CN115906275A - A method for quickly evaluating the energy distribution of solar-powered aircraft based on GUI visualization - Google Patents

Abstract

The invention provides a method for quickly evaluating energy distribution of a solar aircraft based on GUI visualization, which comprises the steps of predicting the surface temperature of a photovoltaic cell by utilizing a heat transfer model of the surface of a wing to obtain the surface temperature change of the photovoltaic cell; the method comprises the following steps of (1) researching the real-time state and input and output conditions of each energy flow system by constructing a multi-system strong coupling solar aircraft energy flow model; the flight trajectory of the aircraft is regulated and optimized by using a genetic algorithm and taking the thrust, the attack angle and the roll angle as independent variables, so that the energy distribution condition and the flight performance of the solar aircraft are improved. The method has stronger applicability, and is suitable for calculating and optimizing the energy distribution of the solar aircraft with any parameter and under different flight states in a three-dimensional space.

Description

A method for quickly evaluating the energy distribution of solar aircraft based on GUI visualization

Technical Field

The invention belongs to the field of aerospace technology, and in particular relates to a method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization.

Background Art

Solar aircraft have the advantages of being clean and pollution-free, breaking through the range and altitude limits of fossil fuel aircraft, and have great application value. Solar aircraft convert solar radiation energy into electrical energy through photovoltaic modules. Part of the electrical energy is supplied to the electric propulsion system to generate thrust to maintain hovering flight and normal operation of electronic equipment, and the remaining electrical energy is stored in secondary batteries for nighttime cruising. The photoelectric conversion efficiency of photovoltaic modules is controlled by solar energy, and electronic equipment requires a reasonable energy supply. In addition, energy distribution needs to change from time to time to ensure the flight attitude of the aircraft during the cruising process. Therefore, the energy distribution of the entire aircraft will directly affect the overall performance.

There are many systems involved in the energy flow of solar aircraft, including the atmospheric environment, photovoltaic cell system, battery system, power system and the movement of the aircraft itself. Therefore, it is necessary to build a solar aircraft energy component model that takes into account different actual working conditions.

Among all the systems involved in energy flow, photovoltaic cells are most severely affected by the environment. First, photovoltaic cells can only use the component of solar radiation perpendicular to the cell surface. The traditional method of calculating the solar incident ray vector is limited to the ground coordinate system, and the aircraft attitude is not fully considered. Secondly, the output of photovoltaic cells is related to the actual surface temperature. Since photovoltaic cells convert most of the solar energy into heat energy when generating electricity, the surface temperature of photovoltaic cells is not completely equal to the ambient temperature. Therefore, it is necessary to model the input and output of photovoltaic cells under different conditions.

At present, the conversion rate of photovoltaic cells is low, the energy supply is unstable under different environmental conditions, and the energy storage of batteries is limited due to their own capacity limitations. In order to ensure sufficient energy supply during the flight of the aircraft and realize the long-duration flight of solar aircraft, how to manage and distribute energy is one of the important issues that need to be considered when designing the aircraft. Predicting the energy distribution of the entire aircraft and optimizing the existing flight conditions are the only way to study solar aircraft.

In order to accurately simulate the energy distribution of solar drones, Zhu Bingjie and others conducted relevant research. They studied the relationship between photovoltaic cells and the attitude angle of solar unmanned aerial vehicles. By referring to the relationship between the solar incident ray vector in the airship and the airship body coordinate system, they could obtain an accurate solution for solar radiation. However, the efficiency of the photovoltaic modules was assumed to be constant in this study, and the relationship between the output and input of the energy system could not accurately reflect the relationship between the efficiency of the photovoltaic modules and the environmental conditions. Liu Li and others introduced the energy flow of solar/hydrogen aircraft in detail based on weight energy balance, power matching and energy management strategies. However, this study only simulated the energy distribution during uniform flight and could not be directly applied to actual flight.

Adjusting the flight attitude is one of the methods to optimize energy distribution. Wang Xiangyu proposed a multi-objective collaborative path planning algorithm for solar unmanned aerial vehicles, which uses an improved ant colony algorithm to find the optimal path and optimize energy distribution. However, this study assumes that photovoltaic cells always output at maximum power, and cannot simulate and optimize arbitrary flight trajectories. Ni et al. used neural networks to control the changes in thrust, angle of attack, and roll angle to maximize energy, but this study assumed that the efficiency of photovoltaic modules was a constant.

Summary of the invention

In order to solve the problems existing in the prior art, the present invention proposes a method for quickly evaluating the energy distribution of solar aircraft based on GUI visualization, which can predict the energy distribution of solar aircraft. In this method, the surface temperature of photovoltaic cells is predicted by using the heat transfer model of the wing surface to obtain the temperature change of the photovoltaic cell surface; by constructing a multi-system strongly coupled solar aircraft energy flow model, the real-time state and input and output of each participating energy flow system are studied; using genetic algorithms, the flight trajectory of the aircraft is regulated and optimized with thrust, angle of attack and roll angle as independent variables, so as to improve the energy distribution and flight performance of the solar aircraft.

