Aircraft electric power propulsion system

Abstract

The invention discloses an aircraft electric power propulsion system, and relates to the technical field of aerospace. Compared with an existing aircraft electric power propulsion system, the aircraft electric power propulsion system solves the problems that a radial magnetic motor is basically adopted in an existing aircraft distributed electric power propulsion system, motors are not reasonably matched, and the weight of an electric power system is increased; and the weight of the power system is further increased due to an additional packaging structure and the use of a relatively long lead wire for transmitting electric energy. An electric aircraft distributed electric power system layout is provided, an electric power system providing propulsion power uses a radial magneto, an electric power system providing lift force uses an axial magneto, and the total weight of the power systems is reduced by utilizing the advantages of different forms of motors. And through the integrated design scheme of the motor controller and the motor, the integration degree of the aircraft and optimization of the internal structure of the aircraft are further improved, the endurance performance and reliability of the aircraft are improved, and powerful support is provided for application in the field of aircrafts.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and in particular to an aircraft electric propulsion system. Background Art

[0002] As the energy density of power batteries increases, electric power systems are increasingly used in transportation. The automotive industry is the fastest and most successful. Similarly, electric power systems are increasingly used in aircraft. At present, electric aircraft are rapidly emerging and developing at home and abroad.

[0003] At present, the electric propulsion system used on aircraft generally adopts a distributed layout, and the typical application case is eVTOL. This type of aircraft generally arranges the electric power system longitudinally in front and behind to provide propulsion power, and its characteristic is that the propeller disk rotation plane is perpendicular to the flight direction of the aircraft. In addition, multiple electric power systems are arranged according to certain rules in the plane where the aircraft wing surface is located to provide lift, and its characteristic is that the propeller disk rotation plane is parallel to the aircraft wing surface, or perpendicular to the direction of gravity.

[0004] All distributed electric propulsion systems of aircraft models disclosed in the prior art basically use radial magnetic motors, and most motor controllers use independent design solutions. Independent motor controllers require additional packaging structures, which increases the gravity of the power system; and the motor controller is far away from the motor, requiring longer wires to transmit electrical energy, further increasing the weight of the power system. All distributed electric power systems of existing aircraft use radial magnetic motors, and the motors are not reasonably matched, resulting in an increase in the weight of the electric power system.

[0005] In order to solve the above problems, the present invention proposes an aircraft electric propulsion system. Summary of the invention

[0006] The purpose of the present invention is to provide an aircraft electric propulsion system to solve the problems raised in the background technology:

[0007] The distributed electric propulsion systems of existing aircraft basically use radial magnetic motors, which are not reasonably matched with the motors, resulting in an increase in the weight of the electric power system; and the motor controllers that mostly use independent design solutions require additional packaging structures, which increases the gravity of the power system; and the motor controller is far away from the motor, requiring a longer wire to transmit electrical energy, further increasing the weight of the power system.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] An aircraft electric propulsion system comprises: a DC battery, a power distribution module, a flight control system, lift propellers axially arranged at four vertices in a rectangular shape, and a propeller radially arranged at the top; the DC battery is intelligently managed by a BMS system, and the output voltage is reduced by DC / DC conversion to provide power for the flight control system, and the flight control system is used to control the operation of the lift propeller and the propeller; the DC battery also provides working voltage for the four lift propellers and the propeller through the power distribution module.

[0010] Preferably, the voltage range of the DC battery is 600-800V.

[0011] Preferably, the four lift propellers all adopt an electrically-controlled integrated axial magnetic motor, and the propulsion propeller adopts an electrically-controlled integrated radial magnetic motor; the magnetic flux direction of the electrically-controlled integrated axial magnetic motor is parallel to the motor rotation axis, and the magnetic flux direction of the electrically-controlled integrated radial magnetic motor is distributed along the radial direction of the motor.

