CN115906275B - Method for rapidly evaluating energy distribution of solar aircraft based on GUI (graphical user interface) visualization - Google Patents
Method for rapidly evaluating energy distribution of solar aircraft based on GUI (graphical user interface) visualization Download PDFInfo
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Abstract
The invention provides a method for quickly evaluating the energy distribution of a solar aircraft based on GUI (graphical user interface) visualization, which comprises the steps of predicting the surface temperature of a photovoltaic cell by using a heat transfer model on the surface of a wing to obtain the temperature change of the surface of the photovoltaic cell; the real-time state and the input and output conditions of each participating energy flow system are researched by constructing a multi-system strong-coupling solar aircraft energy flow model; and the flight track of the aircraft is regulated and optimized by using a genetic algorithm and taking thrust, attack angle and roll angle as independent variables, so that the energy distribution condition and flight performance of the solar aircraft are improved. The method has strong applicability and is suitable for calculation and optimization of the energy distribution of the solar aircraft with any parameters and under different flight states in a three-dimensional space.
Description
Technical Field
The invention belongs to the technical field of aerospace, and particularly relates to a method for rapidly evaluating energy distribution of a solar aircraft based on GUI (graphical user interface) visualization.
Background
The solar energy aircraft has the advantages of cleanness, no pollution and the like, breaks through the range and altitude limit of the fossil fuel aircraft, and has extremely high application value. The solar energy aircraft converts solar radiation energy into electric energy through the photovoltaic assembly, part of the electric energy is supplied to the electric propulsion system to generate thrust to maintain the slow flight and normal operation of electronic equipment, and the rest electric energy is stored in the secondary battery for use in night cruising. The photoelectric conversion efficiency of the photovoltaic module is controlled by solar energy, the electronic equipment needs reasonable energy supply, and the energy distribution needs to be changed from time to time in order to ensure the flying attitude cruising process of the aircraft. Thus, full energy distribution will directly impact overall performance.
There are numerous systems involved in the energy flow of solar aircraft, the atmospheric environment, the photovoltaic cell system, the battery system, the power system and the movement of the aircraft itself. Therefore, a solar energy aircraft energy component model considering actual different working conditions needs to be constructed.
Among all the systems involved in energy flow, photovoltaic cells are most severely affected by the environment. Firstly, the photovoltaic cell can only utilize the component of solar radiation perpendicular to the surface of the cell, the traditional expression method for calculating the solar incident ray vector is limited in a ground coordinate system, and the consideration of the attitude of the aircraft is insufficient; secondly, the output of the photovoltaic cell is related to the actual temperature of the surface of the photovoltaic cell, and as the photovoltaic cell can convert most of solar energy into heat energy when generating electric energy, the surface temperature of the photovoltaic cell is not completely equal to the ambient temperature, so that modeling research on the input and output of the photovoltaic cell under different conditions is needed.
At present, the conversion rate of a photovoltaic cell is lower, the energy supply under different environmental conditions is unstable, and the energy storage of a storage battery is limited due to the limit of the capacity of the storage battery, so that the energy supply is sufficient in the flight process of an aircraft, the long-endurance flight of the solar aircraft is realized, how to perform energy management and distribution is one of important problems to be considered when the aircraft is designed, and the prediction of the total energy distribution and the optimization of the existing flight working conditions are the necessary routes in the process of researching the solar aircraft.
In order to accurately simulate the energy distribution of the solar unmanned aerial vehicle, related researches are carried out, zhu Bingjie and the like, the relation between the photovoltaic cell and the attitude angle of the solar unmanned aerial vehicle is researched, and the accurate solution of solar radiation can be obtained by referring to the relation between the incident solar ray vector in the airship and the airship body coordinate system, but the efficiency of the photovoltaic module is assumed to be unchanged in the research, and the change relation of the efficiency of the photovoltaic module along with the environmental conditions cannot be accurately reflected when the output and the input of the energy system are researched. Liu Li and the like describe the energy flow of a solar/hydrogen energy aircraft in detail based on weight energy balance, power matching and energy management strategies, but the research only simulates the energy distribution condition during uniform speed flight and cannot be directly applied to actual flight.
Adjusting the attitude of the flight is one of the methods to optimize the energy distribution. Wang Xiangyu proposes a multi-objective collaborative path planning algorithm for a solar unmanned aerial vehicle, which utilizes an improved ant colony algorithm to find an optimal path and optimize energy distribution, however, in this study it is assumed that photovoltaic cells always output at maximum power, and any flight trajectory cannot be simulated and optimized. Ni, etc. adopts neural networks to control the changes in thrust, angle of attack and roll angle to achieve energy maximization, but in this study it is assumed that the photovoltaic module efficiency is constant.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for rapidly evaluating the energy distribution of a solar aircraft based on GUI visualization, which can predict the energy distribution situation of the solar aircraft. In the method, the temperature of the surface of the photovoltaic cell is predicted by utilizing a heat transfer model of the surface of the wing, so as to obtain the temperature change of the surface of the photovoltaic cell; the real-time state and the input and output conditions of each participating energy flow system are researched by constructing a multi-system strong-coupling solar aircraft energy flow model; and the flight track of the aircraft is regulated and optimized by using a genetic algorithm and taking thrust, attack angle and roll angle as independent variables, so that the energy distribution condition and flight performance of the solar aircraft are improved.
