Optimization Control Method, Device and System for Performance of Hydrogen Fuel Cell for Aircraft

Abstract

The present invention provides an optimization control method, device and system for the performance of a hydrogen fuel cell for an aircraft. The method includes obtaining battery performance data of at least one dimension of the hydrogen fuel cell for the aircraft in the flight environment of the aircraft; detecting and processing the battery performance data to respectively obtain at least two types of information among the corresponding battery performance change information, performance data format information and battery environment parameter information, where the battery environment parameter information represents parameter information of the flight environment where the hydrogen fuel cell is located; in response to abnormal information matching among at least two types of information among the battery environment parameter information, performance data format information and battery performance change information, obtaining a battery performance impact factor corresponding to the battery performance data of the dimension; generating a battery performance optimization control result of the battery performance data according to the battery performance impact factor; and performing performance optimization processing on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data.

Description

Technical Field

[0001] This application relates to the fields of hydrogen fuel cells and information technology, and particularly to an optimization control method, device, and system for the performance of hydrogen fuel cells used in aircraft. Background Art

[0002] With the growing global demand for clean energy and sustainable transportation solutions, hydrogen fuel cells, as an efficient energy conversion technology, have become a key research direction for aircraft power systems. Hydrogen fuel cells are regarded as a potential alternative for future aircraft power systems due to their high energy density, environmental friendliness, and fast refueling.

[0003] The performance of hydrogen fuel cells directly affects the endurance, safety, and economy of aircraft. However, in actual operation, hydrogen fuel cells are affected by various factors, including the chemical reaction kinetics inside the battery, external environmental conditions (such as temperature, humidity, and pressure), and the efficiency of the battery management system. The complex interaction of these factors makes the optimization control of hydrogen fuel cell performance a technical challenge.

[0004] Traditional hydrogen fuel cell performance control methods mainly rely on empirically set control strategies and simple feedback regulation mechanisms. Lack of comprehensive analysis of multi-dimensional parameters of battery performance and real-time optimization capabilities often makes it difficult to adapt to rapidly changing flight conditions and battery states. The accuracy of hydrogen fuel cell performance optimization control is poor, resulting in the battery performance not reaching the best. Summary of the Invention

[0005] Embodiments of this application provide an optimization control method, device, and system for the performance of hydrogen fuel cells used in aircraft, which can use intelligent algorithms to achieve precise optimization control of hydrogen fuel cell performance, thereby improving the performance of hydrogen fuel cells used in aircraft.

[0006] On the one hand, an optimization control method for the performance of hydrogen fuel cells used in aircraft is provided. The method includes:

[0007] Obtain at least one-dimensional battery performance data of the hydrogen fuel cell used in the aircraft flight environment;

[0008] Detect and process the battery performance data of each dimension to obtain at least two types of information among the corresponding battery performance change information, performance data format information, and battery environment parameter information. The battery environment parameter information is used to characterize the parameter information of the flight environment where the hydrogen fuel cell is located;

[0009] In response to the abnormal information matching between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain, from a pre-set abnormal matching relationship set, a battery performance impact factor corresponding to the battery performance data of the dimension, where the battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell;

[0010] Generate a battery performance optimization control result for the battery performance data of the at least one dimension according to the at least one battery performance impact factor corresponding to the battery performance data of the at least one dimension;

[0011] Perform performance optimization processing on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0012] On the other hand, a battery performance optimization control device for the performance of a hydrogen fuel cell used in an aircraft is also provided, including:

[0013] An acquisition module, configured to acquire at least one dimension of battery performance data of a hydrogen fuel cell used in an aircraft flight environment;

[0014] A detection module, configured to perform detection processing on the battery performance data of each dimension to respectively obtain at least two types of information among the corresponding battery performance change information, performance data format information, and battery environment parameter information, where the battery environment parameter information is used to characterize the parameter information of the flight environment where the hydrogen fuel cell is located;

[0015] A matching module, configured to, in response to the abnormal information matching between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain, from a pre-set abnormal matching relationship set, a battery performance impact factor corresponding to the battery performance data of the dimension, where the battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell;

[0016] A result generation module, configured to generate a battery performance optimization control result for the battery performance data of the at least one dimension according to the at least one battery performance impact factor corresponding to the battery performance data of the at least one dimension;

[0017] A performance optimization module, configured to perform performance optimization processing on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0018] On the other hand, a computer system is provided, which includes a processor and a memory. The memory is used to store at least one segment of computer program, and the at least one segment of computer program is loaded and executed by the processor to implement the optimization control method for battery performance in the embodiments of the present application.

[0019] The embodiments of the present application provide an optimization control method for the performance of a hydrogen fuel cell for an aircraft. Obtain at least one-dimensional battery performance data of the hydrogen fuel cell for the aircraft in the flight environment of the aircraft; perform detection processing on each dimension of battery performance data in the at least one-dimensional battery performance data to respectively obtain at least two types of information among the corresponding battery performance change information, performance data format information, and battery environment parameter information. The battery environment parameter information characterizes the parameter information of the flight environment where the hydrogen fuel cell is located; in response to an abnormal information match among at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain a battery performance impact factor corresponding to the dimension of battery performance data from a pre-set abnormal match relationship set. The battery performance impact factor characterizes the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell; generate an optimization control result for the battery performance of the at least one-dimensional battery performance data according to at least one battery performance impact factor corresponding to the at least one-dimensional battery performance data; perform performance optimization processing on the performance of the hydrogen fuel cell according to the optimization control result of the battery performance of the at least one-dimensional battery performance data. This solution can comprehensively consider multiple dimensions of battery performance, detect and respond to changes in battery performance in real time, and dynamically optimize battery performance through intelligent algorithms, realizing precise optimization control of the performance of the hydrogen fuel cell for the aircraft, thereby improving the performance of the hydrogen fuel cell for the aircraft.

[0020] In addition, the solution of the present application can extract battery performance change information, data format type information, and battery environment parameter information from battery performance data to form features, and optimize battery performance based on these features, enriching the methods for optimizing the control of hydrogen fuel cells, and can greatly improve the accuracy of optimizing the control of hydrogen fuel cell performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 is a schematic diagram of the implementation environment of an optimization control method for the performance of a hydrogen fuel cell for an aircraft provided according to an embodiment of the present application;

[0023] Figure 2 is a flowchart of a method for optimizing the performance of a hydrogen fuel cell for an aircraft according to an embodiment of the present application;

[0024] Figure 3 is a schematic structural diagram of a device for optimizing the performance of a hydrogen fuel cell for an aircraft according to an embodiment of the present application;

[0025] Figure 4 is another schematic structural diagram of a device for optimizing the performance of a hydrogen fuel cell for an aircraft according to an embodiment of the present application;

[0026] Figure 5 is a schematic structural diagram of a computer system according to an embodiment of the present application. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0028] In the present application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. It should be understood that there is no logical or chronological dependence between "first", "second", and "nth", nor are the quantity and execution order limited.

