Energy management system of hybrid power unmanned aerial vehicle

Abstract

The invention discloses an energy management system of a hybrid power unmanned aerial vehicle, and relates to the technical field of hybrid power unmanned aerial vehicle energy management, comprising the following steps: collecting flight condition parameters of the hybrid power unmanned aerial vehicle in real time; synchronously obtaining energy unit state parameters of the hybrid power unmanned aerial vehicle; generating a power distribution strategy among N power sources in the hybrid power unmanned aerial vehicle; executing the power distribution strategy, predicting the energy of the hybrid power unmanned aerial vehicle, and completing the response and management of the energy. Intelligent coordination and real-time optimization of multi-energy input of the hybrid power unmanned aerial vehicle are achieved, thermal stress and mechanical loss of key components are remarkably reduced, the adaptability to complex or unknown flight scenes is enhanced, and therefore on the premise that the flight safety and task reliability are guaranteed, the flight speed of the hybrid power unmanned aerial vehicle is improved. And cooperative improvement of the endurance performance and the system life is realized.

Description

Technical Field

[0001] This invention relates to the field of energy management technology for hybrid unmanned aerial vehicles (UAVs), and in particular to an energy management system for hybrid UAVs. Background Technology

[0002] With the rapid development of drone technology, its application in logistics transportation, agricultural plant protection, emergency rescue and other fields is becoming increasingly widespread. Against this backdrop, hybrid drones, due to their combination of the high responsiveness of electric systems and the high energy density of internal combustion engine systems, are gradually becoming an important development direction for improving the overall performance of drones, and the demand for efficient and intelligent energy management systems is becoming increasingly urgent.

[0003] However, existing energy management strategies for hybrid-powered drones generally suffer from problems such as simple control logic, insufficient energy distribution, and inability to respond to changes in flight conditions in real time. Most systems lack the ability to comprehensively perceive and coordinate the dynamic characteristics of battery status, engine efficiency, and flight missions, resulting in low energy utilization efficiency, premature aging of key components, and even the risk of power interruption under complex flight conditions. In addition, existing solutions often do not fully consider seamless switching and load balancing between different power sources, making it difficult to achieve a balance between maximizing endurance and system reliability. There is an urgent need for a management system that can intelligently coordinate multiple energy inputs and dynamically optimize energy distribution to solve these problems. Summary of the Invention

[0004] In view of the problems existing in the current Internet of Things-based multi-source power grid information fusion and system, this invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that the existing hybrid-powered UAV energy management system lacks the ability to comprehensively perceive flight conditions and energy status, making it difficult to achieve dynamic, coordinated, and optimized energy allocation among multiple power sources, resulting in low energy utilization efficiency, high component wear and tear, and insufficient flight reliability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide an energy management system for a hybrid-powered unmanned aerial vehicle (UAV), comprising: a flight status perception module for real-time acquisition of flight condition parameters of the UAV; an energy status monitoring module for synchronously acquiring energy unit status parameters of the UAV; a dynamic energy allocation decision module for constructing a multi-objective optimization algorithm based on flight condition parameters and energy unit status parameters to generate a power allocation strategy among N power sources in the UAV; a multi-source cooperative control module for executing the power allocation strategy and coordinating the dynamic cooperative operation of multiple power sources in the UAV; and an adaptive learning optimization module for recording historical operating data of the UAV, iteratively updating historical operating data, predicting the energy of the UAV, and completing energy response and management.

[0008] As a preferred embodiment of the energy management system for the hybrid unmanned aerial vehicle of the present invention, the flight state perception module includes a flight motion parameter acquisition submodule, a mission phase identification submodule, and an environmental disturbance perception submodule.

[0009] The flight motion parameter acquisition submodule is used to output the motion state parameters of the hybrid-powered UAV through the fusion of the airborne inertial measurement unit and the global navigation satellite system.