[0012] Preferably, the electrically controlled integrated axial magnetic motor and the electrically controlled integrated radial magnetic motor both adopt an integrated design of a motor controller and a motor, and are cooled by water cooling or air cooling.

[0013] Preferably, the integrated design of the motor controller and the motor also integrates a wireless communication model to enable the system to communicate wirelessly with a ground monitoring center.

[0014] Preferably, the integrated design of the motor controller and the motor uses carbon fiber composite materials to make an integrated housing, and is also provided with heat dissipation channels and heat dissipation fins made of phase change materials or nanomaterials; and electromagnetic shielding technology is also used to reduce the impact of electromagnetic interference on the equipment.

[0015] Preferably, the structure of the heat sink fins includes wavy heat sink fins, honeycomb heat sink fins, spiral heat sink fins, hollow heat sink fins, multi-layer three-dimensional heat sink fins, curved three-dimensional heat sink fins, composite structure heat sink fins, deformable heat sink fins, adjustable heat sink fins, bionic heat sink fins, and modular heat sink fins; the heat sink fins are also prepared in combination with 3D printing technology.

[0016] Preferably, the motor controller and motor integrated design also integrates sensors, power modules and energy storage systems.

[0017] Preferably, the motor controller and the motor integrated design scheme further sets an automatic intelligent control method according to the running state of the motor and the external environmental conditions, and the automatic intelligent control method automatically adjusts the control parameters according to the real-time running state of the motor and the real-time external environmental conditions;

[0018] The automatic intelligent control method is specifically as follows:

[0019] S1: Real-time collection of the operating status and external environmental conditions of the motor controller and motor, and pre-processing and storage of data based on big data technology;

[0020] S2: Control simulation of the motor controller and motor integrated structure based on neural network, as well as optimization adjustment of layout and wiring;

[0021] The neural network takes the preprocessed running state of the motor controller and the motor and the external environmental conditions as input to build a BiLSTM-FCN network. The BiLSTM-FCN network respectively builds a parallel improved BiLSTM network and FCN network, and finally connects the outputs of the improved BiLSTM network and the FCN network through a connection function, and then outputs the control result through a connection layer and an output layer;

[0022] The output of the improved BiLSTM network is as follows:

[0023]

[0024] in, and They are the output results of the forward layer at time t and time t-1 respectively; and They are the output results of the backward layer at time t and time t-1 respectively; and are the weight coefficients between the input layer and the forward layer and the backward layer. is the weight coefficient between the forward propagation unit at time t-1 and the forward propagation unit at time t; is the weight coefficient between the backward propagation unit at time t-1 and the backward propagation unit at time t; and are the weight coefficients between the forward layer, the backward layer and the output layer respectively; f(·) is the internal calculation function of the neuron; g(·) is the function for calculating the total result of the forward layer and the backward layer; y t is the output result of the BiLSTM network at time t;

[0025] The attention mechanism is also integrated into each propagation unit of the improved BiLSTM network, as follows:

[0026] Atten t =αtanh(Wx t +B)

[0027]

[0028] Among them, Atten t is the value of the attention probability distribution at time t; α, W and b are model learning parameters; wt is the attention weight; τ is the dimension of the model input vector; h t is the output at time t;

[0029] The FCN network contains three time convolution blocks, each layer has 128, 256 and 128 filters respectively. Each convolution layer is implemented by the ReLU activation function, and then connected to a global pooling layer for output; the convolution kernel of the FCN network is a sliding window on the time series, which extracts the short-term features of the time series and is updated as follows:

[0030]

[0031] Among them, z i and z j are the i-th and j-th feature maps respectively; σ is the activation function; ω ij and b are model learning parameters;

[0032] The BiLSTM-FCN network also learns model parameters based on the harmony search algorithm, as follows:

[0033] The control balance degree U={u1,u2,Λ,u M} as the optimization target, M is the total number of control signal combinations between the motor and the motor controller, and the relevant parameters are initialized;

