Parameter optimization matching method for electric propulsion system of small electric multi-mode aircraft

Abstract

The present application relates to a kind of electric propulsion system parameter optimization matching method of small electric multi-mode aircraft, belong to the field of aircraft technology.The present application is according to mission profile and task demand, to the target function and constraint condition screening of multi-mode in task working condition, reduce the complexity of parameter matching, establish multi-objective efficiency optimization model to describe the parameter matching problem of multi-mode electric propulsion system, by physical programming method, the multi-objective optimization model is converted into the preference function optimization problem of design target, reduce the difficulty of weight selection and the time-consuming of calculation, the exponential smoothing penalty function is converted into the unconstrained optimization problem to equation constraint in model and traditional penalty function to inequality constraint, by genetic algorithm, the model is solved, iteration and optimization, obtain the optimal multi-mode electric propulsion system parameter under task demand.The feasibility of parameter matching result is verified using theoretical power consumption analysis and experiment two methods, to obtain the optimal matching electric propulsion system.

Description

Technical Field

[0001] This invention relates to a method for optimizing and matching the parameters of the electric propulsion system of a small electric multi-mode aircraft, belonging to the field of aircraft technology. Background Technology

[0002] With the increasing complexity of mission environments and requirements, small electric multi-mode aircraft are being used more and more widely due to their advantages such as being green and pollution-free, having flexible flight capabilities, being multifunctional, low-cost, and easy to maintain. Electric propulsion systems, as the energy and power source for small electric multi-mode aircraft, replace fuel-powered systems, reducing environmental pollution and energy crises. As a core component of electric multi-mode aircraft, optimizing its parameter matching and increasing the efficiency of the electric propulsion system in the main flight modes and operating conditions is of significant practical importance, thereby increasing the endurance of multi-mode electric aircraft.

[0003] Existing small electric multi-mode aircraft only perform efficiency matching under specific operating conditions, maintaining high propulsion efficiency only under selected flight conditions while neglecting the efficiency of other flight modes. This is especially true when multiple modes are deeply involved in the aircraft's operation, failing to consider the overall efficiency across the entire flight profile. Because the power requirements and energy supply vary significantly between different modes, it is necessary to comprehensively consider the power and energy requirements of the multi-mode electric propulsion system, matching optimal multi-mode electric propulsion system parameters to increase overall efficiency. Summary of the Invention

[0004] This paper addresses the parameter matching and optimization problem of the electric propulsion system for small electric multi-mode aircraft. The main objective of this invention is to provide a method for optimizing and matching the parameters of the electric propulsion system of a small electric multi-mode aircraft. Based on the mission profile and requirements, the method selects the main operating conditions in each of the multiple modes, reducing the complexity of the electric propulsion system parameter matching problem. A multi-objective efficiency optimization model is established to describe the parameter matching problem of the multi-mode electric propulsion system. A physical programming method is used to reduce the difficulty of selecting the multi-objective weights, weakening the time-consuming nature of the multi-objective efficiency optimization problem and making the parameter matching process of the multi-mode electric propulsion system more flexible. Inequalities and equality constraints in the multi-objective efficiency optimization model are transformed into an unconstrained optimization problem using a penalty function, with the equality constraints smoothed using an exponential penalty function. Based on the multi-objective efficiency optimization model, the power consumption of the electric propulsion system under each flight condition is comprehensively calculated according to the flight profile and topology of the multi-mode aircraft, avoiding inappropriate energy and power matching during generation and transmission, which could lead to energy loss and waste, and power mismatch. A genetic algorithm with global optimization capabilities is used to solve the constructed efficiency optimization model to quickly obtain the optimal multi-mode electric propulsion system parameters. Finally, the feasibility of the matched multi-mode electric propulsion system parameter results is verified through both theoretical and experimental methods.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] This invention discloses a parameter optimization and matching method for the electric propulsion system of a small electric multi-mode aircraft. First, based on the mission profile and requirements, objective functions and constraints are selected for the mission conditions in multiple modes, establishing a multi-objective efficiency optimization model to describe the parameter matching problem of the multi-mode electric propulsion system. Using a physical programming method, the design objective of the multi-objective efficiency optimization model is transformed into preference function optimization. Using a penalty function method, the equality and inequality constraints of the multi-objective efficiency optimization model are transformed into an unconstrained optimization problem. An exponential smooth penalty function is used for equality constraints, and a traditional penalty function is used for inequality constraints. Based on the multi-objective efficiency optimization model, the power consumption of the electric propulsion system under each flight condition is calculated according to the flight profile requirements and the aircraft's topology. A genetic algorithm with global optimization capabilities is used to solve, iterate, and optimize the model to quickly obtain the optimal multi-mode electric propulsion system parameters. Finally, for the optimized and matched multi-mode electric propulsion system parameters, the feasibility of the parameter matching results is verified using both theoretical power consumption analysis and experimental methods to obtain the optimally matched electric propulsion system.

[0007] A method for optimizing and matching the parameters of the electric propulsion system of a small electric multi-mode aircraft includes the following steps:

[0008] Step 1: Based on the type of small electric multi-mode aircraft and mission requirements, construct the topology of the multi-mode electric propulsion system and determine the flight profile;

[0009] The small electric multi-mode aircraft includes tiltrotor, tiltwing, compound wing, and tail-seat aircraft.

