Energy management method for hydrogen fuel hybrid power unmanned aerial vehicle of OPSA-ECMS

Abstract

The invention discloses an OPSA-ECMS hydrogen fuel hybrid power unmanned aerial vehicle energy management method, and the method comprises the steps: building an unmanned aerial vehicle hybrid power system model employing a hydrogen fuel cell as a main power supply and a lithium ion battery as an auxiliary power supply, and building an energy management system model. According to the method, the OPSA and the ECMS are combined, the minimum hydrogen consumption and the highest efficiency are taken as optimization objectives, the OPSA is adopted to calculate an optimal reference energy distribution scheme in an off-line manner under a known preset route, and the ECMS is adopted to optimize the reference energy distribution scheme in an on-line manner in the task execution process of the unmanned aerial vehicle, so that the optimal energy distribution of the system is realized. According to the method, OPSA off-line calculation and ECMS on-line optimization are combined, the high energy density of hydrogen fuel and the quick response characteristic of the battery are exerted to the maximum extent, and the overall energy utilization efficiency is improved.

Description

Technical Field

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

[0002] In recent years, drones and related technologies have achieved rapid development, and their application scope has gradually expanded. In particular, rotary-wing drones have become the mainstream models in the market due to their simple structure, convenient operation and relatively low cost. With the advancement of technology, rotary-wing drones are widely used in many fields such as agricultural plant protection, power inspection, disaster relief, logistics and transportation, news reporting, wildlife observation and film and television shooting. However, most commercial rotary-wing drones currently rely mainly on lithium-ion batteries as the only energy source. Due to the low energy density of lithium-ion batteries, these drones are insufficient in terms of flight endurance. In addition, although increasing battery capacity can extend the endurance, the mass of the battery increases accordingly, which weakens its endurance gain. Especially in long-term or large-scale tasks (such as inspection tasks), traditional battery-powered drones need to be frequently charged or replaced, which seriously affects the continuity and efficiency of the mission. This problem significantly restricts the application of rotary-wing drones in more complex or continuous mission scenarios.

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

[0004] Energy management strategy is a key technology for optimizing the energy efficiency of hybrid UAVs. At present, 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. The design of rule-based strategies is relatively simple, but it needs to rely on expert experience to set rules, and its applicability is low for the complex and changeable working conditions of UAVs. In contrast, strategies based on optimization algorithms 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 their high computational complexity and the need to master global working condition information in advance. Offline optimization strategies based on particle swarm optimization (PSO) can plan the optimal power distribution in advance based on the predetermined path information, and have been successful in hybrid electric vehicles. However, in the field of UAVs, applications based on PSO are still in the exploratory stage. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention proposes an OPSA-ECMS hydrogen fuel hybrid UAV energy management method. 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 ,xi3 ,...,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.

[0007] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, 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, and whether to accept the new solution is determined according to the loop condition. 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.

[0008] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, the update formula of each particle speed and position is expressed as:

[0009] In the formula, 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, c1 and c2 are the self-learning factor and the 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, and N is the initial population size.

[0010] As a preferred technical solution of the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, 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:

[0011] 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.

[0012] As a preferred technical solution of the energy management method of hydrogen fuel hybrid UAV 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; 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:

[0013] 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.

[0014] As a preferred technical solution of the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, in step S1, the simplified dynamic model of the UAV can be expressed as:

[0015] 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.

[0016] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, the propeller torque M is first calculated. i , a propeller model is established, and the mathematical expression of the model can be expressed as:

[0017] 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.

[0018] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV of OPSA-ECMS, the equivalent circuit motor model is to obtain the equivalent voltage U according to the speed and torque. mi and equivalent current I mi , the motor model can be expressed as follows;

[0019] 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 can be described as:

[0020] 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 the sum of all ESC input powers, which can be expressed as:

[0021] At this point, the required power of the rotorcraft is calculated.