[0034] u ab =u min +(u max -u min )×r1

[0035] Among them, u ab is the harmony vector of the initialization of the a-th motor signal and the control signal of the b-th motor controller, that is, the combination vector of the a-th motor signal and the control signal of the b-th motor controller; u max and u min are the upper and lower limits of the solution; r1 is a random number between (0,1);

[0036] Initialize the harmony memory HM, randomly generate m harmonies in the search domain through the above formula, that is, the combination of the control signal of the motor and the motor controller, and add them to the harmony memory HM shown by the matrix:

[0037]

[0038] Among them, F[·] is the harmony measurement function, that is, the control signal combination effect measurement function;

[0039] Update the harmony vector u before the kth iteration ab (k) Update as follows:

[0040]

[0041] BW(k)=BW min +(BW max -BW min )×r7

[0042] Among them, r2, r3, r4, r5, r6 and r7 are all random numbers between (0,1); is a randomly generated harmony vector, i.e., a control signal combination; u new,ab (k) is the harmony vector after the k-th iteration update; BW(k) is the harmony bandwidth of the k-th iteration, that is, the control signal combination limit; BW max and BW min are the maximum and minimum values ​​of the harmonic bandwidth respectively; p1 is the probability of storing the harmonic memory bank, i.e., the probability of storing the control signal combination; p2 is the probability of fine-tuning the gene, i.e., the probability of fine-tuning the control signal combination;

[0043] The updated harmony vector u new,ab and the worst harmony vector u in the initial harmony memory HM worst The harmony metric function value is compared, if u new,ab The metric function value of (k) is less than u worst The metric function value is u new,ab (k) Replace u worst ;Otherwise, the harmony in HM remains unchanged;

[0044] Check whether the algorithm termination condition is met; if so, output the global optimal solution and the algorithm ends; otherwise, return to continue updating the harmony vector;

[0045] S3: Performing predictive diagnostic maintenance on the motor and the controller based on the neural network;

[0046] S4: Regularly test and verify the motor controller and motor integration structure.

[0047] Compared with the prior art, the present invention provides an aircraft electric propulsion system, which has the following beneficial effects:

[0048] The present invention provides a distributed electric power system layout for an electric aircraft, wherein the electric power system providing propulsion power uses a radial magnetic motor, and the electric power system providing lift uses an axial magnetic motor, and the total weight of the power system is reduced by optimally utilizing the advantages of different forms of motors. The motor adopts a solution of integrating a motor controller and a motor, which can reduce the length of the wire, thereby further reducing the total weight of the power system. The integrated design of the motor controller and the motor further improves the integration of the aircraft and the optimization of the internal structure of the aircraft, improves the endurance performance and reliability of the aircraft, and provides strong support for applications in the field of aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the system architecture mentioned in Embodiment 1 of the present invention;

[0050] Figure 2 A schematic diagram showing a cross-sectional comparison between the radial flux motor and the axial flux motor mentioned in Embodiment 1 of the present invention;

[0051] Figure 3 This is a schematic diagram of data comparison between the radial flux motor and the axial flux motor mentioned in Example 1 of the present invention;

[0052] Figure 4 This is a structural schematic diagram of the heat dissipation fin mentioned in Embodiment 1 of the present invention;

[0053] Figure 5 This is a schematic diagram of the structure of the second heat sink fin mentioned in Embodiment 1 of the present invention;

[0054] Figure 6 This is a schematic diagram of the third structure of the heat dissipation fin mentioned in Embodiment 1 of the present invention;

[0055] Figure 7 This is a schematic diagram showing the variation of the cruising range with the weight of the aircraft mentioned in Embodiment 1 of the present invention;

[0056] Figure 8 This is a schematic diagram of the change in the flight time with the aircraft weight mentioned in Example 1 of the present invention.

