An integrated method for high energy efficiency planning and management based on hydrogen-powered drones

Abstract

The present invention relates to an integrated method for high-energy-efficiency planning and management based on hydrogen-powered UAVs. Aiming at the autonomous flight problem of hydrogen-powered UAVs in a high-dynamic flight environment, firstly, a trajectory planning method based on B-spline curves is used to plan a high-energy-efficiency flight trajectory for the autonomous flight of the UAV; secondly, based on the planned trajectory information, a global optimization method is used to provide a global SOC reference trajectory for subsequent energy management; finally, in the real-time optimization and control of the hydrogen-lithium hybrid system, an extended Kalman filter is used to identify and update the fuel cell model parameters, and an adaptive equivalent hydrogen consumption minimum strategy is used to provide a real-time optimization solution for online energy management, wherein the global SOC reference trajectory is introduced into the equivalent hydrogen consumption factor to provide global guidance. The present invention realizes the high-energy-efficiency integrated optimization of trajectory planning and energy management in hydrogen-powered UAVs, improves energy efficiency, and is suitable for UAVs to perform long-endurance autonomous flight missions in complex environments.

Description

Technical Field

[0001] The present invention relates to an integrated method for high energy efficiency planning and management based on hydrogen energy UAV, which solves the problems of autonomous and safe flight in highly dynamic and complex flight environments, energy-saving optimization and control of hybrid UAVs, and high energy efficiency integration. Background Art

[0002] Drones have been widely used in many fields. However, when performing long-range flight missions such as power inspection, search and reconnaissance, and environmental monitoring, traditional lithium battery-driven drones still have great limitations in terms of range and endurance. The emergence of hydrogen-powered drones provides a better solution for long-flight missions. Currently, hydrogen-powered drones are mainly hybrid drones driven by hydrogen fuel cells and lithium batteries. Hydrogen fuel cells can output high power for a long time, but the power response is slow. Therefore, lithium batteries are usually used as auxiliary power sources to cope with sudden changes in power demand. Compared with traditional drones, hydrogen-powered hybrid drones have significantly improved endurance and can support a variety of long-range flight missions. For existing hydrogen drones, the planning and control methods can be further optimized to improve energy efficiency and reduce energy consumption. Current research focuses on drone trajectory planning and hybrid drive system energy management. When a UAV performs an autonomous flight mission, it is necessary to plan a reference flight trajectory for it in advance. On the one hand, the trajectory must meet basic requirements such as safety and smoothness. On the other hand, the performance of the reference flight trajectory directly affects the energy consumption of the UAV when performing the flight mission. Therefore, on the basis of meeting the feasibility, designing a high-efficiency trajectory planning method for hydrogen-powered UAVs is crucial for long-duration flight of UAVs. The hybrid power system requires an energy management strategy (EMS) to reasonably allocate power to meet the power demand. Global optimization can provide a global optimization solution but cannot cope with real-time sudden power changes. Online optimization can obtain the required power in real time and solve the instantaneous optimal solution. However, the step-by-step execution of trajectory planning and energy management only realizes decoupled optimization, and the planning information is not fully utilized. Therefore, by introducing planning information into the management layer to provide reference guidance, the coordinated optimization of trajectory planning and energy management is achieved, which provides a potential effective solution for further reducing energy consumption and improving energy efficiency.

[0003] At present, the research on trajectory planning mainly focuses on finding a safe and feasible shortest path, and there is less research on the planning of high-efficiency trajectories. The document "Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments" adopts a trajectory planning method based on safe channels and Bezier curves to transform the trajectory shortest time optimization problem into a second-order cone programming problem, which greatly simplifies the solution process of polynomial trajectories. At the same time, current trajectory planning and energy management are usually carried out step by step through decoupling. Patent No. CN201910471374.4 proposes an energy control method for a hybrid propulsion system of a UAV based on flight data. It uses clustering algorithms and BP neural networks to realize power prediction for different flight stages, realizes global planning for different flight stages, and uses particle swarm algorithms to achieve instantaneous optimal control in real-time energy management; however, this method only collects historical flight data for model training, and does not take into account trajectory planning information. Patent No. CN202211181365.X proposes an energy management method for a ground unmanned aerial vehicle platform. The invention designs a complete set of unmanned driving controllers that can realize path planning and energy management, and introduces planning information into energy management, and uses the total mileage of the planned path to realize the correction and charging of the power generation power; however, this method does not use more sufficient trajectory information to guide energy optimization, and does not achieve true collaborative optimization.