The technical solution of the present invention is:

The method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization comprises the following steps:

Step 1: Determine the key parameters of the solar aircraft and its systems involved in energy flow, and construct a solar aircraft energy flow model with strong coupling of multiple systems;

Step 2: Design the initial space motion trajectory of the solar aircraft, taking the aircraft thrust, angle of attack and roll angle as the key flight parameters, and obtain the motion characteristics including flight speed, acceleration, yaw angle parameters and force characteristics including lift and drag parameters under the characteristics of large aspect ratio and low Reynolds number. The power required to maintain the flight attitude of the aircraft during flight is obtained by multiplying the thrust and speed.

Step 3: Conduct energy distribution evaluation of solar aircraft based on GUI visualization: take the key parameters of the systems involved in energy flow in step 1 and the thrust, angle of attack and roll angle of the aircraft as input, and output the current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of each system involved in energy flow of the solar aircraft under given working conditions according to the internal power consumption relationship of the aircraft. According to the output parameter curve, analyze the energy storage conditions of photovoltaic cells and batteries, and evaluate the energy distribution;

Step 4: Optimize flight conditions and energy distribution:

The thrust, angle of attack and roll angle of the aircraft under different operating conditions are constrained, and the battery state of charge at the end of the simulation is greater than or equal to the state of charge at the beginning of the simulation, and the photovoltaic cell can output the maximum energy as the optimization goal. The initial flight conditions with thrust, angle of attack and roll angle as independent variables are generated, and a set of flight conditions with the best battery state of charge and battery input and output are screened out using a genetic algorithm.

Furthermore, in step 1, the systems of the solar aircraft involved in energy flow include a solar radiation system, a photovoltaic cell system, a battery system, and a thrust system including a motor and a propeller.

Furthermore, the key parameters include the 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 include the aspect ratio, reference wing area and take-off weight parameter information of the solar aircraft;

Solar radiation parameters include flight altitude, flight longitude and latitude, and flight time;

Photovoltaic cell parameters include open circuit voltage, short circuit current, maximum power point voltage and maximum power point current, and photovoltaic cell laying rate parameter information of photovoltaic cells under standard conditions;

Key battery parameters include battery capacity, the corresponding relationship between state of charge and open circuit voltage, and internal resistance parameter information;

The key parameters of the propeller include lift coefficient, drag coefficient, installation angle and propeller disc diameter parameter information.

Furthermore, the model includes a solar irradiance model, a heat transfer model, a photovoltaic cell model, a battery model, a propeller model, an aircraft kinematic model and an aircraft dynamic model. The mathematical expressions of each model are as follows:

Solar irradiance model:

Where S _total represents the total solar irradiance; S _beam represents the direct irradiance; S _diffuse represents the diffuse irradiance; S _pv represents the solar irradiance that the photovoltaic cell can actually receive; the solar incident light unit vector is _ns ; the photovoltaic cell surface normal unit vector is _np ; _nb = [0,0,1] ^T ; _Teb represents the transformation matrix from the ground coordinate system to the body axis system, ^and according to its relationship with the aircraft attitude angle:

Among them, Ψ represents the yaw angle, θ represents the pitch angle, and Φ represents the roll angle;

Heat transfer model:

Wherein, q″ _rad,sun represents the thermal radiation flux of the sun to the photovoltaic cell; q″ _rad,wing represents the difference between the thermal radiation flux of the wing surface and the external environment; q″ _conv represents the heat convection flux between the upper surface of the wing and the ambient atmosphere; q″ _elec represents the electric energy flux generated by the photovoltaic cell in this process; ρ _pv represents the surface 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 the time;

Photovoltaic cell model:

Wherein, V _pv and I _pv are the output voltage and output current of the photovoltaic cell respectively; I _sc , V _oc , I _m , V _m are the short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage under any condition respectively; under standard conditions, the short-circuit current when the photovoltaic cell circuit is short-circuited is I _scref , the open-circuit voltage when the circuit is open is V _ocref , the maximum power point current corresponding to the maximum value of the battery output power is I _mref , and the maximum power point voltage at this time is V _mref ; S _pv is the solar irradiance received by the photovoltaic cell per unit area per unit time, which is calculated according to the longitude and latitude, time and altitude parameters; S _ref represents the reference solar irradiance; ΔT is the difference between the actual temperature of the battery and the reference temperature of the battery, ΔT=TT _ref ; T _ref represents the reference temperature of the battery; ΔS=SS _ref , which is the difference between the actual solar irradiance received by the battery and the reference solar irradiance; e is the base of the natural logarithm; a, b, c are compensation coefficients;

