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

Abstract

The invention discloses a fuel cell and lithium battery hybrid unmanned aerial vehicle semi-physical test system and method. The system comprises a hybrid power module used for simulating power output of a fuel cell and a lithium battery; the flight dynamics control module is used for resolving attitude information of the unmanned aerial vehicle; the control instruction sending module is used for sending a motion instruction of the unmanned aerial vehicle to the flight control instruction resolving module; the flight control instruction resolving module is used for resolving a control instruction to obtain motion instruction information; the ground station interface monitoring module is used for recording the flight attitude data of the unmanned aerial vehicle returned by the flight control instruction resolving module; the three-axis flight attitude simulation module receives the motion instruction information and makes attitude response; the real-time simulation module is used for receiving feedback information of the three-axis flight attitude simulation module and simulating the flight state of the unmanned aerial vehicle in a virtual scene; the 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; according to the invention, accurate and efficient simulation of the unmanned aerial vehicle can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle testing, and more specifically, to a semi-physical testing system and method for a fuel cell and lithium battery hybrid unmanned aerial vehicle. Background Art

[0002] Compared with traditional lithium-powered battery drones, new fuel cell and lithium battery hybrid drones have the characteristics of long range and high load capacity, and are widely used in engineering fields such as power inspection, farming operations, and terrain exploration. When drones perform different flight missions, they need to change loads significantly and quickly. In order to reduce development costs, it is necessary to develop a hybrid model and design an efficient energy management strategy to reasonably configure the capacity of fuel cells and lithium batteries and their output power to simulate actual operation. While meeting the overall energy consumption of the drone, ensure that the hybrid system is in a safe, stable and efficient working state.

[0003] However, the design of hybrid UAV system is complex, and the cost of UAV equipped with fuel cells is high and the test flight tolerance is low. Therefore, before the hybrid UAV is officially put into use, the reliability of its power system and flight control system must be verified to prevent mechanical, circuit and communication failures in actual flight and cause unnecessary loss of manpower and material resources. How to conduct semi-physical test experiments on the designed UAV is also widely concerned. Semi-physical simulation, also known as hardware-in-the-loop simulation, connects real hardware devices to the software simulation system. Compared with pure digital simulation, it is closer to the real situation. The natural disturbance factors introduced are also conducive to testing the performance of the flight control algorithm, and the information obtained is more accurate.

[0004] Based on semi-physical testing, in order to study the performance of the flight control system and power system of the UAV in the actual ground environment, a three-axis flight turntable is usually used for ground testing of the UAV. The ground test can better show the pitch, roll and yaw angles of the UAV in the actual environment, and observe the flight posture of the UAV more intuitively.

[0005] By designing a hybrid UAV semi-physical test platform for fuel cells and lithium batteries, it is possible to reduce the cost of physical development while testing the reliability and stability of the semi-physical system and improving its reusability.

[0006] The existing data include research on semi-physical simulation technology of UAVs, fuel cells and lithium battery hybrid semi-physical simulation technology, but there is still a gap in the research on the collaborative simulation of hybrid power and flight control of fuel cell and lithium battery hybrid UAVs.

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

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

[0009] The existing technical solutions for hybrid UAV testing have the following technical problems:

[0010] 1. The physical development and design costs of hybrid UAVs are expensive. The core components involved in this type of UAV, such as fuel cell systems, energy control modules, and UAV bodies, all have high manufacturing and procurement costs. For example, although fuel cell technology has high energy density and environmental advantages, its key materials and manufacturing processes are complex, resulting in costs far exceeding traditional batteries. In addition, as the core of the hybrid system, the energy control module needs to accurately manage the energy distribution between the fuel cell and the lithium battery. Its design and implementation also require a lot of R&D investment. At the same time, the physical parts of the UAV, including the body structure, flight control system, sensors, and communication modules, are also expensive. In actual operation, since UAV flights involve a variety of complex environments and unpredictable factors, improper operation or sudden failures often lead to equipment damage, which not only causes expensive hardware losses, but may also trigger additional maintenance, repair or replacement costs.

