Hydrogen ammonia power supply battery power generation efficiency improvement method and device, equipment and medium

Abstract

The application provides a hydrogen-ammonia-powered battery power generation efficiency improving method and device, equipment and medium, and belongs to the technical field of hydrogen-ammonia batteries. The method comprises the following steps: acquiring current working condition parameters of a hydrogen-ammonia-powered battery power generation system; determining a solution space based on the matching degree of the current working condition parameters and each historical working condition parameter in a historical database; iteratively processing a gas flow combination in the hydrogen-ammonia-powered battery power generation system based on the solution space to obtain a target gas flow combination; wherein the target gas flow combination comprises a target ammonia flow, a target hydrogen flow and a target air flow; and controlling the hydrogen-ammonia-powered battery power generation system based on the target ammonia flow, the target hydrogen flow and the target air flow to improve the hydrogen-ammonia-powered battery power generation efficiency. The hydrogen-ammonia-powered battery power generation efficiency improving method and device, equipment and medium provided by the application can improve the hydrogen-ammonia-powered battery power generation efficiency.

Description

Technical Field

[0001] This application belongs to the field of hydrogen ammonia battery technology, and more specifically, it relates to a method, apparatus, equipment, and medium for improving the power generation efficiency of hydrogen ammonia powered batteries. Background Technology

[0002] Hydrogen-ammonia powered batteries use ammonia as a highly efficient hydrogen carrier. They generate hydrogen through ammonia cracking and are combined with fuel cells to generate electricity. They have the advantages of convenient energy storage and transportation and zero carbon emissions, and have broad application prospects in distributed power generation, marine power, backup power and other fields.

[0003] Currently, the gas supply control of hydrogen-ammonia power batteries mostly adopts a fixed metering ratio. When the system load, ambient temperature, and cracking state fluctuate, the flow ratio of ammonia, hydrogen and air is difficult to adapt to the optimal operating state in real time. This can easily lead to problems such as excessive gas supply increasing parasitic power consumption, insufficient gas supply causing incomplete electrochemical reaction, and incomplete ammonia cracking. As a result, the energy utilization rate of hydrogen-ammonia power batteries is poor during actual operation, and the overall power generation efficiency is difficult to maintain at the optimal level. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment, and medium for improving the power generation efficiency of hydrogen ammonia-powered batteries, so as to improve the power generation efficiency of hydrogen ammonia batteries.

[0005] A first aspect of this application provides a method for improving the power generation efficiency of a hydrogen-ammonia powered battery, applied to a hydrogen-ammonia powered battery power generation system, comprising:

[0006] Obtain the current operating parameters of the hydrogen-ammonia-powered battery power generation system;

[0007] Calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database;

[0008] In response to any historical operating condition parameter having a matching degree greater than a preset matching degree, the historical solution space corresponding to the historical operating condition parameter with the highest matching degree is obtained as the target solution space; with the goal of improving battery power generation efficiency, the gas flow combination in the hydrogen-ammonia powered battery power generation system is optimized within the target solution space to determine the target gas flow combination;

[0009] The target gas flow combination includes the target ammonia flow, the target hydrogen flow, and the target air flow; the historical solution space corresponding to each historical operating condition parameter is determined based on the location of the historical optimal solution corresponding to that historical operating condition parameter.

[0010] Since the matching degree of each historical operating condition parameter is no greater than the preset matching degree, with the goal of improving battery power generation efficiency, the gas flow combination in the hydrogen-ammonia powered battery power generation system is optimized within the preset solution space to determine the target gas flow combination; where the target solution space belongs to the preset solution space.

[0011] The hydrogen-ammonia powered battery power generation system is controlled based on target ammonia flow rate, target hydrogen flow rate, and target air flow rate to improve the battery power generation efficiency of hydrogen-ammonia powered batteries.

[0012] A second aspect of this application provides a device for improving the power generation efficiency of a hydrogen-ammonia powered battery, applied to a hydrogen-ammonia powered battery power generation system, comprising:

[0013] The data acquisition module is used to acquire the current operating parameters of the hydrogen-ammonia-powered battery power generation system;

[0014] The matching degree calculation module is used to calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database.

