Space-time joint rotating speed optimization method for hybrid propulsion system of hovercar

Abstract

The invention discloses an aerocar hybrid propulsion system space-time joint rotating speed optimization method, which comprises the following steps of: constructing a space factor analysis method: firstly, acquiring position information, gradient, road conditions, air resistance data and current speed through a sensor and a navigation system; time factor analysis is further carried out, and driving time, weather forecast and traffic condition data are collected in real time; after independent analysis of space and time factors is completed, a space-time joint genetic optimization algorithm is designed; an optimization result is applied to an actual hybrid propulsion system, the rotating speed of the hybrid propulsion system is adjusted in real time through an adaptive learning algorithm, a prediction model and a strategy are optimized, and therefore the long-term performance of the system is further improved. According to the method, a space-time joint optimization strategy is introduced, the overall performance of the system is improved, efficient and low-consumption propulsion control is achieved, and the performance bottleneck caused by fixed time point and single-dimension optimization in an existing method is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization control of a flying car power system, and in particular to a space-time combined speed optimization method for a flying car hybrid propulsion system. Background Art

[0002] With the widespread application of hybrid propulsion systems in aviation, navigation and land transportation, especially in emerging fields such as flying cars and low-altitude economy, how to achieve efficient operation of propulsion systems in a changing working environment has become a technical problem that the industry urgently needs to solve. As an important carrier of low-altitude economy, the performance of flying cars is directly affected by the efficiency of hybrid propulsion systems. In the pursuit of efficient and environmentally friendly low-altitude travel methods, it is particularly important to optimize the hybrid propulsion system. Existing hybrid propulsion system speed optimization methods generally focus on the optimization of the spatial dimension, that is, the speed is adjusted according to the spatial position or working condition of the system at a specific time point. However, under the complex working conditions such as frequent take-off and landing of flying cars, low-speed cruising and high-speed flight, and the changing flight requirements in the low-altitude economy, this method often ignores the dynamic changes of the system operation state in the time dimension, resulting in problems such as reduced efficiency and increased fuel consumption of the propulsion system when running for a long time or frequently switching working conditions, thereby restricting the practicality of flying cars and the development potential of the low-altitude economy. Therefore, in view of the characteristics of flying cars and low-altitude economy, exploring the speed optimization method of hybrid propulsion system that integrates time dimension and space dimension is of great significance for improving the overall efficiency of the system and promoting the sustainable development of low-altitude economy.

[0003] In the existing research on the speed optimization method of hybrid propulsion system, for example, the Chinese invention patent application number CN202410727933.4, entitled "A hybrid power control method and system for aircraft", proposes an energy management method based on flight altitude, which optimizes the output power of the power system by controlling the fuel supply to improve the overall energy efficiency; the Chinese invention patent application number CN201910471374.4, entitled "A method for energy control of hybrid propulsion system of unmanned aerial vehicle based on flight data", proposes an energy control method based on flight history data in spatial dimension, by classifying the flight phases, establishing the energy prediction model and the required power time series model for each phase, and realizing the optimal control of the instantaneous power output of the power system; the Chinese invention patent number CN202310030161.4, entitled "A hybrid propulsion system for underwater vehicles and its working method", uses radar to detect spatial position and environmental status for power distribution to improve propulsion efficiency. However, these methods often face the following two major problems in practical applications: First, in terms of the dynamic response of speed control, existing research usually optimizes the spatial state at a specific moment, lacks a pre-aiming mechanism and a dynamic adjustment mechanism that considers the time dimension, and cannot cope with the speed demand differences caused by changes in working conditions during the long-term operation of the hybrid propulsion system, which leads to a decrease in the overall efficiency of the system; secondly, in terms of improving the overall performance of the system, the existing methods mostly adopt a separate optimization strategy based on the spatial dimension, and fail to form a collaborative optimization mechanism in the spatial and temporal dimensions. This makes it difficult for the system to maintain a high energy efficiency under complex working conditions, especially under frequently changing working conditions, the fuel consumption of the system tends to increase significantly. Therefore, how to jointly optimize the speed of the hybrid propulsion system of a flying car in the two dimensions of space and time, and develop a spatiotemporal joint optimization control method that adapts to dynamic working conditions to achieve efficient and low-consumption operation of the flying car propulsion system, has become a key issue that restricts the large-scale promotion and application of hybrid propulsion systems. The present invention aims to solve this technical bottleneck, improve the overall performance of the hybrid propulsion system, reduce fuel consumption, and is suitable for the optimization control of various hybrid propulsion systems through a spatiotemporal joint speed optimization method. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing space-time joint speed optimization method of the hybrid propulsion system of the flying car, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a space-time joint speed optimization method for a flying car hybrid propulsion system, which aims to improve the overall performance of the system, achieve efficient and low-consumption propulsion control, and overcome the performance bottleneck caused by fixed time points and single-dimensional optimization in existing methods by introducing a space-time joint optimization strategy.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing the time-space combined speed of a hybrid propulsion system of a flying car, comprising the following steps: Step 1: Construct a spatial factor analysis method. First, the location information, slope, road conditions, air resistance data, and current speed are collected by sensors and navigation systems. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain influence model are established to evaluate the impact of different spatial factors on the efficiency of the propulsion system and derive the optimal speed range under the current spatial conditions. Step 2: Based on the model parameters of air resistance and terrain analysis in step 1, further time factor analysis is performed. By collecting driving time, weather forecast, and traffic condition data in real time, a time dynamic rolling optimization model is constructed to predict the impact of environmental changes on the propulsion system speed in the future time period, and the optimal speed adjustment strategy of the system at different time points is obtained to ensure the best performance of the system on the entire timeline; Step 3: After completing the separate analysis of space and time factors, design a spatiotemporal joint genetic optimization algorithm to comprehensively consider space and time factors, set a multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency, and perform iterative solutions until the objective function reaches the global optimal solution and outputs the optimal speed value; Step 4: Based on the optimization results in step 3, the optimization results are applied to the actual hybrid propulsion system. The speed of the hybrid propulsion system is adjusted in real time through the adaptive learning algorithm, and the prediction model and strategy are optimized to further improve the long-term performance of the system and ultimately achieve efficient and low-consumption operation of the hybrid propulsion system.

