Hydrogen fuel hybrid power unmanned aerial vehicle energy management method giving consideration to rapidity and energy conservation

Abstract

The invention discloses a hydrogen fuel hybrid power unmanned aerial vehicle energy management method considering rapidity and energy saving, and relates to the technical field of hydrogen fuel hybrid power unmanned aerial vehicle energy management. The method comprises the following steps: establishing an unmanned aerial vehicle demand power and battery SOC model; generating an unmanned aerial vehicle control parameter fuzzy logic rule; and solving the newest position and speed through a particle swarm optimization algorithm to improve the energy efficiency and balance electric quantity of the unmanned aerial vehicle. The unmanned aerial vehicle demand power model and the battery SOC model are constructed, and the membership function and the fuzzy rule of the fuzzy controller are updated according to the parameters optimized by the PSO algorithm, so that the response of the system to the input is adjusted, the control output is optimized, and the effective control and management of the dynamic system are realized. The fuzzy logic controller can better adapt to system changes, and control precision and system performance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen fuel hybrid power UAV energy management, and in particular to a hydrogen fuel hybrid power UAV energy management method that takes into account both rapidity and energy saving. Background Art

[0002] Hydrogen-fueled hybrid drones are increasingly being used due to their lightweight and high energy density. Hybrid systems complicate power distribution and energy flow within the drone. Energy management is crucial for improving overall drone economics and extending the life of power components.

[0003] The energy management strategies of hybrid power systems are divided into rule-based, optimization-based, equivalent factor-based and learning-based strategies. However, rule-based energy management strategies usually require certain expert experience, and the strategies required for different scenarios or working conditions are designed based on experience; optimization-based energy management strategies usually construct a cost function according to the response of the system to be tested to the working conditions, and optimize certain goals under constraints, such as reducing fuel consumption and accelerating power response; equivalent factor-based strategies require more accurate mathematical models, and optimize power distribution by constructing an equivalent factor of hydrogen consumption; learning-based energy management strategies have good adaptability, such as those based on deep deterministic policy gradient (DDPG) strategies, but there is a problem of over-estimation of action values. For this reason, the present invention proposes a hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving. Summary of the Invention

[0004] The purpose of the present invention is to provide a hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving. By constructing a UAV demand power model and a battery SOC model, and updating the membership function and fuzzy rules of the fuzzy controller according to the parameters optimized by the PSO algorithm, the system response to the input is adjusted, the control output is optimized, and effective control and management of the dynamic system is achieved.

[0005] According to a first aspect of the present invention, to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for energy management of a hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving, comprising the following steps:

[0006] Build the UAV power demand model and battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and battery SOC model;

[0007] Construct a fuzzy logic controller, set fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC, and perform fuzzification and defuzzification processing through membership functions to control the output power of the UAV's fuel cell and power battery;

[0008] The optimal position and speed are solved by the particle swarm optimization algorithm, the membership function and fuzzy rules of the fuzzy controller are iteratively updated, the output power of the UAV fuel cell and power battery is optimized, and the final optimized fuzzy logic controller parameters are obtained.

[0009] Furthermore, the UAV power demand model and battery SOC model are constructed, and the required power and battery SOC are obtained through the UAV power demand model and battery SOC model, as follows:

[0010] The required power is obtained through dynamic analysis of the UAV flight process, and the required power is allocated to the fuel cell and power battery, thereby obtaining the battery SOC change model;

[0011] The long-flight operating condition is constructed according to actual flight requirements and is divided into five stages: slow taxiing stage, take-off stage, climb stage, cruise flight stage and deceleration landing stage;

[0012] According to the UAV power analysis, the different attack angles and trajectory angles at each stage are obtained to establish the required power function. The lift L and drag D are expressed as:

[0013]

[0014] Where ρ represents the air density at the current flight altitude, V is the relative flight speed, S W Represents the wing area, C L represents the lift coefficient, C D represents the drag coefficient;

[0015] During the flight of the aircraft, the horizontal and vertical forces acting on it are as follows:

[0016] Tsinα+L=mgcosγ

[0017] Tcosα-D-mgsinγ=ma

[0018] Where L represents the lift generated by different components in the wind axis reference frame; D represents the drag experienced by the aircraft; T represents the thrust given to the aircraft by the propeller; α is the angle of attack; γ is the orbital angle;

[0019] The propulsion power of the aircraft during the climb phase is expressed as:

[0020] P prop =TV

[0021] The battery SOC is calculated based on the open circuit voltage, internal resistance and current of the power battery, and the SOC change model is established. The battery SOC is expressed as:

[0022]

[0023] Where SOC new is the current battery SOC, I ess is the battery current, Q ess is the capacity of the battery pack.