[0010] The multi-mode mainly includes rotor mode, fixed-wing mode, and conversion mode;

[0011] The multi-mode electric propulsion system topology includes: a lithium battery pack, a lithium battery management system, an electronic governor, a motor, and a propeller;

[0012] The mission profile of the rotor mode includes vertical takeoff, vertical descent, and multi-point hovering flight conditions in rotor mode;

[0013] The mission profile of fixed-wing aircraft includes cruise, climb, ceiling flight, hovering, and maximum level flight speed flight modes;

[0014] The mission profile for switching modes is the switching mode between rotor mode and fixed-wing mode;

[0015] The flight profile is a typical multi-mode mission profile, which is a combination of the three mission profiles mentioned above.

[0016] Step 2: Based on the flight profile and flight requirements, select the objective function and constraints for the mission conditions in the multi-mode, and establish a multi-objective efficiency optimization model to represent the parameter matching problem of the multi-mode electric propulsion system;

[0017] Compared with the electric propulsion system of conventional fixed-wing UAVs, the multi-mode electric propulsion system has multiple modes to switch between, large variations in power demand between modes, and different durations for each operating condition. The objective function and constraints in the multi-mode system are selected mainly based on the duration of the mission and the importance of the operating conditions; the multi-objective efficiency optimization model is shown in Equation (1).

[0018]

[0019] Where η1(x), η2(x) and η s (x) represent the 1st, 2nd, ..., sth objective functions for efficiency optimization; T max It represents the maximum thrust, W represents the aircraft's weight, and κ is the ratio coefficient between the aircraft's thrust and weight; T j T and V represent the required thrust and actual output thrust under the desired flight power, respectively; j V and X represent the required speed and actual flight speed under the desired flight power, respectively; x i Design variables for parameter matching; and , which are the upper and lower bounds of the design variables, respectively; m and n are the equality constraints for parameter matching and the number of design variables, respectively;

[0020] Step 3: Based on the type of the multi-objective efficiency optimization model, select the preference function type in the physical planning for the objective function type in equation (1), and transform the constraints in equation (1) into an unconstrained optimization problem;

[0021] Step 3 specifically includes the following steps:

[0022] Step 3.1: Based on the multi-objective efficiency optimization model, select the soft preference type where larger is always better in physical programming, set the preference boundary values ​​for each objective in the multi-mode efficiency optimization, solve the piecewise preference curves for each efficiency objective based on the set boundary values, and construct the comprehensive preference function of the multi-mode efficiency optimization problem as the objective function P of the physical programming optimization model. af The constrained comprehensive preference function model is shown in equation (2):

[0023]

[0024] Where, n sc C represents the number of efficiency objectives in a multi-objective model. ηi P represents the value of each efficiency target; ηiThis represents the preference function value corresponding to the efficiency objective.

[0025] Step 3.2: The constrained comprehensive preference function model in equation (2) is transformed into an unconstrained optimization model using a penalty function, which is expressed as equation (3); where the equality constraint uses an exponential smooth penalty function and the inequality constraint uses a traditional penalty function;

[0026]

[0027] Among them, f j f(x) and g(x) represent the expected value and actual value in the equality constraint, respectively; g(x) is the inequality constraint; r1 is the weight coefficient of the equality constraint; r2 is the weight coefficient of the exponential penalty function; δ is the weight coefficient of the penalty function of the constraint condition.

[0028] Step 4: Based on the flight profile and topology of the multi-mode aircraft in Step 1, calculate the power consumption of the electric propulsion system topology under each flight condition in the flight profile according to Equation (3) in Step 3.

[0029] The power consumption calculation of the multi-mode electric propulsion system is based on the power requirements of the flight profile. The propeller, motor, electronic speed controller, and lithium battery in the topology generate or transfer energy to meet the mission requirements. If the energy and power are not properly matched during the generation and transfer process, it will cause energy loss and waste, resulting in power mismatch.

[0030] Step 4 specifically includes the following steps:

[0031] Step 4.1: Use the tensile coefficient C t Power coefficient C p and propeller efficiency η p To describe the performance of a propeller in an electric propulsion system, the propeller's thrust coefficient C... t and power coefficient C p and efficiency η p The calculation is as follows:

[0032]

[0033]

[0034]

[0035] In the formula, J represents the propeller advance ratio, expressed as: P is the propeller output power; P e ρ is the propeller input shaft power; ω is the air density; Q is the propeller rotational speed; D is the propeller torque; and ρ is the propeller diameter.

[0036] Under thrust and power requirements, the propeller performance calculation is related not only to the propeller diameter D, rotational speed ω, and inflow velocity V in equations (4)-(6), but also to the propeller pitch θ. Since there are numerous propeller performance parameters and types, the principle analysis is difficult and the computational load is large. This invention uses a BP neural network to utilize existing propeller parameters, and through training, establishes a reasonable network structure to form a proxy model with a certain accuracy and fast matching. When establishing the propeller proxy model, the inputs are the propeller diameter D, pitch θ, rotational speed ω, and inflow velocity V, and the output is the propeller thrust coefficient C. t and power coefficient C p In addition, four input variables (propeller diameter D, pitch θ, rotational speed ω, and inflow velocity V) of the propeller surrogate model were selected as design variables for the multi-mode efficiency optimization model.

