Fuel cell and lithium battery hybrid unmanned aerial vehicle semi-physical test system and method

Abstract

The application discloses a fuel cell and lithium battery hybrid unmanned aerial vehicle semi-physical test system and method, which comprises a hybrid power module for simulating power output of fuel cells and lithium batteries; a flight dynamics control module for solving unmanned aerial vehicle attitude information; a control instruction sending module for sending unmanned aerial vehicle motion instructions to a flight control instruction solving module; the flight control instruction solving module is used for solving control instructions to obtain motion instruction information; a ground station interface monitoring module is used for recording unmanned aerial vehicle flight attitude data returned by the flight control instruction solving module; a three-axis flight attitude simulation module receives motion instruction information and makes attitude response; a real-time simulation module is used for receiving feedback information of the three-axis flight attitude simulation module to simulate unmanned aerial vehicle flight states in a virtual scene; and an unmanned aerial vehicle 3D modeling and visual module is used for recording real-time simulation machine data and control information of the flight dynamics control module.

Description

Technical Field

[0001] This invention relates to the technical field of drone testing, and more specifically, to a hardware-in-the-loop testing system and method for fuel cell and lithium battery hybrid drones. Background Technology

[0002] Compared to traditional lithium-ion battery-powered drones, the new fuel cell and lithium-ion battery hybrid drones feature longer range and higher payload capacity, making them widely applicable in engineering fields such as power line inspection, agricultural operations, and terrain exploration. Drones require significant and rapid payload adjustments when performing different flight missions. To reduce development costs, a hybrid power model needs to be developed, and efficient energy management strategies need to be designed to rationally configure the capacity and output power of the fuel cell and lithium-ion batteries to simulate actual operation. This ensures that the overall energy consumption of the drone is met while maintaining the hybrid power system in a safe, stable, and efficient operating state.

[0003] However, hybrid-powered drone systems are complex to design, and fuel cell-equipped drones are expensive and have low fault tolerance during test flights. Therefore, before being officially put into use, the reliability of the power system and flight control system of hybrid-powered drones must be verified to prevent unnecessary losses of manpower and resources due to mechanical, circuit, and communication failures during actual flight. How to conduct semi-physical testing experiments on the designed drones has also attracted widespread attention. Semi-physical simulation, also known as hardware-in-the-loop simulation, integrates real hardware devices into a software simulation system. Compared to pure digital simulation, it is closer to real-world conditions, and the introduced natural disturbances are beneficial for testing the performance of flight control algorithms, resulting in more accurate information.

[0004] Based on semi-physical testing, in order to study the performance of the flight control system and power system of UAVs in actual ground environments, a three-axis flight turntable is usually used for ground testing of UAVs. Ground testing can better demonstrate the pitch, roll and yaw angles of UAVs in actual environments and provide a more intuitive observation of the UAV's flight attitude.

[0005] By designing a hybrid drone hardware test platform based on fuel cells and lithium batteries, it is possible to test the reliability and stability of the hardware system while reducing the cost of physical development, thereby improving its reusability.

[0006] Existing data includes research on hardware-in-the-loop simulation technology for unmanned aerial vehicles (UAVs) and hardware-in-the-loop simulation technology for hybrid power systems using fuel cells and lithium batteries. However, research on the collaborative simulation of hybrid power systems and flight control for fuel cell and lithium battery UAVs remains lacking.

[0007] Existing research typically models and simulates the hybrid power system (such as a combination of fuel cells and lithium batteries) and flight control system (such as attitude control and navigation control) of unmanned aerial vehicles (UAVs) separately.

[0008] Due to the lack of effective data interaction between the two systems, the real-time status of the propulsion system (such as battery level and power output) cannot be fed back to the flight control system in a timely manner, resulting in poor coordination during actual flight. In actual flight, the UAV's propulsion system and flight control system need to work closely together. The flight control system needs to adjust its flight strategy based on the status of the propulsion system, while the propulsion system needs to optimize energy allocation according to the requirements of the flight mission. However, the data interaction in existing models is limited or insufficient, leading to poor system performance when dealing with complex flight missions. Due to insufficient coordination between the propulsion system and flight control system, existing models have poor stability. Under different flight conditions, the model may exhibit inconsistent behavior, or even cause flight control failure.