[0011] 2. It is difficult to configure the hybrid model parameters and the flight control parameters of the drone, and mechanical and circuit failures are prone to occur during the actual drone testing. The flight control parameters of the drone need to be highly coordinated with the hybrid system, which further increases the difficulty of configuration. The flight control system is responsible for the attitude, speed, altitude and other control of the drone, and its parameter configuration directly affects the stability and flight performance of the drone. Due to the high coupling between the power system and the flight control system, even a small parameter adjustment may trigger a chain reaction, resulting in system instability. The complex coupling of the model makes the parameter adjustment process extremely complicated when debugging on the actual drone.

[0012] 3. The existing semi-physical simulation system has poor scalability. For power inspection drones, different types of drones are often required to complete different inspection tasks, such as long-term cruising, high-load flight, complex terrain navigation, etc. However, the existing semi-physical simulation system is often designed for a specific type of drone or a specific mission environment, which makes it difficult for the system to adapt to a variety of drones and diverse mission requirements. Summary of the invention

[0013] In order to solve the deficiencies in the prior art, the present invention provides a fuel cell and lithium battery hybrid UAV semi-physical testing system, which can solve the technical problem of poor adaptability of the existing UAV testing system.

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

[0015] A fuel cell and lithium battery hybrid UAV semi-physical test system, including: 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 vision module;

[0016] Among them, the hybrid power module is used to simulate the power output of fuel cells and lithium batteries; the flight dynamics control module is used to solve the UAV attitude information sent back by the real-time simulator and the flight control command solving module; the control command sending module is used to send the UAV motion command to the flight control command solving module; the flight control command solving module is used to solve the control command of the UAV, and transmit the solved motion command information to the three-axis flight attitude simulation module; the ground station interface monitoring module is used to record the UAV flight attitude data sent back by the flight control command solving module; the three-axis flight attitude simulation module is used to receive the motion command information of the flight control command solving module and make corresponding attitude responses; the real-time simulation module is used to receive the feedback information of the three-axis flight attitude simulation module to simulate the flight state of the UAV in the virtual scene; the UAV 3D modeling and vision module is used to record the real-time simulator data and the control information of the flight dynamics control module.

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

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

[0019]

[0020] Among them, m flowis the mass flow rate of hydrogen consumed by the fuel cell, Power load is the power provided by the fuel cell to the load, F is the Faraday constant, η DCDC is the efficiency of the DC converter on the back side of the fuel cell, n FC is the number of cells in series in the fuel cell, V FC is the fuel cell output voltage.

[0021] Preferably, the established air supply model includes an air compressor model to ensure the stability of the stack power:

[0022]

[0023] Among them, m flow,Air The air supply mass flow rate for the fuel cell, Power compressor is the air compressor input power; η compressor is the air compressor compression efficiency, r c is the compression ratio, R is the gas constant, Z is the average compression factor of air, T in is the air temperature sucked by the air compressor, K is the specific heat ratio of the air, M Air is the molar mass of air.

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

[0025]

[0026] Among them, m cooling,Air is the mass flow rate of cooling air, ΔT is the heat exchange temperature difference of cooling air, C p is the average specific heat capacity of cooling air.

[0027] Preferably, the established lithium battery output voltage model is used to calculate the output voltage and power change 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 is the output voltage of the lithium battery, V OC is the open circuit voltage of the lithium battery, I L is the connected load current, R0 is the internal circuit resistance of the lithium battery, V p1 and V p2 They are the equivalent circuit voltage inside the lithium battery.

[0030] Preferably, the DC converter model connected to the lithium battery and the fuel cell is established to stabilize the output voltage of the fuel cell and the lithium battery to ensure the normal operation of the load. The mathematics of the DC converter model is as follows:

[0031]

[0032] in, is the DC / DC converter output power (in W), is the DC / DC converter input current (in A), is the DC / DC converter input voltage (in V), η DC is the DC / DC converter efficiency (in %).

