Battery system control method and device and storage medium

Abstract

The invention discloses a battery system control method and device and a storage medium, and belongs to the technical field of fuel cells. The method comprises the following steps: performing multi-model decomposition processing on a model structure corresponding to the solid oxide fuel cell system by using an included angle-based equilibrium multi-model decomposition method to obtain a plurality of linear sub-models; based on a static input-output curve corresponding to the solid oxide fuel cell system, constructing an included angle-based fuzzy weighting function corresponding to each linear sub-model; according to an included angle-based fuzzy weighting function corresponding to each linear sub-model, performing weighting and synthesis processing on each sub-prediction controller to obtain a global prediction controller; each sub-predictive controller is designed and obtained based on each linear sub-model; and performing optimization control on the solid oxide fuel cell system based on the global prediction controller. By using the technical scheme provided by the invention, the influence of the interference signal on the output voltage can be effectively suppressed, and the fuel utilization rate is ensured to be within a safe range.

Description

Technical Field

[0001] The present invention relates to the technical field of solid oxide fuel cells, and in particular to a battery system control method, device and storage medium. Background Art

[0002] Fuel cell technology is the fourth generation of power generation technology after hydropower, thermal power and nuclear power. It has a series of advantages such as high power generation efficiency, fast response speed, low pollution, low noise and high reliability, bringing revolutionary changes to the energy industry. Among them, Solid Oxide Fuel Cell (SOFC) has the advantages of wide fuel application range and high-temperature waste heat recovery in addition to the above advantages. Therefore, solid oxide fuel cell technology has broad application prospects in large-scale power generation, distributed power generation, combined heat and power, transportation and peak-shaving energy storage, and is known as the most promising new energy power generation technology in the 21st century.

[0003] However, the solid oxide fuel cell system is a very complex nonlinear system. In the past decade, a variety of control technologies have been proposed for fuel cell systems, such as traditional PID control (Proportional Integral Derivative control), fuzzy control, neural network, robust control, predictive control, etc. In recent years, the multi-model control method has achieved good control effects. The multi-model control method based on the decomposition-synthesis principle can effectively transform complex nonlinear control problems into a combination of several simple linear control problems by decomposition; the nonlinear control problem is solved by solving the linear control problem. The multi-model control method has the characteristic of simplifying the complex, which makes it widely used in the field of nonlinear control. However, in the process of controlling the solid oxide fuel cell system, it is necessary to ensure that its output voltage is quickly and accurately stabilized at the expected value, and to ensure that the fuel utilization rate is within the expected range. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to 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 improving the reliability of solid oxide fuel cells.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In one aspect, 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 balanced multi-model decomposition method, a multi-model decomposition process is performed on the model structure corresponding to the solid oxide fuel cell system to obtain a preset number of linear sub-models;

[0009] Based on the static input-output curve corresponding to the solid oxide fuel cell system, constructing an angle-based fuzzy weighting function corresponding to each linear sub-model;

[0010] According to the angle-based fuzzy weighting function 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] Based on the global predictive controller, the solid oxide fuel cell system is optimized and controlled.

[0012] In some possible implementations, the output voltage is used as a scheduling variable of the solid oxide fuel cell system; and determining 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: , , They represent the derivatives corresponding to the hydrogen partial pressure, oxygen partial pressure and water vapor partial pressure respectively; represent the partial pressure of hydrogen, oxygen and water vapor respectively; Indicates the hydrogen input flow rate; represents the time constant of hydrogen flow; represents the time constant of water flow; represents the time constant of oxygen flow; represents the hydrogen molar constant; represents the water molar constant; represents the oxygen molar constant; Indicates setting constant; represents the hydrogen-oxygen ratio; represents the current of the solid oxide fuel cell system; represents the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the battery stack; represents the standard battery electromotive force; represents the universal gas constant; represents absolute temperature; represents the Faraday constant; Represents ohmic loss.

[0016] In some possible implementations, the angle-based balanced 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 a preset number of linear sub-models, including:

[0017] make , , ;

[0018] The model structure corresponding to the solid oxide fuel cell system corresponding to equations (1) and (2) is subjected to multi-model decomposition processing by using an angle-based balanced multi-model decomposition method to obtain a preset number of linear sub-model sets corresponding to equation (3):

[0019] ; (3)

[0020] in, ; The solid oxide fuel cell system is at the equilibrium point The state space matrix coefficients of the linear sub-model at ; Respectively represent the solid oxide fuel cell system The state value, input value and output value at each equilibrium point; They respectively represent the difference between the state variable of the solid oxide fuel cell system and the state value, the difference between the input variable and the input value, and the difference between the output variable and the output value.