[0057] Meaning of the symbols in the figure:

[0058] 1. DC battery; 2. Power distribution module; 3. Flight control system; 4. Lift propeller; 5. Propulsion propeller. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] The present invention provides a distributed electric power system layout for an electric aircraft. The electric power system that provides propulsion power uses a radial magnetic motor, and the electric power system that provides lift uses an axial magnetic motor. By optimally utilizing the advantages of motors of different forms, the total weight of the power system is reduced. The motor adopts a solution that integrates a motor controller and a motor, which can reduce the length of the wire, thereby further reducing the total weight of the power system. The integrated design of the motor controller and the motor further improves the integration of the aircraft and the optimization of the internal structure of the aircraft, improves the endurance performance and reliability of the aircraft, and provides strong support for applications in the aircraft field. Specifically, it includes the following contents.

[0061] Embodiment 1:

[0062] See also Figure 1-2 The present invention discloses an aircraft electric propulsion system, comprising: a DC battery 1, a power distribution module 2, a flight control system 3, a lifting propeller 4 arranged axially at four vertices in a rectangular shape, and a propulsion propeller 5 arranged radially at the top; the DC battery 1 is intelligently managed through a BMS system, and the output voltage is reduced through DC / DC conversion to provide power for the flight control system 3, and the flight control system 3 is used to control the operation of the lifting propeller 4 and the propulsion propeller 5; the DC battery 1 also provides working voltage for the four lifting propellers 4 and the propulsion propeller 5 through the power distribution module 2. The voltage range of the DC battery 1 is 600V to 800V, and the BMS system is used to intelligently manage and maintain each unit in the battery, prevent the battery from being overcharged and over-discharged, extend the battery life, and monitor the battery status.

[0063] The four lift propellers 4 all use an electrically controlled integrated shaft magnetic motor, and the propulsion propeller 5 uses an electrically controlled integrated radial magnetic motor; the magnetic flux direction of the electrically controlled integrated shaft magnetic motor is parallel to the motor rotation axis, and the magnetic flux direction of the electrically controlled integrated radial magnetic motor is distributed along the radial direction of the motor. The electrically controlled integrated shaft magnetic motor and the electrically controlled integrated radial magnetic motor both use an integrated motor controller and motor design, and the cooling method is preferably water cooling, or air cooling.

[0064] Existing electric aircraft, especially eVTOL, generally adopt a fixed-wing and multi-rotor composite configuration. Most power units use a distributed electric propulsion system, one part of which provides the pull or thrust required for fixed-wing mode cruise flight, and the other part provides the lift required for vertical take-off and landing flight. For this type of aircraft, fixed-wing mode cruise flight has a long operating time, generally ranging from 30 minutes to several hours. The power is relatively low and the torque is relatively small. Multi-rotor mode vertical take-off and landing flight has a short operating time, generally around 5 minutes. The power is relatively high and the torque is relatively large. For fixed-wing mode cruise flight, its operating time is long, and the main requirements for the power system motor are high durability, high reliability, and good thermal stability. For multi-rotor mode vertical take-off and landing flight, its operating time is short, and the main requirements for the power system motor are high peak power, high peak torque, high power-to-weight ratio, and high heat dissipation efficiency.

[0065] Reference Figure 2 In this embodiment, the motor is divided into a radial flux motor and an axial flux motor. The magnetic flux direction of the radial flux motor is distributed along the radial direction of the motor, and the magnetic flux direction of the axial flux motor is parallel to the motor rotation axis. The data of the radial flux motor and the axial flux motor are compared, and the comparison results can be referred to Figure 3 , comparing radial flux motors and axial flux motors. Radial flux motors have a century-long application history, and their design methods and production technologies are mature, with the characteristics of low cost and high reliability, suitable for long-term operation. Axial flux motors have a shorter and more direct flux path, which helps to maintain a strong magnetic field, which helps to improve the torque, power density and efficiency of the motor. The flux path of the axial flux motor is unidirectional, and crystal-oriented steel can be used to reduce iron losses and further improve efficiency. The windings of the axial flux motor can be in direct contact with the casing, and the casing can be made of materials with higher thermal conductivity, thereby improving the cooling efficiency of the motor.