[0015] The solution space determination module is used to: 1) obtain the historical solution space corresponding to the historical operating condition parameter with the highest matching degree when the matching degree of any historical operating condition parameter is greater than a preset matching degree, and use this as the target solution space; 2) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the target solution space to determine the target gas flow combination, with the goal of improving battery power generation efficiency; and 3) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the preset solution space when the matching degree of each historical operating condition parameter is not greater than a preset matching degree, with the goal of improving battery power generation efficiency, to determine the target gas flow combination, thus obtaining the target gas flow combination; wherein, the target solution space belongs to the preset solution space; the target gas flow combination includes the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate; the historical solution space corresponding to each historical operating condition parameter is determined based on the position of the historical optimal solution corresponding to that historical operating condition parameter;

[0016] The flow control module is used to control the hydrogen-ammonia-powered battery power generation system based on the target ammonia flow rate, target hydrogen flow rate, and target air flow rate, so as to improve the power generation efficiency of the hydrogen-ammonia-powered battery.

[0017] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for improving the power generation efficiency of a hydrogen-ammonia powered battery.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for improving the power generation efficiency of a hydrogen-ammonia powered battery.

[0019] The beneficial effects of the hydrogen-ammonia powered battery power generation efficiency improvement method, apparatus, equipment, and medium provided in this application embodiment are as follows:

[0020] This application embodiment obtains the current operating parameters of the hydrogen-ammonia battery power generation system and performs matching calculations with historical operating parameters. It can distinguish whether similar historical operating conditions have occurred. If similar historical operating conditions exist, the historical solution space with the highest matching degree is directly used as the target solution space to optimize the flow combination, eliminating the need for a full-domain traversal search, reducing the optimization computation load, and improving the speed of flow ratio determination. If no similar historical operating conditions exist, optimization is performed using a preset solution space, ensuring the completeness and feasibility of the optimization process. Compared to traditional fixed metering ratio control, this application embodiment dynamically determines the target flow combination of ammonia, hydrogen, and air based on operating condition matching. This allows for real-time adaptation to operating condition fluctuations, avoiding parasitic power consumption caused by excessive gas supply and insufficient response due to insufficient gas supply. This application embodiment improves gas supply adaptability and increases the power generation efficiency of the hydrogen-ammonia battery through adaptive operating condition matching and scenario-specific flow optimization, ensuring continuous, stable, and efficient system operation under different operating conditions. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A schematic flowchart illustrating a method for improving the power generation efficiency of a hydrogen-ammonia powered battery according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the structure of a hydrogen-ammonia-powered battery power generation system provided in an embodiment of this application;

[0024] Figure 3 A structural block diagram of a hydrogen-ammonia powered battery power generation efficiency improvement device provided in an embodiment of this application;

[0025] Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0028] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for improving the power generation efficiency of a hydrogen-ammonia powered battery according to an embodiment of this application. This method can be used in a hydrogen-ammonia powered battery power generation system and may include steps S101-S105.

[0029] S101: Obtain the current operating parameters of the hydrogen-ammonia-powered battery power generation system.

[0030] In this embodiment, as Figure 2 As shown, the hydrogen-ammonia fuel cell power generation system can consist of an ammonia storage unit, an ammonia flow regulating valve, an ammonia decomposition hydrogen production unit, an air source, an air flow regulating valve, a hydrogen replenishment source, a hydrogen flow regulating valve, a hydrogen-ammonia fuel cell reactor, and a current output unit.

[0031] The ammonia storage unit stores liquid ammonia or high-pressure gaseous ammonia. An ammonia flow control valve regulates the flow rate of ammonia entering the ammonia decomposition unit, determining the hydrogen production rate. The ammonia decomposition hydrogen production unit decomposes ammonia into hydrogen and nitrogen under high temperature and catalytic action, providing the primary fuel for the fuel cell. An air source provides the oxidant required for the fuel cell reaction. An air flow control valve regulates the air flow entering the reactor, controlling the oxygen supply to match the hydrogen reaction requirements. A hydrogen replenishment source provides additional hydrogen to supplement or regulate the hydrogen supply. A hydrogen flow control valve controls the flow rate of replenished hydrogen, which mixes with the hydrogen produced from ammonia decomposition before entering the reactor. Hydrogen and oxygen undergo an electrochemical reaction in the hydrogen-ammonia fuel cell reactor, converting chemical energy into electrical energy.