[0009] Existing technical solutions for testing hybrid-powered drones have the following technical problems:

[0010] 1. The development and design costs of hybrid-powered drones are high. The core components involved in such drones, such as fuel cell systems, energy control modules, and the drone airframe, all have high manufacturing and procurement costs. For example, while fuel cell technology offers advantages in high energy density and environmental friendliness, its key materials and manufacturing processes are complex, resulting in costs far exceeding those of traditional batteries. Furthermore, the energy control module, as the core of the hybrid power system, requires precise management of energy distribution between the fuel cell and lithium battery; its design and implementation also require significant R&D investment. Simultaneously, the physical components of the drone, including the airframe structure, flight control system, sensors, and communication modules, are also expensive. In actual operation, because drone flight involves various complex environments and unpredictable factors, improper operation or sudden malfunctions often lead to equipment damage, causing not only costly hardware losses but also potentially additional maintenance, repair, or replacement costs.

[0011] 2. Configuring the parameters of the hybrid power model and the UAV's flight control parameters is difficult, and mechanical and electrical failures are prone to occur during UAV physical testing. The UAV's flight control parameters need to be highly coordinated with the hybrid power system, which further increases the difficulty of configuration. The flight control system is responsible for controlling the UAV's attitude, speed, altitude, etc., and its parameter configuration directly affects the UAV's stability and flight performance. Due to the high coupling between the power system and the flight control system, even a small parameter adjustment can trigger a chain reaction, leading to system instability. The complex coupling of the model makes the parameter tuning process extremely complex when debugging on a physical UAV.

[0012] 3. Existing hardware-in-the-loop (HIL) simulation systems have poor scalability. For power line inspection drones, different types of drones are often required to complete different inspection tasks, such as long-duration cruises, high-load flights, and navigation in complex terrain. However, existing HIL simulation systems are often designed for specific types of drones or specific mission environments, making it difficult for the system to adapt to various drones and diverse mission requirements. Summary of the Invention

[0013] To address the shortcomings of existing technologies, this invention provides a hybrid drone testing system based on fuel cells and lithium batteries, which solves the technical problem of poor adaptability in existing drone testing systems.

[0014] The present invention adopts the following technical solution.

[0015] A hybrid unmanned aerial vehicle (UAV) system based on fuel cells and lithium batteries includes: a hybrid power module, a flight dynamics control module, a control command sending module, a flight control command solving module, a ground station interface monitoring module, a three-axis flight attitude simulation module, a real-time simulation module, and a UAV 3D modeling and visual display module.

[0016] The system comprises the following modules: a hybrid power module to simulate the power output of fuel cells and lithium batteries; a flight dynamics control module to calculate the UAV attitude information returned by the real-time simulator and the flight control command calculation module; a control command sending module to send motion commands to the flight control command calculation module; a flight control command calculation module to calculate the UAV control commands and transmit the calculated motion command information to the three-axis flight attitude simulation module; a ground station interface monitoring module to record the UAV flight attitude data returned by the flight control command calculation module; a three-axis flight attitude simulation module to receive the motion command information from the flight control command calculation module and make corresponding attitude responses; a real-time simulation module to receive feedback information from the three-axis flight attitude simulation module and simulate the UAV flight state in a virtual scene; and a UAV 3D modeling and visualization module to record the real-time simulator data and the control information from the flight dynamics control module.

[0017] Preferably, for the mechanism modeling process of the power system, a hybrid power module of UAV fuel cell and lithium battery is designed. The constructed hybrid power module includes: hydrogen supply model, air supply model, cooling system model, lithium battery output voltage model, DC converter model and energy management model.

[0018] Preferably, the established hydrogen supply model is used to guarantee the output power of the fuel cell:

[0019]

[0020] Where, m flowFor the hydrogen consumption mass flow rate of fuel cells, Power load The power supplied by the fuel cell to the load, F is the Faraday constant, η DCDC For the efficiency of the DC-DC converter on the back side of the fuel cell, n FC V represents the number of cells in series in a fuel cell. FC This is the output voltage of the fuel cell.

[0021] Preferably, the established air supply model includes an air compressor model to ensure stable power of the fuel cell stack.

[0022]

[0023] Where, m flow,Air Power supplies air mass flow rate to fuel cells compressor η is the input power for the air compressor. compressor For the air compressor compression efficiency, r c R is the compression ratio, Z is the gas constant, and T is the average compressibility factor of air. in K represents the temperature of the air drawn into the air compressor, K represents the specific heat ratio of the air, and M represents the specific heat ratio of the air. Air Let be the molar mass of air.

[0024] Preferably, the established cooling system model is used to maintain a constant stack temperature:

[0025]

[0026] Where, m cooling,Air Let C be the mass flow rate of the cooling air, ΔT be the heat exchange temperature difference of the cooling air, and C be the temperature difference of the cooling air. p This represents the average specific heat capacity of the cooling air.

