An Energy Management Method for a Hydrogen Fuel Hybrid UAV with OPSA-ECMS

Abstract

The present invention discloses an energy management method for a hydrogen fuel hybrid unmanned aerial vehicle of OPSA-ECMS. The management method includes establishing a hybrid power system model of the unmanned aerial vehicle with a hydrogen fuel cell as the main power source and a lithium-ion battery as the auxiliary power source, and building an energy management system model. Combining OPSA and ECMS, with the minimum hydrogen consumption and the highest efficiency as the optimization objectives, under the known predetermined flight path, OPSA is used for offline calculation of the optimal reference energy distribution scheme, and during the execution of the task by the unmanned aerial vehicle, ECMS is used for online optimization of the reference energy distribution scheme, so as to achieve the optimal energy distribution of the system. The present invention combines the offline calculation of OPSA and the online optimization of ECMS, maximally exerts the high energy density of hydrogen fuel and the fast response characteristics of the battery, and improves the overall energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to an energy management method for a hydrogen fuel hybrid unmanned aerial vehicle of OPSA-ECMS. Background Art

[0002] In recent years, unmanned aerial vehicles (UAVs) and their related technologies have witnessed rapid development, and their application scope has been gradually expanding. Especially, rotor UAVs have become the mainstream models in the market due to their simple structure, convenient operation, and relatively low cost. With technological progress, rotor UAVs are widely used in multiple fields such as agricultural plant protection, power inspection, disaster relief, logistics transportation, news reporting, wildlife observation, and film shooting. However, currently, most commercial rotor UAVs mainly rely on lithium-ion batteries as the sole energy source. Limited by the relatively low energy density of lithium-ion batteries, such UAVs have deficiencies in flight endurance. In addition, although increasing the battery capacity can extend the endurance time, the mass of the battery increases accordingly, which instead weakens the endurance gain. Especially in long-duration or large-scale tasks (such as inspection tasks), UAVs powered by traditional batteries need to be frequently charged or have their batteries replaced, seriously affecting the continuity and efficiency of the tasks. This problem significantly restricts the application of rotor UAVs in more complex or continuous task scenarios.

[0003] Hydrogen fuel cells have attracted much attention due to their efficient process of directly converting chemical energy into electrical energy. Their output products are only water and heat, and they have characteristics such as high energy density and environmental friendliness. Compared with lithium-ion batteries, hydrogen fuel cells can significantly improve the endurance ability only by adjusting the capacity of the hydrogen storage cylinder, and their hydrogen refueling time is much shorter than the battery charging time. However, during the discharge process of hydrogen fuel cells, processes such as the supply of hydrogen and oxygen, electrochemical reactions, and electron transfer are involved. These processes usually require a certain reaction time, resulting in relatively "soft" output characteristics and a relatively slow response speed, making it difficult to meet the instantaneous high-power demands of UAVs in complex external environments or rapid flight attitude changes. Therefore, combining hydrogen fuel cells with lithium-ion batteries to construct a hybrid power system can achieve power complementarity through energy storage units, thereby optimizing the system performance and significantly improving the adaptability of UAVs in diverse task scenarios.

[0004] Energy management strategy is a key technology for the energy efficiency optimization of hybrid unmanned aerial vehicles (UAVs). Currently, most research focuses on the field of hybrid electric vehicles, and its strategies can be roughly divided into two categories: rule-based and optimization algorithm-based. Rule-based strategies are relatively simple to design, but they rely on expert experience for rule setting, and their applicability is low for the complex and changeable working conditions of UAVs. In contrast, optimization algorithm-based strategies can achieve better performance under variable working conditions by dynamically adjusting energy distribution. Among them, global optimization methods such as dynamic programming and quadratic programming can achieve the optimal solution, but their real-time application is limited due to high computational complexity and the need to master global working condition information in advance. The offline optimization strategy based on particle swarm optimization (PSO) can pre-plan the optimal power distribution based on the predetermined path information and has achieved success in hybrid electric vehicles. However, in the field of UAVs, the application based on PSO is still in the exploratory stage. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention proposes an energy management method for a hydrogen fuel hybrid UAV based on OPSA-ECMS.

[0007] The UAV uses a hybrid power system with a hydrogen fuel cell as the main power source and a lithium-ion battery as the auxiliary power source to provide energy; the energy management system uses OPSA for offline calculation according to the pre-set UAV cruise path and control strategy, plans the energy distribution scheme with the highest energy efficiency, collects real-time data of the system physical layer, uses ECMS to track the energy distribution scheme, and online optimizes the energy distribution.

[0008] It includes the following technical steps:

[0009] Step S1. Establish a hydrogen fuel hybrid UAV system model: The system model includes a UAV dynamics model, a fuel cell model, a lithium-ion battery model, and a bidirectional DC / DC converter model; among them, the UAV dynamics model calculates the torque and thrust in the UAV body coordinate system through simplification, the fuel cell constructs its output voltage mathematical model based on polarization characteristics, the lithium-ion battery constructs an autoregressive ergodic equivalent circuit model based on experimental data, and the bidirectional DC / DC converter model constructs a switching model for voltage and current control.