The technical scheme of the invention is as follows:
the method for rapidly evaluating the energy distribution of the solar aircraft based on the GUI visualization comprises the following steps:
step 1: determining key parameters of a solar aircraft and a system participating in energy flow, and constructing a multi-system strong-coupling solar aircraft energy flow model;
step 2: designing a space motion track of an initial solar aircraft, taking the thrust, attack angle and rolling angle of the aircraft as key flight parameters, obtaining the motion characteristics including the flight speed, acceleration and yaw angle parameters and the stress characteristics including the lift force and resistance parameters under the characteristic of high aspect ratio and low Reynolds number, and obtaining the power required by maintaining the flight attitude of the aircraft in the flight process by the product of the thrust and the speed;
step 3: performing GUI-based visualized energy distribution evaluation of the solar aircraft: taking key parameters of the system participating in energy flow in the step 1 and thrust, attack angle and roll angle of the aircraft as inputs, outputting current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of the solar aircraft participating in the energy flow system under given working conditions according to the energy consumption relationship inside the aircraft, analyzing the parametric energy storage conditions of the photovoltaic cell and the storage battery according to the output parameter curves, and evaluating the energy distribution condition;
step 4: and (3) carrying out flight condition and energy distribution optimization:
and restraining the ranges of the thrust, attack angle and roll angle of the aircraft under different working conditions so that the charge state of the storage battery at the end of simulation is larger than or equal to the charge state at the beginning of simulation, and the maximum possible output energy of the photovoltaic cell is an optimization target, generating initial flight working conditions taking the thrust, attack angle and roll angle as independent variables, and screening a group of flight working conditions with the optimal charge state of the storage battery and optimal input and output of the battery by utilizing a genetic algorithm.
Further, in step 1, the system of the solar energy aircraft participating in the energy flow comprises a solar radiation system, a photovoltaic cell system, a storage battery system and a thrust system comprising a motor and a propeller.
Further, the key parameters comprise overall parameters of the solar aircraft, solar radiation parameters, photovoltaic cell parameters, battery key parameters and propeller key parameters; wherein:
the overall parameters of the solar aircraft comprise the aspect ratio, the reference wing area and the takeoff weight parameter information of the solar aircraft;
the solar radiation parameters comprise flight altitude, flight longitude and latitude and flight time;
the photovoltaic cell parameters comprise parameter information of open-circuit voltage, short-circuit current, maximum power point voltage, maximum power point current and photovoltaic cell laying rate of the photovoltaic cell in a standard state;
the key parameters of the storage battery comprise the corresponding relation of the battery capacity, the state of charge and the open-circuit voltage and the internal resistance parameter information;
the key parameters of the propeller comprise lift coefficient, drag coefficient, installation angle and propeller diameter parameter information.
Further, the model comprises a solar irradiance model, a heat transfer model, a photovoltaic cell model, a storage battery model, a propeller model, an aircraft kinematics model and an aircraft dynamics model, and the mathematical expression of each model is as follows:
solar irradiance model:
wherein S is total Representing the total solar irradiance; s is S beam Represents direct irradiance; s is S diffuse Representing the scattered irradiance; s is S pv Representing the solar irradiance that the photovoltaic cell can actually receive; the unit vector of the incident light of the sun is n s The method comprises the steps of carrying out a first treatment on the surface of the The unit vector of the surface normal of the photovoltaic cell is n p ；n b ＝[0,0,1] T ；T e b Representing a transformation matrix from a ground coordinate system to a body axis system, according to the relation with the attitude angle of the aircraft, the transformation matrix comprises:
wherein ψ represents a yaw angle, θ represents a pitch angle, and Φ represents a roll angle;
and (3) a heat transfer model:
wherein q' rad,sun Representing the heat radiation flux of the sun to the photovoltaic cells; q' rad,wing Representing the difference between the wing surface and the heat radiation flux of the external environment; q' conv Representing the heat convection flux between the upper surface of the wing and the ambient atmosphere; q' elec Representing the flux of electrical energy generated by the photovoltaic cells during this process; ρ pv Representing the areal density of the photovoltaic cell; (c) p ) pv Represents the specific heat capacity of the photovoltaic cell; t represents the wing surface temperature; t represents time;
photovoltaic cell model:
wherein V is pv And I pv Respectively outputting voltage and output current of the photovoltaic cell; i sc ,V oc ,I m ,V m Respectively short-circuit current, open-circuit voltage, maximum power point current and under any conditionMaximum power point voltage; under standard conditions, the short-circuit current of the photovoltaic cell circuit when in short-circuit is I scref The open circuit voltage at the time of open circuit is V ocref The corresponding maximum power point current is I when the output power of the battery reaches the maximum value mref The maximum power point voltage at this time is V mref ；S pv Solar irradiance received by a photovoltaic cell in unit area in unit time is calculated according to longitude and latitude, time and height parameters; s is S ref Representing a reference solar irradiance; Δt is the difference between the actual temperature of the battery and the reference temperature of the battery, Δt=t-T ref ；T ref Representing a battery reference temperature; Δs=s-S ref The difference value between the actual solar irradiance received by the battery and the reference solar irradiance is obtained; e is a natural logarithmic base; a, b, c are compensation coefficients;
storage battery model:
wherein the ideal voltage source voltage V ocbat Represents the electromotive force of the battery; r is (r) 0 And r 1 Representing the internal resistance of the storage battery; c (C) 1 A capacitor connected in parallel with the resistor; i bat Indicating the total current flowing through the battery; v (V) bat A voltage representative of a load across the battery; c (C) bata An amount of electricity representing a full charge state of the battery; c (C) bat0 Representing the electric quantity of the storage battery at the initial moment; SOC represents the state of charge of the battery, SOC 0 Representing the state of charge at the initial time; ≡I bat dt is the integral of the current through the battery from the initial state to the in-state over time;
propeller model:
wherein ρ represents the atmospheric density; c (C) Lp Representing the lift coefficient of the propeller; t (T) p Representing the propeller thrust; η (eta) p Representing propeller efficiency;the included angle between the local speed and the rotation plane is phi p ；γ p Is a rising resistance angle; s is S p Is the area of the propeller; v represents the aircraft speed;
aircraft dynamics model:
wherein V represents the flight speed; t represents thrust; d represents flight resistance; l represents the lift of the aircraft; w is the weight of the aircraft; alpha represents an attack angle, which is an angle between the speed direction and the axis of the aircraft; gamma represents the climbing angle;
is a roll angle; psi is the yaw angle; pitch angle θ=α+γ; g represents gravitational acceleration;
aircraft kinematic model:
wherein x, y and h respectively represent the displacement of the aircraft relative to the initial moment position under the ground coordinate system.
