Optimization control method, device and system for performance of hydrogen fuel cell for aircraft

Abstract

The invention provides an optimization control method, device and system for the performance of a hydrogen fuel cell for an aircraft. The method comprises the following steps: acquiring at least one dimension of cell performance data of the hydrogen fuel cell for the aircraft in a flight environment of the aircraft; the battery performance data are detected, at least two kinds of information of corresponding battery performance change information, performance data format information and battery environment parameter information are obtained, and the battery environment parameter information represents parameter information of the flight environment where the hydrogen fuel battery is located; in response to abnormal information matching between at least two types of information in the battery environment parameter information, the performance data format information and the battery performance change information, obtaining a battery performance influence 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 influence factor; and performing performance optimization processing on the performance of the hydrogen fuel cell according to a cell performance optimization control result of the cell performance data.

Description

Technical Field

[0001] The present application relates to the field of hydrogen fuel cells and information technology, and in particular to a method, device and system for optimizing the performance control of hydrogen fuel cells for aircraft. Background Art

[0002] As the global demand for clean energy and sustainable transportation solutions grows, hydrogen fuel cells, as an efficient energy conversion technology, have become a key research direction for aircraft power systems. Hydrogen fuel cells are considered 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 many 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 performance optimization control of hydrogen fuel cells a technical challenge.

[0004] Traditional hydrogen fuel cell performance control methods mainly rely on empirically set control strategies and simple feedback adjustment mechanisms. They lack the ability to comprehensively analyze and optimize multi-dimensional battery performance parameters in real time and often have difficulty adapting to rapidly changing flight conditions and battery status. The accuracy of hydrogen fuel cell performance optimization control is poor, resulting in failure to achieve optimal battery performance. Summary of the invention

[0005] The embodiments of the present application provide a method, device and system for optimizing the performance of hydrogen fuel cells for aircraft, which can use intelligent algorithms to achieve precise optimization control of the performance of hydrogen fuel cells, thereby improving the performance of hydrogen fuel cells for aircraft.

[0006] In one aspect, a method for optimizing the performance of a hydrogen fuel cell for an aircraft is provided, the method comprising: 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; generating a battery performance optimization control result of the battery performance data in the at least one dimension according to at least one battery performance influencing factor corresponding to the battery performance data in the at least one dimension; The hydrogen fuel cell performance is optimized according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0007] On the other hand, a battery performance optimization control device for a hydrogen fuel cell for an aircraft is also provided, comprising: 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 result generating module, configured to generate a battery performance optimization control result of the battery performance data of the at least one dimension according to at least one battery performance influencing factor 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 battery performance optimization control result of the battery performance data of the at least one dimension.

[0008] On the other hand, a computer system is provided, which 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 and executed by the processor to implement the battery performance optimization control method in the embodiment of the present application.

[0009] The embodiment of the present application provides a method for optimizing and controlling the performance of a hydrogen fuel cell for an aircraft, which comprises obtaining battery performance data of at least one dimension of a hydrogen fuel cell for an aircraft in an aircraft flight environment; detecting and processing the battery performance data of each dimension of the battery performance data of the at least one dimension, and obtaining 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 represents parameter information of the flight environment in which the hydrogen fuel cell is located; in response to an abnormal information match between at least two types of information of 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, wherein the battery performance influencing factor represents 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 of the battery performance data of the at least one dimension according to the at least one battery performance influencing factor corresponding to the battery performance data of the at least one dimension; and performing performance optimization processing on the hydrogen fuel cell performance according to the battery performance optimization control result of the battery performance data of the at least one dimension. 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 to achieve precise optimization control of hydrogen fuel cell performance, thereby improving the performance of hydrogen fuel cells used in aircraft.

[0010] In addition, the present application scheme can extract battery performance change information, data format type information and battery environmental parameter information from the battery performance data to form features, and optimize battery performance based on the features, thereby enriching the methods of hydrogen fuel cell optimization control and greatly improving the accuracy of hydrogen fuel cell performance optimization control. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a schematic diagram of an implementation environment of a battery performance optimization control method for a hydrogen fuel cell for an aircraft provided in an embodiment of the present application; Figure 2 It is a flow chart of a battery performance optimization control method for a hydrogen fuel cell for an aircraft provided in accordance with an embodiment of the present application; Figure 3It is a structural schematic diagram of a battery performance optimization control device for a hydrogen fuel cell for an aircraft provided in an embodiment of the present application; Figure 4 is another structural schematic diagram of a battery performance optimization control device for a hydrogen fuel cell for an aircraft provided in an embodiment of the present application; Figure 5 It is a structural diagram of a computer system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0014] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with basically the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0015] In the present application, the term "at least one" means one or more, and the term "plurality" means two or more.