[0021] In some possible implementations, 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 includes a first linear sub-model, a second linear sub-model, and a third linear sub-model; the angle-based balanced multi-model decomposition method is used to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system, and the preset number of linear sub-models obtained include:

[0022] Using an angle-based balanced multi-model decomposition method, a multi-model decomposition process is performed on the model structure corresponding to the solid oxide fuel cell system to obtain the first linear sub-model, the second linear sub-model and the 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 corresponding to each linear sub-model based on the static input-output curve corresponding to the solid oxide fuel cell system includes:

[0030] Setting a plurality of grid points for the static input-output curve, and determining a slope angle at each grid point;

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

[0032] Determining a slope angle corresponding to a 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 angle, a fuzzy weighting function based on the angle corresponding to each linear sub-model is constructed.

[0034] In some possible implementations, constructing an angle-based fuzzy weighting function corresponding to 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, a normalized angle between the slope angle and the slope angle corresponding to each linear sub-model is determined, as shown in formula (4):

[0036] ; (4)

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

[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 corresponding to each linear sub-model is constructed, as shown in formula (5):

[0039] ; (5)

[0040] in, ; (6)

[0041] Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model; is the preset parameter; satisfy ; 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 of the sub-predictive controllers is designed based on each of the linear sub-models in the following manner:

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

[0044] ; (7)

[0045] Subject to

[0046] , ; (8)

[0047] in, and It is The weight matrix of the sub-predictive controller is It is The setpoint signal of each sub-predictive controller; represents the operating time of the solid oxide fuel cell system, It is The control time domain of the sub-predictive controller is It is The prediction time domain of each sub-predictive controller; The input variables of the solid oxide fuel cell system are represented by The difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The minimum difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The maximum difference between the input values ​​at the equilibrium points; The output variable of the solid oxide fuel cell system is represented by The difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The minimum difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The maximum value of the difference between the output values ​​at the equilibrium points;

[0048] Solve the quadratic programming problem and get the optimal solution ,

[0049] Based on the optimal solution , determine the output of the sub-predictive controller, as shown in formula (9),

[0050] ; (9)

[0051] in, Indicates The output of the sub-predictive controller is The solid oxide fuel cell system The input value at the equilibrium point.

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

[0053] According to the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is weighted averaged to obtain a global predictive controller, as shown in formula (10):

[0054] ; (10)

[0055] in, represents the output of the global predictive controller, Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model is: Indicates The output of the sub-predictive controller.

[0056] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are 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, in which at least one instruction and at least one program are stored, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the battery system control method as described above.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] In the present invention, by determining the model structure corresponding to the solid oxide fuel cell system, and then using the angle-based balanced multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is subjected to multi-model decomposition processing to obtain a preset number of linear sub-models, and a sub-prediction controller corresponding to each linear sub-model can be designed based on each linear sub-model, and a complex nonlinear system can be decomposed into a simple linear system, thereby improving the convenience and efficiency of controlling the solid oxide fuel cell system; then, based on the static input-output curve corresponding to the solid oxide fuel cell system, a fuzzy weighting function based on the angle corresponding to each linear sub-model is constructed, and each sub-prediction controller is weighted and synthesized according to the fuzzy weighting function based on the angle corresponding to each linear sub-model to obtain a global prediction controller, and then based on the global prediction controller, the solid oxide fuel cell system is optimized and controlled, and the output voltage of the solid oxide fuel cell can be tracked by the set value and anti-interference control, so that the output voltage can quickly and accurately track the change of the set value, and effectively suppress the influence of the interference signal on the output voltage, and can ensure that the fuel utilization rate is within a safe range, thereby improving the working performance and reliability of the solid oxide fuel cell, and improving the stability and safety of the operation of the solid oxide fuel cell system, thereby improving the effectiveness and flexibility of the control of the solid oxide fuel cell system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 is a flow chart of a battery system control method provided by an embodiment of the present invention;

[0062] Figure 2 is a curve schematic diagram of a fuzzy weighting function based on an angle corresponding to each linear sub-model provided by an embodiment of the present invention;

[0063] Figure 3 It is a curve diagram of output voltage, control signal and fuel utilization rate of a solid oxide fuel cell under set value tracking control based on angle fuzzy weighted global predictive controller provided in an embodiment of the present application;

[0064] Figure 4 It is a curve diagram of 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 angle fuzzy weighting provided in an embodiment of the present application;

