Electric multi-copter optimal forward flight endurance estimation method, system, device and medium

Abstract

This invention discloses a method, system, device, and medium for estimating the optimal forward flight range of an electric multi-rotor drone. The method includes: establishing performance models of key components of the electric multi-rotor drone's power system, wherein the key components include at least a propeller, a drive motor, and a battery; establishing a fuselage level flight state model of the electric multi-rotor drone; acquiring the state parameters and environmental parameters of the multi-rotor drone in level flight; integrating the performance models of the key components of the electric multi-rotor drone's power system and the fuselage level flight state model; and traversing the forward flight speed search range to solve for the forward flight range of the electric multi-rotor drone at various forward flight speeds, thereby obtaining the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor drone. The model of this invention fully considers the performance changes of the power system under dynamic flight conditions, and compared with simple empirical formulas, it can more accurately reflect actual energy consumption, providing a more reliable basis for range assessment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, system, device, and medium for estimating the optimal forward flight range of an electric multirotor, which is especially suitable for quickly and accurately predicting its flight range during the design and mission planning stages. Background Technology

[0002] Currently, the endurance assessment of electric multi-rotor UAVs mainly employs two types of methods: empirical estimation based on simple physical formulas and detailed modeling relying on high-precision simulations or extensive experimental measurements. Empirical estimation methods are typically based on assumptions about energy density and average power under ideal conditions. While simple in form, they have limited accuracy and struggle to reflect the impact of complex flight states, nonlinear characteristics of the propulsion system, and changes in environmental conditions. High-precision modeling methods, such as fluid simulation based on computational fluid dynamics or co-simulation of complex electromechanical systems, while providing relatively accurate results, suffer from problems such as complex modeling, high computational resource consumption, and reliance on detailed component parameters, limiting their application in rapid iterative design and preliminary scheme evaluation.

[0003] Furthermore, existing evaluation methods typically fail to adequately consider the performance of the propulsion system, particularly the thrust and power characteristics of the propeller under different incoming flow conditions, as well as the coupling relationship between motor efficiency and current consumption. As the primary energy-consuming component of electric multi-rotor UAVs, the performance of the propulsion system is significantly affected by changes in state parameters such as flight speed and air density, impacting overall energy consumption. The lack of accurate and efficient modeling of the propulsion system's performance under real-world flight conditions is a major reason for the low accuracy or limited practicality of existing endurance evaluation methods.

[0004] Therefore, there is an urgent need in this field for a solution that can quickly and accurately evaluate the optimal forward flight range of electric multirotors using limited data in the early stages of design, in order to make up for the shortcomings of existing technologies in terms of accuracy and efficiency. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for estimating the optimal forward flight range of an electric multirotor. While ensuring a certain level of evaluation accuracy, it significantly reduces the demand for data volume and computing resources and is suitable for the rapid design and mission planning stages of electric multirotor UAVs.

[0006] A method for estimating the optimal forward flight range of an electric multirotor includes the following steps:

[0007] Establish performance models of key components of the power system of an electric multi-rotor UAV, wherein the key components include at least a propeller, a drive motor, and a battery;

[0008] Establish a model of the fuselage in level flight state of an electric multi-rotor UAV;

[0009] The system acquires the state and environmental parameters of the multi-rotor UAV in level flight, integrates the performance models of key components of the electric multi-rotor UAV power system and the level flight state model of the fuselage, and iterates through the forward flight speed search range to solve the forward flight range of the electric multi-rotor UAV at each forward flight speed, thereby obtaining the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV.

[0010] Furthermore, the propeller performance model is represented as follows:

[0011] ;

[0012] ;

[0013] ;

[0014] In the formula, T, Q, and P represent the thrust, torque, and motor output power generated by the propeller, respectively. , and These are the thrust coefficient, power coefficient, and torque coefficient, respectively. ; air density; This refers to the propeller speed; This is the diameter of the propeller.

[0015] Furthermore, the propeller thrust coefficient and power coefficient Expressed as propeller speed Pitch ratio Compared with progress The relevant functions; and based on experimentally collected data or existing public datasets, multinomial regression is used to analyze the thrust coefficient. and power coefficient Fit the relevant functions;

[0016] When estimating the performance parameters of the same propeller model using propeller data, the thrust coefficient is... and power coefficient The relevant functions are represented as follows:

[0017] ;

[0018] In the formula, and These are the predicted values ​​for the thrust coefficient and power coefficient, respectively. These are the polynomial coefficients obtained by fitting using the least squares method;

[0019] When estimating the performance parameters of different propeller models using propeller data, the thrust coefficient is... and power coefficient The relevant functions are represented as follows:

[0020] ;

[0021] In the formula, The coefficients are polynomials obtained by fitting data from different propeller models using the least squares method.

