Fuzzy control-based multi-stack fuel cell energy management method and system

Abstract

The invention discloses a fuzzy control-based multi-stack fuel cell energy management method and a fuzzy control-based multi-stack fuel cell energy management system. The method comprises the following steps of constructing an energy system comprising a multi-stack fuel cell and a lithium battery; constructing a fuzzy logic controller by taking the required power of the energy system and the charge state of the lithium battery as input variables and taking the output power of the multi-stack fuel battery as an output variable; establishing a hydrogen consumption function of the energy system according to the relationship between the output power of the multi-stack fuel cells and the lithium batteries and the hydrogen consumption, and establishing an optimization function by taking the value of the minimum hydrogen consumption function as a target; and solving the optimization function to obtain an optimal parameter of the fuzzy logic controller, and controlling the output power of the multi-stack fuel cell according to the optimal parameter. According to the method, the energy consumption of the fuel cell is optimized, the use of hydrogen fuel is reduced, and the energy utilization efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy management method and system for multiple fuel cell stacks based on fuzzy control. Background Art

[0002] As a clean and efficient energy conversion technology, fuel cell stacks have demonstrated their potential in providing grid stability, achieving load balancing, supporting distributed generation, and integrating with renewable energy. These technical advantages make fuel cell stacks play an important role in the clean transformation of grid energy.

[0003] In order to improve the performance and efficiency of hydrogen fuel cell stacks, existing control technologies usually use fuzzy logic controllers. Fuzzy logic controllers define input and output variables, establish fuzzy sets and membership functions, formulate fuzzy rule bases, perform fuzzification, fuzzy reasoning and defuzzification, and ultimately achieve precise control of fuel cell stacks. This method can quickly respond to system changes, adapt to complex and changing working conditions, improve the robustness and adaptability of the system, and is particularly suitable for dynamically changing and multi-variable fuel cell systems.

[0004] Although fuzzy logic controllers have shown certain advantages in fuel cell stack control, there are still some problems that need to be solved. The design and parameter adjustment of fuzzy logic controllers often rely on expert experience and trial and error methods, which may lead to inaccurate and inefficient controller design. In addition, existing fuzzy logic controllers may have deficiencies in real-time performance and computational efficiency, especially in application scenarios with high dynamic response requirements. Summary of the invention

[0005] The present invention proposes an energy management method and system for multiple fuel cell stacks based on fuzzy control, which solves the problems of poor real-time performance and poor calculation efficiency of existing fuel cell stack control methods.

[0006] In order to solve the above technical problems, the present invention provides an energy management method for multiple fuel cell stacks based on fuzzy control, comprising the following steps:

[0007] Step S1: constructing an energy system including multiple fuel cell stacks and lithium batteries;

[0008] Step S2: Taking the required power of the energy system and the state of charge of the lithium battery as input variables and the output power of the multiple fuel cell stacks as output variables, a fuzzy logic controller is constructed;

[0009] Step S3: establishing a hydrogen consumption function of the energy system according to the relationship between the output power and hydrogen consumption of the plurality of fuel cells and lithium batteries, and establishing an optimization function with the goal of minimizing the value of the hydrogen consumption function;

[0010] Step S4: Solving the optimization function to obtain the optimal parameters of the fuzzy logic controller, and controlling the output power of the multiple fuel cell stacks according to the optimal parameters.

[0011] Preferably, the construction of the fuzzy logic controller in step S2 comprises the following steps:

[0012] Step S21: defining a plurality of membership functions, and deriving the fuzzy sets of the input variables and the output variables according to the plurality of membership functions;

[0013] Step S22: defining a mapping relationship between fuzzy sets of input variables and output variables as a fuzzy rule;

[0014] Step S23: define a plurality of free variables of the membership function, and use the free variables as parameters of the fuzzy logic controller.

[0015] Preferably, seven membership functions mf1-mf7 are constructed in step S21, and mf1-mf7 correspond to very low, medium-low, low, medium, relatively high, medium-high and very high, respectively.