[0016] The following explains the terms used in this application.

[0017] The field of artificial intelligence (AI) is dedicated to simulating and expanding human intelligence, involving theories, methods, technologies and application systems for perceiving the environment, acquiring knowledge and applying knowledge to achieve the best results. As a branch of computer science, AI aims to understand the nature of intelligence and create machines that can simulate human intelligent responses, with perception, reasoning and decision-making capabilities.

[0018] AI technology is an interdisciplinary field that covers both hardware and software technologies. Hardware includes sensors, AI chips, etc., while software includes computer vision, speech processing, natural language processing, and machine learning / deep learning. Among them, computer vision (CV) technology enables machines to "see", that is, to use cameras and computers instead of human eyes to perform recognition, measurement, and image processing to obtain information from images or multi-dimensional data. Computer vision technology includes image processing, recognition, semantic understanding, retrieval, OCR, video processing, semantic understanding, content / behavior recognition, three-dimensional reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, as well as biometric recognition technologies such as face recognition and fingerprint recognition.

[0019] Hydrogen fuel cells for aircraft are energy conversion devices designed specifically for aircraft. They convert the chemical energy of hydrogen directly into electrical energy through electrochemical reactions to provide power for aircraft. This battery system contains multiple key components, including electrodes, electrolytes, bipolar plates, and gas diffusion layers. It can operate at high efficiency and has the characteristics of low noise and zero emissions. Due to the strict requirements of aircraft on energy density and weight, hydrogen fuel cells are designed with special attention to lightweight and compactness to adapt to the space and weight restrictions of aircraft. In addition, considering the safety and reliability during flight, hydrogen fuel cells for aircraft must also have characteristics such as fast response, high stability and long life to ensure stable power supply under various flight conditions. With the advancement of technology, hydrogen fuel cells have become a hot spot in the research of new energy technologies in the aviation field, and are expected to be commercialized in the future to provide aircraft with more environmentally friendly and efficient energy solutions.

[0020] Optimal control of aircraft hydrogen fuel cell performance refers to the application of a series of technologies and strategies to improve the performance of hydrogen fuel cell systems in aircraft and ensure their reliability and efficiency. This includes precise monitoring and dynamic adjustment of battery operating parameters, such as voltage, current, temperature and pressure, to adapt to different flight conditions and load requirements.

[0021] Traditional hydrogen fuel cell performance control methods mainly rely on empirically set control strategies and simple feedback adjustment mechanisms, lacking the ability to comprehensively analyze and optimize multi-dimensional battery performance parameters in real time. In addition, due to the lack of accurate identification of battery performance influencing factors and dynamic adjustment mechanisms, these methods are often difficult to adapt to rapidly changing flight conditions and battery status, resulting in suboptimal battery performance.

[0022] 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 and more precise control of battery performance. Research has mostly focused on battery systems for ground applications, and research on performance optimization control methods specific to aircraft hydrogen fuel cells is still limited. 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.

[0023] In one embodiment, for ease of understanding, please refer to Figure 1 , Figure 1This is an application environment diagram of the optimization control of the performance of hydrogen fuel cells for aircraft in the embodiment of the present application. The method of the embodiment of the present application is applied to the optimization control system based on the performance of hydrogen fuel cells for aircraft. The optimization control system of the performance of hydrogen fuel cells for aircraft includes: a battery performance optimization control platform, a battery performance detection device 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 that provides 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 distribution networks (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 to this. The terminal device and the battery performance optimization control platform can be directly or indirectly connected by wired or wireless communication, and the embodiment of the present application is not limited here.

[0024] Among them, the battery performance detection equipment can be installed in the aircraft battery system, and collect battery performance data (temperature, pressure, current, voltage, gas flow, etc.) of at least one dimension of the aircraft hydrogen fuel cell in the aircraft flight environment. It can be directly or indirectly connected to the battery performance optimization control platform through wired or wireless communication, and after collecting the battery performance data, it is uploaded to the battery performance optimization control platform for processing. In one embodiment, the battery performance detection equipment is responsible for real-time monitoring of 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, which are used to collect real-time performance data of the battery operation status.

[0025] The battery performance optimization control platform is used to: obtain battery performance data of at least one dimension of a hydrogen fuel cell for an aircraft in an aircraft flight simulation environment; detect and process the battery performance data of each dimension to obtain corresponding battery performance change information, data format type information and at least two types of information of battery environmental parameter information, wherein the battery environmental information is environmental parameter information of the environment in which the hydrogen fuel cell is located; in response to abnormal information matching between the battery environmental parameter information, the performance data format type and at least two types of information of the battery performance change information, obtain a battery performance influencing factor corresponding to the battery performance data of the dimension from a preset abnormal matching relationship set, 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; generate a battery performance optimization control result of the battery performance data of the at least one dimension according to the at least one battery performance influencing factor corresponding to the battery performance data of the at least one dimension; and perform performance optimization processing on the hydrogen fuel cell performance according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0026] The terminal device interacts with the battery performance optimization control platform to control the parameters of the battery performance optimization control platform and receive the performance optimization results sent by the battery performance optimization control platform for display so that the staff can make judgments and adjustments for further optimization.