[0022] Furthermore, the drive motor performance model adopts a torque-based approach. The current model is expressed as follows:

[0023] ;

[0024] In the formula, This is the equivalent current of the motor. and The fitting coefficients are denoted as .

[0025] Furthermore, the battery performance model is expressed as follows:

[0026] ;

[0027] In the formula, For battery life, and These are battery capacity and battery current, respectively. This refers to the battery's safe charge rating.

[0028] Furthermore, the fuselage level flight state model of the electric multi-rotor UAV is represented as follows:

[0029] ;

[0030] In the formula, This indicates the forward drag of the drone in forward flight mode; Indicates the drag coefficient; This represents the actual inflow velocity of the drone, which is the sum of the drone's forward flight speed and the incoming airflow velocity.

[0031] Furthermore, the forward flight range of the electric multi-rotor UAV at various forward flight speeds is solved by iterating through the search range of forward flight speeds, specifically including:

[0032] Solving the pitch angle of the UAV: ​​Calculate the actual inflow velocity of the UAV based on the incoming flow velocity and the forward flight velocity of the UAV. Solve the forward flight drag of the UAV in the forward flight state based on the actual inflow velocity of the UAV and the fuselage level flight state model of the electric multi-rotor UAV. Then, combine the weight of the UAV with the force balance equation to solve the pitch angle of the UAV in the uniform forward flight state.

[0033] Solving for the rotational speed of each propeller: Combining the weight of the UAV and the pitch angle of the UAV in uniform forward flight, the thrust of each propeller is calculated, and then the rotational speed of the propeller in uniform forward flight is calculated by combining the propeller performance model.

[0034] Solving for the drone's flight time and range: The propeller torque is obtained by combining the propeller speed under constant forward flight conditions with the propeller performance model. Then, the equivalent current of the motor is obtained by combining the drive motor performance model. The battery current is obtained based on the equivalent current of the motor. Then, the flight time of the drone under constant forward flight conditions is obtained by combining the battery performance model. Finally, the forward flight range of the electric multi-rotor drone is calculated by combining the forward flight speed of the drone.

[0035] Based on the above process, the forward flight range of the electric multi-rotor UAV at each forward flight speed is solved by traversing the forward flight speed search range, and then the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV are obtained by comparison.

[0036] Secondly, an optimal forward flight range estimation system for electric multi-rotor aircraft is provided, including:

[0037] The power system modeling module is used to establish the performance models of key components of the power system of an electric multi-rotor UAV, which include at least propellers, drive motors and batteries.

[0038] The fuselage level flight state modeling module is used to establish a fuselage level flight state model of an electric multi-rotor UAV.

[0039] The optimal forward flight range solution module is used to obtain the state parameters and environmental parameters of the multi-rotor UAV in level flight state, and integrate the performance model of the key components of the electric multi-rotor UAV power system and the fuselage level flight state model. Within the forward flight speed search range, it traverses and solves the forward flight range of the electric multi-rotor UAV at each forward flight speed, and then obtains the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV.

[0040] Thirdly, an electronic device is provided, comprising:

[0041] A memory on which computer programs are stored;

[0042] A processor is used to load and execute the computer program to implement the previously described method for estimating the optimal forward flight range of an electric multirotor.

[0043] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned method for estimating the optimal forward flight range of an electric multirotor.

[0044] This invention proposes a method, system, device, and medium for estimating the optimal forward flight range of an electric multirotor, which has the following advantages compared with existing technologies:

[0045] 1. Balance between accuracy and practicality: The model fully considers the performance changes of the power system under dynamic flight conditions (such as different forward speeds). Compared with simple empirical formulas, it can more accurately reflect actual energy consumption and provide a more reliable basis for range assessment.

[0046] 2. High data efficiency: By introducing dimensional analysis and dimensionless parameter modeling, the dependence on a large amount of detailed experimental data is significantly reduced, and a model with good generalization ability can be established with only limited test data.