[0016] Preferably, the fuzzy rules in step S22 include:

[0017] (1) When the state of charge of the lithium battery is low and the power demand of the energy system is also low, the fuel cell should output higher power;

[0018] (2) When the state of charge of the lithium battery is low and the power demand of the energy system is high, the fuel cell should output higher power;

[0019] (3) When the state of charge of the lithium battery is high and the power demand of the energy system is low, the fuel cell should output lower power;

[0020] (4) When the state of charge of the lithium battery is high and the power demand of the energy system is also high, the fuel cell should output lower power.

[0021] Preferably, the free variables in step S23 include vertex x1, lower left corner x2, lower right corner x3, lower scale x4, lower left lag x5 and lower right lag x6.

[0022] Preferably, the expression for the output power of the plurality of fuel cell stacks in step S3 is:

[0023] P FC =V FC I FC =nV cell I FC ;

[0024] V FC =nV cell ;

[0025] V cell =E Nerest -V act -V ohm -V con ;

[0026]

[0027] V ohm =R FC I FC ;

[0028]

[0029] In the above formula, P FC is the output power of multiple fuel cell stacks; V FC is the output voltage of multiple fuel cell stacks; I FC is the output current of the multiple fuel cell stacks; n is the number of fuel cells in the multiple fuel cell stacks; V cell 、E Nerest 、V act 、V ohm 、V con , T FC , R FC , k con and I FC max They are the output voltage, Nestor voltage, activation polarization voltage drop, ohmic voltage drop, concentration polarization voltage drop, operating temperature, hydrogen pressure, oxygen pressure, oxygen concentration, internal resistance, concentration polarization voltage coefficient and maximum current of a single fuel cell respectively; ε1, ε2, ε3 and ε4 are constants respectively.

[0030] Preferably, the expression of the hydrogen consumption function in step S3 is:

[0031] H FCT (X) = H FC (X)+H bat (X);

[0032] X=[x1 x2 x3 x4 x5 x6] T ;

[0033] H FC (X) = αX

[0034] α=[α1α2α3α4α5α6]

[0035] H bat (X) = βX;

[0036] β=[β1β2β3β4β5β6]

[0037]

[0038] In the above formula, H FC (X), H bat (X) are the equivalent hydrogen consumption functions of fuel cells and lithium batteries respectively; α and β are weight matrices; α1, α2, α3, α4, α5, α6, β1, β2, β3, β4, β5, β6 are weight coefficients; H FC , H bat Respectively represent the hydrogen consumption of multiple fuel cell stacks and the hydrogen consumption of lithium batteries; P FC is the output power of multiple fuel cell stacks; η FC is the efficiency of multiple fuel cell stacks; P bat is the output power of the lithium battery; η bat is the efficiency of lithium battery; η c , η d are the charging efficiency and discharging efficiency of lithium batteries respectively; t ini and t end Represent the initial time and the end time respectively.

[0039] Preferably, in step S4, the optimization function is solved by using a proxy model optimization algorithm and an adaptive particle swarm optimization algorithm, which includes the following steps:

[0040] Step S41: Initializing the parameter set of the fuzzy logic controller and the parameters of the agent model optimization algorithm;

[0041] Step S42: Calculate the hydrogen consumption corresponding to each group of parameters in the parameter set, and take the group of parameters with the minimum hydrogen consumption as the first optimal solution;

[0042] Step S43: constructing a proxy model according to the current parameter set, interpolating the optimization function using the proxy model to obtain a proxy function, randomly generating a candidate solution set of the proxy function, and selecting a second optimal solution from the candidate solution set;

[0043] Step S44: comparing the hydrogen consumption corresponding to the first optimal solution and the second optimal solution, taking the solution with the smaller hydrogen consumption as the current optimal solution, and adding the current optimal solution to the parameter set;

[0044] Step S45: repeating steps S42 to S44 until the current number of iterations reaches the set maximum number of iterations, and outputting the parameter set;

[0045] Step S46: inputting the parameter set into an adaptive particle swarm optimization algorithm to obtain an optimal parameter combination.