[0037] Step 4.2: The motor is a brushless permanent magnet DC motor. Using the equivalent circuit method to describe the internal losses of the motor, the motor efficiency η is... m It is expressed as follows:

[0038]

[0039] Among them, P m I is the input shaft power of the motor; I is the input operating current of the motor, expressed as: I = (2πK v Q / 60)+I0, where K v Here, I0 is the no-load current, and U is the motor input operating voltage, expressed as: U=(60ω / 2πK v )+IR, where R is the internal resistance of the motor.

[0040] Step 4.3: The electronic speed controller adjusts the input voltage of the lithium battery pack using pulse width modulation technology, thereby adjusting the input operating voltage U of the motor and controlling the motor speed ω; the relationship between the motor voltage and the lithium battery pack voltage is expressed as:

[0041] U=βU bat (8)

[0042] Where β represents the PWM signal, and the motor is at its maximum speed when β = 100%; U bat This refers to the rated voltage of the lithium battery pack.

[0043] The electronic speed controller, while controlling the motor voltage, must also meet the maximum power requirements of the multi-mode propulsion system. and maximum current I max Constraint requirements; therefore, the constraints of the electronic speed controller under the task profile are expressed as:

[0044]

[0045] Step 4.4: The performance of the lithium battery pack is designed using a series and parallel connection of individual cells; the number of cells in series N in the battery pack. s byU bat and the rated voltage U of a single battery cell The decision can be made using N. s =U bat / U cell Indicates the number of battery packs connected in parallel, N. p With the battery's maximum design power Battery peak discharge power Maximum energy of a single battery cell and battery pack maximum energy Relevant, that is, expressed as:

[0046]

[0047] Step 4.5: The power consumption of the electric propulsion system topology under various flight conditions in the flight profile is generated by the lithium battery pack and transferred to the electronic governor, motor, and propeller to output the required power. The efficiency and power loss of the electric propulsion system are represented by (11) and (12), respectively. Among them, since the efficiency η of the electronic governor is... esc and lithium battery pack discharge efficiency η bat The variation is small under flight conditions, so a constant coefficient is used to represent it;

[0048] η tot =η p η m η esc η bat (11)

[0049] P = η tot P bat (12)

[0050] In the formula, η tot P represents the overall efficiency of the electric propulsion system. bat The power generated by the lithium battery pack, and not exceeding the maximum design power.

[0051] Step 5: Use a genetic algorithm with global optimization capability to solve, iterate and optimize the unconstrained comprehensive preference function model of equation (3) to obtain the optimal electric propulsion system parameters of the small electric multi-mode aircraft; and obtain the optimized matching electric propulsion system.

[0052] Step 6: Verify the optimized electric propulsion system results to determine if they meet the spacecraft's mission requirements. Verification methods can be divided into theoretical and experimental verification. Theoretical verification involves reverse-engineering the multi-mode electric propulsion system matching results to verify whether the mission requirements and profile can be met. If the requirements are not met, the system selection and parameter matching are re-performed. Experimental verification involves conducting ground experiments on the multi-mode electric propulsion system based on the matching results to verify the feasibility of the parameter matching results.

[0053] Step 6 is implemented using the following steps:

[0054] Step 6.1: Theoretical verification methods can be used to verify both power and energy. The maximum power P of the multi-mode electric propulsion system e The maximum power requirements for rotor mode, switching mode, and fixed-wing mode under the mission profile should be met, as can be expressed as follows:

[0055]

[0056] in, This represents the maximum power required in fixed-wing mode. P represents the maximum power required in rotor mode. t max This represents the maximum power required in the conversion mode.

[0057] The lithium battery pack provides the energy required for the multi-mode electric propulsion system. The energy matching result is verified by the remaining energy ΔE after the multi-mode electric propulsion system completes the task, which is expressed by Equation (14).

[0058]

[0059] If the matching result cannot meet the power and energy requirements in the mission profile, it is necessary to re-select and re-optimize the parameters.

[0060] Step 6.2: Verify the feasibility of the parameter matching results of the multi-mode electric propulsion system using ground-based testing. The testing mainly consists of static and dynamic tests. To conduct multi-mode electric propulsion system testing, this invention discloses a rapid multi-condition testing platform for electric propulsion systems, capable of simultaneously performing static and dynamic tests to verify the parameter matching results of the multi-mode electric propulsion system.

[0061] This invention discloses a rapid multi-condition test platform for an electric propulsion system, including a front fan system, a main test frame, a wireless data transmission module, a computer, and an electric propulsion system of the aircraft under test (consisting of a lithium battery pack under test, an electronic speed controller under test, a motor under test, and a propeller under test).

[0062] The objective of this invention is achieved through the following technical solution: The electric propulsion system of the aircraft under test is connected and fixed on the main test frame. During static testing, the front fan system is not operational. During dynamic testing, to ensure the stability of the incoming airflow speed, tests are first conducted at different wind speed measurement points. The difference between the wind speed at multiple locations and the speed of the incoming fan is measured to find a stable region of the target wind speed, which is consistent with the incoming airflow speed. Next, the main test frame containing the electric propulsion system of the aircraft under test is placed in this region for testing. Then, with the front fan system powered by a DC regulated power supply and the incoming airflow speed controlled by a microcontroller, the parameters of the aircraft's electric propulsion system are tested. The test results are transmitted to a computer wirelessly. Finally, the optimized and matched parameter results are verified based on the dynamic test results.