[0066] When the electric aircraft electric propulsion system provided in this embodiment is applied to electric aircraft, especially the layout technical solution of the distributed electric power system of the eVTOL aircraft, the fixed-wing mode cruise flight power uses a radial flux motor, and the multi-rotor vertical take-off and landing mode power uses an axial flux motor. And the electronic control of the two motors is integrated inside the motor. The electronic control is integrated with the motor, thereby reducing the length of the wire and the cooling system, thereby reducing the weight of the electric power system.

[0067] The distributed electric power system layout technical solution for electric aircraft provided in this embodiment can optimally utilize the advantages of different forms of motors and reduce the total weight of the power system. In addition, the motor used in this embodiment is an integrated motor controller, which can reduce the length of the wire, thereby further reducing the total weight of the power system.

[0068] The integrated design of the motor controller and the motor uses carbon fiber composite materials to make an integrated shell. The use of carbon fiber composite materials, a lightweight structural parameter, can further reduce the weight of the entire system, improve the fuel efficiency of the aircraft or increase the cruising range.

[0069] It is also provided with heat dissipation channels and heat dissipation fins made of phase change materials or nano materials; the structures of the heat dissipation fins include wavy heat dissipation fins, honeycomb heat dissipation fins, spiral heat dissipation fins, hollow heat dissipation fins, multi-layer three-dimensional heat dissipation fins, curved three-dimensional heat dissipation fins, composite structure heat dissipation fins, deformable heat dissipation fins, adjustable heat dissipation fins, bionic heat dissipation fins, modular heat dissipation fins; it can also be prepared in combination with 3D printing technology. The structure of some heat dissipation fins can be referred to Figure 4-6 The shapes of the heat sink fins can include special-shaped structures, three-dimensional structures, composite structures and adjustable structures, etc., which are mainly used to improve heat dissipation efficiency, reduce volume and weight, enhance stability and reliability, and provide better heat dissipation solutions for the integrated design of motor controller and motor.

[0070] Electromagnetic shielding technology is also used to reduce the impact of electromagnetic interference on equipment and improve the electromagnetic compatibility of the system. Electromagnetic shielding technology can reduce the impact of electromagnetic interference on other electronic equipment and improve the safety and reliability of aircraft.

[0071] The integrated design of motor controller and motor also integrates sensors, power modules and energy storage systems, and integrates temperature sensors, current sensors, voltage sensors, etc. into the motor controller to achieve real-time monitoring of the motor and controller. These sensors can provide accurate operating parameters to help intelligent control systems achieve more precise control and predictive maintenance. Integrate power modules (such as IGBT, MOSFET, etc.) with motor controllers to reduce the size and weight of the system. At the same time, integrated power modules can improve the reliability and efficiency of the system and reduce costs. Energy storage systems (such as batteries, supercapacitors, etc.) are also integrated with motor controllers and motors to form a complete electric propulsion system. This can improve the energy density and cruising range of the system, while also simplifying the structure and installation of the system.

[0072] The integrated design of the motor controller and the motor also sets an automatic intelligent control method according to the running state of the motor and the external environmental conditions, and automatically adjusts the control parameters according to the real-time running state of the motor and the real-time external environmental conditions based on the automatic intelligent control method; the details are as follows:

[0073] S1: Real-time collection of the operating status and external environmental conditions of the motor controller and motor, and pre-processing and storage of data based on big data technology;

[0074] S2: Control simulation of the motor controller and motor integrated structure based on neural network, as well as optimization adjustment of layout and wiring;

[0075] The neural network takes the preprocessed motor controller and motor operating status and external environmental conditions as input to build a BiLSTM-FCN network. The BiLSTM-FCN network builds parallel improved BiLSTM networks and FCN networks respectively. Finally, the outputs of the improved BiLSTM network and FCN network are connected through the connection function, and the control results are output through the connection layer and the output layer.

