Power distribution method, system and equipment for hydrogen energy hybrid power system and storage medium

Abstract

The invention discloses a hydrogen energy hybrid power system power distribution method, system and device and a storage medium, and is applied to the field of fuel cells, and the method comprises the steps: calculating the optimal output power of a fuel cell system assembly through a global distribution system of a hybrid system based on an equivalent hydrogen consumption minimum model; based on the optimal output power of the fuel cell system assembly and according to the distribution rule of the multi-module fuel cell system, the optimal output power of each fuel cell system under the fuel cell system assembly is calculated; the distribution rule comprises the optimal economic performance and the own characteristics of each fuel cell system. An upper layer calculates and distributes the optimal output power of a fuel cell system assembly through a hybrid system global distribution system; and the lower layer utilizes the distribution rule of the multi-module fuel cell system, fully considers the self characteristics and economic performance of each fuel cell system, distributes the optimal output power of the fuel cell system assembly to each fuel cell system, and realizes accurate regulation and control.

Description

Technical Field

[0001] The present invention relates to the field of fuel cells, and particularly to a method, system, device and storage medium for power distribution of a hydrogen energy hybrid system. Background Art

[0002] The hydrogen energy hybrid system can meet the stringent requirements for large peak power and high load-carrying power in application scenarios such as large transportation equipment and industrial heavy machinery. Such a power system generally consists of multiple fuel cell systems and multiple power battery systems to form a hybrid power system, and through the coordinated operation of the two, efficient and stable energy output is achieved. Traditional power distribution methods are difficult to consider the differences between individual fuel cell systems while ensuring the overall performance of the system.

[0003] Therefore, how to optimize the overall performance of the system and fully consider the differences between different fuel cell systems to achieve precise power distribution is a technical problem that urgently needs to be solved at present. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, system, device and storage medium for power distribution of a hydrogen energy hybrid system, which solves the problem in the prior art that it is impossible to optimize the overall performance while considering the differences between different fuel cell systems to achieve precise power distribution.

[0005] To solve the above technical problems, the present invention provides a method for power distribution of a hydrogen energy hybrid system, including:

[0006] Through the hybrid system global distribution system, based on the minimum equivalent hydrogen consumption model, calculate the optimal output power of the fuel cell system assembly;

[0007] Based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system, calculate the optimal output power of each fuel cell system under the fuel cell system assembly; the distribution rules include optimal economic performance and the own characteristics of each fuel cell system.

[0008] Optionally, through the hybrid system global distribution system, based on the minimum equivalent hydrogen consumption model, calculating the optimal output power of the fuel cell system assembly includes:

[0009] Based on the principle of minimum total hydrogen consumption, construct the minimum equivalent hydrogen consumption model; the principle of minimum total hydrogen consumption is to control the output power of the fuel cell system assembly and the power battery system assembly so that the total hydrogen consumption of the hydrogen energy hybrid system reaches the minimum;

[0010] Based on the power balance law of the DC bus, transform the minimum equivalent hydrogen consumption model to obtain a transformed model;

[0011] Solve the conversion model to obtain the optimal output power of the power battery system assembly;

[0012] Based on the optimal output power of the power battery system assembly, the power balance law, and the output limit of the fuel cell system assembly, obtain the optimal output power of the fuel cell system assembly.

[0013] Optionally, the allocation rules of the multi-module fuel cell system include: the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, and the operation parameter limit and variable load rate limit of each fuel cell system.

[0014] Optionally, the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, including:

[0015] Construct an economic performance model according to the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaching the optimal state. The economic performance model is:

[0016] ;

[0017] is the output power of the i-th fuel cell system; represents the total cost; and are the hydrogen cost consumed by the i-th fuel cell system and the fuel cell system life loss cost respectively; k represents the moment, representing the value at a certain moment; n represents the number of fuel cell systems;

[0018] The hydrogen cost consumed by the i-th fuel cell system The calculation formula of is:

[0019] ;

[0020] ;

[0021] is the current market hydrogen price, is the hydrogen consumption of the i-th fuel cell system, is the output power of the i-th fuel cell system, is the efficiency of the i-th fuel cell system, is the low calorific value of hydrogen;

[0022] The life loss cost of the i-th fuel cell system The calculation formula of is:

[0023] ;

[0024] ;

[0025] is the life attenuation rate of the i-th fuel cell system; is the purchase price of the i-th fuel cell system; , , are the voltage attenuation values of the i-th fuel cell system caused under low power, unit load power change, and high power conditions respectively; t1 and t2 are the durations of the i-th fuel cell system under low power and high power conditions respectively; and are the boundary values of the high and low power regions of the i-th fuel cell respectively; is the allowable voltage attenuation value of the i-th fuel cell from the start of use to the end of its life.

[0026] Optionally, based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system, the optimal output power of each fuel cell system under the fuel cell system assembly is calculated, including:

[0027] Based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system, determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm;

[0028] Based on the change of the optimal output power of the fuel cell system assembly, adaptively optimize the initial position of the particle swarm to obtain the initial position of the particle swarm;

[0029] Based on the preset parameters of the particle swarm optimization algorithm, the initial position of the particle swarm, the limitations of the optimization parameters, and the optimization objective, use the particle swarm optimization algorithm to optimize the optimization parameters to obtain the optimal output power of each fuel cell system.

[0030] Optionally, based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system, determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm, including:

[0031] Take the output power of each fuel cell system as the optimization parameter;

[0032] Take the first rule in the distribution rules of the multi-module fuel cell system as the optimization objective; the first rule is that the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state;

[0033] Take the second rule, the third rule, and the fourth rule in the distribution rules of the multi-module fuel cell system as the constraints of the optimization parameters; the second rule is set based on the optimal output power of the fuel cell system assembly, and the third rule is set according to the characteristics of each fuel cell system itself.

[0034] Optionally, adaptively optimize the initial position of the particle swarm based on the change of the optimal output power of the fuel cell system assembly to obtain the initial position of the particle swarm, including:

[0035] Calculate the difference in the optimal output power of the fuel cell system assembly based on the optimal output power of the fuel cell system assembly at the previous moment and the optimal output power of the fuel cell assembly at the current moment;

[0036] Calculate the central value of the initial position of the particle swarm at the current moment based on the optimal particle position at the previous moment and the difference in the optimal output power of the fuel cell system assembly;

[0037] Use the Latin hypercube sampling method to initialize the position of the particle swarm with the central value of the initial position of the particle swarm at the current moment as the center and the variable load limit and output limit of each fuel cell system as the upper and lower boundaries to obtain the initial position of the particle swarm.

[0038] The present invention also provides a power distribution system for a hydrogen energy hybrid system, including:

[0039] An upper layer control module for calculating the optimal output power of the fuel cell system assembly based on the equivalent hydrogen consumption minimum model through the hybrid system global distribution system;

[0040] A lower layer control module for calculating the optimal output power of each fuel cell system under the fuel cell system assembly based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system.