[0008] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car of the present invention, the step 1 specifically includes: 11) Collect the location information of the vehicle or equipment, environmental data such as slope, road conditions, air resistance and current speed parameters through sensors and navigation systems, pre-process and filter these signals, and use low-pass filters to smooth the speed signal: , where v raw is the current speed signal, τ is the time constant of the filter, and s is the Laplace operator; 12) Based on the collected pre-processed data, establish an air resistance model: ,in, is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the relative speed; Through simulation and analysis, the impact of different spatial factors on the efficiency of the propulsion system is evaluated, and finally the optimal speed range ω under the current spatial conditions is obtained by solving the optimization problem. opt , and design controllers that adapt to different space conditions: , where the input power P input The input power P is a function of the rotation speed ω, slope θ and current speed v. onput is a function of the rotation speed ω.

[0009] As a preferred solution of the method for optimizing the time-space joint speed of the hybrid propulsion system of the flying car of the present invention, the step 2 specifically includes: 21) By collecting time-related dynamic data in real time, such as travel time t, weather forecast W(t), and traffic conditions T(t), these data are combined with the results of spatial factor analysis to build a time dynamic model and perform data fusion processing to generate more accurate input signals: , where a 0 、a 1 、a 2 and a 3 is a constant coefficient; 22) Using the time dynamic model and the fused data, predict the impact of environmental changes on the propulsion system speed in the future time period. Based on the prediction results, design a time optimization controller with the goal of optimizing the following performance indicators: , where t 0 is the starting time, t f is the end time, input power P input And the output power P onput are all functions of time t; by solving the optimal control problem, the optimal speed adjustment strategy ω(t) of the system at different time points is obtained to ensure the best performance of the system on the entire time axis; 23) The time optimization controller is designed in coordination with the space controller, and the control matrix K(t,θ) is used to ensure the smooth operation of the system under different time and space conditions: , where u(t) is the control input and x(t) is the state vector.

[0010] As a preferred solution of the method for optimizing the time-space joint speed of the hybrid propulsion system of the flying car of the present invention, the step three specifically includes: 31) Set a multi-objective optimization function (such as minimizing fuel consumption, maximizing propulsion efficiency, etc.) and define the optimization objectives: , where f i (x) is the individual objective function, x is the optimization variable; and based on the coordinated output of the spatial controller and the temporal optimization controller, the genetic algorithm parameters population size N, crossover probability Pc, and mutation probability Pm are selected and initialized, and iterative solutions are performed under different spatiotemporal conditions; 32) Through the iterative process of the genetic algorithm, the speed is continuously adjusted to evaluate the individual xi output by each generation of the genetic algorithm. (t) The fitness F(x i (t) ), until the multi-objective optimization function reaches the global optimal solution, and finally outputs the optimal speed value ω opt , and design a global controller to execute this optimal solution: .