[0004] In summary, existing UAVs lack high-energy-efficiency optimization and control capabilities in highly dynamic and complex flight environments and long-duration flight missions. It is urgent to overcome the problem of integrated high-energy-efficiency planning and management of hydrogen-powered UAVs under autonomous flight missions. Summary of the invention

[0005] The technical problem solved by the present invention is: for the energy-saving autonomous flight problem of existing UAVs in highly dynamic and complex flight environments and long-duration flight missions, the shortcomings of the existing technology are overcome, and a high-energy-efficiency planning and management integrated method based on hydrogen-powered UAVs is provided, which realizes the integrated research on trajectory planning and energy management of hydrogen-powered UAVs in complex flight environments, significantly improves energy efficiency and flight time, and can adapt to highly dynamic flight environments, realizing high-energy-efficiency long-duration autonomous flight.

[0006] The technical solution of the present invention is: a high-efficiency planning and management integrated method based on hydrogen-powered UAVs, establishing energy-saving flight trajectory planning based on B-spline curves, and generating a SOC reference trajectory through SOC planning, and using an adaptive equivalent hydrogen consumption minimum strategy in the online optimization control of hydrogen-powered UAVs to achieve coordinated optimization of global reference and real-time energy management. The specific implementation steps are as follows:

[0007] In the first step, we use the trajectory planning method based on B-spline curves to establish flight channel security constraints and establish a cost function that considers trajectory energy efficiency to achieve energy-saving trajectory planning, providing a reference flight trajectory for autonomous flight of the UAV. The specific implementation steps are as follows:

[0008] Establish a trajectory expression based on B-spline curve:

[0009] ,

[0010] in, is the time parameterized trajectory representation, time ,in is the starting time, is the terminal time, position control point vector is selected as the variable to be optimized in the trajectory planner, where the control point is a spatial position point, the basis function of the B-spline curve is the time polynomial to be designed, which can be obtained by the following de Boor-Cox recursive formula:

[0011] ,

[0012] For Basis Functions , is the order of the spline curve. indivual Sub-B-spline basis function , need to use There are p+2 nodes in total, and the n basis functions in the B-spline curve correspond to a set of node vectors ,in , here we use the quasi-uniform node partitioning method to divide the node vector, where , and the remaining nodes are evenly divided.

[0013] Known starting point coordinates With the end point coordinates , establish the equality constraints between the start point and the end point:

[0014] ,

[0015] ,

[0016] Using the convex hull characteristics of the B-spline curve, safety constraints based on the flight corridor are established:

[0017] ,

[0018] The control point Bound to In a convex polyhedron, , , For the convex polyhedron hyperplane coefficients, and Respectively represent the number of hyperplanes and control points in the convex polyhedron, is the number of convex polyhedrons in the flight corridor.

[0019] Similarly, velocity constraints and acceleration constraints can be established:

[0020] ,

[0021] in is the speed control point, For this control point along The coordinates of the direction, are the lower and upper bounds of the velocity constraint, is the acceleration control point, For this control point along The coordinates of the direction, are the lower and upper bounds of the acceleration constraint.

[0022] In order to achieve high energy efficiency trajectory planning and ensure trajectory smoothness, a cost function considering trajectory energy efficiency is established:

[0023] ,

[0024] in is the minimum acceleration function and can be considered as a substitute for the exact trajectory energy, while This is a trajectory smoothing term, and the adjustment factor is introduced this time Balance energy and smoothness requirements, by adjusting Minimize trajectory energy consumption while ensuring trajectory smoothness.