[0010] The mission phase identification submodule is used to analyze the current flight mission phase of the hybrid-electric UAV based on the waypoint sequence and operation instructions issued by the flight control system.

[0011] The environmental disturbance perception submodule is used to acquire environmental disturbance parameters that affect the flight energy consumption of the hybrid UAV using the onboard sensing devices of the hybrid UAV.

[0012] As a preferred embodiment of the energy management system for the hybrid unmanned aerial vehicle of the present invention, the energy status monitoring module includes an electrical energy unit monitoring submodule, a fuel power unit monitoring submodule, and a multi-source coupling interface monitoring submodule.

[0013] The power unit monitoring submodule is used to collect the operating status parameters of the power unit in the hybrid UAV through high-precision current and voltage sensors and the battery management system.

[0014] The fuel power unit monitoring submodule is used to acquire the operating status parameters of the fuel power unit based on the fuel power sensing unit;

[0015] The multi-source coupling interface monitoring submodule is used to detect the power interaction boundary and energy conversion loss characteristics of the electric power unit and the fuel power unit in the power combiner mechanism of the hybrid UAV.

[0016] As a preferred embodiment of the energy management system for the hybrid unmanned aerial vehicle of the present invention, the dynamic energy allocation decision module includes an operating condition energy fusion modeling submodule, a multi-objective optimization solution submodule, and a strategy mapping output submodule;

[0017] The operating condition energy fusion modeling submodule is used to perform spatiotemporal alignment and feature coupling between flight operating condition parameters and energy unit state parameters to generate a joint state vector characterizing the current system operating state.

[0018] The multi-objective optimization solution submodule is used to construct a multi-objective optimization algorithm based on the joint state vector and solve for the Pareto optimal solution set online;

[0019] The strategy mapping output submodule is used to select a power allocation scheme that is suitable for the current flight phase from the Pareto optimal solution set according to the task priority weight, and output the real-time power command sequence of N power sources in the hybrid UAV.

[0020] As a preferred embodiment of the energy management system for the hybrid unmanned aerial vehicle of the present invention, the multi-source cooperative control module includes a power command parsing submodule, a power source execution scheduling submodule, and a cooperative operation closed-loop correction submodule.

[0021] The power command parsing submodule is used to receive the power allocation strategy output by the dynamic energy allocation decision module and decouple the power allocation strategy into torque and speed control targets corresponding to each power source.

[0022] The power source execution scheduling submodule is used to generate coordinated operation scheduling parameters for the electric power unit and the combustion power unit according to the control objectives;

[0023] The collaborative operation closed-loop correction submodule is used to adjust the execution parameters in real time based on the actual output response and command deviation of multiple power sources in order to maintain the synchronization of dynamic collaborative operation.

[0024] As a preferred embodiment of the energy management system for the hybrid unmanned aerial vehicle of the present invention, the adaptive learning optimization module includes an operational data collection submodule, an energy behavior modeling submodule, and a strategy parameter online tuning submodule.

[0025] The operational data collection submodule is used to perform timestamp alignment and structured storage of flight condition parameters, energy unit status parameters and corresponding power allocation strategies to form a historical operational dataset of the hybrid unmanned aerial vehicle.

[0026] The energy behavior modeling submodule is used to train a lightweight time-series prediction network based on historical operational datasets to establish a mapping relationship between flight mission characteristics and energy consumption response of multiple power sources.

[0027] The online optimization submodule for strategy parameters is used to feed back the predicted energy demand trend to the dynamic energy allocation decision module, driving the adaptive update of weight coefficients and constraint boundaries in the multi-objective optimization algorithm.

