Energy management method for electric aircraft hybrid energy system and storage medium

Abstract

The present application relates to a kind of energy management method and storage medium of electric aircraft composite energy system, method includes: before electric aircraft takes off, according to flight plan, draw power demand-time curve;According to the output characteristics of each unit of electric aircraft composite energy system, construct energy optimization scheduling model with minimum use cost minimization as optimization target;Input electric aircraft each unit technical parameter and power demand-time curve, model solution is carried out, obtain composite energy system energy scheduling plan;In the real-time flight process of electric aircraft, with the minimum deviation of electric aircraft real-time power requirement and composite energy system energy scheduling plan as optimization target, construct system energy real-time control model, obtain the actual energy management strategy of composite energy system.Compared with prior art, by pre-flight scheduling optimization and flight real-time operation optimization, the method proposed has the advantages of high energy utilization efficiency, good economy, high feasibility and the like.

Description

Technical Field

[0001] This invention relates to the field of electric aircraft control technology, and in particular to an energy management method and storage medium for an electric aircraft composite energy system. Background Technology

[0002] Developing electric aircraft technology based on new energy technologies is an effective way to simplify the aircraft's energy structure, improve energy utilization and reliability, and reduce carbon emissions. Existing electric aircraft technologies are mostly based on pure lithium-ion battery technology, but due to the energy density limitations of lithium-ion batteries, they cannot meet the energy and power requirements of medium- and long-haul commercial aircraft. A composite energy system based on hydrogen storage systems—hydrogen fuel cells, lithium-ion batteries, and supercapacitors—can better achieve complementary advantages among energy units and is a feasible solution for promoting the large-scale development of electric aircraft. However, the complex and variable flight conditions of aircraft, the varying characteristics of each unit in the composite energy system, and the complex coupling relationships pose significant challenges to the development of energy management methods for composite energy systems and also significantly affect system safety and economy. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art by providing an energy management method and storage medium for an electric aircraft hybrid energy system, thereby improving the energy utilization efficiency of the hybrid energy system.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] An energy management method for an electric aircraft hybrid energy system includes the following steps:

[0006] S1: Before the electric aircraft takes off, plot the power demand-time curve of the electric aircraft according to the flight plan of the electric aircraft.

[0007] S2: Based on the output characteristics of each unit of the composite energy system of the electric aircraft and the operating constraints, construct an energy optimization scheduling model with the goal of minimizing the energy cost of the composite energy system.

[0008] S3: Input the technical parameters of each unit of the electric aircraft and the power demand-time curve into the energy optimization scheduling model, solve the model, and obtain the energy scheduling plan of the composite energy system;

[0009] S4: During the real-time flight of the electric aircraft, using the energy scheduling plan of the composite energy system as a baseline, calculate the deviation between the real-time power demand of the electric aircraft and the energy scheduling plan of the composite energy system, and construct a real-time energy control model of the system with the minimum deviation as the optimization objective, so as to obtain the actual energy management strategy of the composite energy system.

[0010] Furthermore, the power demand-time curve of the electric aircraft includes the power demand-time curves of the electric aircraft during the takeoff, climb, cruise, and landing phases.

[0011] Furthermore, the composite energy system includes a hydrogen fuel cell, a lithium-ion battery, and a supercapacitor;

[0012] During takeoff and climb, the hydrogen fuel cell, lithium-ion battery and supercapacitor are used for power.

[0013] During the cruise and landing phases, the system is powered by hydrogen fuel cells and lithium-ion batteries.

[0014] Furthermore, the optimization objective of the energy optimization scheduling model is:

[0015]

[0016] In the formula, E total The total energy cost of the electric aircraft's combined energy system; and These represent the remaining electrical energy of the lithium-ion battery and the supercapacitor, respectively, after the flight. and These represent the remaining electrical energy of the lithium-ion battery and the supercapacitor, respectively, after the flight; β ele and β H The prices for charging and hydrogen refueling are respectively, P t FC Let η be the output power of the fuel cell system at time t. E,H Δt is the electro-hydrogen conversion coefficient, and Δt is the energy management and scheduling time interval.