[0010] Step S2. Construct an OPSA offline optimization system: In the D-dimensional space, the position of each particle represents a potential solution to an optimization problem. Let Xi =(x i1 , x i2 , x i3 ,..., x iD ), V i =(v i1 , v i2 , v i3 ,..., v iD ) are the current position and velocity of the i-th pursuer particle, and a suitable efficiency index function is selected to calculate the fitness of each particle, and based on this, the advantages and disadvantages of each particle are evaluated; the iterative solutions of the particles in each generation of the particle swarm are applied to the updated solutions in each generation of the simulated annealing algorithm, and according to the loop condition, it is decided whether to accept the new solution. If not accepted, the update is rejected and the particle searches and iterates again;

[0011] Step S3. Construct an ECMS online optimization system: Use ECMS to construct an optimization objective function, and dynamically optimize the energy distribution of fuel cells and lithium-ion batteries according to the offline optimization results, real-time data, and constraint conditions.

[0012] As a preferred technical solution of an energy management method for a hydrogen fuel hybrid unmanned aerial vehicle with OPSA-ECMS, in step S2, the iterative solutions of the particles in each generation of the particle swarm are applied to the updated solutions in each generation of the simulated annealing algorithm, and according to the loop condition, it is decided whether to accept the new solution. If not accepted, the update is rejected and the particle searches and iterates again, including the following technical steps:

[0013] S21. Initialize parameters: inertia weight w, learning factors c 1 , c 2 , annealing start and end temperatures T 0 , T, and annealing speed a 1 ;

[0014] S22. Initialize the current particle position X i and velocity V i ;

[0015] S23. Calculate the fitness value J k of each particle. If the fitness value J k > individual extreme value P best , then let the individual extreme value P best = fitness value J k for update. If the individual extreme value P best > global extreme value G best , then let the global extreme value G best = individual extreme value P best for update;

[0016] S24. Update the velocity and position of each particle, and limit the velocity to Vmax inside;

[0017] S25. Calculate the change in fitness value △J caused by the movement of the particle position. If △J < 0 or exp(-△J / T) > ε, accept the new position; otherwise, reject it and the particle position remains unchanged. If the new value is accepted, lower the temperature , where a 2 is the Boltzmann constant; otherwise, do not lower the temperature and return to S23;

[0018] S6. Determine whether the termination condition is satisfied. If the termination condition is satisfied, stop the search and output the result; if not, return and continue the iteration.

[0019] As a preferred technical solution of an energy management method for a hydrogen fuel hybrid unmanned aerial vehicle with OPSA-ECMS, the update formulas for the velocity and position of each particle are expressed as:

[0020]

[0021] In the formula, v ij , pop ij are respectively the particle velocity and position of the i-th particle at the k-th iteration in the j (j = 1, 2,..., n) dimension. w is the inertia weight, c 1 , c 2 are respectively the self-learning factor and the swarm learning factor. rand is a random number between 0 and 1, local best is the best individual position of the particle, pop is the particle position, global best is the global best position of the population, and N is the size of the initial population.

[0022] As a preferred technical solution of an energy management method for a hydrogen fuel hybrid unmanned aerial vehicle with OPSA-ECMS, in step S3, the optimization objective function is the sum of the powers of fuel cell, lithium-ion battery discharge, and lithium-ion battery charging, that is:

[0023]

[0024] Among them, P fc represents the power of the fuel cell, P batt represents the power of the lithium-ion battery, P in represents the power of lithium-ion battery charging, a 3、 β 3 are respectively the equivalent factors for lithium-ion battery charging and lithium-ion battery discharge. F 1 represents the optimization objective function during lithium-ion battery discharge, and F 2 represents the optimization objective function during lithium-ion battery charging.

[0025] As a preferred technical solution of the energy management method for a hydrogen fuel hybrid unmanned aerial vehicle of OPSA-ECMS, in step S3, the constraint conditions include system output power constraint conditions, charge and discharge power constraint conditions, and state constraint conditions;

[0026] The system output power constraint condition is:

[0027] P load (k)=P fc (k)+P batt (k)

[0028] In the formula, P load (k) represents the reference value of the system load power demand, and P fc (k), P batt (k) respectively represent the output powers of the fuel cell and the lithium-ion battery;

[0029] The charge and discharge power constraint conditions:

[0030]

[0031] Among them, P fc-max is the maximum output power of the fuel cell, P fc-min is the minimum output power of the fuel cell, and a 4、 β 4 are respectively the equivalent factors for lithium-ion battery charging and lithium-ion battery discharging, and P batt-max、 P batt-min are respectively the maximum discharge power and the maximum charge power of the lithium-ion battery;

[0032] The state constraint conditions:

[0033] SOC min ≤SOC batt (k)≤SOC max

[0034] In the formula, SOC min 、SOC max respectively represent the maximum value and the minimum value of the SOC of the lithium-ion battery.

