Aviation aircraft hybrid power system high-precision safety modeling method and system based on multiple levels
By using multi-level modeling and limit state functions, the problems of insufficient modeling accuracy of hybrid-electric aircraft systems and reliance on experience in traditional safety analysis are solved, thus achieving high-precision system safety analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-10
AI Technical Summary
In existing technologies, the modeling accuracy of hybrid-electric aircraft systems is insufficient, and traditional safety analysis methods rely on experience and cannot effectively handle complex coupled failure problems, resulting in inaccurate system safety analysis.
A multi-level modeling approach is adopted to construct system functional level, component level and equation-based operation model. The physical mechanism and data interaction of each component are described mathematically to establish a high-precision hybrid power system model, and the safety analysis is quantified by limit state function.
It achieves high-precision safety modeling of hybrid power systems, can objectively quantify the safety of complex systems, avoids the limitations of traditional methods, and provides comprehensive safety analysis.
Smart Images
Figure CN121637835A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aircraft technology, and more specifically to a high-precision safety modeling method and system for multi-level aircraft hybrid power systems. Background Technology
[0002] With the iteration of new energy technologies globally, the aviation industry is gradually advancing its electrification transformation. However, current battery energy density limits the long-endurance flight requirements of pure electric aircraft. Against this backdrop, hybrid electric aircraft have become an important transitional solution. Hybrid electric aircraft combine the advantages of high fuel energy density and high electric power utilization efficiency, effectively balancing range and energy efficiency. Compared to traditional fuel-powered aircraft, hybrid electric aircraft have significantly increased system complexity. Specifically, in addition to retaining the traditional fuel engine, hybrid electric aircraft add key components such as electric motors and batteries. Furthermore, the internal coupling relationships and energy flow of the electric propulsion system are tight, directly leading to a significant increase in system safety risks. Therefore, simulation and testing of a single subsystem alone cannot ensure the overall performance and safe and reliable operation of the aircraft; research on the overall safety of the complex hybrid electric system is essential.
[0003] Currently, hybrid power system modeling still faces the problem of insufficient modeling accuracy. Taking the modeling of the internal combustion engine in a hybrid system as an example, the current common modeling method is based on numerical tables. This method itself has low modeling accuracy, and the hybrid power system architecture has the requirement to match the new engine configuration. Numerical table-based modeling cannot accurately represent the characteristics and power output patterns of the new engine configuration, nor can it dynamically reflect the impact of internal engine faults, ultimately limiting the accuracy of system analysis. Therefore, it is necessary to further refine the model and perform multi-level, high-precision modeling of the hybrid system.
[0004] Furthermore, directly applying traditional system safety analysis methods (such as fault tree analysis, failure mode and effects analysis, and common cause analysis) to the safety analysis of hybrid power systems still faces significant challenges and limitations. Specifically, traditional safety analysis methods have two major limitations: first, they are highly subjective and experience-dependent, with the analysis process heavily reliant on the personal experience of safety analysts, making it difficult to guarantee the completeness, consistency, and accuracy of the analysis results, and easily leading to biases due to human factors; second, they cannot address complex coupled failure problems. The various components of a hybrid power system have complex matching relationships, which can easily lead to the problem of mutually coupled failure modes. Traditional top-down tree or tabular analysis cannot effectively incorporate the impact of such coupling relationships, nor can it quantify the system's safety status.
[0005] Therefore, proposing a high-precision safety modeling method and system for multi-level aircraft hybrid power systems to address the difficulties in existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] In view of this, the present invention provides a high-precision safety modeling method and system for multi-level aircraft hybrid power systems to solve the technical problems existing in the prior art.
[0007] To achieve the above objectives, the present invention provides the following technical solution: A high-precision safety modeling method for multi-level hybrid power systems of aircraft includes the following steps: S1. Construct a multi-level modeling framework for hybrid-powered aircraft, and divide the modeling levels. The multi-level modeling framework includes a system function level model, a high-precision component level model, and an equation-based operation model. S2. For the components of the hybrid power system, clarify the physical mechanism of each component, construct a high-precision component-level model through mathematical description, and form an equation-based operation model; S3. Based on the established high-precision component-level model, the collaborative interaction between component-level models is realized by identifying the data interaction mechanism between each component model. Combined with the power control system model, a hybrid power system model is established. The actual working process of the system is described by equations, forming a system functional-level model. S4. Perform simulation calculations on the hybrid power system model to obtain the variation law of the hybrid power system model performance parameters with engine operation, and verify whether the hybrid power system model has passed the test verification. If not, proceed to S5; if yes, proceed to S6. S5. Based on the experimental verification results, correct the hybrid power system model parameters and return to S2; S6. Based on the rated boundary of the verified hybrid power system model, select safety-affecting attribute parameters, construct the time-varying limit state function of the system, form the whole machine safety model, and realize the quantitative judgment of the system safety state.