[0024] Furthermore, according to the fuzzy logic strategy, the two inputs of the fuzzy logic controller are the power battery SOC and the required power of the UAV power system for the fuel cell, and the output is a variable value based on the required power, where the power battery SOC is described using 5 fuzzy sets, the required power is described using 3 fuzzy sets, and the variable value of the required power is described using 4 fuzzy sets.

[0025] Furthermore, a fuzzy logic controller is constructed to set fuzzy variables and fuzzy rules according to the required power of the UAV and the battery SOC, and fuzzification and defuzzification are performed through the membership function to control the output power of the UAV fuel cell and power battery. The details are as follows:

[0026] (41) Fuzzy sets are used to describe input and output respectively, and Gaussian distribution function is used for membership mapping. The Gaussian membership function is determined by two parameters σ and c:

[0027]

[0028] Where x is the input variable, σ is positive and refers to the standard deviation of the normal distribution, and the parameter c refers to the mean of the normal distribution, which is used to determine the center of the curve;

[0029] (42) The fuzzy control rules use AND-OR and IF-THEN logical operations. Let A be the fuzzy set of battery SOC, B be the fuzzy set of required power, and C be the fuzzy set of required power variable values. The relationship between A, B, and C is expressed as If A AND B then C, thus obtaining the ternary fuzzy relationship matrix R:

[0030] R = (A × B) T1 ×C

[0031] Thus the fuzzy set of output quantity C is obtained:

[0032] C = (A × B) T2 ×R

[0033] Among them, T2 is the row vector transformation;

[0034] (43) The battery charge and discharge are determined based on the battery SOC and the required power (low, medium, or high). When performing defuzzification, the centroid algorithm is used to calculate the centroid of the fuzzy set and the membership function graph to obtain the accurate output value, as follows:

[0035] Assume that the graph corresponding to the aggregate fuzzy set is a plate with uniform thickness and density. Find a straight line perpendicular to the x-axis on this plate so that the plate is in equilibrium when placed on the line. The x-axis coordinate of the line is the centroid of the function corresponding to the aggregate fuzzy set. The basic steps of the centroid algorithm are as follows:

[0036] (43.1) Initialization: Select the initial number of categories and category centroids;

[0037] (43.2) Traverse each pixel in the image, calculate the distance between this pixel and each centroid, and classify this pixel into the category of the centroid closest to it;

[0038] (43.3) Update the centroid of each category: Calculate the average color of the pixels of each category as the new centroid of this category;

[0039] (43.4) Repeat steps (43.2) and (43.3) until the class centroid no longer changes or the maximum number of iterations is reached.

[0040] Furthermore, the particle swarm optimization algorithm is used to solve the optimal position and speed, and the membership function and fuzzy rules of the fuzzy controller are iteratively updated to obtain the final optimized fuzzy logic controller parameters, as follows:

[0041] (51) Initialize the particle's position x i and speed v i , the velocity update is expressed as:

[0042] v i+1 =ωv i +c1r1(p i -x i )+c2r2(p g -x i )

[0043] The location update is expressed as:

[0044] x i+1 =x i +v i+1

[0045] Where x is the position of the particle; v is the velocity of the particle; ω is the inertia weight, which controls the impact of the previous velocity at the current velocity; c1 and c2 are positive constants; r1 and r2 are random numbers in the interval [0, 1].

[0046] (52) Set the maximum number of iterations k, and update the objective function value J at the kth iteration. new , calculate the change in the objective function value ΔJ=|J new -J previous|, if ΔJ is less than the convergence threshold, the convergence condition is met and the iteration is stopped. At this time, the final optimized fuzzy logic controller parameters are obtained.