[0063] Beneficial effects

[0064] 1. Existing small electric multi-mode aircraft do not consider the comprehensive matching effect of the electric propulsion system. This invention discloses a parameter optimization matching method for the electric propulsion system of a small electric multi-mode aircraft. Based on the mission profile and mission requirements, the objective function and constraint conditions of the mission conditions in multiple modes are screened to reduce the complexity of parameter matching. A multi-objective efficiency optimization model is established to describe the parameter matching problem of the multi-mode electric propulsion system. The multi-objective optimization model is transformed into a preference function optimization problem of the design objective through physical programming methods, reducing the difficulty of weight selection and computation time. The equality constraints of the exponential smoothing penalty function and the inequality constraints of the traditional penalty function in the model are transformed into unconstrained optimization problems. The model is solved, iterated, and optimized through a genetic algorithm to obtain the optimal multi-mode electric propulsion system parameters under the mission requirements.

[0065] 2. This invention discloses a parameter optimization and matching method for the electric propulsion system of a small electric multi-mode aircraft. It comprehensively considers the accuracy and feasibility of power consumption calculation for the multi-mode electric propulsion system, avoiding energy loss and waste, and power mismatch problems caused by inappropriate energy and power matching during generation and transmission. The power consumption model mainly consists of the propeller, motor, electronic speed controller, and lithium battery pack in the topology. The propeller parameter model uses a BP neural network to utilize existing propeller parameters, avoiding computational time issues. A high-precision and fast-matching surrogate model is formed through training. An equivalent circuit model is used to accurately and quickly describe the motor and its internal losses. The electronic speed controller is constrained by the maximum power and current requirements in the circuit for safety. Finally, based on the dual constraints of energy and power requirements in the multi-mode mission, the optimal lithium battery pack is matched for power supply.

[0066] 3. This invention discloses a method for optimizing and matching the electric propulsion system parameters of a small electric multi-mode aircraft. It employs both theoretical and experimental methods to verify the optimized multi-mode electric propulsion parameters. The theoretical verification method checks whether the optimized matching parameters meet mission requirements from both power and energy perspectives. The experimental method uses a rapid multi-condition test platform for electric propulsion systems disclosed in this invention, including a front fan system, a main test frame, a wireless data transmission module, a computer, and the electric propulsion system of the aircraft under test. Static and dynamic tests are conducted by checking whether the front fan generates incoming airflow speed and the distance between the front fan and the main test frame to verify the optimized and matched multi-mode electric propulsion system parameter results. Attached Figure Description

[0067] Figure 1 This invention discloses an optimization matching flowchart for a method of optimizing and matching parameters of an electric propulsion system for a small electric multi-mode aircraft.

[0068] Figure 2 This is a schematic diagram of the BP neural network training of propeller parameters in the electric propulsion system parameter optimization and matching method for a small electric multi-mode aircraft disclosed in this invention.

[0069] Figure 3 This invention discloses a method for optimizing and matching the parameters of an electric propulsion system for a small electric multi-mode aircraft, and a rapid multi-condition test platform for an electric propulsion system.

[0070] Figure 4 The topology of the multi-mode electric propulsion system of the small electric multi-mode aircraft in the specific embodiment is shown below;

[0071] Figure 5 This is a typical multi-mode flight mission profile of a small electric multi-mode aircraft in a specific embodiment;

[0072] Figure 6 In this specific embodiment, the larger the physical plan, the better the soft preference type.

[0073] Among them, 1—computer; 2—wireless data transmission module; 3—lithium battery pack under test; 4—electronic speed controller under test; 5—main test frame; 6—motor under test; 7—propeller under test; 8—front fan system; 5.1—speed sensor; 5.2—torque sensor; 5.3—tension sensor; 5.4—airspeed sensor; 5.5—current and voltage sensor; 8.1—front fan; 8.2—front DC regulated power supply; 8.3—microcontroller. Detailed Implementation

[0074] To better illustrate the purpose and advantages of the present invention, the following description, in conjunction with an example of parameter optimization and matching of the electric propulsion system of a small electric multi-mode aircraft and the accompanying drawings, further explains the content of the present invention.

[0075] Example 1:

[0076] To verify the feasibility and beneficial effects of the electric propulsion system parameter optimization and matching method for a small electric multi-mode aircraft disclosed in this invention, the technical solution of this invention is clearly and thoroughly described below in the embodiments. For example... Figure 1 The diagram shows the optimization matching flowchart of the electric propulsion system parameter optimization matching method for a small electric multi-mode aircraft disclosed in this invention. The specific implementation steps are as follows:

[0077] Step 1: Based on the type of small electric multi-mode aircraft and mission requirements, construct the topology of the multi-mode electric propulsion system and determine the flight profile;

[0078] The small electric multi-mode aircraft is a small electric tiltrotor drone, which mainly includes rotor mode, fixed-wing mode and switching mode.

[0079] The topology of the multi-mode electric propulsion system is as follows: Figure 4 As shown, it includes a propeller, motor, electronic speed controller, lithium battery pack, and lithium battery management system.