[0025] Based on the derivative properties of the B-spline curve, the above cost function and related constraints can be transformed into the following quadratic programming problem:

[0026] ,

[0027] in is the position control point vector mentioned above, and are the Hessian matrices corresponding to the energy function and the smoothing function, respectively, which can be obtained from the basis function vector. are the coefficient matrices and vectors of equality constraints, are the coefficient matrices and vectors of the inequality constraints.

[0028] In the second step, based on the trajectory information planned in the first step, the power curve of the flight process is obtained, a hydrogen-lithium hybrid system model is established, and a global optimization method is used to obtain a global optimization solution for energy distribution during autonomous flight. The specific implementation steps are as follows:

[0029] First, based on the planned flight trajectory and the dynamics of the quadrotor drone, the power required for the autonomous flight of the drone is estimated:

[0030] ,

[0031] in Power required The estimated value of For the first time in the quadcopter The power of the rotor motor, For the corresponding motor speed, the constant parameter Can be obtained from motor parameters.

[0032] The specific expression for establishing the fuel cell polarization curve model is:

[0033] ,

[0034] in, is the fuel cell output voltage, is the Nernst potential energy, is the reaction activation loss, is the ohmic loss, is the concentration loss, is the number of single-cell batteries in the battery stack, is the stack temperature, and are the pressures of hydrogen and oxygen entering the fuel cell stack, is the stack current, It is a semi-empirical parameter.

[0035] In the global energy allocation, a global SOC planning method is used to plan a globally optimized SOC reference trajectory. First, a cost function is established with the minimum hydrogen consumption in the entire period as the optimization goal. Secondly, constraint conditions are established based on the power device limitations in the hybrid system. Finally, the optimization problem of the energy allocation is as follows:

[0036] ,

[0037] in is the instantaneous hydrogen consumption, and are the power of fuel cell and lithium battery respectively. , , , , For power constraints, the lithium battery charge reference state As a state variable, is the SOC kinetic equation of the lithium battery. The instantaneous hydrogen consumption is ,in is the lower heating value of hydrogen, is the fuel cell efficiency, which can be expressed as Polynomial function of .

[0038] Here, the Pontryagin minimum principle is used to obtain the global optimal solution of the optimization problem. The Hamiltonian function is:

[0039] ,

[0040] in is the co-state factor, and the SOC reference optimization trajectory can be obtained by the following formula:

[0041] ,

[0042] In the third step, when the hydrogen-powered UAV performs online energy management, the extended Kalman filter method (EKF) is used to identify the fuel cell parameters and update the model, and then the adaptive equivalent hydrogen consumption minimum strategy based on the SOC optimized reference trajectory is used to complete the real-time hybrid power system power allocation task. The specific implementation steps are as follows:

[0043] In the above-established fuel cell model, the semi-empirical parameters These parameters are easily affected by the environment, including temperature and altitude. Therefore, in a highly dynamically changing flight environment, the extended Kalman filter method is used to estimate the parameters and update the fuel cell model in real time.

[0044] After obtaining the real-time fuel cell model, an adaptive equivalent hydrogen consumption minimization strategy is designed to perform online energy management. The equivalent hydrogen consumption is , where the equivalent hydrogen consumption of lithium battery is , is the equivalent factor. Based on the fuel cell parameters identified by the extended Kalman filter, the optimization problem of the adaptive equivalent hydrogen consumption minimum strategy can be constructed as follows:

[0045] ,

[0046] Among them, the parameters for:

[0047] ,

[0048] In the adaptive equivalent hydrogen consumption minimization strategy, the SOC reference trajectory is introduced to update the equivalent factor:

[0049] ,

[0050] in For the The equivalent factor of the iteration, The SOC reference trajectory is The sampling value at the time, is the current SOC value, is a positive proportionality coefficient.