[0035] As a preferred technical solution of the energy management method for a hydrogen fuel hybrid unmanned aerial vehicle of OPSA-ECMS, in step S1, the simplified dynamic model of the unmanned aerial vehicle can be expressed as:

[0036]

[0037] Among them, R b e represents the rotation matrix; F bis the total thrust of the drone in the body coordinate system; x, y, and z represent the position of the drone in three-dimensional space; is expressed as the second derivative of x, y, and z, that is, the acceleration components of the drone in the three-dimensional space coordinate system; Φ, θ, and ψ represent the attitude angles of the drone; represents the first derivative of the above attitude angles and is the Euler angular velocity; is expressed as the second derivative of the above attitude angles and is the Euler angular acceleration; Ix, Iy, and Iz respectively represent the moments of inertia of each axis of the drone; Mx, My, and Mz respectively represent the torques of the drone in the body coordinate system; m represents the mass of the drone; g represents the acceleration due to gravity; represents the thrust of the drone in the z direction of the body coordinate system.

[0038] As an optimal technical solution of the energy management method for a hydrogen fuel hybrid drone of OPSA-ECMS, first calculate the propeller torque M i , establish a model for the propeller, and the mathematical expression of this model can be expressed as:

[0039]

[0040] Among them, M i represents the torque generated by the i-th propeller, N i represents the rotational speed of each propeller, D p represents the propeller diameter, ρ represents the atmospheric density, c M represents the torque coefficient.

[0041] As an optimal technical solution of the energy management method for a hydrogen fuel hybrid drone of OPSA-ECMS, the equivalent circuit motor model is to obtain the equivalent voltage U mi and the equivalent current I mi according to the rotational speed and torque, and the motor model can be expressed as follows;

[0042]

[0043] Among them, U m0 , I m0 , R m , K v respectively represent the no-load voltage, no-load current, nominal resistance, and no-load rotational speed of the motor, M i represents the torque generated by the i-th propeller, N i represents the rotational speed of each propeller, w i is the angular velocity of the i-th propeller;

[0044] According to the control signal, the rotational speed of the motor is adjusted through the electronic speed controller, and the mathematical model of the electronic speed controller can be described as:

[0045]

[0046] Among them, I ei is the input current of the electronic speed controller, U ei and R e respectively represent the voltage and resistance of the electronic speed controller, and U mi represents the equivalent voltage of the motor, and I mi represents the equivalent current of the motor;

[0047] Therefore, the total power required by the drone is the sum of the input powers of all electronic speed controllers, which can be expressed as:

[0048]

[0049] Thus, the required power of the rotary-wing drone is calculated.

[0050] As an optimal technical solution of the energy management method for a hydrogen fuel hybrid drone of OPSA-ECMS, the fitness value J of each particle k can be expressed as:

[0051] J k = w 1 η soc + w 2 (1 - E total )

[0052] Among them, w 1 and w 2 are weight coefficients, and η soc is the efficiency of the lithium-ion battery.

[0053] As an optimal technical solution of the energy management method for a hydrogen fuel hybrid drone of OPSA-ECMS, the total energy utilization efficiency E total is defined as the ratio of the effective energy output by the fuel cell and the lithium-ion battery to the input energy:

[0054]

[0055] Among them, E input is the input energy, and E useful is the effective energy; the SOC curve of the highest energy efficiency is denoted as SOC ref .

[0056] The beneficial effects of the present invention are:

[0057] (1) The energy management strategy realizes the effective combination of OPSA and ECMS, can plan the use of energy in advance according to the route length and terrain characteristics, further optimize the endurance, and improve the fuel economy of the system.

[0058] (2) The energy management system adopts a penalty function based on the state of charge of the lithium-ion battery to dynamically correct the initial value of the charge-discharge equivalent factor in the algorithm, which can effectively improve the SOC balance and charging efficiency of the lithium-ion battery and extend its service life.

[0059] (3) The energy management system combines global optimization with local optimization, and efficiently performs energy allocation optimization for online random disturbances. Under online working conditions, it reduces the calculation cost, improves the energy utilization rate, and has good practicability. Description of the Drawings

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0061] Figure 1 It is the energy management strategy flowchart of OPSA-ECMS;

[0062] Figure 2 It is the architecture block diagram of the UAV hydrogen fuel hybrid system;

[0063] Figure 3 It is the UAV flight mission profile diagram;

[0064] Figure 4 It is the UAV energy allocation scheme diagram during the mission execution stage;

[0065] Figure 5 It is the lithium-ion battery SOC change diagram during the mission execution stage. Detailed Embodiments

[0066] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0067] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0068] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0069] Embodiment 1

[0070] Overall, the flowchart of the OPSA-ECMS energy management strategy is as Figure 1 shown.

[0071] First, a UAV hydrogen fuel hybrid power system model is established.