[0080] A typical multi-mode flight mission profile of the small electric tiltrotor UAV is shown below. Figure 5 As shown, it includes vertical takeoff, tilting from rotor mode to fixed-wing mode, multiple cruise flights, multiple hovering flights, tilting from fixed-wing mode to rotor mode, and vertical landing.

[0081] The task requirements include:

[0082] The takeoff gravity W of the drone is 73.5 N;

[0083] In rotor mode, the number of hovering times is 10, the duration of each hovering time is 60 seconds, the hovering speed is 0 m / s, and the hovering pull of a single motor is 24.5 N.

[0084] In fixed-wing mode, the cruise duration is 1 hour, the cruise speed is 10 m / s, and the thrust required for a single motor during cruise is 4.5 N.

[0085] In rotor mode, the average power for vertical takeoff is 1016w, and the duration is 30s. The average power for vertical landing is 603w, and the duration is 30s.

[0086] The average power in tilt mode is 1083W, and the duration is 15s.

[0087] The maximum thrust-to-gravity ratio factor κ of the UAV during flight is 1.8;

[0088] Number of rotors: 3; Number of propellers: 2

[0089] Lithium battery cell voltage U cell 3.6V;

[0090] Single lithium battery capacity: 25Wh;

[0091] Step 2: Based on the flight profile and flight requirements, select the objective function and constraints for the mission conditions in the multi-mode, and establish a multi-objective efficiency optimization model to represent the parameter matching problem of the multi-mode electric propulsion system;

[0092] For the multi-mode electric propulsion system of this UAV, the optimal efficiency is selected when the UAV hovers in rotor mode and cruises in fixed mode, ensuring safe transition to tilt mode and quickly performing optimal parameter matching optimization. The multi-mode efficiency optimization model is then expressed by equation (1):

[0093]

[0094] In equation (15): The optimal efficiency optimization objectives are for cruise flight and hovering flight, respectively.

[0095] Step 3: Based on the type of multi-objective efficiency optimization model, select the preference function in physical planning for the type of objective function in equation (15), and transform the constraints in equation (15) into an unconstrained optimization problem;

[0096] Step 3 specifically includes the following steps:

[0097] Step 3.1: Based on the multi-objective efficiency optimization model, select the "bigger is better" soft preference type in the physical planning, such as... Figure 6 As shown in the table, the present invention sets the preference boundary values ​​for each objective in multi-mode efficiency optimization.

[0098]

[0099] Based on the set boundary values, the piecewise preference curves of each efficiency objective are solved, and the comprehensive preference function of the multi-mode efficiency optimization problem is constructed as the objective function P of the physical programming optimization mode. af The constrained comprehensive preference function model is shown in equation (16);

[0100]

[0101] Step 3.2: The equality constraints and inequality constraints in equation (16) are transformed into an unconstrained optimization problem by using a penalty function. The equality constraints are given by an exponential smooth penalty function, and the inequality constraints are given by a traditional penalty function. The unconstrained preference function optimization model is then expressed by equation (17).

[0102]

[0103] In the formula, r1 and r2 take values ​​of 4 and 10 respectively, and δ takes a value of 0.2;

[0104] Step 4: Based on the flight profile and topology of the multi-mode aircraft in Step 1, calculate the power demand, transmission and loss of the electric propulsion system topology under each flight condition in Step 3 according to Equation (17).

[0105] Step 4 specifically includes the following steps:

[0106] Step 4.1: Based on a large number of existing propeller parameters and types, a BP neural network propeller surrogate model is established. For example... Figure 2 As shown, the inputs to the propeller proxy model are the propeller diameter D, pitch θ, rotational speed ω, and inflow velocity V, and the output is the propeller thrust coefficient C. t and power coefficient C p The input layer had 4 neurons, the hidden layer had 2 neurons, and the hidden layer had 10 neurons. 70% of the propeller data was used for training, 15% for validation, and 15% for testing. Mean squared error (MSE) and R-correlation coefficient (R) were selected as evaluation metrics for the propeller surrogate model. After normalized training, the BP neural network propeller surrogate model showed an MSE of 0.0098 and an R-correlation coefficient of 0.982, indicating that the surrogate model's accuracy is feasible and can meet the calculation requirements for propeller efficiency, power, and thrust in multi-mode electric propulsion systems.

[0107] During the optimization process, four input variables (propeller diameter D, pitch θ, rotational speed ω, and inflow velocity V) of the propeller proxy model were selected as design variables, and the range of design variables is represented by equation (18).