[0011] As a preferred solution of the method for optimizing the time-space joint speed of the hybrid propulsion system of the flying car of the present invention, the step 4 specifically includes: 41) The results of the genetic optimization algorithm are applied to the actual hybrid propulsion system through a global control system, which continuously monitors the current spatiotemporal conditions and system state x(t,θ), adjusts the output of the optimization controller in real time, and performs secondary optimization based on new data input. The control law is expressed as: , where K(ω opt， t,θ) is the control matrix, x(t) is the state vector; 42) The system continuously accumulates data during operation through an adaptive learning algorithm, optimizes the prediction model F^(t) and control strategy to further improve the long-term performance of the system and achieve efficient and low-consumption operation of the hybrid propulsion system. The adaptive control module dynamically adjusts to sudden environmental changes by updating the control gain K(t) in real time: , where K 0 is the initial control matrix, ΔK(t) is the increment of the control matrix.

[0012] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car of the present invention, the space-time joint speed optimization method of the hybrid propulsion system of the flying car is based on a hybrid propulsion system, and the hybrid propulsion system includes: a turboshaft main propulsion module, an auxiliary electric propulsion module, a planetary gear power coupling module, a rotor module, a drive axle, wheels, an energy management module, a space-time joint speed optimization controller, a real-time monitoring unit and an environmental parameter collection unit; The turboshaft main propulsion module and the auxiliary electric propulsion module are used to provide hybrid propulsion functions. The two are connected in parallel through a planetary gear power coupling module to jointly drive the propulsion system; the energy management module is responsible for allocating and adjusting energy supply according to real-time operating conditions to ensure efficient operation of the system; the spatiotemporal joint speed optimization controller combines space optimization and time optimization strategies to perform spatiotemporal joint optimization control on the speed of the propulsion system; the real-time monitoring unit is used to receive data from the environmental parameter collection unit, and continuously monitor the operating status of the system, and provide necessary feedback information to support dynamic adjustment of the speed optimization controller, thereby achieving efficient and low-consumption operation of the hybrid propulsion system.

[0013] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car described in the present invention, wherein: the turboshaft main propulsion module includes an air intake device, a compressor, a combustion chamber, a gas generator turbine, and a power turbine; the air intake device is connected to the compressor to provide compressed air; the compressor is connected to the combustion chamber to deliver the compressed air into the combustion chamber; the combustion chamber is connected to the gas generator turbine, and the generated gas drives the gas generator turbine to rotate; the gas generator turbine is connected to the power turbine, and drives the power turbine to rotate through the connecting shaft.

[0014] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car described in the present invention, the planetary gear power coupling module includes a sun gear, a planetary gear, an inner gear ring and a planetary carrier; the planetary carrier is connected to a power turbine output shaft; the auxiliary electric propulsion module is connected to the sun gear through a clutch; the inner gear ring is connected to the rotor module through a transfer case; the planetary gear includes a sub-planetary gear, a sub-planetary gear and a sub-planetary gear, which is connected to a drive shaft through a connecting rod device.

[0015] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car described in the present invention, the transfer case includes an input shaft, a gear transmission mechanism, a shunt mechanism, a propeller connection mechanism, a control and adjustment mechanism, and a structural support and sealing mechanism; the input shaft is connected to the inner gear ring, and the power is decelerated by the gear transmission mechanism and then transmitted to the input end of the rotor module through the shunt mechanism; the control and adjustment mechanism is connected to the gear transmission mechanism and the shunt mechanism to adjust the transmission of power and realize the appropriate distribution of power; the structural support and sealing mechanism surrounds the outer edge of the transfer case and the input shaft area and the support points inside the transfer case.

