CN-122021378-A - Multi-element hydrogen storage-oriented unmanned aerial vehicle capacity configuration optimization method and system

CN122021378ACN 122021378 ACN122021378 ACN 122021378ACN-122021378-A

Abstract

A multi-element hydrogen storage oriented unmanned aerial vehicle capacity configuration optimization method and system comprise the steps of constructing physical models of hydrogen fuel cells, lithium battery packs and hydrogen storage tanks of different hydrogen storage forms, taking hydrogen storage forms and corresponding hydrogen storage parameters as decision variables, generating constraints of power supply of the hydrogen fuel cells and the lithium battery packs by combining the physical models of the hydrogen fuel cells, the lithium battery packs and the hydrogen storage tanks of different hydrogen storage forms, wherein the constraints of power supply comprise energy balance constraints and power constraints, constructing constraints of hydrogen storage by combining the physical models of the hydrogen storage tanks of different hydrogen storage forms, the constraints of hydrogen storage comprise unified maximum hydrogen supply rate constraints and thermal management coupling constraints, constructing a plurality of objective functions, linearizing nonlinear parameters taking the decision variables as independent variables in all constraint conditions and the objective functions, and performing multi-objective optimization by an epsilon-constraint method. The invention obviously improves the overall economy and performance of the unmanned aerial vehicle energy system.

Inventors

LIU YUEXIN
DUAN CHENYANG
CHE CHAO
He Diecong
LI HAITAO
SHI WEIHENG
LIU CHANG

Assignees

国网江苏省电力有限公司常州供电分公司

Dates

Publication Date: 20260512
Application Date: 20251215

Claims (12)