[0108]

[0109] Based on the thrust and inflow velocity requirements of each mode in the multi-mode model, and the independent variables of the four propeller surrogate modes, the propeller power coefficient C can be calculated by the BP neural network propeller surrogate model using equations (4) to (6). p Tensile coefficient C t and efficiency η p ;

[0110] Step 4.2: The motor is installed according to the (T / W) requirements of the task. max And to determine the preliminary parameters of the UAV takeoff gravity W value, voltage parameter K. v The motor speed is 160 r / min·V, the no-load current I0 is 0.5 A, and the internal resistance R of the motor is 0.178 Ω. Therefore, the efficiency matching of the motor is based on the equivalent circuit method, using equation (7) according to the power P required by the input shaft of the propeller.e And perform matching calculations for the power supply of the lithium battery pack;

[0111] Step 4.3: The electronic speed controller adjusts the voltage of the input lithium battery pack using pulse width modulation technology to regulate the input operating voltage U of the motor, thereby controlling the motor speed ω, as expressed by equation (8). While controlling the motor voltage, the electronic speed controller also needs to meet the maximum power requirements of the multi-mode propulsion system. and maximum current I max The constraint requirement is expressed by equation (9);

[0112] Step 4.4: The parameters of the lithium battery pack are constrained by both multi-mode energy and power requirements. On the one hand, it needs to provide the energy required by the aircraft throughout the entire mission profile; on the other hand, it needs to meet the continuous and peak power requirements of rotor mode and transition mode during multi-mode changes. The performance of the lithium battery pack is expressed as the number of cells connected in series, N. s And the number of parallel connections N p Perform matching design; where the number of battery packs in series N s Select rated voltage U bat If the voltage is 44.4V, then the number of lithium batteries connected in series can be calculated as N. s =44.4 / 3.7=12. The number of battery packs connected in parallel, N. p Matching with the battery's maximum design power Battery peak discharge power Maximum energy of a single battery cell and battery pack maximum energy The matching relationship is expressed by equation (10);

[0113] Step 4.5: The efficiency and power loss of the electric propulsion system under various flight conditions in the flight profile are represented by (11) and (12), respectively; where the efficiency of the electronic speed controller η is... esc and lithium battery discharge efficiency η bat The efficiency values ​​are set to 0.95 for all parameters, which vary little under flight conditions. The remaining parameters are calculated based on the matching of the multi-mode electric propulsion system.

[0114] Step 5: Use a genetic algorithm with global optimization capabilities to discretely solve the comprehensive preference model of multi-mode efficiency. The three design variables, propeller diameter D, pitch θ, and rotational speed ω, are discretized into integers to facilitate matching with existing propeller parameters, as well as to iterate and optimize the model, and obtain the optimal electric propulsion system parameters for the small electric multi-mode aircraft.

[0115] The comprehensive preference model optimization results for the multi-mode electric propulsion system of a small electric tiltrotor UAV are as follows: propeller 21 inches, blade pitch 6 inches, motor efficiency 90.4%, and maximum current I of the electronic speed controller. maxThe capacity is 36A, and the lithium battery is connected in parallel with N. p The number of series connections is 2, and the number of series connections is N. s It is 12;

[0116] Step 6: Verify the optimized electric propulsion system results to determine if they meet the spacecraft's mission requirements. Verification methods can be divided into theoretical and experimental verification. Theoretical verification can be performed by reverse engineering the multi-mode electric propulsion system matching results to verify whether the mission requirements and profile can be met. If the requirements are not met, the system selection and parameter matching will be re-performed. Experimental verification involves conducting ground experiments on the multi-mode electric propulsion system based on the matching results to verify the feasibility of the parameter matching results.

[0117] Step 6 is implemented using the following steps:

[0118] Step 6.1: Theoretical verification can be performed from both power and energy perspectives. The maximum power P of the multi-mode electric propulsion system of a small electric tiltrotor UAV. e The power output is 1814W, which meets the maximum power requirements for rotor mode, switching mode, and fixed-wing mode in step 1; the lithium battery energy is matched to the energy E. bat The energy required for the flight profile is 600Wh, which is greater than the energy required for the flight profile (vertical takeoff and landing energy: 19.9Wh, multiple rolls energy: 146.6Wh, multiple hovering: 209.6Wh, cruise flight: 132.9Wh, total 509Wh).

[0119] Step 6.2: Verify the feasibility of the parameter matching results of the multi-mode electric propulsion system using ground tests. The tests are mainly divided into static tests and dynamic tests. This embodiment discloses a rapid multi-condition test platform for an electric propulsion system, such as... Figure 3 As shown. The example test platform includes a computer 1, a wireless data transmission module 2, a lithium battery pack under test 3, an electronic speed controller under test 4, a main test frame 5, a motor under test 6, a propeller under test 7, and a front fan system 8. The test platform can perform static and dynamic tests simultaneously to verify the parameter matching results of the multi-mode electric propulsion system;

[0120] Computer 1 displays, records, and outputs the test results of the electric propulsion system;

[0121] Wireless data transmission module 2 wirelessly transmits the test parameter results of the electric propulsion system to computer 1;

[0122] The multi-mode electric propulsion system under test mainly consists of a lithium battery under test 3, an electronic speed controller under test 4, a motor under test 6, a propeller under test 7, and fixed components, providing thrust for multi-mode flight of a small electric tiltrotor UAV. The root of the propeller under test 7 is fastened to the motor under test 6, and they rotate together to provide the thrust required for flight. The motor under test 6 is mechanically fastened to the main test frame 5 through fixed components. The motor under test 6 is sequentially connected to a tension sensor 5.3, a torque sensor 5.2, and a speed sensor 5.1, and then connected to the electronic speed controller under test 4. The electronic speed controller under test controls the input operating voltage of the motor under test. The lithium battery pack 3 is connected to the electronic speed controller 4 to provide the necessary energy for the circuit.