[0065] Figure 5 It is a structural schematic diagram of a battery system control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0068] In the embodiments of the present invention, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

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

[0070] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0071] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0072] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0073] Figure 1 It is a flowchart of a battery system control method provided by an embodiment of the present invention. This specification provides method operation steps such as the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in the order shown in the embodiment or the figure or in parallel (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 1 As shown, the above method may include:

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

[0075] In a specific embodiment, a solid oxide fuel cell is a fuel cell that uses a solid oxide as an electrolyte and operates at high temperatures. Optionally, the solid oxide fuel cell has the following advantages: the solid oxide fuel cell is a fully solid structure, and there is no corrosion problem and electrolyte loss problem caused by the use of liquid electrolytes, which can increase the service life of the battery; it operates in a high temperature environment, not only does the electrocatalyst not need to use precious metals, but it can also directly use natural gas, coal gas and hydrocarbons as fuel, which can simplify the fuel cell system and save costs, among other advantages.

[0076] In an optional embodiment, the output voltage is used as a scheduling variable of the solid oxide fuel cell system; the above-mentioned determination of the model structure corresponding to the solid oxide fuel cell system includes:

[0077] ; (1)

[0078] ; (2)

[0079] Based on equations (1) and (2), the model structure corresponding to the solid oxide fuel cell system is determined, where: , , They represent the derivatives corresponding to the hydrogen partial pressure, oxygen partial pressure and water vapor partial pressure respectively; represent the partial pressure of hydrogen, oxygen and water vapor respectively; Indicates the hydrogen input flow rate; represents the time constant of hydrogen flow; represents the time constant of water flow; represents the time constant of oxygen flow; represents the hydrogen molar constant; represents the water molar constant; represents the oxygen molar constant; Indicates setting constant; represents the hydrogen-oxygen ratio; represents the current of the solid oxide fuel cell system; represents the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the battery stack; represents the standard battery electromotive force; represents the universal gas constant; represents absolute temperature; represents the Faraday constant; Represents ohmic loss.

[0080] In a specific embodiment, equations (1) and (2) describe a solid oxide fuel cell system, and the output voltage is used as a scheduling variable of 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. in the system can be adjusted according to the output voltage, so that the output voltage can accurately follow the preset set value and maintain near the 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 in combination with 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 an angle-based balanced multi-model decomposition method to perform multi-model decomposition processing on a model structure corresponding to the solid oxide fuel cell system to obtain a preset number of linear sub-models;

[0082] In a specific embodiment, the angle-based balanced multi-model decomposition method is a modeling and control strategy for complex nonlinear systems. The nonlinear system can be decomposed into multiple linear sub-models based on the angle, and the original nonlinear system can be accurately described 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-mentioned angle-based balanced 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 a preset number of linear sub-models including:

[0084] make , , ;

[0085] The angle-based balanced multi-model decomposition method is used to perform multi-model decomposition on the model structure corresponding to the solid oxide fuel cell system corresponding to equations (1) and (2), and a set of linear sub-models corresponding to equation (3) is obtained:

[0086] ; (3)

[0087] in, ; The solid oxide fuel cell system is at equilibrium point The state space matrix coefficients of the linear sub-model at ; They represent the solid oxide fuel cell system The state value, input value and output value at each equilibrium point; They respectively represent the difference between the state variable and the state value, the difference between the input variable and the input value, and the difference between the output variable and the output value of the solid oxide fuel cell system.

[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 includes a first linear sub-model, a second linear sub-model and a third linear sub-model; the above-mentioned balanced multi-model decomposition method based on the angle is used to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system, and the preset number of linear sub-models obtained include:

[0089] The model structure corresponding to the solid oxide fuel cell system is decomposed by a balanced multi-model decomposition method based on an angle to obtain a first linear sub-model, a second linear sub-model and a third linear sub-model; at this time, ;