[0047] 3. Lightweight and fast computation: It adopts a computationally efficient model such as multinomial regression, which makes the performance evaluation and endurance calculation process fast, making it very suitable for multi-scheme iteration and optimization in the early stage of electric multi-rotor UAV design.

[0048] 4. Modularity and scalability: The method adopts a modular design, with relatively independent power system models, environmental parameters, flight states, etc., which facilitates adjustment and expansion according to specific electric multi-rotor UAV configurations and mission scenarios.

[0049] 5. Possesses optimization guidance capability: Unlike traditional methods that only provide single-state endurance assessment, this method can proactively seek optimization and directly output the longest endurance under a specific configuration and its corresponding optimal forward flight speed, providing key decision-making basis for UAV mission path planning and flight control parameter setting. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is an implementation framework diagram of the optimal forward flight range estimation method for electric multirotors provided in this embodiment of the invention;

[0052] Figure 2 This is a force diagram of an electric multi-rotor UAV in level flight provided by an embodiment of the present invention;

[0053] Figure 3 This is a flowchart of the algorithm for solving the optimal forward flight range provided in an embodiment of the present invention;

[0054] Figure 4This is a uniform flight speed-drag diagram provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0056] like Figure 1 As shown, the optimal forward flight range estimation method for electric multirotors of the present invention includes the following steps:

[0057] Step 1: Establish performance models of key components of the electric multi-rotor UAV power system. The key components include at least the propeller, drive motor, and battery.

[0058] Propeller performance model: In the propulsion system of an unmanned aerial vehicle (UAV), the propeller is the key component that mainly provides the UAV's flight power, and its performance can be expressed as a function of the thrust coefficient. Power coefficient and torque coefficient The function, where The equations relating the thrust and torque generated by the propeller to the output power of the motor are as follows:

[0059] ;

[0060] ;

[0061] ;

[0062] In the formula, T, Q, and P represent the thrust, torque, and motor output power generated by the propeller, respectively. air density; This refers to the propeller speed; This is the diameter of the propeller.

[0063] Among them, air density From temperature Determined by both altitude h and elevation, it can be estimated using the following formula:

[0064] ;

[0065] in, , is the standard air density at sea level under standard atmospheric pressure, and at the same time, temperature It is also correlated with altitude h, and this relationship can be expressed by the following formula:

[0066] ;

[0067] in, , which is the standard atmospheric temperature at sea level under standard atmospheric pressure.

[0068] For the propeller, a thrust coefficient and power coefficient model based on dimensionless analysis is used for modeling, utilizing the Buckingham model. Theorem, which states that the thrust coefficient of a propeller... and power coefficient Expressed as propeller speed Pitch ratio Compared with progress Related functions, This represents the actual inflow velocity of the drone; and based on experimentally collected data or existing publicly available datasets, multinomial regression is used to evaluate the thrust coefficient. and power coefficient Fit the relevant functions.

[0069] The core steps are as follows: First, raw data such as propeller thrust, torque, and motor output power are obtained through experimental measurements or by collecting publicly available datasets. Second, based on dimensionless analysis theory, the raw data are converted into dimensionless parameters, such as thrust coefficient, power coefficient, pitch ratio, and advance ratio. Finally, using these dimensionless parameters as input, polynomial regression is performed using the least squares method to solve for the polynomial coefficients that minimize the error between the model's predicted and measured values ​​of the propeller performance model. This method ensures that the model originates from experimental data and possesses generalization ability based on physical principles.

[0070] Since experimental data or existing public datasets cannot cover all propeller models, the following two cases are considered when fitting propeller performance parameters:

[0071] (1) When estimating the performance parameters of propellers of the same model using propeller data, the thrust coefficient and power coefficient The relevant functions are represented as follows:

[0072] ;

[0073] In the formula, and These are the predicted values ​​for the thrust coefficient and power coefficient, respectively. These are the polynomial coefficients obtained by fitting using the least squares method;

[0074] (2) When estimating the performance parameters of different propeller models using propeller data, the thrust coefficient and power coefficient The relevant functions are represented as follows:

[0075] ;

[0076] In the formula, The coefficients are polynomials obtained by fitting data from different propeller models using the least squares method.

