can be designed, decomposing the complex nonlinear system into a simple linear system, thereby improving the convenience and efficiency of solid oxide fuel cell system control. Next, based on the static input-output curve of the solid oxide fuel cell system, an angle-based fuzzy weighting function is constructed for each linear sub-model. Based on the angle-based fuzzy weighting function for each linear sub-model, each sub-predictive controller is weighted and synthesized to obtain a global predictive controller. Then, based on the global predictive controller, the solid oxide fuel cell system is optimized for control. This allows for setpoint tracking and anti-interference control of the solid oxide fuel cell output voltage, enabling the output voltage to quickly and accurately track changes in the setpoint, effectively suppressing the influence of interference signals on the output voltage, and ensuring fuel utilization within a safe range. This improves the working performance and reliability of the solid oxide fuel cell, as well as the stability and safety of the solid oxide fuel cell system, thereby enhancing the effectiveness and flexibility of solid oxide fuel cell system control. Attached Figure Description

[0060] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic flowchart of a battery system control method provided in an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of a fuzzy weighting function based on the included angle for each linear sub-model provided in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the output voltage, control signal, and fuel utilization rate of a solid oxide fuel cell under setpoint tracking control based on a global predictive controller with fuzzy weighting of included angle, provided in an embodiment of this application.

[0064] Figure 4 This is a schematic diagram of the output voltage, control signal, and fuel utilization rate of a solid oxide fuel cell based on an angle-based fuzzy weighted global predictive controller for anti-interference control, provided in an embodiment of this application.

[0065] Figure 5 This is a schematic diagram of the structure of a battery system control device provided in an embodiment of the present invention. Detailed Implementation

[0066] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0067] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0068] In this embodiment of the invention, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0069] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0070] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0071] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0072] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0073] Figure 1 This is a flowchart illustrating a battery system control method according to an embodiment of the present invention. This specification provides the method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive methods, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the method can be executed sequentially according to the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 1 As shown, the above method may include:

[0074] S101: Determine the model structure corresponding to the solid oxide fuel cell system;

[0075] In one specific embodiment, a solid oxide fuel cell is a fuel cell that uses solid oxide as an electrolyte and operates at high temperatures. Optionally, solid oxide fuel cells have the following advantages: solid oxide fuel cells have an all-solid structure, eliminating the corrosion and electrolyte loss problems associated with using liquid electrolytes, thus improving battery life; operating in a high-temperature environment, they not only do not require precious metals for the electrocatalyst, but can also directly use natural gas, coal gas, and hydrocarbons as fuel, simplifying the fuel cell system and saving costs, among other advantages.

[0076] In an optional embodiment, the output voltage is used as the scheduling variable for the solid oxide fuel cell system; the model structure for determining the solid oxide fuel cell system includes:

[0077] ; (1)

[0078] ; (2)

[0079] The model structure corresponding to the solid oxide fuel cell system is determined based on equations (1) and (2), where, , , These represent the derivatives corresponding to the partial pressures of hydrogen, oxygen, and water vapor, respectively. These represent the partial pressures of hydrogen, oxygen, and water vapor, respectively. Indicates the hydrogen input flow rate; The time constant representing the hydrogen gas flow; This represents the time constant of the water flow; The time constant representing the oxygen flow; This represents the molar constant of hydrogen. Represents the water molar constant; This represents the molar constant of oxygen; Indicates a constant value; Indicates the hydrogen-to-oxygen ratio; This indicates the current in the solid oxide fuel cell system; This indicates the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the fuel cell stack; This represents the standard battery electromotive force; Represents the universal gas constant; Indicates absolute temperature; Denotes Faraday's constant; This indicates an ohmic loss.

[0080] In a specific embodiment, equations (1) and (2) describe a solid oxide fuel cell system. The output voltage is used as the scheduling variable for the solid oxide fuel cell system. Optionally, during the operation of the solid oxide fuel cell system, the battery temperature, fuel flow rate, oxidant flow rate, current density, etc., can be adjusted according to the output voltage to ensure that the output voltage accurately follows the preset set value and remains near that set value, thereby effectively suppressing the influence of interference signals on the output voltage. Specifically, the fuel flow rate can be the hydrogen input flow rate, and the oxidant flow rate can be the oxygen input flow rate. Optionally, the set value can be set according to actual application requirements. Specifically, the set value range of the output voltage of the solid oxide fuel cell system can be [310, 360) V.