[0028] In a preferred embodiment of the energy management system for the hybrid-powered unmanned aerial vehicle (UAV) of the present invention, the formula for constructing the multi-objective optimization algorithm is as follows:

[0029]

[0030] in, Represents the power distribution control vector. Indicates the output power of the power unit. This indicates the output power of the combustion unit. This indicates the total power requirement for the drone's current flight. This indicates the overall energy efficiency of the hybrid power system. Indicates battery wear indicators. This indicates the thermomechanical loss index of the combustion unit. This represents the battery loss weighting coefficient. This represents the weighting coefficient for combustion unit losses. Indicates the rate of change of power. Indicates the lower limit of battery state of charge. Indicates the upper limit of the battery's state of charge. Indicates the battery's state of charge. Indicates the first The output power of each power source Indicates the first The maximum permissible output power of each power source.

[0031] Secondly, embodiments of the present invention provide an energy management method for a hybrid-powered unmanned aerial vehicle (UAV), comprising: real-time acquisition of flight condition parameters of the UAV; synchronous acquisition of energy unit status parameters of the UAV; based on the flight condition parameters and energy unit status parameters, constructing a multi-objective optimization algorithm to generate a power allocation strategy among N power sources in the UAV; executing the power allocation strategy to coordinate the dynamic collaborative operation of multiple power sources in the UAV; recording historical operating data of the UAV, iteratively updating the historical operating data, predicting the energy of the UAV, and completing energy response and management.

[0032] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the energy management system of the hybrid unmanned aerial vehicle described above.

[0033] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the energy management system of the hybrid unmanned aerial vehicle described above.

[0034] The beneficial effects of this invention are as follows: By constructing an energy management system integrating flight status perception, energy status monitoring, dynamic energy allocation decision-making, multi-source collaborative control, and adaptive learning optimization, this invention achieves intelligent coordination and real-time optimization of multiple energy inputs for hybrid-powered UAVs. Based on precise flight condition parameters and energy unit status parameters, the system can dynamically generate and execute optimal power allocation strategies, effectively improving energy utilization efficiency and extending endurance. Simultaneously, through seamless switching and load balancing control of multiple power sources, it significantly reduces thermal stress and mechanical losses in key components, slowing down the aging process. Furthermore, relying on the adaptive learning mechanism, the system can continuously accumulate mission experience, continuously optimize energy management strategies, and enhance its adaptability to complex or unknown flight scenarios, thereby achieving a synergistic improvement in endurance performance and system lifespan while ensuring flight safety and mission reliability. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0036] Figure 1 A flowchart of an energy management system for a hybrid unmanned aerial vehicle (UAV) provided as an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of an energy management system for a hybrid unmanned aerial vehicle (UAV) provided in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the structure of a medium in an energy management system for a hybrid unmanned aerial vehicle (UAV) provided in an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the structure of a computing device for an energy management system of a hybrid unmanned aerial vehicle (UAV) provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0043] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0044] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0045] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] Example

[0047] Reference Figure 1 and Figure 2This is the first embodiment of the present invention, which provides an energy management system for a hybrid unmanned aerial vehicle, comprising:

[0048] S1: Flight status perception module, used to collect flight status parameters of hybrid-powered UAVs in real time.

[0049] Among them, the energy management system flight status perception module of the hybrid unmanned aerial vehicle includes a flight motion parameter acquisition submodule, a mission phase identification submodule, and an environmental disturbance perception submodule.

[0050] The energy management system of the hybrid-electric drone includes a flight motion parameter acquisition submodule, which integrates the flight motion parameters of the hybrid-electric drone with the global navigation satellite system via an onboard inertial measurement unit.

[0051] The energy management system task phase identification submodule of the hybrid UAV is used to analyze the current flight task phase of the hybrid UAV based on the waypoint sequence and operation instructions issued by the flight control system.

[0052] The environmental disturbance perception submodule of the energy management system for hybrid-electric drones is used to acquire environmental disturbance parameters that affect the flight energy consumption of hybrid-electric drones using the onboard sensors of the hybrid-electric drone.

[0053] S2: Energy Status Monitoring Module, used to synchronously acquire the energy unit status parameters of the hybrid UAV.

[0054] Among them, the energy status monitoring module of the energy management system of the hybrid UAV includes an electrical energy unit monitoring submodule, a fuel power unit monitoring submodule, and a multi-source coupling interface monitoring submodule.