[0027] The optimization control system provided in the embodiment 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, thereby achieving precise optimization control of hydrogen fuel cell performance, thereby improving the performance of hydrogen fuel cells used in aircraft.

[0028] The optimization control method for the performance of hydrogen fuel cells for aircraft provided in the embodiment of the present application can be executed by a battery performance optimization control platform. In some embodiments, the battery performance optimization control platform can be composed of a computer device as a terminal or a server. The following first takes the computer device as an example of a server to introduce the optimization control method for the performance of hydrogen fuel cells for aircraft provided in the embodiment of the present application.

[0029] Figure 2 is a flow chart of a method for optimizing the performance of a hydrogen fuel cell for an aircraft provided in accordance with an embodiment of the present application, see Figure 2 In the embodiment of the present application, the battery performance optimization control platform is used as an example to illustrate. The optimization control method includes the following steps: 201. Obtain battery performance data of at least one dimension of a hydrogen fuel cell for an aircraft in an aircraft flight environment.

[0030] Among them, the battery performance data of hydrogen fuel cells for aircraft are parameters or data that can describe and evaluate the performance of hydrogen fuel cells in aircraft applications. Each dimension represents a specific aspect of 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 be specifically introduced: Electrochemical performance data Electrochemical performance is the key to evaluating the core functions of hydrogen fuel cells, among which open circuit voltage (OCV) refers to the voltage level of the battery when there is no load. The operating voltage refers to the voltage of the battery in actual operation, while the current density involves the current flow per unit area, and the power density refers to the power output of the battery per unit area.

[0031] Thermal Management Performance Data Thermal management is critical for hydrogen fuel cells. Battery temperature data reflects the temperature state of the battery during operation, which directly affects the performance and life of the battery. Heat flux density data describes the heat flow per unit area, which is critical for evaluating battery heat dissipation requirements and designing effective cooling systems.

[0032] Mechanical properties data The mechanical performance of a hydrogen fuel cell involves the stability and strength of its structure when subjected to mechanical loads. Vibration and shock tolerance data show how the battery performs under the vibration and shock encountered in aircraft operation, which is critical to ensuring flight safety.

[0033] Safety performance data Safety performance data includes gas leakage rate and pressure change, which are important indicators for hydrogen fuel cell safety monitoring. Gas leakage rate data monitors the leakage of hydrogen and oxygen, while pressure change data focuses on the fluctuation of internal pressure of the battery. Together, they ensure the safety of the battery system.

[0034] Environmental adaptability data Environmental adaptability data covers parameters such as ambient temperature, humidity and atmospheric pressure, which describe how external environmental conditions affect battery performance. This is crucial for evaluating the performance of batteries in different climates and flight altitudes.

[0035] Durability and lifespan data Durability and lifespan data focus on the performance changes of batteries during long-term use. Cycle life data indicates the performance degradation of batteries after multiple charge and discharge cycles, while aging data monitors the performance degradation of batteries after continuous operation.

[0036] Energy Management Performance Data Energy management performance data includes energy efficiency and charge and discharge rate. Energy efficiency data measures the efficiency of the battery in converting chemical energy into electrical energy, while charge and discharge rate data describes the speed at which the battery charges and discharges. These data are critical to optimizing the energy output and input of the battery.

[0037] In the embodiment of the present application, the battery performance detection equipment such as sensors installed on the hydrogen fuel cell system can be used to collect data in real time and transmit it to the ground battery performance optimization control platform for analysis. Through in-depth analysis of these data, the performance status of the battery can be evaluated, and the battery performance optimization control platform such as the battery management system (BMS) can be optimized, and corresponding optimization strategies can be formulated to ensure the reliable operation of the hydrogen fuel cell in the aircraft.

[0038] In practical applications, for example, it is necessary to clarify the specific needs of data acquisition, including determining key performance parameters such as voltage, current, temperature and pressure, which are essential for evaluating the performance of the battery. Based on these requirements, appropriate sensors are selected for precise measurement to ensure that their measurement range, accuracy and response time can meet specific performance monitoring requirements. Subsequently, sensors are arranged in various key parts of the hydrogen fuel cell system, such as the battery stack, gas pipeline and cooling system. At the same time, data acquisition hardware is configured, including data acquisition cards (DAQ), signal conditioners and interface devices, and appropriate sampling frequencies and data resolutions are set to ensure that the collected data is both accurate and reliable.