[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 a specific embodiment, using an angle-based balanced multi-model decomposition method to perform multi-model decomposition processing on a model structure corresponding to a solid oxide fuel cell system to obtain three linear sub-models may include: using an angle-based grid decomposition method to grid the solid oxide fuel cell system, with a network density of 0.02, to determine 32 grid points; linearizing the solid oxide fuel cell system corresponding to each grid point to obtain 32 linear sub-models; using Calculate the degree of nonlinearity in a solid oxide fuel cell system where is the nominal model selected based on the maximum-minimum selection; It is A linearized model; yes and The normalized angle between the linearized models and the nominal model is the maximum angle between the 32 linearized models and the nominal model, which is expressed as the nonlinearity of the solid oxide fuel cell system. ,because , at this time, the solid oxide fuel cell system cannot be controlled by a single linear controller. Using the angle-based balanced multi-model decomposition method, the 32 linear models are clustered and obtained. Linear sub-models, where the initial threshold is set to 0.2, the step size is set to 0.002, and the number of clusters is set to ; Reduce the threshold, the threshold decreases by one step, and the self-balanced multi-model decomposition method based on the angle is used to re-cluster the 32 linear models, and obtain Linear sub-models, where threshold = threshold - step size, ;like , then the decomposition ends and the decomposition result is Otherwise, the threshold is continuously reduced until the decomposition is completed. The final threshold for the end of decomposition is 0.174, and the solid oxide fuel cell system can be decomposed into three linear sub-models.

[0097] In a specific embodiment, a preset number of linear sub-models can be set in combination with 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 the three linear sub-models can be used for control, thereby achieving global optimization and control of complex nonlinear systems, and improving the convenience and efficiency of controlling the solid oxide fuel cell system.

[0098] S103: constructing an angle-based fuzzy weighting function corresponding to each linear sub-model based on a static input-output curve corresponding to the solid oxide fuel cell system;

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

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

[0101] Setting a plurality of grid points for a static input-output curve and determining a slope angle at each grid point;

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

[0103] determining a slope angle corresponding to a target time of 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 corresponding to each linear sub-model is constructed.

[0105] In a specific embodiment, the multiple grid points set for the static input-output curve can be a series of discrete points on the curve, and the series of discrete points can form a corresponding curve by connecting; based on the grid points, the characteristics of the curve, such as slope, intercept, etc., can be calculated and analyzed. Optionally, setting multiple grid points for the static input-output curve can include: first determining the range of the horizontal axis and the vertical axis 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; secondly, in combination with actual application requirements, setting a suitable step size to determine the number of grid points, the smaller the step size, the denser the grid points; finally, based on the determined range and step size of the horizontal axis and the vertical axis of the curve, a series of discrete grid points are generated. Optionally, the number of grid points can be set in combination with 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 a specific embodiment, the slope angle at each grid point may be determined as follows:

[0107] ,in, ;

[0108] in, Indicates the static input-output curve The slope angle at each grid point; Indicates the static input-output curve The slope at each grid point.

[0109] Based on the slope angle at each grid point, the slope angle at any two grid points can be determined as follows:

[0110] , ;

[0111] According to the slope angles at any two grid points, the maximum angle is determined, that is, the maximum absolute value of the difference between the slope angles at any two grid points. The maximum absolute value of the difference between the slope angles at any two grid points is the target angle, as shown in the following formula:

[0112] ;

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

[0114] ;in, ;

[0115] in, Solid oxide fuel cell system The slope angle corresponding to the moment is At the moment, the slope angle of the static input-output curve of the solid oxide fuel cell system is The moment corresponds to the target moment; express At this moment, the slope of the static input-output curve of the solid oxide fuel cell system corresponds to.

[0116] In an optional embodiment, the above-mentioned construction of the angle-based fuzzy weighting function corresponding to 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 formula (4):

[0118] ; (4)

[0119] in, represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model, Solid oxide fuel cell system The slope angle corresponding to the moment, The moment corresponds to the target moment; represents the slope angle corresponding to each linear sub-model, represents the target angle;

[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 corresponding to each linear sub-model is constructed, as shown in formula (5):

[0121] ; (5)

[0122] in, ; (6)

[0123] Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model; is the preset parameter; satisfy ; Represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

[0124] In a specific embodiment, It can reflect the similarity between the solid oxide fuel cell system and each linear sub-model. The larger it is, the smaller the angle between the two is, and the more similar the solid oxide fuel cell system is to the linear sub-model; The smaller it is, the larger the angle between the two is, and the less similar the solid oxide fuel cell system is to the linear sub-model. It can be adjusted according to actual application needs. Specifically, , at this time, the control effect on the solid oxide fuel cell system is optimal. Optionally, according to the above-mentioned angle-based balanced multi-model decomposition method, the model structure corresponding to the solid oxide fuel cell system is subjected to multi-model decomposition processing, and the obtained first linear sub-model, second linear sub-model and third linear sub-model can respectively determine the normalized angle between the slope angle and the slope angle corresponding to the first linear sub-model, the normalized angle between the slope angle and the slope angle corresponding to the second linear sub-model, and the normalized angle between the slope angle and the slope angle corresponding to the third linear sub-model, and then the angle-based fuzzy weighting function corresponding to the first linear sub-model, the angle-based fuzzy weighting function corresponding to the second linear sub-model, and the angle-based fuzzy weighting function corresponding to the third linear sub-model can be determined respectively. Optionally, Figure 2 is a curve diagram of a fuzzy weighting function based on an angle corresponding to each linear sub-model provided by an embodiment of the present invention; Figure 2 As shown, A curve representing the angle-based fuzzy weighting function corresponding to the first linear sub-model; A curve representing the angle-based fuzzy weighting function corresponding to the second linear sub-model; The curve representing the angle-based fuzzy weighting function corresponding to the third linear sub-model.