[0041] The present invention also provides a power distribution device for a hydrogen energy hybrid system, including:

[0042] A memory for storing a computer program;

[0043] A processor for implementing the power distribution method of the hydrogen energy hybrid system as described above when executing the computer program.

[0044] The present invention also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, the power distribution method of the hydrogen energy hybrid system as described above is implemented.

[0045] It can be seen that in the present invention, through the hybrid system global distribution system, based on the minimum equivalent hydrogen consumption model, the optimal output power of the fuel cell system assembly is calculated; based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system, the optimal output power of each fuel cell system under the fuel cell system assembly is calculated; the distribution rules include the optimal economic performance and the self-characteristics of each fuel cell system. The present invention adopts a hierarchical energy management strategy. The upper layer calculates and distributes the optimal output power of the fuel cell system assembly through the hybrid system global distribution system based on the minimum equivalent hydrogen consumption model; the lower layer uses the distribution rules of the multi-module fuel cell system, fully considering the self-characteristics and economic performance of each fuel cell system, and distributes the optimal output power of the fuel cell system assembly to each fuel cell system, realizing precise power regulation.

[0046] In addition, the present invention also provides a power distribution system, device and storage medium for a hydrogen energy hybrid system, which also have the above beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0048] Figure 1 It is a flowchart of a power distribution method for a hydrogen energy hybrid system provided by an embodiment of the present invention;

[0049] Figure 2 It is an example diagram of the topological structure of a hydrogen energy hybrid system provided by an embodiment of the present invention;

[0050] Figure 3 It is an example diagram of a hierarchical control strategy provided by an embodiment of the present invention;

[0051] Figure 4 It is an example flowchart of a power distribution method for a hydrogen energy hybrid system provided by an embodiment of the present invention;

[0052] Figure 5 It is a schematic structural diagram of a power distribution device for a hydrogen energy hybrid system provided by an embodiment of the present invention;

[0053] Figure 6 It is a schematic structural diagram of a power distribution device for a hydrogen energy hybrid system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] In the process of exploration and development in the current energy field, hydrogen energy has become one of the most potential energy options due to its significant advantages of cleanliness and high efficiency. The hydrogen energy hybrid system has emerged as the times require, and its original design intention is to meet the stringent requirements for large peak power and relatively high pulling power in application scenarios such as large transportation equipment and industrial heavy machinery.

[0056] When the traditional power management strategy of the hydrogen energy hybrid system deals with such a complex hydrogen energy hybrid system, although it can perform power distribution on the fuel cell system assembly and the power battery system assembly as a whole and meet the basic operation requirements of the system to a certain extent, it cannot penetrate into specific individual fuel cell systems to perform refined power regulation according to their individual differences. For example, for a fuel cell system with a relatively low hydrogen consumption but a relatively fast life loss, it is difficult for the traditional strategy to effectively balance the relationship between its hydrogen consumption and life loss while ensuring the overall performance of the system; for a fuel cell system with relatively special output characteristics, the traditional power distribution method cannot fully utilize its advantages either.

[0057] Therefore, the present invention proposes a power distribution method for a hydrogen energy hybrid system, which can not only perform scientific and reasonable power distribution on the fuel cell system assembly and the power battery system assembly to optimize the overall performance of the system, but also fully consider the differences between different fuel cell systems and perform precise distribution of the multi-module fuel cell system according to these differences. In this way, on the premise of meeting the power demand of the system, the energy utilization efficiency is maximized, the operation cost is reduced, and the service life of the entire power system is extended, thereby promoting the wide application and sustainable development of the multi-energy coupling power system based on hydrogen energy in various fields.

[0058] For details, please refer to Figure 1 , Figure 1 which is a flowchart of a power distribution method for a hydrogen energy hybrid system provided by an embodiment of the present invention. The method may include:

[0059] S101: Through the hybrid system global distribution system, calculate the optimal output power of the fuel cell system assembly based on the minimum equivalent hydrogen consumption model.

[0060] The execution entity of this embodiment is the control end or the management end. Step S101 follows the upper-layer control strategy, that is, at the upper-layer control level, the equivalent hydrogen consumption minimum model is used to determine the optimal output power combination of the fuel cell system assembly and the power battery system assembly through precise calculation and analysis, so as to achieve the goal of the lowest overall hydrogen consumption. This process comprehensively considers various factors in the system operation to ensure the efficient utilization of energy while meeting the power demand.

[0061] This embodiment describes the hydrogen energy hybrid system, and specific reference can be made to Figure 2 , Figure 2 which is an example diagram of the topological structure of a hydrogen energy hybrid system provided by an embodiment of the present invention. The hydrogen energy hybrid system is composed of a fuel cell system assembly and a power battery system assembly, and through the collaborative work of the two, efficient and stable energy output is achieved. The fuel cell system assembly is composed of 4 groups of fuel cell systems; the power battery system assembly is composed of 4 groups of power battery systems. The fuel cell system mainly converts the chemical energy of hydrogen into electrical energy through electrochemical reactions, with the characteristics of high energy conversion efficiency and strong endurance; while the power battery system relies on the charge and discharge of the battery to store and release electrical energy, with the advantages of fast response speed and high power density. The fuel cell system and the power battery system are the two core components of this power system, and there are fundamental differences between them. Different fuel cell systems have different output characteristics, and the four fuel cell systems have output characteristics (maximum output power, minimum output power, maximum load change rate) and life characteristics (fuel cell system attenuation characteristics).

[0062] Furthermore, the above-mentioned optimal output power of the fuel cell system assembly calculated through the hybrid system global distribution system based on the equivalent hydrogen consumption minimum model specifically includes:

[0063] Step 11: Based on the principle of minimum total hydrogen consumption, an equivalent hydrogen consumption minimum model is constructed; the principle of minimum total hydrogen consumption is to control the output power of the fuel cell system assembly and the power battery system assembly so that the total hydrogen consumption of the hydrogen energy hybrid system reaches the minimum.

[0064] The equivalent hydrogen consumption minimum model is constructed according to the principle of minimum equivalent hydrogen consumption and is an instantaneous optimization method. The basic idea is to convert the electrical energy consumed by the power battery system into hydrogen consumption, making it comparable to the hydrogen consumption of the fuel cell system. By controlling the relationship between the output power of the fuel cell system and the vehicle demand power, the total hydrogen consumption of the system within a unit control cycle is minimized to obtain the optimal economic performance.

[0065] The expression of the equivalent hydrogen consumption minimum model is:

[0066] .

[0067] Among them, is the theoretical value of the optimal output power of the fuel cell system assembly calculated based on the principle of minimum equivalent hydrogen consumption; is the total hydrogen consumption of the hydrogen - energy hybrid system; is the instantaneous hydrogen consumption of the fuel cell system assembly, which is a function of the output power of the fuel cell system assembly, and the relationship between the two can be equivalent to a linear relationship; is the instantaneous hydrogen consumption of the power battery system assembly; k is a correction coefficient; and are the upper limit and the lower limit of the SOC (State Of Charge) of the power battery system assembly respectively; and are the minimum value and the maximum value of the voltage of the power battery system assembly respectively; and are the maximum value and the minimum value of the output power of the fuel cell system assembly respectively; It should be noted that the optimal output power of the fuel cell system assembly is the actual power output value.