[0039] 202. Detect and process 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 environmental parameter information, wherein the battery environmental information is environmental parameter information of the environment in which the hydrogen fuel cell is located.

[0040] After obtaining at least one dimension of battery performance data, the battery performance optimization control platform can detect, analyze and process the battery performance data to extract corresponding battery performance change information, performance data format information and battery environmental parameter information.

[0041] Among them, battery performance change information refers to data that describes how the performance parameters of the battery change over time during operation. This information is crucial for identifying the working state and health state of the battery and predicting its life. For example, voltage changes, changes in current during battery charging and discharging, changes in battery output power, changes in battery status, and other transitions from a healthy state to a faulty state.

[0042] The data format information describes how the battery performance data is represented and processed, including data type (analog or digital), unit (such as volts, amperes), accuracy, resolution, and data structure. Understanding this information is essential for accurate data collection, effective transmission, reliable storage, and in-depth analysis. For example, in one embodiment, the data format type information includes: Data type: whether the data is analog or digital, continuous or discrete. Units and dimensions: the measurement units of the data, such as volts (V), amperes (A), watts (W), etc. Accuracy and resolution: the accuracy of data acquisition and the minimum change that can be detected. Data structure: the organization form of data, such as time series, vectors, matrices, etc.

[0043] The battery environment parameter information describes the external environmental conditions of the hydrogen fuel cell, which may affect the performance and life of the battery. In one embodiment, it specifically includes: Temperature: The ambient temperature of the battery when it is operating affects the chemical reaction rate and battery efficiency.

[0044] Humidity: Ambient humidity may affect the insulation properties and corrosion rate of the battery.

[0045] Pressure: The pressure of the hydrogen and oxygen supply affects the chemical reaction kinetics of the battery.

[0046] Air Quality: Pollutants in the environment may affect the performance and life of the battery.

[0047] Vibration and shock: Vibration and shock encountered in flight may affect the structural integrity of the battery.

[0048] In one embodiment, the battery performance optimization control platform can analyze the battery performance data of each dimension of the at least one dimension based on data mining and analysis technology to obtain battery performance change information, data format type information and battery environmental parameter information.

[0049] 203. 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 influencing factor corresponding to the battery performance data of the dimension is obtained from a pre-set set of abnormal matching relationships, and 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.

[0050] The pre-set abnormal matching relationship set is a database or knowledge base defined in the system design phase, which contains the abnormal patterns that may occur in hydrogen fuel cells under different conditions and 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.

[0051] Specifically, the abnormal matching relationship set is a set of predefined rules or patterns that describe the correlation between different dimensions in the hydrogen fuel cell performance data (such as battery performance change information, data format type information, and battery environmental parameter information), as well as the abnormal conditions or potential problems that may be indicated when specific combinations of these dimensions appear. The battery performance influencing factors of the battery performance data of each dimension are given, providing a basis and reference for subsequent accurate optimization and control of the battery.

[0052] Among them, the battery performance influencing 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 influencing factor refers to the key parameters that can quantitatively characterize and predict the influence of the battery performance data of the corresponding dimension on the overall battery performance. In one embodiment, it can be a weight value that characterizes the degree of influence of the battery performance data of this dimension on the overall battery performance. For example, the weight of the influence of the battery charging and discharging current on the battery performance, etc.

[0053] In one embodiment, the abnormal matching relationship set includes the battery performance data, battery environmental parameters, and the battery performance impact factor corresponding to each dimension performance parameter under various abnormal matching modes or states between the performance data format, which can be presented in a table. If an abnormal information match occurs, the impact factor corresponding to each dimension of battery performance data under the abnormal state can be queried from the abnormal matching relationship set. The mapping relationship set can be set according to the knowledge base and practical experience.

[0054] In an embodiment 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 environmental parameter information, the performance data format information and the battery performance change information; such as whether there is an abnormal match between the battery environmental parameter and the performance data format information, such as whether there is an abnormal match between the battery environmental parameter information and the battery performance change information; if there is an abnormal information match between at least two types of information, then from a pre-set abnormal matching relationship set, a battery performance influencing factor corresponding to the battery performance data of the dimension is obtained; and subsequently, the battery performance optimization control will be performed on the battery according to the battery performance influencing factor of the battery performance data of each dimension.