1. The unmanned aerial vehicle capacity allocation optimization method for multi-element hydrogen storage is characterized by comprising the steps of supplying power to an unmanned aerial vehicle system, wherein the power supply comprises a hydrogen fuel cell and a lithium battery pack, and a hydrogen storage tank supplies hydrogen to the hydrogen fuel cell, and the method is characterized by comprising the following steps: Constructing physical models of hydrogen storage tanks of hydrogen fuel cells, lithium battery packs and different hydrogen storage forms, wherein the hydrogen storage forms comprise high-pressure gaseous hydrogen storage, liquid hydrogen storage and/or solid hydrogen storage; generating constraints of power supply of the hydrogen fuel cell and the lithium battery pack by taking the selection of the hydrogen storage form and corresponding hydrogen storage parameters as decision variables and combining physical models of the hydrogen fuel cell, the lithium battery pack and hydrogen storage tanks of different hydrogen storage forms, wherein the constraints of power supply comprise energy balance constraints and power constraints, constructing constraints of hydrogen storage by combining physical models of the hydrogen storage tanks of different hydrogen storage forms, and constructing a plurality of objective functions, wherein the constraints of hydrogen storage comprise unified maximum hydrogen supply rate constraints and thermal management coupling constraints; Linearizing all constraint conditions and nonlinear parameters taking decision variables as independent variables in the objective function, converting the objective function into a mixed integer linear problem, and performing multi-objective optimization through an epsilon-constraint method.
2. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 1, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: the energy balance constraint is specifically as follows: The method comprises the steps of multiplying the electric efficiency of a hydrogen fuel cell system by the lower heating value of hydrogen and multiplying the electric efficiency of the hydrogen fuel cell system by the total effective available hydrogen mass to be used as the energy supply of the hydrogen fuel cell, multiplying the charge-discharge efficiency of a lithium battery pack by the available energy of the lithium battery pack, obtaining the electric efficiency of the hydrogen fuel cell system by a physical model of the hydrogen fuel cell, obtaining the available energy of the lithium battery pack by the physical model of the lithium battery pack, and obtaining the energy of the lithium battery pack by a battery pack; the energy balance constraint is that the energy supply of the hydrogen fuel cell and the available energy of the lithium battery pack are equal to or more than the sum of the aerodynamic, propulsion and mission load power consumption of the unmanned aerial vehicle.
3. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 2, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: The effective available hydrogen mass is specifically: if the high-pressure gaseous hydrogen is stored, the effective available hydrogen mass of the high-pressure gaseous hydrogen is the total hydrogen mass of initial storage corresponding to the high-pressure gaseous hydrogen, otherwise, the total hydrogen mass is 0; If the liquid hydrogen is stored, the effective available hydrogen mass of the liquid hydrogen is the initial stored total hydrogen mass corresponding to the liquid hydrogen, subtracting the total evaporation loss mass, otherwise, the total evaporation loss mass is 0, wherein the total evaporation loss mass is the liquid hydrogen density multiplied by the standardized daily evaporation rate multiplied by the task total navigation time; if solid hydrogen is stored, the effective available hydrogen mass of the solid hydrogen is the total hydrogen mass of initial storage corresponding to the solid hydrogen multiplied by the reversible efficiency of the solid hydrogen, otherwise, the effective available hydrogen mass of the solid hydrogen is 0; And adding the effective available hydrogen mass of the high-pressure gas hydrogen storage, the effective available hydrogen mass of the liquid hydrogen storage and the effective available hydrogen mass of the solid hydrogen storage to obtain the total effective available hydrogen mass.
4. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 1, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: The power constraint is specifically as follows: the power constraints include power balance constraints and power response synergy constraints; the power balance constraint is that the maximum output power of the hydrogen fuel cell plus the maximum output power of the lithium battery pack is larger than or equal to the maximum value of the unmanned plane power; The power response co-constraint is that the power versus time derivative of the hydrogen fuel cell is less than or equal to the dynamic gain function corresponding to the hydrogen storage form multiplied by the hydrogen mass versus time derivative.
5. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 4, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: the dynamic gain function is specifically: For high-pressure gaseous hydrogen storage, the dynamic gain function is a set constant value; For liquid hydrogen storage, the dynamic gain function is obtained by looking up a table according to the temperature of the gasifier; For solid hydrogen storage, the dynamic gain function is obtained by looking up a table according to the bed temperature and mass flow rate.
6. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 1, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: The unified maximum hydrogen supply rate constraint is specifically: the unified maximum hydrogen supply rate is greater than or equal to the set rate of maximum output power requirement of the hydrogen fuel cell; the unified maximum hydrogen supply rate is added by the maximum hydrogen supply rates of the high-pressure gas hydrogen storage, the liquid hydrogen storage and the solid hydrogen storage, and if the high-pressure gas hydrogen storage, the liquid hydrogen storage or the solid hydrogen storage does not exist, the maximum hydrogen supply rate corresponding to the hydrogen storage form is 0; Inputting the maximum value of the pressure of the hydrogen storage tank and the valve flow at each moment to a physical simulation model to simulate to obtain the hydrogen supply rate of the high-pressure gaseous hydrogen storage at each moment, wherein the maximum value of the hydrogen supply rates of the high-pressure gaseous hydrogen storage at each moment is the maximum hydrogen supply rate of the high-pressure gaseous hydrogen storage; Inputting the evaporation pressure of the Dewar bottle and the maximum heat exchange value of the gasifier at each moment to a physical simulation model to simulate so as to obtain the hydrogen supply rate of the liquid hydrogen storage at each moment, wherein the maximum value in the hydrogen supply rate of the liquid hydrogen storage at each moment is the maximum hydrogen supply rate of the liquid hydrogen storage; and inputting the metal hydride bed temperature and the metal hydride charge state at each moment into a physical simulation model to simulate to obtain the hydrogen supply rate of the solid hydrogen storage at each moment, wherein the maximum value of the hydrogen supply rate of the solid hydrogen storage at each moment is the maximum hydrogen supply rate of the solid hydrogen storage.
7. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 1, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: the thermal management coupling constraint is specifically: The real-time waste heat power of the fuel cell plus the heat exchange power between the environment and the reaction bed is greater than or equal to the desorption reaction enthalpy of the solid-state hydrogen storage material multiplied by the real-time hydrogen release rate of the solid-state hydrogen storage plus other heat losses.
8. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 3, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: the plurality of objective functions specifically include: the decision targets are the corresponding hydrogen storage parameters of the selected hydrogen storage form and the selected one or more hydrogen storage forms, the initial pressure and the storage tank capacity for the high-pressure gaseous hydrogen storage parameters, the Dewar bottle capacity for the liquid hydrogen storage parameters, the material type and the capacity for the solid hydrogen storage parameters, and the material type is converted into numerical data; the multiple objective functions are respectively minimizing total cost, minimizing total mass and maximizing total dead time; obtaining total cost and total navigation time under different decision variables through simulation, and fitting the total cost and total navigation time into a nonlinear model of the decision variables; The total mass is the sum of the masses of the selected hydrogen storage forms; For high-pressure gaseous hydrogen storage, the mass is the mass of a storage tank plus the total hydrogen mass initially stored, wherein the mass of the storage tank is the set first proportional coefficient multiplied by the working pressure and then multiplied by the capacity of the storage tank plus the fixed mass of an end structure; For liquid hydrogen storage, the mass is the Dewar flask mass plus the total hydrogen mass initially stored, wherein the Dewar flask mass is the second set proportionality coefficient multiplied by the 0.8 th power of the volume; For solid state hydrogen storage, the mass is the sum of the mass of the metal hydride, the mass of the reaction vessel, and the mass of the thermal management system, the mass of the metal hydride being the effective available hydrogen mass for solid state hydrogen storage divided by the mass hydrogen storage density of the material.
9. The unmanned aerial vehicle capacity configuration optimization method for multi-element hydrogen storage according to claim 1, wherein the unmanned aerial vehicle capacity configuration optimization method is characterized by comprising the following steps of: Linearizing nonlinear parameters taking decision variables as independent variables in all constraint conditions and objective functions, wherein the nonlinear parameters comprise the following specific steps: piecewise linearization using SOS2 constraints is employed to linearize nonlinear parameters with decision variables as arguments.
10. A multi-element hydrogen storage oriented unmanned aerial vehicle capacity configuration optimization system based on the method of any one of claims 1-9, comprising a model construction module, a constraint condition and objective function construction module, a linearization module and a multi-objective optimization module, characterized in that: The model building module is used for building physical models of hydrogen storage tanks of hydrogen fuel cells, lithium battery packs and different hydrogen storage forms, wherein the hydrogen storage forms comprise high-pressure gaseous hydrogen storage, liquid hydrogen storage and/or solid hydrogen storage; The constraint condition and objective function construction module is used for generating constraints of power supply by combining physical models of hydrogen fuel cells, lithium battery packs and hydrogen storage tanks of different hydrogen storage forms by taking hydrogen storage forms and corresponding hydrogen storage parameters as decision variables, wherein the constraints of power supply comprise energy balance constraints and power constraints, constructing constraints of hydrogen storage by combining physical models of hydrogen storage tanks of different hydrogen storage forms, and constructing a plurality of objective functions; the linearization module is used for linearizing all constraint conditions and nonlinear parameters taking decision variables as independent variables in the objective function, and converting the objective function into a mixed integer linearity problem; And the multi-objective optimization module is used for performing multi-objective optimization through an epsilon-constraint method.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 9 when the computer program is executed.
12. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the method according to any of claims 1 to 9.