[0008] Optionally, the components in S2 include: engine components, motor components, and battery pack components.
[0009] Optionally, for engine components, modeling can be carried out from the thermodynamic, hydrodynamic, and mechanical dynamics domains, following the law of conservation of energy; Aerodynamic modeling is performed using the ideal gas equation, as shown in the following formula: (1) in, For pressure, For volume, For fluid mass, The gas constant is For temperature; Fluid mass and energy are calculated using the continuity equation and energy dynamics equation, as shown in the following formulas: (2) (3) in, For gas density, For the specific internal energy of the gas, and These are the mass flow rates at the inlet section and the outlet section, respectively. and These are the import enthalpy and the export enthalpy, respectively. and These are fuel mass flow rate and lower heating value, respectively. For heat flow, For mechanical power, t For time; The formula for determining the flow characteristics of the intake and exhaust valves is as follows: This method utilizes a single-zone actual circulation approach to evaluate in-cylinder phenomena. (4) Under critical flow conditions, the following equation is used: (5) in, and These are the pressure and temperature at the intake valve, respectively. This refers to the exhaust manifold pressure. For valve opening area, Specific heat ratio, For flow coefficient, The gas constant is For fluid mass, For time; The displacement of the piston from top dead center is determined based on the crankshaft angle to achieve dynamic coupling between mechanical dynamics and thermodynamics. The formula is as follows: (6) in, Where is the crank radius. The length of the link. This represents the displacement of the piston from top dead center. This refers to the crankshaft rotation angle; The combustion model uses a three-parameter Wiebe function to apply the combustion rate, and the heat transfer model uses the Woschni correlation. After the simulation converges, the effective power of the engine is extracted.
[0010] Optionally, for motor components, including the generator and drive motor, the least squares method is used to fit the bench test or experimental data to obtain the generator efficiency model, as shown in the following formula: (7) in, For generator power generation efficiency; and These are the generator's rotational speed and output power, respectively. The efficiency of the drive motor model under both generating and discharging states was obtained by least squares fitting with respect to the motor speed. and output power The function is given by the following formula: (8) (9) in, The power generation efficiency of the drive motor model. The efficiency of the drive motor model under discharge conditions; The formula for calculating the battery current value during battery discharge or charging based on efficiency is as follows: (10) (11) in, and These are the total motor torque and the torque output from the motor to the shaft, respectively. This refers to the real-time speed of the motor. The moment of inertia of the motor. and These are the battery's output voltage and battery current, respectively. When the motor is in working condition, the electric motor is working. , For motor efficiency.
[0011] Optionally, for battery pack components, an equivalent circuit based on the internal resistance model is used to construct the battery model, as shown in the following formula: (12) (13) (14) in, This refers to the battery's output power. and These are the battery's output voltage and battery current, respectively. and These are the battery's terminal voltage and internal resistance, respectively. For battery initial value; This refers to the battery capacity.
[0012] Optionally, the hybrid power system model in S3 includes: an engine-generator model, a power battery model, a drive motor model, and a computational ensemble module; the engine and generator are mechanically coupled, and both rotate at the same speed; the power battery model is based on the generator power... and drive motor power Calculate the current state It achieves energy interaction with the drive motor through an inverter.
[0013] Optionally, in S5, the safety-affecting attribute parameters are power and energy.