[0081] S102: Using the angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into multiple models to obtain a preset number of linear sub-models.

[0082] In a specific embodiment, the angle-based equilibrium multi-model decomposition method is a modeling and control strategy for complex nonlinear systems. It can decompose the nonlinear system into multiple linear sub-models based on the angle, and achieve an accurate description of the original nonlinear system by balancing multiple linear sub-models. The angle can be determined based on the change in the slope of the static input-output curve corresponding to the solid oxide fuel cell system.

[0083] In an optional embodiment, the above-described angle-based equilibrium multi-model decomposition method is used to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system, resulting in a predetermined number of linear sub-models, including:

[0084] make , , ;

[0085] Using the angle-based equilibrium multi-model decomposition method, the model structures corresponding to the solid oxide fuel cell system in equations (1) and (2) are decomposed into multiple models to obtain a set of a predetermined number of linear sub-models corresponding to equation (3):

[0086] ; (3)

[0087] in, ; It is the solid oxide fuel cell system at the equilibrium point The state-space matrix coefficients of the linear submodel at the given point; These represent the first and second parts of the solid oxide fuel cell system. The state value, input value, and output value at each equilibrium point; These represent the differences between the state variables and state values, the differences between the input variables and input values, and the differences between the output variables and output values ​​of the solid oxide fuel cell system, respectively.

[0088] In an optional embodiment, the preset expected value range of the output voltage of the solid oxide fuel cell system is [310, 360)V, and the preset number of linear sub-models include a first linear sub-model, a second linear sub-model, and a third linear sub-model; the above-mentioned angle-based equilibrium multi-model decomposition method is used to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system to obtain the preset number of linear sub-models, including:

[0089] Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a first linear sub-model, a second linear sub-model, and a third linear sub-model. At this point, ;

[0090] The output voltage range of the first linear sub-model is [310, 325); the matrix coefficients of the first linear sub-model include:

[0091] ;

[0092] The output voltage range of the second linear sub-model is [325, 339); the matrix coefficients of the second linear sub-model include:

[0093] ;

[0094] The output voltage range of the third linear sub-model is [339, 360); the matrix coefficients of the third linear sub-model include:

[0095] .

[0096] In one specific embodiment, the solid oxide fuel cell system model structure is decomposed using an angle-based equilibrium multi-model decomposition method to obtain three linear sub-models. This may include: meshing the solid oxide fuel cell system using an angle-based gridding decomposition method, determining 32 grid points with a network density of 0.02; linearizing the solid oxide fuel cell system corresponding to each grid point to obtain 32 linear sub-models; and using... Calculate the nonlinearity of the solid oxide fuel cell system, where, It is a nominal model selected based on the maximum-minimum selection; It is the first A linear model; yes and The normalized angle between the 32 linearized models and the nominal model represents the degree of nonlinearity of the solid oxide fuel cell system. Calculations show that... ,because At this point, the solid oxide fuel cell system cannot be controlled by a single linear controller. Using an angle-based equilibrium multi-model decomposition method, 32 linearized models are clustered to obtain... There are 1 linear sub-model, where the initial threshold is set to 0.2, the step size to 0.002, and the number of clusters is [not specified]. The threshold is reduced by a step size, and the 32 linearized models are re-clustered using a self-balancing multi-model decomposition method based on the included angle. There are linear sub-models, where the threshold is set to the threshold minus the step size. ;like The decomposition ends when the result is obtained. The threshold is reduced to three linear sub-models; conversely, the threshold is further reduced until the decomposition ends. The final threshold for the decomposition is 0.174, and the solid oxide fuel cell system can be decomposed into three linear sub-models.

[0097] In one specific embodiment, the preset number of linear sub-models can be set according to actual application requirements. Specifically, for the solid oxide fuel cell system of the present invention, it can be decomposed into three linear sub-models. By decomposing the solid oxide fuel cell system into three linear sub-models, the system behavior can be accurately and simply described, and control can be performed using the three linear sub-models. This enables global optimization and control of complex nonlinear systems and improves the convenience and efficiency of controlling the solid oxide fuel cell system.