[0017] Furthermore, the operational constraints include bus power balance constraints, energy storage device energy balance constraints, power constraints of each system unit, and energy constraints of each system unit;

[0018] The expression for the bus power balance constraint is:

[0019]

[0020] In the formula, and These represent the charging efficiency, discharging efficiency, charging power at time t, and discharging power of the lithium-ion battery, respectively. and These represent the supercapacitor's charging efficiency, discharging efficiency, charging power at time t, and discharging power, respectively; P t FC and P load,t η represents the output power of the fuel cell system at time t and the power demand of the electric aircraft at time t, respectively; DCand η M These are the conversion efficiencies of the DC-DC bidirectional converter and the high-speed motor, respectively.

[0021] The expression for the energy balance constraint of the energy storage device is:

[0022]

[0023]

[0024] In the formula, and These represent the remaining electrical energy of the lithium-ion battery at the initial time, time t-1, and time t, respectively. and These represent the remaining electrical energy of the supercapacitor at the initial time, time t-1, and time t, respectively; Δt is the energy management and scheduling time interval.

[0025] The expressions for the power constraints of each unit in the system are as follows:

[0026]

[0027] In the formula, and These are the maximum permissible charge and discharge rates for lithium-ion batteries, respectively. and These are the maximum permissible charge and discharge rates of the supercapacitor, respectively. and These are 0-1 variables representing whether the lithium-ion battery is being charged and discharged; and These are 0-1 variables indicating whether the supercapacitor is charging and discharging; and These represent the maximum permissible ramp / ramp rates for fuel cells; W LB W SC and W FC These are the design capacities for lithium-ion batteries, supercapacitors, and fuel cells, respectively. The output power of the fuel cell at time t-1;

[0028] The expressions for the energy constraints of each unit in the system are as follows:

[0029]

[0030] In the formula, m H η represents the design hydrogen storage capacity of the hydrogen storage system. E,H This is the electro-hydrogen conversion coefficient, which is the conversion coefficient between hydrogen and the power generated by the fuel cell.

[0031] Furthermore, the technical parameters of each unit of the electric aircraft include: the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the lithium-ion battery; the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the supercapacitor; the design capacity and maximum allowable uphill / downhill ramp rate of the fuel cell; the design hydrogen storage capacity and electro-hydrogen conversion coefficient of the hydrogen storage system; the energy conversion efficiency of the DC-DC bidirectional inverter and the high-speed motor; and the charging and hydrogen refueling prices.

[0032] Furthermore, the energy dispatch plan for the composite energy system includes time-of-flight power output curves for fuel cells, lithium-ion batteries, and supercapacitors.

[0033] Furthermore, the expression for the optimization objective of the real-time energy control model of the system is:

[0034]

[0035] In the formula, ξ t Let t be the deviation between the actual power output of the composite energy system and the planned power output at time t; and These represent the actual remaining electrical energy of the lithium-ion battery and the supercapacitor at time t, respectively. Let t be the actual output power of the fuel cell at time t; Let t be the remaining electrical energy of the lithium-ion battery at time t; P represents the remaining electrical energy of the supercapacitor at time t. t FC w represents the output power of the fuel cell system at time t. LB w SC and w FC These are penalty factors for changes in power output of lithium-ion batteries, supercapacitors, and fuel cells, respectively.

[0036] Furthermore, the constraints satisfied by the real-time energy control model of the system are:

[0037]

[0038] In the formula, and These represent the actual charging power and discharging power of the lithium-ion battery at time t, and the actual charging power and discharging power of the supercapacitor, respectively. and These represent the actual output power of the fuel cell at time t and time t-1, respectively. and These are the 0-1 variables representing whether the lithium-ion battery is actually charging and discharging at time t; and These are the 0-1 variables representing whether the supercapacitor is actually charging and discharging at time t.

[0039] The present invention also provides a machine-readable storage medium on which a computing program is stored, the computing program being executed by a processor using the method described above.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] (1) The present invention uses a two-stage energy management optimization method of pre-flight scheduling optimization and real-time flight operation optimization, which can significantly reduce the energy cost of the composite energy system while meeting the actual energy / power requirements of electric aircraft, thereby significantly improving the operating economy of electric aircraft.