[0125] In the above embodiment, each linear sub-model can be synthesized based on the angle-based fuzzy weighting function corresponding to each linear sub-model to represent the global nonlinear characteristics of the solid oxide fuel cell system, so that the complex nonlinear system can be described and controlled by a simple linear sub-model, thereby improving the convenience and efficiency of controlling the solid oxide fuel cell system.

[0126] S104: weighting and synthesizing each sub-predictive controller according to the angle-based fuzzy weighting function corresponding to each linear sub-model to obtain a 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 formula (7):

[0129] ; (7)

[0130] Subject to

[0131] , ; (8)

[0132] in, and It is The weight matrix of the sub-predictive controller is It is The setpoint signal of each sub-predictive controller; represents the operating time of the solid oxide fuel cell system, It is The control time domain of the sub-predictive controller is It is The prediction time domain of each sub-predictive controller; The input variables of the solid oxide fuel cell system are represented by The difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are The minimum difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are The maximum difference between the input values ​​at the equilibrium points; The output variables of the solid oxide fuel cell system are The difference between the output values ​​at the equilibrium points, The output variables of the solid oxide fuel cell system are The minimum difference between the output values ​​at the equilibrium points, The output variables of the solid oxide fuel cell system are The maximum value of the difference between the output values ​​at the equilibrium points;

[0133] Solve the quadratic programming problem and get the optimal solution ,

[0134] Based on the optimal solution , determine the output of the sub-predictive controller, as shown in formula (9),

[0135] ; (9)

[0136] in, Indicates The output of the sub-predictive controller is Solid oxide fuel cell system The input value at the equilibrium point.

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

[0138] In an optional embodiment, the above-mentioned weighted and synthesized processing is performed on each sub-predictive controller according to the angle-based fuzzy weighting function corresponding to each linear sub-model to obtain a global predictive controller including:

[0139] According to the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is weighted averaged to obtain the global predictive controller, as shown in formula (10):

[0140] ; (10)

[0141] in, represents the output of the global predictive controller, Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model is: Indicates The output of the sub-predictive controller.

[0142] In a specific embodiment, the global predictive controller is used to optimize and control the solid oxide fuel cell system, which can achieve the optimal working performance of the solid oxide fuel cell, thereby improving the power generation efficiency. According to the angle-based fuzzy weighting function corresponding to each linear sub-model, a corresponding weight can be assigned to each sub-predictive controller, and each sub-predictive controller is weighted averaged to determine the global controller, thereby achieving control using three sub-predictive controllers.

[0143] In the above embodiment, based on the angle-based fuzzy weighted function corresponding to each linear sub-model, each sub-predictive controller is synthesized to obtain a global controller, and the solid oxide fuel cell is optimized and controlled to ensure that the output voltage quickly and accurately tracks the changes in the set value and ensure 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: Optimizing and controlling the solid oxide fuel cell system based on a global predictive controller.

[0145] In a specific embodiment, the global predictive controller adjusts the flow rate of hydrogen entering the solid oxide fuel cell system according to the expected value of the output voltage and the output voltage monitored in real time to ensure the stability of the output voltage.

[0146] In a specific embodiment, the solid oxide fuel cell system is optimized and controlled not only to ensure that the output voltage quickly and accurately tracks the change of the set value, but also to ensure that the fuel utilization rate is within a safe range. Optionally, the fuel utilization rate can directly affect the working performance of the solid oxide fuel cell system, as shown in the following formula:

[0147] ;

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

[0149] In a specific embodiment, when determining Afterwards, it can be detected whether the control signal meets the safety range of fuel utilization. Optionally, In the case of ;exist In the case of ;exist In the case of , thereby ensuring that the fuel utilization is within a safe range and better optimizing the control of the solid oxide fuel cell system.