[0029] In the present application, the term "at least one" means one or more, and the meaning of "a plurality" means two or more.

[0030] Hereinafter, the terms related to the present application will be explained.

[0031] The field of artificial intelligence (AI) is dedicated to simulating and expanding human intelligence, and involves theories, methods, technologies, and application systems for perceiving the environment, acquiring knowledge, and applying knowledge to obtain the best results. As a branch of computer science, AI aims to understand the essence of intelligence and create machines that can simulate human intelligent responses, and has functions of perception, reasoning, and decision-making.

[0032] AI technology is an interdisciplinary field that encompasses both hardware and software technologies. In terms of hardware, it includes sensors, AI chips, etc. In terms of software, it includes computer vision, speech processing, natural language processing, and machine learning / deep learning, etc. Among them, computer vision (CV) technology enables machines to "see", that is, to use cameras and computers to replace human eyes for recognition, measurement, and image processing to obtain information in images or multi-dimensional data. Computer vision technology includes image processing, recognition, semantic understanding, retrieval, OCR, video processing, semantic understanding, content / behavior recognition, 3D reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc., as well as biometric recognition technologies such as face recognition and fingerprint recognition.

[0033] The hydrogen fuel cell for aircraft is an energy conversion device designed specifically for aircraft. It directly converts the chemical energy of hydrogen into electrical energy through an electrochemical reaction to provide power for the aircraft. This battery system contains multiple key components, including electrodes, electrolytes, bipolar plates, and gas diffusion layers, etc., and can operate at high efficiency, with the characteristics of low noise and zero emissions. Due to the strict requirements of aircraft for energy density and weight, the hydrogen fuel cell for aircraft pays special attention to lightweight and compact design during design to adapt to the space and weight limitations of aircraft. In addition, considering the safety and reliability during flight, the hydrogen fuel cell for aircraft also needs to have characteristics such as fast response, high stability, and long life to ensure stable power supply under various flight conditions. With the progress of technology, the hydrogen fuel cell has become a hot topic in the research of new energy technologies in the aviation field and is expected to achieve commercial application in the future to provide a more environmentally friendly and efficient energy solution for aircraft.

[0034] The optimization control of the performance of the hydrogen fuel cell for aircraft refers to the application of a series of technologies and strategies aimed at improving the performance of the hydrogen fuel cell system in aircraft and ensuring its reliability and efficiency. This includes the precise monitoring and dynamic adjustment of battery operating parameters, such as voltage, current, temperature, and pressure, etc., to adapt to different flight conditions and load requirements.

[0035] Traditional methods for controlling the performance of hydrogen fuel cells mainly rely on empirically set control strategies and simple feedback adjustment mechanisms, lacking the comprehensive analysis and real-time optimization ability of multi-dimensional parameters of battery performance. In addition, due to the lack of accurate identification of battery performance influencing factors and dynamic adjustment mechanisms, these methods often have difficulty adapting to rapidly changing flight conditions and battery states, resulting in the battery performance not reaching the best.

[0036] With the development of computer technologies such as artificial intelligence and big data, modern battery management systems have begun to integrate advanced data analysis and machine learning technologies to achieve a deeper understanding of battery performance and more precise control. Most research has focused on battery systems for ground applications, and there is still limited research on performance optimization control methods specific to aircraft hydrogen fuel cells. Therefore, it is necessary to develop a new performance optimization control method for aircraft hydrogen fuel cells that can comprehensively consider multiple dimensions of battery performance, detect and respond to changes in battery performance in real time, and dynamically adjust battery performance through intelligent algorithms to maximize battery performance, thereby improving the performance and reliability of aircraft hydrogen fuel cells.

[0037] In one embodiment, for ease of understanding, please refer to Figure 1 , Figure 1 which is an application environment diagram for the performance optimization control of aircraft hydrogen fuel cells in the embodiments of the present application. The method of the embodiments of the present application is applied to an optimization control system based on the performance of aircraft hydrogen fuel cells. The optimization control system for the performance of aircraft hydrogen fuel cells includes: a battery performance optimization control platform, battery performance detection equipment, and a terminal device; wherein, the battery performance optimization control platform can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (CDN), and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal device and the battery performance optimization control platform can be directly or indirectly connected through wired or wireless communication methods, and the embodiments of the present application do not limit this.

[0038] Among them, the battery performance detection equipment can be installed in the aircraft battery system to collect battery performance data of at least one dimension (temperature, pressure, current, voltage, gas flow, etc.) of the aircraft hydrogen fuel cell in the aircraft flight environment, and can be directly or indirectly connected to the battery performance optimization control platform through wired or wireless communication methods, and upload the collected battery performance data to the battery performance optimization control platform for processing after collection. In one embodiment, the battery performance detection equipment is responsible for real-time monitoring of the key performance parameters of the hydrogen fuel cell. The battery performance detection equipment can be a sensor device. For example, it can be a sensor array, specifically including sensors such as temperature, pressure, current, voltage, and gas flow, for collecting real-time performance data of the battery operating state.

[0039] The battery performance optimization control platform is used for: obtaining at least one - dimensional battery performance data of the hydrogen fuel cell for an aircraft in an aircraft flight simulation environment; detecting and processing the battery performance data of each dimension to respectively obtain at least two types of information among the corresponding battery performance change information, data format type information, and battery environment parameter information, where the battery environment information is the environmental parameter information of the environment where the hydrogen fuel cell is located; in response to the abnormal information matching among at least two types of information among the battery environment parameter information, the performance data format type, and the battery performance change information, obtaining, from a pre - set abnormal matching relationship set, a battery performance impact factor corresponding to the battery performance data of the dimension, where the battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell; generating a battery performance optimization control result for the battery performance data of the at least one dimension according to the at least one battery performance impact factor corresponding to the at least one dimension of battery performance data; and performing performance optimization processing on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0040] The terminal device interacts with the battery performance optimization control platform, is used for controlling the parameters of the battery performance optimization control platform, and receiving and displaying the performance optimization results sent by the battery performance optimization control platform so that the staff can make judgments and adjustments for further optimization.

