A Spatiotemporal Joint Rotational Speed Optimization Method for a Hybrid Propulsion System of a Flying Car

Abstract

The present invention discloses a spatiotemporal joint speed optimization method for a hybrid propulsion system of a flying car, comprising the following steps: constructing a spatial factor analysis method, first by collecting location information, slope, road conditions, air resistance data, and current speed through sensors and navigation systems; further performing a temporal factor analysis by collecting real-time travel time, weather forecast, and traffic condition data; after completing the separate analyses of spatial and temporal factors, designing a spatiotemporal joint genetic optimization algorithm; applying the optimization results to an actual hybrid propulsion system, adjusting the speed of the hybrid propulsion system in real time through an adaptive learning algorithm, and optimizing the prediction model and strategy, thereby further improving the long-term performance of the system. By introducing a spatiotemporal joint optimization strategy, the present invention aims to improve the overall performance of the system, achieve efficient and low-cost propulsion control, and overcome the performance bottlenecks caused by fixed time points and single-dimensional optimization in existing methods.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized control of a flying car power system, and in particular to a method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car. Background Art

[0002] With the widespread application of hybrid propulsion systems in aviation, maritime, and land transportation, and their increasingly crucial role in emerging fields such as flying cars and the low-altitude economy, achieving efficient propulsion system operation in a dynamic operating environment has become a pressing technical challenge for the industry. As a key vehicle for the low-altitude economy, the performance of flying cars is directly impacted by the effectiveness of their hybrid propulsion systems. Optimizing hybrid propulsion systems is crucial in the pursuit of efficient and environmentally friendly low-altitude travel. Existing methods for optimizing the speed of hybrid propulsion systems generally focus on spatial optimization, adjusting the speed based on the system's spatial position or operating conditions at a specific point in time. However, given the complex operating conditions of flying cars, such as frequent takeoffs and landings, and the switching between low-speed cruising and high-speed flight, as well as the volatile flight demands of the low-altitude economy, these methods often overlook the dynamic changes in the system's operating state over time. This leads to reduced propulsion system efficiency and increased fuel consumption during prolonged operation or frequent switching between operating conditions, thus limiting 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] Existing research on hybrid propulsion system speed optimization methods includes, for example, Chinese invention patent application number CN202410727933.4, entitled “A hybrid power control method and system for aircraft,” which proposes an energy management method based on flight altitude. This method optimizes the output power of the power system by controlling the fuel supply to improve overall energy efficiency. Chinese invention patent application number CN201910471374.4, entitled “A method for energy control of a hybrid propulsion system for unmanned aerial vehicles based on flight data,” proposes an energy control method based on flight history data in the spatial dimension. By classifying the flight phases, a prediction model for the energy required for each phase and a required power time series model are established to achieve optimal control of the instantaneous power output of the power system. 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 conditions to distribute power and improve propulsion efficiency. However, these methods often face the following two major problems in practical applications:

[0004] First, regarding the dynamic response of speed control, existing research typically optimizes the spatial state at a specific moment, lacking a pre-aiming mechanism or dynamic adjustment mechanism that considers the temporal dimension. This makes it impossible to address the variations in speed requirements caused by varying operating conditions during the long-term operation of a hybrid propulsion system, resulting in reduced overall system efficiency. Second, regarding improving overall system performance, existing methods often employ separate optimization strategies based on the spatial dimension, failing to develop a collaborative optimization mechanism that encompasses both spatial and temporal dimensions. This makes it difficult for the system to maintain high energy efficiency under complex operating conditions, and particularly under frequently changing operating conditions, where fuel consumption often increases significantly. Therefore, how to jointly optimize the speed of a flying car hybrid propulsion system in both spatial and temporal dimensions, and develop a spatiotemporal joint optimization control method that adapts to dynamic operating conditions to achieve efficient and low-energy operation of the flying car propulsion system, has become a key issue hindering the large-scale deployment of hybrid propulsion systems. The present invention aims to address this technical bottleneck by utilizing a spatiotemporal joint speed optimization method to improve the overall performance of the hybrid propulsion system and reduce fuel consumption. This method is applicable to the optimization control of various hybrid propulsion systems. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] In view of the problems existing in the above-mentioned existing methods for optimizing the spatiotemporal speed of hybrid propulsion systems of flying cars, the present invention is proposed.

[0007] Therefore, the purpose of the present invention is to provide a spatiotemporal joint speed optimization method for a flying car hybrid propulsion system. By introducing a spatiotemporal joint optimization strategy, the method 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.