[0150] In a specific embodiment, Figure 3 1 is a curve diagram of the output voltage, control signal and fuel utilization rate of a solid oxide fuel cell under a set value tracking control based on a global predictive controller with angle fuzzy weighting provided in an embodiment of the present application; specifically, Figure 3 As shown, Figure 3 The top graph is the output voltage curve of the solid oxide fuel cell under the set value tracking control of the global predictive controller based on angle fuzzy weighting. Figure 3 The middle diagram is the curve of the control signal of the set value tracking control of the solid oxide fuel cell under the global predictive controller based on angle fuzzy weighting. Figure 3 The bottom graph is a curve of the fuel utilization rate of the solid oxide fuel cell under the set value tracking control of the global predictive controller based on angle fuzzy weighting; under the global predictive controller, the output voltage of the solid oxide fuel cell quickly and accurately tracks the changes in the set value, and the fuel utilization rate changes within a safe range. The control effect of the global predictive controller is excellent and reliable.

[0151] In a specific embodiment, Figure 4 1 is a curve diagram of the output voltage, control signal and fuel utilization rate of a solid oxide fuel cell under an anti-interference control based on a global predictive controller with angle fuzzy weighting provided in an embodiment of the present application; specifically, Figure 4 As shown, Figure 4 The top graph is the curve of the output voltage of the solid oxide fuel cell under the anti-interference control of the global predictive controller based on angle fuzzy weighting. Figure 4 The middle diagram is the curve of the control signal of the anti-interference control of the solid oxide fuel cell under the global predictive controller based on angle fuzzy weighting. Figure 4 The bottom graph is the curve of the fuel utilization rate of the solid oxide fuel cell under the anti-interference control of the global predictive controller based on angle fuzzy weighting; 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 time and adjust the output voltage to the set value quickly and accurately, thereby effectively suppressing the influence of the interference signal on the output voltage; and the fuel utilization rate changes within a safe range, so the control effect of the global predictive controller is excellent and reliable.

[0152] It can be seen from the technical solutions provided in the above embodiments of this specification that this specification determines the model structure corresponding to the solid oxide fuel cell system, and then uses the angle-based balanced multi-model decomposition method to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system to obtain a preset number of linear sub-models. Based on each linear sub-model, a sub-prediction controller corresponding to each linear sub-model can be designed, and a complex nonlinear system can be decomposed into a simple linear system, thereby improving the convenience and efficiency of controlling the solid oxide fuel cell system; then, based on the static input-output curve corresponding to the solid oxide fuel cell system, a fuzzy weighted function based on the angle corresponding to each linear sub-model is constructed, and according to The angle-based fuzzy weighted function corresponding to each linear sub-model is used to weight and synthesize 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 and controlled. The output voltage of the solid oxide fuel cell can be tracked by set values ​​and subjected to anti-interference control, so that the output voltage can quickly and accurately track changes in the set values, and 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 working performance and reliability of the solid oxide fuel cell, as well as improving the stability and safety of the solid oxide fuel cell system operation, thereby improving the effectiveness and flexibility of the control of the solid oxide fuel cell system.

[0153] The embodiment of the present invention further provides a battery system control device, correspondingly, Figure 5 is a schematic diagram of the structure of a battery system control device provided by an embodiment of the present invention; Figure 5 As shown, the above device comprises:

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

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

[0156] A data construction module 530, configured to construct an angle-based fuzzy weighting function corresponding to each linear sub-model based on a static input-output curve corresponding to the solid oxide fuel cell system;

[0157] A prediction controller synthesis module 540 is used to perform weighting and synthesis processing on each sub-prediction controller according to the angle-based fuzzy weighting function corresponding to each linear sub-model to obtain a global prediction controller; each sub-prediction 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 of the solid oxide fuel cell system; the model determination module 510 is specifically used to:

[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: , , They represent the derivatives corresponding to the hydrogen partial pressure, oxygen partial pressure and water vapor partial pressure respectively; represent the partial pressure of hydrogen, oxygen and water vapor respectively; Indicates the hydrogen input flow rate; represents the time constant of hydrogen flow; represents the time constant of water flow; represents the time constant of oxygen flow; represents the hydrogen molar constant; represents the water molar constant; represents the oxygen molar constant; Indicates setting constant; represents the hydrogen-oxygen ratio; represents the current of the solid oxide fuel cell system; represents the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the battery stack; represents the standard battery electromotive force; represents the universal gas constant; represents absolute temperature; represents the Faraday constant; Represents ohmic loss.