[0016] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car of the present invention, the energy management module input includes real-time working condition information and system energy demand signal; the output is a fuel supply control signal for the turboshaft main propulsion module and a torque scheduling signal for the auxiliary electric propulsion module. By dynamically adjusting the energy distribution ratio, the energy management module ensures the overall energy efficiency and endurance of the system;

[0017] The spatiotemporal combined speed optimization controller is used to optimize the speed of the turboshaft main propulsion module and the auxiliary electric propulsion module according to the operating state of the system and the external environmental conditions. Its input includes the system state information from the real-time monitoring unit and the energy allocation strategy of the energy management module; its output is a speed adjustment instruction, which is sent to the speed control system of the turboshaft main propulsion module and the motor control system of the auxiliary electric propulsion module respectively; through the adjustment of the spatiotemporal combined speed optimization controller, the system can achieve the best balance between power output and fuel / power consumption;

[0018] The real-time monitoring unit is used to continuously monitor the operating status of the propulsion system, and its input includes combustion chamber temperature, compressor pressure, turboshaft main propulsion module speed, auxiliary electric propulsion module bus current parameters, and external environmental information, wherein the external environmental information is provided by the environmental parameter collection unit; its output is the filtered parameters, which are sent to the energy management module and the time-space joint speed optimization controller; through the feedback of the real-time monitoring unit, the system can adjust the working parameters in time to prevent faults and maintain efficient and stable operation.

[0019] Beneficial effects of the present invention: 1. The present invention not only takes into account the influence of spatial factors on the efficiency of the hybrid propulsion system, but also realizes the accurate prediction and control of the optimal speed of the system under different spatial conditions. At the same time, based on the real-time collected dynamic time data and combined with the spatial analysis results, it realizes the dynamic optimization control of the system under the conditions of time-space coupling.

[0020] 2. The present invention comprehensively considers the two major factors of time and space. By constructing a time dynamic model and a space influence model, and introducing a time-space joint genetic optimization algorithm, it can achieve multi-objective optimization control of the hybrid propulsion system under different time and space conditions, such as iterative optimization of the rotation speed under different time and space conditions through a genetic algorithm. When environmental conditions change, the system can automatically adjust the optimal control strategy according to the changes in time and space factors, ensuring that the efficiency of the propulsion system is maximized and fuel consumption is minimized in a complex environment, significantly improving the overall performance of the system.

[0021] 3. In the real-time optimization control process, the present invention adopts an adaptive learning algorithm and a global controller design. While monitoring the current spatiotemporal conditions and system status, it can perform real-time adjustments and secondary optimizations based on new data inputs. Even in the event of partial failures or sudden changes in the environment, the system can still achieve active fault-tolerant control, maintain the efficient operation of the hybrid propulsion system, and further improve the long-term stability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 It is a schematic structural diagram of the hybrid propulsion system of the present invention; Figure 2 This is a schematic diagram of the structure of the turboshaft main propulsion module; Figure 3It is a schematic diagram of the structure of the planetary gear power coupling module; Figure 4 This is a schematic diagram of the connection status of the planetary gear power coupling module; Figure 5 Schematic diagram of the transfer case structure; Figure 6 The figure is a flow chart of the principle of the method of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0026] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0027] Reference Figure 1-Figure 6 , provides a method for optimizing the time-space joint speed of a hybrid propulsion system of a flying car, comprising the following steps: Step 1: Construct a spatial factor analysis method. First, the location information, slope, road conditions, air resistance data, and current speed are collected by sensors and navigation systems. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain influence model are established to evaluate the impact of different spatial factors on the efficiency of the propulsion system and derive the optimal speed range under the current spatial conditions. Step 2: Based on the model parameters of air resistance and terrain analysis in step 1, further time factor analysis is performed. By collecting driving time, weather forecast, and traffic condition data in real time, a time dynamic rolling optimization model is constructed to predict the impact of environmental changes on the propulsion system speed in the future time period, and the optimal speed adjustment strategy of the system at different time points is obtained to ensure the best performance of the system on the entire timeline; Step 3: After completing the separate analysis of space and time factors, design a spatiotemporal joint genetic optimization algorithm to comprehensively consider space and time factors, set a multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency, and perform iterative solutions until the objective function reaches the global optimal solution and outputs the optimal speed value; Step 4: Based on the optimization results in step 3, the optimization results are applied to the actual hybrid propulsion system. The speed of the hybrid propulsion system is adjusted in real time through the adaptive learning algorithm, and the prediction model and strategy are optimized to further improve the long-term performance of the system and ultimately achieve efficient and low-consumption operation of the hybrid propulsion system.