[0098] S103: Based on the static input-output curves of the solid oxide fuel cell system, construct the fuzzy weighting function based on the included angle for each linear sub-model;

[0099] In one specific embodiment, the static input-output curve corresponding to the solid oxide fuel cell system can be the relationship curve between the input and output of the solid oxide fuel cell under static conditions. Optionally, the static input-output curve corresponding to the solid oxide fuel cell system can be the relationship curve between the output voltage and current density of the solid oxide fuel cell under static conditions. The fuzzy weighting function based on the included angle corresponding to each linear sub-model is used to realize the weighted sum of different linear sub-models, thereby synthesizing a global predictive controller.

[0100] In an optional embodiment, the construction of the angle-based fuzzy weighting function for each linear sub-model based on the static input-output curve of the solid oxide fuel cell system includes:

[0101] Set multiple grid points for the static input-output curve and determine the slope angle at each grid point;

[0102] The target angle is determined based on the slope angle at each grid point; the target angle is the maximum absolute value of the difference between the slope angles at any two grid points.

[0103] Determine the slope angle corresponding to the target time for the solid oxide fuel cell system;

[0104] Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, a fuzzy weighting function based on the angle is constructed for each linear sub-model.

[0105] In one specific embodiment, the multiple grid points set for the static input-output curve can be a series of discrete points on the curve, which can be connected to form a corresponding curve. Based on the grid points, the characteristics of the curve, such as slope and intercept, can be calculated and analyzed. Optionally, setting multiple grid points for the static input-output curve may include: first, determining the range of the horizontal and vertical axes of the curve; specifically, the horizontal axis of the curve can be the hydrogen input flow rate, and the vertical axis of the curve can be the output voltage; second, determining the number of grid points by setting an appropriate step size according to actual application requirements; the smaller the step size, the denser the grid points; finally, generating a series of discrete grid points based on the determined range of the horizontal and vertical axes of the curve and the step size. Optionally, the number of grid points can be set according to actual application requirements; specifically, 70 grid points can be distributed on the static input-output curve corresponding to the solid oxide fuel cell system.

[0106] In one specific embodiment, the slope angle at each grid point can be determined using the following formula:

[0107] ,in, ;

[0108] in, Represents the first [number]th [unit] on the static input-output curve. Slope angle at each grid point; Represents the first [number]th [unit] on the static input-output curve. The slope at each grid point.

[0109] The slope angle between any two grid points can be determined based on the slope angle at each grid point, as shown in the following formula:

[0110] , ;

[0111] Determine the maximum included angle between any two grid points, which is the maximum absolute value of the difference between the slope angles at any two grid points. This maximum absolute value of the difference between the slope angles at any two grid points is the target included angle, as shown in the following formula:

[0112] ;

[0113] The slope angle corresponding to the target time of a solid oxide fuel cell system can be the slope angle corresponding to the system reaching the target time, as shown in the following formula:

[0114] ;in, ;

[0115] in, Indicates a solid oxide fuel cell system The slope angle corresponding to time t, i.e. At what moment does the slope angle of the static input-output curve of the solid oxide fuel cell system correspond? The time corresponds to the target time; express At any given time, the slope of the static input-output curve corresponding to the solid oxide fuel cell system.

[0116] In an optional embodiment, the construction of the angle-based fuzzy weighting function for each linear sub-model, based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, includes:

[0117] Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model is determined, as shown in equation (4).

[0118] ; (4)

[0119] in, This represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model. Indicates a solid oxide fuel cell system The slope angle corresponding to time 1. The time corresponds to the target time; This represents the slope angle corresponding to each linear sub-model. Indicates the included angle of the target;

[0120] Based on the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model, a fuzzy weighting function based on the angle is constructed for each linear sub-model, as shown in Equation (5).

[0121] ; (5)

[0122] in, ; (6)

[0123] Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model; These are preset parameters; satisfy ; This represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

[0124] In one specific embodiment This can reflect the similarity between the solid oxide fuel cell system and each linear sub-model. The larger the value, the smaller the angle between the two, and the more similar the solid oxide fuel cell system is to this linear sub-model; The smaller the angle, the larger the angle between the two, and the less similar the solid oxide fuel cell system is to this linear sub-model. It can be adjusted according to actual application needs, specifically... At this point, the control effect of the solid oxide fuel cell system is optimal. Optionally, based on the above-mentioned angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a multi-model system. The resulting first linear sub-model, second linear sub-model, and third linear sub-model can be used to determine the normalized angles between the slope angles corresponding to the slope angles of the first, second, and third linear sub-models, respectively. Furthermore, the angle-based fuzzy weighting functions corresponding to the first, second, and third linear sub-models can be determined respectively. Optionally, Figure 2 This is a schematic diagram of a fuzzy weighting function based on the included angle for each linear sub-model provided in an embodiment of the present invention; as shown below. Figure 2 As shown, The curve representing the angle-based fuzzy weighting function corresponding to the first linear sub-model; The curve representing the angle-based fuzzy weighting function corresponding to the second linear sub-model; The curve represents the angle-based fuzzy weighting function corresponding to the third linear sub-model.