[0027] Preferably, the established lithium battery output voltage model is used to calculate the output voltage and power changes of the lithium battery in the hybrid power system:

[0028] V L =V OC -I L *R0-V p1 -V p2

[0029] Among them, V L V is the output voltage of the lithium battery. OC I is the open-circuit voltage of the lithium battery. L R0 is the load current, and R0 is the internal circuit resistance of the lithium battery. p1 and V p2 These are the equivalent circuit voltages inside the lithium battery.

[0030] Preferably, the established DC-DC converter model connecting the lithium battery and fuel cell is used to stabilize the output voltage of the fuel cell and lithium battery, ensuring the normal operation of the load. The mathematical model of the DC-DC converter is as follows:

[0031]

[0032] in, The output power of the DC / DC converter (in watts). Input current (in amperes) for the DC / DC converter. η is the input voltage (in V) of the DC / DC converter. DC Efficiency of DC / DC converter (in %).

[0033] Preferably, a rule-based energy management model is established to allocate power between the lithium battery and the fuel cell, and to obtain the SOC value of the input lithium battery and the load power P. load Fuel cell rated power P FC,nom Maximum discharge power P of lithium battery Li，Max,Discharge Maximum charging power P of lithium battery Li，Max，Charge And based on the acquired data, allocate the output power of the fuel cell to P. FC The charging power and discharging power of the lithium battery are respectively P Li，Charge and P Li，Discharge ;

[0034] When the SOC of a lithium battery is less than 0.2 and P load >P FC，nom When the simulation program terminates, the output power of the fuel cell is insufficient to meet the normal operation of the load.

[0035] When the SOC of a lithium battery is less than 0.2, P load ≤P FC,nom At that time, the lithium battery charging power is P Li,Charge =P FC,nom -P load The fuel cell output power is P FC =P FC,nom If P Li,Charge <P Li，Max，Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise, P Li，Charge =P Li，Max，Charge Fuel cell output power P FC，nom =P Li，Max，Charge +P load ;

[0036] When the lithium battery SOC is in the range [0.2, 0.8) and P load ≤P FC，nom At that time, the charging power P of the lithium batteryLi，Charge =P FC，nom -P load The fuel cell output power is P FC =P FC，nom If P Li，Charge <P Li，Max，Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise, P Li，Charge =P Li，Max，Charge Fuel cell output power P FC，nom =P Li，Max，Charge +P load ;

[0037] When the lithium battery SOC is in the range [0.2, 0.8) and P load ≥P FC，nom At that time, the discharge power of the lithium battery is P. Li，Discharge =P load -P FC，nom The fuel cell output power is P FC =P FC，nom If P Li，Discharge >P Li，Max，Discharge If the result is negative, it indicates that the load power is too high and the maximum discharge power of the lithium battery does not support the load operation, and the simulation program will be terminated.

[0038] This invention also proposes a semi-physical testing method for fuel cell and lithium battery hybrid drones, utilizing the aforementioned fuel cell and lithium battery hybrid drone semi-physical testing system, comprising the following steps:

[0039] Step 1: Check the electrical and communication connections of each module. If the connections are normal, run the real-time simulation module and input the UAV movement commands through the control command sending module.

[0040] Step 2: The flight control command calculation module calculates the motion commands of the UAV and inputs the motion commands into the flight dynamics control module to obtain the power required by the UAV under different motion commands.

[0041] Step 3: Determine if the power demand of the drone is equal to the power output of the hybrid power module. If yes, proceed to step 4; otherwise, adjust the air supply parameters of the hybrid power module.

[0042] Step 4: Determine whether the drone is performing a specific task. If so, calculate the drone's motion commands and adjust the air supply parameters of the hybrid power system until the drone's power demand equals the power output of the hybrid system.

[0043] Step 5: Observe the motion state of the three-axis flight turntable in the three-axis flight attitude simulation module, observe the UAV flight status in the UAV 3D modeling and visual module and the ground station interface monitoring module, and record the test data.

[0044] The present invention also proposes a terminal, including a processor and a storage medium;

[0045] The storage medium is used to store instructions;

[0046] The processor is used to operate according to the instructions to execute the steps of the fuel cell and lithium battery hybrid drone semi-physical testing method.

[0047] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the semi-physical testing method for the fuel cell and lithium battery hybrid unmanned aerial vehicle.

[0048] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] 1. This invention offers better economic efficiency. Actual flight testing of hybrid-powered drones is costly, especially in high-risk environments where expensive fuel cells and flight control boards can easily be damaged. Traditional drone testing does not include the fuel cell power system. Through hardware-in-the-loop simulation, various flight scenarios and operating conditions can be simulated in the laboratory, reducing the frequency and cost of actual testing of fuel cell drones.