[0068] The expression of the correction coefficient k can be:

[0069] .

[0070] Among them, μ is the balance coefficient of SOC, and this value is related to the types, numbers and specific performances of each power battery system in the hydrogen - energy hybrid system.

[0071] The instantaneous hydrogen consumption of the fuel cell system assembly is expressed as:

[0072] .

[0073] Among them, both a and b are constants.

[0074] The instantaneous hydrogen consumption of the power battery system assembly is expressed as:

[0075] ;

[0076] is the output power of the power battery system assembly; , are the average charge - discharge efficiency of the power battery system assembly; , are the average power and the average instantaneous hydrogen consumption of the fuel cell system assembly respectively; , The charge and discharge efficiency of the power battery system assembly, respectively, is a function of SOC, and the expression can be:

[0077] ;

[0078] Among them, and are the charge and discharge internal resistances of the lithium battery, respectively, and can be regarded as constants.

[0079] Step 12: Based on the power balance law of the DC bus, transform the equivalent hydrogen consumption minimum model to obtain a transformed model.

[0080] The DC bus of the hydrogen energy hybrid system of the fuel cell / power battery system satisfies the following power balance law:

[0081] .

[0082] represents the required power on the bus.

[0083] According to the above power balance law, transform in the equivalent hydrogen consumption minimum model to:

[0084] Then the problem of minimizing the hydrogen fuel consumption of the hybrid system will be transformed into:

[0085] .

[0086] In the instantaneous optimization process, the required power on the bus is a constant; because b is also a constant, the above formula can be further transformed into:

[0087] .

[0088] Substitute the expression of the instantaneous hydrogen consumption of the above power battery system assembly into the above formula to obtain Formula 1:

[0089] .

[0090] Perform a mathematically equivalent substitution on the above formula, assuming:

[0091] ;

[0092] Therefore, the above Formula 1 can be further transformed into:

[0093] ;

[0094] The total voltage U bat of the power battery system satisfies the following constraints:

[0095] ;

[0096] Therefore,

[0097] ;

[0098] The expression of the final conversion model is:

[0099] .

[0100] Step 13: Solve the conversion model to obtain the optimal output power of the power battery system assembly.

[0101] The solution process of the conversion model can refer to the solution of the quadratic equation of one variable. The minimum point of the quadratic equation of one variable .

[0102] For the equation corresponding to the first row in the conversion model, , when x ≤ (i.e., ), = ; when x (i.e., ), = x = ; when x > 1 (i.e., > 1), = 1.

[0103] For the equation corresponding to the second layer in the conversion model, , when x (i.e., ), = 1; when x (i.e., ), = x = ; when x > (i.e., > ), = .)

[0104] Substitute , and it can be known that: The solution of the minimum hydrogen consumption is:

[0105] ;

[0106] Substitute the above formula into the expression of the conversion model, and it can be obtained:

[0107] ;

[0108] For rail transit vehicles, the vehicle operation time is long and the required power is high. , that is , therefore .

[0109] Under the above conditions

[0110] ;

[0111] Assume:

[0112] ;

[0113] By further simplifying and solving the analytical solution of the optimization problem, the optimal output power of the power battery system assembly is as follows: After arranging the above formula, we get:

[0114] ;

[0115] The optimal output power of the power battery system assembly can be obtained through the above formula under the distribution rule based on the principle of minimum equivalent hydrogen consumption.

[0116] Step 14: Based on the optimal output power of the power battery system assembly, the power balance law, and the output limit of the fuel cell system assembly, obtain the optimal output power of the fuel cell system assembly.

[0117] Judge according to the required power on the bus and the output limits of each fuel cell system, and finally obtain the optimal output power of the fuel cell system assembly and the optimal output power

[0118] :

[0119] S102: Based on the optimal output power of the fuel cell system assembly and according to the distribution rule of the multi-module fuel cell system, calculate the optimal output power of each fuel cell system under the fuel cell system assembly; the distribution rule includes the optimal economic performance and the own characteristics of each fuel cell system.

[0120] Specifically, during the actual operation of the hydrogen energy hybrid system, after receiving the command of the optimal output power of the fuel cell system assembly, the specific output power of each fuel cell system specifically depends entirely on the specific power distribution strategy adopted. That is, the lower-level strategy comes into play, and according to the distribution rules of the multi-module fuel cell system, the optimal output power of the fuel cell system assembly is accurately distributed to each fuel cell system. It should be noted that in the calculation process of the optimal output power of each fuel cell system, this embodiment does not limit the specific solution algorithm. For example, it can be a particle swarm optimization algorithm, or it can also be a genetic algorithm.

[0121] This embodiment takes into account that there are certain differences between each fuel cell system. Therefore, the distribution rules of this embodiment include the optimal economic performance and the own characteristics of each fuel cell system. Further, the above-mentioned distribution rules of the multi-module fuel cell system can be further specifically included: the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, and the operating parameter limits and variable load rate limits of each fuel cell system.

[0122] Specifically, the distribution rules of the multi-module fuel cell system fully consider the own characteristics of each fuel cell system. Each fuel cell system has its specific operating parameter limits, such as its respective maximum and minimum output powers, which determine the power range that they can provide or accept under different working conditions. At the same time, the maximum and minimum variable load rates of each fuel cell system cannot be ignored, and this parameter reflects the ability of the fuel cell system to respond to power changes. Only by taking these factors into account in the distribution rules can it be ensured that each fuel cell system can operate in a safe and efficient state, thereby ensuring the stability and reliability of the entire multi-module fuel cell system. The specific formula expression of the distribution rules formulated based on the characteristics of each fuel cell system is:

[0123] .

[0124] and are respectively the minimum and maximum values of the output power of the i-th fuel cell system ; and are respectively the minimum and maximum values of the variable load rate of the i-th fuel cell system .

[0125] Specifically, the specific economic performance is optimally reflected in the following two key aspects: On the one hand, when each fuel cell system outputs the same power, due to differences in its internal structure, catalyst performance, reaction conditions, etc., the hydrogen consumption of each fuel cell system will vary. This means that in actual operation, even when outputting the same power, the hydrogen consumption of different fuel cell systems is different, which in turn affects the energy utilization efficiency and operating cost of the entire hydrogen energy hybrid system. On the other hand, after each fuel cell system completes its operation, the life loss situation also varies greatly. This is mainly because different fuel cell systems are subjected to different working conditions such as working pressure, temperature change, and load fluctuation during operation. The combined effect of these factors leads to obvious differences in the service life of each fuel cell system. Based on this, the allocation rules of the multi-module fuel cell system comprehensively consider the above differences between different fuel cell systems to ensure that while minimizing the total hydrogen consumption of the fuel cell system, the overall service life of the fuel cell system is maximally extended.