[0055] The power unit monitoring submodule of the energy management system for hybrid-powered drones is used to collect the operating status parameters of the power unit in the hybrid-powered drone through high-precision current and voltage sensors and the battery management system.

[0056] The fuel power unit monitoring submodule of the energy management system for hybrid unmanned aerial vehicles is used to acquire the operating status parameters of the fuel power unit based on the fuel power sensor unit;

[0057] The multi-source coupling interface monitoring submodule of the energy management system for hybrid unmanned aerial vehicles (UAVs) is used to detect the power interaction boundary and energy conversion loss characteristics of the electrical power unit and the fuel power unit in the power combiner mechanism of the hybrid UAV.

[0058] S3: Dynamic energy allocation decision module, used to construct a multi-objective optimization algorithm based on flight condition parameters and energy unit state parameters, and generate a power allocation strategy among N power sources in a hybrid UAV.

[0059] Among them, the dynamic energy allocation decision module of the energy management system of the hybrid UAV includes a working condition energy fusion modeling submodule, a multi-objective optimization solution submodule, and a strategy mapping output submodule;

[0060] The energy management system of the hybrid unmanned aerial vehicle (UAV) uses the operating condition energy fusion modeling submodule to perform spatiotemporal alignment and feature coupling between flight operating condition parameters and energy unit state parameters to generate a joint state vector characterizing the current system operating state.

[0061] The multi-objective optimization solution submodule for the energy management system of a hybrid unmanned aerial vehicle (UAV) is used to construct a multi-objective optimization algorithm based on the joint state vector of the energy management system of the UAV and solve the Pareto optimal solution set online.

[0062] The energy management system strategy mapping output submodule of the hybrid-electric UAV is used to select a power allocation scheme that is suitable for the current flight phase from the Pareto optimal solution set according to the task priority weight, and output the real-time power command sequence of N power sources in the hybrid-electric UAV.

[0063] S4: Multi-source cooperative control module, used to execute power allocation strategies and coordinate the dynamic cooperative operation of multiple power sources in a hybrid-electric UAV.

[0064] Among them, the multi-source collaborative control module of the energy management system of the hybrid UAV includes a power command parsing submodule, a power source execution scheduling submodule, and a collaborative operation closed-loop correction submodule.

[0065] The power command parsing submodule of the energy management system of the hybrid UAV is used to receive the power allocation strategy output by the dynamic energy allocation decision module and decouple the power allocation strategy into the torque and speed control targets corresponding to each power source;

[0066] The power source execution scheduling submodule of the energy management system for hybrid unmanned aerial vehicles is used to generate coordinated operation scheduling parameters for the electric power unit and the combustion power unit according to the control objectives.

[0067] The energy management system of the hybrid-powered UAV uses a collaborative operation closed-loop correction submodule to adjust execution parameters in real time based on the actual output response and command deviation of multiple power sources in order to maintain the synchronization of dynamic collaborative operation.

[0068] S5: Adaptive learning optimization module, used to record historical operation data of hybrid UAV, iteratively update historical operation data, predict the energy of hybrid UAV, and complete energy response and management.

[0069] Among them, the adaptive learning optimization module of the energy management system of the hybrid UAV includes a sub-module for collecting operational data, a sub-module for modeling energy behavior, and a sub-module for online tuning of strategy parameters.

[0070] The energy management system operation data collection submodule of the hybrid UAV is used to perform timestamp alignment and structured storage of flight condition parameters, energy unit status parameters and corresponding power allocation strategies to form a historical operation dataset of the hybrid UAV.

[0071] The energy behavior modeling submodule of the energy management system for hybrid unmanned aerial vehicles is used to train a lightweight time-series prediction network based on historical operation datasets to establish a mapping relationship between flight mission characteristics and energy consumption response of multiple power sources.