[0041] The optimization control system provided by the embodiments of the present application can comprehensively consider multiple dimensions of battery performance, detect and respond to changes in battery performance in real time, and dynamically optimize battery performance through intelligent algorithms, realizing precise optimization control of the performance of the hydrogen fuel cell for an aircraft, thereby improving the performance of the hydrogen fuel cell for an aircraft.

[0042] The optimization control method for the performance of the hydrogen fuel cell for an aircraft provided by the embodiments of the present application can be executed by the battery performance optimization control platform. In some embodiments, the battery performance optimization control platform can be composed of a computer device, a terminal, or a server. First, taking the computer device as a server as an example, the optimization control method for the performance of the hydrogen fuel cell for an aircraft provided by the embodiments of the present application is introduced.

[0043] Figure 2 is a flowchart of an optimization control method for the performance of a hydrogen fuel cell for an aircraft provided by an embodiment of the present application. Refer to Figure 2 In the embodiments of the present application, taking the execution by the battery performance optimization control platform as an example for illustration. The optimization control method includes the following steps:

[0044] 201. Obtain at least one - dimensional battery performance data of the hydrogen fuel cell for an aircraft in the aircraft flight environment.

[0045] Among them, the battery performance data of the aircraft hydrogen fuel cell are parameters or data that can describe and evaluate the performance of the hydrogen fuel cell in aircraft applications. Each dimension represents a specific aspect of the battery performance. In one embodiment, the battery performance data may include: electrochemical performance data, thermal management performance data, mechanical performance data, safety performance data, environmental adaptability data, durability and life data, energy management performance data, etc. The following will introduce them specifically:

[0046] Electrochemical performance data

[0047] Electrochemical performance is the key to evaluating the core function of the hydrogen fuel cell. Among them, the open circuit voltage (OCV) refers to the voltage level of the battery when there is no load. The working voltage represents the voltage of the battery under actual operating conditions, while the current density involves the current flow per unit area, and the power density refers to the power output by the battery per unit area.

[0048] Thermal management performance data

[0049] Thermal management is crucial for the hydrogen fuel cell. The battery temperature data reflects the temperature state during battery operation and directly affects the performance and life of the battery. The heat flux density data describes the heat flow per unit area, which is crucial for evaluating the heat dissipation requirements of the battery and designing an effective cooling system.

[0050] Mechanical performance data

[0051] The mechanical performance of the hydrogen fuel cell involves the stability and strength of its structure under mechanical loads. The vibration and shock tolerance data indicate the performance of the battery under vibrations and shocks encountered during aircraft operation, which is crucial for ensuring flight safety.

[0052] Safety performance data

[0053] The safety performance data includes the gas leakage rate and pressure change, which are important indicators for the safety monitoring of the hydrogen fuel cell. The gas leakage rate data monitors the leakage of hydrogen and oxygen, while the pressure change data focuses on the fluctuations of the internal pressure of the battery. Both jointly ensure the safety of the battery system.

[0054] Environmental adaptability data

[0055] The environmental adaptability data covers parameters such as environmental temperature, humidity, and atmospheric pressure, which describe how external environmental conditions affect the battery performance. This is crucial for evaluating the performance of the battery under different climates and flight altitudes.

[0056] Durability and life data

[0057] Durability and lifespan data focus on the performance changes of the battery during long-term use. Cycle life data indicates the performance degradation of the battery after undergoing multiple charge and discharge cycles, while aging data monitors the performance degradation of the battery during continuous operation.

[0058] Energy management performance data

[0059] Energy management performance data includes energy efficiency and charge and discharge rates. Energy efficiency data measures the efficiency of the battery in converting chemical energy into electrical energy, and charge and discharge rate data describes the speed of battery charging and discharging. These data are crucial for optimizing the energy output and input of the battery.

[0060] In the embodiments of this application, it can be collected in real time through battery performance detection devices such as sensors installed on the hydrogen fuel cell system and analyzed by transmitting it to the ground battery performance optimization control platform. Through in-depth analysis of these data, the performance status of the battery can be evaluated, such as optimizing the battery management system (BMS), and corresponding optimization strategies can be formulated to ensure the reliable operation of the hydrogen fuel cell in the aircraft.

[0061] In practical applications, for example, it is necessary to clarify the specific requirements for data collection, including determining key performance parameters such as voltage, current, temperature, and pressure, etc. These parameters are crucial for evaluating the performance of the battery. Based on these requirements, appropriate sensors are selected for accurate measurement to ensure that their measurement range, accuracy, and response time can meet specific performance monitoring requirements. Subsequently, sensors are arranged at various key parts of the hydrogen fuel cell system, such as the battery stack, gas pipeline, and cooling system, etc. At the same time, data acquisition hardware is configured, including a data acquisition card (DAQ), signal conditioner, and interface device, and appropriate sampling frequencies and data resolutions are set to ensure that the collected data is both accurate and reliable.

[0062] 202. Detect and process the battery performance data in each dimension, and respectively obtain at least two types of information among the corresponding battery performance change information, performance data format information, and battery environment parameter information. The battery environment information is the environmental parameter information of the environment where the hydrogen fuel cell is located.

[0063] After obtaining the battery performance data in at least one dimension, the battery performance optimization control platform can detect, analyze, and process the battery performance data to extract the corresponding battery performance change information, performance data format information, and battery environment parameter information.

[0064] Among them, the battery performance change information refers to the data that describes how the performance parameters of the battery change over time during operation. This information is crucial for identifying the working state, health state of the battery, and predicting its lifespan. For example, the voltage change, the change of current during the charging and discharging process of the battery, the change of the battery output power, and the transition process from the healthy state to the fault state such as the change of the battery state.

[0065] Among them, the data format information describes the representation and processing methods of the battery performance data, including data type (analog or digital), unit (such as volts, amperes), precision, resolution, and data structure. Understanding this information is essential for the accurate acquisition, effective transmission, reliable storage, and in-depth analysis of the data. For example, in one embodiment, the data format type information includes:

[0066] Data type, whether the data is an analog signal or a digital signal, continuous or discrete. Unit and dimension, the measurement unit of the data, such as volts (V), amperes (A), watts (W), etc. Precision and resolution, the precision of data acquisition and the minimum change that can be detected. Data structure, the organization form of the data, such as time series, vector, matrix, etc.

[0067] Among them, the battery environmental parameter information describes the external environmental conditions in which the hydrogen fuel cell is located, and these conditions may affect the performance and lifespan of the battery. In one embodiment, it specifically includes:

[0068] Temperature: The environmental temperature when the battery is working, which affects the chemical reaction rate and battery efficiency.