Battery Model:

Among them, the ideal voltage source voltage V _ocbat represents the electromotive force of the battery; r ₀ and r ₁ represent the internal resistance of the battery; C ₁ is the capacitor connected in parallel with the resistor; I _bat represents the total current flowing through the battery; V _bat represents the voltage of the load at both ends of the battery; C _bata represents the charge of the battery in a fully charged state; C _bat0 represents the charge of the battery at the initial moment; SOC represents the state of charge of the battery, and SOC ₀ represents the state of charge at the initial moment; ∫I _bat dt is the integral of the current passing through the battery from the initial state to the current state over time;

Propeller Model:

Wherein, ρ represents the atmospheric density; C _Lp represents the propeller lift coefficient; T _p represents the propeller thrust; η _p represents the propeller efficiency; the angle between the local speed and the rotation plane is Φ _p ; γ _p is the lift-drag angle; S _p is the propeller area; V represents the flight speed of the aircraft;

Aircraft dynamics model:

为滚转角；Ψ为偏航角；俯仰角θ＝α+γ；g表示重力加速度；Among them, V represents the flight speed; T represents the thrust; D represents the flight resistance; L represents the lift of the aircraft; W represents the weight of the aircraft; α represents the angle of attack, which is the angle between the speed direction and the axis of the aircraft; γ represents the climb angle;

is the roll angle; Ψ is the yaw angle; the pitch angle θ＝α+γ; g represents the acceleration due to gravity;

Aircraft kinematic model:

Among them, x, y, and h represent the displacement of the aircraft relative to its initial position in the ground coordinate system.

Furthermore, in step 3, the energy management strategy during flight is as follows: photovoltaic cells generate electricity to provide energy for the entire aircraft, and storage batteries are used to store electricity when the photovoltaic cell production capacity is sufficient, and are used to discharge to provide energy for the aircraft when the photovoltaic cell production capacity is insufficient. For the storage battery, its charge state is always evaluated to keep it within the range of (0.1,1) to ensure that the battery is not overcharged or over-discharged; during the whole process, the thrust system and airborne equipment are energy-consuming systems.

Furthermore, in step 3, the GUI visualization input sub-interfaces are: 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 the basic model parameters and flight condition parameters of the aircraft; the position time information interface guides the user to input the initial state information such as the latitude and longitude, altitude, etc. of the aircraft simulation initial state; the photovoltaic cell and battery parameter interface respectively inputs the battery related parameters, including battery current, voltage and resistance parameters; the thrust system interface inputs the relevant parameters of the motor and propeller, including the motor efficiency parameters and the propeller lift and drag coefficient, installation angle, propeller diameter and number of propellers;

The output curves of the GUI visualization output sub-interface are: the curve of solar radiation intensity changing with time, the curve of wing surface temperature changing with time, the curve of photovoltaic cell power, battery power, airborne equipment power and flight power changing with time, the curve of current and voltage of photovoltaic cells and batteries changing with time, the curve of photovoltaic cell efficiency, battery efficiency and propeller efficiency changing with time, the curve of battery state of charge changing with time, the curve of aircraft angle of attack, angle of climb, pitch angle, yaw angle and roll angle changing with time, the curve of aircraft thrust, speed and altitude changing with time, the horizontal and vertical coordinates of the aircraft and their changing curves with time and the flight path of the aircraft in three-dimensional space.

Beneficial Effects

The present invention provides a method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization, which uses 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 uses a genetic algorithm to effectively improve the flight performance of the solar aircraft. The present invention has strong applicability and is suitable for energy distribution calculation and optimization of solar aircraft with any parameters and under different flight conditions in three-dimensional space.

Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

Figure 1: Schematic diagram of the design method of the present invention for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization

Figure 2: Energy flow model of solar aircraft with multi-system strong coupling according to the present invention

Figure 3: Space motion trajectory of the initial solar-powered aircraft of the present invention

Figure 4: Energy distribution of all systems of the present invention

Figure 5: GUI-based visualization of the energy distribution assessment system interface of the present invention

Figure 6: Flowchart of the energy allocation and trajectory optimization algorithm of the present invention

Figure 7: The optimized space motion trajectory of the solar-powered aircraft of the present invention

Figure 8: Photovoltaic cell output curves before and after optimization of the present invention

FIG9 : Battery state of charge curves before and after optimization of the present invention

DETAILED DESCRIPTION

Embodiments of the present invention are described in detail below. The embodiments are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

The prior art lacks analysis of key components such as photovoltaic modules, and insufficient research on energy distribution and optimization of solar aircraft under complex working conditions. The present invention combines the mathematical and physical models of photovoltaic cells, batteries and propellers, uses equivalent circuits and heat transfer models, and describes in detail the energy distribution and power characteristics of solar aircraft. Combined with the aircraft kinematics and dynamics equations, the energy distribution of solar aircraft under different working conditions can be calculated and evaluated. 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 results, a user-friendly and visual full-machine energy rapid evaluation software is also constructed. The schematic diagram of the design method for rapid evaluation of solar aircraft energy distribution based on GUI visualization is shown in Figure 1.