[0072] The online optimization submodule for the energy management system of hybrid unmanned aerial vehicles (UAVs) is used to feed back the predicted energy demand trend to the dynamic energy allocation decision module, driving the adaptive update of weight coefficients and constraint boundaries in the multi-objective optimization algorithm.

[0073] Furthermore, this invention constructs a closed-loop, intelligent, and adaptive hybrid-powered UAV energy management system through five closely related technical steps, S1 to S5: First, the S1 flight status perception module collects flight condition parameters in real time, including motion status, mission stage, and environmental disturbances, comprehensively depicting the current operating scenario of the UAV; the S2 energy status monitoring module simultaneously acquires the energy unit status parameters of the electric power unit, the combustion power unit, and their coupling interfaces, accurately grasping the availability and health status of each power source; based on this, the S3 dynamic energy allocation decision module fuses the two types of parameters to generate a joint state vector, constructs and solves a multi-objective optimization algorithm, and generates a power allocation strategy that takes into account endurance, energy loss, and response smoothness; subsequently, the S4 multi-source collaborative control module parses this strategy into specific control commands, schedules the electric power unit and the combustion power unit to perform start-stop, switching, and load allocation, and ensures precise synchronization of collaborative operations through closed-loop correction; finally, the S5 adaptive learning optimization module continuously collects historical operating data, establishes an energy consumption prediction model, and feeds back the prediction results for online tuning of the weights and constraints of the optimization algorithm, enabling the system to have continuous evolution capabilities. The entire technical solution achieves comprehensive perception of flight conditions and energy status, dynamic optimization allocation driven by multiple objectives, high-precision collaborative execution, and experience-based autonomous iteration. This effectively solves the defects of existing technologies, such as simple control logic, rigid energy allocation, inability to respond to changes in operating conditions in real time, and lack of seamless multi-source collaboration. It significantly improves energy utilization efficiency, system reliability, and mission adaptability.

[0074] In a preferred embodiment, an energy management method for a hybrid-powered unmanned aerial vehicle (UAV) includes: real-time acquisition of flight condition parameters of the UAV; synchronous acquisition of energy unit status parameters of the UAV; construction of a multi-objective optimization algorithm based on the flight condition parameters and energy unit status parameters to generate a power allocation strategy among N power sources in the UAV; execution of the power allocation strategy to coordinate the dynamic collaborative operation of multiple power sources in the UAV; recording historical operating data of the UAV, iteratively updating the historical operating data, predicting the energy of the UAV, and completing energy response and management.

[0075] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0076] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0077] In summary, this invention constructs an energy management system integrating flight status perception, energy status monitoring, dynamic energy allocation decision-making, multi-source collaborative control, and adaptive learning optimization. This system achieves intelligent coordination and real-time optimization of multiple energy inputs for hybrid-powered UAVs. Based on precise flight condition parameters and energy unit status parameters, the system can dynamically generate and execute optimal power allocation strategies, effectively improving energy utilization efficiency and extending endurance. Simultaneously, through seamless switching and load balancing control of multiple power sources, it significantly reduces thermal stress and mechanical losses of key components, slowing down the aging process. Furthermore, relying on the adaptive learning mechanism, the system can continuously accumulate mission experience, continuously optimize energy management strategies, and enhance its adaptability to complex or unknown flight scenarios. Thus, while ensuring flight safety and mission reliability, it achieves a synergistic improvement in endurance performance and system lifespan.

[0078] Reference Figure 3 and Figure 4 After introducing the method and system of exemplary embodiments of the present invention, the following references are made. Figure 3 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 3 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program for the energy management system of the hybrid-electric drone is run by the processor, it will implement the steps described in the above method implementation, such as: real-time acquisition of flight condition parameters of the hybrid-electric drone; synchronous acquisition of energy unit status parameters of the hybrid-electric drone; based on the flight condition parameters and energy unit status parameters, constructing a multi-objective optimization algorithm to generate a power allocation strategy among the N power sources in the hybrid-electric drone; executing the power allocation strategy to coordinate the dynamic cooperative operation of multiple power sources in the hybrid-electric drone; recording historical operating data of the hybrid-electric drone, iteratively updating the historical operating data, predicting the energy of the hybrid-electric drone, and completing energy response and management. The specific implementation methods of each step will not be repeated here.