[0069] Humidity: The environmental humidity may affect the insulation performance and corrosion rate of the battery.

[0070] Pressure: The pressure of hydrogen and oxygen supply, which affects the chemical reaction kinetics of the battery.

[0071] Air quality: Pollutants in the environment may affect the performance and lifespan of the battery.

[0072] Vibration and shock: The vibration and shock encountered during flight may affect the structural integrity of the battery.

[0073] In one embodiment, the battery performance optimization control platform can, based on data mining and analysis techniques, analyze each dimension of the battery performance data in the at least one dimension of battery performance data to obtain battery performance change information, data format type information, and battery environmental parameter information.

[0074] 203. In response to the abnormal information matching among at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain a battery performance impact factor corresponding to the battery performance data of the dimension from a pre-set abnormal matching relationship set, where the battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell.

[0075] The pre-set abnormal matching relationship set is a database or knowledge base defined during the system design stage, which contains the abnormal patterns that may occur in the hydrogen fuel cell under different conditions, as well as their corresponding influencing factors and consequences. This set is established through comprehensive consideration of historical data, experimental results, expert experience, and theoretical analysis, and is used to quickly identify and respond to abnormal situations during real-time monitoring and data analysis.

[0076] Specifically, the abnormal matching relationship set is a group of pre-defined rules or patterns that describe the correlation between different dimensions (such as battery performance change information, data format type information, and battery environment parameter information) in the hydrogen fuel cell performance data, as well as the abnormal states or potential problems that may be indicated when specific combinations of these dimensions occur, and give the battery performance impact factors of the battery performance data of each dimension, providing a basis and reference for subsequent accurate optimization control of the battery.

[0077] Among them, the battery performance impact factor can characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell. The battery performance impact factor refers to those key parameters that can quantitatively characterize and predict the impact of the battery performance data of the corresponding dimension on the overall battery performance. In one embodiment, it can be a weight value, characterizing the degree of influence of the battery performance data of this dimension on the entire battery performance. For example, the weight of the battery charge and discharge current on the battery performance, etc.

[0078] In one embodiment, the abnormal matching relationship set includes the battery performance impact factors corresponding to each dimension performance parameter under various abnormal matching patterns or states among the battery performance data, battery environment parameters, and performance data formats, and can be presented in the form of a table. If abnormal information matching occurs, the impact factors corresponding to the battery performance data of each dimension in this abnormal state can be queried from the abnormal matching relationship set. This mapping relationship set can be set according to the knowledge base and practical experience.

[0079] In the embodiments of the present application, the battery optimization control platform can detect whether there is an abnormal information match between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information; for example, whether there is an abnormal match between the battery environment parameters and the performance data format information, and whether there is an abnormal match between the battery environment parameter information and the battery performance change information; if there is an abnormal information match between at least two types of information, then from the pre-set abnormal match relationship set, obtain the battery performance impact factor corresponding to the battery performance data of the dimension; subsequently, the battery performance will be optimized and controlled according to the battery performance impact factor of the battery performance data of each dimension.

[0080] In practice, based on experience, historical data, and knowledge base, there is a correlation between different dimensions (such as battery performance change information, data format type information, and battery environment parameter information) in the performance data of hydrogen fuel cells. Under a specific battery environment, the battery performance change information and the performance data format show a certain regularity (that is, the data match each other). If there is an abnormal match between the data, it indicates that the performance of the hydrogen fuel cell is abnormal or not in the optimal state. At this time, it is necessary to optimize and control the performance of the hydrogen fuel cell to improve the battery performance.

[0081] For example, a sudden change in environmental parameters (such as temperature, humidity, or pressure) may cause a change in battery performance data. If the change in battery performance data is within the expected range, it indicates normalcy; if it does not meet the expectation, it means that the battery performance needs further optimization and control. Another example is that a sudden change in environmental parameters (such as temperature, humidity, or pressure) will affect system failures or errors. At this time, it may affect the state of the performance data acquisition device or the way the system represents and processes the battery performance data. At this time, if a change in the format of the battery performance data is found, it indicates a battery system error or failure, which will affect battery performance management, and the overall battery performance will inevitably decline. At this time, it is necessary to optimize and control the battery performance in a timely manner to quickly bring the battery performance to the best state.

[0082] In one embodiment, the battery optimization control platform can detect whether there is an abnormal information match between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information. It can first detect whether there is an abnormal match between the battery environment parameter information and the battery performance change information. If there is an abnormal match, then detect whether there is an abnormal match between the battery environment parameter information and the performance data format information. Of course, it can also first detect the abnormal match between the battery environment parameter information and the performance format information, and then detect the abnormal match with the battery performance change information. The specific detection order can be set according to actual needs.

[0083] In one embodiment, the battery performance impact factor of each dimension includes: first sub-weight information and second sub-weight information.

[0084] In response to an abnormal information match occurring between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain a battery performance impact factor corresponding to the battery performance data of the dimension from a pre-set abnormal match relationship set, including:

[0085] If an abnormal information match occurs between the battery environment parameter information and the battery performance change information, obtain first sub-weight information corresponding to the battery performance data of the dimension in the first sub-abnormal match relationship set;

[0086] If an abnormal information match occurs between the battery environment parameter information and the performance data format information, obtain second sub-weight information corresponding to the battery performance data of the dimension from the second sub-abnormal match relationship set.

[0087] In an embodiment of the present invention, if there is an abnormal match between the battery environment parameter and the battery performance change information in each dimension data, the battery performance optimization control platform obtains first sub-weight information corresponding to each dimension data from the first sub-abnormal match relationship set. Among them, the first sub-abnormal match relationship set is the part (first sub-weight information) corresponding to the abnormal match between the battery environment parameter and the battery performance change information in the preset abnormal match relationship.

[0088] The first sub-abnormal match relationship set includes first sub-weight information corresponding to each dimension performance data in the abnormal match state between the battery environment parameter and the battery performance change information. The first sub-weight information characterizes the influence degree of each dimension performance data on the battery performance in the abnormal match state between the battery environment parameter and the battery performance change information.

[0089] In an embodiment of the present invention, if there is an abnormal match between the battery environment parameter and the performance data format information in each dimension data, the battery performance optimization control platform obtains second sub-weight information corresponding to each dimension data from the second sub-abnormal match relationship set. Among them, the second sub-abnormal match relationship set is the part corresponding to the abnormal match between the battery environment parameter and the performance data format information in the preset abnormal match relationship.