[0126] Furthermore, the comprehensive economy of the hydrogen consumption and life loss of each fuel cell system reaches the optimal state, which can specifically include:

[0127] Construct an economic performance model based on the comprehensive economy of the hydrogen consumption and life loss of each fuel cell system reaching the optimal state. The economic performance model is:

[0128] ;

[0129] is the output power of the i-th fuel cell system; represents the total cost; and are the hydrogen cost consumed by the i-th fuel cell system and the cost of life loss of the fuel cell system respectively; k represents the time, representing the value at a certain moment; n represents the number of fuel cell systems;

[0130] The hydrogen cost consumed by the i-th fuel cell system The calculation formula of is:

[0131] ;

[0132] ;

[0133] is the current market hydrogen price, is the hydrogen consumption of the i-th fuel cell system, is the output power of the i-th fuel cell system, is the efficiency of the i-th fuel cell system, is the lower heating value of hydrogen;

[0134] The life loss cost of the i-th fuel cell system The calculation formula is:

[0135] ;

[0136] ;

[0137] is the life attenuation rate of the i-th fuel cell system; is the purchase price of the i-th fuel cell system; , , are the voltage attenuation values of the i-th fuel cell system caused by low power, unit load power change and high power conditions respectively; t1 and t2 are the durations of the i-th fuel cell system under low power and high power conditions respectively; and are the boundary values of the high and low power regions of the i-th fuel cell respectively; is the allowable voltage attenuation value of the i-th fuel cell from the start of use to the end of its life.

[0138] It should be noted that large-scale demand power supply equipment has a relatively high demand level for electricity. In order to continuously and stably meet its power demand, and considering that frequent start-stop will have a greater impact on the life of the fuel cell system, after the equipment is started, the fuel cell system will maintain a continuous operation state and will not be easily shut down. Based on this, the loss caused by the start-stop of the fuel cell system is not considered in the embodiment. Instead, when evaluating the life loss cost of the fuel cell system, the life loss caused by the fuel cell system itself during high-power operation, low-power operation and power change is mainly considered. Moreover, the life cost of the fuel cell system is quantified in this embodiment. Through a unique life cost conversion method, the real-time loss of the life of the fuel cell system during the operation of the hydrogen energy hybrid system is established an equivalent relationship with the hydrogen consumption of the system in a quantified form. It can be seen that this method can effectively extend the service life of the fuel cell system while reducing hydrogen consumption, achieving a dual optimization of the system energy utilization efficiency and the equipment service life.

[0139] Finally, the expression of the allocation rule of the multi-module fuel cell system is:

[0140] .

[0141] Furthermore, based on the optimal output power of the fuel cell system assembly and according to the allocation rule of the multi-module fuel cell system, the optimal output power of each fuel cell system under the fuel cell system assembly is calculated, which specifically includes:

[0142] Step 21: Determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system.

[0143] Specifically, in this embodiment, based on the optimal output power of the fuel cell system assembly obtained from the upper-layer control strategy in step S101 and the distribution rules of the module fuel cell systems in the lower-layer control strategy, the particle swarm optimization algorithm is used for optimization. During the optimization process using the particle swarm optimization algorithm, it is necessary to determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm.

[0144] Furthermore, determining the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system may specifically include:

[0145] Step 211: Take the output power of each fuel cell system as an optimization parameter.

[0146] Specifically, the optimization parameter is the optimal output power of each fuel cell system finally solved, that is, the output power of each fuel cell system at this moment. Therefore, the particle position is the output power of the fuel cell system, and the particle velocity is the direction and distance of the particle's movement in the next iteration.

[0147] Step 212: Take the first rule in the distribution rules of the multi-module fuel cell system as the optimization objective; the first rule is that the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state.

[0148] Specifically, the goal of optimizing the particle swarm optimization algorithm is to ensure the optimal comprehensive economy of hydrogen consumption and life loss of the fuel cell system, that is, the first rule in the distribution rules of the multi-module fuel cell system:

[0149] .

[0150] Step 213: Take the second rule, third rule, and fourth rule in the distribution rules of the multi-module fuel cell system as the limitations of the optimization parameters; the second rule is set based on the optimal output power of the fuel cell system assembly, and the third rule is set according to the characteristics of each fuel cell system itself.

[0151] Specifically, the limitations of the optimization parameters are divided into three aspects: (1) The sum of the output powers of each fuel cell system is the output power of the fuel cell system assembly allocated by the upper-layer strategy; (2) The output powers of each fuel cell system meet their respective output limits of the fuel cell systems; (3) The difference between the output power of each fuel cell system at this moment and its output power at the previous moment is less than the variable load limit, that is:

[0152] 。

[0153] Step 22: Adaptive optimization of the initial position of the particle swarm is performed based on the change of the optimal output power of the fuel cell system assembly to obtain the initial position of the particle swarm.

[0154] Under the power distribution management control strategy proposed in this embodiment, the power distribution system of the hydrogen energy hybrid system can obtain the total required power of the hydrogen energy hybrid system in real time, that is, the total required power of the bus. Subsequently, according to the hierarchical power distribution management strategy, this total required power is distributed in real time, and the distributed power signal is transmitted to different power systems in time to meet the actual power consumption requirements. In the control process of the entire strategy, since the real-time rule needs to be strictly followed, the response speed of the hierarchical power distribution management strategy becomes the key factor affecting the system performance. As Figure 3 shown, in this embodiment, there are differences in the time occupancy ratios of the upper-layer control module and the lower-layer control module during operation, and the lower-layer control module occupies a relatively large proportion in the entire strategy response process. Therefore, further optimizing the response speed of the lower-layer control is crucial for improving the performance of the entire hierarchical energy management strategy. Therefore, in this embodiment, adaptive optimization of the initial position of the particle swarm is performed based on the change of the optimal output power of the fuel cell system assembly to obtain the initial position of the particle swarm, so as to improve the speed.

[0155] Furthermore, the above-mentioned adaptive optimization of the initial position of the particle swarm based on the change of the optimal output power of the fuel cell system assembly to obtain the initial position of the particle swarm may specifically include:

[0156] Step 221: Calculate the difference in the optimal output power of the fuel cell system assembly based on the optimal output power of the fuel cell system assembly at the previous moment and the optimal output power of the fuel cell assembly at the current moment.