[0077] Drive motor performance model: Electric multi-rotor drones generally use brushless DC motors, and the equivalent current of the motor... It is the direct physical quantity that provides energy to it and is closely related to the system's endurance; under static or fixed advance ratio conditions, thrust and torque Strongly correlated, this embodiment uses torque-based... The current model characterizes the performance model of the drive motor, as specifically expressed below:

[0078] ;

[0079] In the formula, This is the equivalent current of the motor. and The fitting coefficients are obtained by using the least squares method to solve polynomial regression based on experimentally collected data or existing public datasets.

[0080] Battery performance model: In the power system of an electric multi-rotor UAV, the main function of the battery is to provide power for the flight of the electric multi-rotor UAV. This embodiment mainly models the discharge time of the battery under stable current. In actual flight of a multi-rotor UAV, the battery will retain 15%-20% of its safe charge. Therefore, the battery discharge time model adopted in this invention is expressed as follows:

[0081] ;

[0082] In the formula, For battery life, and These are battery capacity and battery current, respectively. The battery's safety factor can generally be set to 0.8~0.85.

[0083] Step 2: Establish a model of the fuselage in level flight state of the electric multi-rotor UAV.

[0084] Level flight is the most common flight state for electric multi-rotor drones. For drones in flight, the main challenge is aerodynamic drag. Figure 2 This is a schematic diagram of the forces acting on a UAV in level flight. The fuselage model in level flight of this embodiment is represented by the UAV aerodynamic drag model as follows:

[0085] ;

[0086] In the formula, This indicates the forward drag of the drone in forward flight. Indicates the drag coefficient; The actual inflow velocity of the drone is the sum of the drone's forward flight velocity V1 and the incoming flow velocity V0.

[0087] Step 3: Obtain the state parameters and environmental parameters of the multi-rotor UAV in level flight state, and integrate the performance model of the key components of the electric multi-rotor UAV power system and the fuselage level flight state model. Within the search range of forward flight speed, traverse and solve the forward flight range of the electric multi-rotor UAV at each forward flight speed, and then obtain the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV.

[0088] The description of a multi-rotor drone in level flight under normal circumstances can be simplified as follows: Let the weight of an electric multi-rotor drone in level flight be... ,have Each rotor (belonging to the state parameters of a multi-rotor UAV), the corresponding environmental parameter is altitude. ,temperature Incoming flow velocity Problem objective: Based on the input parameters, determine the optimal flight range (maximum flight distance) of an electric multi-rotor drone. .

[0089] Specifically, the forward flight range of an electric multi-rotor UAV at any constant forward speed can be calculated using the following steps:

[0090] Step 1: Determine the pitch angle of the UAV.

[0091] During the drone's uniform forward flight, the forward velocity V1 is the velocity relative to the ground coordinate system. This is used to calculate the actual inflow velocity. When considering the superposition of airflow velocities, the actual inflow velocity is: , The airflow velocity;

[0092] Based on the actual inflow velocity of the UAV and the fuselage level flight model of the electric multi-rotor UAV, the forward drag of the UAV in forward flight state can be solved: ;

[0093] Since the drone is flying forward at a constant speed, the pitch angle of the drone in this state can be solved using the force balance equation and the drone's weight G. .

[0094] Step 2: Determine the rotational speed of each propeller.

[0095] During actual forward flight of a drone, there are slight differences in the actual thrust and rotational speed of each propeller. However, these differences are negligible under low-speed forward flight conditions. Therefore, by combining the weight of the drone and the pitch angle during uniform forward flight, the thrust of each propeller can be calculated. , This refers to the number of propellers;

[0096] Combining propeller performance model The propeller speed under uniform forward flight conditions can be obtained by solving the problem. .

[0097] Step 3: Determine the drone's flight time and range.

[0098] Based on the propeller speed during uniform forward flight of the drone Combining the propeller performance model The propeller torque is obtained by solving the problem. ;

[0099] The equivalent current of the motor is obtained by solving the performance model of the drive motor: ;

[0100] The battery current is obtained by solving for the equivalent current of the motor: , It consumes current for other devices;

[0101] The flight time of the drone during constant-speed forward flight was obtained by combining the battery performance model: ;

[0102] Then, the forward flight range of the electric multi-rotor drone was calculated by combining the forward flight speed of the drone. .