[0072] The UAV hydrogen fuel hybrid power system model is as Figure 2 shown, and specifically includes a UAV dynamics model, a fuel cell model, a lithium-ion battery model, and a bidirectional DC / DC converter model. Among them, the UAV dynamics model calculates the torque and thrust in the UAV body coordinate system through simplification. The fuel cell constructs its output voltage mathematical model based on polarization characteristics. The lithium-ion battery constructs an autoregressive ergodic equivalent circuit model based on experimental data. The bidirectional DC / DC converter model constructs a switching model for voltage and current control;

[0073] First, model each key component. The simplified UAV dynamics model can be expressed as:

[0074]

[0075] Among them, R b e represents the rotation matrix; F b is the total thrust of the UAV in the body coordinate system; x, y, and z represent the position of the UAV in three-dimensional space; is expressed as the second derivative of x, y, and z, that is, the acceleration component of the UAV in the three-dimensional space coordinate system; Φ, θ, and ψ represent the attitude angles of the UAV; is expressed as the first derivative of the above attitude angles, which is the Euler angular velocity; is expressed as the second derivative of the above attitude angles, which is the Euler angular acceleration; Ix, Iy, and Iz respectively represent the moments of inertia of each axis of the UAV; Mx, My, and Mz respectively represent the torques of the UAV in the body coordinate system; m represents the mass of the UAV; g represents the acceleration due to gravity; represents the thrust of the UAV in the z direction of the body coordinate system.

[0076] First, calculate the propeller torque M i , and establish a model for the propeller. The mathematical expression of this model can be expressed as:

[0077]

[0078] Among them, M i represents the torque generated by the i-th propeller, N i represents the rotational speed of each propeller, D p represents the propeller diameter, ρ represents the atmospheric density, c M represents the torque coefficient;

[0079] The equivalent circuit motor model obtains the equivalent voltage U mi and the equivalent current I mi based on the rotational speed and torque, and the motor model can be expressed as follows;

[0080]

[0081] Among them, U m0 、I m0 、R m 、K v respectively represent the no-load voltage, no-load current, nominal resistance, and no-load rotational speed of the motor, M i represents the torque generated by the i-th propeller, N i represents the rotational speed of each propeller, w i is the angular velocity of the i-th propeller;

[0082] According to the control signal, the rotational speed of the motor can be adjusted through the electronic speed controller, and the mathematical model of the electronic speed controller can be described as:

[0083]

[0084] Among them, I ei is the input current of the electronic speed controller, U ei and R e respectively represent the voltage and resistance of the electronic speed governor, U mi represents the equivalent voltage of the motor, I mi represents the equivalent current of the motor;

[0085] Therefore, the total power required by the UAV is the sum of the input powers of all electronic speed controllers and can be expressed as:

[0086]

[0087] So far, the required power of the rotor UAV has been calculated.

[0088] The fuel cell constructs its output voltage mathematical model based on the polarization characteristics, the lithium-ion battery constructs the autoregressive ergodic equivalent circuit model based on the experimental data, and the bidirectional DC / DC converter model constructs the switching model for voltage and current control.

[0089] It should be noted that the OPSA-ECMS energy management strategy is based on in-depth modeling and intelligent optimization of the UAV hydrogen fuel hybrid system. Its core lies in achieving optimal energy allocation through precise calculation and real-time scheduling. The system dynamically monitors and feeds back the performance and working status of each key component (such as fuel cells, lithium batteries, electronic speed controllers, bidirectional DC / DC converters, etc.), and can not only effectively allocate the power output of the battery and fuel cell under different flight states, but also avoid energy waste under extreme conditions, maximizing the endurance and flight stability. Specifically, the OPSA-ECMS strategy accurately calculates the power demand at each moment during the flight process through the UAV dynamics model, and combines the polarization characteristics of the fuel cell and the charge and discharge characteristics of the lithium battery to adjust the power output mode in real time. As an efficient power source, the fuel cell provides stable continuous power, while the lithium battery responds quickly under short-term high power demands to make up for the insufficient output of the fuel cell. The precise control of the bidirectional DC / DC converter ensures the smooth conversion and regulation of voltage and current, ensuring the high efficiency and safety of the system in energy supply.

[0090] Secondly, construct the OPSA offline optimization system.

[0091] Calculate the optimal allocation scheme offline according to the UAV flight route information. The UAV flight mission profile is as Figure 3 shown, and use OPSA to perform offline optimization on it.