[0033] Preferably, a rule-based energy management model is established to allocate the power of lithium batteries and fuel cells, and obtain the SOC value of the input lithium battery, the load power P load , fuel cell rated power P FC,nom 、Lithium battery maximum discharge power P Li，Max,Discharge 、Lithium battery maximum charging power P Li，Max，Charge ; and according to the acquired data, the output power of the fuel cell is allocated as P FC , the charging power and discharging power of lithium battery are P Li，Charge and P Li，Discharge ;

[0034] When the SOC of the lithium battery is less than 0.2, P load >P FC，nom When , the simulation program is terminated, and the output power of the fuel cell is not enough to meet the normal operation of the load;

[0035] When the SOC of the lithium battery is less than 0.2, P load ≤P FC,nom When 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 [0.2, 0.8) and P load ≤P FC，nom When the charging power of lithium battery is PLi，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 [0.2, 0.8) and P load ≥P FC，nom When 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 , it indicates that the load power is too large and the maximum discharge power of the lithium battery cannot support the load operation, so the simulation program is terminated.

[0038] The present invention also proposes a fuel cell and lithium battery hybrid UAV semi-physical testing method, using the fuel cell and lithium battery hybrid UAV semi-physical testing system, including the following steps:

[0039] Step 1, check the electrical connection and communication connection of each module. If the connection is normal, run the real-time simulation module and input the UAV motion command through the control command sending module;

[0040] Step 2: The flight control command solving module solves the UAV motion command, and inputs the motion command into the flight dynamics control module to solve and obtain the required power of the UAV under different action commands;

[0041] Step 3, determine whether the power demand of the UAV is equal to the power output of the hybrid power module, if so, proceed to step 4, otherwise adjust the air supply parameters of the hybrid power module;

[0042] Step 4, determining whether the UAV is to perform a specific task, and if so, solving the UAV motion command and adjusting the air supply parameters of the hybrid system until the UAV power demand is equal to 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 flight state of the UAV in the UAV 3D modeling and vision module and the ground station interface monitoring module, and record the test data.

[0044] The present invention also provides a terminal, comprising 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 on which a computer program is stored. When the program is executed by a processor, the steps of the semi-physical testing method of the fuel cell and lithium battery hybrid drone are implemented.

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

[0049] 1. The present invention is more economical in terms of technology. The actual flight test of hybrid UAV is very costly, especially in high-risk environments, which can easily damage the expensive fuel cell and flight control board. Traditional UAV testing does not include the power system part of the fuel cell. Through semi-physical simulation, various flight scenarios and working conditions can be simulated in the laboratory, reducing the frequency and cost of actual testing of fuel cell UAVs.

[0050] 2. Greatly shorten the R&D cycle. The traditional semi-physical test system has a single test structure, and the configuration of the hybrid system usually requires physical reference. However, by quickly iterating and verifying the design in a virtual environment, the sophisticated mathematical models and underlying data exchange chains designed on the three simulation platforms of Dymola, Simulink and Unity can quickly compare and optimize different solutions, which can greatly shorten the UAV development cycle.

[0051] 3. Simulate the performance of fixed-wing drones under extreme conditions. With the support of real-time simulators, by combining Unity3D software to write extreme scenarios such as high and low temperatures, strong winds, and thunderstorms, the flight performance of drones and the use of fuel cells and lithium batteries can be simulated and observed without exposing physical drones to dangerous conditions. In addition, the flight control algorithm can be quickly iterated and optimized in a real-time simulation environment to ensure flight stability and control accuracy when performing different tasks, especially significantly enhancing the ability to cope with complex environments.

[0052] 4. The present invention can realize real-time debugging and optimization. The power and flight control system model based on the combination of Dymola and Simulink can provide real-time feedback on the operating status of the system, construct the mechanism model of the fuel cell and lithium battery and the energy management model in Dymola, construct the flight control system model of the UAV in Simulink, import the FMU model into Simulink for parameter collaborative adjustment, and the operator can adjust the flight control algorithm and energy management scheme in real time according to the simulation results. When the energy management strategy needs to be adjusted, it only needs to be designed in Dymola, realizing decentralized management of power system simulation, avoiding single point failure, achieving optimal power distribution through dynamic adjustment of the energy management model, improving energy efficiency, extending the flight time of the UAV, and realizing collaborative simulation between the power system and the flight control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural block diagram of the semi-physical test system of the fuel cell and lithium battery hybrid UAV in the present invention;

[0054] Figure 2 It is a flow chart of the semi-physical testing method for fuel cell and lithium battery hybrid UAVs.