[0032] In this embodiment, hydrogen is the fuel that directly participates in power generation, ammonia is one of the sources of hydrogen, and oxygen in the air is the oxidant. By coordinating and optimizing the flow rates of ammonia, hydrogen, and air, the fuel supply and oxidant supply in the hydrogen-ammonia battery power generation system can be optimally matched, avoiding problems such as incomplete reaction, fuel waste, catalyst poisoning, and excessive power consumption of auxiliary equipment caused by insufficient or excessive fuel, thereby improving the power generation efficiency and operational stability of the hydrogen-ammonia battery.

[0033] In this embodiment, the operating parameters of the power generation system can be used to describe the current operating state of the hydrogen ammonia-powered battery power generation system, such as load size, temperature, and reaction pressure.

[0034] S102: Calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database.

[0035] In this embodiment, the historical database stores the parameters of each historical operating condition during the historical optimization process, as well as the optimization results under each historical operating condition parameter. The matching degree can be calculated by calculating Euclidean distance or cosine similarity, etc.

[0036] S103: In response to any historical operating condition parameter having a matching degree greater than a preset matching degree, obtain the historical solution space corresponding to the historical operating condition parameter with the highest matching degree as the target solution space; with the goal of improving battery power generation efficiency, optimize the gas flow combination in the hydrogen-ammonia powered battery power generation system within the target solution space to determine the target gas flow combination.

[0037] In this embodiment, when any historical operating condition parameter in the historical database has a matching degree greater than a preset matching degree with the current operating condition parameter, it indicates that the current operating condition has a relatively similar scenario in the historical optimization process. Under similar operating condition scenarios, the optimal gas flow combination is also relatively similar. Therefore, the historical solution space corresponding to the historical operating condition parameter with the highest matching degree can be obtained and used as the target solution space.

[0038] In this embodiment, the target gas flow rate combination includes the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate. The historical solution space corresponding to each historical operating condition parameter is determined based on the position of the historical optimal solution corresponding to that historical operating condition parameter.

[0039] In this embodiment, the optimization of gas flow rate combinations can be achieved based on the particle swarm optimization (PSO) algorithm. Each particle in the PSO algorithm is positioned as [ammonia flow rate, hydrogen flow rate, air flow rate] (the order can be set arbitrarily), and each particle's position represents a solution. However, considering that the solution space of the PSO algorithm is generally based on empirical settings, it is usually set relatively large to ensure that no optimal solution is missed. But a larger solution space reduces the efficiency of searching for the optimal solution and increases the convergence time. In this embodiment, if the current operating parameters have a high degree of matching with a certain historical operating parameter in the historical optimization process (greater than a preset matching degree), then the optimal gas flow rate combinations under the two operating parameter conditions are also relatively close in distance in the solution space. Therefore, a relatively smaller solution space can be determined based on the optimization results corresponding to this historical operating parameter to accelerate the convergence speed and improve the efficiency of searching for the optimal solution.

[0040] In one embodiment, the process of determining the historical solution space corresponding to each historical operating condition parameter includes:

[0041] Obtain the location of the historical optimal solution corresponding to the historical operating condition parameter; the solution corresponding to the location of the historical optimal solution is used to characterize the optimal gas flow combination under the historical operating condition parameter; determine the locations of the N closest historical suboptimal solutions to the location of the historical optimal solution; N is a positive integer; determine the historical fluctuation space based on the N locations of the historical suboptimal solutions; determine the historical solution space corresponding to the historical operating condition parameter based on the historical fluctuation space.

[0042] In this embodiment, N can be set based on experience, such as 10 or 20, or it can be set based on the particle size of the particle swarm itself; the larger the particle size, the larger N. The historical optimal solution position refers to the gas flow combination that has been optimized and has the highest power generation efficiency under the given historical operating parameters. The suboptimal solution position refers to the gas flow combination that is second only to the optimal solution.

[0043] In this embodiment, the historical fluctuation space refers to the range of gas flow fluctuation determined based on the N suboptimal solutions identified above. In order to ensure that no optimal solution is missed, the determined historical fluctuation space can be appropriately expanded to determine the historical solution space corresponding to the historical operating condition parameter.

[0044] In this embodiment, if the current operating condition parameter is completely consistent with a certain historical operating condition parameter in the historical database, the position of the historical optimal solution corresponding to the historical operating condition parameter that is exactly the same as the current operating condition parameter can be directly retrieved as the target gas flow combination.