[0050] 2. Significantly shortens the R&D cycle. Traditional semi-physical testing systems have a simple testing structure, and the configuration of the hybrid power system usually requires a physical reference. However, by rapidly iterating and verifying designs in a virtual environment, the sophisticated mathematical models and underlying data exchange chains designed on the three simulation platforms of Dymola, Simulink, and Unity enable rapid comparison and optimization of different solutions, which can greatly shorten the UAV development cycle.

[0051] 3. Simulate the performance of fixed-wing UAVs under extreme conditions. With the support of a real-time simulator, extreme scenarios such as high and low temperatures, strong winds, and thunderstorms can be simulated using Unity3D software to observe the flight performance of UAVs and the usage status of fuel cells and lithium batteries, without exposing the physical UAV to hazardous conditions. Furthermore, the real-time simulation environment allows for rapid iteration and optimization of flight control algorithms, ensuring flight stability and control accuracy when performing different tasks, and significantly enhancing the ability to cope with complex environments.

[0052] 4. This invention enables real-time debugging and optimization. Based on the combined power and flight control system model of Dymola and Simulink, it can provide real-time feedback on the system's operating status. The mechanism models of fuel cells and lithium batteries, as well as the energy management model, are built in Dymola, while the UAV's flight control system model is built in Simulink. The FMU model is imported into Simulink for parameter coordination adjustment. Operators can adjust the flight control algorithm and energy management scheme in real time based on simulation results. When the energy management strategy needs adjustment, it only needs to be designed in Dymola, achieving decentralized management of the power system simulation, avoiding single points of failure. Optimal power allocation is achieved through dynamic adjustment of the energy management model, improving energy efficiency, extending the UAV's endurance, and realizing collaborative simulation between the power system and flight control system. Attached Figure Description

[0053] Figure 1 This is a block diagram of the semi-physical testing system for fuel cell and lithium battery hybrid unmanned aerial vehicles in this invention;

[0054] Figure 2 This is a schematic diagram of the semi-physical testing method for fuel cell and lithium battery hybrid drones.

[0055] Figure 3 This is a schematic diagram of the verification structure of a hardware-in-the-loop (HIL) test system for unmanned aerial vehicles (UAVs). Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0057] like Figure 1 As shown, this invention constructs a hybrid unmanned aerial vehicle (UAV) system using fuel cells and lithium batteries. The system includes a hybrid power module, a flight dynamics control module, a control command sending module, a flight control command solving module, a ground station interface monitoring module, a three-axis flight attitude simulation module, a real-time simulation module, and a UAV 3D modeling and visual display module.

[0058] The system comprises the following modules: a hybrid power module to simulate the power output of fuel cells and lithium batteries, providing flight power for the UAV; a flight dynamics control module to calculate the UAV attitude information returned by the real-time simulator and the flight control command calculation module; a control command sending module to send motion commands to the flight control command calculation module; a flight control command calculation module to calculate the UAV control commands and transmit the resulting motion commands to the three-axis flight attitude simulation module; a ground station interface monitoring module to record the UAV flight attitude data returned by the flight control command calculation module, including pitch, yaw, roll rate, and angular acceleration; a three-axis flight attitude simulation module to receive motion command information from the flight control command calculation module and respond accordingly; a real-time simulation module to receive feedback information from the three-axis flight attitude simulation module and simulate the UAV flight state in a virtual scene; and a UAV 3D modeling and visualization module to record data from the real-time simulator and control information from the flight dynamics control module.

[0059] For the mechanism modeling process of the power system, a hybrid power module of fuel cell and lithium battery was designed using Dymola simulation software. The constructed hybrid power module includes: hydrogen supply model, air supply model, cooling system model, lithium battery output voltage model, and DC-DC converter model of lithium battery and fuel cell.

[0060] Specifically, the established hydrogen supply model is used to ensure the output power of the fuel cell:

[0061]

[0062] Where, m flow Power is the mass flow rate of hydrogen consumed in a fuel cell (in g / s). load The power supplied by the fuel cell to the load (in W), F is the Faraday constant (in C / mol), and η is the power delivered by the fuel cell to the load. DCDC n represents the efficiency (in %) of the DC-DC converter on the back side of the fuel cell. FC V represents the number of cells in series in a fuel cell. FC This refers to the output voltage of the fuel cell (in V).

[0063] The established air supply model includes an air compressor model to ensure stable power output from the fuel cell stack.