[0055] Figure 3 It is a schematic diagram of the verification structure of the UAV semi-physical test system. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0057] like Figure 1 As shown, the present invention constructs a semi-physical test system for a fuel cell and lithium battery hybrid UAV, and the test 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 vision module;

[0058] Among them, the hybrid power module is used to simulate the power output of fuel cells and lithium batteries to provide flight power guarantee for the UAV; the flight dynamics control module is used to solve the UAV attitude information sent back by the real-time simulator and the flight control command solving module; the control command sending module is used to send the UAV motion command to the flight control command solving module; the flight control command solving module is used to solve the control command of the UAV and transmit the obtained motion command to the three-axis flight attitude simulation module; the ground station interface monitoring module is used to record the UAV flight attitude data sent back by the flight control command solving module; among them, the motion commands include pitch, yaw, roll angular velocity, and angular acceleration; the three-axis flight attitude simulation module is used to receive the motion command information of the flight control command solving module and make corresponding attitude responses; the real-time simulation module is used to receive the feedback information of the three-axis flight attitude simulation module to simulate the flight state of the UAV in the virtual scene; the UAV 3D modeling and vision module is used to record the real-time simulator data and the control information of the flight dynamics control module.

[0059] In view of the mechanism modeling process of the power system, the Dymola simulation software is used to design the hybrid power module of the fuel cell and lithium battery. The constructed hybrid power module includes: hydrogen supply model, air supply model, cooling system model, lithium battery output voltage model, and 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] Among them, m flow is the mass flow rate of hydrogen consumed by the fuel cell (in g / s), Power load is the power provided by the fuel cell to the load (in W), F is the Faraday constant (in C / mol), η DCDC is the efficiency of the DC converter on the back side of the fuel cell (in %), n FC is the number of cells in series in the fuel cell, V FC is the fuel cell output voltage (in V).

[0063] The established air supply model includes an air compressor model to ensure the stability of the stack power:

[0064]

[0065] Among them, m flow，Air is the fuel cell air supply mass flow rate (in g / s), Power compressor is the air compressor input power (in W); η compressor is the air compressor compression efficiency, rc is the compression ratio, R is the gas constant (in J / (mol·K)), Z is the average air compression factor, T in is the air temperature sucked by the air compressor (in °C), K is the specific heat ratio of the air, M Air is the molar mass of air (in g / mol).

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

[0067]

[0068] Among them, m cooling，Air is the mass flow rate of cooling air (in g / s), ΔT is the heat exchange temperature difference of cooling air (in °C), C p is the average specific heat capacity of cooling air (in 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 the hybrid system:

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

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

[0072] The DC converter model connected to the lithium battery and fuel cell is established to stabilize the output voltage of the fuel cell and lithium battery within a certain range to ensure the normal operation of the load. The mathematical model of the converter is shown in the following formula:

[0073]

[0074] in, is the DC / DC converter output power (in W), is the DC / DC converter input current (in A), is the DC / DC converter input voltage (in V), η DC is the DC / DC converter efficiency (in %).

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

[0076] Get the SOC value of the lithium battery and the load power P load , fuel cell rated power P FC,nom 、Lithium battery maximum discharge power P Li，Max，Discharge 、Lithium battery maximum charging power P Li，Max，Charge ; According to the power demand obtained, the output power of the fuel cell is allocated to P FC , the charging power and discharging power of lithium battery are P Li，Charge and P Li，Discharge .

[0077] When the SOC of the lithium battery is less than 0.2, P load >P FC，nom When , the simulation program is terminated, and the output power of the fuel cell is not enough to meet the normal operation of the load;

[0078] When the SOC of the lithium battery is less than 0.2, P load ≤P FC,nom When 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 [0.2, 0.8) and P load ≤P FC,nom When the charging power of lithium battery 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 ;

[0080] When the lithium battery SOC is in [0.2, 0.8) and Pload ≥P FC，nom When 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 , it indicates that the load power is too large and the maximum discharge power of the lithium battery cannot support the load operation, so the simulation program is terminated.