[0045] S104: In response to the fact that the matching degree of each historical operating condition parameter is not greater than the preset matching degree, with the goal of improving battery power generation efficiency, the gas flow combination in the hydrogen-ammonia powered battery power generation system is optimized within the preset solution space to determine the target gas flow combination, and the target gas flow combination is obtained.

[0046] In this embodiment, if the matching degree of each historical operating condition parameter is not greater than the preset matching degree, it means that the reference value of the data in the historical database is not great. At this time, the gas flow combination in the hydrogen-ammonia battery power generation system can be optimized within the preset solution space to determine the target gas flow combination.

[0047] In this embodiment, the original solution space corresponding to the historical operating condition parameters stored in the historical database during the optimization iteration may be a preset solution space or a (historical) target solution space determined through the aforementioned S103 step. The range of the preset solution space is larger than that of the target solution space, and the preset solution space includes the target solution space.

[0048] S105: Control the hydrogen-ammonia powered battery power generation system based on the target ammonia flow rate, target hydrogen flow rate, and target air flow rate to improve the power generation efficiency of the hydrogen-ammonia powered battery.

[0049] In this embodiment, the ammonia flow regulating valve can be controlled based on the determined target ammonia flow rate, the air flow regulating valve can be controlled based on the target air flow rate, and the hydrogen flow regulating valve can be controlled based on the target hydrogen flow rate, so that the flow rates of each gas match the calculated target gas flow rate combination, thereby improving the power generation efficiency of the hydrogen-ammonia powered battery.

[0050] In this embodiment, the method for improving the power generation efficiency of hydrogen-ammonia powered batteries further includes updating the historical database based on the current operating parameters and the target gas flow rate combination.

[0051] In this embodiment, after the target gas flow rate combination is calculated, historical data can be updated to facilitate the subsequent determination of the optimal gas flow rate combination.

[0052] As can be seen from the above, this embodiment of the application obtains the current operating parameters of the hydrogen-ammonia battery power generation system and performs matching calculations with historical operating parameters. This allows it to distinguish whether similar historical operating conditions have occurred in the past. If similar historical operating conditions exist, the historical solution space with the highest matching degree is directly used as the target solution space to optimize the flow combination, eliminating the need for a full-domain traversal search, reducing the optimization computation load, and improving the speed of flow ratio determination. If no similar historical operating conditions exist, the system switches to a preset solution space for optimization, ensuring the completeness and feasibility of the optimization process. Compared to traditional fixed metering ratio control, this embodiment of the application dynamically determines the target flow combination of ammonia, hydrogen, and air based on operating condition matching. This allows for real-time adaptation to operating condition fluctuations, avoiding parasitic power consumption caused by excessive gas supply and insufficient response caused by insufficient gas supply. This embodiment of the application improves gas supply adaptability and increases the power generation efficiency of the hydrogen-ammonia battery through adaptive operating condition matching and scenario-specific flow optimization, ensuring that the system can operate continuously, stably, and efficiently under different operating conditions.

[0053] In one embodiment of this application, determining the historical fluctuation space based on N historical suboptimal solution locations includes:

[0054] For each dimension, extract the values ​​of N historical suboptimal solutions in that dimension to obtain the set of suboptimal solutions for that dimension; determine the dimensional fluctuation range based on the set of suboptimal solutions for that dimension.

[0055] The historical fluctuation space is determined based on the dimensional fluctuation range corresponding to each dimension.

[0056] In this embodiment, the position of the particle is [ammonia flow rate, hydrogen flow rate, air flow rate] at each iteration, so each historical suboptimal solution position contains multiple identical dimensions; each dimension is used to characterize one gas flow rate in the gas flow rate combination.

[0057] This embodiment also takes into account that different gases may have different degrees of change under the same operating condition parameters. Therefore, in this embodiment, a corresponding fluctuation range is determined for each dimension.

[0058] For example, if N=3, the N historical suboptimal solutions are: ammonia flow rate 82, hydrogen flow rate 99, air flow rate 178; ammonia flow rate 79, hydrogen flow rate 101, air flow rate 182; ammonia flow rate 81, hydrogen flow rate 98, air flow rate 179 (units omitted). The ammonia dimension set is {82, 79, 81}, with a fluctuation range of 79~82. The hydrogen dimension set is {99, 101, 98}, with a fluctuation range of 98~101. The air dimension set is {178, 182, 179}, with a fluctuation range of 178~182. Historical fluctuation ranges are: ammonia flow rate: 79~82, hydrogen flow rate: 98~101, air flow rate: 178~182. In this embodiment and subsequent embodiments, the gas flow rate values ​​are only examples, and the units of gas flow rate are all standard liters per minute (SLM) or cubic feet per minute (CFM), etc.