[0014] Optionally, the time-varying limit state function expression for the system in S5 is: (15) (16) (17) in, For the safety boundary of the limit state function, For the system output of the hybrid-electric aircraft system analysis model, when When the working boundary exceeds the critical value for failure, it is considered an unsafe state. Let be the time-varying limit state function of the system. This is the power limit state function. Let be the energy limit state function. and These are the power and energy required to overcome flight drag during the takeoff phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the takeoff phase, respectively. and This refers to the power and energy generated by the speed changes during the takeoff phase of flight. and These are the engine and battery power figures that vary with flight time during the takeoff phase. and These are the engine and battery energy levels that change over time during the takeoff phase; and These are the power and energy required to overcome flight drag during the cruise phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the cruise phase, respectively. and These represent the power and energy of speed changes during the cruise phase of flight. and These are the engine and battery power variations over operating time during the cruise phase. and These are the engine and battery energy values that change over time during the cruise phase; and These are the power and energy required to overcome flight drag during the descent phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the descent phase, respectively. and These represent the power and energy of the speed change during the descent phase of flight. and These are the engine and battery power figures as a function of time during the landing phase. and These represent the engine and battery energy levels as the landing phase changes over time.
[0015] A high-precision safety modeling system for multi-level aircraft hybrid power systems, executing any of the aforementioned high-precision safety modeling methods for multi-level aircraft hybrid power systems, includes a construction framework module, a model construction module, a collaborative interaction module, a simulation calculation module, an experimental verification module, and a quantitative judgment module connected sequentially; wherein, Framework module: Construct a multi-level modeling framework for hybrid-powered aircraft, divide the modeling levels, and the multi-level modeling framework includes a system function level model, a high-precision component level model, and an equation-based operation model. Model building module: For the components of the hybrid power system, the physical mechanism of each component is clarified, and a high-precision component-level model is built through mathematical description to form an equation-based operating model; Collaborative Interaction Module: Based on the established high-precision component-level model, the collaborative interaction between component-level models is realized by identifying the data interaction mechanism between each component model. Combined with the power control system model, a hybrid power system model is established. The actual working process of the system is described by equations, forming a system functional-level model. Simulation Calculation Module: The hybrid power system model is simulated to obtain the variation law of the hybrid power system model's performance parameters with engine operation, and to verify whether the hybrid power system model has passed the experimental verification. If not, it enters the experimental verification module; if yes, it enters the quantitative judgment module. Experimental verification module: Based on the experimental verification results, correct the parameters of the hybrid power system model and return to the model building module; Quantitative Judgment Module: Based on the rated boundary of the verified hybrid power system model, safety-affecting attribute parameters are selected, a time-varying limit state function of the system is constructed, a whole-machine safety model is formed, and the quantitative judgment of the system safety state is realized.
[0016] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a high-precision safety modeling method and system for multi-level aircraft hybrid power systems, the beneficial effects of which are: It can construct multi-level, high-precision hybrid electric propulsion system architectures using formal mathematical formulas, and by establishing limit state functions, it can objectively and quantitatively characterize the safety of complex hybrid power systems from a system-level perspective. This effectively avoids the limitations of traditional experience-based safety analysis methods when facing complex coupled problems, and ultimately achieves a more comprehensive safety analysis of complex systems. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0018] Figure 1 A flowchart illustrating a high-precision safety modeling method for multi-level hybrid power systems of aircraft provided by this invention; Figure 2 A schematic diagram of the hybrid power system architecture for a series electric propulsion aircraft provided by the present invention; Figure 3 This is a typical mission profile of a vertical takeoff and landing aircraft of a certain tonnage provided by the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] See Figure 1 As shown, this invention discloses a high-precision safety modeling method for multi-level aircraft hybrid power systems, comprising the following steps: S1. Construct a multi-level modeling framework for hybrid-powered aircraft, and divide the modeling levels. The multi-level modeling framework includes a system function level model, a high-precision component level model, and an equation-based operation model. S2. For the components of the hybrid power system, clarify the physical mechanism of each component, construct a high-precision component-level model through mathematical description, and form an equation-based operation model; S3. Based on the established high-precision component-level model, the collaborative interaction between component-level models is realized by identifying the data interaction mechanism between each component model. Combined with the power control system model, a hybrid power system model is established. The actual working process of the system is described by equations, forming a system functional-level model. S4. Perform simulation calculations on the hybrid power system model to obtain the variation law of the hybrid power system model performance parameters with engine operation, and verify whether the hybrid power system model has passed the test verification. If not, proceed to S5; if yes, proceed to S6. S5. Based on the experimental verification results, correct the hybrid power system model parameters and return to S2; S6. Based on the rated boundary of the verified hybrid power system model, select safety-affecting attribute parameters, construct the time-varying limit state function of the system, form the whole machine safety model, and realize the quantitative judgment of the system safety state.