[0051] The advantages of the present invention compared with the prior art are: the present invention relates to an integrated method for high-energy-efficiency planning and management based on a hydrogen-powered UAV. Aiming at the defects and shortcomings of existing UAVs in terms of endurance anxiety in highly dynamic and complex flight environments and long-duration flight missions and low energy efficiency in existing decoupled trajectory planning and energy management methods, an energy-saving trajectory planning based on B-spline curves is designed, which provides a safe, feasible and energy-efficient flight trajectory for UAVs, and realizes high-energy-efficiency flight of UAVs in autonomous flight missions; furthermore, an online energy management method based on SOC reference trajectory is designed, which uses the planned trajectory information to generate a globally optimized SOC reference trajectory, providing global guidance for online management, and the use of adaptive online energy management strategies can cope with real-time power mutations and environmental changes.

[0052] Compared with traditional decoupling methods, the integrated method designed in the present invention can cope with complex flight environments, and significantly improves energy efficiency and reduces energy consumption through collaborative optimization of planning and management and coupling of global and online optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a design flow chart of an integrated method for high energy efficiency planning and management based on hydrogen-powered UAVs according to the present invention. DETAILED DESCRIPTION

[0054] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0055] like Figure 1As shown, the present invention relates to an integrated method for high energy efficiency planning and management based on hydrogen energy UAV. In the first step, a trajectory planning method based on B-spline curve is used to establish a flight channel to ensure safety constraints and a cost function considering trajectory energy efficiency to realize energy-saving trajectory planning, so as to provide a reference flight trajectory for autonomous flight of the UAV; in the second step, based on the trajectory information planned in the first step, the power curve of the flight process is obtained, a hydrogen-lithium hybrid power system model is established, and a global optimization method is used to obtain the global optimization solution of energy distribution in autonomous flight, and the global SOC optimized reference trajectory is selected as the reference quantity in subsequent online energy management; in the third step, when the hydrogen energy UAV performs online energy management, the extended Kalman filter method (EKF) is used to identify the fuel cell parameters and update the model, and then the adaptive equivalent hydrogen consumption minimum strategy based on the SOC optimized reference trajectory is used to complete the real-time hybrid power system power distribution task. The integrated method of trajectory planning and energy management designed based on hydrogen energy UAV of the present invention can adapt to complex environments, maintain high efficiency to perform long-term flight tasks, has the characteristics of real-time and high energy efficiency, and is suitable for performing long-duration autonomous flight tasks in complex flight environments.

[0056] The specific implementation steps are as follows:

[0057] In the first step, we use the trajectory planning method based on B-spline curves to establish flight channel security constraints and establish a cost function that considers trajectory energy efficiency to achieve energy-saving trajectory planning, providing a reference flight trajectory for autonomous flight of the UAV. The specific implementation steps are as follows:

[0058] Establish a trajectory expression based on B-spline curve:

[0059] ,

[0060] in, is the time parameterized trajectory representation, time ,in is the starting time, is the terminal time, position control point vector is selected as the variable to be optimized in the trajectory planner, where the control point is a spatial position point, the basis function of the B-spline curve is the time polynomial to be designed, which can be obtained by the following de Boor-Cox recursive formula:

[0061] ,

[0062] For Basis Functions , is the order of the spline curve. indivual Sub-B-spline basis function , need to use There are p+2 nodes in total, and the n basis functions in the B-spline curve correspond to a set of node vectors ,in , here we use the quasi-uniform node partitioning method to divide the node vector, where , and the remaining nodes are evenly divided.

[0063] Known starting point coordinates With the end point coordinates , establish the equality constraints between the start point and the end point:

[0064] ,

[0065] ,

[0066] Using the convex hull characteristics of the B-spline curve, safety constraints based on the flight corridor are established:

[0067] ,

[0068] The control point Bound to In a convex polyhedron, , , For the convex polyhedron hyperplane coefficients, and Respectively represent the number of hyperplanes and control points in the convex polyhedron, is the number of convex polyhedrons in the flight corridor.