[0157] In this embodiment, the lower-layer control module is a particle swarm optimization algorithm based on the distribution rules of a multi-module fuel cell system. The optimization results and speed of this algorithm are affected by many factors, among which the initial values of the particle swarm have a relatively large impact. If the initial values of the particles are randomly initialized, without changing any parameters, the results of multiple optimizations may not necessarily converge to a global or local optimal solution, and an invalid solution may also be obtained. Therefore, particle initialization is a very important step, which is related to the speed and direction of optimization convergence in the entire optimization process. If the initialization range of the particles is selected well, the optimization convergence time can be greatly shortened, and it is not easy to fall into a local optimal solution. In this embodiment, the initial positions of the particle swarm are adaptively optimized based on the change of the optimal output power of the fuel cell system assembly. The optimal output power of the fuel cell system assembly obtained by the upper-layer control module at the previous moment is , and the optimal output power of the fuel cell system assembly obtained by the upper-layer control module at this moment is .

[0158] Step 222: Calculate the central value of the initial position of the particle swarm at the current moment according to the difference between the best particle position at the previous moment and the optimal output power of the fuel cell system assembly.

[0159] Specifically, the central value of the initial position of the particle swarm at the current moment is obtained according to the difference between the best particle position at the previous moment and the optimal output power of the fuel cell system :

[0160] .

[0161] Among them, i represents the dimension of the particles in the particle swarm optimization algorithm, that is, different fuel cell systems; n represents the number of fuel cell systems.

[0162] Step 223: Taking the central value of the initial position of the particle swarm at the current moment as the center, and using the variable load limit and output limit of each fuel cell system as the upper and lower boundaries, the Latin hypercube sampling method is used to initialize the positions of the particle swarm to obtain the initial positions of the particle swarm.

[0163] Specifically, taking the central value of the initial position of the particle swarm at the current moment as the center, and using the variable load limit and output limit of different fuel cell systems as the upper and lower boundaries, the Latin hypercube sampling (abbreviated as LHS) method is used to initialize the particle positions, which can ensure that the positions of the particle swarm are evenly distributed and scattered in the feasible region, and thus obtain the initial positions of the particle swarm at each step.

[0164] Step 23: Based on the parameters of the preset particle swarm optimization algorithm, the initial positions of the particle swarm, the constraints of the optimization parameters, and the optimization objective, use the particle swarm optimization algorithm to optimize the optimization parameters to obtain the optimal output power of each fuel cell system.

[0165] It can be understood that during the optimization process using the particle swarm optimization algorithm, the velocity also needs to be updated. The velocity update formula: Although it is called velocity in terms of expression, in fact, it is the distance and direction of the particle's next iterative movement, that is, a position vector.

[0166] 。

[0167] Among them, the inertial part , which is composed of the inertial weight and the particle's own velocity, represents the particle's trust in its previous own motion state; the cognitive part , represents the particle's own thinking, that is, the part of the particle's own experience, which can be understood as the distance and direction between the particle's current position and its own historical optimal position; the social part , represents the information sharing and cooperation among particles, that is, the experience from other excellent particles in the group, which can be understood as the distance and direction between the particle's current position and the group's historical optimal position.

[0168] Parameter definition of the velocity update formula: N is the size of the particle swarm; i is the particle serial number, i = 1, 2, …, N; D is the particle dimension; d is the particle dimension serial number, d = 1, 2, …, D; k is the number of iterations; ω is the inertial weight; c1 is the individual learning factor; c2 is the group learning factor; r1, r2 are random numbers within the interval [0, 1] to increase the randomness of the search; is the velocity vector of particle i in the d-th dimension at the k-th iteration; is the position vector of particle i in the d-th dimension at the k-th iteration; is the historical optimal position of particle i in the d-th dimension at the k-th iteration, that is, after the k-th iteration, the optimal solution obtained by the i-th particle (individual) search; is the historical optimal position of the group in the d-th dimension at the k-th iteration, that is, after the k-th iteration, the optimal solution in the entire particle swarm.

[0169] In the process of using the particle swarm optimization algorithm for optimization, it is necessary to set the parameters of the particle swarm optimization algorithm: (1) The particle swarm size N. A smaller population size is likely to fall into local optimality; a larger population size can improve convergence and find the global optimal solution faster, but correspondingly, the computational amount of each iteration will increase, resulting in a slower algorithm response speed; and when the population size increases to a certain level, further increase will no longer have a significant effect. (2) The number of iterations K. If the number of iterations is too small, the solution is unstable, and if it is too large, it is time-consuming and affects the algorithm response time. (3) The inertia weight ω. The inertia weight represents the influence of the velocity of the previous generation of particles on the velocity of the current generation of particles, or the degree of trust of the particle in its current motion state. The particle performs inertial motion based on its own velocity. When solving practical optimization problems, it is often desired to first perform global search to quickly converge the search space to a certain region, and then perform local fine search to obtain a high-precision solution. In the present invention, an adaptive adjustment strategy is adopted. As the number of iterations increases, the inertia weight ω continuously decreases, so that the particle swarm algorithm has strong global convergence ability in the initial stage and strong local convergence ability in the later stage. The calculation formula is as follows:

[0170] .

[0171] Among them, is the maximum inertia weight; is the minimum inertia weight; is the current number of iterations; is the maximum number of iterations.

[0172] (4) Learning factors: c1, c2. Also known as the acceleration coefficient or acceleration factor. c1 represents the weight of the part of the particle's next action that comes from its own experience, and is the acceleration weight that pushes the particle to the individual optimal position ; c2 represents the weight of the part of the particle's next action that comes from the experience of other particles, and is the acceleration weight that pushes the particle to the swarm optimal position ; In the process of using the particle swarm optimization algorithm for optimization, it is also necessary to optimize the stopping criterion: (1) Reaching the maximum number of iteration steps; (2) Acceptable satisfactory solution: Stop optimizing when the difference between the fitness value of the optimal solution after the previous iteration and the fitness value of the optimal solution after the current iteration is less than a certain value; (3) Reaching the longest policy response time: When the response time reaches the limit value, output the fuel cell system power value corresponding to the fitness value of the optimal solution;

[0173] Applying the hydrogen energy hybrid system power distribution method provided by the embodiments of the present invention, through the hybrid system global distribution system, based on the equivalent hydrogen consumption minimum model, the optimal output power of the fuel cell system assembly is calculated; based on the optimal output power of the fuel cell system assembly, and according to the distribution rules of the multi-module fuel cell system, the optimal output power of each fuel cell system under the fuel cell system assembly is calculated; the distribution rules include the optimal economic performance and the own characteristics of each fuel cell system. This method adopts a hierarchical power distribution strategy. The upper layer calculates and distributes the optimal output power of the fuel cell system assembly through the hybrid system global distribution system based on the equivalent hydrogen consumption minimum model; the lower layer uses the distribution rules of the multi-module fuel cell system, fully considering the own characteristics and economic performance of each fuel cell system, and distributes the optimal output power of the fuel cell system assembly to each fuel cell system, realizing precise power regulation.

[0174] Moreover, at the upper layer control level, based on the principle of minimum equivalent hydrogen consumption, through precise calculation and analysis, the optimal output power combination of the fuel cell system assembly and the power battery system assembly is explored to achieve the goal of the lowest overall hydrogen consumption. This process comprehensively considers various factors in system operation to ensure efficient utilization of energy while meeting power requirements. This optimized distribution reduces the hydrogen consumption of the system from the source and lays a solid foundation for improving energy utilization efficiency.