[0021] Specifically, considering the architectural characteristics of hybrid propulsion systems, a high-precision model of the hybrid propulsion system is constructed. This includes building a system functional level model, a component-level high-precision physical model, and an equation-based operational model. In addition, a whole-machine safety analysis model is constructed by combining safety analysis concepts, thus achieving high-precision safety modeling of the hybrid propulsion system based on a multi-level model.
[0022] Furthermore, the components in S2 include: engine components, motor components, and battery pack components.
[0023] Furthermore, for engine components, modeling is carried out from the thermodynamic, hydrodynamic, and mechanical dynamic domains, following the law of conservation of energy; Specifically, a high-precision modeling method is used to form an equation-based operating model.
[0024] Aerodynamic modeling is performed using the ideal gas equation, as shown in the following formula: (1) in, For pressure, For volume, For fluid mass, The gas constant is For temperature; Fluid mass and energy are calculated using the continuity equation and energy dynamics equation, as shown in the following formulas: (2) (3) in, For gas density, For the specific internal energy of the gas, and These are the mass flow rates at the inlet section and the outlet section, respectively. and These are the import enthalpy and the export enthalpy, respectively. and These are fuel mass flow rate and lower heating value, respectively. For heat flow, For mechanical power, t For time; The formula for determining the flow characteristics of the intake and exhaust valves is as follows: This method utilizes a single-zone actual circulation approach to evaluate in-cylinder phenomena. (4) Under critical flow conditions, the following equation is used: (5) in, and These are the pressure and temperature at the intake valve, respectively. This refers to the exhaust manifold pressure. For valve opening area, Specific heat ratio, For flow coefficient, The gas constant is For fluid mass, For time; The displacement of the piston from top dead center is determined based on the crankshaft angle to achieve dynamic coupling between mechanical dynamics and thermodynamics. The formula is as follows: (6) in, Where is the crank radius. The length of the link. This represents the displacement of the piston from top dead center. This refers to the crankshaft rotation angle; Specifically, according to The value can be used to calculate the volume in equations (2) and (3). The air compression process is determined by equation (3) for each crankshaft angle step change.
[0025] The combustion model uses a three-parameter Wiebe function to apply the combustion rate, and the heat transfer model uses the Woschni correlation. After the simulation converges, the effective power of the engine is extracted.
[0026] Furthermore, for the motor components, including the generator and the drive motor, the least squares method is used to fit the bench test or experimental data to obtain the generator efficiency model, as shown in the following formula: (7) in, For generator power generation efficiency; and These are the generator's rotational speed and output power, respectively. Specifically, due to the mechanical coupling between the engine and generator in a series hybrid system, the generator rotates at the same speed as the engine.
[0027] Similar to generators, the dynamic effects of drive motors are ignored.
[0028] The efficiency of the drive motor model under both generating and discharging states was obtained by least squares fitting with respect to the motor speed. and output power The function is given by the following formula: (8) (9) in, The power generation efficiency of the drive motor model. The efficiency of the drive motor model under discharge conditions; The formula for calculating the battery current value during battery discharge or charging based on efficiency is as follows: (10) (11) in, and These are the total motor torque and the torque output from the motor to the shaft, respectively. This refers to the real-time speed of the motor. The moment of inertia of the motor. and These are the battery's output voltage and battery current, respectively. When the motor is in working condition, the electric motor is working. , For motor efficiency.
[0029] Furthermore, for battery pack components, an equivalent circuit based on the internal resistance model is used to construct the battery model, as shown in the following formula: (12) (13) (14) in, This refers to the battery's output power. and These are the battery's output voltage and battery current, respectively. and These are the battery's terminal voltage and internal resistance, respectively. For battery initial value; This refers to the battery capacity.
[0030] Specifically, for batteries, since the charging and discharging process involves very complex electrochemical reactions, and its electrochemical characteristics are a nonlinear time-varying function involving multiple variables, the effects of temperature and discharge rate on the battery's internal resistance and terminal voltage are ignored, and an equivalent circuit based on the internal resistance model is adopted.
[0031] Furthermore, the hybrid power system model in S3 includes: an engine-generator model, a power battery model, a drive motor model, and a computational assembly module; the engine and generator are mechanically coupled, and both rotate at the same speed; the power battery model is based on the generator power... and drive motor power Calculate the current state It achieves energy interaction with the drive motor through an inverter.