Further, in step 3, the energy management strategy in the flight is: the photovoltaic cell generates electric energy to provide energy for the whole aircraft, the storage battery is used for storing the electric energy when the photovoltaic cell generates enough energy, and is used for discharging to provide energy for the aircraft when the photovoltaic cell generates insufficient energy, and the state of charge of the storage battery is evaluated at all times so as to ensure that the state of charge of the storage battery is always in a (0.1, 1) interval, thereby ensuring that the storage battery is overcharged and overdischarged; the thrust system and the onboard equipment are both energy-consuming systems in the whole process.
Further, in step 3, the GUI visualized input sub-interfaces are respectively: aircraft flight information interface, position time information interface, photovoltaic cell parameter interface, battery parameter interface and thrust system parameter interface:
the aircraft flight information interface inputs basic model parameters and flight condition parameters of the aircraft; the position time information interface guides a user to input information of initial states such as longitude, latitude, altitude and the like of the aircraft in which the simulation initial state is located; the parameter interfaces of the photovoltaic cell and the storage battery respectively input relevant parameters of the battery, including current and voltage of the battery and resistance parameters; inputting relevant parameters of a motor and a propeller by a thrust system interface, wherein the relevant parameters comprise motor efficiency parameters, a lift resistance coefficient of the propeller, a mounting angle, a diameter of the propeller and the number of the propellers;
the output sub-interface output curves of the GUI visualization are respectively: a solar radiation intensity time-varying curve, a wing surface temperature time-varying curve, a photovoltaic cell power, a battery power, an on-board equipment power and a flight demand power time-varying curve, a photovoltaic cell and battery current voltage time-varying curve, a photovoltaic cell efficiency, a battery efficiency and a propeller efficiency time-varying curve, a battery state of charge time-varying curve, an aircraft attack angle, a climb angle, a pitch angle, a yaw angle and a roll angle time-varying curve, an aircraft thrust, a speed and altitude time-varying curve, an aircraft abscissa and time-varying curve and an aircraft flight path in three-dimensional space.
Advantageous effects
The invention provides a method for quickly evaluating the energy distribution of a solar aircraft based on GUI (graphical user interface) visualization, which adopts a solar aircraft energy component model under actual complex working conditions, a key flight parameter model under the flight working conditions of the solar aircraft and an energy management strategy to quickly predict and analyze the energy distribution of the solar aircraft; and the flight performance of the solar aircraft is effectively improved by utilizing a genetic algorithm. The method has strong applicability and is suitable for calculation and optimization of the energy distribution of the solar aircraft with any parameters and under different flight states in a three-dimensional space.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1: the invention relates to a design method schematic diagram for rapidly evaluating energy distribution of a solar aircraft based on GUI (graphical user interface) visualization
Fig. 2: the multi-system strong-coupling solar aircraft energy flow model
Fig. 3: the initial solar energy aircraft of the invention has a space motion track
Fig. 4: energy distribution of all systems of the invention
Fig. 5: GUI-based visual energy distribution evaluation system interface of the invention
Fig. 6: the invention relates to an energy distribution and track optimization algorithm flow chart
Fig. 7: the optimized solar aircraft space motion trail of the invention
Fig. 8: the invention optimizes the output curve of the front and rear photovoltaic cells
Fig. 9: the invention optimizes the charge state curve of the storage battery before and after
Detailed Description
The following detailed description of embodiments of the invention is exemplary and intended to be illustrative of the invention and not to be construed as limiting the invention.
In the prior art, the analysis of key components such as a photovoltaic module is lacking, and the energy distribution and optimization research of the solar aircraft under the complex working condition is insufficient. According to the invention, the mathematical physical model of the photovoltaic cell, the storage battery and the propeller is combined, the equivalent circuit and the heat transfer model are utilized to describe the energy distribution and the power characteristic of the solar aircraft in detail, and the calculation and the evaluation of the energy distribution of the solar aircraft under different working conditions can be performed by combining the kinematics and the dynamics equation of the aircraft. In order to find the optimal energy distribution, a genetic algorithm is used to optimize the flight path. In order to intuitively display the evaluation result, full-energy rapid evaluation software with friendly interface and visualization is also constructed. A schematic diagram of a design method for rapid assessment of solar aircraft energy distribution based on GUI visualization is shown in fig. 1.