[0047] According to a second aspect of the present invention, the present invention provides a hydrogen fuel hybrid UAV energy management system that takes into account both rapidity and energy saving, which is used to implement the above-mentioned hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving, comprising:

[0048] The first building module is used to build a UAV power demand model and a battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and the battery SOC model;

[0049] The second building block is used to build a fuzzy logic controller, which sets fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC, and performs fuzzification and defuzzification processing through membership functions to control the output power of the UAV's fuel cell and power battery;

[0050] The optimization output module is used to solve the optimal position and speed through the particle swarm optimization algorithm, iteratively update the membership function and fuzzy rules of the fuzzy controller, optimize the output power of the UAV fuel cell and power battery, and obtain the final optimized fuzzy logic controller parameters.

[0051] According to the third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, it adopts the above-mentioned hydrogen fuel hybrid drone energy management method that takes into account both speed and energy saving.

[0052] According to a fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving.

[0053] The present invention has at least the following beneficial effects:

[0054] 1. The present invention converts precise values ​​into fuzzy values ​​through fuzzy processing methods, and uses fuzzy rules to express the relationship between input and output, making the strategy more flexible, improving the robustness of the system, and effectively dealing with the uncertainty of UAV power requirements.

[0055] 2. The present invention uses a particle swarm algorithm (PSO) for optimization based on a fuzzy rule strategy. Particles fly in the search space and track two "extreme values" to update their own position and velocity. The PSO algorithm automatically adjusts the key parameters of the fuzzy logic controller, achieving more accurate and adaptive energy distribution, thereby improving the hydrogen fuel economy of the system.

[0056] 4. The present invention reduces energy pressure and improves DC bus voltage stability, thereby ensuring the service life of the fuel cell and battery to a certain extent.

[0057] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of the energy management method of the present invention;

[0059] Figure 2 This is a schematic diagram of the framework of the energy management method of the present invention;

[0060] Figure 3 Schematic diagram of the structure of the UAV power system of the present invention;

[0061] Figure 4 The input and output fuzzy sets and membership function relationships of the fuzzy logic controller in the present invention, where (a) represents the battery SOC, (b) represents the required power, and (c) represents the variable value of the required power, that is, the required variable of the power battery. DETAILED DESCRIPTION

[0062] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0063] SOC (State of Charge): refers to the state of charge of the battery, that is, the percentage of remaining power to total capacity, which is used to reflect the current power level of the battery.

[0064] See also Figure 1-Figure 2 The present invention provides a technical solution: a hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving, comprising the following steps:

[0065] S1. Build a UAV power demand model and a battery SOC model, and use these models to obtain the required power and battery SOC, as follows:

[0066] The required power is obtained through dynamic analysis of the UAV flight process, and the required power is allocated to the fuel cell and power battery, thereby obtaining the battery SOC change model;

[0067] The long-flight operating condition is constructed according to actual flight requirements and is divided into five stages: slow taxiing stage, take-off stage, climb stage, cruise flight stage and deceleration landing stage;

[0068] like Figure 3 As shown in the figure, based on the UAV power analysis, the different attack angles and trajectory angles at each stage are used to establish the required power function. The lift L and drag D are expressed as:

[0069]

[0070] Where ρ represents the air density at the current flight altitude, V is the relative flight speed, S W Represents the wing area, C L represents the lift coefficient, C D represents the drag coefficient;

[0071] During the flight of the aircraft, the horizontal and vertical forces acting on it are as follows:

[0072] Tsinα+L=mgcosγ

[0073] Tcosα-D-mgsinγ=ma

[0074] Where L represents the lift generated by different components in the wind axis reference frame; D represents the drag experienced by the aircraft; T represents the thrust given to the aircraft by the propeller; α is the angle of attack; γ is the orbital angle;

[0075] The propulsion power of the aircraft during the climb phase is expressed as:

[0076] P prop =TV

[0077] The battery SOC is calculated based on the open circuit voltage, internal resistance and current of the power battery, and the SOC change model is established. The battery SOC is expressed as:

[0078]

[0079] Where SOC new is the current battery SOC, I ess is the battery current, Q ess is the capacity of the battery pack;

[0080] S2. Construct a fuzzy logic controller to set fuzzy variables and fuzzy rules based on the UAV's required power and battery SOC. Fuzzification and defuzzification are then performed using membership functions to control the output power of the UAV's fuel cell and power battery. The details are as follows:

[0081] like Figure 4 As shown in the figure, according to the fuzzy logic strategy, the two inputs of the fuzzy logic controller are the power battery SOC and the power demanded by the UAV power system for the fuel cell, and the output is a variable value based on the power demand. The power battery SOC is described by 5 fuzzy sets, the power demand is described by 3 fuzzy sets, and the variable value of the power demand is described by 4 fuzzy sets.