[0059] As can be seen from the above, the embodiments of this application extract the values ​​of N historical suboptimal solutions according to the gas flow dimension, determine the fluctuation range of each dimension, and then synthesize the historical fluctuation space. Since different gases have different reasonable changes under the same operating conditions, dimensional processing can accurately capture the flow fluctuation patterns of ammonia, hydrogen, and air, avoiding the deviation of fluctuation range caused by multi-gas mixed calculation. For example, in the embodiments, each gas independently determines the fluctuation range, making the historical fluctuation space more consistent with the actual distribution of suboptimal solutions, providing an accurate basis for the subsequent determination of the historical solution space and reducing the invalid optimization range.

[0060] In one embodiment of this application, determining the historical solution space corresponding to the historical operating condition parameter based on the historical fluctuation space includes:

[0061] Obtain the similarity between the current operating condition parameter and the historical operating condition parameter with the highest matching degree, and mark it as the target similarity; determine the expansion coefficient for expanding the solution space based on the target similarity; the target similarity and the expansion coefficient are negatively correlated; the expansion coefficient has a preset upper limit value; expand the historical fluctuation space based on the expansion coefficient to obtain the historical solution space corresponding to the historical operating condition parameter.

[0062] In this embodiment, a higher target similarity indicates that the current operating parameters are closer to a certain historical operating parameter, and the optimal gas combination is also closer. Therefore, the expansion coefficient can be appropriately smaller to prevent excessive expansion of the solution space. Conversely, if the target similarity is smaller, the expansion coefficient can be appropriately larger to avoid missing the optimal solution. At the same time, the expansion coefficient also has a corresponding preset upper limit value to prevent excessive expansion from exceeding the preset solution space range.

[0063] In this embodiment, the expansion coefficient can be determined based on target similarity through mapping relationships or linear relationships. The specific mapping relationship and the slope and intercept in the linear relationship can be set based on multiple experiments. The expansion coefficient can be a value greater than 1, such as 1.1, 1.5, etc. In this embodiment, the historical fluctuation space can be expanded based on the expansion coefficient to finally obtain the historical solution space corresponding to the historical operating condition parameter. In this embodiment, when expanding the historical fluctuation space, both the upper and lower limits of the historical fluctuation space should be expanded. For example, in the aforementioned embodiment, the fluctuation range of ammonia flow rate is 79~82, hydrogen flow rate is 98~101, and air flow rate is 178~182. If the expansion coefficient is selected as 1.1, the upper and lower limits of each gas flow rate need to be expanded synchronously and proportionally. Taking ammonia flow rate as an example, first calculate the original fluctuation range width (82-79=3). After expansion, the range width is 3×1.1=3.3. Based on the center of the original range ((79+82) / 2=80.5), the upper and lower limits after expansion are calculated to be 80.5-1.65=78.85 and 80.5+1.65=82.15. In engineering applications, it can be simplified to 79~82 or retained to one decimal place according to the accuracy requirements. Similarly, the original range width of hydrogen flow rate is 3, and the expanded range width is 3.3, with a center of 99.5. The expanded upper and lower limits are 97.85~101.15. The original range width of air flow rate is 4, and the expanded range width is 4.4, with a center of 180. The expanded upper and lower limits are 177.8~182.2. This method of simultaneously expanding the upper and lower limits ensures that the center position of the historical fluctuation space remains unchanged and is always distributed around the historical optimal solution. It avoids the solution space shift caused by unilateral expansion, thereby ensuring that the subsequent optimization process always unfolds within a reasonable range. It neither misses potential optimal solutions nor introduces irrelevant invalid flow intervals, further improving the rationality and practicality of the historical solution space and providing accurate and reliable range support for subsequent flow combination optimization.

[0064] In one embodiment of this application, optimizing the gas flow combination in a hydrogen-ammonia-powered battery power generation system within a target solution space to determine a target gas flow combination includes:

[0065] Initialize multiple particles; the position of each initialized particle is used to characterize a gas flow combination;

[0066] The gas flow combination in the hydrogen-ammonia battery power generation system is iterated based on the target solution space and the preset fitness function until the difference of the fitness function values ​​after M consecutive iterations is less than the preset iteration threshold, thus obtaining the target gas flow combination; wherein, the fitness function value after each iteration is determined based on the fitness function and the optimal position of the population after that iteration.