[0125] In the above embodiments, each linear sub-model can be synthesized based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, thereby representing the global nonlinear characteristics of the solid oxide fuel cell system. This allows the complex nonlinear system to be described and controlled through simple linear sub-models, thus improving the convenience and efficiency of controlling the solid oxide fuel cell system.

[0126] S104: Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, each sub-predictive controller is weighted and synthesized to obtain the global predictive controller; each sub-predictive controller is designed based on each linear sub-model.

[0127] In an optional embodiment, each sub-predictive controller is designed based on each linear sub-model in the following manner:

[0128] Consider the objective function of equation (7):

[0129] (7)

[0130] Subject to

[0131] , ; (8)

[0132] in, and It is the first The weight matrix of each predictive controller, It is the first The setpoint signal of the individual predictive controller; Indicates the operating time of the solid oxide fuel cell system. It is the first The control time domain of the sub-predictive controller It is the first Prediction time domain of individual predictive controllers; This indicates that the input variables of the solid oxide fuel cell system are related to the first... The difference between the input values ​​at each equilibrium point Represents the input variables of a solid oxide fuel cell system and the first The minimum difference between the input values ​​at each equilibrium point Represents the input variables of a solid oxide fuel cell system and the first The maximum difference between the input values ​​at each equilibrium point; Represents the output variables of a solid oxide fuel cell system and the first... The difference between the output values ​​at each equilibrium point Represents the output variables of a solid oxide fuel cell system and the first... The minimum difference between the output values ​​at each equilibrium point Represents the output variables of a solid oxide fuel cell system and the first... The maximum difference between the output values ​​at each equilibrium point;

[0133] Solve the quadratic programming problem to obtain the optimal solution. ,

[0134] Based on the optimal solution The output of the sub-predictive controller is determined as shown in equation (9).

[0135] ; (9)

[0136] in, Indicates the first The output of the individual predictive controller Indicates the first solid oxide fuel cell system The input values ​​at each equilibrium point.

[0137] In a specific embodiment, the objective function based on equation (7) and the constraints based on equation (8) together constitute a quadratic programming problem. This quadratic programming problem can be used to design a model predictive controller. By minimizing the objective function while satisfying the constraints, the optimal control input is determined, thereby determining the output of the sub-predictive controller. Optionally, the optimal solution... The optimal control input can be obtained. For the output of the sub-predictive controller. Optional, .

[0138] In an optional embodiment, the above-described weighting and synthesis process of each sub-predictive controller based on the angle-based fuzzy weighting function corresponding to each linear sub-model to obtain the global predictive controller includes:

[0139] Based on the angle-based fuzzy weighting function corresponding to each linear sub-model, a weighted average is performed on each sub-predictive controller to obtain the global predictive controller, as shown in Equation (10).

[0140] ; (10)

[0141] in, This represents the output of the global predictive controller. Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model. Indicates the first The output of the individual predictive controller.

[0142] In one specific embodiment, a global predictive controller is used to optimize and control the solid oxide fuel cell system, achieving optimal performance and thus improving power generation efficiency. Based on the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is assigned a corresponding weight, and a weighted average is performed on each sub-predictive controller to determine the global controller, thereby enabling control using three sub-predictive controllers.

[0143] In the above embodiments, based on the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is synthesized into a global controller to optimize and control the solid oxide fuel cell. This ensures that the output voltage quickly and accurately tracks the change of the set value and that the fuel utilization rate is within a safe range, thereby improving the working performance and reliability of the solid oxide fuel cell.

[0144] S105: Based on a global predictive controller, optimize the control of the solid oxide fuel cell system.

[0145] In one specific embodiment, the global predictive controller adjusts the hydrogen flow rate into the solid oxide fuel cell system based on the expected output voltage value and the real-time monitored output voltage to ensure the stability of the output voltage.