[0032] Specifically, taking a series electric propulsion hybrid aircraft system as an example, the modeling of each sub-module is shown in the attached figure. Figure 2 As shown, it includes: an engine-generator set model, a power battery model, a drive motor model, and a computational assembly module. Each sub-model is a corresponding input / output encapsulation. Specifically, for the engine and generator, due to the mechanical coupling between the engine and generator, the engine speed... With generator speed Similarly, generator efficiency is calculated using a model, which in turn determines the generator's output power, torque, and speed at the current moment. The battery model uses generator power... and drive motor The input is used to calculate the current state. It is connected to the drive motor via an inverter, thereby providing energy to the motor when energy is insufficient. The generator can charge the battery when the engine has remaining energy. The calculation assembly module includes the specific calculation process inside each module, and further establishes and completes the system functional level model, which is the basis for subsequent whole machine safety analysis.
[0033] For the actual flight process of a hybrid-powered aircraft, taking the power demand profile of a certain tonnage vertical takeoff and landing aircraft as the design input, its mission profile is decomposed and initially defined as the takeoff and climb phase, cruise phase, and descent and landing phase. A typical mission profile is shown in the attached figure. Figure 3 As shown.
[0034] According to the law of conservation of energy, the power and energy required by an electric propulsion hybrid-electric aircraft in each mission segment are respectively determined by the power required to overcome flight drag. and energy Power required to overcome gravity and climb to a designated altitude and energy Power of speed change during flight and energy It consists of three parts, and the functional expressions for power and energy are as follows: (18) in, Power required for the aircraft The aircraft requires energy; in, Must meet: (19) in, For total thrust, For the speed of the aircraft, As resistance, For flight altitude, For the aircraft's gravity, Power required for the aircraft It is the acceleration due to gravity. For time.
[0035] The input power and energy of a hybrid power system consist of two parts: one is the conversion of internal energy into mechanical energy by the engine, and the other is the contribution from the battery, defined as follows: (20)
[0037] in, Input power to the hybrid power system, To provide energy to the hybrid power system.
[0038] Furthermore, in S5, the safety-affecting attribute parameters are power and energy.
[0039] Furthermore, the time-varying limit state function expression for the system in S5 is: (15) (16) (17) in, For the safety boundary of the limit state function, For the system output of the hybrid-electric aircraft system analysis model, when When the working boundary exceeds the critical value for failure, it is considered an unsafe state. Let be the time-varying limit state function of the system. This is the power limit state function. Let be the energy limit state function. and These are the power and energy required to overcome flight drag during the takeoff phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the takeoff phase, respectively. and This refers to the power and energy generated by the speed changes during the takeoff phase of flight. and These are the engine and battery power figures that vary with flight time during the takeoff phase. and These are the engine and battery energy levels that change over time during the takeoff phase; and These are the power and energy required to overcome flight drag during the cruise phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the cruise phase, respectively. and These represent the power and energy of speed changes during the cruise phase of flight. and These are the engine and battery power variations over operating time during the cruise phase. and These are the engine and battery energy values that change over time during the cruise phase; and These are the power and energy required to overcome flight drag during the descent phase, respectively. and These are the power and energy required to overcome gravity and climb to a designated altitude during the descent phase, respectively. and These represent the power and energy of the speed change during the descent phase of flight. and These are the engine and battery power figures as a function of time during the landing phase. and These represent the engine and battery energy levels as the landing phase changes over time.
[0040] Specifically, the safety status of the hybrid-powered aircraft is quantitatively assessed by calculating the limit state function of the hybrid system as it changes over time at each mission phase. Based on the safety analysis results, a quantitative basis can be provided for predicting changes in the safety of the hybrid system and proposing targeted maintenance recommendations.