[0079] It should be noted that examples of computer-readable storage media for the energy management system of hybrid unmanned aerial vehicles may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0080] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 4 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0081] Figure 4 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 4 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0082] like Figure 4 As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0083] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0084] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0085] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0086] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 406. Figure 4 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0087] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it collects flight condition parameters of the hybrid UAV in real time; synchronously acquires the energy unit status parameters of the hybrid UAV; constructs a multi-objective optimization algorithm based on the flight condition parameters and energy unit status parameters to generate a power allocation strategy among the N power sources in the hybrid UAV; executes the power allocation strategy to coordinate the dynamic cooperative operation of multiple power sources in the hybrid UAV; records the historical operation data of the hybrid UAV, iteratively updates the historical operation data, predicts the energy of the hybrid UAV, and completes energy response and management.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0089] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the energy management system units of a hybrid unmanned aerial vehicle is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0090] The energy management system of the hybrid-powered UAV, described as a separate component, may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0091] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0092] If the energy management system function of a hybrid-powered drone is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the energy management system method for the hybrid-powered drone according to various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] Finally, it should be noted that the above embodiments of the energy management system for hybrid-powered drones are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention for the energy management system of hybrid-powered drones should be determined by the scope of the claims.

[0094] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. An energy management system for a hybrid unmanned aerial vehicle (UAV), characterized in that: include, The flight status perception module is used to collect flight condition parameters of the hybrid-electric UAV in real time. The energy status monitoring module is used to synchronously acquire the energy unit status parameters of the hybrid UAV; The dynamic energy allocation decision module is used to construct a multi-objective optimization algorithm based on flight condition parameters and energy unit state parameters to generate a power allocation strategy among N power sources in a hybrid UAV. The multi-source collaborative control module is used to execute power allocation strategies and coordinate the dynamic collaborative operation of multiple power sources in a hybrid-electric UAV. The adaptive learning optimization module is used to record historical operating data of the hybrid-electric drone, iteratively update the historical operating data, predict the energy of the hybrid-electric drone, and complete energy response and management.

2. The energy management system for a hybrid unmanned aerial vehicle as described in claim 1, characterized in that: The flight status perception module includes a flight motion parameter acquisition submodule, a mission phase identification submodule, and an environmental disturbance perception submodule. The flight motion parameter acquisition submodule is used to output the motion state parameters of the hybrid-powered UAV through the fusion of the airborne inertial measurement unit and the global navigation satellite system. The mission phase identification submodule is used to analyze the current flight mission phase of the hybrid-electric UAV based on the waypoint sequence and operation instructions issued by the flight control system. The environmental disturbance perception submodule is used to acquire environmental disturbance parameters that affect the flight energy consumption of the hybrid UAV using the onboard sensing devices of the hybrid UAV.

3. The energy management system for a hybrid unmanned aerial vehicle as described in claim 2, characterized in that: The energy status monitoring module includes an electrical energy unit monitoring submodule, a fuel power unit monitoring submodule, and a multi-source coupling interface monitoring submodule; The power unit monitoring submodule is used to collect the operating status parameters of the power unit in the hybrid UAV through high-precision current and voltage sensors and the battery management system. The fuel power unit monitoring submodule is used to acquire the operating status parameters of the fuel power unit based on the fuel power sensing unit; The multi-source coupling interface monitoring submodule is used to detect the power interaction boundary and energy conversion loss characteristics of the electric power unit and the fuel power unit in the power combiner mechanism of the hybrid UAV.