[0008] To solve the above technical problems, the present invention provides the following technical solution: a method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car, comprising the following steps:

[0009] Step 1: Construct a spatial factor analysis method. First, the sensors and navigation system collect location information, slope, road conditions, air resistance data, and current speed. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain impact model are established to evaluate the impact of different spatial factors on the propulsion system efficiency and determine the optimal speed range under the current spatial conditions.

[0010] Step 2: Based on the model parameters of air resistance and terrain analysis in Step 1, further time factor analysis is performed. By collecting real-time driving time, weather forecast, and traffic condition data, 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. The optimal speed adjustment strategy for the system at different time points is determined to ensure the best performance of the system throughout the entire timeline.

[0011] Step 3: After completing the separate analysis of spatial and temporal factors, a spatiotemporal joint genetic optimization algorithm is designed to comprehensively consider spatial and temporal factors. A multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency is set, and the algorithm is iteratively solved until the objective function reaches the global optimal solution and the optimal speed value is output.

[0012] 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 an 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.

[0013] As a preferred solution of the method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car according to the present invention, step 1 specifically includes:

[0014] 11) The sensors and navigation system collect the vehicle or equipment’s location information, environmental data such as slope, road conditions, air resistance, and current speed parameters, and pre-process and filter these signals. The speed signal is smoothed using a low-pass filter: , where v raw is the current speed signal, τ is the time constant of the filter, and s is the Laplace operator;

[0015] 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;

[0016] By simulating and analyzing the impact of different spatial factors on the propulsion system efficiency, the optimal speed range ω under the current spatial conditions is finally 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 speed ω, slope θ and current speed v. onput is a function of the rotational speed ω.

[0017] As a preferred solution of the method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car according to the present invention, step 2 specifically includes:

[0018] 21) By collecting real-time dynamic data related to time, such as travel time t, weather forecast W(t), and traffic conditions T(t), and combining these data with the results of spatial factor analysis, a temporal dynamic model is constructed and the data is fused to generate more accurate input signals: , where a0, a1, a2 and a3 are constant coefficients;

[0019] 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-optimized 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 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;

[0020] 23) The time optimization controller and the spatial controller are designed in coordination, 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.

[0021] As a preferred solution of the method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car according to the present invention, step three specifically includes:

[0022] 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, and 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.

[0023] 32) Through the iterative process of genetic algorithm, the speed is continuously adjusted to evaluate the individual xi output by each generation of 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: .

[0024] As a preferred solution of the method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car according to the present invention, step 4 specifically includes:

[0025] 41) The results of the genetic optimization algorithm are applied to the actual hybrid propulsion system through a global control system. The current spatiotemporal conditions and system state x(t,θ) are continuously monitored, the output of the optimization controller is adjusted in real time, and a secondary optimization is performed based on the 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;

[0026] 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-energy 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.

[0027] As a preferred embodiment of the spatiotemporal combined speed optimization method for a hybrid propulsion system of a flying car according to the present invention, the spatiotemporal combined speed optimization method for a hybrid propulsion system of a flying car is based on a hybrid propulsion system comprising 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 spatiotemporal combined speed optimization controller, a real-time monitoring unit, and an environmental parameter collection unit.

[0028] 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 working 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 of 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, providing necessary feedback information to support dynamic adjustment of the speed optimization controller, thereby achieving efficient and low-consumption operation of the hybrid propulsion system.

[0029] As a preferred solution of the spatiotemporal combined speed optimization method for the hybrid propulsion system of a flying car according to the present invention, 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 a connecting shaft.

[0030] As a preferred solution of the spatiotemporal combined speed optimization method for the hybrid propulsion system of a flying car according to the present invention, the planetary gear power coupling module includes a sun gear, planetary gears, an inner ring gear, 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 via a clutch; the inner ring gear is connected to the rotor module via a transfer case; the planetary gear includes a sub-planetary gear, a sub-planetary gear, and a sub-planetary gear, which are connected to a drive shaft via a connecting rod device.

[0031] As a preferred embodiment of the spatiotemporal combined speed optimization method for the hybrid propulsion system of a flying car according to the present invention, the transfer case includes an input shaft, a gear transmission mechanism, a diverter 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 ring gear, and after the power is decelerated by the gear transmission mechanism, it is transmitted to the input end of the rotor module through the diverter mechanism. The control and adjustment mechanism is connected to the gear transmission mechanism and the diverter mechanism to regulate the power transmission and achieve appropriate power distribution. The structural support and sealing mechanism surrounds the outer edge of the transfer case, the input shaft area, and the support points inside the transfer case.