[0067] In this embodiment, the optimization process is implemented based on the particle swarm optimization algorithm. Initializing the particles involves generating multiple gas flow combinations in the target solution space. During particle iteration, hyperparameters such as the learning rate and inertia weights can be set based on the model's default parameters until the difference in the fitness function values ​​after M consecutive iterations is less than a preset iteration threshold. The optimal position of the population at this point is then taken as the target gas flow combination. M is a positive integer set based on preferences and experience.

[0068] In this embodiment, the dependent variable of the fitness function is the battery power generation efficiency, and the independent variable is calculated based on the ammonia flow rate, hydrogen flow rate, and air flow rate. In this embodiment, the calculation formula for the fitness function can be set independently. In one implementation, the fitness function can be:

[0069] ,in, Represents the fitness function. Indicates power generation efficiency. This refers to the ammonia flow rate. Hydrogen flow rate, For airflow, This indicates the effective output power under the current operating conditions. Indicates the current operating parameters. This represents the total input chemical energy.

[0070] In this embodiment, , This indicates that ammonia has a low calorific value. This indicates that hydrogen has a low calorific value, and oxygen from the air acts as an oxidant in the reaction, converting the chemical energy of the fuel into electrical energy. Therefore, the energy input does not include the energy value of air. In this embodiment, The relationship between ammonia flow rate, hydrogen flow rate, and air flow rate and the effective output power can be obtained by fitting the calculated relationship between these parameters under various operating conditions.

[0071] As can be seen from the above, in the embodiments of this application, the higher the target similarity, the closer the current operating parameters are to the historical operating parameters, the smaller the difference in the optimal flow combination, and the smaller the expansion coefficient expansion can avoid redundancy in the solution space and improve the optimization speed; the lower the target similarity, the more the expansion coefficient is increased, the less the optimal solution is missed, and the upper limit setting can prevent the solution space from exceeding the preset range, ensure the rationality of optimization, and realize the dynamic adaptive adjustment of the solution space.

[0072] Corresponding to the method for improving the power generation efficiency of hydrogen-ammonia powered batteries in the above embodiments, Figure 3 This is a structural block diagram of a hydrogen-ammonia powered battery efficiency improvement device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The hydrogen ammonia-powered battery power generation efficiency improvement device 20 is applied to a hydrogen ammonia-powered battery power generation system. The hydrogen ammonia-powered battery power generation efficiency improvement device 20 includes: a data acquisition module 21, a matching degree calculation module 22, a solution space determination module 23, and a flow control module 24.

[0073] Among them, the data acquisition module 21 is used to acquire the current operating parameters of the hydrogen ammonia power battery power generation system;

[0074] The matching degree calculation module 22 is used to calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database.

[0075] Solution space determination module 23 is used to: 1) obtain the historical solution space corresponding to the historical operating condition parameter with the highest matching degree when the matching degree of any historical operating condition parameter is greater than a preset matching degree, as the target solution space; 2) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the target solution space to determine the target gas flow combination with the goal of improving battery power generation efficiency; and 3) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the preset solution space when the matching degree of each historical operating condition parameter is not greater than a preset matching degree, with the goal of improving battery power generation efficiency, to determine the target gas flow combination, thus obtaining the target gas flow combination; wherein, the target solution space belongs to the preset solution space; the target gas flow combination includes the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate; the historical solution space corresponding to each historical operating condition parameter is determined based on the position of the historical optimal solution corresponding to that historical operating condition parameter;

[0076] The flow control module 24 is used to control the hydrogen-ammonia powered battery power generation system based on the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate, so as to improve the power generation efficiency of the hydrogen-ammonia powered battery.

[0077] In one embodiment of this application, the solution space determination module 23 is specifically used to obtain the location of the historical optimal solution corresponding to the historical operating condition parameter; the solution corresponding to the location of the historical optimal solution is used to characterize the optimal gas flow combination under the historical operating condition parameter.

[0078] Determine the positions of the N nearest historical suboptimal solutions to the historical best solution; N is a positive integer.

[0079] The historical fluctuation space is determined based on the positions of N historical suboptimal solutions.

[0080] The historical solution space corresponding to the historical operating condition parameters is determined based on the historical fluctuation space.