[0103] To achieve optimal range estimation, the core objective is to find the forward flight speed with the highest energy efficiency. Generally, the maximum level flight distance of an electric multi-rotor drone does not occur at its maximum speed. This is because higher level flight speeds result in greater drag and increased current, leading to reduced flight time. Furthermore, due to the maximum motor current limitation, the speed of an electric multi-rotor drone in level flight is within a finite range. Therefore, a numerical iterative method can be used to find the maximum value. Figure 3 The flowchart for the numerical iterative algorithm to find the optimal forward flight range is shown below, with specific steps as follows:

[0104] S1: Define the speed search range: Set the forward flight speed search range to 0 to the drone's maximum level flight speed. (It can usually be set to a large value).

[0105] S2: Initialize relevant parameter values: Determine airflow velocity Initial optimal driving range = 0, corresponding to the optimal forward speed = 0.

[0106] S3: Velocity Traversal Search: Within the search range of the forward velocity, traverse all possible forward velocities V1 with an appropriate step size, and perform the following calculations for each velocity point:

[0107] Calculate the actual inflow velocity ;

[0108] Solving the UAV pitch angle at the current velocity based on the force balance equation ;

[0109] Calculate the required rotational speed using a propeller performance model. ;

[0110] Calculate propeller torque Then, the equivalent current of the motor can be calculated. And verify that it is less than the motor's maximum current limit;

[0111] Calculate battery current ;

[0112] If the current constraint is satisfied, then calculate the driving range S at the current speed.

[0113] S4: Update the optimal solution: compare the remaining range at the current speed. Best-ever driving range ,like > Then update and .

[0114] S5: Output the final result: After traversing all speed points, output the globally optimal solution, i.e., the optimal driving range. and its corresponding optimal forward flight speed .

[0115] Experimental evaluation and verification.

[0116] By using publicly available databases, ground tests, or actual flight test data, the power system model and the final optimal range prediction results are verified and their accuracy evaluated to ensure the effectiveness and reliability of the method.

[0117] The experimental verification process of this invention was conducted on a computer with a main frequency of 2.9GHz and 16GB of memory, under the Win10 operating system. The experimental verification steps are as follows:

[0118] Step 1: Modeling the performance parameters of the propeller and motor:

[0119] This step can be replaced by a public database. In this implementation case, a self-built simplified incoming flow test bench is used to collect propeller and motor data and record propeller and motor related data with incoming flow speed in the range of 0-10 m / s. Taking the T5146 propeller as an example, the relevant components and equipment used in the test are shown in Table 1.

[0120] ;

[0121] The data that can be obtained from the power system test bench is shown in Table 2.

[0122] ;

[0123] Table 3 shows some of the test data from the power system test bench.

[0124] ;

[0125] The simplified incoming flow test bench provides wind speeds at various levels as shown in Table 4.

[0126] ;

[0127] Based on the wind speed results, the corresponding forward ratio can be calculated. .

[0128] The specific fitting process is as follows:

[0129] Propeller performance model fitting: advance ratio Rotation speed and pitch ratio As an independent variable, the thrust coefficient and power coefficient As the dependent variable, a design matrix for a second-order polynomial model is constructed. Then, the model coefficients are solved using the linear least squares algorithm by calling the `fitlm` function in MATLAB.

[0130] Motor model fitting: Equivalent current and torque model of the motor, which incorporates torque... As the independent variable, the equivalent current of the motor As the dependent variable, we also use MATLAB's fitlm function to perform linear least squares fitting to directly obtain the torque-current coefficient.

[0131] The fitted result is obtained using the MATLAB fitting algorithm described above. , , The model results are shown below:

[0132] ;

[0133] ;

[0134] .

[0135] Step 2: Outdoor flight test of the drone.

[0136] In order to identify the drag parameters of the UAV fuselage and to verify and evaluate the modeling results of the UAV power system, in this embodiment, an FX200 aircraft was used for a flight test. The relevant configuration of the UAV is shown in Table 5 below.

[0137] ;

[0138] Repeat the outdoor level flight experiment with pre-flight speeds of 2m / s, 4m / s, 6m / s, 8m / s, and 10m / s respectively, to obtain multiple sets of level flight data at different speeds.

[0139] Step 3: Identification of drag parameters of electric multi-rotor UAV fuselage.

[0140] By using multiple sets of uniform flight data at different speeds, combined with the fuselage drag parameter model adopted in this invention, the fuselage drag parameters of the UAV can be identified. Figure 4 To fit the speed-drag relationship at various speeds in the flight data, based on the measured drag data at different forward flight speeds, linear regression was used to fit the relationship between drag and the square of speed, resulting in a drag coefficient of 0.41 for the FX200 aircraft.