[0146] In one specific embodiment, optimized control of the solid oxide fuel cell system is not only to ensure that the output voltage quickly and accurately tracks changes in the setpoint, but also to ensure that fuel utilization remains within a safe range. Optionally, fuel utilization can directly affect the operating performance of the solid oxide fuel cell system, as shown in the following formula:

[0147] ;

[0148] Specifically, the safe range for fuel utilization rate is [0.7, 0.9]. Maintaining the fuel utilization rate within the safe range can ensure the safety and stability of the solid oxide fuel cell system.

[0149] In one specific embodiment, when determining Then, it can be detected whether the control signal meets the safe range of fuel utilization rate. Optionally, in In that case, then ;exist In that case, then ;exist In that case, then This ensures that fuel utilization is within a safe range and allows for better optimization and control of the solid oxide fuel cell system.

[0150] In one specific embodiment Figure 3 This is a schematic diagram of the output voltage, control signal, and fuel utilization rate of a solid oxide fuel cell under setpoint tracking control based on a global predictive controller with fuzzy weighting of included angle, provided in an embodiment of this application; specifically, as shown... Figure 3 As shown, Figure 3 The topmost diagram shows the output voltage curve of a solid oxide fuel cell under setpoint tracking control based on an angle-based fuzzy weighted global predictive controller. Figure 3 The diagram in the middle shows the curve of the control signal for setpoint tracking control in a solid oxide fuel cell based on a global predictive controller with fuzzy weighting of the included angle. Figure 3 The bottommost diagram shows the fuel utilization curve of a solid oxide fuel cell under setpoint tracking control based on a global predictive controller with fuzzy weighted angle. Under the global predictive controller, the output voltage of the solid oxide fuel cell quickly and accurately tracks changes in the setpoint, and the fuel utilization varies within a safe range. The global predictive controller has excellent control performance and is reliable.

[0151] In one specific embodiment Figure 4 This is a schematic diagram of the output voltage, control signal, and fuel utilization rate of a solid oxide fuel cell under anti-interference control based on a global predictive controller with fuzzy weighting based on the included angle, provided in an embodiment of this application; specifically, as shown... Figure 4 As shown, Figure 4 The topmost diagram shows the output voltage curve of a solid oxide fuel cell under anti-interference control based on a global predictive controller with fuzzy weighting of the included angle. Figure 4 The diagram in the middle shows the curve of the control signal for anti-interference control in a solid oxide fuel cell based on a global predictive controller with fuzzy weighting of the included angle. Figure 4 The bottommost diagram shows the fuel utilization curve of a solid oxide fuel cell under anti-interference control based on a global predictive controller with fuzzy weighted angle. Under the global predictive controller, when interference occurs and the output voltage deviates from the set value, the global predictive controller will adjust the control in a timely manner to quickly and accurately adjust the output voltage to the set value, thereby effectively suppressing the influence of interference signals on the output voltage. Furthermore, the fuel utilization rate varies within a safe range, so the global predictive controller has excellent and reliable control performance.

[0152] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification determines the model structure corresponding to the solid oxide fuel cell system, and then uses an angle-based equilibrium multi-model decomposition method to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system, obtaining a preset number of linear sub-models. Based on each linear sub-model, a sub-predictive controller corresponding to each linear sub-model can be designed, which can decompose a complex nonlinear system into a simple linear system, thereby improving the convenience and efficiency of controlling the solid oxide fuel cell system. Next, based on the static input-output curve corresponding to the solid oxide fuel cell system, an angle-based fuzzy weighting function is constructed for each linear sub-model, according to... Each linear sub-model corresponds to a fuzzy weighting function based on the included angle. Weighting and synthesis are performed on each sub-predictive controller to obtain a global predictive controller. Then, based on the global predictive controller, the solid oxide fuel cell system is optimized for control. This allows for setpoint tracking and anti-interference control of the solid oxide fuel cell output voltage, enabling it to quickly and accurately track changes in the setpoint and effectively suppress the impact of interference signals on the output voltage. Furthermore, it ensures fuel utilization remains within a safe range, thereby improving the performance and reliability of the solid oxide fuel cell, as well as the stability and safety of the solid oxide fuel cell system. Ultimately, this enhances the effectiveness and flexibility of solid oxide fuel cell system control.