[0022] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV based on OPSA-ECMS, the fitness value J of each particle is k It can be expressed as: J k =w1η soc +w2(1-E total ) Among them, w1 and w2 are weight coefficients, η soc is the efficiency of lithium-ion batteries.

[0023] As an optimal technical solution for the energy management method of hydrogen fuel hybrid UAV based on OPSA-ECMS, the total energy utilization efficiency E total Defined as the ratio of the effective energy output of fuel cells and lithium-ion batteries to the input energy:

[0024] 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 .

[0025] The beneficial effects of the present invention are: (1) The energy management strategy achieves an effective combination of OPSA and ECMS. It can plan energy use in advance according to the route length and terrain characteristics, further optimize the cruising range, and improve the fuel economy of the system.

[0026] (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 and 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.

[0027] (3) The energy management system combines global optimization with local optimization, and efficiently optimizes energy allocation for online random disturbances. Under online working conditions, it reduces the computing cost, improves energy utilization, and has good practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 It is the energy management strategy flow chart of OPSA-ECMS; Figure 2 This is the architecture block diagram of the hydrogen fuel hybrid system for UAVs; Figure 3 This is a profile diagram of the UAV flight mission; Figure 4 This is a diagram of the energy allocation scheme for the UAV during the mission execution phase; Figure 5 This is the SOC change diagram of the lithium-ion battery during the mission execution phase. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1 In summary, the flow chart of the OPSA-ECMS energy management strategy is as follows: Figure 1 shown.

[0033] First, a hydrogen fuel hybrid power system model for UAV is established.

[0034] UAV hydrogen fuel hybrid system model Figure 2 As shown, it specifically includes a UAV dynamics model, a fuel cell model, a lithium-ion battery model, and a bidirectional DC / DC converter model. The UAV dynamics model is simplified to calculate the torque and thrust in the UAV fuselage coordinate system. The fuel cell constructs its output voltage mathematical model based on the polarization characteristics. The lithium-ion battery constructs an autoregressive state ergodic equivalent circuit model based on experimental data. The bidirectional DC / DC converter model constructs a switch model to control voltage and current. First, the key components are modeled, and the simplified UAV dynamics model can be expressed as:

[0035] 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.

[0036] First calculate the propeller torque M i , a propeller model is established, and the mathematical expression of the model can be expressed as:

[0037] 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; 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 can be expressed as follows;

[0038] 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 can be adjusted by the ESC. The mathematical model of the ESC can be described as:

[0039] 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 the sum of all ESC input powers, which can be expressed as:

[0040] At this point, the required power of the rotorcraft is calculated.

[0041] The fuel cell constructs its output voltage mathematical model based on the polarization characteristics, the lithium-ion battery constructs an autoregressive equivalent circuit model based on experimental data, and the bidirectional DC / DC converter model constructs a switch model to control voltage and current.

[0042] It should be noted that the OPSA-ECMS energy management strategy is based on the in-depth modeling and intelligent optimization of the hydrogen fuel hybrid power system of the UAV. Its core lies in achieving the optimal allocation of energy through precise calculation and real-time scheduling. The system can not only effectively allocate the power output of batteries and fuel cells under different flight conditions, but also avoid energy waste under extreme conditions, and maximize endurance and flight stability through dynamic monitoring and feedback of the performance and working status of key components (such as fuel cells, lithium batteries, electric regulators, bidirectional DC / DC converters, etc.). Specifically, the OPSA-ECMS strategy accurately calculates the power demand at each moment during the flight through the UAV dynamics model, and adjusts the power output mode in real time by combining the polarization characteristics of the fuel cell and the charge and discharge characteristics of the lithium battery. As a high-efficiency power source, the fuel cell provides stable and continuous power, while the lithium battery responds quickly to short-term high power demand 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 efficiency and safety of the system in energy supply.

[0043] Secondly, build the OPSA offline optimization system.

[0044] The optimal allocation plan is calculated offline based on the UAV flight route information. The UAV flight mission profile is as follows: Figure 3 As shown, OPSA is used to optimize it offline.