[0028] Among them, step one specifically includes: 11) Collect vehicle or equipment location information and environmental data such as slope through sensors and navigation systems , road conditions, air resistance and current speed parameters, and pre-process and filter these signals, and use a low-pass filter to smooth the speed signal: , where v raw is the current speed signal, τ is the time constant of the filter, and s is the Laplace operator; 12) Based on the collected pre-processed data, establish an air resistance model: ,in, is the air density, C d is the air resistance coefficient, A is the windward area, and v is the relative speed. The impact of different spatial factors on the efficiency of the propulsion system is evaluated through simulation and analysis, and finally the optimal speed range ω under the current spatial conditions is obtained by solving the optimization problem. opt , and design controllers that adapt to different space conditions: , where the input power P input The input power P is a function of the rotation speed ω, slope θ and current speed v. onput is a function of the rotation speed ω.

[0029] Furthermore, step 2 specifically includes: 21) By collecting time-related dynamic data in real time, such as travel time t, weather forecast W(t), and traffic conditions T(t), these data are combined with the results of spatial factor analysis to build a time dynamic model and perform data fusion processing to generate more accurate input signals: , where a 0 、a 1 、a 2 and a 3 is a constant coefficient; 22) Using the time dynamic model and the fused data, predict the impact of environmental changes on the propulsion system speed in the future time period. Based on the prediction results, design a time optimization controller with the goal of optimizing the following performance indicators: , where t 0 is the starting time, t f is the end time, input power P input And the output power P onput are all functions of time t; by solving the optimal control problem, the optimal speed adjustment strategy ω(t) of the system at different time points is obtained to ensure the best performance of the system on the entire time axis; 23) The time optimization controller is designed in coordination with the space controller, and the control matrix K(t,θ) is used to ensure the smooth operation of the system under different time and space conditions: , where u(t) is the control input and x(t) is the state vector.

[0030] Among them, step three specifically includes: 31) Set a multi-objective optimization function (such as minimizing fuel consumption, maximizing propulsion efficiency, etc.) and define the optimization objectives: , where f i (x) is the individual objective function, x is the optimization variable; and based on the coordinated output of the spatial controller and the temporal optimization controller, the genetic algorithm parameters population size N, crossover probability Pc, and mutation probability Pm are selected and initialized, and iterative solutions are performed under different spatiotemporal conditions; 32) Through the iterative process of the genetic algorithm, the speed is continuously adjusted to evaluate the individual xi output by each generation of the genetic algorithm. (t) The fitness F(x i (t) ), until the multi-objective optimization function reaches the global optimal solution, and finally outputs the optimal speed value ω opt , and design a global controller to execute this optimal solution: .

[0031] Furthermore, step 4 specifically includes: 41) The results of the genetic optimization algorithm are applied to the actual hybrid propulsion system through a global control system, which continuously monitors the current spatiotemporal conditions and system state x(t,θ), adjusts the output of the optimization controller in real time, and performs secondary optimization based on new data input. The control law is expressed as: , where K(ω opt，t,θ) is the control matrix, x(t) is the state vector; 42) The system continuously accumulates data during operation through an adaptive learning algorithm, optimizes the prediction model F^(t) and control strategy to further improve the long-term performance of the system and achieve efficient and low-consumption operation of the hybrid propulsion system. The adaptive control module dynamically adjusts to sudden environmental changes by updating the control gain K(t) in real time: , where K 0 is the initial control matrix, ΔK(t) is the increment of the control matrix.

[0032] The space-time joint speed optimization method of the hybrid propulsion system of the flying car is based on the hybrid propulsion system, which includes: a turboshaft main propulsion module 1, an auxiliary electric propulsion module 2, a planetary gear power coupling module 3, a rotor module 4, a drive axle 5, wheels 6, an energy management module 7, a space-time joint speed optimization controller 8, a real-time monitoring unit 9 and an environmental parameter collection unit 10; The turboshaft main propulsion module 1 and the auxiliary electric propulsion module 2 are used to provide hybrid propulsion function. The two are connected in parallel through the planetary gear power coupling module 3 to jointly drive the propulsion system; the energy management module 7 is responsible for allocating and adjusting the energy supply according to the real-time working conditions to ensure the efficient operation of the system; the spatiotemporal joint speed optimization controller 8 combines space optimization and time optimization strategies to perform spatiotemporal joint optimization control on the speed of the propulsion system; the real-time monitoring unit 9 is used to receive data from the environmental parameter collection unit 10, and continuously monitor the operating status of the system, and provide necessary feedback information to support the dynamic adjustment of the speed optimization controller, thereby realizing efficient and low-consumption operation of the hybrid propulsion system.