[0046] Preferably, the step S43 of selecting the second optimal solution from the candidate solution set comprises the following steps:

[0047] Step S431: Calculate each candidate point in the candidate solution set and sampling point set The shortest distance between

[0048]

[0049] In the formula, Represents the location of the points in the training set;

[0050] Step S432: Set the maximum and minimum values ​​of the proxy function value S max , S min , the maximum and minimum values ​​of the shortest distance between the candidate point and the sampling point D max , D min for:

[0051]

[0053] Step S433: Generate an ascending weight set {σ1,σ2,…,σ κ}, if mod(τ,κ)≠0, let σ S ←σ mod(τ,κ) , otherwise let σ S ←σ κ , where mod() is the modulus function;

[0054] Step S434: Let σ D ←1-σ S ;

[0055] Step S435: If S min ≠S max ,make Otherwise,

[0056] Step S436: If D min ≠D max ,make Otherwise,

[0057] Step S437: Update the current optimal solution:

[0058]

[0059] Step S438: Repeat steps S435 to S437 until the current number of iterations reaches the set maximum number of iterations, and output the current optimal solution as the second optimal solution.

[0060] The present invention also provides an energy management system for multiple fuel cell stacks based on fuzzy control, which is implemented based on the above-mentioned energy management method for multiple fuel cell stacks based on fuzzy control, and includes: a data acquisition module, an energy management module and a control module;

[0061] The data acquisition module is used to collect operating data of multiple fuel cells and lithium batteries;

[0062] The energy management module is used to distribute the output power of multiple fuel cells and lithium batteries according to the operating data collected by the data collection module to meet the power demand of the system;

[0063] The control module is used to control the output power of multiple fuel cells and lithium batteries according to the control signal output by the energy management module.

[0064] The benefits of the present invention include at least:

[0065] 1. The fuzzy logic controller can handle uncertainty and nonlinear problems. By taking the required power and the state of charge of the lithium battery as input variables, it can achieve precise control of the output power of multiple fuel cell stacks.

[0066] 2. By precisely controlling the output power of multiple fuel cells, the energy consumption of the fuel cells is optimized, the use of hydrogen fuel is reduced, and the energy efficiency of the system is improved; at the same time, the configuration can be adjusted according to different energy requirements and environmental conditions, with good flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of a system framework of an embodiment of the present invention;

[0069] Figure 3 A schematic diagram of a fuzzy logic controller and a membership function according to an embodiment of the present invention;

[0070] Figure 4 A schematic diagram showing the relationship between the parameters of the fuzzy logic controller and the hydrogen consumption according to an embodiment of the present invention;

[0071] Figure 5 A schematic diagram of a flow chart of an agent model optimization algorithm according to an embodiment of the present invention;

[0072] Figure 6Schematic diagram of the process of the adaptive particle swarm optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying 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 belong to the protection scope of the present invention.

[0074] like Figure 1 As shown, an embodiment of the present invention provides an energy management method for multiple fuel cell stacks based on fuzzy control, comprising the following steps:

[0075] Step S1: construct an energy system including multiple fuel cell stacks and lithium batteries.

[0076] like Figure 2 As shown, the system framework of the embodiment of the present invention consists of two main parts: a physical system and a cloud system, including a physical energy system and a fuel cell stack cloud model.

[0077] Specifically, the physical energy system includes a multi-stack fuel cell power generation system, a lithium battery energy storage system, a data acquisition system, a local controller and a DC / DC converter.

[0078] The fuel cell stack cloud model includes a fuel cell stack digital twin model and a parameter control system. The fuel cell stack cloud model is a model built in the cloud based on cloud computing technology. It can optimize and calculate the controller parameters in the cloud in combination with the fuel cell stack digital twin model, and send them to the physical energy system. The local controller is used to control the physical energy system to obtain the most optimized and most hydrogen-saving operating state of the physical energy system.