[0045] In 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 ), 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 the decision on whether to accept the new solution is made according to the loop condition. If not, the update is rejected and the particles re-search and iterate. The implementation of the control method specifically includes the following steps: S1, initialization parameters: inertia weight w, learning factors c1, c2, annealing start and end temperatures T0, T, annealing speed a1; S2. Initialize the current particle position X i and speed V i ; 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; 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:

[0046] 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, c1 and c2 are the self-learning factor and the 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; 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; 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.

[0047] The fitness value J of OPSA particles k It can be 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; Total energy efficiency E total It can be defined as the ratio of the effective energy output of fuel cells and lithium-ion batteries to the input energy:

[0048] 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 .

[0049] It should be noted that the OPSA offline optimization system combines particle swarm optimization (PSO) and simulated annealing algorithms to solve the optimal energy allocation problem in UAV flight missions. The innovation of this system lies in applying intelligent optimization algorithms to UAV energy management, using particle swarms to search for the optimal solution in multidimensional space, and optimizing the global optimal solution through the annealing mechanism of simulated annealing, so as to maximize the energy utilization efficiency. First, the OPSA offline optimization system calculates the optimal energy allocation plan offline through 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 use efficiency index of the fuel cell and lithium battery. The position and speed of the particle change dynamically in space and are updated according to the fitness function. The particle will adjust according to the feedback information of the individual optimal position and the group optimal position, and gradually approach 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 quickly finds the optimal solution in high-dimensional space through collaborative search. Specifically, each position and velocity update of a particle is not only affected by its own historical experience (self-learning factor), but also guided by the experience of other particles in the group (group learning factor). In addition, in order to prevent falling into the local optimal solution, the OPSA system also combines the temperature control mechanism in the simulated annealing algorithm to reduce the uncertainty of the system by gradually lowering the "temperature" during the iteration process, helping the system to jump out of the local optimal solution. In each iteration, the position and velocity updates of particles are affected by the inertia weight and the random factor, forming a balance between exploration and utilization. The inertia weight allows the particles to maintain a certain inertia during the search process, thereby preventing the search process from being too random; while the random factor provides diversity for the particle search and enhances the global search capability of the system. When the particle position update causes a change in the fitness value, the system decides whether to continue searching by randomly choosing whether to accept the new position. This mechanism ensures that the system can jump out of the local solution in the global search and avoids premature convergence. The goal of the system is to maximize the efficiency of energy utilization, which is specifically manifested as the ratio of the effective energy output of fuel cells and lithium batteries to the input energy. Through particle swarm optimization, the system can dynamically adjust the energy allocation ratio of the battery according to the specific needs of the flight mission, optimize energy use, and improve the overall energy utilization efficiency. The improvement of energy efficiency can not only extend the flight time of the UAV, but also reduce energy waste, improve the operating efficiency of the UAV and the overall performance of the system. Therefore, the OPSA offline optimization system realizes the intelligent energy management in the hybrid power system of the UAV by combining the dual optimization mechanisms of particle swarm optimization and simulated annealing. Through accurate modeling and calculation of flight mission routes, mission requirements and system status, the system can quickly generate the best energy allocation plan in the offline stage and provide strong support for actual flight missions.This strategy not only ensures efficient use of energy during flight, but also enhances the adaptability and flexibility of the UAV system in complex mission environments.

[0050] Finally, build the ECMS online optimization system.

[0051] The main optimization goal of the energy management strategy is to minimize the total consumption of the hybrid system during the entire operation process. Therefore, ECMS is used to design the optimization objective function; the optimization objective function is the sum of the power of the fuel cell, lithium-ion battery discharge, and lithium-ion battery charging, that is:

[0052] 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, and F1 is the equivalent factor of lithium-ion battery discharging (P batt ≥0), F2 represents the optimization objective function when the lithium-ion battery is charged (P batt <0) optimization objective function; P in Indicates the power of lithium-ion battery during charging, which can be expressed as: P in =(-P batt )·η DCDC ·η batt Among them, η DCDC is the efficiency of the DC / DC converter, η batt is the charging efficiency of lithium-ion batteries. System output power constraints: P load (k)=P fc (k)+P batt (k) Among them, P load (k) represents the system load power demand reference value, P fc (k), P batt (k) represents the output power of the fuel cell and the lithium-ion battery respectively. The charging and discharging power constraint condition is:

[0053] 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、 Pbatt-min They are the maximum discharge power and maximum charge power of lithium-ion batteries respectively.