[0123] The main test frame 5 consists of a speed sensor 5.1, a torque sensor 5.2, a tension sensor 5.3, an airspeed sensor 5.4, a current and voltage sensor 5.5, and a fixing component. It senses and tests the parameters of the electric propulsion system. The fixing component mechanically secures the lithium battery pack 3 under test, the electronic speed controller 4 under test, and the motor 6 under test within the electric propulsion system. The speed sensor 5.1, torque sensor 5.2, and tension sensor 5.3 respectively sense the speed ω, torque Q, and tension T of the motor 6 under test and the propeller 7 under test. The airspeed sensor 5.4 senses the incoming flow velocity, and the current and voltage sensor 5.5 senses the current and voltage in the circuit when the electric propulsion system is operating. The main test frame 5 not only perfectly secures the electric propulsion system under test but also accurately and quickly senses the output parameters of the electric propulsion system under test.

[0124] The front fan system 8 mainly consists of a front fan 8.1, a front DC regulated power supply 8.2, a microcontroller 8.3, and a fixing component, providing a stable incoming airflow speed for dynamic testing of the electric propulsion system. The fixing component mechanically secures the front fan 8.1 to the main test frame 5 at the same height, ensuring that the propeller 7 under test is located at the center of the incoming airflow speed. The front DC regulated power supply 8.2 is electrically connected to the front fan 8.1, providing a stable incoming airflow speed through a stable voltage. The microcontroller 8.3 controls the magnitude of the incoming airflow speed. Furthermore, by controlling the distance between the front fan system 8 and the main test frame 5, both the stability and magnitude of the incoming airflow speed are simultaneously controlled.

[0125] Based on the aforementioned multi-condition rapid test platform for electric propulsion systems, dynamic tests in level flight and static tests in hovering, involving parameter matching of the electric propulsion system in multiple modes, were conducted. The test results are as follows:

[0126] Static hovering test: propeller thrust: 24.5 N, motor efficiency: 85%, current: 6.02 A, power consumption: 234.8 Wh; Dynamic cruise test: incoming current speed: 10 m / s, propeller thrust: 4.52 N, motor efficiency: 83%, current: 1.14 A, power consumption: 170.5 Wh. The tested motor efficiency was lower than the optimized matching result, but the matched lithium battery energy could cover the actual extra energy consumed, meeting mission requirements. Furthermore, a limit power test was conducted on the electric propulsion system. The maximum static thrust of a single electric propulsion system was 55.6 N, greater than 1.8 times the required thrust, meeting mission requirements and verifying the feasibility of the matched electric propulsion system.