The first step is to determine the key parameters of the solar aircraft and its systems involved in energy flow, and to construct a multi-system strongly coupled solar aircraft energy flow model. The constructed multi-system strongly coupled solar aircraft energy flow model is shown in Figure 2. In the multi-system strongly coupled model shown in Figure 2, the influence of solar irradiance and direction, aircraft attitude angle and thermal effect on the efficiency and output power of photovoltaic cells is considered, the change of battery charge state and power over time based on the first-order RC equivalent circuit model is considered, the change of propeller model efficiency and process ratio over time based on blade element theory is considered, and the relationship between the aircraft motion trajectory in three-dimensional space and the aircraft thrust, angle of attack, speed, attitude angle and lift and drag is considered. According to the output energy of photovoltaic cells, the remaining power of batteries, and the energy consumed by aircraft, based on the law of energy conservation, the changes in the production capacity and energy storage of photovoltaic cells and batteries can be obtained.

The systems involved in energy flow of solar aircraft include solar radiation system, photovoltaic cell system, battery system and thrust system including motor and propeller.

The overall parameters of the solar aircraft include the aspect ratio, reference wing area and take-off weight parameter information of the solar aircraft;

Solar radiation parameters include flight altitude, flight longitude and latitude, and flight time;

Photovoltaic cell parameters include open circuit voltage, short circuit current, maximum power point voltage and maximum power point current, and photovoltaic cell laying rate parameter information of photovoltaic cells under standard conditions;

Key battery parameters include battery capacity, the corresponding relationship between state of charge and open circuit voltage, and internal resistance parameter information;

The key parameters of the propeller include lift coefficient, drag coefficient, installation angle and propeller disc diameter parameter information.

In this embodiment, the aspect ratio of the solar aircraft is determined to be 22, the reference wing area is 240m ² , and the take-off weight of the aircraft is 9960N; the flight altitude of the aircraft at the initial moment is 10km, the initial flight longitude and latitude are 40° east longitude and 120° north latitude, and the initial flight time is 00:00 on March 21; the total open circuit voltage of the photovoltaic cells on the solar aircraft under standard conditions 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 cells on the wing surface is 0.88; the battery capacity of the battery is 500Ah, the initial state of charge is 0.65, and the total open circuit voltage is 209V; the propeller blades adopt NACA 23012 airfoils, the lift coefficient and the drag coefficient are obtained from the airfoil library, the installation angle is 20°, and the propeller disc diameter is 2m.

During the modeling process, the input and output of the systems involved in the energy flow of the solar aircraft will be analyzed separately, and models of each system will be established separately, including a photovoltaic cell model that couples the aircraft attitude angle and thermal effect obtained by combining the equivalent circuit model using coordinate transformation and heat transfer laws. More accurate system model parameters are input, and based on the coupling relationship between the systems involved in the energy flow and the energy management strategy based on the law of energy conservation, the real-time production capacity of photovoltaic cells and the remaining power and total capacity of the battery are used to output the production capacity and energy storage changes of photovoltaic cells and batteries, and obtain the energy distribution of the solar aircraft.

The models involved include solar irradiance model, heat transfer model, photovoltaic cell model, battery model, propeller model, aircraft kinematic model and aircraft dynamic model. The mathematical expressions of each model are as follows:

Solar irradiance model:

Where S _total represents the total solar irradiance; S _beam represents the direct irradiance; S _diffuse represents the diffuse irradiance; S _pv represents the solar irradiance that the photovoltaic cell can actually receive; the solar incident light unit vector is _ns ; the photovoltaic cell surface normal unit vector is _np ; _nb = [0,0,1] ^T ; _Teb represents the transformation matrix from the ground coordinate system to the body axis system, ^and according to its relationship with the aircraft attitude angle:

Among them, Ψ represents the yaw angle, θ represents the pitch angle, and Φ represents the roll angle.

Heat transfer model:

Among them, q″ _rad,sun represents the thermal radiation flux of the sun to the photovoltaic cell; q″ _rad,wing represents the difference in thermal radiation flux between the wing surface and the external environment; q″ _conv represents the heat convection flux between the upper surface of the wing and the ambient atmosphere; q″ _elec represents the electric energy flux generated by the photovoltaic cell in this process; ρ _pv represents the surface density of the photovoltaic cell; (c _p ) _pv represents the specific heat capacity of the photovoltaic cell; T represents the wing surface temperature; and t represents time.