[0064]

[0065] Where, m flow，Air Power provides air mass flow rate (in g / s) for fuel cells. compressor η is the input power of the air compressor (in W); compressor For the air compressor compression efficiency, rc R is the compression ratio, R is the gas constant (unit: J / (mol·K)), Z is the average compressibility factor of air, and T is the gas constant. in K represents the temperature of the air drawn into the air compressor (in °C), K represents the specific heat ratio of air, and M represents the specific heat ratio of air. Air This is the molar mass of air (in g / mol).

[0066] The established cooling system model is used to maintain a constant stack temperature:

[0067]

[0068] Where, m cooling，Air ΔT is the mass flow rate of the cooling air (in g / s), ΔT is the heat exchange temperature difference of the cooling air (in °C), and C p This is the average specific heat capacity of the cooling air (unit: J / (kg·℃)).

[0069] The established lithium battery output voltage model is used to calculate the output voltage and power changes of the lithium battery in a hybrid power system:

[0070] V L =V OC -I L *R0-V p1 -V p2

[0071] Among them, V L The output voltage of the lithium battery (in V), V OC I is the open-circuit voltage of the lithium battery (in V). L R0 is the connected load current (in A), R0 is the internal circuit resistance of the lithium battery (in Ω), V p1 and V p2 These are the equivalent circuit voltages inside the lithium battery (in V).

[0072] A model of a DC-DC converter connecting a lithium battery and a fuel cell is established to stabilize the output voltage of the fuel cell and lithium battery within a certain range, ensuring the normal operation of the load. The mathematical model of the converter is shown below:

[0073]

[0074] in, The output power of the DC / DC converter (in watts). Input current (in amperes) for the DC / DC converter. η is the input voltage (in V) of the DC / DC converter. DC Efficiency of DC / DC converter (in %).

[0075] Establish a rule-based energy management model to rationally allocate the power of lithium batteries and fuel cells, specifically including:

[0076] Obtain the SOC value and load power P of the lithium battery. load Fuel cell rated power P FC,nom Maximum discharge power P of lithium battery Li，Max，Discharge Maximum charging power P of lithium battery Li，Max，Charge The output power of the fuel cell is allocated as P based on the acquired power demand. FC The charging power and discharging power of the lithium battery are respectively P Li，Charge and P Li，Discharge .

[0077] When the SOC of a lithium battery is less than 0.2 and P load >P FC，nom When the simulation program terminates, the output power of the fuel cell is insufficient to meet the normal operation of the load.

[0078] When the SOC of a lithium battery is less than 0.2, P load ≤P FC,nom At that time, the lithium battery charging power is P Li，Charge =P FC,nom -P load The fuel cell output power is P FC =P FC，nom If P Li，Charge <P Li，Max，Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise, P Li，Charge =P Li，Max,Charge Fuel cell output power P FC，nom =P Li，Max,Charge +P load ;

[0079] When the lithium battery SOC is in the range [0.2, 0.8) and P load ≤P FC,nom At that time, the charging power P of the lithium battery Li，Charge =P FC,nom -P load The fuel cell output power is P FC =P FC，nom If P Li，Charge <P Li，Max，Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise, P Li,Charge =P Li,Max，Charge Fuel cell output power P FC，nom =P Li，Max,Charge +P load ;

[0080] When the lithium battery SOC is in the range [0.2, 0.8) and Pload ≥P FC，nom At that time, the discharge power of the lithium battery is P. Li，Discharge =P load -P FC，nom The fuel cell output power is P FC =P FC，nom If P Li，Discharge >P Li，Max，Discharge If the result is negative, it indicates that the load power is too high and the maximum discharge power of the lithium battery does not support the load operation, and the simulation program will be terminated.

[0081] Preferably, to prevent overcharging and over-discharging of lithium batteries, the SOC value is typically set between 0.2 and 0.8.

[0082] The fuel cell and lithium battery hybrid power modules are converted into model description files (FMI) and then imported into the flight dynamics control module designed based on MATLAB / Simulink software for co-simulation via the Functional Mock-up Interface (FMI).

[0083] The flight dynamics control module and the established UAV 3D modeling and visual module are connected via API using UDP communication.

[0084] The fixed-wing UAV flight dynamics control module, built with MATLAB / Simulink, transmits attitude calculation and control commands back by the UAV to the UAV ground station interface and the flight dynamics control module for calculation via a real-time simulator.

[0085] The flight control command calculation module is mainly a flight control board, which is connected to the three-axis flight attitude simulation module via CAN communication signals.