[0127] The above detailed description is a further explanation of the purpose, technical solution and beneficial effects of the invention. It should be understood that the above description is only a specific implementation example of the present invention and is only used to explain the present invention. It is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing and matching the parameters of the electric propulsion system of a small electric multi-mode aircraft, characterized in that: Includes the following steps: Step 1: Based on the type of small electric multi-mode aircraft and mission requirements, construct the topology of the multi-mode electric propulsion system and determine the flight profile; Multi-mode mainly includes rotor mode, fixed-wing mode, and switching mode; The multi-mode electric propulsion system topology includes: a lithium battery pack, a lithium battery management system, an electronic governor, a motor, and a propeller; The mission profile of the rotor mode includes vertical takeoff, vertical descent, and multi-point hovering flight conditions in rotor mode; The mission profile of fixed-wing aircraft includes cruise, climb, ceiling flight, hovering, and maximum level flight speed flight modes; The mission profile for switching modes is the switching mode between rotor mode and fixed-wing mode; The flight profile is a typical multi-mode mission profile, which is a combination of the three mission profiles mentioned above. Step 2: Based on the flight profile and flight requirements, select the objective function and constraints for the mission conditions in the multi-mode, and establish a multi-objective efficiency optimization model to represent the parameter matching problem of the multi-mode electric propulsion system. The multi-objective efficiency optimization model is as follows: As shown; in, , and These are the 1st, 2nd, ...th efficiency optimizations, respectively. One objective function; It is the maximum thrust. Indicates the weight of the aircraft. It is the ratio of the aircraft's thrust to its gravity. and These are the required thrust and the actual output thrust under the expected flight power, respectively. and These are the required speed under the desired flight power and the actual flight speed, respectively. Design variables for parameter matching; and These are the upper and lower bounds of the design variable, respectively; and These represent the equality constraints for parameter matching and the number of design variables, respectively. Step 3: Based on the type of multi-objective efficiency optimization model, select the preference function in physical planning for the type of objective function in equation (1), and transform the constraints in equation (1) into an unconstrained optimization problem; Step 3.1: Based on the multi-objective efficiency optimization model, select the soft preference type where larger is better in physical programming, set the preference boundary values ​​for each objective in the multi-mode efficiency optimization, solve the piecewise preference curves for each efficiency objective based on the preference boundary values, and construct the comprehensive preference function of the multi-mode efficiency optimization problem as the objective function of the physical programming optimization model. The constrained comprehensive preference function model is as follows: As shown: in, The number of efficiency objectives in a multi-objective model; This indicates the value of each efficiency target; This represents the preference function value corresponding to the efficiency objective; Step 3.2: Use a penalty function to transform the constrained comprehensive preference function model in equation (2) into an unconstrained comprehensive preference function model, expressed as equation (3); where the equality constraint uses an exponential smooth penalty function, and the inequality constraint uses a traditional penalty function; in, and These represent the expected value and the actual value in the equality constraint, respectively. Inequality constraints; These are the weighting coefficients for the equality constraints; These are the weighting coefficients of the exponential penalty function; The penalty function weight coefficients for the constraints; Step 4: Based on the flight profile and topology of the multi-mode aircraft in Step 1, calculate the power demand, transmission and loss of the electric propulsion system topology under each flight condition in Step 3 according to Equation (3). Step 4.1: Use the tensile coefficient Power coefficient and propeller efficiency To describe the performance of a propeller in an electric propulsion system, the propeller's thrust coefficient, power coefficient, and efficiency are calculated as follows: In the formula, The propeller advance ratio is expressed as: ; This refers to the output power of the propeller. This is the power input to the propeller shaft. air density; This refers to the propeller speed; This refers to the propeller torque; The diameter of the propeller; Step 4.2: The motor is a brushless permanent magnet DC motor. Using the equivalent circuit method to describe the internal losses of the motor, the motor efficiency is... It is expressed as follows: in, This refers to the input power of the motor shaft. The input operating current for the motor is expressed as: ,in For voltage parameters, This is the no-load current; The input operating voltage for the motor is expressed as: ,in This is the internal resistance of the motor; Step 4.3: The electronic speed controller adjusts the input voltage of the lithium battery pack using pulse width modulation technology to regulate the motor's input operating voltage. This controls the motor speed. The relationship between the motor voltage and the lithium battery pack voltage is expressed as follows: in, Indicates a PWM signal, when The motor is at its maximum speed at %. This refers to the rated voltage of the lithium battery pack. The electronic speed controller needs to control the motor voltage while meeting the maximum power requirements of the multi-mode propulsion system. and maximum current Given the constraints required, the constraints of the electronic speed controller under the task profile are expressed as follows: Step 4.4: The lithium battery pack utilizes a series-parallel design for individual cells; the number of cells connected in series in the battery pack... Depend on and the rated voltage of a single battery Decision, that is Number of parallel battery packs With the maximum design power of the battery pack Battery peak discharge power Maximum energy of a single battery cell and battery pack maximum energy Relevant, that is, expressed as: Step 4.5: The power consumption of the electric propulsion system topology under various flight conditions in the flight profile is generated by the lithium battery pack and transferred to the electronic governor, motor, and propeller to output the required power. The efficiency and power loss of the electric propulsion system are then expressed by the following formulas: Japanese style This indicates that, among other things, due to the efficiency of the electronic speed controller... and lithium battery pack discharge efficiency The variation is small under flight conditions, so a constant coefficient is used to represent it; In the formula, The total efficiency of the electric propulsion system; The power generated by the lithium battery pack, and not exceeding the maximum design power. ; Step 5: Use a genetic algorithm with global optimization capabilities to optimize the formula. The unconstrained integrated preference function model is solved, iterated, and optimized to obtain the optimal electric propulsion system parameters for the small electric multi-mode aircraft; thus, the optimally matched electric propulsion system is obtained.

2. The method for optimizing and matching the electric propulsion system parameters of a small electric multi-mode aircraft as described in claim 1, characterized in that: It also includes step six, verifying whether the optimized electric propulsion system results meet the mission requirements of the aircraft; the verification methods are divided into two types: theoretical verification and experimental verification. Theoretical verification method: Based on the matching results of the multi-mode electric propulsion system, reverse verification is performed to see if the mission requirements and mission profile can be met. If the requirements are not met, the selection and parameter matching are carried out again. The theoretical verification method is specifically implemented by verifying both power and energy; the maximum power of the multi-mode electric propulsion system. It should meet the maximum power requirements required in the rotor mode, conversion mode and fixed-wing mode under the mission profile, expressed by equation (13); in, This represents the maximum power required in fixed-wing mode. This represents the maximum power required in rotor mode. This represents the maximum power required in the conversion mode; The lithium-ion battery pack provides the energy required for the multi-mode electric propulsion system, and the remaining energy after the multi-mode electric propulsion system completes the mission is used. To verify the energy matching results, use the formula... express; If the matching result cannot meet the power and energy requirements in the mission profile, it is necessary to re-select and re-optimize the parameters. Experimental verification method: Conduct ground experiments on a multi-mode electric propulsion system based on the matching results to verify the feasibility of the parameter matching results; The experimental verification method is implemented as follows: the feasibility of the parameter matching results of the multi-mode electric propulsion system is verified using a ground test platform; the test is mainly divided into static test and dynamic test.

3. The method for optimizing and matching the electric propulsion system parameters of a small electric multi-mode aircraft as described in claim 1, characterized in that: Propeller thrust coefficient in step 4.1 and power coefficient It is obtained through the following method: From equations (4) to (6), it can be seen that the propeller thrust coefficient and power coefficient With propeller diameter Rotation speed and inflow rate Yes, it is related to the pitch of the propeller. Yes, there is a relationship; a backpropagation neural network is used to build a propeller surrogate model using existing propeller parameters, which can quickly obtain the thrust coefficients of different types of propellers. and power coefficient That is, the input to the propeller proxy model is the propeller diameter. Pitch Rotation speed and inflow rate The output is the propeller thrust coefficient. and power coefficient .

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