[0079] Specifically, a fuel cell is a device that generates electrical energy through an electrochemical reaction between hydrogen and oxygen. The output voltage of a single fuel cell is:

[0080] V cell =E Nerest -V act -V ohm -V con ;

[0081]

[0082] V ohm =R FC I FC ;

[0083]

[0084] In the above formula, V cell 、E Nerest 、V act 、V ohm and V con They represent the output voltage, Nestor voltage, activation polarization voltage drop, ohmic voltage drop and concentration polarization voltage drop of a single fuel cell respectively; T FC , and They represent the operating temperature, hydrogen pressure and oxygen pressure of the fuel cell respectively; ε1, ε2, ε3 and ε4 are constants respectively; is the oxygen concentration; R FC ,I FC , k con and I FC max They are the fuel cell internal resistance, fuel cell current, concentration polarization voltage coefficient and fuel cell maximum current.

[0085] In practical applications, a fuel cell stack is composed of multiple single cells connected in series. Without considering the differences between individual cells, the output voltage of a multi-stack fuel cell is:

[0086] V FC =nV cell ;

[0087] Where n is the number of individual fuel cells connected in series in the multi-stack fuel cell.

[0088] Therefore, the output power of multiple fuel cell stacks is:

[0089] P FC =V FC I FC =nV cell I FC =n(E Nerest -V act -V ohm -V con )I FC .

[0090] Step S2: Taking the required power of the physical energy system and the state of charge of the lithium battery as input variables and the output power of the multiple fuel cell stacks as output variables, a fuzzy logic controller is constructed.

[0091] Specifically, the embodiment of the present invention adopts the second type of fuzzy logic control of Mamdani fuzzy system to deal with uncertainty, and its typical structure is as follows: Figure 3As shown in Figure 1, the input and output variables are described by seven membership functions (mf1-mf7). Assume that the seven membership functions mf 1 to mf 7 here describe the degree of a variable, which are "very small", "medium small", "small", "medium", "large", "medium large" and "very large".

[0092] And the fuzzy rules of the fuzzy logic controller are defined by the following rules:

[0093] (1) When the state of charge of the lithium battery is low and the power demand of the energy system is also low, the fuel cell should output higher power;

[0094] (2) When the state of charge of the lithium battery is low and the power demand of the energy system is high, the fuel cell should output higher power;

[0095] (3) When the state of charge of the lithium battery is high and the power demand of the energy system is low, the fuel cell should output lower power;

[0096] (4) When the state of charge of the lithium battery is high and the power demand of the energy system is also high, the fuel cell should output lower power.

[0097] The final fuzzy rules are shown in Table 1.

[0098] Table 1 Fuzzy rules of fuzzy logic controller

[0099]

[0100]

[0101] Each membership function has six free variables, namely, the vertex, the lower left corner, the lower right corner, the lower scale, the lower left hysteresis and the lower right hysteresis. Let the six parameters of each membership function be: x1, x2, x3, x4, x5, x6. By optimizing these parameters, the purpose of optimizing the fuzzy logic controller can be achieved, thereby achieving the goal of low hydrogen consumption of the fuel cell stack. In the embodiment of the present invention, the input variable of the fuzzy logic controller is defined as the required power of the physical energy system, and the output variable is the output power of multiple fuel cell stacks.

[0102] The six parameters of each membership function: vertex x1, lower left corner x2, lower right corner x3, lower scale x4, lower left hysteresis x5 and lower right hysteresis x6 are taken as optimization parameters, and these parameters together constitute the decision vector X. The relationship between the hydrogen consumption of multiple fuel cells and the parameters of the fuzzy logic controller is shown in Figure 4 As shown, by modifying the parameters of the fuzzy logic controller, the performance of the controlled system can be changed, thereby changing the hydrogen consumption of the multiple fuel cell stacks. FCT(X) consists of two parts: one is the actual hydrogen consumption of multiple fuel cell stacks, and the other is the equivalent hydrogen consumption of multiple fuel cell stacks:

[0103] H FCT (X) = H FC (X)+H bat (X);

[0104]

[0105]

[0106] X=[x1 x2 x3 x4 x5 x6] T

[0107] In the above formula, H FC (X), H bat (X) are the hydrogen consumption function of the fuel cell and the equivalent hydrogen consumption function of the fuel cell respectively; X represents the decision vector; α and β represent weight matrices respectively; α1, α2, α3, α4, α5, α6, β1, β2, β3, β4, β5, and β6 represent weight coefficients.