[0055] In practice, based on experience, historical data and knowledge base, there is a correlation between different dimensions in hydrogen fuel cell performance data (such as battery performance change information, data format type information and battery environmental parameter information). Under a specific battery environment, battery performance change information and performance data format show a certain regularity (that is, the data match). If there is an abnormal match between the data, it means that the performance of the hydrogen fuel cell is abnormal or not in the optimal state. At this time, the performance of the hydrogen fuel cell needs to be optimized and controlled to improve battery performance.

[0056] For example, sudden changes in environmental parameters (such as temperature, humidity or pressure) may cause changes in battery performance data. If the changes in battery performance data are within expectations, it is normal. If they are not as expected, it means that the battery performance needs to be further optimized and controlled. For another example, sudden changes in environmental parameters (such as temperature, humidity or pressure) may affect system failures or errors, which may affect the state of the performance data collection device or the system's representation and processing of battery performance data. At this time, if the format of the battery performance data is found to have changed, it indicates that the battery system is wrong or faulty, 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 so that the battery performance can quickly reach the optimal state.

[0057] In one embodiment, the battery optimization control platform can detect whether an abnormal information match occurs between at least two types of information among the battery environmental parameter information, the performance data format information, and the battery performance change information. The detection may be performed by first detecting whether the battery environmental parameter information and the battery performance change information are abnormally matched. If they are abnormally matched, the detection is then performed to detect whether the battery environmental parameter information and the performance data format information are abnormally matched. Of course, the abnormal match between the battery environmental parameter information and the performance format information may also be detected first, and then the abnormal match with the battery performance change information may be detected. The specific detection order may be set according to actual needs.

[0058] In one embodiment, the battery performance influencing factor of each dimension includes: first sub-weight information and second sub-weight information.

[0059] In response to 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, a battery performance influencing factor corresponding to the battery performance data of the dimension is obtained 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.

[0060] 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 the first sub-weight information corresponding to each dimension data from the first sub-abnormal matching relationship set. The first sub-abnormal matching 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 matching relationship.

[0061] The first sub-abnormal matching relationship set includes the first sub-weight information corresponding to each dimension performance data under the abnormal matching state between the battery environment parameter and the battery performance change information. The first sub-weight information represents the influence degree of each dimension performance data on the battery performance under the abnormal matching state between the battery environment parameter and the battery performance change information.

[0062] In an embodiment of the present invention, if there is an abnormal match between the battery environment parameters and the performance data format information in each dimension data, the battery performance optimization control platform obtains the second sub-weight information corresponding to each dimension data from the second sub-abnormal matching relationship set. The second sub-abnormal matching relationship set is the part corresponding to the abnormal match between the battery environment parameters and the performance data format information in the preset abnormal matching relationship.

[0063] The second sub-abnormal matching relationship set includes the second sub-weight information corresponding to each dimension performance data under the abnormal matching state between the battery environment parameters and the performance data format information. The second sub-weight information represents the degree of influence of each dimension performance data on the battery performance under the abnormal matching state between the battery environment parameters and the performance data format information changes.

[0064] It should be understood that using the battery environmental parameter information, the performance data format information and the battery performance change information as features or factors to determine battery performance optimization control can further mine high-value data for performance optimization control and improve the accuracy of performance optimization control compared to the existing optimization that relies solely on a few performance parameters.

[0065] 204. Generate a battery performance optimization control result of the battery performance data in the at least one dimension according to at least one battery performance influencing factor corresponding to the battery performance data in the at least one dimension.

[0066] In the embodiment of the present application, after obtaining the battery performance data of each dimension and its corresponding battery performance influencing factor, the battery performance optimization control platform can determine the battery performance optimization control result of the battery performance data of each dimension.

[0067] The battery performance optimization control result indicates whether the hydrogen fuel cell is in an optimizable state, which may specifically include the battery performance optimizable state, normal state, etc. If the battery is in an optimizable state, it means that from the perspective of a certain dimension of battery performance data, the battery performance is not in a normal state or an optimal state, and at this time, the battery performance may need to be optimized and controlled.

[0068] 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: 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 including the key data in the battery performance data of at least one dimension is greater than a preset proportion, obtain the factor adjustment parameter corresponding to the battery performance data of each dimension according to the preset key information mapping set; According to at least one battery performance influencing factor corresponding to the battery performance data of at least one dimension, a performance optimization control result of the battery performance data of at least one dimension is generated, including: according to at least one battery performance influencing factor and at least one factor adjustment parameter corresponding to the battery performance data of at least one dimension, a performance optimization control result of the battery performance data of at least one dimension is generated.