[0082] (S21) Fuzzy sets are used to describe input and output respectively, and Gaussian distribution function is used for membership mapping. The Gaussian membership function is determined by two parameters σ and c:

[0083]

[0084] Where x is the input variable, σ is positive and refers to the standard deviation of the normal distribution, and the parameter c refers to the mean of the normal distribution, which is used to determine the center of the curve;

[0085] (S22) The fuzzy control rules use AND-OR and IF-THEN logical operations. Let A be the fuzzy set of battery SOC, B be the fuzzy set of required power, and C be the fuzzy set of required power variable values. The relationship between A, B, and C is expressed as If A AND B then C, thus obtaining the ternary fuzzy relationship matrix R:

[0086] R = (A × B) T1 ×C

[0087] Thus the fuzzy set of output quantity C is obtained:

[0088] C = (A × B) T2 ×R

[0089] Among them, T2 is the row vector transformation;

[0090] (S23) The battery charge and discharge are determined based on the battery SOC and the required power (low, medium, or high). When performing the defuzzification process, a centroid algorithm is used to calculate the centroid of the fuzzy set and the membership function graph to obtain an accurate output value, as follows:

[0091] Assume that the graph corresponding to the aggregate fuzzy set is a plate with uniform thickness and density. Find a straight line perpendicular to the x-axis on this plate so that the plate is in equilibrium when placed on the line. The x-axis coordinate of the line is the centroid of the function corresponding to the aggregate fuzzy set. The basic steps of the centroid algorithm are as follows:

[0092] (S23.1) Initialization: Select the initial number of categories and category centroids;

[0093] (S23.2) traverse each pixel in the image, calculate the distance between this pixel and each centroid, and classify this pixel into the category of the centroid closest to it;

[0094] (S23.3) Update the centroid of each category: calculate the average color of the pixels of each category as the new centroid of this category;

[0095] (S23.4) Repeat steps (S23.2) and (S23.3) until the class centroid no longer changes or the maximum number of iterations is reached;

[0096] It should be noted that each rule of the fuzzy control rule has equal weight. The algorithm used for fuzzy set aggregation is the maximum (max) algorithm. The centroid algorithm is used for defuzzification. Each pixel point is traversed and its weighted sum in the x and y directions is calculated. Finally, the centroid coordinates are obtained by dividing the sum:

[0097] y=y+D(m,n)×m

[0098] x=x+D(m,n)×n

[0099] sum=sum+D(m,n)

[0100]

[0101]

[0102] Among them, (m,n) represents the pixel coordinates, and D(m,n) represents the pixel size;

[0103] S3. The optimal position and speed are solved using the particle swarm optimization (PSO) algorithm. The membership function and fuzzy rules of the fuzzy controller are iteratively updated to optimize the output power of the UAV fuel cell and power battery. The final optimized fuzzy logic controller parameters are obtained:

[0104] (S31) Initialize the particle's position x i and speed v i , the velocity update is expressed as:

[0105] v i+1 =ωv i +c1r1(p i -x i )+c2r2(p g -x i )

[0106] The location update is expressed as:

[0107] x i+1 =x i +v i+1

[0108] Where x is the position of the particle; v is the velocity of the particle; ω is the inertia weight, which controls the impact of the previous velocity on the current velocity; c1 and c2 are positive constants, which control the social and individual behavior of each particle; r1 and r2 are random numbers in the interval [0, 1], which help to broaden the exploration of the problem search space;

[0109] (S32) Set the maximum number of iterations k, and update the objective function value J at the kth iteration. new , calculate the change in the objective function value ΔJ=|J new -J previous |, if ΔJ is less than the convergence threshold, the convergence condition is met and the iteration is stopped. At this time, the final optimized fuzzy logic controller parameters are obtained.

[0110] In summary, the present invention constructs a UAV power demand model and a battery SOC model, and updates the membership function and fuzzy rules of the fuzzy controller according to the parameters optimized by the PSO algorithm, thereby adjusting the system's response to the input, optimizing the control output, and realizing effective control and management of the dynamic system.