[0069] Similarly, velocity constraints and acceleration constraints can be established:

[0070] ,

[0071] in is the speed control point, For this control point along The coordinates of the direction, are the lower and upper bounds of the velocity constraint, is the acceleration control point, For this control point along The coordinates of the direction, are the lower and upper bounds of the acceleration constraint.

[0072] In order to achieve energy-efficient trajectory planning and ensure trajectory smoothness, a cost function that considers trajectory energy efficiency is established:

[0073] ,

[0074] in is the minimum acceleration function and can be considered as a substitute for the exact trajectory energy, while This is a trajectory smoothing term, and the adjustment factor is introduced this time Balance energy and smoothness requirements, by adjusting Minimize trajectory energy consumption while ensuring trajectory smoothness.

[0075] Based on the derivative properties of the B-spline curve, the above cost function and related constraints can be transformed into the following quadratic programming problem:

[0076] ,

[0077] in is the position control point vector mentioned above, and are the Hessian matrices corresponding to the energy function and the smoothing function, respectively, which can be obtained from the basis function vector. are the coefficient matrices and vectors of equality constraints, are the coefficient matrices and vectors of the inequality constraints.

[0078] In the second step, based on the trajectory information planned in the first step, the power curve of the flight process is obtained, a hydrogen-lithium hybrid system model is established, and a global optimization method is used to obtain a global optimization solution for energy distribution during autonomous flight. The specific implementation steps are as follows:

[0079] First, based on the planned flight trajectory and the dynamics of the quadrotor drone, the power required for the autonomous flight of the drone is estimated:

[0080] ,

[0081] in Power required The estimated value of For the first time in the quadcopter The power of the rotor motor, For the corresponding motor speed, the constant parameter Can be obtained from motor parameters.

[0082] The specific expression for establishing the fuel cell polarization curve model is:

[0083] ,

[0084] in, is the fuel cell output voltage, is the Nernst potential energy, is the reaction activation loss, is the ohmic loss, is the concentration loss, is the number of single-cell batteries in the battery stack, is the stack temperature, and are the pressures of hydrogen and oxygen entering the fuel cell stack, is the stack current, It is a semi-empirical parameter.

[0085] In the global energy allocation, a global SOC planning method is used to plan a globally optimized SOC reference trajectory. First, a cost function is established with the minimum hydrogen consumption in the entire period as the optimization goal. Secondly, constraint conditions are established based on the power device limitations in the hybrid system. Finally, the optimization problem of the energy allocation is as follows:

[0086] ,

[0087] in is the instantaneous hydrogen consumption, and are the power of fuel cell and lithium battery respectively. , , , , For power constraints, the lithium battery charge reference state As a state variable, is the SOC kinetic equation of the lithium battery. The instantaneous hydrogen consumption is ,in is the lower heating value of hydrogen, is the fuel cell efficiency, which can be expressed as Polynomial function of .

[0088] Here, the Pontryagin minimum principle is used to obtain the global optimal solution of the optimization problem. The Hamiltonian function is:

[0089] ,

[0090] in is the co-state factor, and the SOC reference optimization trajectory can be obtained by the following formula:

[0091] ,

[0092] In the third step, when the hydrogen-powered UAV performs online energy management, the extended Kalman filter method (EKF) is used to identify the fuel cell parameters and update the model, and then the adaptive equivalent hydrogen consumption minimum strategy based on the SOC optimized reference trajectory is used to complete the real-time hybrid power system power allocation task. The specific implementation steps are as follows:

[0093] In the above-established fuel cell model, the semi-empirical parameters These parameters are easily affected by the environment, including temperature and altitude. Therefore, in a highly dynamically changing flight environment, the extended Kalman filter method is used to estimate the parameters and update the fuel cell model in real time.