[0069] In the embodiment of the present application, after obtaining the battery performance data of at least one dimension, the battery performance optimization control platform can also identify the key data of the battery performance data, identify whether the battery performance data is the key characteristic data that significantly affects the battery performance (such as switch voltage, charging and discharging current, battery power, fuel consumption, etc.), and whether the detection device that detects the battery performance data is a detection device of concern or importance (such as battery temperature sensor, voltage and current detection device, etc.); if so, in order to improve the optimization accuracy, it is also necessary to adjust the battery performance influencing factor corresponding to the battery performance data of this dimension, and realize battery performance optimization control based on the adjusted factor. If the number of the key data included in the battery performance data of at least one dimension is greater than the preset number, it indicates that the key battery performance data accounts for a large proportion. At this time, it is necessary to reasonably adjust the weight or degree of influence of each battery performance data on the battery performance.

[0070] Among them, the key data at least includes key performance characteristics that significantly affect battery performance (i.e., key battery performance data) and the identification of battery performance detection equipment. In an embodiment of the present application, a key database can be pre-stored, which includes battery performance characteristics that significantly affect battery performance and the identification of key battery performance detection equipment. When applied, key data can be identified based on the database.

[0071] The key information mapping set includes mapping relationships between different key data and key battery performance influencing factors. When applied, the corresponding key battery performance influencing factors can be queried in the set.

[0072] The battery key performance influencing factor is a parameter used to adjust the battery performance influencing factor, including parameters such as the battery key performance influencing factor adjustment method or adjustment range, for example, it can be a ratio.

[0073] Among them, the preset proportion can be set according to actual needs, such as 70%, 80%, etc.

[0074] In one embodiment, the battery performance optimization control platform can optimize and adjust at least one battery performance influencing factor of the battery performance data of the corresponding dimension according to the factor adjustment parameters corresponding to the battery performance data of each dimension, and obtain at least one target battery performance influencing factor corresponding to the battery performance data of the at least one dimension; and generate a performance optimization control result of the battery performance data of the at least one dimension according to the at least one target battery performance influencing factor corresponding to the basic battery performance data of the at least one dimension.

[0075] In one embodiment, for 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 according to the factor adjustment parameters corresponding to the battery performance data of each dimension, including: for the battery performance data of each dimension, 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; and weighted processing is performed on the battery performance influencing factor corresponding to the battery performance data of each dimension according to the at least one adjustment weight corresponding to the battery performance data of each dimension.

[0076] 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}, pi represents the battery performance influencing factor set corresponding to the performance data mi in dimension i, and Di={d1, d2...di...dn} is the adjustment weight set corresponding to the performance data mi in dimension i. In the embodiment of the present application, the factors in Pi can be adjusted and optimized according to Di, such as weighted {p1, p2...pi...pn}*{d1, d2...di...dn}, where i and n are positive integers greater than 2, and n>i.

[0077] In one embodiment, the battery performance optimization control platform may generate a performance optimization control result of the battery performance data of the at least one dimension according to the at least one target battery performance influencing factor corresponding to the battery performance data of the at least one dimension. For example, in one embodiment, in order to improve the efficiency and accuracy of battery performance optimization, it may include: 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 corresponding to a first battery performance optimization threshold value that exceeds or is equal to the first preset battery performance impact factor, then the battery performance optimization control result of the battery performance data corresponding to the dimension of the first preset battery performance impact factor is set to a battery performance optimizable state; If at least one battery performance impact factor corresponding to the battery performance data of the at least one dimension is less than the first preset battery performance impact factor corresponding to the first battery performance optimization threshold, the at least one target battery performance impact factor corresponding to the battery performance data of the at least one dimension is fused to obtain a fused battery performance impact amplitude value corresponding to the battery performance data of the at least one dimension. A 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.

[0078] In an embodiment of the present application, the battery performance optimization control platform can pre-set a battery performance optimization threshold. If the platform detects that at least one target battery performance influencing factor corresponding to the battery performance data includes a first preset battery performance influencing factor corresponding to the first battery performance optimization threshold, it indicates that the target battery performance influencing factor hits the performance optimization feature, and the battery performance may be abnormal or not in the optimal state. At this time, it can be determined that the battery performance optimization control result corresponding to the battery performance data is a battery performance optimizable state.

[0079] In one embodiment, detecting whether the target battery performance influencing factor includes a first preset battery performance influencing 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 battery performance deviation from the optimal state from the perspective of the corresponding dimensional performance data. If the platform detects that at least one target battery performance influencing factor corresponding to the battery performance data includes a first preset battery performance influencing factor that exceeds or is equal to the first battery performance optimization threshold, it indicates that the target battery performance influencing factor hits the performance optimization feature, and the degree to which the battery's performance deviates from the abnormal state or is not in the optimal state is a high level or a high degree.