[0141] Step 4: Verification of experimental results.

[0142] To verify the ability of this method to estimate the optimal range, the accuracy of the model and the validity of the optimization results need to be checked. Combining the data and results from steps one, two, and three, and using the results from the UAV level flight test log, the predicted current value and the actual value are verified to check the accuracy of the optimal range estimation. Taking the results at a speed of 2 m / s as an example, the obtained parameters and actual current results are shown in Table 6 below.

[0143] ;

[0144] This invention proposes an optimal forward flight range estimation method for electric multirotors, which has the following beneficial effects:

[0145] 1. Balance between accuracy and practicality: The model fully considers the performance changes of the power system under dynamic flight conditions (such as different forward speeds). Compared with simple empirical formulas, it can more accurately reflect actual energy consumption and provide a more reliable basis for range assessment.

[0146] 2. High data efficiency: By introducing dimensional analysis and dimensionless parameter modeling, the dependence on a large amount of detailed experimental data is significantly reduced, and a model with good generalization ability can be established with only limited test data.

[0147] 3. Lightweight and fast computation: It adopts a computationally efficient model such as multinomial regression, which makes the performance evaluation and endurance calculation process fast, making it very suitable for multi-scheme iteration and optimization in the early stage of electric multi-rotor UAV design.

[0148] 4. Modularity and scalability: The method adopts a modular design, with relatively independent power system models, environmental parameters, flight states, etc., which facilitates adjustment and expansion according to specific electric multi-rotor UAV configurations and mission scenarios.

[0149] 5. Possesses optimization guidance capability: Unlike traditional methods that only provide single-state endurance assessment, this method can proactively seek optimization and directly output the longest endurance under a specific configuration and its corresponding optimal forward flight speed, providing key decision-making basis for UAV mission path planning and flight control parameter setting.

[0150] This invention also provides an optimal forward flight range estimation system for electric multirotors, comprising:

[0151] The power system modeling module is used to establish the performance models of key components of the power system of an electric multi-rotor UAV, which include at least propellers, drive motors and batteries.

[0152] The fuselage level flight state modeling module is used to establish a fuselage level flight state model of an electric multi-rotor UAV.

[0153] The optimal forward flight range solution module is used to obtain the state parameters and environmental parameters of the multi-rotor UAV in level flight state, and integrate the performance model of the key components of the electric multi-rotor UAV power system and the fuselage level flight state model. Within the forward flight speed search range, it traverses and solves the forward flight range of the electric multi-rotor UAV at each forward flight speed, and then obtains the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV.

[0154] It should be understood that the functional unit modules in the various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in hardware or software.

[0155] This invention also provides an electronic device, comprising:

[0156] A memory on which computer programs are stored;

[0157] A processor is used to load and execute the computer program to implement the previously described method for estimating the optimal forward flight range of an electric multirotor.

[0158] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for estimating the optimal forward flight range of an electric multirotor as described above.

[0159] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0160] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for estimating the optimal forward flight range of an electric multirotor, characterized in that, Includes the following steps: Establish performance models of key components of the power system of an electric multi-rotor UAV, wherein the key components include at least a propeller, a drive motor, and a battery; Establish a model of the fuselage in level flight state of an electric multi-rotor UAV; The system acquires the state and environmental parameters of a multi-rotor UAV in level flight, and integrates the performance models of key components of the electric multi-rotor UAV's power system and the fuselage level flight state model. Within the search range of forward flight speed, it iterates through and solves the forward flight range of the electric multi-rotor UAV at various forward flight speeds, thereby obtaining the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV. The state parameters of the multi-rotor UAV include the weight and number of rotors of the multi-rotor UAV; the environmental parameters include altitude, temperature, and airflow velocity.

2. The method for estimating the optimal forward flight range of an electric multirotor according to claim 1, characterized in that, The propeller performance model is represented as follows: ； ； ； In the formula, T, Q, and P represent the thrust, torque, and motor output power generated by the propeller, respectively. , and These are the thrust coefficient, power coefficient, and torque coefficient, respectively. ; air density; This refers to the propeller speed; This is the diameter of the propeller.