[0094] After obtaining the real-time fuel cell model, an adaptive equivalent hydrogen consumption minimization strategy is designed to perform online energy management. The equivalent hydrogen consumption is , where the equivalent hydrogen consumption of lithium battery is , is the equivalent factor. Based on the fuel cell parameters identified by the extended Kalman filter, the optimization problem of the adaptive equivalent hydrogen consumption minimum strategy can be constructed as follows:

[0095] ,

[0096] Among them, the parameters for:

[0097] ,

[0098] In the adaptive equivalent hydrogen consumption minimization strategy, the SOC reference trajectory is introduced to update the equivalent factor:

[0099] ,

[0100] in For the The equivalent factor of the iteration, The SOC reference trajectory is The sampling value at the time, is the current SOC value, is a positive proportionality coefficient.

[0101] The method of the present invention can be used to perform long-duration flight missions, achieve high-efficiency trajectories and energy management in a variety of complex environments, adapt to high-dynamic flight environments, and provide stable and energy-saving optimization guidance for UAV flight control. Compared with the traditional decoupling method, it can cope with more complex environmental changes and reduce equivalent hydrogen consumption by more than 10%, meeting the requirements of high energy efficiency and real-time performance.

[0102] The contents not described in detail in the specification of the present invention belong to the prior art known to the professional and technical personnel in this field.

Claims

1. A high-efficiency planning and management integrated method based on hydrogen-powered drones, characterized in that: The following steps are involved: In the first step, a trajectory planning method based on B-spline curves is used to establish flight channel safety constraints and a cost function that considers trajectory energy efficiency to achieve energy-saving trajectory planning, providing a reference flight trajectory for autonomous flight of UAVs. In the second step, based on the trajectory information planned in the first step, the power curve of the flight process is obtained, a hydrogen-lithium hybrid system model is established, and a global optimization method is used to obtain the global optimization solution for energy distribution during autonomous flight. The global SOC optimization reference trajectory is selected as the reference quantity in subsequent online energy management. In the third step, when the hydrogen-powered UAV performs online energy management, the extended Kalman filter method (EKF) is used to identify the fuel cell parameters and update the model, and then the adaptive equivalent hydrogen consumption minimum strategy based on the SOC optimized reference trajectory is used to complete the real-time hybrid power system power allocation task; In the first step, a trajectory planning method based on B-spline curves is used to establish a flight channel to ensure safety constraints and a cost function that considers trajectory energy efficiency to achieve energy-saving trajectory planning, providing a reference flight trajectory for autonomous flight of the UAV. The specific implementation steps are as follows: Establish a trajectory expression based on B-spline curve: ， in, is the time parameterized trajectory representation, time ,in is the starting time, is the terminal time, position control point vector is selected as the variable to be optimized in the trajectory planner, where the control point is a spatial position point, the basis function of the B-spline curve is the time polynomial to be designed, which can be obtained by the following de Boor-Cox recursive formula: ， For Basis Functions , is the order of the spline curve. indivual Sub-B-spline basis function , need to use There are p+2 nodes in total, and the n basis functions in the B-spline curve correspond to a set of node vectors ,in , here we use the quasi-uniform node partitioning method to divide the node vector, where , the remaining nodes are evenly divided; Known starting point coordinates With the end point coordinates , establish the equality constraints between the start point and the end point: ， ， Using the convex hull characteristics of the B-spline curve, safety constraints based on the flight corridor are established: ， The control point Bound to In a convex polyhedron, , , For the convex polyhedron hyperplane coefficients, and Respectively represent the number of hyperplanes and control points in the convex polyhedron, Then is the number of convex polyhedrons in the flight corridor; Similarly, velocity constraints and acceleration constraints can be established: ， in is the speed control point, is the speed control point, The coordinates of the direction, The lower and upper bounds of the speed constraint, is the acceleration control point, For this control point along The coordinates of the direction, are the lower and upper bounds of the acceleration constraint; In order to achieve high energy efficiency trajectory planning and ensure trajectory smoothness, a cost function considering trajectory energy efficiency is established: ， in is the minimum acceleration function and can be considered as a substitute for the exact trajectory energy, while This is a trajectory smoothing term, and the adjustment factor is introduced this time Balance energy and smoothness requirements, by adjusting Minimize trajectory energy consumption while ensuring trajectory smoothness; Based on the derivative properties of the B-spline curve, the above cost function and related constraints can be transformed into the following quadratic programming problem: ， in is the position control point vector mentioned above, and are the Hessian matrices corresponding to the energy function and the smoothing function, respectively, which can be obtained from the basis function vector. are the coefficient matrices and vectors of equality constraints, is the coefficient matrix and vector of inequality constraints; In the third step, when the hydrogen-powered UAV performs online energy management, the extended Kalman filter method EKF is used to identify the fuel cell parameters and update the model, and then the adaptive equivalent hydrogen consumption minimum strategy based on the SOC optimized reference trajectory is used to complete the real-time hybrid power system power allocation task. The specific implementation steps are as follows: In the above-established fuel cell model, the semi-empirical parameters These parameters are easily affected by the environment, including temperature and altitude. Therefore, in a highly dynamic flight environment, the extended Kalman filter method is used to estimate the parameters and update the fuel cell model in real time. After obtaining the real-time fuel cell model, an adaptive equivalent hydrogen consumption minimization strategy is designed to perform online energy management. The equivalent hydrogen consumption is , where the equivalent hydrogen consumption of lithium battery is , is the equivalent factor; based on the fuel cell parameters identified by the extended Kalman filter, the optimization problem of the adaptive equivalent hydrogen consumption minimum strategy can be constructed as follows: ， Among them, the parameters for: ， In the adaptive equivalent hydrogen consumption minimization strategy, the SOC reference trajectory is introduced to update the equivalent factor: ， in For the The equivalent factor of the iteration, The SOC reference trajectory is The sampling value at the time, is the current SOC value, is a positive proportionality coefficient.