[0080] In an embodiment of the present application, if all target battery performance influencing factors of battery performance data of a certain dimension are less than the first preset battery performance influencing factor corresponding to the first battery performance optimization threshold, it indicates that the battery has not deviated from the optimal performance state from the perspective of battery performance of this dimension. At this time, in order to improve the accuracy of performance optimization, it is necessary to further process the target battery performance influencing factors of the battery performance data of the dimension for secondary confirmation. For example, the battery performance optimization control platform can fuse all target battery performance influencing factors corresponding to the battery performance data of at least one dimension to obtain the fused battery performance impact amplitude value corresponding to the battery performance data of at least one dimension; and generate the performance optimization control result of the battery performance data of at least one dimension according to the fused battery performance impact amplitude value and the second battery performance optimization threshold.

[0081] Among them, the amplitude value of the fusion battery performance impact represents the impact amplitude of the fusion target battery performance impact factor on the overall battery performance under the corresponding dimension battery performance. The larger the amplitude, the greater the impact, and vice versa.

[0082] There are many fusion methods. For example, for the performance data of each dimension, a fusion weight corresponding to at least one target battery performance influencing factor can be obtained (for example, the correlation between the target battery performance influencing 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 influencing factors are weighted and summed according to the fusion weight, and the weighted sum result is used as the battery performance impact amplitude value after fusion.

[0083] In one embodiment, a performance optimization control result of the battery performance data of at least one dimension is generated according to the battery performance impact amplitude value after fusion and the battery performance optimization threshold, including: if the battery performance impact amplitude value after fusion is greater than a second battery performance optimization threshold, then 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, then determining 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 battery performance normal state (that is, a battery performance state that does not require optimization).

[0084] Among them, the second battery performance optimization threshold can be set according to an empirical value. If the battery performance impact amplitude value after fusion 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 optimal or ideal state from the overall perspective of the battery performance data in a certain dimension. In the embodiment of the present application, the first and second battery performance optimization thresholds can be used to perform a secondary confirmation of the performance optimization control state of the battery, which can prevent misjudgment and improve the accuracy of optimization.

[0085] For example, the sequence M={m1, m2…mi…mn} represents a data set of battery performance data in at least one dimension, the factor set P={p1, p2…pi…pn}, pi represents the set of target battery performance influencing factors corresponding to the performance data mi in dimension i (such as electrochemical performance dimension), pi={pi1, pi2…pin}. If all 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 means that the battery performance in this dimension has not reached the optimization standard. In order to prevent misjudgment (considering the complexity of the flight environment) and ensure the accuracy of battery performance optimization, it is also necessary to fuse 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}, qi1n represents the weight corresponding to the factor pin, and then, the weighted sum of pi is performed according to q to obtain the impact of the fused battery performance under the i-dimensional data. Specifically, the formula is used: p'=pi1*qi1+pi2*qi2+...pin*qin; p' is compared with the second battery performance optimization threshold B. If it is greater than B, it means that the battery has reached the performance optimization standard under the i-dimensional state, and the battery performance test result is determined to be in an optimizable state, otherwise it cannot be optimized. 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 standard for the overall performance optimization of the battery under i-dimensional data (such as electrochemical performance dimension), and the first battery performance optimization threshold A represents the standard for the local performance optimization of the battery under i-dimensional data (such as electrochemical performance dimension), and the overall optimization standard is greater than the local optimization standard.

[0086] 205. Perform performance optimization processing on the hydrogen fuel cell performance according to the battery performance optimization control result of the battery performance data of the at least one dimension.

[0087] In one embodiment, after obtaining the battery performance optimization control result of the battery performance data in each dimension, the battery performance optimization control platform can perform performance optimization processing on the battery according to the battery performance optimization control result. Specifically, in order to improve the optimization efficiency and accuracy, the battery performance optimization control result of the battery performance data in at least one dimension is determined as the number of results of the battery optimizable state; and the hydrogen fuel cell performance is optimized according to the number of results.

[0088] For example, if the number of results is greater than the preset optimization number, 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 predicted optimization number, 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 to be optimized.

[0089] In one embodiment, the proportion of battery performance data whose battery performance optimization control results are in a battery-optimizable state can be calculated based on the number of results (dividing the number of results by the number of performance dimensions), and the proportion can be used to finally determine whether the battery needs to be optimized. If the 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 yet reached the level that requires optimization or does not need to be optimized.

[0090] After determining that the hydrogen fuel cell needs to be optimized, the battery performance optimization control platform can optimize the battery parameters. For example, battery optimization measures may include: Load management: By adjusting the load, the battery operates in its optimal performance range to avoid overload or underload. Temperature control: Maintain the battery at the optimal operating temperature to improve the chemical reaction rate and efficiency. Pressure regulation: Optimize the supply pressure 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 demand. For example, in one embodiment, after determining that optimization is required, the energy management system can be started to monitor and adjust the battery's charging and discharging process, and an auxiliary power system can be used in conjunction with supercapacitors or secondary batteries to smooth power output and improve system stability. In addition, it is also possible to switch to high-performance catalysts and optimize electrode structures to improve electrochemical reaction efficiency, use backup electrolyte materials, improve ion conductivity and chemical stability, and so on.