[0081] In one embodiment of this application, each historical suboptimal solution position contains multiple identical dimensions; each dimension is used to characterize a gas flow rate in a gas flow rate combination; the solution space determination module 23 is further used to extract N historical suboptimal solution positions under each dimension to obtain a set of suboptimal solutions corresponding to that dimension; and to determine the dimensional fluctuation range corresponding to that dimension based on the set of suboptimal solutions corresponding to that dimension.

[0082] The historical fluctuation space is determined based on the dimensional fluctuation range corresponding to each dimension.

[0083] In one embodiment of this application, the solution space determination module 23 is further used to obtain the similarity between the current working condition parameter and the historical working condition parameter with the highest matching degree, and mark it as the target similarity.

[0084] The expansion coefficients used for solution space expansion are determined based on target similarity; target similarity and expansion coefficients are negatively correlated; the expansion coefficients have a preset upper limit value;

[0085] The historical solution space corresponding to the historical operating condition parameters is obtained by expanding the historical fluctuation space based on the expansion coefficient.

[0086] In one embodiment of this application, the optimization process is implemented based on the particle swarm optimization algorithm; the solution space determination module 23 is further used to initialize multiple particles; the position corresponding to each initialized particle is used to characterize a gas flow combination;

[0087] The gas flow combination in the hydrogen-ammonia battery power generation system is iterated based on the target solution space and the preset fitness function until the difference of the fitness function values ​​after M consecutive iterations is less than the preset iteration threshold, thus obtaining the target gas flow combination; wherein, the fitness function value after each iteration is determined based on the fitness function and the optimal position of the population after that iteration.

[0088] In one embodiment of this application, the dependent variable of the fitness function is the battery power generation efficiency, and the independent variable of the fitness function is calculated based on the ammonia flow rate, hydrogen flow rate, and air flow rate.

[0089] In one embodiment of this application, the hydrogen-ammonia powered battery power generation efficiency improvement device 20 further includes: a database update module, used to update the historical database based on the current operating parameters and the target gas flow combination.

[0090] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of the data acquisition module 21, matching degree calculation module 22, solution space determination module 23, and flow control module 24 are shown.

[0091] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0092] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0093] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0094] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the hydrogen-ammonia powered battery power generation efficiency improvement method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0095] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0096] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0097] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in 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 implementations should not be considered beyond the scope of this application.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0103] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for improving the power generation efficiency of a hydrogen-ammonia powered battery, characterized in that, Applications include: Hydrogen-ammonia powered battery power generation systems Obtain the current operating parameters of the hydrogen-ammonia-powered battery power generation system; Calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database; In response to any historical operating condition parameter having a matching degree greater than a preset matching degree, the historical solution space corresponding to the historical operating condition parameter with the highest matching degree is obtained as the target solution space. With the goal of improving battery power generation efficiency, the gas flow combination in the hydrogen-ammonia powered battery power generation system is optimized within the target solution space to determine the target gas flow combination. The target gas flow combination includes the target ammonia flow, the target hydrogen flow, and the target air flow. The historical solution space corresponding to each historical operating condition parameter is determined based on the position of the historical optimal solution corresponding to that historical operating condition parameter. In response to the fact that the matching degree of each historical operating condition parameter is not greater than the preset matching degree, with the goal of improving battery power generation efficiency, the gas flow combination in the hydrogen-ammonia powered battery power generation system is optimized within the preset solution space to determine the target gas flow combination, thereby obtaining the target gas flow combination; wherein, the target solution space belongs to the preset solution space; The hydrogen-ammonia powered battery power generation system is controlled based on the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate to improve the power generation efficiency of the hydrogen-ammonia powered battery. The process of determining the historical solution space corresponding to each historical operating condition parameter includes: Obtain the location of the historical optimal solution corresponding to the historical operating condition parameter; the solution corresponding to the historical optimal solution location is used to characterize the optimal gas flow combination under the historical operating condition parameter. Determine the positions of the N nearest historical suboptimal solutions to the aforementioned historical optimal solution; N is a positive integer; The historical fluctuation space is determined based on the positions of the N historical suboptimal solutions. Obtain the similarity between the current operating condition parameters and the historical operating condition parameters with the highest matching degree, and mark it as the target similarity; An expansion coefficient for expanding the solution space is determined based on the target similarity; the target similarity is negatively correlated with the expansion coefficient; the expansion coefficient has a preset upper limit value; The historical fluctuation space is expanded based on the expansion coefficient to obtain the historical solution space corresponding to the historical operating condition parameter.