First, key parameters of the solar aircraft and the systems participating in energy flow are determined, and a multi-system strong-coupling solar aircraft energy flow model is constructed. The constructed multi-system strongly coupled solar aircraft energy flow model is shown in fig. 2. In the multi-system strong coupling model shown in fig. 2, the influence of solar irradiance magnitude and direction, aircraft attitude angle and thermal effect on photovoltaic cell efficiency and output power is considered, the change condition of storage battery charge state and power with time based on a first-order RC equivalent circuit model is considered, the change condition of propeller model efficiency and process ratio with time based on a phyllin theory is considered, the relation of aircraft motion track in a three-dimensional space, aircraft thrust, attack angle, speed, attitude angle and lifting resistance is considered, and the capacity energy storage change of the photovoltaic cell and the storage battery can be obtained based on energy conservation according to the photovoltaic cell output energy magnitude, the storage battery residual capacity and the energy consumed by the aircraft.
Systems in which solar energy aircraft participate in energy flow include solar radiation systems, photovoltaic cell systems, battery systems, and thrust systems including electric motors and propellers.
The overall parameters of the solar aircraft comprise the aspect ratio, the reference wing area and the takeoff weight parameter information of the solar aircraft;
the solar radiation parameters comprise flight altitude, flight longitude and latitude and flight time;
the photovoltaic cell parameters comprise parameter information of open-circuit voltage, short-circuit current, maximum power point voltage, maximum power point current and photovoltaic cell laying rate of the photovoltaic cell in a standard state;
the key parameters of the storage battery comprise the corresponding relation of the battery capacity, the state of charge and the open-circuit voltage and the internal resistance parameter information;
the key parameters of the propeller comprise lift coefficient, drag coefficient, installation angle and propeller diameter parameter information.
In the embodiment, the aspect ratio of the solar aircraft is 22 and the reference wing area is 240m 2 The take-off weight of the aircraft is 9960N; the flying height of the aircraft at the initial moment is 10km, and the aircraft is initially flyingThe longitude and latitude are 40 degrees of east longitude and 120 degrees of north latitude, and the flight time at the initial moment is 3 months and 21 days 00:00; the total open circuit voltage of the photovoltaic cell on the solar aircraft under the standard state is 440V, the short circuit current is 116.4A, the maximum power point voltage is 360V, and the maximum power point current is 103.2A, and the laying rate of the photovoltaic cell on the wing surface is 0.88; the battery capacity of the storage battery is 500Ah, the initial charge state is 0.65, and the total open circuit voltage is 209V; the propeller blade adopts NACA 23012 airfoil, the lift coefficient and the resistance coefficient are obtained from an airfoil library, the installation angle is 20 degrees, and the diameter of the propeller disc is 2m.
In the modeling process, input and output of systems of the solar energy aircraft participating in energy flow are respectively analyzed, models of the systems are respectively built, the models comprise photovoltaic cell models which are obtained by utilizing coordinate transformation and heat transfer laws and combining an equivalent circuit model and are coupled with thermal effects, more accurate system model parameters are input, and the energy distribution situation of the solar energy aircraft is obtained by utilizing the real-time energy production situation of the photovoltaic cells, the residual electric quantity and the total capacity of the storage battery and the energy storage change of the photovoltaic cells and the storage battery based on the coupling relation among the systems participating in energy flow and based on an energy conservation energy management strategy.
The model comprises a solar irradiance model, a heat transfer model, a photovoltaic cell model, a storage battery model, a propeller model, an aircraft kinematics model and an aircraft dynamics model, and the mathematical expression of each model is as follows:
solar irradiance model:
wherein S is total Representing the total solar irradiance; s is S beam Represents direct irradiance; s is S diffuse Representing the scattered irradiance; s is S pv Representing the solar irradiance that the photovoltaic cell can actually receive; the unit vector of the incident light of the sun is n s The method comprises the steps of carrying out a first treatment on the surface of the The unit vector of the surface normal of the photovoltaic cell is n p ；n b ＝[0,0,1] T ；T e b Representing a transformation matrix from a ground coordinate system to a body axis system, according to the relation with the attitude angle of the aircraft, the transformation matrix comprises:
wherein ψ represents the yaw angle, θ represents the pitch angle, and Φ represents the roll angle.
And (3) a heat transfer model:
wherein q' rad,sun Representing the heat radiation flux of the sun to the photovoltaic cells; q' rad,wing Representing the difference between the wing surface and the heat radiation flux of the external environment; q' conv Representing the heat convection flux between the upper surface of the wing and the ambient atmosphere; q' elec Representing the flux of electrical energy generated by the photovoltaic cells during this process; ρ pv Representing the areal density of the photovoltaic cell; (c) p ) pv Represents the specific heat capacity of the photovoltaic cell; t represents the wing surface temperature; t represents time.
Photovoltaic cell model:
wherein V is pv And I pv Respectively outputting voltage and output current of the photovoltaic cell; i sc ,V oc ,I m ,V m Respectively short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage under any condition; under standard conditions, the short-circuit current of the photovoltaic cell circuit when in short-circuit is I scref The open circuit voltage at the time of open circuit is V ocref The corresponding maximum power point current is I when the output power of the battery reaches the maximum value mref The maximum power point voltage at this time is V mref ；S pv Solar irradiance received by photovoltaic cells per unit area per unit timeCalculating according to longitude and latitude, time and height parameters; s is S ref ＝1000W/m 2 Representing a reference solar irradiance; Δt is the difference between the actual temperature of the battery and the reference temperature of the battery, Δt=t-T ref . Battery reference temperature T ref ＝298.15K；ΔS＝S-S ref The difference value between the actual solar irradiance received by the battery and the reference solar irradiance is obtained; e is a natural logarithmic base, e= 2.71838; a, b, c are compensation coefficients, a=0.0025/K, b=0.0005W/m 2 ,c＝0.00288/K。
Storage battery model:
wherein the ideal voltage source voltage V ocbat Represents the electromotive force of the battery; r is (r) 0 And r 1 Representing the internal resistance of the storage battery; c (C) 1 A capacitor connected in parallel with the resistor; i bat Indicating the total current flowing through the battery; v (V) bat A voltage representative of a load across the battery; c (C) bata The electric quantity representing the full charge state of the storage battery, namely the rated capacity, is expressed as Ah; c (C) bat0 Representing the electric quantity of the storage battery at the initial moment; SOC represents the state of charge of the battery, SOC 0 Representing the state of charge at the initial time; ≡I bat dt is the integral of the current through the battery from the initial state to the in-state over time.