[0086] The flight control board transmits the UAV's attitude back to the ground station interface monitoring module via MAVLink communication signals.

[0087] The control command sending module is used to send control commands. The control command sending module includes a drone remote controller. The drone remote controller and the flight control module are connected via UDP communication signals to realize the process of sending control commands.

[0088] The three-axis flight attitude simulation module and the real-time simulation module are connected via PWM signals. The three-axis flight attitude simulation module transmits control commands from the ESC and servo motors to the real-time simulation module.

[0089] The real-time simulation module is connected to the three-axis flight attitude simulation module via RS232 communication signal, transmitting the UAV's position and linear acceleration to the three-axis flight attitude simulation module.

[0090] The real-time simulation module establishes a connection with the computer via the TCP / IP network protocol.

[0091] The test method for the hybrid unmanned aerial vehicle (UAV) hardware-in-the-loop system of the present invention is as follows: Figure 2 As shown, the connection status of each sub-component is first checked at the physical hardware and communication software levels. After confirming that everything is correct, the real-time simulator is run, the host computer is opened to run the flight control system, and the UAV motion-related commands are input into the ground station software. The flight commands are solved in the control system to calculate the power demand sequence required by the UAV. It is determined whether the output power of the current hybrid power system model meets the requirements of the UAV. If not, the air supply parameters of the hybrid power system are adjusted to be equal to the power required by the UAV. It is determined whether a specific flight mission needs to be performed. If so, the flight control system automatically plans the route and calculates the power demand, and then adjusts the output power of the hybrid system. After that, the motion status of the three-axis flight turntable is observed, and the flight status of the UAV in the three-dimensional virtual space is detected in the visual interface and the ground station. During the operation of the UAV semi-physical system, the flight data and operation process are archived.

[0092] like Figure 2 As shown, this invention discloses a method for constructing and operating a hybrid unmanned aerial vehicle (UAV) system based on fuel cells and lithium batteries, comprising the following steps:

[0093] Step 1: Check the electrical and communication connections of each module. If the connections are normal, run the real-time simulation module and input the UAV movement commands through the control command sending module.

[0094] Step 2: The flight control command calculation module calculates the motion commands of the UAV and inputs the motion commands into the flight dynamics control module to obtain the power required by the UAV under different motion commands.

[0095] Step 3: Determine if the power demand of the drone is equal to the power output of the hybrid power module. If yes, proceed to step 4; otherwise, adjust the air supply parameters of the hybrid power module.

[0096] Specifically, the power distribution between the lithium battery and the fuel cell is redistributed through the energy management module. The State of Charge (SOC) is calculated based on the mathematical model of the lithium battery to determine its charge and discharge state. Then, the hydrogen and air supply to the fuel cell is adjusted to regulate the output power, ensuring that the load power equals the sum of the lithium battery power and the fuel cell power.

[0097] Step 4: Determine whether the drone is performing a specific task. If so, calculate the drone's motion commands and adjust the air supply parameters of the hybrid power system until the drone's power demand equals the power output of the hybrid system.

[0098] Among them, specific tasks include the drone's flight states of takeoff, climb, variable speed and constant speed cruise, descent, and landing.

[0099] Step 5: Observe the motion state of the three-axis flight turntable in the three-axis flight attitude simulation module, observe the UAV flight status in the UAV 3D modeling and visual module and the ground station interface monitoring module, and record the test data.

[0100] like Figure 3 As shown in the figure, as a specific embodiment, the semi-physical testing method for fuel cell and lithium battery hybrid drones proposed in this invention is as follows:

[0101] Step 1: Design the energy management control module and the hybrid power module of hydrogen fuel cell and lithium battery using the Modelica language;

[0102] The hybrid power module includes the following sub-models:

[0103] A hydrogen transport and control model was established. The mass balance equation of hydrogen in the anode channel was used to calculate the proton exchange capacity and the changes in inlet and outlet gas flow rates. A hydrogen flow control module was designed.

[0104] An air pump control model was established, and the air flow rate into the fuel cell stack was controlled using a control algorithm based on the air inlet and outlet pressure ratio and the compressor MAP diagram.

[0105] Based on existing resistance-capacitance models, a second-order Thevenin lithium battery mathematical model is established to accurately describe the dynamic response and electrochemical process of lithium batteries.

[0106] A fuel cell stack model was established, and the current and voltage output modules of the fuel cell were established based on electrode electrochemical potential, Nernst equation, electrode activation polarization loss law, Ohmic polarization loss law and concentration difference polarization loss law.