3. The method for estimating the optimal forward flight range of an electric multirotor according to claim 2, characterized in that, The thrust coefficient of the propeller and power coefficient Expressed as propeller speed Pitch ratio Compared with progress The relevant functions; and based on experimentally collected data or existing public datasets, multinomial regression is used to analyze the thrust coefficient. and power coefficient Fit the relevant functions; When estimating the performance parameters of the same propeller model using propeller data, the thrust coefficient is... and power coefficient The relevant functions are represented as follows: ； In the formula, and These are the predicted values ​​for the thrust coefficient and power coefficient, respectively. These are the polynomial coefficients obtained by fitting using the least squares method; When estimating the performance parameters of different propeller models using propeller data, the thrust coefficient is... and power coefficient The relevant functions are represented as follows: ； In the formula, The coefficients are polynomials obtained by fitting data from different propeller models using the least squares method.

4. The method for estimating the optimal forward flight range of an electric multirotor according to claim 1, characterized in that, The drive motor performance model adopts a torque-based approach. The current model is expressed as follows: ； In the formula, This is the equivalent current of the motor. and The fitting coefficients are denoted as .

5. The method for estimating the optimal forward flight range of an electric multirotor according to claim 1, characterized in that, The battery performance model is represented as follows: ； In the formula, For battery life, and These are battery capacity and battery current, respectively. This refers to the battery's safe charge rating.

6. The method for estimating the optimal forward flight range of an electric multirotor according to claim 1, characterized in that, The fuselage of an electric multi-rotor drone in level flight mode is represented as follows: ； In the formula, This indicates the forward drag of the drone in forward flight mode; Indicates the drag coefficient; This represents the actual inflow velocity of the drone, which is the sum of the drone's forward flight speed and the incoming airflow velocity.

7. The method for estimating the optimal forward flight range of an electric multirotor according to claim 1, characterized in that, The forward flight range of the electric multi-rotor UAV at various forward flight speeds is calculated by iterating through the search range of forward flight speeds, specifically including: Solving the pitch angle of the UAV: ​​Calculate the actual inflow velocity of the UAV based on the incoming flow velocity and the forward flight velocity of the UAV. Solve the forward flight drag of the UAV in the forward flight state based on the actual inflow velocity of the UAV and the fuselage level flight state model of the electric multi-rotor UAV. Then, combine the weight of the UAV with the force balance equation to solve the pitch angle of the UAV in the uniform forward flight state. Solving for the rotational speed of each propeller: Combining the weight of the UAV and the pitch angle of the UAV in uniform forward flight, the thrust of each propeller is calculated, and then the rotational speed of the propeller in uniform forward flight is calculated by combining the propeller performance model. Solving for the drone's flight time and range: The propeller torque is obtained by combining the propeller speed under constant forward flight conditions with the propeller performance model. Then, the equivalent current of the motor is obtained by combining the drive motor performance model. The battery current is obtained based on the equivalent current of the motor. Then, the flight time of the drone under constant forward flight conditions is obtained by combining the battery performance model. Finally, the forward flight range of the electric multi-rotor drone is calculated by combining the forward flight speed of the drone. Based on the above process, the forward flight range of the electric multi-rotor UAV at each forward flight speed is solved by traversing the forward flight speed search range, and then the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV are obtained by comparison.

8. A system for estimating the optimal forward flight range of an electric multirotor, characterized in that, include: The power system modeling module is used to establish the performance models of key components of the power system of an electric multi-rotor UAV, which include at least propellers, drive motors and batteries. The fuselage level flight state modeling module is used to establish a fuselage level flight state model of an electric multi-rotor UAV. The optimal forward flight range solution module is used to obtain the state parameters and environmental parameters of the multi-rotor UAV in level flight state. It integrates the performance models of key components of the electric multi-rotor UAV's power system and the fuselage level flight state model, and iterates through the forward flight speed search range to solve for the forward flight range of the electric multi-rotor UAV at each forward flight speed, thereby obtaining the optimal forward flight range and corresponding forward flight speed of the electric multi-rotor UAV. Among them, the state parameters of the multi-rotor UAV include the weight of the multi-rotor UAV and the number of rotors; the environmental parameters include altitude, temperature and airflow velocity.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor is configured to load and execute the computer program to implement the optimal forward flight range estimation method for electric multirotors as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optimal forward flight range estimation method for electric multirotors as described in any one of claims 1 to 7.

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