Propeller model:
wherein ρ represents the atmospheric density; c (C) Lp Representing the lift coefficient of the propeller; t (T) p Representing the propeller thrust; η (eta) p Representing propeller efficiency; the included angle between the local speed and the rotation plane is phi p ；γ p Is a rising resistance angle; s is S p Is the area of the propeller; v denotes the aircraft speed.
Aircraft dynamics model:
wherein V represents the flight speed; t represents thrust; d represents flight resistance; l represents the lift of the aircraft; w is the weight of the aircraft; alpha represents an attack angle, which is an angle between the speed direction and the axis of the aircraft; gamma represents the climbing angle;
is a roll angle; psi is the yaw angle; pitch angle θ=α+γ; g represents the gravitational acceleration. The thrust, attack angle and roll angle are independent variables, and other parameters in the formula are obtained by combining the three independent variables with the initial speed, acceleration, attack angle, attitude angle, climbing angle, flying height and flying position and then carrying out equation solving in the formula (7).
Aircraft kinematic model:
wherein x, y and h respectively represent the displacement of the aircraft relative to the initial moment position under the ground coordinate system.
And secondly, designing the space motion trail of the initial solar aircraft.
The thrust, attack angle and roll angle of the aircraft are taken as key flight parameters, the motion characteristics including the flight speed, acceleration and yaw angle parameters and the stress characteristics including the lift and resistance parameters are obtained under the characteristic of high aspect ratio and low Reynolds number, and the power required for maintaining the flight attitude of the aircraft in the flight process is obtained by the product of the thrust and the speed. The initial solar aircraft spatial motion profile is shown in fig. 3.
Third, an energy distribution assessment of the solar aircraft based on the GUI visualization is performed.
The key parameters of the system participating in the energy flow in the first step, the thrust, attack angle and roll angle of the aircraft are taken as inputs, current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of the solar aircraft participating in the energy flow system under given working conditions are output according to the energy consumption relation inside the aircraft, and the parametric energy storage conditions of the photovoltaic cell and the storage battery are analyzed according to the output parameter curves, so that the energy distribution condition is estimated.
The energy management strategy in the flight is as follows: the photovoltaic cell generates electric energy to provide energy for the whole aircraft, the storage battery is used for storing the electric energy when the photovoltaic cell generates enough energy, and is used for discharging to provide energy for the aircraft when the photovoltaic cell generates insufficient energy, and the state of charge of the storage battery is evaluated at all times so as to ensure that the state of charge of the storage battery is always within a (0.1, 1) interval, namely the overcharge and overdischarge of the battery are ensured; the thrust system and the onboard equipment are both energy-consuming systems in the whole process. The energy distribution of all the systems is obtained by combining the MATLAB tool and the energy management strategy and the power, current voltage and efficiency parameters or the state of charge parameters of the states of the systems participating in energy flow as shown in figure 4.
Based on the energy distribution evaluation of the GUI visualization, the parameters entered during the visualization include: the key parameters of the equivalent circuit of the aircraft battery, the parameters of the aspect ratio, the weight and the wing reference area of the aircraft and the flight track parameters taking the thrust, the attack angle and the roll angle as independent variables can simply, quickly and accurately analyze the full-energy flow condition, and predict the flight movement characteristics, the surface temperature parameters and the change condition of the power, the efficiency parameters and the electric characteristic parameters of each key energy system in the specified time under various complex working conditions and different parameter states. The user can predict the energy distribution of the aircraft under different flight conditions by adjusting the input parameters in the program, so that the application range of the software is increased, and the adaptability of the software is improved. The software displays all calculation results in the form of a graph, so that the expression of the state of the aircraft and the energy distribution state of the aircraft is more visual, and a user can conveniently analyze specific situations.
The GUI visualization-based energy distribution evaluation system input and output windows are shown in fig. 5. The input sub-interfaces are respectively: an aircraft flight information interface, a position time information interface, a photovoltaic cell parameter interface, a storage battery parameter interface and a thrust system parameter interface.
The aircraft flight information interface mainly inputs basic model parameters and flight working condition parameters of an aircraft, the position time information interface guides a user to input information of initial states such as longitude and latitude, altitude and the like of the aircraft in which an initial state is simulated, the photovoltaic cell and storage battery parameter interface respectively inputs relevant parameters of a battery, including battery current voltage and resistance parameters, and the thrust system interface inputs relevant parameters of a motor and a propeller, including motor efficiency parameters, a resistance coefficient of the propeller, a mounting angle, a propeller diameter and the number of the propellers. The output curves of the output sub-interfaces are respectively as follows: a solar radiation intensity time-varying curve, a wing surface temperature time-varying curve, a photovoltaic cell power, a battery power, an on-board equipment power and a flight demand power time-varying curve, a photovoltaic cell and battery current voltage time-varying curve, a photovoltaic cell efficiency, a battery efficiency and a propeller efficiency time-varying curve, a battery state of charge time-varying curve, an aircraft attack angle, a climb angle, a pitch angle, a yaw angle and a roll angle time-varying curve, an aircraft thrust, a speed and altitude time-varying curve, an aircraft abscissa and time-varying curve and an aircraft flight path in three-dimensional space. Wherein the power profile over time can reflect the flow of energy within the aircraft.