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

[0164] make , , ;

[0165] The model structure corresponding to the solid oxide fuel cell system corresponding to equations (1) and (2) is subjected to multi-model decomposition processing by using an angle-based balanced multi-model decomposition method to obtain a preset number of linear sub-model sets corresponding to equation (3):

[0166] ; (3)

[0167] in, ; The solid oxide fuel cell system is at the equilibrium point The state space matrix coefficients of the linear sub-model at ; Respectively represent the solid oxide fuel cell system The state value, input value and output value at each equilibrium point; They respectively represent the difference between the state variable of the solid oxide fuel cell system and the state value, the difference between the input variable and the input value, and the difference between the output variable and the output value.

[0168] 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 includes 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 to:

[0169] Using an angle-based balanced multi-model decomposition method, a multi-model decomposition process is performed on the model structure corresponding to the solid oxide fuel cell system to obtain the first linear sub-model, the second linear sub-model and the 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, configured to set a plurality of 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 value of the 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] A data construction unit is used to construct an angle-based fuzzy weighting function corresponding to each linear sub-model based on the slope angle, the slope angle corresponding to each linear sub-model and the target angle.

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

[0180] A normalized angle determination unit is used to determine a 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 formula (4),

[0181] ; (4)

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

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

[0184] ; (5)

[0185] in, ; (6)

[0186] Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model; is the preset parameter; satisfy ; 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 device further comprises: a prediction controller determination module, configured to:

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

[0189] ; (7)

[0190] Subject to

[0191] , ; (8)

[0192] in, and It is The weight matrix of the sub-predictive controller is It is The setpoint signal of each sub-predictive controller; represents the operating time of the solid oxide fuel cell system, It is The control time domain of the sub-predictive controller is It is The prediction time domain of each sub-predictive controller; The input variables of the solid oxide fuel cell system are represented by The difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The minimum difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The maximum difference between the input values ​​at the equilibrium points; The output variable of the solid oxide fuel cell system is represented by The difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The minimum difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The maximum value of the difference between the output values ​​at the equilibrium points;

[0193] Solve the quadratic programming problem and get the optimal solution ,

[0194] Based on the optimal solution , determine the output of the sub-predictive controller, as shown in formula (9),

[0195] ; (9)

[0196] in, Indicates The output of the sub-predictive controller is The solid oxide fuel cell system The input value at the equilibrium point.

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

[0198] According to the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is weighted averaged to obtain a global predictive controller, as shown in formula (10):

[0199] ; (10)

[0200] in, represents the output of the global predictive controller, Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model is: Indicates The output of the sub-predictive controller.

[0201] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

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

[0203] The embodiment of the present invention also provides a storage medium, and the computer-readable storage medium can be a tangible device that holds and stores 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 of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or protruding structures in grooves on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

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

[0205] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related 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 "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through 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., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

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

[0207] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Process or multiple flow charts and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0208] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple flow charts and / or boxes Figure 1 A function specified in one or more boxes.

[0209] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple flow charts and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0210] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present invention. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square frame in the block diagram and / or flow chart, and the combination of the square frames in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0211] Finally, it should be noted that the embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all within the protection of the present invention.

Claims

1. A battery system control method, characterized in that: The method comprises: Determine the model structure corresponding to the solid oxide fuel cell system; Using an angle-based balanced multi-model decomposition method, a multi-model decomposition process is performed on the model structure corresponding to the solid oxide fuel cell system to obtain a preset number of linear sub-models; Based on the static input-output curve corresponding to the solid oxide fuel cell system, constructing an angle-based fuzzy weighting function corresponding to each linear sub-model; According to the angle-based fuzzy weighting function 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.

2. The battery system control method according to claim 1, characterized in that: Using the output voltage as a scheduling variable of the solid oxide fuel cell system; Determining the model structure corresponding to the solid oxide fuel cell system includes: ； （1） ； （2） The model structure corresponding to the solid oxide fuel cell system is determined based on equations (1) and (2), wherein: , , They represent the derivatives corresponding to the hydrogen partial pressure, oxygen partial pressure and water vapor partial pressure respectively; represent the partial pressure of hydrogen, oxygen and water vapor respectively; Indicates the hydrogen input flow rate; represents the time constant of hydrogen flow; represents the time constant of water flow; represents the time constant of oxygen flow; represents the hydrogen molar constant; represents the water molar constant; represents the oxygen molar constant; Indicates setting constant; represents the hydrogen-oxygen ratio; represents the current of the solid oxide fuel cell system; represents the output voltage of the solid oxide fuel cell system; Indicates the number of batteries in the battery stack; represents the standard battery electromotive force; represents the universal gas constant; represents absolute temperature; represents the Faraday constant; Represents ohmic loss.