[0090] The second sub-abnormal match relationship set includes second sub-weight information corresponding to each dimension performance data in the abnormal match state between the battery environment parameter and the performance data format information. The second sub-weight information characterizes the influence degree of each dimension performance data on the battery performance in the abnormal match state between the battery environment parameter and the change of the performance data format information.

[0091] It should be understood that using the battery environment parameter information, the performance data format information, and the battery performance change information as features or factors to judge the battery performance optimization control can further explore high-value data for performance optimization control and improve the accuracy of performance optimization control compared with the existing method that only relies on a few performance parameters for optimization.

[0092] 204. Generate a battery performance optimization control result for the battery performance data of at least one dimension according to at least one battery performance impact factor corresponding to the battery performance data of at least one dimension.

[0093] In an embodiment of the present application, after obtaining the battery performance data of each dimension and its corresponding battery performance impact factor, the battery performance optimization control platform can determine the battery performance optimization control result of the battery performance data of each dimension.

[0094] Among them, the battery performance optimization control result indicates whether the hydrogen fuel cell is in an optimizable state, which may specifically include a battery performance optimizable state, a normal state, etc. If the battery is in an optimizable state, it indicates that from the perspective of the battery performance data of a certain dimension, the battery performance is not in a normal state or the best state. At this time, it may be necessary to optimize the performance of the battery.

[0095] In one embodiment, in order to improve the accuracy of battery performance optimization, the method of the embodiment of the present application may further include:

[0096] Identify and process the key data of the battery performance data of at least one dimension. The key data at least includes: the key performance characteristics of the hydrogen fuel cell and the identification information of the battery performance detection device; if the proportion of the battery performance data containing the key data in the battery performance data of at least one dimension is greater than a preset proportion, obtain the factor adjustment parameters corresponding to the battery performance data of each dimension from the preset key information mapping set;

[0097] Generating a performance optimization control result for the battery performance data of at least one dimension according to at least one battery performance impact factor corresponding to the battery performance data of at least one dimension includes: generating a performance optimization control result for the battery performance data of at least one dimension according to at least one battery performance impact factor corresponding to the battery performance data of at least one dimension and at least one factor adjustment parameter.

[0098] In the embodiments of the present application, after the battery performance optimization control platform obtains battery performance data in at least one dimension, it can also identify the key data of the battery performance data, identify whether the battery performance data is key feature data that significantly affects battery performance (such as switching voltage, charging and discharging current, battery power, fuel consumption, etc.), and detect whether the detection device for the battery performance data is a concerned or important detection device (such as a battery temperature sensor, a voltage and current detection device, etc.); if so, in order to improve the optimization accuracy, it is also necessary to adjust the battery performance impact factor corresponding to the battery performance data in this dimension, and implement battery performance optimization control based on the adjusted factor. If the number of the key data included in the battery performance data in at least one dimension is greater than a preset number, it indicates that the proportion of the key battery performance data is relatively large. At this time, it is necessary to reasonably adjust the influence weight or degree of each battery performance data on the battery performance.

[0099] Among them, the key data includes at least the key performance characteristics that significantly affect battery performance (i.e., key battery performance data) and the identification of the battery performance detection device. In the embodiments of the present application, a key database can be pre-stored, and this database includes the battery performance characteristics that significantly affect battery performance and the identification of the key battery performance detection devices. When applying, the key data can be identified based on the database.

[0100] Among them, the key information mapping set includes the mapping relationship between different key data and the battery key performance impact factor. When applying, the corresponding battery key performance impact factor can be queried in this set.

[0101] Among them, the battery key performance impact factor is a parameter used to adjust the battery performance impact factor, including parameters such as the adjustment method or adjustment amplitude of the battery key performance impact factor. For example, it can be a ratio.

[0102] Among them, the preset proportion can be set according to actual needs, such as 70%, 80%, etc.

[0103] In one embodiment, the battery performance optimization control platform can, for the battery performance data in each dimension, optimize and adjust at least one battery performance impact factor of the battery performance data in the corresponding dimension according to the factor adjustment parameter corresponding to the battery performance data in each dimension, and obtain at least one target battery performance impact factor corresponding to the battery performance data in at least one dimension; generate a performance optimization control result of the battery performance data in at least one dimension according to the at least one target battery performance impact factor corresponding to the battery basic performance data in at least one dimension.

[0104] In one embodiment, for the battery performance data of each dimension, according to the factor adjustment parameters corresponding to the battery performance data of each dimension, at least one battery performance influencing factor of the battery performance data of the corresponding dimension is optimized and adjusted, including: for the battery performance data of each dimension, according to at least one adjustment weight corresponding to the battery performance data of each dimension, wherein one battery performance influencing factor corresponds to one adjustment weight; performing a weighting process on the battery performance influencing factors corresponding to the battery performance data of each dimension according to at least one adjustment weight corresponding to the battery performance data of each dimension.

[0105] For example, the sequence M = {m1, m2... mi... mn} represents a data set of battery performance data of at least one dimension, the factor set Pi = {p1, p2... pi... pn}, where pi represents the set of battery performance influencing factors corresponding to the performance data mi in the i-th dimension, and Di = {d1, d2... di... dn} is the set of adjustment weights corresponding to the performance data mi in the i-th dimension. In the embodiment of the present application, the factors in Pi can be adjusted and optimized according to Di, such as weighting, i.e., {p1, p2... pi... pn} * {d1, d2... di... dn}, where i and n are positive integers greater than 2, and n > i.

[0106] In one embodiment, the battery performance optimization control platform can generate the performance optimization control results of the battery performance data of at least one dimension in various ways according to at least one target battery performance influencing factor corresponding to the battery performance data of at least one dimension. For example, in one embodiment, in order to improve the efficiency and accuracy of battery performance optimization, it can include:

[0107] If at least one of the at least one target battery performance influencing factors corresponding to the battery performance data of at least one dimension includes a first preset battery performance influencing factor that exceeds or is equal to the first battery performance optimization threshold, then set the battery performance optimization control result of the battery performance data of the dimension corresponding to the first preset battery performance influencing factor to the battery performance optimizable state;

[0108] If all of the at least one battery performance influencing factors corresponding to the battery performance data of at least one dimension are less than the first preset battery performance influencing factor corresponding to the first battery performance optimization threshold, then perform a fusion process on the at least one target battery performance influencing factors corresponding to the battery performance data of at least one dimension to obtain a fused battery performance influence amplitude value corresponding to the battery performance data of at least one dimension. Generate the performance optimization control result of the battery performance data of at least one dimension according to the fused battery performance influence amplitude value and the second battery performance optimization threshold.