And fourthly, carrying out flight working condition and energy distribution optimization.
The flying process comprises low-altitude spiral, climbing flight, high-altitude flight and gliding, wherein the ranges of the thrust, attack angle and roll angle of the aircraft under different working conditions are constrained, so that the charge state of the storage battery at the end of simulation is larger than or equal to the charge state at the initial stage of simulation, the maximum output energy of the photovoltaic cell is an optimization target, the initial flying working conditions taking the thrust, attack angle and roll angle as independent variables are generated, and the cross, genetic and variation are utilized to screen out a group of flying working conditions with the optimal charge state of the storage battery and the optimal input and output of the battery. The optimization process needs to set the population number and the maximum iteration number, and the calculation is stopped when the iteration is equal to the maximum iteration number or the optimized working condition is basically unchanged.
The optimization objective is expressed in mathematical expression as: max f 1 ＝SOC end -SOC 0 ＞0，max f 2 ＝∫P pm dt. Wherein SOC is end Indicating the state of charge, SOC, of the battery at the end of the simulation 0 Representing the state of charge of the storage battery at the initial moment of simulation, P pm The maximum power which can be output by the storage battery is represented, and t represents the simulation moment. Based on the concept of gravitational potential energy storage and two optimization targets, the genetic algorithm is utilized to improve the high-altitude long-endurance performance of the solar aircraft and output an optimization result. Setting the population quantity N po And maximum number of iterations N it 。p c Represents the crossover probability, p m Representing the mutation probability of the real coding vector. And (3) judging the dominant level of individuals in the population, carrying out rapid non-dominant sequencing, and screening out reasonable new parent generation according to the crowding degree. Generating a new population through crossover and mutation operations, and continuing iteration until an optimization target is met. A flow chart of the energy distribution and trajectory optimization algorithm is shown in fig. 6.
The total solar energy in one day per unit area after optimization is 5.25 multiplied by 10 8 J/m 2 Compared with the optimization before the optimization, the method improves the temperature by 5 multiplied by 10 5 J/m 2 The solar energy receivable by the photovoltaic cell is 2.78X10 7 J/m 2 Compared with the optimization, the method is improved by 1.2 multiplied by 10 5 J/m 2 The method comprises the steps of carrying out a first treatment on the surface of the After optimization, the total maximum energy which can be output by the photovoltaic cell is 9.92 multiplied by 10 8 J is increased by 5.4X10 than before optimization 7 J; the total energy output of the photovoltaic cell is 7.53 multiplied by 10 8 J is increased by 4.8X10 than before optimization 7 J; the total energy of discharge of the storage battery is 3.05X10 8 J is reduced by 2.8X10 than before optimization 7 J; the total energy of the battery charge is 3.17 multiplied by 10 8 J is increased by 1.4X10 before optimization 6 J; when the aircraft spirals around 10km, the total energy consumed by the aircraft is 3.23 multiplied by 10 8 J is reduced by 5.8X10 than before optimization 7 J; the state of charge of 24:00 after optimization is 0.667, 0.087 is increased before optimization, 0.017 is increased when the state of charge is 0:00, and the storage battery can supply energy to the aircraft under the working condition until the first timeThe two-day photovoltaic cells can be powered individually. The optimized flight trajectory is shown in fig. 7, and the comparison of the photovoltaic cell output and the battery state of charge before and after optimization is shown in fig. 8 and 9, respectively.
The energy distribution optimizing process divides the flight process into four working conditions of spiral flight at the 10km height, climbing flight, spiral flight at the 15km height and gliding according to a genetic algorithm, and the state of charge of the storage battery is set to be in the (0.1, 1) interval. The method comprises the steps of generating N with thrust, attack angle and roll angle as independent variables by taking the maximum total maximum energy which can be output by a photovoltaic cell in the simulation process as an optimization target, wherein the charge state of a 24:00 storage battery on the same day is greater than or equal to the charge state on the same day as 0:00 and the charge state at the simulation end time is the maximum p Screening a group of flight conditions with optimal charge state of the storage battery and optimal input and output of the storage battery by utilizing intersection, inheritance and variation of the initial flight conditions, and population quantity N p Set to 20, maximum number of iterations N it 500, stopping calculation when the iteration is equal to the maximum iteration number or the optimized working condition is basically unchanged.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made in the above embodiments by those skilled in the art without departing from the spirit and principles of the invention.