[0092] In the D-dimensional space, the position of each particle represents a potential solution to an optimization problem. Let X i =(x i1 ,x i2 ,x i3 ,...,x iD ) and V i =(v i1 ,v i2 ,v i3 ,...,v iD ) be the current position and velocity of the i-th pursuer particle. Select a suitable effectiveness index function to calculate the fitness of each particle, and use this to evaluate the quality of each particle. Apply the iterative solutions of the particles in each generation of the particle swarm to the updated solutions in each generation of the simulated annealing algorithm, and decide whether to accept the new solution according to the loop condition. If not accepted, reject the update, and the particle searches and iterates again. The specific implementation of the control method includes the following steps:

[0093] S1. Initialize parameters: inertia weight w, learning factors c 1 、c 2 , annealing start and end temperatures T 0 、T, annealing speed a 1 ;

[0094] S2. Initialize the current particle position X i and speed V i ;

[0095] S3. Calculate the fitness value J of each particle k , if the fitness value J k >Individual extreme value P best , then let the individual extreme value P best = Adaptation value J k Update, if the individual extreme value P best >Global extremum G best , then let the global extreme value G best = Individual extreme value P best Make updates;

[0096] S4, update the speed and position of each particle, and limit the speed to V max The speed and position update formula for the kth iteration can be expressed as:

[0097]

[0098] Where v ij 、pop ij are the particle velocity and position of the i-th particle in the k-th iteration in the j (j=1, 2, …, n) dimension, w is the inertia weight, c 1 、c 2 They are self-learning factor and group learning factor, rand is a random number between 0 and 1, local best is the best individual position of the particle, pop is the particle position, global best is the global optimal position of the population, N is the initial population size;

[0099] S5. Calculate the change in fitness value △J caused by the movement of the particle position. If △J<0 or exp(-△J / T)>ε( is a random number between [0,1]), the new position is accepted; otherwise, the particle position does not change. If the new value is accepted, the temperature is lowered. Otherwise, do not cool down and return to S3;

[0100] S6. Determine whether the termination condition is met (the condition is generally set as the number of iterations or calculation accuracy). If the termination condition is met, stop searching and output the result; if not, return to continue iterating.

[0101] The fitness value J of OPSA particles k It can be expressed as:

[0102] J k =w 1 ηsoc +w 2 (1 - E total )

[0103] where w 1 and w 2 are weight coefficients, and η soc is the efficiency of the lithium - ion battery;

[0104] The total energy utilization efficiency E total can be defined as the ratio of the effective energy output from the fuel cell and the lithium - ion battery to the input energy:

[0105]

[0106] where E input is the input energy, and E useful is the effective energy; the SOC curve of the highest energy efficiency is denoted as SOC ref .

[0107] It should be noted that the OPSA offline optimization system combines the Particle Swarm Optimization (PSO) and the Simulated Annealing algorithm to solve the optimal energy allocation problem in the UAV flight mission. The innovation of this system lies in the application of intelligent optimization algorithms to UAV energy management. It uses the particle swarm to search for the optimal solution in multi-dimensional space and optimizes the global optimal solution through the annealing mechanism of simulated annealing, so as to maximize the energy usage efficiency. First, the OPSA offline optimization system calculates the optimal energy allocation plan offline based on the UAV flight route information. During the optimization process, the position of each particle represents a potential solution, and the fitness of the particle is evaluated by the energy efficiency and usage effectiveness index of the fuel cell and the lithium battery. The position and velocity of the particle change dynamically in space and are updated according to the fitness function. The particle will be adjusted according to the feedback information of the individual best position and the global best position, gradually approaching the global optimal solution. The key to particle swarm optimization lies in its parallel search strategy. Each particle represents the possibility of a solution, and the particle swarm can quickly find the optimal solution in high-dimensional space through collaborative search. Specifically, each update of the particle's position and velocity is affected not only by its own historical experience (self-learning factor) but also by the experience of other particles in the group (group learning factor). In addition, to prevent falling into local optimal solutions, the OPSA system also combines the temperature control mechanism in the simulated annealing algorithm. By gradually reducing the "temperature" during the iteration process, it reduces the uncertainty of the system and helps the system jump out of local optimal solutions. In each iteration, the update of the particle's position and velocity is affected by the inertia weight and the random factor, forming a balance between exploration and exploitation. The inertia weight enables the particle to maintain a certain inertia during the search process, thus avoiding the search process from being too random; while the random factor provides diversity for the particle search and enhances the global search ability of the system. When the change in the fitness value is caused by the update of the particle position, it is decided whether to continue the search by randomly choosing whether to accept the new position. This mechanism ensures that the system can jump out of local solutions in the global search and avoids premature convergence. The goal of the system is to maximize the energy utilization efficiency, which is specifically manifested as the ratio of the effective energy output of the fuel cell and the lithium battery to the input energy. Through particle swarm optimization, the system can dynamically adjust the energy allocation ratio of the battery according to the specific requirements of the flight mission, optimize the energy usage, and improve the overall energy utilization efficiency. The improvement of energy efficiency can not only extend the endurance time of the UAV but also reduce energy waste, improve the operation efficiency of the UAV and the comprehensive performance of the system. Therefore, the OPSA offline optimization system realizes the intelligent energy management in the UAV hybrid power system by combining the dual optimization mechanisms of particle swarm optimization and simulated annealing. Through the accurate modeling and calculation of the flight mission route, mission requirements, and system status, the system can quickly generate the best energy allocation plan in the offline stage and provide strong support for the actual flight mission.This strategy not only ensures the efficient use of energy during flight but also enhances the adaptability and flexibility of the UAV system in complex mission environments.