[0041] and Figure 1 Corresponding to the method described above, the present invention also provides a high-precision safety modeling system for multi-level aircraft hybrid power systems, used for... Figure 1The specific implementation of the method includes, in sequence, a framework construction module, a model construction module, a collaborative interaction module, a simulation calculation module, an experimental verification module, and a quantitative judgment module; among them, Framework module: Construct a multi-level modeling framework for hybrid-powered aircraft, divide the modeling levels, and the multi-level modeling framework includes a system function level model, a high-precision component level model, and an equation-based operation model. Model building module: For the components of the hybrid power system, the physical mechanism of each component is clarified, and a high-precision component-level model is built through mathematical description to form an equation-based operating model; Collaborative Interaction Module: Based on the established high-precision component-level model, the collaborative interaction between component-level models is realized by identifying the data interaction mechanism between each component model. Combined with the power control system model, a hybrid power system model is established. The actual working process of the system is described by equations, forming a system functional-level model. Simulation Calculation Module: The hybrid power system model is simulated to obtain the variation law of the hybrid power system model's performance parameters with engine operation, and to verify whether the hybrid power system model has passed the experimental verification. If not, it enters the experimental verification module; if yes, it enters the quantitative judgment module. Experimental verification module: Based on the experimental verification results, correct the parameters of the hybrid power system model and return to the model building module; Quantitative Judgment Module: Based on the rated boundary of the verified hybrid power system model, safety-affecting attribute parameters are selected, a time-varying limit state function of the system is constructed, a whole-machine safety model is formed, and the quantitative judgment of the system safety state is realized.
[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0043] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A high-precision safety modeling method for a hybrid power system of an aerial vehicle based on multiple levels, characterized in that, The method comprises the following steps: S1, constructing a multi-level modeling framework of a hybrid aircraft, dividing modeling levels, and the multi-level modeling framework comprising a system function level model, a high-precision component level model, and an equation-based operation model; S2, for components of the hybrid power system, explicitly defining the physical mechanism of each component, constructing a high-precision component level model through mathematical description, and forming an equation-based operation model; S3, according to the established high-precision component level model, realizing the collaborative interaction between component level models by identifying the data interaction mechanism between the models, combining a power control system model to establish a hybrid power system model, describing the actual working process of the system through equations, and forming a system function level model; S4, simulating and calculating the hybrid power system model to obtain the variation law of the performance parameters of the hybrid power system model with the engine operation, and verifying whether the hybrid power system model is verified by test, if not, entering S5; if yes, entering S6; S5, according to the test verification result, correcting the parameters of the hybrid power system model, and returning to S2; S6, based on the rated boundary of the verified hybrid power system model, selecting a safety influence attribute parameter, constructing a system time-varying limit state function, forming a whole machine safety model, and realizing quantitative determination of the safety state of the system.
2. The method of claim 1, wherein, The components in S2 include engine components, motor components, and battery pack components.
3. The method of claim 2, wherein, For the engine components, modeling is carried out from the thermodynamic domain, the fluid mechanics domain, and the mechanical dynamics domain, and the energy conservation law is followed; An ideal gas equation is used for aerodynamics modeling, and the formula is as follows: （1） wherein, P is pressure, V is volume, m is fluid mass, R is the gas constant, T is temperature; The fluid mass and energy are calculated through the continuity equation and the energy dynamic equation, and the formula is as follows: （2） （3） wherein, is the gas density, is the gas specific internal energy, and are the mass flow rate at the inlet cross section and the mass flow rate at the outlet cross section, respectively, and are the specific enthalpy at the inlet and the specific enthalpy at the outlet, respectively, and are the fuel mass flow rate and the lower heating value, respectively, is the heat flow rate, is the mechanical power, t is the time; A single-zone real cycle method is used to evaluate in-cylinder phenomena, and the formula for determining the flow characteristics of the intake and exhaust valves is as follows: （4） In the case of critical flow, the following equation is used: （5） wherein, and Pv is the pressure at the intake valve and Tv is the temperature at the intake valve, Pexh is the exhaust manifold pressure, A is the valve opening area, Cv is the specific heat capacity ratio, Cf is the flow coefficient, R is the gas constant, m is the fluid mass, t is time; The displacement of the piston from the top dead center is determined according to the crank angle value to realize dynamic coupling of mechanical dynamics and thermodynamics, and the formula is as follows: （6） wherein, is the crank radius, is the connecting rod length, is the displacement of the piston from top dead center, is the crank angle; A three-parameter Wiebe function is used to apply the combustion rate in the combustion model, and the Woschni correlation is used in the heat transfer model, and the engine effective power is extracted after simulation convergence.