4. The energy management system for a hybrid unmanned aerial vehicle as described in claim 3, characterized in that: The dynamic energy allocation decision module includes an operating condition energy fusion modeling submodule, a multi-objective optimization solution submodule, and a strategy mapping output submodule. The operating condition energy fusion modeling submodule is used to perform spatiotemporal alignment and feature coupling between flight operating condition parameters and energy unit state parameters to generate a joint state vector characterizing the current system operating state. The multi-objective optimization solution submodule is used to construct a multi-objective optimization algorithm based on the joint state vector and solve for the Pareto optimal solution set online; The strategy mapping output submodule is used to select a power allocation scheme that is suitable for the current flight phase from the Pareto optimal solution set according to the task priority weight, and output the real-time power command sequence of N power sources in the hybrid UAV.

5. The energy management system for a hybrid unmanned aerial vehicle as described in claim 4, characterized in that: The multi-source collaborative control module includes a power command parsing submodule, a power source execution scheduling submodule, and a collaborative operation closed-loop correction submodule. The power command parsing submodule is used to receive the power allocation strategy output by the dynamic energy allocation decision module and decouple the power allocation strategy into torque and speed control targets corresponding to each power source. The power source execution scheduling submodule is used to generate coordinated operation scheduling parameters for the electric power unit and the combustion power unit according to the control objectives; The collaborative operation closed-loop correction submodule is used to adjust the execution parameters in real time based on the actual output response and command deviation of multiple power sources in order to maintain the synchronization of dynamic collaborative operation.

6. The energy management system for a hybrid unmanned aerial vehicle as described in claim 5, characterized in that: The adaptive learning optimization module includes a runtime data collection submodule, an energy behavior modeling submodule, and a policy parameter online tuning submodule. The operational data collection submodule is used to perform timestamp alignment and structured storage of flight condition parameters, energy unit status parameters and corresponding power allocation strategies to form a historical operational dataset of the hybrid unmanned aerial vehicle. The energy behavior modeling submodule is used to train a lightweight time-series prediction network based on historical operational datasets to establish a mapping relationship between flight mission characteristics and energy consumption response of multiple power sources. The online optimization submodule for strategy parameters is used to feed back the predicted energy demand trend to the dynamic energy allocation decision module, driving the adaptive update of weight coefficients and constraint boundaries in the multi-objective optimization algorithm.

7. The energy management system for a hybrid unmanned aerial vehicle as described in claim 6, characterized in that: The formula for constructing the multi-objective optimization algorithm is as follows: in, Represents the power distribution control vector. Indicates the output power of the power unit. This indicates the output power of the combustion unit. This indicates the total power requirement for the drone's current flight. This indicates the overall energy efficiency of the hybrid power system. Indicates battery wear indicators. This indicates the thermomechanical loss index of the combustion unit. This represents the battery loss weighting coefficient. This represents the weighting coefficient for combustion unit losses. Indicates the rate of change of power. Indicates the lower limit of battery state of charge. Indicates the upper limit of the battery's state of charge. Indicates the battery's state of charge. Indicates the first The output power of each power source Indicates the first The maximum permissible output power of each power source.

8. An energy management method for a hybrid unmanned aerial vehicle (UAV), based on the energy management system for a hybrid UAV according to any one of claims 1 to 7, characterized in that: include, Real-time acquisition of flight condition parameters of hybrid-powered UAVs; Synchronously acquire the energy unit status parameters of the hybrid-powered UAV; Based on flight condition parameters and energy unit state parameters, a multi-objective optimization algorithm is constructed to generate a power allocation strategy among N power sources in a hybrid UAV. Execute power allocation strategies to coordinate the dynamic cooperative operation of multiple power sources in hybrid-powered UAVs; Record historical operational data of hybrid-powered drones, iteratively update historical operational data, predict the energy of hybrid-powered drones, and complete energy response and management.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the energy management system for any of the hybrid unmanned aerial vehicles according to claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the energy management system for any of the hybrid unmanned aerial vehicles according to claims 1 to 7.

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