[0111] Example 2:

[0112] This embodiment provides a hydrogen fuel hybrid UAV energy management system that takes into account both rapidity and energy conservation, and is used to implement the aforementioned hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy conservation, including:

[0113] The first building module is used to build a UAV power demand model and a battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and the battery SOC model;

[0114] The second building block is used to build a fuzzy logic controller, which sets fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC, and performs fuzzification and defuzzification processing through membership functions to control the output power of the UAV's fuel cell and power battery;

[0115] The optimization output module is used to solve the optimal position and speed through the particle swarm optimization algorithm, iteratively update the membership function and fuzzy rules of the fuzzy controller, optimize the output power of the UAV fuel cell and power battery, and obtain the final optimized fuzzy logic controller parameters.

[0116] Specifically, the above-mentioned first building module, second building module and optimization output module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of energy optimization management of the hydrogen fuel hybrid UAV based on the above-mentioned hydrogen fuel hybrid UAV energy management method that takes into account both speed and energy saving; the above-mentioned first building module, second building module and optimization output module can perform operations according to the specific steps given in the hydrogen fuel hybrid UAV energy management method that takes into account both speed and energy saving.

[0117] It should be noted that it should be understood that the division of the various modules of the above system is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by processing elements; or all be implemented in the form of hardware; or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the shared remote driving system building module can be a separately established processing element, or it can be integrated into a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device to perform the functions of the above-mentioned signal processing module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or software instructions.

[0118] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0119] Example 3:

[0120] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, the above-mentioned hydrogen fuel hybrid unmanned aerial vehicle energy management method that takes into account both rapidity and energy saving is adopted.

[0121] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses, etc.

[0122] Furthermore, the processor may adopt a central processing unit (CPU). Of course, depending on the actual usage, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0123] Example 4:

[0124] The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to implement the above-mentioned hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving.

[0125] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that computer-readable medium includes but is not limited to the above-mentioned components.

[0126] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0127] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can be a central element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there can be a central element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only embodiment.

[0128] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0129] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

1. A hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving, characterized by: The following steps are involved: Build the UAV power demand model and battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and battery SOC model; Construct a fuzzy logic controller, set fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC, and perform fuzzification and defuzzification processing through membership functions to control the output power of the UAV's fuel cell and power battery; The optimal position and speed are solved by the particle swarm optimization algorithm, the membership function and fuzzy rules of the fuzzy controller are iteratively updated, the output power of the UAV fuel cell and power battery is optimized, and the final optimized fuzzy logic controller parameters are obtained.

2. The energy management method for hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving according to claim 1 is characterized in that: Construct the UAV power demand model and battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and battery SOC model, as follows: The required power is obtained through dynamic analysis of the UAV flight process, and the required power is allocated to the fuel cell and power battery, thereby obtaining the battery SOC change model; The long-flight operating condition is constructed according to actual flight requirements and is divided into five stages: slow taxiing stage, take-off stage, climb stage, cruise flight stage and deceleration landing stage; According to the UAV power analysis, the different attack angles and trajectory angles at each stage are obtained to establish the required power function. The lift L and drag D are expressed as: Where ρ represents the air density at the current flight altitude, V is the relative flight speed, S W Represents the wing area, C L represents the lift coefficient, C D represents the drag coefficient; During the flight of the aircraft, the horizontal and vertical forces acting on it are as follows: Tsinα+L=mgcosγ Tcosα-D-mgsinγ=ma Where L represents the lift generated by different components in the wind axis reference frame; D represents the drag experienced by the aircraft; T represents the thrust given to the aircraft by the propeller; α is the angle of attack; γ is the orbital angle; The propulsion power of the aircraft during the climb phase is expressed as: P prop =TV The battery SOC is calculated based on the open circuit voltage, internal resistance and current of the power battery, and the SOC change model is established. The battery SOC is expressed as: Where SOC new is the current battery SOC, I ess is the battery current, Q ess is the capacity of the battery pack.

3. The energy management method for hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving according to claim 2 is characterized in that: According to the fuzzy logic strategy, the two inputs of the fuzzy logic controller are the power battery SOC and the power demanded by the UAV power system for the fuel cell, and the output is a variable value based on the power demand. The power battery SOC is described by 5 fuzzy sets, the power demand is described by 3 fuzzy sets, and the variable value of the power demand is described by 4 fuzzy sets.