[0042] (2) This invention enables the rapid formulation of energy management plans and real-time optimization of energy management strategies for electric aircraft composite energy systems under different flight conditions, which is conducive to the rapid promotion and practice of electric aircraft technology based on composite energy systems. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating an energy management method for an electric aircraft hybrid energy system provided in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of a composite energy system provided in an embodiment of the present invention;

[0045] Figure 3 This is a planned power demand-time curve for an electric aircraft provided in an embodiment of the present invention;

[0046] Figure 4 This is a power scheduling plan curve of each unit of the electric aircraft composite energy system obtained during the pre-flight scheduling optimization stage, provided in an embodiment of the present invention.

[0047] Figure 5 This is a power demand-time curve of an electric aircraft provided in an embodiment of the present invention;

[0048] Figure 6 This is a graph showing the actual power output curves of each unit of an electric aircraft composite energy system obtained during the real-time flight optimization phase, as provided in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0051] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0052] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed during use. They are only for the convenience of describing this invention and 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 this invention.

[0053] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0054] Furthermore, terms such as "horizontal" and "vertical" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0055] Example 1

[0056] This embodiment provides an energy management method for a hybrid energy system of an electric aircraft, comprising two stages: pre-flight scheduling optimization and real-time flight operation optimization, specifically including the following steps:

[0057] S1: Before the electric aircraft takes off, plot the power demand-time curve of the electric aircraft according to the flight plan of the electric aircraft.

[0058] S2: Based on the output characteristics of each unit of the composite energy system of the electric aircraft and the operating constraints, construct an energy optimization scheduling model with the goal of minimizing the energy cost of the composite energy system.

[0059] S3: Input the technical parameters of each unit of the electric aircraft and the power demand-time curve into the energy optimization scheduling model, solve the model, and obtain the energy scheduling plan of the composite energy system;

[0060] S4: During the real-time flight of the electric aircraft, using the energy scheduling plan of the composite energy system as a baseline, calculate the deviation between the real-time power demand of the electric aircraft and the energy scheduling plan of the composite energy system, and construct a real-time energy control model of the system with the minimum deviation as the optimization objective, thereby obtaining the actual energy management strategy of the composite energy system and performing real-time control of the electric aircraft.

[0061] The power demand-time curve of the electric aircraft generally includes the power demand-time curves of the electric aircraft during the takeoff, climb, cruise and landing phases.

[0062] Based on the power output characteristics of each unit in the composite energy system, preferably, the hydrogen storage system—hydrogen fuel cell, lithium-ion battery and supercapacitor—is used to provide power during takeoff and climb; while the hydrogen storage system—hydrogen fuel cell and lithium-ion battery—is used to provide power during cruise and landing.

[0063] The optimization objective of the energy optimization scheduling model is:

[0064]

[0065] In the formula, E total The total energy cost of the electric aircraft's combined energy system; and These represent the remaining electrical energy of the lithium-ion battery and the supercapacitor, respectively, after the flight. and These represent the remaining electrical energy of the lithium-ion battery and the supercapacitor, respectively, after the flight; β ele and β H The prices for charging and hydrogen refueling are respectively, P t FC Let η be the output power of the fuel cell system at time t. E,H Δt is the electro-hydrogen conversion coefficient, and Δt is the energy management and scheduling time interval.

[0066] Various operational constraints include: bus power balance constraints, energy storage device energy balance constraints, power constraints of each system unit, and energy constraints of each system unit.

[0067] The expression for the bus power balance constraint is:

[0068]

[0069] In the formula, and These represent the charging efficiency, discharging efficiency, charging power at time t, and discharging power of the lithium-ion battery, respectively. and These represent the supercapacitor's charging efficiency, discharging efficiency, charging power at time t, and discharging power, respectively; P t FC and P load,t η represents the output power of the fuel cell system at time t and the power demand of the electric aircraft at time t, respectively; DC and η M These are the conversion efficiencies of the DC-DC bidirectional converter and the high-speed motor, respectively.