Photovoltaic cell model:

Wherein, V _pv and I _pv are the output voltage and output current of the photovoltaic cell respectively; I _sc , V _oc , I _m , V _m are the short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage under any condition respectively; under standard conditions, the short-circuit current of the photovoltaic cell when the circuit is short-circuited is I _scref , the open-circuit voltage when the circuit is open is V _ocref , the maximum power point current corresponding to the maximum value of the battery output power is I _mref , and the maximum power point voltage at this time is V _mref ; S _pv is the solar irradiance received by the photovoltaic cell per unit area per unit time, which is calculated according to the longitude and latitude, time and altitude parameters; S _ref =1000W/m ² , indicating the reference solar irradiance; ΔT is the difference between the actual temperature of the battery and the reference temperature of the battery, ΔT=TT _ref . The battery reference temperature T _ref = 298.15K; ΔS = SS _ref , which is the difference between the actual solar irradiance received by the battery and the reference solar irradiance; e is the natural logarithm base, e = 2.71838; a, b, c are compensation coefficients, a = 0.0025/K, b = 0.0005W/m ² , c = 0.00288/K.

Battery Model:

Among them, the ideal voltage source voltage V _ocbat represents the electromotive force of the battery; _r0 and _r1 represent the internal resistance of the battery; _C1 is the capacitor in parallel with the resistor; I _bat represents the total current flowing through the battery; V _bat represents the voltage of the load at both ends of the battery; C _bata represents the power of the battery in a fully charged state, that is, the rated capacity, in Ah; C _bat0 represents the power of the battery at the initial moment; SOC represents the state of charge of the battery, SOC ₀ represents the state of charge at the initial moment; ∫I _bat dt is the integral of the current passing through the battery from the initial state to the current state over time.

Propeller Model:

Among them, ρ represents the atmospheric density; C _Lp represents the propeller lift coefficient; T _p represents the propeller thrust; η _p represents the propeller efficiency; the angle between the local speed and the rotation plane is Φ _p ; γ _p is the lift-drag angle; S _p is the propeller area; and V represents the flight speed of the aircraft.

Aircraft dynamics model:

为滚转角；Ψ为偏航角；俯仰角θ＝α+γ；g表示重力加速度。推力、攻角和滚转角为自变量，式中其它参数均由这三个自变量结合初始速度、加速度、攻角、姿态角、爬升角、飞行高度和飞行位置，带入到式(7)中解方程得到。Among them, V represents the flight speed; T represents the thrust; D represents the flight resistance; L represents the lift of the aircraft; W represents the weight of the aircraft; α represents the angle of attack, which is the angle between the speed direction and the axis of the aircraft; γ represents the climb angle;

is the roll angle; Ψ is the yaw angle; the pitch angle θ＝α+γ; g represents the gravity acceleration. Thrust, angle of attack and roll angle are independent variables, and the other parameters are obtained by combining these three independent variables with initial velocity, acceleration, angle of attack, attitude angle, climb angle, flight altitude and flight position, and then substituting them into equation (7) to solve the equation.

Aircraft kinematic model:

Among them, x, y, and h represent the displacement of the aircraft relative to its initial position in the ground coordinate system.

The second step is to design the space motion trajectory of the initial solar aircraft.

Taking the aircraft thrust, angle of attack and roll angle as key flight parameters, the motion characteristics including flight speed, acceleration, yaw angle parameters and the force characteristics including lift and drag parameters under the characteristics of large aspect ratio and low Reynolds number are obtained. The power required to maintain the flight attitude during the flight of the aircraft is obtained by multiplying the thrust and speed. The spatial motion trajectory of the initial solar aircraft is shown in Figure 3.

The third step is to carry out energy distribution evaluation of solar aircraft based on GUI visualization.

Taking the key parameters of the systems involved in the energy flow in the first step and the thrust, angle of attack and roll angle of the aircraft as input, according to the internal power consumption relationship of the aircraft, the current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of each system involved in the energy flow of the solar aircraft under given working conditions are output. According to the output parameter curve, the energy storage conditions of photovoltaic cells and batteries are analyzed to evaluate the energy distribution situation.

The energy management strategy during flight is: photovoltaic cells generate electricity to provide energy for the entire aircraft, and the storage battery is used to store electricity when the photovoltaic cell capacity is sufficient. When the photovoltaic cell capacity is insufficient, it is used to discharge to provide energy for the aircraft. For the storage battery, its state of charge is always evaluated to keep it in the range of (0.1,1), that is, to ensure that the battery is not overcharged or over-discharged; the thrust system and airborne equipment are energy-consuming systems throughout the process. Using MATLAB tools and the above energy management strategy, combined with the power, current, voltage and efficiency parameters of each system involved in the energy flow or the state of charge parameters of the state, the energy distribution of all systems is obtained as shown in Figure 4.