[0109] In the embodiments of the present application, the battery performance optimization control platform can preset a battery performance optimization threshold. If at least one target battery performance impact factor corresponding to the battery performance data detected by the platform includes a first preset battery performance impact factor that exceeds or is equal to the first battery performance optimization threshold, it indicates that the target battery performance impact factor hits the performance optimization feature, and the performance of the battery may be abnormal or not in the best state. At this time, it can be determined that the battery performance optimization control result corresponding to the battery performance data is the battery performance optimizable state.

[0110] In one embodiment, detecting whether the target battery performance impact factor includes a first preset battery performance impact factor that exceeds or is equal to the first battery performance optimization threshold is to determine the degree or level (which can be divided into high, medium, and low levels) of the deviation of the battery performance from the best state from the perspective of the performance data of the corresponding dimension. If at least one target battery performance impact factor corresponding to the battery performance data detected by the platform includes a first preset battery performance impact factor that exceeds or is equal to the first battery performance optimization threshold, it indicates that the target battery performance impact factor hits the performance optimization feature, and the degree of deviation of the battery performance from the abnormal or not in the best state is at a high level or a high degree.

[0111] In the embodiments of the present application, if all the target battery performance impact factors of the battery performance data of a certain dimension are less than the first preset battery performance impact factor corresponding to the first battery performance optimization threshold, it indicates that from the perspective of the battery performance of this dimension, the battery does not deviate from the best performance state. At this time, in order to improve the accuracy of performance optimization, it is also necessary to further process the target battery performance impact factors of the battery performance data of the dimension for secondary confirmation. For example, the battery performance optimization control platform can perform a fusion process on all the target battery performance impact factors corresponding to the battery performance data of the at least one dimension to obtain a fused battery performance impact amplitude value corresponding to the battery performance data of the at least one dimension; generate a performance optimization control result for the battery performance data of the at least one dimension according to the fused battery performance impact amplitude value and the second battery performance optimization threshold.

[0112] Among them, the fused battery performance impact amplitude value characterizes the magnitude of the impact of the fused target battery performance impact factor on the overall battery performance under the corresponding dimension of the battery performance. The larger the amplitude value, the greater the impact, and vice versa.

[0113] Among them, there can be various fusion methods. For example, for the performance data of each dimension, at least one fusion weight corresponding to the target battery performance impact factor can be obtained (for example, the correlation between the target battery performance impact factors can be analyzed, and weights can be assigned according to the correlation). One factor corresponds to one fusion weight, and the target battery performance impact factors are weighted and summed according to the fusion weights, and the weighted sum result is used as the fused battery performance impact amplitude value.

[0114] In one embodiment, generating a performance optimization control result of the battery performance data of the at least one dimension according to the fused battery performance influence amplitude value and the battery performance optimization threshold includes: if the fused battery performance influence amplitude value is greater than the second battery performance optimization threshold, determining that the battery performance optimization control result of the battery performance data corresponding to the dimension of the fused battery performance influence amplitude value is a battery performance optimizable state; if the fused battery performance influence amplitude value is not greater than the second battery performance optimization threshold, determining that the battery performance detection result of the battery performance data corresponding to the dimension of the fused battery performance influence amplitude value is a battery performance normal state (i.e., a state where the battery performance does not need to be optimized).

[0115] Among them, the second battery performance optimization threshold can be set according to empirical values. If the fused battery performance influence amplitude value is greater than the second battery performance optimization threshold, it indicates whether the battery performance needs to be optimized or the degree of deviation from the best or ideal state from the overall perspective of the battery performance data of a certain dimension. In the embodiments of the present application, the first and second battery performance optimization thresholds can be used to reconfirm the battery performance optimization control state, which can prevent misjudgment and improve the accuracy of optimization.

[0116] For example, the sequence M = {m1, m2 …… mi …… mn} represents a data set of battery performance data for at least one dimension, and the factor set P = {p1, p2 …… pi …… pn}, where pi represents the set of target battery performance impact factors corresponding to the performance data mi in the i-th dimension (such as the electrochemical performance dimension), and pi = {pi1, pi2 …… pin}. If all the target battery performance factors in the factor set pi are less than a (the factor corresponding to the first battery performance optimization threshold A), it indicates that the battery performance in this dimension does not yet meet the optimization standard. To prevent misjudgment (considering the complexity of the flight environment) and ensure the accuracy of battery performance optimization, it is also necessary to perform a fusion process on the factors in the factor set pi. Specifically, the weight corresponding to each factor in the factor set pi can be obtained, that is, qi = {qi1, qi2 …… qin}, where qi1n represents the weight corresponding to the factor pin. Then, a weighted sum of pi is performed according to q to obtain the fused battery performance impact amplitude under the i-dimensional data. Specifically, the formula is used: p’ = pi1 * qi1 + pi2 * qi2 + …… + pin * qin; compare p’ with the second battery performance optimization threshold B. If it is greater than B, it indicates that the battery meets the performance optimization standard in the i-th dimension, and the battery performance detection result is determined to be in an optimizable state; otherwise, it is not optimizable. Among them, the second battery performance optimization threshold B is greater than the first battery performance optimization threshold A. The second battery performance optimization threshold B represents the overall performance optimization standard of the battery under the i-dimensional data (such as the electrochemical performance dimension), and the first battery performance optimization threshold A represents the local performance optimization standard of the battery under the i-dimensional data (such as the electrochemical performance dimension). The overall optimization standard is greater than the local optimization standard.

[0117] 205. Perform a performance optimization process on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data for the at least one dimension.

[0118] In one embodiment, after obtaining the battery performance optimization control result of the battery performance data for each dimension, the battery performance optimization control platform can perform a performance optimization process on the battery according to the battery performance optimization control result. Specifically, to improve the optimization efficiency and accuracy, determine the number of results of the battery performance optimization control result of the battery performance data for the at least one dimension that are in the battery optimizable state; perform a performance optimization process on the performance of the hydrogen fuel cell according to the number of results.

[0119] For example, if the number of results is greater than the preset optimization number, it means that from the perspective of the battery performance in multiple dimensions, the battery needs to be optimized. If it is not greater than the predicted optimization number, it means that from the perspective of the battery performance in multiple dimensions, the battery has not reached the level that needs to be optimized or does not need to be optimized, etc.