Claims (5)
1. A method for rapidly assessing solar aircraft energy distribution based on GUI visualization, characterized by: the method comprises the following steps:
step 1: determining key parameters of a solar aircraft and a system participating in energy flow, and constructing a multi-system strong-coupling solar aircraft energy flow model;
the model comprises a solar irradiance model, a heat transfer model, a photovoltaic cell model, a storage battery model, a propeller model, an aircraft kinematics model and an aircraft dynamics model, and the mathematical expression of each model is as follows:
solar irradiance model:
wherein S is total Representing the total solar irradiance; s is S beam Represents direct irradiance; s is S diffuse Representing the scattered irradiance; s is S pv Representing the solar irradiance that the photovoltaic cell can actually receive; the unit vector of the incident light of the sun is n s The method comprises the steps of carrying out a first treatment on the surface of the The unit vector of the surface normal of the photovoltaic cell is n p ；n b ＝[0,0,1] T ；T e b Representing a transformation matrix from a ground coordinate system to a body axis system, according to the relation with the attitude angle of the aircraft, the transformation matrix comprises:
wherein ψ represents a yaw angle, θ represents a pitch angle, and Φ represents a roll angle;
and (3) a heat transfer model:
wherein q' rad,sun Representing the heat radiation flux of the sun to the photovoltaic cells; q' rad,wing Representing the difference between the wing surface and the heat radiation flux of the external environment; q' conv Representing the heat convection flux between the upper surface of the wing and the ambient atmosphere; q' elec Representing the flux of electrical energy generated by the photovoltaic cells during this process; ρ pv Representing the areal density of the photovoltaic cell; (c) p ) pv Represents the specific heat capacity of the photovoltaic cell; t represents the wing surface temperature; t represents time;
photovoltaic cell model:
wherein V is pv And I pv Respectively outputting voltage and output current of the photovoltaic cell; i sc ,V oc ,I m ,V m Respectively short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage under any condition; under standard conditions, the short-circuit current of the photovoltaic cell circuit when in short-circuit is I scref The open circuit voltage at the time of open circuit is V ocref The corresponding maximum power point current is I when the output power of the battery reaches the maximum value mref The maximum power point voltage at this time is V mref ；S pv Solar irradiance received by a photovoltaic cell in unit area in unit time is calculated according to longitude and latitude, time and height parameters; s is S ref Representing a reference solar irradiance; Δt is the difference between the actual temperature of the battery and the reference temperature of the battery, Δt=t-T ref ；T ref Representing a battery reference temperature; Δs=s-S ref The difference value between the actual solar irradiance received by the battery and the reference solar irradiance is obtained; e is a natural logarithmic base; a, b, c are compensation coefficients;
storage battery model:
wherein the ideal voltage source voltage V ocbat Represents the electromotive force of the battery; r is (r) 0 And r 1 Representing the internal resistance of the storage battery; c (C) 1 A capacitor connected in parallel with the resistor; i bat Indicating the total current flowing through the battery; v (V) bat A voltage representative of a load across the battery; c (C) bata An amount of electricity representing a full charge state of the battery; c (C) bat0 Representing the electric quantity of the storage battery at the initial moment; SOC represents the state of charge of the battery, SOC 0 Representing the state of charge at the initial time; ≡I bat dt is the integral of the current through the battery from the initial state to the in-state over time;
propeller model:
wherein ρ represents the atmospheric density; c (C) Lp Representing the lift coefficient of the propeller; t (T) p Representing the propeller thrust; η (eta) p Representing propeller efficiency; the included angle between the local speed and the rotation plane is phi p ；γ p Is a rising resistance angle; s is S p Is the area of the propeller; v represents the aircraft speed;
aircraft dynamics model:
wherein V represents the flight speed; t represents thrust; d represents flight resistance; l represents the lift of the aircraft; w is the weight of the aircraft; alpha represents an attack angle, which is an angle between the speed direction and the axis of the aircraft; gamma represents the climbing angle;
is a roll angle; psi is the yaw angle; pitch angle θ=α+γ; g represents gravitational acceleration;
aircraft kinematic model:
wherein x, y and h respectively represent displacement of the aircraft relative to the initial moment position under a ground coordinate system;
step 2: designing a space motion track of an initial solar aircraft, taking the thrust, attack angle and rolling angle of the aircraft as key flight parameters, obtaining the motion characteristics including the flight speed, acceleration and yaw angle parameters and the stress characteristics including the lift force and resistance parameters under the characteristic of high aspect ratio and low Reynolds number, and obtaining the power required by maintaining the flight attitude of the aircraft in the flight process by the product of the thrust and the speed;
step 3: performing GUI-based visualized energy distribution evaluation of the solar aircraft: taking key parameters of the system participating in energy flow in the step 1 and thrust, attack angle and roll angle of the aircraft as inputs, outputting current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of the solar aircraft participating in the energy flow system under given working conditions according to the energy consumption relationship inside the aircraft, analyzing the parametric energy storage conditions of the photovoltaic cell and the storage battery according to the output parameter curves, and evaluating the energy distribution condition;
step 4: and (3) carrying out flight condition and energy distribution optimization:
and restraining the ranges of the thrust, attack angle and roll angle of the aircraft under different working conditions so that the charge state of the storage battery at the end of simulation is larger than or equal to the charge state at the beginning of simulation, and the maximum possible output energy of the photovoltaic cell is an optimization target, generating initial flight working conditions taking the thrust, attack angle and roll angle as independent variables, and screening a group of flight working conditions with the optimal charge state of the storage battery and optimal input and output of the battery by utilizing a genetic algorithm.
2. A method for rapidly assessing solar aircraft energy distribution based on GUI visualization as claimed in claim 1, wherein: in step 1, the system of the solar energy aircraft participating in energy flow comprises a solar radiation system, a photovoltaic cell system, a storage battery system and a thrust system comprising a motor and a propeller.