[0070] The expression for the energy balance constraint of the energy storage device is:

[0071]

[0072]

[0073] In the formula, and These represent the remaining electrical energy of the lithium-ion battery at the initial time, time t-1, and time t, respectively. and These represent the remaining electrical energy of the supercapacitor at the initial time, time t-1, and time t, respectively; Δt is the energy management and scheduling time interval.

[0074] The expressions for the power constraints of each unit in the system are as follows:

[0075]

[0076] In the formula, and These are the maximum permissible charge and discharge rates for lithium-ion batteries, respectively. and These are the maximum permissible charge and discharge rates of the supercapacitor, respectively. and These are 0-1 variables representing whether the lithium-ion battery is being charged and discharged; and These are 0-1 variables indicating whether the supercapacitor is charging and discharging; and These represent the maximum permissible ramp / ramp rates for fuel cells; W LB W SC and W FC These are the design capacities for lithium-ion batteries, supercapacitors, and fuel cells, respectively. The output power of the fuel cell at time t-1;

[0077] The expressions for the energy constraints of each unit in the system are as follows:

[0078]

[0079] In the formula, m H η represents the design hydrogen storage capacity of the hydrogen storage system. E,H This is the electro-hydrogen conversion coefficient, which is the conversion coefficient between hydrogen and the power generated by the fuel cell.

[0080] The technical parameters of each unit of the electric aircraft include, but are not limited to: the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the lithium-ion battery; the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the supercapacitor; the design capacity and maximum allowable uphill / downhill ramp rate of the fuel cell; the design hydrogen storage capacity and electro-hydrogen conversion coefficient of the hydrogen storage system; the energy conversion efficiency of the DC-DC bidirectional inverter and the high-speed motor; and the charging and hydrogen refueling prices.

[0081] The energy dispatch plan for the composite energy system described in step S3 includes the time-sharing power output curves of fuel cells, lithium-ion batteries, and supercapacitors.

[0082] The system energy real-time operation optimization method described in step S4, for each time t, when the actual power demand... With planned power demand P load,t When discrepancies exist, the original energy dispatch plan is revised based on the actual charging and discharging status and capacity of each unit in the composite energy system, with the goal of minimizing the deviation between the actual power output of the composite energy system and the plan, so as to meet the system's energy supply demand and power balance requirements.

[0083] The expression for the optimization objective of the real-time energy control model of the system is:

[0084]

[0085] In the formula, ξ t Let t be the deviation between the actual power output of the composite energy system and the planned power output at time t; and These represent the actual remaining electrical energy of the lithium-ion battery and the supercapacitor at time t, respectively. Let t be the actual output power of the fuel cell at time t; Let t be the remaining electrical energy of the lithium-ion battery at time t; P represents the remaining electrical energy of the supercapacitor at time t. t FC w represents the output power of the fuel cell system at time t. LB w SC and w FC These are penalty factors for changes in power output of lithium-ion batteries, supercapacitors, and fuel cells, respectively.

[0086] The constraints satisfied by the real-time energy control model of the system are:

[0087]

[0088] In the formula, and These represent the actual charging power and discharging power of the lithium-ion battery at time t, and the actual charging power and discharging power of the supercapacitor, respectively. and These represent the actual output power of the fuel cell at time t and time t-1, respectively. and These are the 0-1 variables representing whether the lithium-ion battery is actually charging and discharging at time t; and These are the 0-1 variables representing whether the supercapacitor is actually charging and discharging at time t.

[0089] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a machine-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part 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 methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] Therefore, this embodiment also provides a machine-readable storage medium, characterized in that the machine-readable storage medium stores a computing program, and the computing program is executed by a processor using the method described above.