Energy distribution evaluation based on GUI visualization. During the visualization process, the input parameters include: key parameters of the aircraft battery equivalent circuit, aircraft aspect ratio, weight and wing reference area parameters, and flight trajectory parameters with thrust, angle of attack and roll angle as independent variables. It can analyze the energy flow of the whole aircraft concisely, quickly and accurately, and predict the flight motion characteristics, surface temperature parameters and power, efficiency parameters and electrical characteristic parameters of each key energy system 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, which increases the scope of software application and improves the adaptability of the software. The software presents all calculation results in the form of a curve chart, making the expression of the aircraft state and its energy distribution state more intuitive and convenient for users to analyze specific situations.

The input and output windows of the energy distribution assessment system based on GUI visualization are shown in Figure 5. The input sub-interfaces are: 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 mainly inputs the basic model parameters and flight condition parameters of the aircraft. The position and time information interface guides the user to input the initial state information such as the latitude and longitude, altitude, etc. of the aircraft simulation initial state. The photovoltaic cell and battery parameter interface respectively inputs the battery related parameters, including battery current, voltage and resistance parameters. The thrust system interface inputs the relevant parameters of the motor and propeller, including the motor efficiency parameters and the propeller lift and drag coefficient, installation angle, propeller diameter and number of propellers. The output sub-interface outputs the following curves: the curve of solar radiation intensity changing with time, the curve of wing surface temperature changing with time, the curve of photovoltaic cell power, battery power, airborne equipment power and flight power changing with time, the curve of photovoltaic cell and battery current and voltage changing with time, the curve of photovoltaic cell efficiency, battery efficiency and propeller efficiency changing with time, the curve of battery charge state changing with time, the curve of aircraft attack angle, climb angle, pitch angle, yaw angle and roll angle changing with time, the curve of aircraft thrust, speed and altitude changing with time, the curve of aircraft horizontal and vertical coordinates changing with time and the flight path of the aircraft in three-dimensional space. Among them, the power change curve over time can reflect the flow of energy in the aircraft.

The fourth step is to optimize flight conditions and energy distribution.

The flight process is divided into low-altitude hovering, climbing flight, high-altitude flight, and gliding. The thrust, angle of attack, and roll angle of the aircraft under different working conditions are constrained. The battery state of charge at the end of the simulation is greater than or equal to the state of charge at the beginning of the simulation, and the photovoltaic cell can output the maximum energy as the optimization goal. The initial flight conditions with thrust, angle of attack, and roll angle as independent variables are generated. The battery state of charge and battery input and output are selected by crossover, inheritance, and mutation. The optimization process requires setting the population size and the maximum number of iterations. The calculation stops when the iteration is equal to the maximum number of iterations or the optimized working conditions remain basically unchanged.

The optimization objective is expressed in mathematical expressions as: max f ₁ = SOC _end - SOC ₀ > 0, max f ₂ = ∫ P _pm dt. Among them, SOC _end represents the state of charge of the battery at the end of the simulation, SOC ₀ represents the state of charge of the battery at the initial moment of the simulation, P _pm represents the maximum power that the battery can output, and t represents the time of the simulation. Using the idea of gravitational potential energy storage, based on two optimization objectives, the genetic algorithm is used to improve the high-altitude and long-flight performance of the solar aircraft, and the optimization results are output. Set the population size N _po and the maximum number of iterations N _it . _{P c} represents the crossover probability, and P _m represents the mutation probability of the real coding vector. By judging the dominant level of individuals in the population, fast non-dominant sorting is performed, and a reasonable new parent generation is selected according to the degree of crowding. A new population is generated through crossover and mutation operations, and it continues to iterate until the optimization objective is met. The flow chart of the energy allocation and trajectory optimization algorithm is shown in Figure 6.

After optimization, the total solar energy per unit area in one day is 5.25×10 ⁸ J/m ² , which is 5×10 ⁵ J/m ² higher than before optimization. The solar energy that can be received by photovoltaic cells is 2.78×10 ⁷ J/m ² , which is 1.2×10 ⁵ J/m ² higher than before optimization. After optimization, the total maximum energy that can be output by photovoltaic cells is 9.92×10 ⁸ J, which is 5.4×10 ⁷ J higher than before optimization. The actual total energy output of photovoltaic cells is 7.53×10 ⁸ J, which is 4.8×10 ⁷ J higher than before optimization. The total energy discharged by the battery is 3.05×10 ⁸ J, which is 2.8×10 ⁷ J lower than before optimization. The total energy charged by the battery is 3.17×10 ⁸ J, which is 1.4×10 ⁶ J higher than before optimization. When hovering at about 10 km, the total energy consumed by the aircraft is 3.23×10 ⁸ J, which is 5.8×10 ⁷ J less than before optimization; after optimization, the state of charge at 24:00 is 0.667, which is 0.087 more than before optimization and 0.017 more than 0:00. Under this condition, the battery can supply energy to the aircraft until the photovoltaic cell can supply power alone the next day. The optimized flight trajectory is shown in Figure 7, and the comparison of the photovoltaic cell output and the battery state of charge before and after optimization is shown in Figures 8 and 9, respectively.