[0081] Preferably, in order to prevent the lithium battery from being overcharged and over-discharged, the SOC value is usually set to between 0.2 and 0.8.

[0082] The fuel cell and lithium battery hybrid power modules are converted into model description files in the form of functional mock-up units (FMUs) through the functional mock-up interface (FMI) and imported into the flight dynamics control module designed based on MATLAB / Simulink software for joint simulation.

[0083] The flight dynamics control module is connected to the established drone 3D modeling and vision module via API using UDP communication.

[0084] The fixed-wing UAV flight dynamics control module is built with MATLAB / Simulink. The attitude calculation control instructions sent back by the UAV are transmitted to the UAV ground station interface and the flight dynamics control module through a real-time simulator for solution.

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

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

[0087] The control instruction sending module is used to send control instructions. The control instruction sending module includes a drone remote controller. The drone remote controller is connected to the flight control module through a UDP communication signal to realize the control instruction sending process.

[0088] The three-axis flight attitude simulation module is connected to the real-time simulation module through a PWM signal, and the three-axis flight attitude simulation module transmits the control instructions of the electric regulator and the servo to the real-time simulation module.

[0089] The real-time simulation module is connected to the three-axis flight attitude simulation module through RS232 communication signals, and transmits the position and linear acceleration of the UAV to the three-axis flight attitude simulation module.

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

[0091] The testing method of the semi-physical system of the hybrid UAV of the present invention is as follows Figure 2 As shown, first check the connection status of each sub-part at the physical hardware and communication software levels; after confirming that everything is correct, run the real-time simulator, open the host computer to run the flight control system, and enter the UAV movement-related instructions in the ground station software; the flight instructions will be solved in the control system to calculate the power requirement sequence required by the UAV; determine whether the output power of the current hybrid power system model meets the needs of the UAV, otherwise adjust the air supply parameters of the hybrid power system to equal the power required by the UAV; determine whether it is necessary to perform a specific flight mission, if so, automatically plan the route in the flight control system, calculate the power requirement, and then adjust the output power of the hybrid system; then observe the movement status of the three-axis flight turntable, and detect the flight status of the UAV in the three-dimensional virtual space in the visual interface and the ground station; archive the flight data and operation process during the operation of the UAV semi-physical system.

[0092] like Figure 2 As shown, the present invention discloses a method for constructing and operating a semi-physical test system for a fuel cell and lithium battery hybrid UAV, comprising the following steps:

[0093] Step 1, check the electrical connection and communication connection of each module. If the connection is normal, run the real-time simulation module and input the UAV motion command through the control command sending module;

[0094] Step 2: The flight control command solving module solves the UAV motion command, and inputs the motion command into the flight dynamics control module to solve and obtain the required power of the UAV under different action commands;

[0095] Step 3, determine whether the power demand of the UAV is equal to the power output of the hybrid power module, if so, proceed to step 4, otherwise adjust the air supply parameters of the hybrid power module;

[0096] Specifically, the power distribution of lithium batteries and fuel cells is reallocated through the energy management module, the SOC is calculated according to the mathematical model of the lithium battery to determine its charge and discharge state, and then the hydrogen and air supply of the fuel cell is adjusted to adjust the output power to ensure that the load power is equal to the sum of the lithium battery power and the fuel cell power.

[0097] Step 4, determining whether the UAV is to perform a specific task, and if so, solving the UAV motion command and adjusting the air supply parameters of the hybrid system until the UAV power demand is equal to the power output of the hybrid system;

[0098] Among them, specific tasks include the drone's take-off, climb, speed change and constant speed cruising, descent, and landing flight states.