[0153] This invention also provides a battery system control device, accordingly, Figure 5 This is a schematic diagram of the structure of a battery system control device provided in an embodiment of the present invention; as shown below. Figure 5 As shown, the above-mentioned device includes:

[0154] Model determination module 510 is used to determine the model structure corresponding to the solid oxide fuel cell system;

[0155] The model decomposition module 520 is used to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system using an angle-based equilibrium multi-model decomposition method to obtain a preset number of linear sub-models.

[0156] The data construction module 530 is used to construct a fuzzy weighting function based on the included angle for each linear sub-model based on the static input-output curve corresponding to the solid oxide fuel cell system.

[0157] The predictive controller synthesis module 540 is used to perform weighting and synthesis processing on each sub-predictive controller according to the fuzzy weighting function based on the included angle corresponding to each linear sub-model, so as to obtain a global predictive controller; each sub-predictive controller is designed based on each linear sub-model.

[0158] The optimization control module 550 is used to optimize the control of the solid oxide fuel cell system based on the global predictive controller.

[0159] In an optional embodiment, the output voltage is used as a scheduling variable for the solid oxide fuel cell system; the model determination module 510 is specifically used for:

[0160] ; (1)

[0161] ; (2)

[0162] The model structure corresponding to the solid oxide fuel cell system is determined based on equations (1) and (2), wherein, , , These represent the derivatives corresponding to the partial pressures of hydrogen, oxygen, and water vapor, respectively. These represent the partial pressures of hydrogen, oxygen, and water vapor, respectively. Indicates the hydrogen input flow rate; The time constant representing the hydrogen gas flow; This represents the time constant of the water flow; The time constant representing the oxygen flow; This represents the molar constant of hydrogen. Represents the water molar constant; This represents the molar constant of oxygen; Indicates a constant value; Indicates the hydrogen-to-oxygen ratio; This represents the current of the solid oxide fuel cell system; This indicates the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the fuel cell stack; This represents the standard battery electromotive force; Represents the universal gas constant; Indicates absolute temperature; Denotes Faraday's constant; This indicates an ohmic loss.

[0163] In an optional embodiment, the model decomposition module 520 is specifically used for:

[0164] make , , ;

[0165] Using the angle-based equilibrium multi-model decomposition method, the model structures corresponding to the solid oxide fuel cell system in equations (1) and (2) are subjected to multi-model decomposition processing to obtain a set of a predetermined number of linear sub-models corresponding to equation (3):

[0166] ; (3)

[0167] in, ; The solid oxide fuel cell system at its equilibrium point The state-space matrix coefficients of the linear submodel at the given point; The solid oxide fuel cell system is respectively represented by the first... The state value, input value, and output value at each equilibrium point; These represent the differences between the state variables and the state values, the differences between the input variables and the input values, and the differences between the output variables and the output values ​​of the solid oxide fuel cell system, respectively.

[0168] In an optional embodiment, the preset expected range of the output voltage of the solid oxide fuel cell system is [310, 360)V, and the preset number of linear sub-models include a first linear sub-model, a second linear sub-model, and a third linear sub-model; the model decomposition module 520 is further specifically used for:

[0169] Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a first linear sub-model, a second linear sub-model, and a third linear sub-model. ;

[0170] The output voltage range of the first linear sub-model is [310, 325); the matrix coefficients of the first linear sub-model include:

[0171] ;

[0172] The output voltage range of the second linear sub-model is [325, 339); the matrix coefficients of the second linear sub-model include:

[0173] ;

[0174] The output voltage range of the third linear sub-model is [339, 360); the matrix coefficients of the third linear sub-model include:

[0175] .

[0176] In an optional embodiment, the data construction module 530 includes:

[0177] A data determination unit is used to set multiple grid points for the static input-output curve and determine the slope angle at each grid point; determine a target angle based on the slope angle at each grid point; the target angle is the maximum value of the absolute difference between the slope angles at any two grid points; and determine the slope angle corresponding to the target time of the solid oxide fuel cell system.

[0178] The data construction unit is used to construct a fuzzy weighting function based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle for each linear sub-model.

[0179] In an optional embodiment, the data construction unit includes:

[0180] The normalized angle determination unit is used to determine the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, as shown in equation (4).