[0120] In one embodiment, the proportion of battery performance data for which the battery performance optimization control result is the battery optimizable state can be calculated based on the number of results (dividing the number of results by the number of dimensions of performance), and the proportion is used to finally confirm whether the battery needs to be optimized. If the quantity proportion is greater than the preset proportion, it means that from the perspective of battery performance in multiple dimensions, the battery needs to be optimized. If it is not greater than the preset proportion, it means that from the perspective of battery performance in multiple dimensions, the battery has not reached the level that requires optimization or does not need optimization, etc.

[0121] After determining that the hydrogen fuel cell needs to be optimized, the battery performance optimization control platform can optimize the parameters of the battery. For example, the battery optimization measures can include: Load management: By adjusting the load, the battery is made to operate in its optimal performance range to avoid overloading or underloading. Temperature control: Maintaining the battery at the optimal operating temperature to increase the chemical reaction rate and efficiency. Pressure regulation: Optimizing the supply pressures of hydrogen and oxygen to ensure the effective supply of reactants and the timely discharge of products. Specific battery optimization measures can be set according to requirements. For example, in one embodiment, after determining the need for optimization, the energy management system can be started to monitor and adjust the charging and discharging process of the battery, and an auxiliary power supply system can be used in conjunction with a supercapacitor or a secondary battery to smooth the power output and improve the system stability. In addition, high-performance catalysts can be switched to use and the electrode structure can be optimized to improve the electro-chemical reaction efficiency, and backup electrolyte materials can be adopted to improve the ion conductivity and chemical stability, etc.

[0122] As can be seen from the above, the embodiment of the present application provides an optimization control for the performance of a hydrogen fuel cell for an aircraft, which can comprehensively consider multiple dimensions of battery performance, detect and respond to changes in battery performance in real time, and dynamically optimize the battery performance through intelligent algorithms, realizing precise optimization control of the performance of the hydrogen fuel cell for an aircraft, thereby improving the performance of the hydrogen fuel cell for an aircraft.

[0123] In addition, the embodiment of the present application can extract battery performance change information, data format type information, and battery environment parameter information from battery performance data to form features, and perform battery performance optimization based on these features, enriching the methods of hydrogen fuel cell optimization control, and can greatly improve the accuracy of hydrogen fuel cell performance optimization control.

[0124] Figure 3 is a block diagram of a battery performance optimization control device for the performance of a hydrogen fuel cell for an aircraft provided according to an embodiment of the present application. This device is used to execute the above battery performance optimization control method. Refer to Figure 4 and the device includes:

[0125] An acquisition module 401, configured to acquire at least one dimension of battery performance data of a hydrogen fuel cell for an aircraft in an aircraft flight environment;

[0126] The detection module 402 is configured to detect and process the battery performance data of each of the dimensions, and respectively obtain at least two types of information from the corresponding battery performance change information, performance data format information, and battery environment parameter information. The battery environment parameter information is used to characterize the parameter information of the flight environment where the hydrogen fuel cell is located;

[0127] The matching module 403 is configured to, in response to abnormal information matching between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtain a battery performance impact factor corresponding to the battery performance data of the dimension from a pre-set abnormal matching relationship set. The battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell;

[0128] The result generation module 404 is configured to generate a battery performance optimization control result for the battery performance data of at least one dimension according to at least one battery performance impact factor corresponding to the battery performance data of at least one dimension;

[0129] The performance optimization module 405 is configured to perform performance optimization processing on the performance of the hydrogen fuel cell according to the battery performance optimization control result of the battery performance data of at least one dimension.

[0130] In one embodiment, referring to Figure 4 , the battery performance optimization control device further includes: a key data module 406;

[0131] The key data module 406 is configured to: identify and process the key data of the battery performance data of at least one dimension. The key data at least includes: the key performance characteristics of the hydrogen fuel cell and the identification information of the battery performance detection device; if the proportion of the battery performance data containing the key data in the battery performance data of at least one dimension is greater than a preset proportion, then obtain a factor adjustment parameter corresponding to the battery performance data of each dimension from a pre-set key information mapping set;

[0132] The result generation module 404 is configured to generate a performance optimization control result for the battery performance data of at least one dimension according to at least one battery performance impact factor and at least one factor adjustment parameter corresponding to the battery performance data of at least one dimension.

[0133] The embodiment of the present application provides an optimization control scheme for the performance of a hydrogen fuel cell used in an aircraft. This scheme can comprehensively consider multiple dimensions of battery performance, detect and respond to changes in battery performance in real time, and dynamically optimize battery performance through intelligent algorithms, realizing precise optimization control of the performance of the hydrogen fuel cell, thereby improving the performance of the hydrogen fuel cell used in the aircraft.

[0134] It should be noted that when the optimization control device provided in the above embodiment performs optimization control, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the optimization control device provided in the above embodiment and the optimization control method embodiment belong to the same concept. For the specific implementation process, please refer to the method embodiment, which will not be elaborated here.

[0135] In the embodiments of the present application, the computer system can be configured as a terminal or a server. When the computer system is configured as a terminal, the terminal is used as the execution subject to implement the technical solutions provided in the embodiments of the present application; when the computer system is configured as a server, the server is used as the execution subject to implement the technical solutions provided in the embodiments of the present application; or, the technical solutions provided in the present application are implemented through the interaction between the terminal and the server. The embodiments of the present application do not make any limitations in this regard.

[0136] Figure 5 FIG. is a schematic structural diagram of a server (computer system) provided by an embodiment of the present application. The server 500 may vary greatly due to different configurations or performances, and may include one or more central processing units 521 (for example, one or more processors) and a memory 532, and one or more storage media for storing application programs or data (for example, one or more mass storage systems). Among them, the memory 532 and the storage media may be transient storage or persistent storage. The program stored in the storage media may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Further, the central processing unit 521 may be configured to communicate with the storage media and execute a series of instruction operations in the storage media on the server 500.

[0137] The steps executed by the server in the above embodiment may be based on the Figure 5 server structure shown. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above may refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0138] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer system (which can be a personal computer, a server, or a network system, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0142] The above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present application.