2. According to claim 1, a high-efficiency planning and management integrated method based on hydrogen-powered drones is characterized by: In the second step, based on the trajectory information planned in the first step, the power curve of the flight process is obtained, a hydrogen-lithium hybrid power system model is established, and a global optimization method is used to obtain a global optimization solution for energy distribution during autonomous flight; the specific implementation steps are as follows: First, based on the planned flight trajectory and the dynamics of the quadrotor drone, the power required for the autonomous flight of the drone is estimated: ， in Power required The estimated value of For the first time in the quadcopter The power of the rotor motor, For the corresponding motor speed, the constant parameter Can be obtained from motor parameters; The specific expression for establishing the fuel cell polarization curve model is: ， in, is the fuel cell output voltage, is the Nernst potential energy, is the reaction activation loss, is the ohmic loss, is the concentration loss, is the number of single-cell batteries in the battery stack, is the stack temperature, and are the pressures of hydrogen and oxygen entering the fuel cell stack, is the stack current, is a semi-empirical parameter; In the global energy allocation, a global SOC planning method is used to plan a globally optimized SOC reference trajectory. First, a cost function is established with the minimum hydrogen consumption in the entire period as the optimization goal. Secondly, constraint conditions are established based on the power device limitations in the hybrid system. Finally, the optimization problem of the energy allocation is as follows: ， in is the instantaneous hydrogen consumption, and are the power of fuel cell and lithium battery respectively. , , , , For power constraints, the lithium battery charge reference state As a state variable, is the SOC kinetic equation of the lithium battery; the instantaneous hydrogen consumption is ,in is the lower heating value of hydrogen, is the fuel cell efficiency, which can be expressed as Polynomial function of ; By solving the state quantity in the above optimization problem , and obtain the SOC optimization reference trajectory.

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