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

[0100] like Figure 3 As shown, as a specific embodiment, the semi-physical testing method of the fuel cell and lithium battery hybrid drone proposed by the present invention is as follows:

[0101] Step 1, use Modelica language to design energy management control module and hybrid power module of hydrogen fuel cell and lithium battery;

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

[0103] Establish a hydrogen transport and control model, use the mass balance equation of hydrogen in the anode flow channel to calculate the proton exchange amount and the change in inlet and outlet gas flow, and design a hydrogen flow control module;

[0104] Establish an air pump control model, and use the control algorithm to control the air flow entering the fuel cell stack according to the air inlet and outlet pressure ratio and the compressor MAP diagram;

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

[0106] Establish a fuel cell stack model, and establish the current and voltage output module of the fuel cell based on the electrode electrochemical potential, Nernst equation, electrode activation polarization loss law, Ohm polarization loss law, and concentration difference polarization loss law;

[0107] Design the DC / DC conversion module according to the fuel cell output voltage fluctuation range and the UAV DC bus voltage requirements;

[0108] Design energy management model and design output time range of fuel cell and lithium battery based on fuzzy decision control method;

[0109] A cooling calculation model is established, mainly based on the heat transfer convection law and empirical formula to establish the air-cooled fuel cell stack and air heat exchange model.

[0110] The hydrogen and air consumption of the fuel cell are used as external input variables. According to the load power, the current density that needs to pass through the fuel cell is calculated, so as to adjust the hydrogen load and hydrogen-air consumption ratio and conduct dynamic output power test.

[0111] Step 2. Encapsulate the model into an FMU model description file through the FMI standard protocol interface and import it into MATLAB / Simulink to define the input and output interfaces of the encapsulated model parameters;

[0112] Step 3. Use Unity3D software to build a 3D model of the fixed-wing UAV, design the UAV 3D model motion control module and data exchange module, and input the UAV's aerodynamic parameters into Simulink;

[0113] Step 4. Figure 3 As shown in the figure, a six degrees of freedom (6DOF) solution module and a motor lift model of a fixed-wing UAV are designed in Simulink, a power model is constructed according to the motor lift model, and then the aerodynamic interpolation parameters of the body are calculated according to the fuselage structure parameters. Then, the aerodynamic force and fuselage moment are calculated to construct the body aerodynamic model, and then the body motion model is constructed according to the body aerodynamic model and the flight dynamics model.

[0114] Step 5. Figure 3 As shown in the figure, the constructed model is simulated in Unity3D and the movement of the drone is observed. After the software part test is completed, the next step of semi-physical test operation is 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 and the three-axis flight turntable, connect the three-axis flight turntable and the real-time simulator, and connect the real-time simulator and computer through the network port;

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

[0117] The beneficial effects of the present invention are that, compared with the prior art, the present invention achieves matching of the power supplied by the hybrid power system with the power required by the UAV, realizes the digital modeling of the UAV and visualization of the control 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, so as to facilitate precise control of the attitude of the UAV when performing different flight missions.

[0118] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0119] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive lists) of computer readable storage medium include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or convex structures in grooves on which instructions are stored, and any suitable combination thereof. Computer readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (e.g., light pulses by optical fiber cables), or electrical signals transmitted by wires.

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

[0121] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state 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 "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fuel cell and lithium battery hybrid drone semi-physical testing system, 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 vision module; Among them, the hybrid power module is used to simulate the power output of fuel cells and lithium batteries; the flight dynamics control module is used to solve the UAV attitude information sent back by the real-time simulator and the flight control command solving module; the control command sending module is used to send the UAV motion command to the flight control command solving module; the flight control command solving module is used to solve the control command of the UAV, and transmit the solved motion command information to the three-axis flight attitude simulation module; the ground station interface monitoring module is used to record the UAV flight attitude data sent back by the flight control command solving module; the three-axis flight attitude simulation module is used to receive the motion command information of the flight control command solving module and make corresponding attitude responses; the real-time simulation module is used to receive the feedback information of the three-axis flight attitude simulation module to simulate the flight state of the UAV in the virtual scene; the UAV 3D modeling and vision module is used to record the real-time simulator data and the control information of the flight dynamics control module.

2. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 1 is characterized in that: According to the mechanism modeling process of the power system, a hybrid power module of the 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.

3. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is characterized in that: The established hydrogen supply model is used to ensure the output power of the fuel cell: Among them, m flow is the mass flow rate of hydrogen consumed by the fuel cell, Power load is the power provided by the fuel cell to the load, F is the Faraday constant, η DCDC is the efficiency of the DC converter on the back side of the fuel cell, n FC is the number of cells in series in the fuel cell, V FC is the fuel cell output voltage.

4. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is characterized in that: The established air supply model includes an air compressor model to ensure the stability of the stack power: Among them, m flow，Air The air supply mass flow rate for the fuel cell, Power compressor is the air compressor input power; η compressor is the air compressor compression efficiency, r c is the compression ratio, R is the gas constant, Z is the average compression factor of air, T in is the air temperature sucked by the air compressor, K is the specific heat ratio of the air, M Air is the molar mass of air.

5. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is characterized in that: The cooling system model is established to maintain a constant temperature of the stack: Among them, m cooling，Air is the mass flow rate of cooling air, ΔT is the heat exchange temperature difference of cooling air, C p is the average specific heat capacity of cooling air.

6. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is 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 the hybrid system: V L =V OC -I L *R0-V p1 -V p2 Among them, V L is the output voltage of the lithium battery, V OC is the open circuit voltage of the lithium battery, I L is the connected load current, R0 is the internal circuit resistance of the lithium battery, V p1 and V p2 They are the equivalent circuit voltage inside the lithium battery.

7. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is characterized in that: The DC converter model connected to the lithium battery and fuel cell is used to stabilize the output voltage of the fuel cell and lithium battery to ensure the normal operation of the load. The mathematics of the DC converter model is as follows: in, is the DC / DC converter output power (in W), is the DC / DC converter input current (in A), is the DC / DC converter input voltage (in V), η DC is the DC / DC converter efficiency (in %).

8. The fuel cell and lithium battery hybrid UAV semi-physical testing system according to claim 2 is characterized in that: Establish a rule-based energy management model to allocate the power of lithium batteries and fuel cells, obtain the SOC value of the input lithium battery, load power P load , fuel cell rated power P FC,nom 、Lithium battery maximum discharge power P Li,Max，Discharge 、Lithium battery maximum charging power P Li,Max，Charge ; and according to the acquired data, the output power of the fuel cell is allocated as P FC , the charging power and discharging power of lithium battery are P Li,Charge and P Li,Discharge ; When the SOC of the lithium battery is less than 0.2, P load >P FC，nom When , the simulation program is terminated, and the output power of the fuel cell is not enough to meet the normal operation of the load; When the SOC of the lithium battery is less than 0.2, P load ≤P FC，nom When 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 SOC is in [0.2, 0.8) and P load ≤P FC，nom When the charging power of lithium battery 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 SOC is in [0.2, 0.8) and P load ≥P FC，nom When 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 , it indicates that the load power is too large and the maximum discharge power of the lithium battery cannot support the load operation, so the simulation program is terminated.

9. A fuel cell and lithium battery hybrid UAV semi-physical testing method, using the fuel cell and lithium battery hybrid UAV semi-physical testing system according to any one of claims 1 to 8, characterized in that: The steps include: Step 1, check the electrical connection and communication connection of each module. If the connection is normal, run the real-time simulation module and input the UAV motion command through the control command sending module; Step 2: The flight control command solving module solves the UAV motion command, and inputs the motion command into the flight dynamics control module to solve and obtain the required power of the UAV under different action commands; Step 3, determine whether the power demand of the UAV is equal to the power output of the hybrid power module, if so, proceed to step 4, otherwise adjust the air supply parameters of the hybrid power module; Step 4, determining whether the UAV is to perform a specific task, and if so, solving the UAV motion command and adjusting the air supply parameters of the hybrid system until the UAV power demand is equal to 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 flight state of the UAV in the UAV 3D modeling and vision module and the ground station interface monitoring module, and record the test data.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the semi-physical testing method of the fuel cell and lithium battery hybrid drone according to claim 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the semi-physical testing method of a fuel cell and lithium battery hybrid drone described in claim 9 are implemented.

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