[0033] As a preferred solution of the space-time joint speed optimization method of the hybrid propulsion system of the flying car of the present invention, the turboshaft main propulsion module 1 includes an air intake device 101, a compressor 102, a combustion chamber 103, a gas generator turbine 104, and a power turbine 105; the air intake device 101 is connected to the compressor 102 to provide compressed air; the compressor 102 is connected to the combustion chamber 103 to send the compressed air into the combustion chamber 103; the combustion chamber 103 is connected to the gas generator turbine 104, and the generated gas drives the gas generator turbine 104 to rotate; the gas generator turbine 104 is connected to the power turbine 105, and drives the power turbine 105 to rotate through the connecting shaft 106.

[0034] Specifically, the planetary gear power coupling module 3 includes a sun gear 301, a planetary gear 302, an inner ring gear 303 and a planetary carrier 304; the planetary carrier 304 is connected to the power turbine output shaft 107; the auxiliary electric propulsion module 2 is connected to the sun gear 301 through a clutch 11; the inner ring gear 303 is connected to the rotor module 4 through a transfer case 12; the planetary gear 302 includes a sub-planetary gear 3021, a sub-planetary gear 3022 and a sub-planetary gear 3023, which is connected to the drive shaft 14 through a connecting rod device 13.

[0035] Furthermore, the transfer case 12 includes an input shaft 1201, a gear transmission mechanism 1202, a diverter mechanism 1203, a propeller connection mechanism 1204, a control and adjustment mechanism 1205, and a structural support and sealing mechanism 1206; the input shaft 1201 is connected to the inner ring gear 303, and after the power is decelerated by the gear transmission mechanism 1202, the power is transmitted to the input end of the rotor module 4 through the diverter mechanism 1203; the control and adjustment mechanism 1205 is connected to the gear transmission mechanism 1202 and the diverter mechanism 1203 to adjust the transmission of power and achieve appropriate power distribution; the structural support and sealing mechanism 1206 surrounds the outer edge of the transfer case 12 and the input shaft 1201 area and the support points inside the transfer case 12.

[0036] The energy management module 7 inputs real-time operating condition information and system energy demand signals; the outputs are fuel supply control signals for the turboshaft main propulsion module 1 and torque dispatch signals for the auxiliary electric propulsion module 2. By dynamically adjusting the energy distribution ratio, the energy management module 7 ensures the overall energy efficiency and endurance of the system. The spatiotemporal combined speed optimization controller 8 is used to optimize the speed of the turboshaft main propulsion module 1 and the auxiliary electric propulsion module 2 according to the operating state of the system and the external environmental conditions. Its input includes the system state information from the real-time monitoring unit 9 and the energy allocation strategy of the energy management module 7; its output is a speed adjustment instruction, which is sent to the speed control system of the turboshaft main propulsion module 1 and the motor control system of the auxiliary electric propulsion module 2 respectively; through the adjustment of the spatiotemporal combined speed optimization controller 8, the system can achieve the best balance between power output and fuel / power consumption; The real-time monitoring unit 9 is used to continuously monitor the operating status of the propulsion system, and its input includes the temperature of the combustion chamber 103, the pressure of the compressor 102, the speed of the turboshaft main propulsion module 1, the bus current parameters of the auxiliary electric propulsion module 2, and the external environmental information, wherein the external environmental information is provided by the environmental parameter collection unit 10; its output is the filtered parameters, which are sent to the energy management module 7 and the time-space joint speed optimization controller 8; through the feedback of the real-time monitoring unit 9, the system can adjust the working parameters in time to prevent faults and maintain efficient and stable operation.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the speed of a hybrid propulsion system of a flying car in time and space, characterized in that: The following steps are involved: Step 1: Construct a spatial factor analysis method. First, the location information, slope, road conditions, air resistance data, and current speed are collected by sensors and navigation systems. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain influence model are established to evaluate the impact of different spatial factors on the efficiency of the propulsion system and derive the optimal speed range under the current spatial conditions. Step 2: Based on the model parameters of air resistance and terrain analysis in step 1, further time factor analysis is performed. By collecting driving time, weather forecast, and traffic condition data in real time, a time dynamic rolling optimization model is constructed to predict the impact of environmental changes on the propulsion system speed in the future time period, and the optimal speed adjustment strategy of the system at different time points is obtained to ensure the best performance of the system on the entire timeline; Step 3: After completing the separate analysis of space and time factors, design a spatiotemporal joint genetic optimization algorithm to comprehensively consider space and time factors, set a multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency, and perform iterative solutions until the objective function reaches the global optimal solution and outputs the optimal speed value; Step 4: Based on the optimization results in step 3, the optimization results are applied to the actual hybrid propulsion system. The speed of the hybrid propulsion system is adjusted in real time through the adaptive learning algorithm, and the prediction model and strategy are optimized to further improve the long-term performance of the system and ultimately achieve efficient and low-consumption operation of the hybrid propulsion system.

2. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 1, characterized in that: The step 1 specifically includes: 11) Collect the location information of the vehicle or equipment, environmental data such as slope, road conditions, air resistance and current speed parameters through sensors and navigation systems, pre-process and filter these signals, and use low-pass filters to smooth the speed signal: , where v raw is the current speed signal, τ is the time constant of the filter, and s is the Laplace operator; 12) Based on the collected pre-processed data, establish an air resistance model: ,in, is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the relative speed; Through simulation and analysis, the impact of different spatial factors on the efficiency of the propulsion system is evaluated, and finally the optimal speed range ω under the current spatial conditions is obtained by solving the optimization problem. opt , and design controllers that adapt to different space conditions: , where the input power P input The input power P is a function of the rotation speed ω, slope θ and current speed v. onput is a function of the rotation speed ω.

3. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 1, characterized in that: The step 2 specifically includes: 21) By collecting time-related dynamic data in real time, such as travel time t, weather forecast W(t), and traffic conditions T(t), these data are combined with the results of spatial factor analysis to build a time dynamic model and perform data fusion processing to generate more accurate input signals: , where a0, a1, a2 and a3 are constant coefficients; 22) Using the time dynamic model and the fused data, predict the impact of environmental changes on the propulsion system speed in the future time period. Based on the prediction results, design a time optimization controller with the goal of optimizing the following performance indicators: , where t0 is the start time, t f is the end time, input power P input And the output power P onput All are functions of time t; By solving the optimal control problem, the optimal speed adjustment strategy ω(t) of the system at different time points is obtained to ensure the best performance of the system on the entire time axis; 23) The time optimization controller is designed in coordination with the space controller, and the control matrix K(t,θ) is used to ensure the smooth operation of the system under different time and space conditions: , where u(t) is the control input and x(t) is the state vector.

4. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 1, characterized in that: The step three specifically includes: 31) Set a multi-objective optimization function (such as minimizing fuel consumption and maximizing propulsion efficiency) and define the optimization objectives: , where f i (x) is the individual objective function, x is the optimization variable; Based on the coordinated output of the spatial controller and the temporal optimization controller, the genetic algorithm parameters population size N, crossover probability Pc, and mutation probability Pm are selected and initialized, and iterative solutions are performed under different spatiotemporal conditions. 32) Through the iterative process of the genetic algorithm, the speed is continuously adjusted to evaluate the individual xi output by each generation of the genetic algorithm. (t) The fitness F(x i (t) ), until the multi-objective optimization function reaches the global optimal solution, and finally outputs the optimal speed value ω opt , and design a global controller to execute this optimal solution: .

5. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 1, characterized in that: The step 4 specifically includes: 41) The results of the genetic optimization algorithm are applied to the actual hybrid propulsion system through a global control system, which continuously monitors the current spatiotemporal conditions and system state x(t,θ), adjusts the output of the optimization controller in real time, and performs secondary optimization based on new data input. The control law is expressed as: , where K(ω opt， t,θ) is the control matrix, x(t) is the state vector; 42) The system continuously accumulates data during operation through an adaptive learning algorithm, optimizes the prediction model F^(t) and control strategy to further improve the long-term performance of the system and achieve efficient and low-consumption operation of the hybrid propulsion system. The adaptive control module dynamically adjusts to sudden environmental changes by updating the control gain K(t) in real time: , where K0 is the initial control matrix and ΔK(t) is the increment of the control matrix.

6. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 1, characterized in that: The method for optimizing the speed of a hybrid propulsion system of a flying car in a time-space joint manner is based on a hybrid propulsion system, wherein the hybrid propulsion system comprises: a turboshaft main propulsion module (1), an auxiliary electric propulsion module (2), a planetary gear power coupling module (3), a rotor module (4), a drive axle (5), wheels (6), an energy management module (7), a time-space joint speed optimization controller (8), a real-time monitoring unit (9), and an environmental parameter collection unit (10); The turboshaft main propulsion module (1) and the auxiliary electric propulsion module (2) are used to provide a hybrid propulsion function, and the two are connected in parallel through a planetary gear power coupling module (3) to jointly drive the propulsion system; the energy management module (7) is responsible for allocating and adjusting energy supply according to real-time working conditions to ensure efficient operation of the system; the spatiotemporal joint speed optimization controller (8) combines space optimization and time optimization strategies to perform spatiotemporal joint optimization control on the speed of the propulsion system; the real-time monitoring unit (9) is used to receive data from the environmental parameter collection unit (10), and continuously monitor the operating status of the system, and provide necessary feedback information to support dynamic adjustment of the speed optimization controller, thereby achieving efficient and low-consumption operation of the hybrid propulsion system.

7. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 6, characterized in that: The turboshaft main propulsion module (1) comprises an air intake device (101), a compressor (102), a combustion chamber (103), a gas generator turbine (104), and a power turbine (105); the air intake device (101) is connected to the compressor (102) to provide compressed air; the compressor (102) is connected to the combustion chamber (103) to deliver compressed air into the combustion chamber (103); the combustion chamber (103) is connected to the gas generator turbine (104) to generate gas to drive the gas generator turbine (104) to rotate; the gas generator turbine (104) is connected to the power turbine (105) to drive the power turbine (105) to rotate via a connecting shaft (106).

8. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 6, characterized in that: The planetary gear power coupling module (3) comprises a sun gear (301), a planetary gear (302), an inner gear ring (303) and a planet carrier (304); the planet carrier (304) is connected to a power turbine output shaft (107); the auxiliary electric propulsion module (2) is connected to the sun gear (301) via a clutch (11); the inner gear ring (303) is connected to the rotor module (4) via a transfer case (12); the planetary gear (302) comprises a sub-planetary gear (3021), a sub-planetary gear (3022) and a sub-planetary gear (3023), which is connected to a drive shaft (14) via a connecting rod device (13).

9. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 8, characterized in that: The transfer case (12) comprises an input shaft (1201), a gear transmission mechanism (1202), a flow diversion mechanism (1203), a propeller connection mechanism (1204), a control and regulation mechanism (1205), and a structural support and sealing mechanism (1206); the input shaft (1201) is connected to the inner gear ring (303), and after the power is decelerated by the gear transmission mechanism (1202), the power is transmitted to the input end of the rotor module (4) through the flow diversion mechanism (1203); the control and regulation mechanism (1205) is connected to the gear transmission mechanism (1202) and the flow diversion mechanism (1203), and adjusts the transmission of power and realizes appropriate distribution of power; the structural support and sealing mechanism (1206) surrounds the outer edge of the transfer case (12) and the input shaft (1201) area and the support points inside the transfer case (12).

10. The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space according to claim 7, characterized in that: The energy management module (7) inputs include real-time operating condition information and system energy demand signals; the outputs are fuel supply control signals for the turboshaft main propulsion module (1) and torque dispatch signals for the auxiliary electric propulsion module (2); by dynamically adjusting the energy distribution ratio, the energy management module (7) ensures the overall energy efficiency and endurance of the system; The spatiotemporal combined speed optimization controller (8) is used to optimize the speed of the turboshaft main propulsion module (1) and the auxiliary electric propulsion module (2) according to the operating state of the system and external environmental conditions, and its input includes system state information from the real-time monitoring unit (9) and the energy allocation strategy of the energy management module (7); its output is a speed adjustment instruction, which is sent to the speed control system of the turboshaft main propulsion module (1) and the motor control system of the auxiliary electric propulsion module (2) respectively; through the adjustment of the spatiotemporal combined speed optimization controller (8), the system can achieve an optimal balance between power output and fuel / electricity consumption; The real-time monitoring unit (9) is used to continuously monitor the operating status of the propulsion system, and its input includes the temperature of the combustion chamber (103), the pressure of the compressor (102), the speed of the turboshaft main propulsion module (1), the bus current parameters of the auxiliary electric propulsion module (2), and external environmental information, wherein the external environmental information is provided by the environmental parameter collection unit (10); its output is the parameters after filtering, which are sent to the energy management module (7) and the time-space joint speed optimization controller (8); through the feedback of the real-time monitoring unit (9), the system can adjust the working parameters in time to prevent faults and maintain efficient and stable operation.

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