[0054] 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.

[0055] ECMS performs online optimization in a limited time domain based on the real-time collected measurement information, the set constraints, the input offline optimization results and the designed objective function to obtain the optimal energy management allocation results of the fuel cell and lithium-ion battery of the UAV at different times.

[0056] 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 the hybrid 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 battery through online optimization, accurately control the coordinated work of the fuel cell and the lithium battery, and ensure that the UAV uses energy efficiently and economically during flight. First, ECMS achieves the goal of minimizing total energy consumption by designing an optimization objective function. The 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 factor of charging and discharging, the system can convert the charging and discharging power into a unified power consumption value, so that the optimization problem can be processed under a consistent metric. In addition, the charging power of the lithium battery also needs to consider the influence of the DC / DC converter and the charging efficiency of the lithium battery. These factors ensure the system's precise control of the energy conversion process. During the actual flight, ECMS uses 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 requirements of the fuel cell and the lithium battery and dynamically adjusts the power distribution between the two to minimize the total power consumption. Different phases of the flight mission may require different energy outputs. For example, the demand for battery power is high during takeoff, while the cruising phase 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. In order to ensure the safety and stability of the system, ECMS also includes multiple constraints. First, 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 charging and discharging power of the fuel cell and lithium battery is also subject to strict maximum output power and minimum output power constraints. In particular, the SOC (State of Charge) range of the lithium battery must be controlled between its maximum and minimum SOC to avoid battery damage caused by excessive discharge or 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, 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, thereby 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 status, external environment and mission requirements. Through fine control of the battery charging and discharging process, the UAV system can maximize energy efficiency and optimize energy distribution during flight.This intelligent, real-time energy management method provides a more flexible and efficient way of utilizing energy for UAV hybrid power systems, laying a solid foundation for achieving long-term flight and mission success.

[0057] Example 2 The following is a detailed introduction based on actual cases: Referring to the power parameters of the hydrogen fuel cell module, the physical parameters of the fuel cell and lithium-ion battery are shown in Table 1: Table 1 Physical parameters of fuel cells and lithium-ion batteries

[0058] The initial constraints for fuel cells and lithium-ion batteries in drones are shown in Table 2: Table 2 Initial constraints and their physical meanings

[0059] The OPSA-ECMS energy management strategy collects real-time signals from drones, such as the SOC of lithium-ion batteries and the hydrogen consumption of fuel cells, for the correction and iterative update of relevant parameters of the prediction model. Then, based on the reference SOC and system load power reference allocation scheme given by OPSA, the optimal solution of the objective function is solved based on ECMS, and new control variables are continuously updated and input into the hybrid power system based on fuel cells and lithium-ion batteries, thus completing a closed-loop control process.

[0060] The simulation sets the initial SOC value of the lithium-ion battery to 0.65. The fuel cell is always in a working environment where the gas partial pressure of hydrogen and oxygen is 200 kPa, the working temperature is 323.15 K, and the operating time is 350 s. The power curves and SOC conversion curves of each power source under the OPSA-ECMS energy management strategy are shown in the following figure. Figure 5 shown.

[0061] from Figure 4 It can be seen that the OPSA-ECMS energy management strategy can meet the system load power requirements, and the fuel cell works in the high-efficiency area, such as Figure 5 As shown, the SOC of the lithium-ion battery is always within the safe operating range, which can improve the overall working 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.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0066] Obviously, those skilled in the art can make various changes and modifications 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 equivalents, 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 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, c1 and c2 are the self-learning factor and the 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, 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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