[0181] ; (4)

[0182] in, This represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model. This refers to the solid oxide fuel cell system. The slope angle corresponding to the time, the The time corresponds to the target time; This represents the slope angle corresponding to each linear sub-model. Indicates the included angle of the target;

[0183] The function construction unit is used to construct a fuzzy weighting function based on the angle between the slope angle and the slope angle corresponding to each linear sub-model, as shown in equation (5).

[0184] ; (5)

[0185] in, ; (6)

[0186] Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model; These are preset parameters; satisfy ; This represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

[0187] In an optional embodiment, the apparatus further includes a prediction controller determination module, configured to:

[0188] Consider the objective function of equation (7):

[0189] (7)

[0190] Subject to

[0191] , ; (8)

[0192] in, and It is the first The weight matrix of each predictive controller, It is the first The setpoint signal of the individual predictive controller; This indicates the operating time of the solid oxide fuel cell system. It is the first The control time domain of the sub-predictive controller It is the first Prediction time domain of individual predictive controllers; This indicates that the input variables of the solid oxide fuel cell system are related to the first... The difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The minimum difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The maximum difference between the input values ​​at each equilibrium point; The output variable of the solid oxide fuel cell system is related to the first... The difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The minimum difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The maximum difference between the output values ​​at each equilibrium point;

[0193] Solving the quadratic programming problem yields the optimal solution. ,

[0194] Based on the optimal solution The output of the sub-predictive controller is determined as shown in equation (9).

[0195] ; (9)

[0196] in, Indicates the first The output of the individual predictive controller The solid oxide fuel cell system represents the first The input values ​​at each equilibrium point.

[0197] In an optional embodiment, the prediction controller synthesis module 540 is specifically used for:

[0198] Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, a weighted average is performed on each sub-predictive controller to obtain the global predictive controller, as shown in equation (10).

[0199] ; (10)

[0200] in, This represents the output of the global predictive controller. Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model. Indicates the first The output of the individual predictive controller.

[0201] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0202] This invention also provides an electronic device, the device comprising: a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the battery system control method as described in any of the method embodiments.

[0203] This invention also provides a storage medium, which can be a tangible device for holding and storing instructions used by an instruction execution device. The computer-readable storage medium can be an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combinations thereof. The computer-readable storage medium used herein is not to be construed as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0204] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0205] The computer program instructions that perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0206] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0207] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more flowcharts and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0208] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more flowcharts and / or boxes Figure 1 The function specified in one or more boxes.

[0209] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more flowcharts and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0210] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0211] Finally, it should be noted that the embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A battery system control method, characterized in that, The method includes: Determine the model structure corresponding to the solid oxide fuel cell system; Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a predetermined number of linear sub-models. Based on the static input-output curves corresponding to the solid oxide fuel cell system, a fuzzy weighting function based on the included angle is constructed for each linear sub-model. Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, each sub-predictive controller is weighted and synthesized to obtain a global predictive controller; each sub-predictive controller is designed based on each linear sub-model. Based on the global predictive controller, the solid oxide fuel cell system is optimized and controlled. The construction of the angle-based fuzzy weighting function for each linear sub-model based on the static input-output curve of the solid oxide fuel cell system includes: Multiple grid points are set for the static input-output curve, and the slope angle at each grid point is determined; The target included angle is determined based on the slope angle at each grid point; the target included angle is the maximum absolute value of the difference between the slope angles at any two grid points; Determine the slope angle corresponding to the target time of the solid oxide fuel cell system; Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, a fuzzy weighting function based on the angle is constructed for each linear sub-model. The construction of the angle-based fuzzy weighting function for each linear sub-model, based on the slope angle, the slope angle corresponding to each linear sub-model, and the target included angle, includes: Based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle, the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model is determined, as shown in equation (4). ； （4） in, This represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model. This refers to the solid oxide fuel cell system. The slope angle corresponding to the time, the The time corresponds to the target time; This represents the slope angle corresponding to each linear sub-model. Indicates the included angle of the target; Based on the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model, a fuzzy weighting function based on the angle is constructed for each linear sub-model, as shown in equation (5). ； （5） in, ; (6) Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model; These are preset parameters; satisfy ; This represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