[0175] Moreover, at the lower layer control level, with the help of the adaptive particle swarm optimization algorithm, according to the unique distribution rules of the multi-module fuel cell system, refined power distribution is carried out for each fuel cell system under the fuel cell system assembly. The adaptive particle swarm optimization algorithm can fully consider these differences and dynamically adjust the power distribution scheme to ensure that each fuel cell system can operate under the most suitable working conditions.

[0176] Moreover, the distribution rules of the multi-module fuel cell system fully consider the specific differences of each fuel cell system, such as the output characteristics and life characteristics differences between fuel cell systems, as well as the comprehensive economy of hydrogen consumption and life loss. At the same time, the own characteristics such as the maximum and minimum output power and the maximum and minimum variable load rates of each fuel cell system are taken into account to ensure the lowest total hydrogen consumption and the longest system life. By intelligently adjusting the power distribution, the hydrogen consumption of the overall fuel cell system is effectively reduced, and at the same time, the service life of each fuel cell system is extended, improving the stability and reliability of the entire system.

[0177] Moreover, a comprehensive evaluation system is established. This system integrates key factors such as the actual price of hydrogen, the acquisition costs of various fuel cell systems, and their respective lifespan characteristics, forming a comprehensive and practical evaluation criterion. Using this evaluation system as the optimization criterion for the hydrogen energy hybrid system can provide strong support for the system's resource allocation and operation decision-making, ensuring that the system can achieve the best operating state under different working conditions.

[0178] Moreover, the quantification of the lifespan cost of the fuel cell system enables, through the conversion of lifespan cost, the real-time loss of the lifespan during the operation of the hydrogen energy hybrid system to be equivalent to the hydrogen consumption of the system, while reducing hydrogen consumption and extending the lifespan of the fuel cell.

[0179] Moreover, it also has the advantage of real-time optimization: it can obtain the total required power of the hydrogen energy hybrid system in real time and perform real-time allocation. Considering that the key to the response speed of the hierarchical energy management strategy lies in the lower-layer strategy, it focuses on optimizing the response speed of the lower-layer strategy. During the operation of the hydrogen energy hybrid system, it can continuously monitor the changes in various parameters of the system, such as power demand fluctuations, hydrogen consumption, battery status, etc., and quickly adjust the power allocation strategy based on this real-time data. In this way, not only is the hydrogen consumption of the hybrid system significantly reduced, but also the service life of the hydrogen energy hybrid system is greatly extended, effectively improving the overall performance and reliability of the system, providing strong technical support for the wide application and sustainable development of the hydrogen energy hybrid system.

[0180] Moreover, a real-time optimization algorithm is provided. This algorithm has powerful memory and optimization functions. When running at any moment, it will automatically cache the best position of the previous moment. Based on this, combined with the actual limits of each fuel cell, the Latin hypercube sampling method is used to optimize the initial position of the particle swarm at this moment. This significantly reduces the processing time of the algorithm, enabling it to quickly respond to the real-time changes of the system, fully meeting the strict requirements of the lower-layer control module for real-time performance, and ensuring the efficient operation of the system in a dynamic environment.

[0181] Moreover, hierarchical control also has the following advantages: (1) Decoupling complexity: Hierarchical control separates global power optimization from module-level allocation. The upper layer only needs to handle two major system assemblies, and the lower layer focuses on a small number of modules, significantly reducing the dimension and computational burden of a single optimization problem; (2) Improving real-time response ability: By quickly calculating the total system power by the upper layer strategy and adaptively initializing the initial particle positions of the lower layer strategy, the overall response time of the hierarchical strategy is shorter than that of the traditional integrated algorithm and is more likely to meet the real-time requirements; (3) Enhancing modularity and scalability: When the fuel cell or power battery module is added or removed from the system, only the algorithm needs to be adjusted accordingly in the lower layer or the upper layer, without reconstructing the entire control strategy, which is easy to iterate and maintain; (4) Balancing hydrogen consumption and lifespan: The lower layer strategy can perform refined control according to the output characteristics and lifespan loss of different modules, achieving the most optimized comprehensive economy; it is often difficult for the integrated strategy to balance both global and local characteristics in the same iteration; (5) Fault isolation and robustness: The hierarchical structure can monitor and isolate single-module anomalies in the lower layer, avoiding affecting the upper layer's global decision-making; in the integrated strategy, a single-point failure may lead to the failure of overall optimization.

[0182] For a better understanding of the present invention, please specifically refer to Figure 4 , Figure 4 which is a flow example diagram of a power distribution method for a hydrogen energy hybrid system provided by an embodiment of the present invention, and specifically may include:

[0183] Receiving the total required power sent by the in-vehicle VCU (Vehicle Control Module), which represents the required power on the bus. The upper control module: performs allocation based on the principle of minimum equivalent hydrogen consumption, and obtains the optimal power of the power battery system assembly (i.e., the optimal output power of the power battery system assembly ), and determines the required power of the fuel cell system assembly at the current moment (i.e., the optimal output power of the fuel cell system assembly at the current moment ) based on the power balance law of the DC bus and the maximum and minimum values of the fuel cell system assembly; the cut-off voltage, charge and discharge resistance, charge and discharge coefficient, and state of charge of the power battery system need to be input during the allocation process. The lower control module: according to the allocation rules of the multi-module fuel cell system (including the output limit and variable load limit of each fuel cell system), initializes the particle swarm parameters using the particle swarm optimization algorithm, and applies the LSH method to initialize the velocity and position of each particle, where the initial position requires the best particle position at the previous moment, which is based on the best particle position at the previous moment, i.e., the optimal output power of each fuel cell system at the current moment Determine. The optimization process is the individual historical optimal position, the population historical optimal position, the individual historical optimal fitness value, and the population historical optimal fitness value, and it is judged whether the end condition is satisfied; if it is satisfied, the optimal particle position is output, that is, the optimal output power of each fuel cell system at the current moment ; if not, update the velocity and position of each particle, calculate the fitness value of each particle, update the individual historical optimal fitness value of each particle and other parameters, such as the inertia weight, the number of iterations, etc., and re-judge whether the updated value satisfies the end condition. The end condition can be that the fitness value between two iterations is less than the minimum difference; or / and the maximum number of iterations is reached; or / and the strategy response time exceeds the limit value

[0184] The power distribution device of the hydrogen energy hybrid system provided by the embodiment of the present invention will be introduced below. The power distribution device of the hydrogen energy hybrid system described below can be correspondingly referred to the power distribution method of the hydrogen energy hybrid system described above

[0185] For details, please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a power distribution device of a hydrogen energy hybrid system provided by an embodiment of the present invention, and may include:

[0186] The upper control module 100 is used to calculate the optimal output power of the fuel cell system assembly based on the equivalent hydrogen consumption minimum model through the global distribution system of the hybrid system

[0187] The lower control module 200 is used to calculate the optimal output power of each fuel cell system under the fuel cell system assembly based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system

[0188] Based on the above embodiment, the upper control module 100 may include:

[0189] The first model construction unit is used to construct the equivalent hydrogen consumption minimum model based on the principle of minimum total hydrogen consumption; the principle of minimum total hydrogen consumption is to control the output power of the fuel cell system assembly and the power battery system assembly so that the total hydrogen consumption of the hydrogen energy hybrid system reaches the minimum

[0190] The conversion unit is used to convert the equivalent hydrogen consumption minimum model based on the power balance law of the DC bus to obtain a conversion model

[0191] The calculation unit is used to solve the conversion model to obtain the optimal output power of the power battery system assembly

[0192] A determination unit for obtaining the optimal output power of the fuel cell system assembly based on the optimal output power of the power battery system assembly, the power balance law, and the output limit of the fuel cell system assembly.