[0108] Step S3: A hydrogen consumption function of the energy system is established based on the relationship between the output power of the multiple fuel cells and lithium batteries and the hydrogen consumption, and an optimization function is established with the goal of minimizing the value of the hydrogen consumption function.

[0109] Specifically, combining the objective function and the decision variables, the optimization problem of the optimal hydrogen consumption of the fuel cell stack is obtained as follows:

[0110]

[0111] Where X = [x1, x2, …, x n ] represents the decision vector, i.e., the fuzzy controller parameter; n is the dimension of the decision vector; and are the upper and lower limits.

[0112] Step S4: Solve the optimization function to obtain the optimal parameters of the fuzzy logic controller, and control the output power of the multiple fuel cell stacks according to the optimal parameters.

[0113] Since the objective function of the embodiment of the present invention has no clear function expression and has a black box characteristic, the gradient information of the objective function is unknown and cannot be solved by some traditional mathematical programming algorithms. Considering the characteristics of the black box function, the embodiment of the present invention chooses to use an algorithm based on the agent model optimization algorithm (SO) and the adaptive particle swarm optimization algorithm (APSO) to solve the objective function.

[0114] The advantage of the SO algorithm is that it can be optimized based on the initial training set, generate a substitute model using an interpolation function, and then generate multiple candidate solutions near the current optimal feasible solution in each iterative step. In addition, based on the substitution criterion and the distance criterion, the best candidate solution is selected, and the training set, the current optimal target value, and the best feasible solution are updated. However, this algorithm often falls into the dilemma of inefficient search in the later stage. Therefore, in an embodiment of the present invention, the SO algorithm and the APSO algorithm are combined, the SO algorithm is used to determine the best feasible solution in the early stage, and the APSO algorithm is used to further refine the feasible solution in the later stage, so that the hydrogen consumption of the entire model is minimized and the numerical value is more accurate.

[0115] The surrogate model optimization algorithm SO is a commonly used method in the fields of engineering computing and machine learning to solve the optimization problem of complex functions. This method constructs a relatively simple new function called a surrogate model by collecting feature points, and then optimizes this model to obtain the optimal solution. Surrogate models are usually divided into interpolation and fitting types, which can effectively approximate the original complex function, especially when the function calculation cost is high or the calculation speed is slow.

[0116] The solution process of the SO algorithm used in the embodiment of the present invention is as follows: Figure 4 As shown, the following steps are included:

[0117] Step S41: Initialize the parameter set of the fuzzy logic controller and the parameters of the agent model optimization algorithm.

[0118] Step S42: Calculate the hydrogen consumption corresponding to each set of parameters in the parameter set, and take the set of parameters with the minimum hydrogen consumption as the first optimal solution X opt :

[0119]

[0120] In the above formula, T represents the constructed training set,

[0121] Step S43: construct a proxy model according to the current parameter set, use the proxy model to interpolate the optimization function to obtain a proxy function, randomly generate a candidate solution set of the proxy function, and select the second optimal solution from the candidate solution set.

[0122] Specifically, the proxy model used in the embodiment of the present invention is an RBF interpolation model, and its expression is:

[0123]

[0124] In the formula, ‖·‖ represents the Euclidean norm; represents the number of samples in the training set; ρ represents The dimension of ; b represents a constant; is the current decision variable; is the i-th decision variable; α i represents the coefficient, α i , ρ and b can be determined by the following equations:

[0125]

[0126] Among them, Ψ, Γ1, Γ2, and E are just operation symbols without special meanings, respectively:

[0127]

[0128] Here, Φ is given by The matrix formed.