4. The method of claim 2, wherein, For the motor components, including the generator and the drive motor, the least squares method is used to fit the generator efficiency model based on bench test or experimental data, and the formula is as follows: （7） wherein, is the power generation efficiency of the generator; and is the rotational speed of the generator and is the output power of the generator, respectively. The efficiency of the drive motor model in the generating state and the discharging state is fitted by the least square method to obtain a function about the motor speed and the output power , and the formula is as follows: （8） （9） wherein, is the efficiency of the drive motor model in the motoring state, is the efficiency of the drive motor model in the generating state. The battery current value is calculated according to the efficiency of the battery discharge or charge, and the formula is as follows: （10） （11） wherein, and are the total torque of the motor and the torque output to the shaft by the motor, respectively, is the real-time speed of the motor, is the moment of inertia of the motor, and are the output voltage of the battery and the current of the battery, respectively, is the operating state of the motor, when operating as a motor, , is the efficiency of the motor.
5. The method of claim 2, wherein, For the battery pack components, an internal resistance model equivalent circuit is used to construct a battery model, and the formula is as follows: （12） （13） （14） wherein, is the output power of the battery; and are the output voltage of the battery and the current of the battery, respectively; and are the terminal voltage of the battery and the internal resistance of the battery, respectively; is the initial value of the battery; is the capacity of the battery.
6. The method of claim 1, wherein, The hybrid system model in S3 includes: engine-generator set model, power battery model, drive motor model and calculation module; the engine is mechanically coupled with the generator, and the rotation speeds of the engine and the generator are the same; the power battery model calculates the power battery state of charge according to the generator power and the drive motor power The current state of is calculated, and energy interaction is realized through the inverter and the drive motor.
7. The method of claim 1, wherein, The safety influence attribute parameter in S5 is power and energy.
8. The method of claim 1, wherein, The expression of the system time-varying limit state function in S5 is: （15） （16） （17） wherein, is a safety boundary of the limit state function, is a system output of the hybrid aircraft system analysis model, when the operational boundary exceeds the critical value of failure, i.e., the unsafe state, is a system time-varying limit state function, is a power limit state function, is an energy limit state function, and are the power and energy to overcome the flight drag during the takeoff phase, respectively, and are the power and energy to overcome the flight gravity to climb to a specified height during the takeoff phase, respectively, and are the power and energy to change the speed during the flight in the takeoff phase, and are the engine and battery power varying with the running time during the takeoff phase, respectively, and are the engine and battery energy varying with the running time during the takeoff phase, respectively; and are the power and energy to overcome the flight drag during the cruise phase, respectively, and are the power and energy to overcome the flight gravity to climb to a specified height during the cruise phase, respectively, and are the power and energy to change the speed during the flight in the cruise phase, respectively, and are the engine and battery power varying with the running time during the cruise phase, respectively, and are the engine and battery energy varying with the running time during the cruise phase, respectively; and are the power and energy to overcome the flight drag during the landing phase, respectively, and are the power and energy to overcome the flight gravity to climb to a specified height during the landing phase, respectively, and are the power and energy to change the speed during the flight in the landing phase, respectively, and are the engine and battery power varying with the running time during the landing phase, respectively, and are the engine and battery energy varying with the running time during the landing phase, respectively.
9. A multi-level based high precision safety modeling system for hybrid electric aircraft systems, characterized by The application of any one of claims 1-8, a multi-level based high-precision safety modeling method for a hybrid power system of an aircraft, comprises a framework construction module, a model construction module, a collaborative interaction module, a simulation calculation module, a test verification module, and a quantitative determination module connected in sequence; wherein, Building framework module: building a multi-level modeling framework of hybrid aircraft, dividing modeling levels, and the multi-level modeling framework contains multi-level models, such as system function level model, high-precision component level model and equation-based operation model; Building model module: for the components of hybrid power system, the physical mechanism of each component is determined, and high-precision component level model is built through mathematical description to form equation-based operation model; Collaborative interaction module: according to the established high-precision component level model, the data interaction mechanism between component models is identified to realize the collaborative interaction between component level models, and the hybrid power system model is established by combining the power control system model to form the system function level model, which describes the actual working process of the system through equations; Simulation calculation module: the hybrid power system model is simulated to obtain the variation law of the performance parameters of the hybrid power system model with the engine operation, and whether the hybrid power system model is verified by test is verified, if not, entering the test verification module; if yes, entering the quantitative judgment module; Test verification module: according to the test verification result, the hybrid power system model parameters are corrected, and the building model module is returned; Quantitative judgment module: based on the rated boundary of the verified hybrid power system model, the safety influence attribute parameters are selected to build the system time-varying limit state function to form the whole machine safety model, and the quantitative judgment of the system safety state is realized.