[0193] Based on the above embodiments, the distribution rules of the multi-module fuel cell system may specifically include: the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, and the operating parameter limits and variable load rate limits of each fuel cell system.

[0194] Based on the above embodiments, the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaching the optimal state may include:

[0195] A second model construction unit for constructing an economic performance model according to the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaching the optimal state. The economic performance model is:

[0196] ;

[0197] is the output power of the i-th fuel cell system; represents the total cost; and are respectively the hydrogen cost consumed by the i-th fuel cell system and the cost of life loss of the fuel cell system; k represents the time, representing the value at a certain time; n represents the number of fuel cell systems;

[0198] The hydrogen cost consumed by the i-th fuel cell system The calculation formula is:

[0199] ;

[0200] ;

[0201] is the current market price of hydrogen, is the hydrogen consumption of the i-th fuel cell system, is the output power of the i-th fuel cell system, is the efficiency of the i-th fuel cell system, is the lower calorific value of hydrogen;

[0202] The cost of life loss of the i-th fuel cell system The calculation formula is:

[0203] ;

[0204] ;

[0205] is the life attenuation rate of the i-th fuel cell system; is the purchase price of the i-th fuel cell system; , , are the voltage attenuation values of the i-th fuel cell system caused under low power, unit load power change, and high power conditions respectively; t1 and t2 are the durations of the i-th fuel cell system under low power and high power conditions respectively; and are the boundary values of the high and low power regions of the i-th fuel cell respectively; is the allowable voltage attenuation value of the i-th fuel cell from the start of use to the end of its life.

[0206] Based on the above embodiments, the determination unit may include:

[0207] A determination subunit, configured to determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm based on the optimal output power of the fuel cell system assembly and the allocation rules of the multi-module fuel cell system;

[0208] An adaptive optimization subunit, configured to adaptively optimize the initial position of the particle swarm based on the change of the optimal output power of the fuel cell system assembly to obtain the initial position of the particle swarm;

[0209] A calculation subunit, configured to optimize the optimization parameters by using the particle swarm optimization algorithm based on the parameters of the preset particle swarm optimization algorithm, the initial position of the particle swarm, the limitations of the optimization parameters, and the optimization objective, so as to obtain the optimal output power of each fuel cell system.

[0210] Based on the above embodiments, the determination subunit includes:

[0211] An optimization parameter determination subunit, configured to use the output power of each fuel cell system as the optimization parameter;

[0212] An optimization objective determination subunit, configured to use the first rule in the allocation rules of the multi-module fuel cell system as the optimization objective; the first rule is that the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state;

[0213] An optimization parameter limitation determination subunit, configured to use the second rule, the third rule, and the fourth rule in the allocation rules of the multi-module fuel cell system as the limitations of the optimization parameters; the second rule is set based on the optimal output power of the fuel cell system assembly, and the third rule is set according to the characteristics of each fuel cell system itself.

[0214] Based on the above embodiments, the adaptive optimization sub-unit includes:

[0215] A difference calculation sub-unit, configured to calculate the difference in the optimal output power of the fuel cell system assembly based on the optimal output power of the fuel cell system assembly at the previous moment and the optimal output power of the fuel cell assembly at the current moment;

[0216] A central value calculation sub-unit, configured to calculate the central value of the initial position of the particle swarm at the current moment according to the optimal particle position at the previous moment and the difference in the optimal output power of the fuel cell system assembly;

[0217] An initial position determination sub-unit, configured to use the central value of the initial position of the particle swarm at the current moment as the center, and use the variable load limit and output limit of each fuel cell system as the upper and lower boundaries, and initialize the position of the particle swarm using the Latin hypercube sampling method to obtain the initial position of the particle swarm.

[0218] It should be noted that the modules and units in the above hydrogen energy hybrid system power distribution device can be changed in order before and after without affecting the logic.

[0219] Applying the hydrogen energy hybrid system power distribution device provided by the embodiments of the present invention, the upper control module 100 is configured to calculate the optimal output power of the fuel cell system assembly through the hybrid system global distribution system based on the minimum equivalent hydrogen consumption model; the lower control module 200 is configured to calculate the optimal output power of each fuel cell system under the fuel cell system assembly based on the optimal output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system. The upper layer of this device calculates and distributes the optimal output power of the fuel cell system assembly through the hybrid system global distribution system; the lower layer uses the distribution rules of the multi-module fuel cell system, fully considering the own characteristics and economic performance of each fuel cell system, and distributes the optimal output power of the fuel cell system assembly to each fuel cell system, realizing precise regulation.

[0220] Next, the hydrogen energy hybrid system power distribution device provided by the embodiments of the present invention will be introduced. The hydrogen energy hybrid system power distribution device described below can be mutually referred to the hydrogen energy hybrid system power distribution method described above.

[0221] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a hydrogen energy hybrid system power distribution device provided by an embodiment of the present invention, and may include:

[0222] A memory 10, configured to store a computer program;

[0223] The processor 20 is used to execute a computer program to implement the above-mentioned hydrogen energy hybrid system power distribution method.

[0224] The memory 10, the processor 20, and the communication interface 31 all complete mutual communication through the communication bus 32.

[0225] In the embodiment of the present invention, the memory 10 is used to store one or more programs. The program may include program codes, and the program codes include computer operation instructions. In the embodiment of the present invention, the memory 10 may store programs for implementing the following functions:

[0226] Through the hybrid system global distribution system, based on the equivalent hydrogen consumption minimum model, calculate the optimal output power of the fuel cell system assembly;

[0227] Based on the optimal output power of the fuel cell system assembly, and according to the distribution rules of the multi-module fuel cell system, calculate the optimal output power of each fuel cell system under the fuel cell system assembly; the distribution rules include optimal economic performance and the own characteristics of each fuel cell system.

[0228] In a possible implementation manner, the memory 10 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function, etc.; the data storage area may store data created during use.