[0129] Randomly generating a candidate solution set for the surrogate function includes the following steps:

[0130] Step S4301: Input the disturbance step size d and the current optimal solution

[0131] Step S4302: in For one dimensional column vector, the i-th candidate point Disturbance count x flag ←0;

[0132] Step S4303: Determine whether r per The jth element in [r per ] j Is it less than the set probability u? per If yes, execute step S4304, otherwise execute step S4305;

[0133] Step S4304: Where normrnd() is a normal random number, x flag ←x flag +1;

[0134] Step S4305: Repeat steps S4303 to S4304 until all Complete judgment;

[0135] Step S4306: If x flag ==0, then randomly from 1 to Select an integer and assign it to j;

[0136] Step S4307: Update

[0137] Step S4308: Repeat steps S4302 to S4307 until the current number of iterations reaches the set maximum number of iterations.

[0138] Selecting the second best solution from the candidate solution set includes the following steps:

[0139] Step S4311: record the candidate point set generated in the previous step as The sampling point set is denoted as Calculate each candidate point and sampling point set in the candidate solution set The shortest distance between

[0140]

[0141] In the above formula, Represents the location of the points in the training set.

[0142] Step S4312: Set the maximum and minimum values ​​of the proxy function value S max , S min , the maximum and minimum values ​​of the shortest distance between the candidate point and the sampling point D max , D min for:

[0143]

[0144] Step S4313: Generate an ascending weight set {σ1,σ2,…,σ κ}, if mod(τ,κ)≠0, let σ S ←σ mod(τ,κ) , otherwise let σ S ←σ κ , where mod() is the modulus function;

[0145] Step S4314: Let σ D ←1-σ S ;

[0146] Step S4315: If S min ≠S max ,make Otherwise,

[0147] Step S4316: If D min ≠D max ,make Otherwise,

[0148] Step S4317: Update the current optimal solution:

[0149]

[0150] Step S4318: Repeat steps S435 to S437 until the current number of iterations reaches the set maximum number of iterations, and output the current optimal solution as the second optimal solution.

[0151] Step S44: Compare the hydrogen consumption corresponding to the first optimal solution and the second optimal solution, take the solution with smaller hydrogen consumption as the current optimal solution, and add the current optimal solution to the parameter set.

[0152] Specifically, if Then update the current optimal solution, current success and failure times as follows:

[0153]

[0154] At this time, if S success Exceeded the maximum number of successes set Then update the current perturbation step size χ n+1 =2χ n 、Current number of successes S success =0.

[0155] like Then update the current success and failure times as follows:

[0156]

[0157] At this time, if F fail Exceeded the maximum number of failures set Then update the current perturbation step size χ n+1 =0.5x n 、Current number of failures F fail =0.

[0158] Step S45: repeating steps S42 to S44 until the current number of iterations reaches the set maximum number of iterations, and outputting the parameter set;

[0159] Step S46: input the parameter set into the adaptive particle swarm optimization algorithm to obtain the optimal parameter combination.

[0160] The solution process of the adaptive particle swarm optimization algorithm (APSO) used in the embodiment of the present invention is as follows: Figure 5As shown. The adaptive particle swarm optimization algorithm (APSO) is an improved algorithm based on the traditional particle swarm optimization (PSO). It improves the performance and convergence speed of the algorithm by adaptively adjusting the algorithm parameters. The core of APSO is to dynamically adjust the algorithm parameters, such as inertia weight and learning factor, according to the convergence of the group, so that particles can search the solution space more effectively. In an embodiment of the present invention, the feasible solution obtained by the above-mentioned proxy model optimization algorithm (SO) can be further refined by the APSO algorithm to obtain the optimal solution.

[0161] First, input the feasible solution set obtained by the SO algorithm above, set k = 1, and define the maximum number of iterations K APSO , then initialize the particle swarm and define the initial and final inertia weights Initial and final teach-in coefficients Initial and final population learning coefficients Initialize the historical optimal position for each individual and hydrogen consumption The entire group initializes the historical optimal position X pop and hydrogen consumption F FCT (X pop ).

[0162] When k≤K APSO When , loop k from 1 to N:

[0163]

[0164] And update according to the formula X pop ,F FCT (X pop ), make k=k+1, until the loop condition is not met, end the loop, and take the control parameters corresponding to the optimal individual as the optimal control parameter combination.