[0108] Finally, an online optimization system for ECMS is constructed.

[0109] The main optimization goal of the energy management strategy is to minimize the total consumption of the hybrid system during the entire operation. Therefore, ECMS is used to design the optimization objective function; the optimization objective function is the sum of the powers of the fuel cell, lithium-ion battery discharge, and lithium-ion battery charging, that is:

[0110]

[0111] Among them, P fc represents the power of the fuel cell, P batt represents the power of the lithium-ion battery, P in represents the power of lithium-ion battery charging, a 3、 β 3 are the equivalent factors for lithium-ion battery charging and lithium-ion battery discharge respectively, F 1 represents the optimization objective function when the lithium-ion battery discharges (P batt ≥0), F 2 represents the optimization objective function when the lithium-ion battery charges (P batt <0);

[0112] P in represents the power of the lithium-ion battery during charging and can be expressed as:

[0113] P in =(-P batt )·η DCDC ·η batt

[0114] Among them, η DCDC is the efficiency of the DC / DC converter, η batt is the charging efficiency of the lithium-ion battery. System output power constraint conditions:

[0115] P load (k)=P fc (k)+P batt (k)

[0116] Among them, P load (k) represents the reference value of the system load power demand, P fc (k), P batt (k) represent the output powers of the fuel cell and the lithium-ion battery respectively. The charging and discharging power constraint conditions are:

[0117]

[0118] Among them, P fc-max is the maximum output power of the fuel cell, and P fc-min is the minimum output power of the fuel cell. a 4、 β 4 are the equivalent factors for lithium-ion battery charging and lithium-ion battery discharging respectively. P batt-max、 P batt-min are the maximum discharge power and the maximum charging power of the lithium-ion battery respectively.

[0119] The state constraint conditions are as follows:

[0120] SOC min ≤SOC batt (k) ≤ SOC max

[0121] In the formula, SOC min and SOC max represent the maximum value and the minimum value of the SOC of the lithium-ion battery respectively.

[0122] Based on the measured information collected in real time, the set constraint conditions, the input offline optimization results, and the designed objective function, ECMS performs online optimization within a finite time domain to obtain the optimal energy management allocation results of the fuel cell and the lithium-ion battery of the UAV at different times.

[0123] It should be noted that the ECMS online optimization system is based on the real-time energy management of fuel cells and lithium-ion batteries in a hybrid power system to minimize the total energy consumption during the entire flight process. The core goal of this strategy is to dynamically adjust the power output of the batteries through online optimization, precisely control the collaborative work of fuel cells and lithium batteries, and ensure that the UAV uses energy efficiently and economically during flight. First of all, ECMS achieves the goal of minimizing the total energy consumption by designing an optimized objective function. This objective function takes the power of the fuel cell, the discharge power of the lithium-ion battery, and the charging power of the lithium-ion battery as the core variables for optimization. By setting the equivalent factors for charge and discharge, the system can convert the charging and discharging powers into a unified power consumption value, enabling the optimization problem to be processed under a consistent measurement standard. In addition, the charging power of the lithium battery also needs to consider the influence of the DC / DC converter and the lithium battery charging efficiency, and these factors ensure the precise control of the energy conversion process by the system. During the actual flight process, ECMS uses the real-time measured flight parameters (such as flight speed, load power demand, etc.) to adjust the output of the objective function according to the dynamic changes of the flight mission. The system calculates the power demands of the fuel cell and the lithium battery, and dynamically adjusts the power distribution between them to minimize the total power consumption. Different stages of the flight mission may require different energy outputs. For example, the power demand for the battery is relatively high during takeoff, while the cruise stage mainly relies on the stable output of the fuel cell. ECMS optimizes the energy distribution of the system in real time according to these changes to ensure that each battery operates within the optimal efficiency range. To ensure the safety and stability of the system, ECMS also includes multiple constraints. First of all, the system needs to meet the reference value of the load power, that is, the power demand of the flight mission for the UAV; at the same time, the charge and discharge powers of the fuel cell and the lithium battery are also strictly restricted by the maximum and minimum output powers. In particular, the SOC (State of Charge) range of the lithium battery must be controlled between its maximum and minimum SOCs to avoid battery damage caused by over-discharge or over-charging. Through this online optimization process based on real-time data, offline optimization results, and objective function design, the ECMS system can continuously adjust the energy management strategy during flight, maximize the energy efficiency of fuel cells and lithium batteries to the greatest extent, and adjust the energy distribution method according to real-time needs. This strategy can not only extend the flight time of the UAV, but also reduce energy waste, thus providing efficient and stable energy supply in a changing flight environment. Therefore, the innovation of the ECMS online optimization system lies in its ability to dynamically adjust the energy usage strategy according to the real-time flight state, external environment, and mission requirements. Through the fine control of the battery charge and discharge process, the UAV system can achieve the maximum energy efficiency and the optimal energy distribution during flight.This intelligent and real-time energy management method provides a more flexible and efficient way of energy utilization for the UAV hybrid power system, laying a solid foundation for achieving long-duration flight and mission success.