Claims

1. A method for optimizing the performance of a hydrogen fuel cell for an aircraft, characterized in that: The method comprises: Acquiring at least one dimension of battery performance data of a hydrogen fuel cell for an aircraft in an aircraft flight environment; Performing detection and processing on the battery performance data of each dimension to obtain corresponding battery performance change information, performance data format information and at least two types of information of battery environment parameter information, wherein the battery environment parameter information is used to characterize parameter information of the flight environment in which the hydrogen fuel cell is located; In response to abnormal information matching between at least two types of information among the battery environmental parameter information, the performance data format information and the battery performance change information, a battery performance impact factor corresponding to the battery performance data of the dimension is obtained from a preset abnormal matching relationship set, wherein the battery performance impact factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell; Identify and process key data of the battery performance data of the at least one dimension, wherein the key data at least includes: key performance characteristics of the hydrogen fuel cell and identification information of a battery performance detection device; If the proportion of battery performance data containing the key data in at least one dimension of battery performance data is greater than a preset proportion, obtaining a factor adjustment parameter corresponding to the battery performance data of each dimension according to a preset key information mapping set; Generating a performance optimization control result of the battery performance data in the at least one dimension according to at least one battery performance influencing factor and at least one factor adjustment parameter corresponding to the battery performance data in the at least one dimension; The hydrogen fuel cell performance is optimized according to the performance optimization control result of the battery performance data of the at least one dimension.

2. The method according to claim 1, characterized in that: Generating a performance optimization control result of the battery performance data in the at least one dimension according to at least one battery performance influencing factor and at least one factor adjustment parameter corresponding to the battery performance data in the at least one dimension, including: According to the factor adjustment parameter corresponding to the battery performance data of each dimension, at least one battery performance influencing factor of the battery performance data of the corresponding dimension is optimized and adjusted to obtain at least one target battery performance influencing factor corresponding to the battery performance data of the dimension; A performance optimization control result of the battery performance data in the at least one dimension is generated according to at least one target battery performance influencing factor corresponding to the battery performance data in the at least one dimension.

3. The method according to claim 2, characterized in that: According to the factor adjustment parameter corresponding to the battery performance data of each dimension, optimizing and adjusting at least one battery performance influencing factor of the battery performance data of the corresponding dimension includes: For each dimension of battery performance data, obtaining an adjustment weight corresponding to the battery performance data in each dimension, wherein one battery performance influencing factor corresponds to one adjustment weight; The battery performance influencing factors corresponding to the battery performance data of each dimension are weighted according to the adjustment weight corresponding to the battery performance data of each dimension.

4. The method according to claim 2, characterized in that: Generating a performance optimization control result of the battery performance data in the at least one dimension according to at least one target battery performance influencing factor corresponding to the battery performance data in the at least one dimension includes: If at least one target battery performance impact factor corresponding to the at least one dimension of battery performance data includes a first preset battery performance impact factor that exceeds or is equal to a first battery performance optimization threshold, then setting the battery performance optimization control result of the battery performance data of the dimension corresponding to the first preset battery performance impact factor to a battery performance optimizable state; If all target battery performance impact factors corresponding to the battery performance data of the at least one dimension are less than the first preset battery performance impact factor corresponding to the first battery performance optimization threshold, all target battery performance impact factors corresponding to the battery performance data of the at least one dimension are fused to obtain a fused battery performance impact amplitude value corresponding to the battery performance data of the at least one dimension; The performance optimization control result of the battery performance data of the at least one dimension is generated according to the fused battery performance impact amplitude value and the second battery performance optimization threshold.

5. The method according to claim 4, characterized in that: The generating of the performance optimization control result of the battery performance data of the at least one dimension according to the post-fusion battery performance impact amplitude value and the second battery performance optimization threshold value comprises: If the battery performance impact amplitude value after fusion is greater than the second battery performance optimization threshold, determining that the battery performance optimization control result of the battery performance data of the dimension corresponding to the battery performance impact amplitude value after fusion is a battery performance optimizable state; If the battery performance impact amplitude value after fusion is not greater than the second battery performance optimization threshold, it is determined that the battery performance detection result of the battery performance data of the dimension corresponding to the battery performance impact amplitude value after fusion is a normal battery performance state.

6. The method according to claim 5, characterized in that The battery performance influencing factor of each dimension includes: first sub-weight information and second sub-weight information; In response to abnormal information matching between at least two types of information among the battery environment parameter information, the performance data format information, and the battery performance change information, obtaining a battery performance influencing factor corresponding to the battery performance data of the dimension from a preset abnormal matching relationship set, including: If an abnormal information match occurs between the battery environment parameter information and the battery performance change information, obtaining first sub-weight information corresponding to the battery performance data of the dimension in the first sub-abnormal matching relationship set; If an abnormal information match occurs between the battery environment parameter information and the performance data format information, second sub-weight information corresponding to the battery performance data of the dimension is obtained from the second sub-abnormal matching relationship set.

7. The method according to any one of claims 1 to 6, characterized in that: The performance optimization process of the hydrogen fuel cell is performed according to the battery performance optimization control result of the battery performance data of the at least one dimension, including: Determine the number of battery performance optimization control results of the battery performance data of the at least one dimension that are battery-optimizable states; The hydrogen fuel cell performance is optimized according to the result quantity.

8. A battery performance optimization control device for hydrogen fuel cells for aircraft, characterized in that: include: An acquisition module, used to acquire battery performance data of at least one dimension of a hydrogen fuel cell for an aircraft in an aircraft flight environment; A detection module, used to detect and process the battery performance data of each dimension, and obtain corresponding battery performance change information, performance data format information and at least two types of information of battery environmental parameter information, wherein the battery environmental parameter information is used to characterize parameter information of the flight environment in which the hydrogen fuel cell is located; A matching module, configured to obtain a battery performance influencing factor corresponding to the battery performance data of the dimension from a preset abnormal matching relationship set in response to an abnormal information match between at least two types of information among the battery environmental parameter information, the performance data format information and the battery performance change information, wherein the battery performance influencing factor is used to characterize the degree of influence of the battery performance data of the corresponding dimension on the performance of the hydrogen fuel cell; A key data module is used to identify and process the key data of the battery performance data of at least one dimension, wherein the key data at least includes: the key performance characteristics of the hydrogen fuel cell and the identification information of the battery performance detection equipment; if the proportion of the battery performance data containing the key data in the battery performance data of at least one dimension is greater than a preset proportion, then obtaining the factor adjustment parameter corresponding to the battery performance data of each dimension according to the preset key information mapping set; A result generating module, configured to generate a performance optimization control result of the battery performance data of the at least one dimension according to at least one battery performance influencing factor and at least one factor adjustment parameter corresponding to the battery performance data of the at least one dimension; A performance optimization module is used to optimize the performance of the hydrogen fuel cell according to the performance optimization control result of the battery performance data in at least one dimension.

9. A computer system, characterized in that: The computer system includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the method for optimizing control of battery performance according to any one of claims 1 to 7.

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