[0091] The following is a specific implementation process using the above method:

[0092] This embodiment focuses on typical flight conditions of a civil passenger aircraft and conducts case calculations. The schematic diagram of the composite energy system structure used in this embodiment is shown below. Figure 2 As shown in the figure. During the pre-flight scheduling optimization phase, the power demand-time curve was plotted based on the electric aircraft's flight plan, as shown in the figure. Figure 3As shown in Table 1, the technical parameters of each component required for the energy optimization scheduling model are listed below, with a unit scheduling time of 0.1 minutes. By solving the energy optimization scheduling model with the objective of minimizing the energy cost of the composite energy system, the power scheduling plans for each unit of the composite energy system are obtained as follows: Figure 4 As shown. From Figure 4 It is evident that the hydrogen storage system-fuel cell, as the main power source of the composite energy system, basically tracks the energy demand curve of the electric aircraft, and its output power is relatively stable; the supercapacitor mainly serves as a power auxiliary during takeoff and climb; and the lithium-ion battery serves as an energy and power supplement to absorb the power demand fluctuations throughout the entire flight phase.

[0093] Table 1 Technical parameters of each component in the system

[0094]

[0095]

[0096] Using the aforementioned energy scheduling plan as a baseline, real-time operational optimization is implemented. It is assumed that the actual power demand curve of the electric aircraft during actual flight is as follows: Figure 5 As shown. By solving the real-time energy control model with the goal of minimizing the energy cost of the hybrid energy system, the actual power output curves of each unit of the electric aircraft hybrid energy system are obtained as follows. Figure 6 As shown. From Figure 6 As can be seen, the deviation between the actual power demand and the planned power demand is mainly met by controlling the charging and discharging power of the lithium-ion battery, and partially by controlling the charging and discharging power of the supercapacitor during takeoff and climb. The power of the fuel cell remains relatively stable. In this embodiment, the actual energy cost of the hybrid energy system is 3598.14 yuan, which is only 70.5% of the full-load energy cost of 5106 yuan, indicating good overall economic efficiency of the system.

[0097] In summary, this embodiment focuses on the typical flight conditions of a civil passenger aircraft, implementing pre-flight scheduling optimization and real-time flight operation optimization. The results show that the energy management method and system for the electric aircraft hybrid energy system proposed in this invention can effectively meet the actual energy / power requirements of the electric aircraft and significantly reduce its energy costs, thereby improving the operational economy of the electric aircraft.

[0098] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. An energy management method for an electric aircraft composite energy system, characterized in that, Includes the following steps: S1: Before the electric aircraft takes off, plot the power demand-time curve of the electric aircraft according to the flight plan of the electric aircraft. S2: Based on the output characteristics of each unit of the composite energy system of the electric aircraft and the operating constraints, construct an energy optimization scheduling model with the goal of minimizing the energy cost of the composite energy system. S3: Input the technical parameters of each unit of the electric aircraft and the power demand-time curve into the energy optimization scheduling model, solve the model, and obtain the energy scheduling plan of the composite energy system; S4: During the real-time flight of the electric aircraft, using the energy scheduling plan of the composite energy system as a baseline, calculate the deviation between the real-time power demand of the electric aircraft and the energy scheduling plan of the composite energy system, and construct a real-time energy control model of the system with the minimum deviation as the optimization objective, so as to obtain the actual energy management strategy of the composite energy system.

2. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The power demand-time curve of the electric aircraft includes the power demand-time curves of the electric aircraft during the takeoff, climb, cruise, and landing phases.

3. The energy management method for an electric aircraft composite energy system according to claim 2, characterized in that, The composite energy system includes a hydrogen fuel cell, a lithium-ion battery, and a supercapacitor. During takeoff and climb, the hydrogen fuel cell, lithium-ion battery and supercapacitor are used for power. During the cruise and landing phases, the system is powered by hydrogen fuel cells and lithium-ion batteries.

4. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The optimization objective of the energy optimization scheduling model is: In the formula, The total energy cost of the electric aircraft's combined energy system; and These represent the remaining electrical energy of the lithium-ion battery and the supercapacitor, respectively, after the flight. and These are the remaining electrical energy of the lithium-ion battery and the supercapacitor before the start of flight; and The prices are for charging and hydrogen refueling, respectively. For fuel cell systems in t Output power at any moment The electro-hydrogen conversion coefficient, This refers to the time interval for energy management and scheduling.

5. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The operational constraints include bus power balance constraints, energy storage device energy balance constraints, power constraints of each system unit, and energy constraints of each system unit. The expression for the bus power balance constraint is: In the formula, , , and These are the charging efficiency and discharging efficiency of lithium-ion batteries, respectively. t The charging power and discharging power at any given time; , , and These are the charging efficiency and discharging efficiency of supercapacitors, respectively. t The charging power and discharging power at any given time; and For fuel cell systems t The output power at any given moment, and the electric aircraft in t Power requirements at any given time; and These are the conversion efficiencies of the DC-DC bidirectional converter and the high-speed motor, respectively. The expression for the energy balance constraint of the energy storage device is: In the formula, , and They are the initial time, t -1 time and t The remaining electrical energy of the lithium-ion battery at any given time; , and They are the initial time, t -1 time and t The remaining electrical energy of the supercapacitor at any given time; For energy management scheduling time intervals; The expressions for the power constraints of each unit in the system are as follows: In the formula, and These are the maximum permissible charge and discharge rates for lithium-ion batteries, respectively. and These are the maximum permissible charge and discharge rates of the supercapacitor, respectively. and These are 0-1 variables representing whether the lithium-ion battery is being charged and discharged; and These are 0-1 variables indicating whether the supercapacitor is charging and discharging; and These are the maximum permissible ramp / ramp rates for the fuel cell; , and These are the design capacities for lithium-ion batteries, supercapacitors, and fuel cells, respectively. for t The output power of the fuel cell at time -1; The expressions for the energy constraints of each unit in the system are as follows: In the formula, The designed hydrogen storage capacity of the hydrogen storage system; This is the electro-hydrogen conversion coefficient, which is the conversion coefficient between hydrogen and the power generated by the fuel cell.

6. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The technical parameters of each unit of the electric aircraft include: the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the lithium-ion battery; the design capacity, maximum allowable charge / discharge rate, and charge / discharge efficiency of the supercapacitor; the design capacity and maximum allowable uphill / downhill ramp rate of the fuel cell; the design hydrogen storage capacity and electro-hydrogen conversion coefficient of the hydrogen storage system; the energy conversion efficiency of the DC-DC bidirectional inverter and the high-speed motor; and the charging and hydrogen refueling prices.

7. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The energy dispatch plan for the composite energy system includes time-of-use power output curves for fuel cells, lithium-ion batteries, and supercapacitors.

8. The energy management method for an electric aircraft composite energy system according to claim 1, characterized in that, The expression for the optimization objective of the real-time energy control model of the system is: In the formula, for t The deviation between the actual power output and the planned power output of the composite energy system at any given time; and They are respectively t The actual remaining electrical energy of lithium-ion batteries and supercapacitors at any given time; for t The actual output power of the fuel cell at any given time; for t The remaining electrical energy of the lithium-ion battery at any given time; for t The remaining electrical energy of the supercapacitor at any given time; For fuel cell systems in t Output power at any given moment; , and These are penalty factors for changes in power output of lithium-ion batteries, supercapacitors, and fuel cells, respectively. This refers to the time interval for energy management and scheduling.

9. The energy management method for an electric aircraft composite energy system according to claim 8, characterized in that, The constraints satisfied by the real-time energy control model of the system are: In the formula, , , and They are respectively t Real-time actual charging power and discharging power of lithium-ion batteries, and actual charging power and discharging power of supercapacitors; and They are respectively t Time and t Actual output power of fuel cell at time -1; and They are respectively t The 0-1 variable representing whether the lithium-ion battery is actually charging and discharging at any given moment; and They are respectively t The 0-1 variable representing whether the supercapacitor is actually charging and discharging at any given moment; and These are the conversion efficiencies of the DC-DC bidirectional converter and the high-speed motor, respectively. and These refer to the charging efficiency and discharging efficiency of lithium-ion batteries, respectively. and These refer to the charging efficiency and discharging efficiency of the supercapacitor, respectively. and These are the maximum permissible uphill and downhill ramp rates for the fuel cell, respectively. and These are the maximum permissible charge and discharge rates for lithium-ion batteries, respectively. and These represent the maximum permissible charge and discharge rates of the supercapacitor. , and These refer to the design capacities of lithium-ion batteries, supercapacitors, and fuel cells, respectively.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores a computational program, which is executed by a processor according to any one of claims 1 to 9.

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