[0124] Embodiment 2

[0125] The following is a specific introduction in combination with actual cases:

[0126] Referring to the power parameters of the hydrogen fuel cell module, the physical parameters of the fuel cell and the lithium-ion battery are shown in Table 1:

[0127] Table 1 Physical parameters of fuel cell and lithium-ion battery

[0128]

[0129] The initial constraints for the fuel cell and lithium-ion battery in the UAV are shown in Table 2:

[0130] Table 2 Initial constraints and their physical meanings

[0131]

[0132] Based on the OPSA-ECMS energy management strategy, by collecting real-time signals of the UAV, such as the SOC of the lithium-ion battery, the hydrogen consumption of the fuel cell, etc., it is used for the correction and iterative update of the relevant parameters of the prediction model. Then, according to the reference SOC given by OPSA and the reference power distribution scheme of the system load power, the optimal solution of the objective function is solved based on ECMS, continuously updating to generate new control variables, and inputting them to the hybrid power system based on the fuel cell-lithium-ion battery, thus completing a closed-loop control process.

[0133] The simulation sets the initial SOC value of the lithium-ion battery to 0.65. The fuel cell is always in an operating environment where the partial pressures of hydrogen and oxygen gases are both 200 kPa and the operating temperature is 323.15 K. The operating duration is 350 s. Under the OPSA-ECMS energy management strategy, the power curves and SOC transformation curves of each power source are as Figure 5 shown.

[0134] From Figure 4 it can be seen that the OPSA-ECMS energy management strategy can meet the system load power demand. The fuel cell operates in the efficient region. As Figure 5 shown, the SOC of the lithium-ion battery is always within the safe operating range, which can improve the overall operating efficiency of the fuel cell, better play the auxiliary role of the lithium-ion battery, and effectively reduce the system hydrogen consumption, improve the economy of the system, and achieve the optimal power distribution of the system.

[0135] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0136] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0138] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0139] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. An OPSA-ECMS hydrogen fuel hybrid UAV energy management method, characterized by: The UAV adopts a hybrid power system with hydrogen fuel cells as the main power source and lithium-ion batteries as the auxiliary power source to provide energy; the energy management system uses OPSA to perform offline calculations based on the pre-set UAV cruise path and control strategy, plans the energy distribution plan with the highest energy efficiency, collects real-time data of the system physical layer, uses ECMS to track the energy distribution plan, and optimizes energy distribution online; The following technical steps are included: Step S1. Establishing a hydrogen fuel hybrid power system model for a UAV: ​​the system model includes a UAV dynamics model, a fuel cell model, a lithium-ion battery model, and a bidirectional DC / DC converter model; wherein the UAV dynamics model calculates the torque and thrust in the coordinate system of the UAV fuselage by simplifying, the fuel cell constructs its output voltage mathematical model based on polarization characteristics, the lithium-ion battery constructs an autoregressive ergodic equivalent circuit model based on experimental data, and the bidirectional DC / DC converter model constructs a switch model for voltage and current control; Step S2. Construct OPSA offline optimization system: In D-dimensional space, the position of each particle represents a potential solution to the optimization problem. Let X i =(x i1 ,x i2 ,x i3 ,...,x iD ), V i =(v i1 ,v i2 ,v i3 ,...,v iD ) is the current position and speed of the ith chasing particle, and selects a suitable efficiency index function as the fitness of each particle, and uses it to evaluate the quality of each particle; the iterative solution of the particles in each generation of particle swarm is applied to the updated solution of each generation in the simulated annealing algorithm, and decides whether to accept the new solution according to the loop condition. If not, the update is rejected and the particles re-search and iterate; Step S3. Construct an ECMS online optimization system: Use ECMS to construct an optimization objective function, and dynamically optimize the energy distribution of fuel cells and lithium-ion batteries based on offline optimization results, real-time data and constraints.

2. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 1 is characterized in that: In step S2, the iterative solution of particles in each generation of particle swarm is applied to the updated solution of each generation in the simulated annealing algorithm. The loop condition is used to decide whether to accept the new solution. If not, the update is rejected and the particles re-search and iterate, including the following technical steps: S21. Initialization parameters: inertia weight w , learning factors c1, c2, annealing start and end temperatures T0, T and annealing speed a1; S22, initialize the current particle position X i and speed V i ; S23. Calculate the fitness value J of each particle k , if the fitness value J k >Individual extreme value P best , then let the individual extreme value P best = Adaptation value J k Update, if the individual extreme value P best >Global extremum G best , then let the global extreme value G best = Individual extreme value P best Make updates; S24, update the speed and position of each particle, and limit the speed to V max Inside; S25. Calculate the change in fitness value △J caused by the movement of the particle position. If △J<0 or exp(-△J / T)>ε, accept the new position; otherwise, reject and the particle position does not change; if the new value is accepted, cool down , where a2 is the Boltzmann constant; otherwise, the temperature is not lowered and the process returns to S23; S6. Determine whether the termination condition is met. If the termination condition is met, stop searching and output the result; if not, return to continue iterating.

3. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 2, characterized in that: The update formula for each particle's velocity and position is expressed as: In the formula, v ij , pop ij Respectively i Particles in j Dimension k The particle velocity and position of the iteration, where j =1, 2, …, n , w is the inertia weight, c1 and c2 are the self-learning factor and group learning factor respectively, rand A random number between 0 and 1, local best is the optimal individual position of the particle, pop is the particle position, global best is the global optimal position of the population, and N is the initial population size.

4. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 1, characterized in that: In step S3, the optimization objective function is the sum of the power of the fuel cell, the lithium-ion battery discharge, and the lithium-ion battery charging, that is: Among them, P fc Represents the power of the fuel cell, P batt Indicates the power of lithium-ion battery, P in Indicates the power of lithium-ion battery charging, a 3、 β3 is the equivalent factor of lithium-ion battery charging and lithium-ion battery discharging, F1 is the optimization objective function when the lithium-ion battery is discharged, and F2 is the optimization objective function when the lithium-ion battery is charged.

5. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 1, characterized in that: In step S3, the constraints include system output power constraints, charge and discharge power constraints, and state constraints; The system output power constraint condition is: P load (k)=P fc (k)+P batt (k) Where P load (k) represents the system load power demand reference value, P fc (k), P batt (k) represents the output power of fuel cell and lithium-ion battery respectively; The charging and discharging power constraints are: Among them, P fc-max is the maximum output power of the fuel cell, P fc-min is the minimum output power of the fuel cell, a 4、 β4 are the equivalent factors of lithium-ion battery charging and lithium-ion battery discharging, P batt-max、 P batt-min They are the maximum discharge power and maximum charge power of lithium-ion batteries respectively; The state constraints are: SOC min ≤SOC batt (k)≤SOC max In the formula, SOC min , SOC max They represent the maximum and minimum SOC values ​​of the lithium-ion battery respectively.

6. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 1, characterized in that: In step S1, the simplified UAV dynamics model is expressed as: Among them, R b e represents the rotation matrix; F b is the total thrust of the drone in the body coordinate system; x, y, z represent the position of the drone in three-dimensional space; It is expressed as the second-order derivative of x, y, and z, that is, the acceleration component of the drone in the three-dimensional space coordinate system; Φ, θ, and ψ represent the attitude angle of the drone; Represents the first-order derivative of the above attitude angle, which is the Euler angular velocity; It is expressed as the second-order derivative of the above attitude angle, which is the Euler angular acceleration; Ix, Iy and Iz represent the moment of inertia of each axis of the UAV respectively; Mx, My and Mz represent the torque of the UAV in the body coordinate system respectively; m represents the mass of the UAV; g represents the acceleration due to gravity; It represents the thrust of the drone in the z direction of the body coordinate system.

7. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 6, characterized in that: First calculate the propeller torque M i , a propeller model is established, and the mathematical expression of the model is expressed as: Among them, M i It is expressed as the torque generated by the i-th propeller, N i Indicates the speed of each propeller, D p represents the propeller diameter, ρ represents the atmospheric density, c M Expressed as torque coefficient.

8. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 7, characterized in that: The equivalent circuit motor model is to obtain the equivalent voltage U based on the speed and torque mi and equivalent current I mi , the motor model is expressed as follows; Among them, U m0 ,I m0 , R m , K v Represent the no-load voltage, no-load current, nominal resistance, and no-load speed of the motor respectively. i It is expressed as the torque generated by the i-th propeller, N i represents the speed of each propeller, w i is the angular velocity of the i-th propeller; According to the control signal, the speed of the motor is adjusted by the ESC. The mathematical model of the ESC is described as: Among them, I ei is the input current of the ESC, U ei and R e Respectively represent the voltage and resistance of the electronic speed regulator, U mi Expressed as the equivalent voltage of the motor, I mi Expressed as the equivalent current of the motor; Therefore, the total power required by the drone is P total Input power P for all ESCs ei The sum is expressed as: At this point, the required power of the rotorcraft is calculated.

9. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 2, characterized in that: The fitness value J of each particle k It is expressed as: J k =w1η soc +w2(1-E total ) Among them, w1 and w2 are weight coefficients, η soc is the lithium-ion battery efficiency, E total is the total energy efficiency.

10. The OPSA-ECMS hydrogen fuel hybrid UAV energy management method according to claim 9, characterized in that: Total energy efficiency E total Defined as the ratio of the effective energy output of fuel cells and lithium-ion batteries to the input energy: Among them, E input is the input energy, E useful is the effective energy; the SOC curve with the highest energy efficiency is recorded as SOC ref .

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