[0076] The output of the improved BiLSTM network is as follows:

[0077]

[0078] in, and They are the output results of the forward layer at time t and time t-1 respectively; and They are the output results of the backward layer at time t and time t-1 respectively; and ωσ1 are the weight coefficients between the input layer and the forward layer and the backward layer respectively; is the weight coefficient between the forward propagation unit at time t-1 and the forward propagation unit at time t; is the weight coefficient between the backward propagation unit at time t-1 and the backward propagation unit at time t; and are the weight coefficients between the forward layer, the backward layer and the output layer respectively; f(·) is the internal calculation function of the neuron; g(·) is the function for calculating the total result of the forward layer and the backward layer; y t is the output result of the BiLSTM network at time t;

[0079] The attention mechanism is also integrated into each propagation unit of the improved BiLSTM network, as follows:

[0080] Atten t =αtanh(Wx t +B)

[0081]

[0082] Among them, Atten t is the value of the attention probability distribution at time t; α, W and b are model learning parameters; w t is the attention weight; τ is the dimension of the model input vector; h t is the output at time t;

[0083] The FCN network contains three time convolution blocks, each layer has 128, 256 and 128 filters respectively. Each convolution layer is implemented by the ReLU activation function, and then connected to a global pooling layer for output; the convolution kernel of the FCN network is a sliding window on the time series to extract the short-term features of the time series, which is updated as follows:

[0084]

[0085] Among them, z i and z j are the i-th and j-th feature maps respectively; σ is the activation function; ω ij and b are model learning parameters;

[0086] The BiLSTM-FCN network also learns model parameters based on the harmony search algorithm, as follows:

[0087] The control balance degree U={u1,u2,Λ,u M} as the optimization target, M is the total number of control signal combinations between the motor and the motor controller, and the relevant parameters are initialized;

[0088] u ab =u min +(u max -u min )×r1

[0089] Among them, u ab is the harmony vector of the initialization of the a-th motor signal and the control signal of the b-th motor controller, that is, the combination vector of the a-th motor signal and the control signal of the b-th motor controller; u max and u min are the upper and lower limits of the solution; r1 is a random number between (0,1);

[0090] Initialize the harmony memory HM, randomly generate m harmonies in the search domain through the above formula, that is, the combination of the control signal of the motor and the motor controller, and add them to the harmony memory HM shown by the matrix:

[0091]

[0092] Among them, F[·] is the harmony measurement function, that is, the control signal combination effect measurement function;

[0093] Update the harmony vector u before the kth iteration ab (k) Update as follows:

[0094]

[0095] BW(k)=BW min +(BWmax -BW min )×r7

[0096] Among them, r2, r3, r4, r5, r6 and r7 are all random numbers between (0,1); ur0 is a randomly generated harmony vector, that is, a control signal combination; u new,ab (k) is the harmony vector after the k-th iteration update; BW(k) is the harmony bandwidth of the k-th iteration, that is, the control signal combination limit; BW max and BW min are the maximum and minimum values ​​of the harmonic bandwidth respectively; p1 is the probability of storing the harmonic memory bank, i.e., the probability of storing the control signal combination; p2 is the probability of fine-tuning the gene, i.e., the probability of fine-tuning the control signal combination;

[0097] The updated harmony vector u new,ab and the worst harmony vector u in the initial harmony memory HM worst The harmony metric function value is compared, if u new,ab The metric function value of (k) is less than u worst The metric function value is u new,ab (k) Replace u worst ;Otherwise, the harmony in HM remains unchanged;

[0098] Check whether the algorithm termination condition is met. If so, output the global optimal solution and the algorithm ends; otherwise, return to continue updating the harmony vector;

[0099] S3: Predictive diagnostic maintenance of motors and controllers based on neural networks;

[0100] S4: Regularly test and verify the motor controller and motor integration structure.