[0091] From the above, it can be seen that the embodiment of the present application provides an optimized control of the performance of hydrogen fuel cells for aircraft, which 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, thereby achieving precise optimized control of hydrogen fuel cell performance, thereby improving the performance of hydrogen fuel cells for aircraft.

[0092] In addition, the embodiments of the present application can extract battery performance change information, data format type information and battery environmental parameter information from the battery performance data to form features, and optimize the battery performance based on the features, thereby enriching the methods of hydrogen fuel cell optimization control and greatly improving the accuracy of hydrogen fuel cell performance optimization control.

[0093] Figure 3 This is a block diagram of a battery performance optimization control device for a hydrogen fuel cell for an aircraft according to an embodiment of the present application. The device is used to execute the above-mentioned battery performance optimization control method, see Figure 4 , the device comprises: An acquisition module 401 is used to acquire battery performance data of at least one dimension of a hydrogen fuel cell for an aircraft in an aircraft flight environment; The detection module 402 is 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 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; A matching module 403 is used to obtain a battery performance impact 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 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; A result generating module 404, configured to generate a battery performance optimization control result of the battery performance data of the at least one dimension according to the at least one battery performance influencing factor corresponding to the battery performance data of the at least one dimension; The performance optimization module 405 is used to perform performance optimization processing on the hydrogen fuel cell performance according to the battery performance optimization control result of the battery performance data in the at least one dimension.

[0094] In one embodiment, reference Figure 4 , the battery performance optimization control device further includes: a key data module 406; The key data module 406 is used to: identify and process the key data of the battery performance data of the 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 including the key data in the battery performance data of the at least one dimension is greater than the preset proportion, then obtain the factor adjustment parameter corresponding to the battery performance data of each dimension according to the preset key information mapping set; The result generation module 404 is used to generate 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.

[0095] The embodiments of the present application provide an optimization control scheme for the performance of hydrogen fuel cells for aircraft. The 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 to achieve precise optimization control of hydrogen fuel cell performance, thereby improving the performance of hydrogen fuel cells for aircraft.

[0096] It should be noted that: the optimization control device provided in the above embodiment only uses the division of the above functional modules as an example when performing optimization control. In actual applications, the above functions can be assigned to different functional modules as needed, 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, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0097] 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 serves as the execution subject to implement the technical solution provided in the embodiments of the present application; when the computer system is configured as a server, the server serves as the execution subject to implement the technical solution provided in the embodiments of the present application; or, the technical solution provided in the present application is implemented through interaction between the terminal and the server, which is not limited in the embodiments of the present application.

[0098] Figure 5 : This is a schematic diagram of the structure of a server (computer system) provided in an embodiment of the present application. The server 500 may have relatively large differences due to different configurations or performances, and may include one or more central processors 521 (for example, one or more processors) and memory 532, and one or more storage media for storing application programs or data (for example, one or more massive storage systems). Among them, the memory 532 and the storage medium may be short-term storage or permanent storage. The program stored in the storage medium may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Furthermore, the central processor 521 may be configured to communicate with the storage medium and execute a series of instruction operations in the storage medium on the server 500.

[0099] The steps performed by the server in the above embodiment can be based on the Figure 5 The 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 above-described systems, devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0100] In the 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 only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0103] 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 this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer system (which can be a personal computer, a server, or a network system, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0104] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the 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; generating a battery performance optimization control result of the battery performance data in the at least one dimension according to at least one battery performance influencing factor corresponding to the battery performance data in the at least one dimension; The hydrogen fuel cell performance is optimized according to the battery 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 The method further comprises: 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 corresponding to the battery performance data in the at least one dimension includes: A performance optimization control result of the battery performance data in the at least one dimension is generated 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.

3. 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 battery performance influencing factor and at least one factor adjustment parameter corresponding to the battery basic 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.

4. The method according to claim 3, 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.

5. The method according to claim 3, 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.

6. The method according to claim 5, characterized in that: The generating 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 battery performance optimization threshold value includes: 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.

7. The method according to claim 6, 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.

8. The method according to any one of claims 1 to 7, 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.

9. 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 result generating module, configured to generate a battery performance optimization control result of the battery performance data of the at least one dimension according to at least one battery performance influencing factor 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 battery performance optimization control result of the battery performance data of the at least one dimension.

10. 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 battery performance optimization control method according to any one of claims 1 to 8.

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