[0032] As a preferred embodiment of the spatiotemporal combined speed optimization method for the hybrid propulsion system of a flying car according to the present invention, the energy management module inputs include real-time operating condition information and system energy demand signals; and outputs fuel supply control signals for the turboshaft main propulsion module and torque scheduling signals 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.

[0033] The spatiotemporal combined speed optimization controller is used to optimize the speed of the turboshaft main propulsion module and the auxiliary electric propulsion module based on the system's operating status and external environmental conditions. Its input includes system status information from the real-time monitoring unit and the energy allocation strategy of the energy management module. Its output is a speed adjustment command, 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 an optimal balance between power output and fuel / electricity consumption.

[0034] 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.

[0035] Beneficial effects of the present invention:

[0036] 1. The present invention not only considers the impact of spatial factors on the efficiency of the hybrid propulsion system, and realizes the accurate prediction and control of the optimal speed of the system under different spatial conditions, but also realizes the dynamic optimization control of the system under the conditions of time-space coupling based on the dynamic time data collected in real time and combined with the results of spatial analysis.

[0037] 2. This invention comprehensively considers both temporal and spatial factors. By constructing a temporal dynamic model and a spatial influence model, and introducing a spatiotemporal joint genetic optimization algorithm, it is able to achieve multi-objective optimization control of the hybrid propulsion system under different temporal and spatial conditions. For example, the genetic algorithm can iteratively optimize the rotational speed under different spatiotemporal conditions. As environmental conditions change, the system can automatically adjust the optimal control strategy based on the changes in spatiotemporal factors, ensuring maximum efficiency and minimum fuel consumption in complex environments, significantly improving the overall system performance.

[0038] 3. The present invention adopts an adaptive learning algorithm and a global controller design in the real-time optimization control process. While monitoring the current spatiotemporal conditions and system status, it can perform real-time adjustments and secondary optimization 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

[0039] 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. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0040] Figure 1 Schematic diagram of the structure of the hybrid propulsion system of the present invention;

[0041] Figure 2This is a schematic diagram of the structure of the turboshaft main propulsion module;

[0042] Figure 3 This is a schematic diagram of the structure of the planetary gear power coupling module;

[0043] Figure 4 This is a schematic diagram of the connection status of the planetary gear power coupling module;

[0044] Figure 5 Schematic diagram of the transfer case structure;

[0045] Figure 6 The figure is a flow chart of the principle of the method of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. 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.

[0048] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0049] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0050] Reference Figures 1-6 , provides a method for optimizing the spatiotemporal speed of a hybrid propulsion system of a flying car, comprising the following steps:

[0051] Step 1: Construct a spatial factor analysis method. First, the sensors and navigation system collect location information, slope, road conditions, air resistance data, and current speed. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain impact model are established to evaluate the impact of different spatial factors on the propulsion system efficiency and determine the optimal speed range under the current spatial conditions.

[0052] Step 2: Based on the model parameters of air resistance and terrain analysis in Step 1, further time factor analysis is performed. By collecting real-time driving time, weather forecast, and traffic condition data, 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. The optimal speed adjustment strategy for the system at different time points is determined to ensure the best performance of the system throughout the entire timeline.

[0053] Step 3: After completing the separate analysis of spatial and temporal factors, a spatiotemporal joint genetic optimization algorithm is designed to comprehensively consider spatial and temporal factors. A multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency is set, and the algorithm is iteratively solved until the objective function reaches the global optimal solution and the optimal speed value is output.

[0054] 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 an 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.

[0055] Among them, step one specifically includes:

[0056] 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;

[0057] 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 influence 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 speed ω, slope θ and current speed v. onput is a function of the rotational speed ω.

[0058] Furthermore, step 2 specifically includes:

[0059] 21) By collecting real-time dynamic data related to time, such as travel time t, weather forecast W(t), and traffic conditions T(t), and combining these data with the results of spatial factor analysis, a temporal dynamic model is constructed and the data is fused to generate more accurate input signals: , where a0, a1, a2 and a3 are constant coefficients;

[0060] 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-optimized 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 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;

[0061] 23) The time optimization controller and the spatial controller are designed in coordination, 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.