3. A method for rapidly assessing solar aircraft energy distribution based on GUI visualization as claimed in claim 1, wherein: the key parameters comprise the overall parameters of the solar aircraft, solar radiation parameters, photovoltaic cell parameters, storage battery key parameters and propeller key parameters; wherein:
the overall parameters of the solar aircraft comprise the aspect ratio, the reference wing area and the takeoff weight parameter information of the solar aircraft;
the solar radiation parameters comprise flight altitude, flight longitude and latitude and flight time;
the photovoltaic cell parameters comprise parameter information of open-circuit voltage, short-circuit current, maximum power point voltage, maximum power point current and photovoltaic cell laying rate of the photovoltaic cell in a standard state;
the key parameters of the storage battery comprise the corresponding relation of the battery capacity, the state of charge and the open-circuit voltage and the internal resistance parameter information;
the key parameters of the propeller comprise lift coefficient, drag coefficient, installation angle and propeller diameter parameter information.
4. A method for rapidly assessing solar aircraft energy distribution based on GUI visualization as claimed in claim 1, wherein: in step 3, the energy management strategy in the flight is: the photovoltaic cell generates electric energy to provide energy for the whole aircraft, the storage battery is used for storing the electric energy when the photovoltaic cell generates enough energy, and is used for discharging to provide energy for the aircraft when the photovoltaic cell generates insufficient energy, and the state of charge of the storage battery is evaluated at all times so as to ensure that the state of charge of the storage battery is always in a (0.1, 1) interval, thereby ensuring that the storage battery is overcharged and overdischarged; the thrust system and the onboard equipment are both energy-consuming systems in the whole process.
5. A method for rapidly assessing solar aircraft energy distribution based on GUI visualization as claimed in claim 1, wherein: in step 3, the GUI visual input sub-interfaces are respectively: aircraft flight information interface, position time information interface, photovoltaic cell parameter interface, battery parameter interface and thrust system parameter interface:
the aircraft flight information interface inputs basic model parameters and flight condition parameters of the aircraft; the position time information interface guides a user to input information of the initial state of longitude and latitude and altitude of the aircraft in the initial simulation state; the parameter interfaces of the photovoltaic cell and the storage battery respectively input relevant parameters of the battery, including current and voltage of the battery and resistance parameters; inputting relevant parameters of a motor and a propeller by a thrust system interface, wherein the relevant parameters comprise motor efficiency parameters, a lift resistance coefficient of the propeller, a mounting angle, a diameter of the propeller and the number of the propellers;
the output sub-interface output curves of the GUI visualization are respectively: a solar radiation intensity time-varying curve, a wing surface temperature time-varying curve, a photovoltaic cell power, a battery power, an on-board equipment power and a flight demand power time-varying curve, a photovoltaic cell and battery current voltage time-varying curve, a photovoltaic cell efficiency, a battery efficiency and a propeller efficiency time-varying curve, a battery state of charge time-varying curve, an aircraft attack angle, a climb angle, a pitch angle, a yaw angle and a roll angle time-varying curve, an aircraft thrust, a speed and altitude time-varying curve, an aircraft abscissa and time-varying curve and an aircraft flight path in three-dimensional space.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104182560A (en) * | 2014-01-08 | 2014-12-03 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | Aircraft flutter prediction and analysis method and device |
| CN107272721A (en) * | 2017-07-11 | 2017-10-20 | 北京昶远科技有限公司 | A kind of solar energy unmanned aerial vehicle flight path planing method flown based on day-night cycle |
| CN109408987A (en) * | 2018-11-01 | 2019-03-01 | 西北工业大学 | A kind of three-dimensional high lift device parametric method for meeting Engineering constraint based on FFD method |
| CN109740878A (en) * | 2018-12-20 | 2019-05-10 | 北京航空航天大学 | A kind of solar powered aircraft continuation of the journey appraisal procedure based on net energy distribution map |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11640180B2 (en) * | 2021-02-17 | 2023-05-02 | Northrop Grumman Systems Corporation | Systems and methods for flight path optimization |
-
2022
- 2022-10-31 CN CN202211345095.1A patent/CN115906275B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104182560A (en) * | 2014-01-08 | 2014-12-03 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | Aircraft flutter prediction and analysis method and device |
| CN107272721A (en) * | 2017-07-11 | 2017-10-20 | 北京昶远科技有限公司 | A kind of solar energy unmanned aerial vehicle flight path planing method flown based on day-night cycle |
| CN109408987A (en) * | 2018-11-01 | 2019-03-01 | 西北工业大学 | A kind of three-dimensional high lift device parametric method for meeting Engineering constraint based on FFD method |
| CN109740878A (en) * | 2018-12-20 | 2019-05-10 | 北京航空航天大学 | A kind of solar powered aircraft continuation of the journey appraisal procedure based on net energy distribution map |
Non-Patent Citations (4)
| Title |
|---|
| A Novel Approach for Optimization of Nozzle Angle and Thrust Vectoring Controller via a Sub-Mutation Genetic Algorithm;MEHMET CEM CATALBAS 等;《International journal of innovative computing, information & control: IJICIC》;第13卷(第6期);1929-1940 * |
| 倾转旋翼飞行器飞行动力学数学建模方法研究;曹芸芸;《中国博士学位论文全文数据库 工程科技Ⅱ辑》(第7期);C031-5 * |
| 基于机翼-帆尾的高纬度跨年驻留太阳能飞机总体参数设计方法;昌敏 等;《航空学报》;第35卷(第6期);1592-1603 * |
| 基于统计能量理论的飞行器壁板高频声振疲劳寿命预报及参数设计;乔扬 等;《工程力学》;第36卷(第9期);230-236、256 * |
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