2. The battery system control method according to claim 1, characterized in that, The output voltage is used as the scheduling variable for the solid oxide fuel cell system. The model structure corresponding to the solid oxide fuel cell system is determined as follows: ； （1） ； （2） The model structure corresponding to the solid oxide fuel cell system is determined based on equations (1) and (2), wherein, , , These represent the derivatives corresponding to the partial pressures of hydrogen, oxygen, and water vapor, respectively. These represent the partial pressures of hydrogen, oxygen, and water vapor, respectively. Indicates the hydrogen input flow rate; The time constant representing the hydrogen gas flow; This represents the time constant of the water flow; The time constant representing the oxygen flow; This represents the molar constant of hydrogen. Represents the water molar constant; This represents the molar constant of oxygen; Indicates a constant value; Indicates the hydrogen-to-oxygen ratio; This represents the current of the solid oxide fuel cell system; This indicates the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the fuel cell stack; This represents the standard battery electromotive force; Represents the universal gas constant; Indicates absolute temperature; Denotes Faraday's constant; This indicates an ohmic loss.

3. The battery system control method according to claim 2, characterized in that, The method of using an angle-based equilibrium multi-model decomposition to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system, resulting in a predetermined number of linear sub-models, includes: make , , ; Using the angle-based equilibrium multi-model decomposition method, the model structures corresponding to the solid oxide fuel cell system in equations (1) and (2) are subjected to multi-model decomposition processing to obtain a set of a predetermined number of linear sub-models corresponding to equation (3): ； （3） in, ; The solid oxide fuel cell system at its equilibrium point The state-space matrix coefficients of the linear submodel at the given point; The solid oxide fuel cell system is respectively represented by the first... The state value, input value, and output value at each equilibrium point; These represent the differences between the state variables and the state values, the differences between the input variables and the input values, and the differences between the output variables and the output values ​​of the solid oxide fuel cell system, respectively.

4. The battery system control method according to claim 3, characterized in that, The preset expected range of the output voltage of the solid oxide fuel cell system is [310, 360)V. The preset number of linear sub-models includes a first linear sub-model, a second linear sub-model, and a third linear sub-model. The preset number of linear sub-models are obtained by using an angle-based equilibrium multi-model decomposition method to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system, including: Using an angle-based equilibrium multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is decomposed into a first linear sub-model, a second linear sub-model, and a third linear sub-model. ; The output voltage range of the first linear sub-model is [310, 325); the matrix coefficients of the first linear sub-model include: ； The output voltage range of the second linear sub-model is [325, 339); the matrix coefficients of the second linear sub-model include: ； The output voltage range of the third linear sub-model is [339, 360); the matrix coefficients of the third linear sub-model include: 。 5. The battery system control method according to claim 4, characterized in that, Each sub-predictive controller is designed based on each linear sub-model in the following manner: Consider the objective function of equation (7): ； （7） Subject to ， ； （8） in, and It is the first The weight matrix of each predictive controller, It is the first The setpoint signal of the individual predictive controller; This indicates the operating time of the solid oxide fuel cell system. It is the first The control time domain of the sub-predictive controller It is the first Prediction time domain of individual predictive controllers; This indicates that the input variables of the solid oxide fuel cell system are related to the first... The difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The minimum difference between the input values ​​at each equilibrium point This indicates that the input variables of the solid oxide fuel cell system are related to the first... The maximum difference between the input values ​​at each equilibrium point; The output variable of the solid oxide fuel cell system is related to the first... The difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The minimum difference between the output values ​​at each equilibrium point The output variable of the solid oxide fuel cell system is related to the first... The maximum difference between the output values ​​at each equilibrium point; Solve the quadratic programming problem to obtain the optimal solution. , Based on the optimal solution The output of the sub-predictive controller is determined as shown in equation (9). ； （9） in, Indicates the first The output of the individual predictive controller The solid oxide fuel cell system represents the first The input values ​​at each equilibrium point.

6. The battery system control method according to claim 1, characterized in that, The step of weighting and synthesizing each sub-predictive controller according to the fuzzy weighting function based on the included angle corresponding to each linear sub-model to obtain the global predictive controller includes: Based on the fuzzy weighting function based on the included angle corresponding to each linear sub-model, a weighted average is performed on each sub-predictive controller to obtain the global predictive controller, as shown in equation (10). ； （10） in, This represents the output of the global predictive controller. Indicates the first The fuzzy weighting function based on the included angle corresponds to each linear sub-model. Indicates the first The output of the individual predictive controller.

7. An electronic device comprising a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the battery system control method as claimed in any one of claims 1 to 6.

8. A computer storage medium storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by a processor to implement the battery system control method as claimed in any one of claims 1 to 6.

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