[0062] Step three specifically includes:

[0063] 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, and 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.

[0064] 32) Through the iterative process of genetic algorithm, the speed is continuously adjusted to evaluate the individual xi output by each generation of 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: .

[0065] Furthermore, step four specifically includes:

[0066] 41) The results of the genetic optimization algorithm are applied to the actual hybrid propulsion system through a global control system. The current spatiotemporal conditions and system state x(t,θ) are continuously monitored, the output of the optimization controller is adjusted in real time, and a secondary optimization is performed based on the 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;

[0067] 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-energy 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.

[0068] The method for optimizing the speed of a hybrid propulsion system of a flying car in time and space 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 controller for optimizing the speed of a hybrid propulsion system of a flying car in time and space, a real-time monitoring unit 9, and an environmental parameter collection unit 10.

[0069] 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 of 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.

[0070] As a preferred solution of the spatiotemporal combined speed optimization method of the hybrid propulsion system of a 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 deliver 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.

[0071] 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.

[0072] 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.

[0073] The energy management module 7 inputs include real-time operating condition information and system energy demand signals; its outputs are fuel supply control signals for the turboshaft main propulsion module 1 and torque scheduling 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.

[0074] 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 based on the system's operating status and external environmental conditions. Its input includes system status 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 command, 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.

[0075] The real-time monitoring unit 9 is used to continuously monitor the operating status of the propulsion system. 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 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.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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 by: The following steps are involved: Step 1: Construct a spatial factor analysis method. First, the sensors and navigation system collect location information, slope, road conditions, air resistance data, and current speed. Then, based on the location information, slope, road conditions, air resistance, and current speed parameters, an air resistance model and a terrain impact model are established to evaluate the impact of different spatial factors on the propulsion system efficiency and determine 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 real-time driving time, weather forecast, and traffic condition data, 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. The optimal speed adjustment strategy for the system at different time points is determined to ensure the best performance of the system throughout the entire timeline. Step 3: After completing the separate analysis of spatial and temporal factors, a spatiotemporal joint genetic optimization algorithm is designed to comprehensively consider spatial and temporal factors. A multi-objective optimization function with the goals of minimizing fuel consumption and maximizing propulsion efficiency is set, and the algorithm is iteratively solved until the objective function reaches the global optimal solution and the optimal speed value is output. 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 time dynamic rolling optimization model and the time-space joint speed optimization strategy are optimized.

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) The location information of the vehicle or equipment, environmental data such as slope, road conditions, air resistance and current speed parameters are collected through sensors and navigation systems, and these signals are pre-processed and filtered, and the speed signal is smoothed using a low-pass filter: Among them, 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: Where ρ is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the relative speed; By simulating and analyzing the impact of different spatial factors on the propulsion system efficiency, the optimal speed range ω under the current spatial conditions is finally 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 speed ω, slope θ and current speed v. onput is a function of the rotational 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 second step specifically includes: 21) By collecting real-time dynamic data related to 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: F(t)=a0+a1t+a2W(t)+a3T(t) Among them, 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: Among them, t0 is the starting time, t f is the end time, input power P input and 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 over the entire time axis; 23) The time optimization controller and the spatial controller are designed in coordination, and the control matrix K(t,θ) is used to ensure the smooth operation of the system under different time and space conditions: u(t)=K(t,θ)·x(t) Among them, 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 up a multi-objective optimization function and define the optimization objectives: f(x)=[f1(x),f2(x),...,f m (x)] Among them, 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. The current spatiotemporal conditions and system state x(t,θ) are continuously monitored, the output of the optimization controller is adjusted in real time, and a secondary optimization is performed based on the new data input. The control law is expressed as: u(t)=K(ω opt ,t,θ)·x(t) Among them, 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 time dynamic model F(t) and the time-space joint speed optimization strategy, and the adaptive control module dynamically adjusts to sudden environmental changes by updating the control gain K(t) in real time: K(t)=K0+ΔK(t) 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 spatiotemporal speed of a hybrid propulsion system of a flying car 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 spatiotemporal 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, providing 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 send 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 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), planetary gears (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 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); 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 diversion mechanism (1203); the control and regulation mechanism (1205) is connected to the gear transmission mechanism (1202) and the diversion mechanism (1203) to regulate the transmission of power and achieve 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 as well as 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 scheduling 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 speeds 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. 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 parameter 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 filtered parameter, which is 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 operating parameters in time to prevent faults from occurring and maintain efficient and stable operation.

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