Energy distribution optimization process According to the genetic algorithm, the flight process is divided into four conditions: hovering flight at an altitude of 10 km, climbing flight, hovering flight at an altitude of 15 km, and gliding. The battery state of charge is always set in the interval of (0.1, 1). The optimization goal is that the battery state of charge at 24:00 on the same day is greater than or equal to the state of charge at 0:00 on the same day and the state of charge is the maximum at the end of the simulation. The maximum total energy that the photovoltaic cell can output during the simulation is the maximum. _Np initial flight conditions with thrust, angle of attack and roll angle as independent variables are generated. By using crossover, inheritance and mutation, a set of flight conditions with the best battery state of charge and battery input and output are selected. The population size _Np is set to 20, the maximum number of iterations _Nit is 500, and the calculation is stopped when the iteration is equal to the maximum number of iterations or the optimized condition is basically unchanged.

Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, substitute and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims (6)

1. A method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization, characterized in that it includes the following steps: Step 1: Determine the key parameters of the solar aircraft and its systems involved in energy flow, and construct a solar aircraft energy flow model with strong coupling of multiple systems; Step 2: Design the initial space motion trajectory of the solar aircraft, taking the aircraft thrust, angle of attack and roll angle as the key flight parameters, and obtain the motion characteristics including flight speed, acceleration, yaw angle parameters and force characteristics including lift and drag parameters under the characteristics of large aspect ratio and low Reynolds number. The power required to maintain the flight attitude of the aircraft during flight is obtained by multiplying the thrust and speed. Step 3: Conduct energy distribution evaluation of solar aircraft based on GUI visualization: take the key parameters of the systems involved in energy flow in step 1 and the thrust, angle of attack and roll angle of the aircraft as input, and output the current and voltage parameters, state of charge parameters, power parameters and efficiency parameter curves of each system involved in energy flow of the solar aircraft under given working conditions according to the internal power consumption relationship of the aircraft. According to the output parameter curve, analyze the energy storage conditions of photovoltaic cells and batteries, and evaluate the energy distribution; Step 4: Optimize flight conditions and energy distribution: The thrust, angle of attack and roll angle of the aircraft under different operating conditions are constrained, and the battery state of charge at the end of the simulation is greater than or equal to the state of charge at the beginning of the simulation, and the photovoltaic cell can output the maximum energy as the optimization goal. The initial flight conditions with thrust, angle of attack and roll angle as independent variables are generated, and a set of flight conditions with the best battery state of charge and battery input and output are screened out using a genetic algorithm. 2. According to the method of claim 1, which is based on GUI visualization to quickly evaluate the energy distribution of a solar aircraft, it is characterized in that: in step 1, the systems of the solar aircraft involved in the energy flow include a solar radiation system, a photovoltaic cell system, a battery system, and a thrust system including a motor and a propeller. 3. According to claim 1, a method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization, characterized in that: the key parameters include the 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 include the aspect ratio, reference wing area and take-off weight parameter information of the solar aircraft; Solar radiation parameters include flight altitude, flight longitude and latitude, and flight time; Photovoltaic cell parameters include open circuit voltage, short circuit current, maximum power point voltage and maximum power point current, and photovoltaic cell laying rate parameter information of photovoltaic cells under standard conditions; Key battery parameters include battery capacity, the corresponding relationship between state of charge and open circuit voltage, and internal resistance parameter information; The key parameters of the propeller include lift coefficient, drag coefficient, installation angle and propeller disc diameter parameter information. 4. According to claim 3, a method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization, characterized in that: the model includes a solar irradiance model, a heat transfer model, a photovoltaic cell model, a battery model, a propeller model, an aircraft kinematic model and an aircraft dynamic model, and the mathematical expressions of each model are as follows: Solar irradiance model:

Where S _total represents the total solar irradiance; S _beam represents the direct irradiance; S _diffuse represents the diffuse irradiance; S _pv represents the solar irradiance that the photovoltaic cell can actually receive; the solar incident light unit vector is _ns ; the photovoltaic cell surface normal unit vector is _np ; _nb = [0,0,1] ^T ; _Teb represents the transformation matrix from the ground coordinate system to the body axis system, ^and according to its relationship with the aircraft attitude angle:

Among them, Ψ represents the yaw angle, θ represents the pitch angle, and Φ represents the roll angle; Heat transfer model:

Wherein, q″ _rad,sun represents the thermal radiation flux of the sun to the photovoltaic cell; q″ _rad,wing represents the difference between the thermal radiation flux of the wing surface and the external environment; q″ _conv represents the heat convection flux between the upper surface of the wing and the ambient atmosphere; q″ _elec represents the electric energy flux generated by the photovoltaic cell in this process; ρ _pv represents the surface 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 the time; Photovoltaic cell model:

Wherein, V _pv and I _pv are the output voltage and output current of the photovoltaic cell respectively; I _sc , V _oc , I _m , V _m are the short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage under any condition respectively; under standard conditions, the short-circuit current when the photovoltaic cell circuit is short-circuited is I _scref , the open-circuit voltage when the circuit is open is V _ocref , the maximum power point current corresponding to the maximum value of the battery output power is I _mref , and the maximum power point voltage at this time is V _mref ; S _pv is the solar irradiance received by the photovoltaic cell per unit area per unit time, which is calculated according to the longitude and latitude, time and altitude parameters; S _ref represents the reference solar irradiance; ΔT is the difference between the actual temperature of the battery and the reference temperature of the battery, ΔT＝TT _ref ; T _ref represents the reference temperature of the battery; ΔS＝SS _ref , which is the difference between the actual solar irradiance received by the battery and the reference solar irradiance; e is the base of the natural logarithm; a, b, c are compensation coefficients; Battery Model:

Among them, the ideal voltage source voltage V _ocbat represents the electromotive force of the battery; r ₀ and r ₁ represent the internal resistance of the battery; C ₁ is the capacitor connected in parallel with the resistor; I _bat represents the total current flowing through the battery; V _bat represents the voltage of the load at both ends of the battery; C _bata represents the charge of the battery in a fully charged state; C _bat0 represents the charge of the battery at the initial moment; SOC represents the state of charge of the battery, and SOC ₀ represents the state of charge at the initial moment; ∫I _bat dt is the integral of the current passing through the battery from the initial state to the current state over time; Propeller Model:

Wherein, ρ represents the atmospheric density; C _Lp represents the propeller lift coefficient; T _p represents the propeller thrust; η _p represents the propeller efficiency; the angle between the local speed and the rotation plane is Φ _p ; γ _p is the lift-drag angle; S _p is the propeller area; V represents the flight speed of the aircraft; Aircraft dynamics model:

Among them, V represents the flight speed; T represents the thrust; D represents the flight resistance; L represents the lift of the aircraft; W represents the weight of the aircraft; α represents the angle of attack, which is the angle between the speed direction and the axis of the aircraft; γ represents the climb angle;

is the roll angle; Ψ is the yaw angle; the pitch angle θ＝α+γ; g represents the acceleration due to gravity; Aircraft kinematic model:

Among them, x, y, and h represent the displacement of the aircraft relative to its initial position in the ground coordinate system. 5. According to claim 1, a method for quickly evaluating the energy distribution of a solar aircraft based on GUI visualization is characterized in that: in step 3, the energy management strategy during the flight process is: the photovoltaic cell generates electricity to provide energy for the entire aircraft, the storage battery is used to store electricity when the photovoltaic cell production capacity is sufficient, and is used to discharge to provide energy for the aircraft when the photovoltaic cell production capacity is insufficient, and for the storage battery, its charge state is constantly evaluated to keep it within the range of (0.1,1) to ensure that the battery is not overcharged or over-discharged; the thrust system and the onboard equipment are energy-consuming systems during the entire process. 6. According to the method of claim 1, the GUI-based visualization method for quickly evaluating the energy distribution of a solar aircraft is characterized in that: in step 3, the GUI visualization input sub-interfaces are respectively: an aircraft flight information interface, a position and time information interface, a photovoltaic cell parameter interface, a battery parameter interface, and a thrust system parameter interface: The aircraft flight information interface inputs the basic model parameters and flight condition parameters of the aircraft; the position time information interface guides the user to input the initial state information such as the latitude and longitude, altitude, etc. of the aircraft simulation initial state; the photovoltaic cell and battery parameter interface respectively inputs the battery related parameters, including battery current, voltage and resistance parameters; the thrust system interface inputs the relevant parameters of the motor and propeller, including the motor efficiency parameters and the propeller lift and drag coefficient, installation angle, propeller diameter and number of propellers; The output curves of the GUI visualization output sub-interface are: the curve of solar radiation intensity changing with time, the curve of wing surface temperature changing with time, the curve of photovoltaic cell power, battery power, airborne equipment power and flight power changing with time, the curve of current and voltage of photovoltaic cells and batteries changing with time, the curve of photovoltaic cell efficiency, battery efficiency and propeller efficiency changing with time, the curve of battery state of charge changing with time, the curve of aircraft angle of attack, climb angle, pitch angle, yaw angle and roll angle changing with time, the curve of aircraft thrust, speed and altitude changing with time, the horizontal and vertical coordinates of the aircraft and their changing curves with time and the flight path of the aircraft in three-dimensional space.

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