3. The battery system control method according to claim 2, characterized in that: The method of using the angle-based balanced multi-model decomposition method to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system to obtain a preset number of linear sub-models includes: make , , ; The model structure corresponding to the solid oxide fuel cell system corresponding to equations (1) and (2) is subjected to multi-model decomposition processing by using an angle-based balanced multi-model decomposition method to obtain a preset number of linear sub-model sets corresponding to equation (3): ； （3） in, ; The solid oxide fuel cell system is at the equilibrium point The state space matrix coefficients of the linear sub-model at ; Respectively represent the solid oxide fuel cell system The state value, input value and output value at each equilibrium point; They respectively represent the difference between the state variable of the solid oxide fuel cell system and the state value, the difference between the input variable and the input value, and the difference between the output variable and the output value.

4. The battery system control method according to claim 3, characterized in that: 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 includes a first linear sub-model, a second linear sub-model and a third linear sub-model; the angle-based balanced multi-model decomposition method is used to perform multi-model decomposition processing on the model structure corresponding to the solid oxide fuel cell system, and the preset number of linear sub-models obtained include: Using an angle-based balanced multi-model decomposition method, a multi-model decomposition process is performed on the model structure corresponding to the solid oxide fuel cell system to obtain the first linear sub-model, the second linear sub-model and the 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 1, characterized in that: The step of constructing the angle-based fuzzy weighting function corresponding to each linear sub-model based on the static input-output curve corresponding to the solid oxide fuel cell system includes: Setting a plurality of grid points for the static input-output curve, and determining a 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 value of the difference between the slope angles at any two grid points; Determining a slope angle corresponding to a 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 corresponding to each linear sub-model is constructed.

6. The battery system control method according to claim 5, characterized in that: The step of constructing an angle-based fuzzy weighting function corresponding to each linear sub-model based on the slope angle, the slope angle corresponding to each linear sub-model, and the target angle includes: Based on the slope angle, the slope angle corresponding to each linear sub-model and the target angle, a normalized angle between the slope angle and the slope angle corresponding to each linear sub-model is determined, as shown in formula (4): ； （4） in, represents the normalized angle between the slope angle and the slope angle corresponding to each linear sub-model, The solid oxide fuel cell system The slope angle corresponding to the moment The time corresponds to the target time; represents the slope angle corresponding to each linear sub-model, represents the target angle; 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 corresponding to each linear sub-model is constructed, as shown in formula (5): ； （5） in, ; (6) Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model; is the preset parameter; satisfy ; represents the sum of the normalized angles between the slope angle and the slope angle corresponding to each linear sub-model.

7. 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 formula (7): ； （7） Subject to ， ； （8） in, and It is The weight matrix of the sub-predictive controller is It is The setpoint signal of each sub-predictive controller; represents the operating time of the solid oxide fuel cell system, It is The control time domain of the sub-predictive controller is It is The prediction time domain of each sub-predictive controller; The input variables of the solid oxide fuel cell system are represented by The difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The minimum difference between the input values ​​at the equilibrium points, The input variables of the solid oxide fuel cell system are represented by The maximum difference between the input values ​​at the equilibrium points; The output variable of the solid oxide fuel cell system is represented by The difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The minimum difference between the output values ​​at the equilibrium points, The output variable of the solid oxide fuel cell system is represented by The maximum value of the difference between the output values ​​at the equilibrium points; Solve the quadratic programming problem and get the optimal solution , Based on the optimal solution , determine the output of the sub-predictive controller, as shown in formula (9), ； （9） in, Indicates The output of the sub-predictive controller is The solid oxide fuel cell system The input value at the equilibrium point.

8. 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 angle-based fuzzy weighting function corresponding to each linear sub-model to obtain a global predictive controller includes: According to the angle-based fuzzy weighting function corresponding to each linear sub-model, each sub-predictive controller is weighted averaged to obtain a global predictive controller, as shown in formula (10): ； （10） in, represents the output of the global predictive controller, Indicates The fuzzy weighting function based on the angle corresponding to the linear sub-model is: Indicates The output of the sub-predictive controller.

9. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the battery system control method as described in any one of claims 1 to 8.

10. A computer storage medium, wherein at least one instruction and at least one program are stored in the computer storage medium, wherein the at least one instruction and the at least one program are loaded and executed by a processor to implement the battery system control method according to any one of claims 1 to 8.

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