[0101] Based on the above automatic intelligent control method to control the propulsion of the aircraft, the specific test results can be referred to Figure 7-8 In the figure, curve 1 shows the change of the cruising range of the aircraft controlled by the above-mentioned automatic intelligent control method, and curve 2 shows the change of the cruising range of the aircraft not controlled by the above-mentioned automatic intelligent control method. It can be seen from the figure that as the weight of the aircraft increases, its cruising range decreases, and the flight time also decreases. The strategy based on this embodiment can reduce the weight of the aircraft, and can effectively improve the cruising range and flight time of the aircraft. The aircraft optimized and controlled based on the above-mentioned automatic intelligent control method can further improve the cruising range and flight time of the aircraft, and improve the navigation efficiency of the aircraft.

[0102] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An aircraft electric propulsion system, characterized in that: include: A DC battery (1), a power distribution module (2), a flight control system (3), lift propellers (4) axially arranged at four vertices in a rectangular shape, and a propulsion propeller (5) radially arranged at the top; the DC battery (1) is intelligently managed through a BMS system, and also reduces the output voltage through DC / DC conversion to provide power for the flight control system (3), and the flight control system (3) is used to control the operation of the lift propellers (4) and the propulsion propeller (5); the DC battery (1) also provides working voltage for the four lift propellers (4) and the one propulsion propeller (5) through the power distribution module (2).

2. An aircraft electric propulsion system according to claim 1, characterized in that: The voltage range of the DC battery (1) is 600-800V.

3. The aircraft electric propulsion system according to claim 1, characterized in that: The four lift propellers (4) all adopt an electrically controlled integrated axial magnetic motor, and the propulsion propeller (5) adopts an electrically controlled integrated radial magnetic motor; the magnetic flux direction of the electrically controlled integrated axial magnetic motor is parallel to the motor rotation axis, and the magnetic flux direction of the electrically controlled integrated radial magnetic motor is distributed along the radial direction of the motor.

4. An aircraft electric propulsion system according to claim 3, characterized in that: The electrically controlled integrated axial magnetic motor and the electrically controlled integrated radial magnetic motor both adopt an integrated design of a motor controller and a motor, and are cooled by water cooling or air cooling.

5. An aircraft electric propulsion system according to claim 4, characterized in that: The motor controller and motor integrated design also integrates a wireless communication model to enable the system to communicate wirelessly with a ground monitoring center.

6. An aircraft electric propulsion system according to claim 5, characterized in that: The integrated design of the motor controller and the motor uses carbon fiber composite materials to make an integrated housing, and is also provided with a heat dissipation channel and heat dissipation fins made of phase change materials or nano materials; and electromagnetic shielding technology is also used to reduce the impact of electromagnetic interference on the equipment.

7. An aircraft electric propulsion system according to claim 6, characterized in that: The structure of the heat sink fins includes wavy heat sink fins, honeycomb heat sink fins, spiral heat sink fins, hollow heat sink fins, multi-layer three-dimensional heat sink fins, curved three-dimensional heat sink fins, composite structure heat sink fins, deformable heat sink fins, adjustable heat sink fins, bionic heat sink fins, and modular heat sink fins; the heat sink fins are also prepared in combination with 3D printing technology.

8. An aircraft electric propulsion system according to claim 7, characterized in that: The motor controller and motor integrated design also integrates sensors, power modules and energy storage systems.