[0165] The method of the present invention realizes the economic optimization of the fuel cell stack supporting the power grid by constructing a digital twin model of the fuel cell stack, combining fuzzy logic control based on uncertain rules, and utilizing an algorithm based on a substitute model and an adaptive particle swarm optimization algorithm. Through this method, the hydrogen consumption of the energy system can be reduced, energy efficiency can be improved, operating costs can be reduced, the service life of the fuel cell stack can be extended, and its competitiveness in practical applications can be enhanced. The present invention is not only of direct significance to improving the performance and economic benefits of the fuel cell stack, but also provides important data support and technical guidance for the development of the entire hydrogen energy industry chain, and promotes the widespread application of fuel cell technology in the field of power grids.

[0166] An embodiment of the present invention also provides an energy management system for multiple fuel cell stacks based on fuzzy control, which is implemented based on the above-mentioned energy management method for multiple fuel cell stacks based on fuzzy control and includes a data acquisition module, an energy management module and a control module.

[0167] The data acquisition module is used to collect the operating data of multiple fuel cells and lithium batteries.

[0168] The energy management module is used to distribute the output power of multiple fuel cells and lithium batteries according to the operating data collected by the data acquisition module to meet the power requirements of the system.

[0169] The control module is used to control the output power of multiple fuel cells and lithium batteries according to the control signal output by the energy management module.

[0170] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0171] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for energy management of multiple fuel cell stacks based on fuzzy control, characterized in that: The following steps are involved: Step S1: constructing an energy system including multiple fuel cell stacks and lithium batteries; Step S2: Taking the required power of the energy system and the state of charge of the lithium battery as input variables and the output power of the multiple fuel cell stacks as output variables, a fuzzy logic controller is constructed; Step S3: establishing a hydrogen consumption function of the energy system according to the relationship between the output power and hydrogen consumption of the plurality of fuel cells and lithium batteries, and establishing an optimization function with the goal of minimizing the value of the hydrogen consumption function; Step S4: Solving the optimization function to obtain the optimal parameters of the fuzzy logic controller, and controlling the output power of the multiple fuel cell stacks according to the optimal parameters.

2. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 1, characterized in that: The construction of the fuzzy logic controller in step S2 comprises the following steps: Step S21: defining a plurality of membership functions, and deriving the fuzzy sets of the input variables and the output variables according to the plurality of membership functions; Step S22: defining a mapping relationship between fuzzy sets of input variables and output variables as a fuzzy rule; Step S23: define a plurality of free variables of the membership function, and use the free variables as parameters of the fuzzy logic controller.

3. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 2 is characterized in that: In step S21, seven membership functions mf1-mf7 are constructed, and mf1-mf7 correspond to very low, medium-low, low, medium, relatively high, medium-high and very high, respectively.

4. The energy management method of a multi-stack fuel cell system based on fuzzy control according to claim 2, characterized in that: The fuzzy rules in step S22 include: (1) When the state of charge of the lithium battery is low and the power demand of the energy system is also low, the fuel cell should output higher power; (2) When the state of charge of the lithium battery is low and the power demand of the energy system is high, the fuel cell should output higher power; (3) When the state of charge of the lithium battery is high and the power demand of the energy system is low, the fuel cell should output lower power; (4) When the state of charge of the lithium battery is high and the power demand of the energy system is also high, the fuel cell should output lower power.

5. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 2, characterized in that: The free variables in step S23 include vertex x1, lower left corner x2, lower right corner x3, lower scale x4, lower left lag x5 and lower right lag x6.

6. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 1, characterized in that: The expression of the output power of the multiple fuel cell stacks in step S3 is: P FC =V FC I FC =nV cell I FC ; In FC =nV cell ; V cell =E Nerest -V act -V ohm -V con ; V ohm =R FC I FC ; In the above formula, P FC is the output power of multiple fuel cell stacks; V FC is the output voltage of multiple fuel cell stacks; I FC is the output current of the multiple fuel cell stacks; n is the number of fuel cells in the multiple fuel cell stacks; V cell 、E Nerest 、V act 、V ohm 、V con , T FC , R FC , k con and I FC max They are the output voltage, Nestor voltage, activation polarization voltage drop, ohmic voltage drop, concentration polarization voltage drop, operating temperature, hydrogen pressure, oxygen pressure, oxygen concentration, internal resistance, concentration polarization voltage coefficient and maximum current of a single fuel cell respectively; ε1, ε2, ε3 and ε4 are constants respectively.

7. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 5, characterized in that: The expression of the hydrogen consumption function in step S3 is: H FCT (X)=H FC (X)+H bat (X); X=[x1 x2 x3 x4 x5 x6] T ; H FC (X)=αX β＝[β1β2β3β4β5β6] In the above formula, H FC (X), H bat (X) are the equivalent hydrogen consumption functions of fuel cells and lithium batteries respectively; α and β are weight matrices; α1, α2, α3, α4, α5, α6, β1, β2, β3, β4, β5, β6 are weight coefficients; H FC , H bat Respectively represent the hydrogen consumption of multiple fuel cell stacks and the hydrogen consumption of lithium batteries; P FC is the output power of multiple fuel cell stacks; η FC is the efficiency of multiple fuel cell stacks; P bat is the output power of the lithium battery; η bat The efficiency of lithium batteries; η c , η d are the charging efficiency and discharging efficiency of lithium batteries respectively; t ini and t end Represent the initial time and the end time respectively.

8. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 1, characterized in that: In step S4, the optimization function is solved by using a proxy model optimization algorithm and an adaptive particle swarm optimization algorithm, which includes the following steps: Step S41: Initializing the parameter set of the fuzzy logic controller and the parameters of the agent model optimization algorithm; Step S42: Calculate the hydrogen consumption corresponding to each group of parameters in the parameter set, and take the group of parameters with the minimum hydrogen consumption as the first optimal solution; Step S43: constructing a proxy model according to the current parameter set, interpolating the optimization function using the proxy model to obtain a proxy function, randomly generating a candidate solution set of the proxy function, and selecting a second optimal solution from the candidate solution set; Step S44: comparing the hydrogen consumption corresponding to the first optimal solution and the second optimal solution, taking the solution with the smaller hydrogen consumption as the current optimal solution, and adding the current optimal solution to the parameter set; Step S45: repeating steps S42 to S44 until the current number of iterations reaches the set maximum number of iterations, and outputting the parameter set; Step S46: inputting the parameter set into an adaptive particle swarm optimization algorithm to obtain an optimal parameter combination.

9. The energy management method of multiple fuel cell stacks based on fuzzy control according to claim 8, characterized in that: The step S43 of selecting the second optimal solution from the candidate solution set comprises the following steps: Step S431: Calculate each candidate point in the candidate solution set and sampling point set The shortest distance between In the formula, Represents the location of the points in the training set; Step S432: Set the maximum and minimum values ​​of the proxy function value S max , S min , the maximum and minimum values ​​of the shortest distance between the candidate point and the sampling point D max , D min for: Step S433: Generate an ascending weight set {σ1,σ2,…,σ κ }, if mod(τ,κ)≠0, let σ S ←σ mod(τ,κ) , otherwise let σ S ←σ κ , where mod() is the modulus function; Step S434: Let σ D ←1-σ S ; Step S435: If S min ≠S max ,make Otherwise, Step S436: If D min ≠D max ,make Otherwise, Step S437: Update the current optimal solution: Step S438: Repeat steps S435 to S437 until the current number of iterations reaches the set maximum number of iterations, and output the current optimal solution as the second optimal solution.

10. An energy management system for multiple fuel cell stacks based on fuzzy control, implemented based on an energy management method for multiple fuel cell stacks based on fuzzy control as claimed in any one of claims 1 to 9, characterized in that: include: Data acquisition module, energy management module and control module; The data acquisition module is used to collect the operating data of multiple fuel cells and lithium batteries; The energy management module allocates the output power of multiple fuel cells and lithium batteries according to the operating data collected by the data collection module to meet the power demand of the system; The control module controls the output power of the multiple fuel cell stacks and lithium batteries according to the control signal output by the energy management module.

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