2. The method for improving the power generation efficiency of a hydrogen-ammonia powered battery as described in claim 1, characterized in that, Each historical suboptimal solution location contains multiple identical dimensions; each dimension is used to characterize a gas flow rate in the gas flow rate combination. The determination of the historical fluctuation space based on the positions of the N historical suboptimal solutions includes: For each dimension, extract the values ​​of the N historical suboptimal solutions in that dimension to obtain the set of suboptimal solutions corresponding to that dimension; determine the dimensional fluctuation range corresponding to that dimension based on the set of suboptimal solutions corresponding to that dimension. The historical fluctuation space is determined based on the dimensional fluctuation range corresponding to each dimension.

3. The method for improving the power generation efficiency of a hydrogen-ammonia powered battery as described in claim 1, characterized in that, The optimization process is implemented based on the particle swarm optimization algorithm; The optimization of the gas flow combination in the hydrogen-ammonia battery power generation system within the target solution space to determine the target gas flow combination includes: Initialize multiple particles; the position of each initialized particle is used to characterize a gas flow combination; Based on the target solution space and a preset fitness function, the gas flow combination in the hydrogen-ammonia battery power generation system is iterated until the difference in fitness function values ​​after M consecutive iterations is less than a preset iteration threshold, thus obtaining the target gas flow combination; wherein, the fitness function value after each iteration is determined based on the fitness function and the optimal position of the population after that iteration.

4. The method for improving the power generation efficiency of a hydrogen-ammonia powered battery as described in claim 3, characterized in that, The dependent variable of the fitness function is the battery power generation efficiency, and the independent variable of the fitness function is calculated based on ammonia flow rate, hydrogen flow rate, and air flow rate.

5. The method for improving the power generation efficiency of a hydrogen-ammonia powered battery as described in claim 1, characterized in that, Also includes: The historical database is updated based on the current operating parameters and the target gas flow rate combination.

6. A device for improving the power generation efficiency of a hydrogen-ammonia powered battery, characterized in that, Applications include: Hydrogen-ammonia powered battery power generation systems The data acquisition module is used to acquire the current operating parameters of the hydrogen-ammonia-powered battery power generation system; The matching degree calculation module is used to calculate the matching degree between the current operating condition parameters and each historical operating condition parameter in the historical database. A solution space determination module is used to: 1) obtain the historical solution space corresponding to the historical operating condition parameter with the highest matching degree when the matching degree of any historical operating condition parameter is greater than a preset matching degree, as the target solution space; 2) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the target solution space to determine a target gas flow combination, with the goal of improving battery power generation efficiency; and 3) optimize the gas flow combination in the hydrogen-ammonia-powered battery power generation system within the preset solution space when the matching degree of each historical operating condition parameter is not greater than the preset matching degree, with the goal of improving battery power generation efficiency, to determine a target gas flow combination, thus obtaining the target gas flow combination; wherein, the target solution space belongs to the preset solution space; the target gas flow combination includes a target ammonia flow rate, a target hydrogen flow rate, and a target air flow rate; the historical solution space corresponding to each historical operating condition parameter is determined based on the historical optimal solution position corresponding to that historical operating condition parameter; A flow control module is used to control the hydrogen-ammonia powered battery power generation system based on the target ammonia flow rate, the target hydrogen flow rate, and the target air flow rate, so as to improve the power generation efficiency of the hydrogen-ammonia powered battery. The solution space determination module is specifically used to obtain the location of the historical optimal solution corresponding to the historical operating condition parameters; the solution corresponding to the location of the historical optimal solution is used to characterize the optimal gas flow combination under the historical operating condition parameters. Determine the positions of the N nearest historical suboptimal solutions to the aforementioned historical optimal solution; N is a positive integer; The historical fluctuation space is determined based on the positions of the N historical suboptimal solutions. Obtain the similarity between the current operating condition parameters and the historical operating condition parameters with the highest matching degree, and mark it as the target similarity; An expansion coefficient for expanding the solution space is determined based on the target similarity; the target similarity is negatively correlated with the expansion coefficient; the expansion coefficient has a preset upper limit value; The historical fluctuation space is expanded based on the expansion coefficient to obtain the historical solution space corresponding to the historical operating condition parameter.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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