4. The energy management method for hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving according to claim 3 is characterized in that: A fuzzy logic controller is constructed to set fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC. Fuzzification and defuzzification are then performed through membership functions to control the output power of the UAV's fuel cell and power battery. The details are as follows: (41) Fuzzy sets are used to describe input and output respectively, and Gaussian distribution function is used for membership mapping. The Gaussian membership function is determined by two parameters σ and c: Where x is the input variable, σ is positive and refers to the standard deviation of the normal distribution, and the parameter c refers to the mean of the normal distribution, which is used to determine the center of the curve; (42) The fuzzy control rules use AND-OR and IF-THEN logical operations. Let A be the fuzzy set of battery SOC, B be the fuzzy set of required power, and C be the fuzzy set of required power variable values. The relationship between A, B, and C is expressed as If A AND B then C. The ternary fuzzy relationship matrix R is obtained: R=(A×B) T1 ×C Thus the fuzzy set of output quantity C is obtained: C=(A×B) T2 ×R Among them, T2 is the row vector transformation; (43) The battery charge and discharge are determined based on the battery SOC and the required power (low, medium, or high). When performing defuzzification, the centroid algorithm is used to calculate the centroid of the fuzzy set and the membership function graph to obtain the accurate output value, as follows: Assume that the graph corresponding to the aggregate fuzzy set is a plate with uniform thickness and density. Find a straight line perpendicular to the x-axis on this plate so that the plate is in equilibrium when placed on the line. The x-axis coordinate of the line is the centroid of the function corresponding to the aggregate fuzzy set. The basic steps of the centroid algorithm are as follows: (43.1) Initialization: Select the initial number of categories and category centroids; (43.2) Traverse each pixel in the image, calculate the distance between this pixel and each centroid, and classify this pixel into the category of the centroid closest to it; (43.3) Update the centroid of each category: Calculate the average color of the pixels of each category as the new centroid of this category; (43.4) Repeat steps (43.2) and (43.3) until the class centroid no longer changes or the maximum number of iterations is reached.

5. The energy management method for hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving according to claim 4 is characterized in that: The optimal position and speed are solved by particle swarm optimization algorithm, and the membership function and fuzzy rules of the fuzzy controller are iteratively updated to obtain the final optimized fuzzy logic controller parameters, as follows: (51) Initialize the particle's position x i and speed v i , the velocity update is expressed as: v i+1 =ωv i +c1r1(p i -x i )+c2r2(p g -x i ) The location update is expressed as: x i+1 =x i +v i+1 Where x is the position of the particle; v is the velocity of the particle; ω is the inertia weight, which controls the impact of the previous velocity at the current velocity; c1 and c2 are positive constants; r1 and r2 are random numbers in the interval [0, 1]. (52) Set the maximum number of iterations k, and update the objective function value J at the kth iteration. new , calculate the change in the objective function value ΔJ=|J new -J previous |, if ΔJ is less than the convergence threshold, the convergence condition is met and the iteration is stopped. At this time, the final optimized fuzzy logic controller parameters are obtained.

6. A hydrogen fuel hybrid UAV energy management system that takes into account both rapidity and energy saving, used to implement the hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving according to any one of claims 1 to 5, characterized in that: include: The first building module is used to build a UAV power demand model and a battery SOC model, and obtain the required power and battery SOC size through the UAV power demand model and the battery SOC model; The second building block is used to build a fuzzy logic controller, which sets fuzzy variables and fuzzy rules according to the UAV's required power and battery SOC, and performs fuzzification and defuzzification processing through membership functions to control the output power of the UAV's fuel cell and power battery; The optimization output module is used to solve the optimal position and speed through the particle swarm optimization algorithm, iteratively update the membership function and fuzzy rules of the fuzzy controller, optimize the output power of the UAV fuel cell and power battery, and obtain the final optimized fuzzy logic controller parameters.

7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the energy management method for hydrogen fuel hybrid UAV that takes into account both rapidity and energy saving as described in any one of claims 1 to 5 is adopted.

8. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to perform the hydrogen fuel hybrid UAV energy management method that takes into account both rapidity and energy saving as described in any one of claims 1 to 5.

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