Description

Multi-element hydrogen storage-oriented unmanned aerial vehicle capacity configuration optimization method and system Technical Field The invention belongs to the technical field of power systems of hydrogen energy unmanned aerial vehicles, and particularly relates to a capacity configuration optimization method and system of an unmanned aerial vehicle for multi-element hydrogen storage. Background The hydrogen fuel cell unmanned aerial vehicle becomes a research hot spot for long-endurance application due to the advantages of high energy density and zero emission. However, the form of storage and transportation of hydrogen energy directly determines the weight, volume, safety and overall performance of the overall energy system. The prior main hydrogen storage technology comprises mature high-pressure gas hydrogen storage technology, low cost, low hydrogen storage density (especially volume density), need of a thick hydrogen storage tank, unfavorable for the weight reduction of an unmanned aerial vehicle, extremely high hydrogen storage density of liquid hydrogen, need of an extremely low-temperature (-253 ℃) heat preservation container, continuous evaporation loss problem, inapplicability to intermittent and long-time tasks, high safety of solid hydrogen storage, low-pressure operation of the solid hydrogen storage by materials such as metal hydride, and possible thermal management problem in the hydrogen absorption and desorption process. The existing unmanned aerial vehicle design generally only adopts a single hydrogen storage form, and cannot be optimally selected according to specific task profiles (such as endurance, voyage, ambient temperature and load power consumption). For example, the simple selection of a liquid hydrogen system for extremely space-time pursuits may be uneconomical in short tasks due to evaporation losses and thermos weight. In addition, when the hydrogen storage system and the storage battery are configured in a hybrid mode, dynamic output characteristics (such as starting speed and power response) of different hydrogen storage modes are huge, and how to cooperatively configure the hydrogen storage capacity and the battery capacity becomes a complex multivariable optimization problem. Disclosure of Invention In order to solve the defects in the prior art, the invention provides an unmanned aerial vehicle capacity configuration optimization method and system for multi-element hydrogen storage. The invention adopts the following technical scheme. The first aspect of the present invention provides a method for optimizing capacity configuration of an unmanned aerial vehicle for multi-element hydrogen storage, wherein power supply of an unmanned aerial vehicle system includes a hydrogen fuel cell and a lithium battery pack, and a hydrogen storage tank supplies hydrogen to the hydrogen fuel cell, comprising: Constructing physical models of hydrogen storage tanks of hydrogen fuel cells, lithium battery packs and different hydrogen storage forms, wherein the hydrogen storage forms comprise high-pressure gaseous hydrogen storage, liquid hydrogen storage and/or solid hydrogen storage; generating constraints of power supply of the hydrogen fuel cell and the lithium battery pack by taking the selection of the hydrogen storage form and corresponding hydrogen storage parameters as decision variables and combining physical models of the hydrogen fuel cell, the lithium battery pack and hydrogen storage tanks of different hydrogen storage forms, wherein the constraints of power supply comprise energy balance constraints and power constraints, constructing constraints of hydrogen storage by combining physical models of the hydrogen storage tanks of different hydrogen storage forms, and constructing a plurality of objective functions, wherein the constraints of hydrogen storage comprise unified maximum hydrogen supply rate constraints and thermal management coupling constraints; Linearizing all constraint conditions and nonlinear parameters taking decision variables as independent variables in the objective function, converting the objective function into a mixed integer linear problem, and performing multi-objective optimization through an epsilon-constraint method. Preferably, the energy balance constraint is specifically: The method comprises the steps of multiplying the electric efficiency of a hydrogen fuel cell system by the lower heating value of hydrogen and multiplying the electric efficiency of the hydrogen fuel cell system by the total effective available hydrogen mass to be used as the energy supply of the hydrogen fuel cell, multiplying the charge-discharge efficiency of a lithium battery pack by the available energy of the lithium battery pack, obtaining the electric efficiency of the hydrogen fuel cell system by a physical model of the hydrogen fuel cell, obtaining the available energy of the lithium battery pack by the physical model of the lithium battery pack, and obtaining the