[0229] In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include NVRAM. The memory stores an operating system and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof. Among them, the operation instructions may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0230] The processor 20 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices. The processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call the program stored in the memory 10.

[0231] The communication interface 31 may be an interface of a communication module for connecting to other devices or systems.

[0232] Of course, it should be noted that Figure 6The structure shown does not constitute a limitation on the power distribution device of the hydrogen energy hybrid system in the embodiments of the present invention. In actual applications, the power distribution device of the hydrogen energy hybrid system may include more or fewer components than those Figure 6 shown, or combine certain components.

[0233] Next, the computer-readable storage medium provided by the embodiments of the present invention will be introduced. The computer-readable storage medium described below can be correspondingly referred to the hydrogen energy hybrid system power distribution method described above.

[0234] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned hydrogen energy hybrid system power distribution method are implemented.

[0235] The computer-readable storage medium may include various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0236] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0237] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0238] Finally, it should also be noted that in this article, relationships such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0239] The above has introduced in detail a method, system, device and computer-readable storage medium for power distribution of a hydrogen energy hybrid system provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A power distribution method for a hydrogen energy hybrid system, characterized in that Including: Through the hybrid system global distribution system, based on the equivalent hydrogen consumption minimum model, calculate the optimal output power of the fuel cell system assembly; Based on the optimal output power of the fuel cell system assembly, and according to the distribution rules of the multi-module fuel cell system, calculate the optimal output power of each fuel cell system under the fuel cell system assembly; the distribution rules include the optimal economic performance and the self-characteristics of each fuel cell system.

2. The power distribution method of the hydrogen energy hybrid system according to claim 1, wherein, Through the hybrid system global distribution system, based on the equivalent hydrogen consumption minimum model, calculate the optimal output power of the fuel cell system assembly, including: Based on the principle of minimum total hydrogen consumption, construct the equivalent hydrogen consumption minimum model; the principle of minimum total hydrogen consumption is to control the output power of the fuel cell system assembly and the power battery system assembly so that the total hydrogen consumption of the hydrogen energy hybrid system reaches the minimum; Based on the power balance law of the DC bus, transform the equivalent hydrogen consumption minimum model to obtain a transformed model; Solve the transformed model to obtain the optimal output power of the power battery system assembly; Based on the optimal output power of the power battery system assembly, the power balance law, and the output limit of the fuel cell system assembly, obtain the optimal output power of the fuel cell system assembly.

3. The power distribution method of the hydrogen energy hybrid system according to claim 1, characterized in that The distribution rules of the multi-module fuel cell system include: the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, and the operation parameter limit and variable load rate limit of each fuel cell system.

4. The power distribution method of the hydrogen energy hybrid system according to claim 3, wherein The comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state, including: Construct an economic performance model according to the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaching the optimal state. The economic performance model is: ； is the output power of the i-th fuel cell system; represents the total cost; and are respectively the hydrogen cost consumed by the i-th fuel cell system and the cost of fuel cell system life loss; k represents the time, representing the value at a certain moment; n represents the number of fuel cell systems; The hydrogen cost consumed by the i-th fuel cell system The calculation formula is as follows: ； ； is the hydrogen price in the current market, is the hydrogen consumption of the i-th fuel cell system, is the output power of the i-th fuel cell system, is the efficiency of the i-th fuel cell system, is the lower heating value of hydrogen; The life loss cost of the i-th fuel cell system The calculation formula is as follows: ； ； is the life attenuation rate of the i-th fuel cell system; is the purchase price of the i-th fuel cell system; , , are the voltage attenuation values of the i-th fuel cell system caused under low power, unit load power change, and high power conditions respectively; t1 and t2 are the durations of the i-th fuel cell system under low power and high power conditions respectively; and are the boundary values of the high and low power regions of the i-th fuel cell respectively; is the allowable voltage attenuation value of the i-th fuel cell from the start of use to the end of its life.

5. The power distribution method of the hydrogen energy hybrid system according to claim 1, characterized in that, Based on the optimal output power of the fuel cell system assembly, and according to the distribution rules of the multi-module fuel cell system, calculate the optimal output power of each fuel cell system under the fuel cell system assembly, including: Based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system, determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm; Based on the change of the optimal output power of the fuel cell system assembly, adaptively optimize the initial position of the particle swarm to obtain the initial position of the particle swarm; Based on the preset parameters of the particle swarm optimization algorithm, the initial position of the particle swarm, the limitations of the optimization parameters, and the optimization objective, use the particle swarm optimization algorithm to optimize the optimization parameters to obtain the optimal output power of each fuel cell system.

6. The power distribution method of the hydrogen energy hybrid system according to claim 5, wherein Based on the optimal output power of the fuel cell system assembly and the distribution rules of the multi-module fuel cell system, determine the optimization objective, optimization parameters, and limitations of the optimization parameters of the particle swarm optimization algorithm, including: Take the output power of each fuel cell system as the optimization parameter; Take the first rule in the distribution rules of the multi-module fuel cell system as the optimization objective; the first rule is that the comprehensive economy of hydrogen consumption and life loss of each fuel cell system reaches the optimal state; Take the second rule, the third rule and the fourth rule in the distribution rules of the multi-module fuel cell system as the constraints of the optimization parameters; the second rule is set based on the best output power of the fuel cell system assembly, and the third rule is set according to the characteristics of each fuel cell system itself.

7. The power distribution method of the hydrogen energy hybrid system according to any one of claims 1 to 6, characterized in that Adaptive optimization of the initial position of the particle swarm is carried out based on the change of the best output power of the fuel cell system assembly to obtain the initial position of the particle swarm, including: Calculate the difference in the best output power of the fuel cell system assembly according to the best output power of the fuel cell system assembly at the previous moment and the best output power of the fuel cell assembly at the current moment; Calculate the central value of the initial position of the particle swarm at the current moment according to the best particle position at the previous moment and the difference in the best output power of the fuel cell system assembly; Take the central value of the initial position of the particle swarm at the current moment as the center, and take the variable load limit and output limit of each fuel cell system as the upper and lower boundaries, and use the Latin hypercube sampling method to initialize the position of the particle swarm to obtain the initial position of the particle swarm.

8. A power distribution system for a hydrogen energy hybrid system, characterized in that, Include: The upper control module is used to calculate the best output power of the fuel cell system assembly through the hybrid system global distribution system based on the equivalent hydrogen consumption minimum model; The lower control module is used to calculate the best output power of each fuel cell system under the fuel cell system assembly based on the best output power of the fuel cell system assembly and according to the distribution rules of the multi-module fuel cell system.

9. A power distribution device for a hydrogen energy hybrid system, characterized in that, Include: A memory for storing computer programs; A processor for implementing the power distribution method of the hydrogen energy hybrid system as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, Computer executable instructions are stored in the computer-readable storage medium, and when the computer executable instructions are loaded and executed by the processor, the power distribution method of the hydrogen energy hybrid system as described in any one of claims 1 to 7 is implemented.

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