[0107] Design a DC / DC conversion module based on the output voltage fluctuation range of the fuel cell and the DC bus voltage requirements of the UAV;

[0108] Design an energy management model and design the output time range of fuel cells and lithium batteries based on fuzzy decision control methods;

[0109] A cooling calculation model was established, mainly based on the heat transfer law and empirical formulas to establish a heat exchange model between the air-cooled electric stack and the air.

[0110] Using the hydrogen and air consumption of the fuel cell as external input variables, the required current density to pass through the fuel cell is calculated based on the load power, thereby adjusting the hydrogen load and hydrogen-air consumption ratio, and conducting dynamic output power testing.

[0111] Step 2. Encapsulate the model into an FMU model description file using the FMI standard protocol interface and import it into MATLAB / Simulink. Define the input and output interfaces for the encapsulated model parameters.

[0112] Step 3. Construct a 3D model of the fixed-wing UAV using Unity3D software, design the motion control module and data exchange module for the UAV 3D model, and input the aerodynamic parameters of the UAV into Simulink;

[0113] Step 4. Figure 3 As shown, a six-degrees-of-freedom (6DOF) solution module and a motor lift model for a fixed-wing UAV are designed in Simulink. A dynamic model is constructed based on the motor lift model. Then, the aerodynamic interpolation parameters of the UAV are calculated based on the fuselage structural parameters. Aerodynamic forces and fuselage torques are then calculated to construct the airframe aerodynamic model. Finally, the airframe motion model is constructed based on the airframe aerodynamic model and the flight dynamics model.

[0114] Step 5. Figure 3 As shown, the constructed model will be simulated in Unity3D and the movement of the drone will be observed. After the software testing is completed, the next step of semi-physical testing will be carried out.

[0115] Step 6. Check the drone connection status in the ground station software. Connect the remote controller and Pixhawk firmware through the receiver or data transmission module. Connect the Pixhawk firmware to the three-axis flight turntable. Connect the three-axis flight turntable to the real-time simulator. Connect the real-time simulator to the computer through the network port.

[0116] Step 7. Run the drone model in Unity3D, start the flight control system simulation model in Simulink, control the flight attitude through the remote controller, observe the drone's motion attitude in the Unity3D interface, and view the drone's position information, speed, and attitude information in QGC software to complete a semi-physical test and method for a fuel cell and lithium battery hybrid drone.

[0117] The beneficial effects of this invention are that, compared with the prior art, this invention achieves matching between the power supplied by the hybrid power system and the power demand of the UAV, realizes digital modeling and visualization of UAV operation training through 3D modeling software, and can intuitively and conveniently adjust various parameters in the fuel cell and lithium battery hybrid power model and flight control algorithm, which facilitates precise control of the UAV's attitude when performing different flight missions.

[0118] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0119] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0120] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0121] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A semi-physical testing system for a fuel cell and lithium battery hybrid unmanned aerial vehicle, characterized in that, include: Hybrid power module, flight dynamics control module, control command sending module, flight control command solving module, ground station interface monitoring module, three-axis flight attitude simulation module, real-time simulation module, UAV 3D modeling and visual module; The system comprises the following modules: a hybrid power module to simulate the power output of fuel cells and lithium batteries; a flight dynamics control module to calculate the UAV attitude information returned by the real-time simulator and the flight control command calculation module; a control command sending module to send motion commands to the flight control command calculation module; a flight control command calculation module to calculate the UAV control commands and transmit the calculated motion command information to the three-axis flight attitude simulation module; a ground station interface monitoring module to record the UAV flight attitude data returned by the flight control command calculation module; a three-axis flight attitude simulation module to receive the motion command information from the flight control command calculation module and make corresponding attitude responses; a real-time simulation module to receive feedback information from the three-axis flight attitude simulation module and simulate the UAV flight state in a virtual scene; and a UAV 3D modeling and visualization module to record real-time simulator data and control information from the flight dynamics control module. For the mechanism modeling process of the power system, a hybrid power module of UAV fuel cell and lithium battery is designed. The constructed hybrid power module includes: hydrogen supply model, air supply model, cooling system model, lithium battery output voltage model, DC converter model and energy management model. The established cooling system model is used to maintain a constant stack temperature. in, The mass flow rate of the cooling air. To cool the air by exchanging heat at different temperatures, The average specific heat capacity of cooling air, This represents the number of cells connected in series in a fuel cell. This is the output voltage of the fuel cell. The power provided by the fuel cell to the load. The efficiency of the DC-DC converter on the back side of the fuel cell; A rule-based energy management model is established to allocate power between lithium batteries and fuel cells, and to obtain the SOC value of the input lithium battery and the load power. P load Fuel cell rated power P FC, nom Maximum discharge power of lithium battery P Li, Max, Discharge Maximum charging power of lithium battery P Li, Max, Charge And allocate the output power of the fuel cell based on the acquired data. P FC The charging power and discharging power of lithium batteries are respectively P Li, Charge and P Li, Discharge .

2. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1, characterized in that, The established hydrogen supply model is used to guarantee the output power of the fuel cell: in, For the hydrogen consumption mass flow rate of fuel cells, The power provided by the fuel cell to the load. It is Faraday's constant.

3. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1, characterized in that, The established air supply model includes an air compressor model to ensure stable power output from the fuel cell stack. in, Mass flow rate of air supplied to fuel cells, Input power to the air compressor; For air compressor compression efficiency, The compression ratio is... The gas constant is The average compressibility factor of air. The temperature of the air drawn into the air compressor. The specific heat ratio of air. Let be the molar mass of air.

4. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1, characterized in that, The established lithium battery output voltage model is used to calculate the output voltage and power changes of the lithium battery in a hybrid power system: in, This refers to the output voltage of the lithium battery. This is the open-circuit voltage of the lithium battery. This is the current of the connected load. This refers to the internal circuit resistance of a lithium battery. and These are the equivalent circuit voltages inside the lithium battery.

5. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1, characterized in that, The established DC-DC converter model connecting the lithium battery and fuel cell is used to stabilize the output voltage of the fuel cell and lithium battery, ensuring the normal operation of the load. The mathematical model of the DC-DC converter is as follows: in, Output power of the DC / DC converter (in W). Input current of the DC / DC converter (in A). Input voltage of the DC / DC converter (in volts). Efficiency of DC / DC converter (in %).

6. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1, characterized in that, When the SOC of a lithium battery is less than 0.2 ppm, at the same time P load > P FC, nom When the simulation program terminates, the output power of the fuel cell is insufficient to meet the normal operation of the load. When the SOC of a lithium battery is less than 0.2, P load ≤ P FC, nom At that time, the lithium battery charging power is P Li, Charge = P FC, nom - P load The fuel cell output power is P FC = P FC, nom ,if P Li, Charge < P Li, Max, Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise... P Li, Charge = P Li, Max, Charge fuel cell output power P FC, nom = P Li, Max, Charge + P load ; When the lithium battery's SOC is in the range of [0.2, 0.8) and P load ≤ P FC, nom At that time, the charging power of the lithium battery P Li, Charge = P FC, nom - P load The fuel cell output power is P FC = P FC, nom ,if P Li, Charge < P Li, Max, Charge The lithium battery is in the charging stage until the SOC reaches 0.8; otherwise... P Li, Charge = P Li, Max, Charge fuel cell output power P FC, nom = P Li, Max, Charge + P load ; When the lithium battery's SOC is in the range of [0.2, 0.8) and P load ≥ P FC, nom At that time, the discharge power of the lithium battery was P Li, Discharge = P load - P FC, nom The fuel cell output power is P FC = P FC, nom ,if P Li, Discharge > P Li, Max, Discharge If the result is negative, it indicates that the load power is too high and the maximum discharge power of the lithium battery does not support the load operation, and the simulation program will be terminated.

7. A semi-physical testing method for a fuel cell and lithium battery hybrid unmanned aerial vehicle (UAV), utilizing the semi-physical testing system for a fuel cell and lithium battery hybrid UAV as described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: Check the electrical and communication connections of each module. If the connections are normal, run the real-time simulation module and input the UAV movement commands through the control command sending module. Step 2: The flight control command calculation module calculates the motion commands of the UAV and inputs the motion commands into the flight dynamics control module to obtain the power required by the UAV under different motion commands. Step 3: Determine if the power demand of the drone is equal to the power output of the hybrid power module. If yes, proceed to step 4; otherwise, adjust the air supply parameters of the hybrid power module. Step 4: Determine whether the drone is performing a specific task. If so, calculate the drone's motion commands and adjust the air supply parameters of the hybrid power system until the drone's power demand equals the power output of the hybrid system. Step 5: Observe the motion state of the three-axis flight turntable in the three-axis flight attitude simulation module, observe the UAV flight status in the UAV 3D modeling and visual module and the ground station interface monitoring module, and record the test data.

8. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the semi-physical testing method for fuel cell and lithium battery hybrid unmanned aerial vehicles according to claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the semi-physical testing method for fuel cell and lithium battery hybrid unmanned aerial vehicles as described in claim 7.

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