9. An aircraft electric propulsion system according to claim 8, characterized in that: The motor controller and motor integrated design scheme also sets an automatic intelligent control method according to the running state of the motor and the external environmental conditions, and the automatic intelligent control method automatically adjusts the control parameters according to the real-time running state of the motor and the real-time external environmental conditions; The automatic intelligent control method is specifically as follows: S1: Real-time collection of the operating status and external environmental conditions of the motor controller and motor, and pre-processing and storage of data based on big data technology; S2: Control simulation of the motor controller and motor integrated structure based on neural network, as well as optimization adjustment of layout and wiring; The neural network takes the preprocessed running state of the motor controller and the motor and the external environmental conditions as input to build a BiLSTM-FCN network. The BiLSTM-FCN network respectively builds a parallel improved BiLSTM network and FCN network, and finally connects the outputs of the improved BiLSTM network and the FCN network through a connection function, and then outputs the control result through a connection layer and an output layer; The output of the improved BiLSTM network is as follows: in, and They are the output results of the forward layer at time t and time t-1 respectively; and They are the output results of the backward layer at time t and time t-1 respectively; and are the weight coefficients between the input layer and the forward layer and the backward layer respectively; is the weight coefficient between the forward propagation unit at time t-1 and the forward propagation unit at time t; is the weight coefficient between the backward propagation unit at time t-1 and the backward propagation unit at time t; and are the weight coefficients between the forward layer, the backward layer and the output layer respectively; f(·) is the internal calculation function of the neuron; g(·) is the function for calculating the total result of the forward layer and the backward layer; y t is the output result of the BiLSTM network at time t; The attention mechanism is also integrated into each propagation unit of the improved BiLSTM network, as follows: Atten t =αtanh(Wx t +B) Among them, Atten t is the value of the attention probability distribution at time t; α, W and b are model learning parameters; w t is the attention weight; τ is the dimension of the model input vector; h t is the output at time t; The FCN network contains three time convolution blocks, each layer has 128, 256 and 128 filters respectively. Each convolution layer is implemented by the ReLU activation function, and then connected to a global pooling layer for output; the convolution kernel of the FCN network is a sliding window on the time series, which extracts the short-term features of the time series and is updated as follows: Among them, z i and z j are the i-th and j-th feature maps respectively; σ is the activation function; ω ij and b are model learning parameters; The BiLSTM-FCN network also learns model parameters based on the harmony search algorithm, as follows: The control balance degree U={u1,u2,Λ,u M } as the optimization target, M is the total number of control signal combinations between the motor and the motor controller, and the relevant parameters are initialized; in ab =in min +(in max -in min )×r1 Among them, u ab is the harmony vector of the initialization of the a-th motor signal and the control signal of the b-th motor controller, that is, the combination vector of the a-th motor signal and the control signal of the b-th motor controller; u max and u min are the upper and lower limits of the solution; r1 is a random number between (0,1); Initialize the harmony memory HM, randomly generate m harmonies in the search domain through the above formula, that is, the combination of the control signal of the motor and the motor controller, and add them to the harmony memory HM shown by the matrix: Among them, F[·] is the harmony measurement function, that is, the control signal combination effect measurement function; Update the harmony vector u before the kth iteration ab (k) Update as follows: BW(k)=BW min +(BW max -BW min )×r7 Among them, r2, r3, r4, r5, r6 and r7 are all random numbers between (0,1); is a randomly generated harmony vector, i.e., a control signal combination; u new,ab (k) is the harmony vector after the k-th iteration update; BW(k) is the harmony bandwidth of the k-th iteration, that is, the control signal combination limit; BW max and BW min are the maximum and minimum values ​​of the harmonic bandwidth respectively; p1 is the probability of storing the harmonic memory bank, i.e., the probability of storing the control signal combination; p2 is the probability of fine-tuning the gene, i.e., the probability of fine-tuning the control signal combination; The updated harmony vector u new,ab and the worst harmony vector u in the initial harmony memory HM worst The harmony metric function value is compared, if u new,ab The metric function value of (k) is less than u worst The metric function value is u new,ab (k) Replace u worst ;Otherwise, the harmony in HM remains unchanged; Check whether the algorithm termination condition is met; if so, output the global optimal solution and the algorithm ends; otherwise, return to continue updating the harmony vector; S3: Performing predictive diagnostic maintenance on the motor and the controller based on the neural network; S4: Regularly test and verify the motor controller and motor integration structure.

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