Performance simulation calculation method for aviation hybrid electric power system

Abstract

The invention discloses a performance simulation calculation method for an aviation hybrid electric power system. The method comprises the following steps: firstly, constructing a sub-component calculation model covering an aerodynamic thermal component, a power generation subsystem, a power conversion and transmission subsystem, a battery subsystem, a motor and an aerodynamic system; establishing a reverse logic calculation framework driven by a load side demand, and realizing hierarchical reverse parameter solution from a load end to a power source end; and establishing a power balance equation at a bus end, carrying out iterative solution by adopting a Newton-Rafson algorithm, and realizing dynamic distribution and coordination control of multi-source energy in combination with an energy constraint module. According to the method, through a reverse solution and bus end balance strategy, the simulation calculation efficiency and convergence of the complex aviation hybrid electric power system are remarkably improved, and meanwhile, the adaptability and expandability of the model to different topological architectures and task profiles are enhanced; and an efficient and high-credibility calculation tool is provided for design, performance evaluation and energy management strategy optimization of the hybrid electric propulsion system of the aircraft.

Description

A Simulation Calculation Method for the Performance of Aero-Hybrid Power Systems Technical Field

[0001] This invention relates to the field of performance simulation of aviation hybrid electric propulsion systems, specifically to a method for performance simulation calculation of aviation hybrid electric propulsion systems. The method achieves performance calculation of hybrid electric propulsion systems by performing inverse logic calculations and establishing balance equations at the bus end. Background Technology

[0002] Hybrid Electric Propulsion System (HEPS) is a new type of aerospace power architecture that deeply integrates traditional gas turbine engines with electric propulsion systems to achieve synergistic operation between aerodynamic and thermodynamic components and electrical components. It aims to improve the overall performance of aircraft and promote the transformation of aerospace power systems towards high efficiency and greenness.

[0003] In the performance simulation modeling of aerospace hybrid electric propulsion systems, traditional methods typically divide the overall model into two major modules based on system function and physical characteristics: aerodynamic and thermodynamic components and electrical components. The aerodynamic and thermodynamic components, centered on the aerospace gas turbine and its supporting systems, have a relatively mature theoretical framework and engineering foundation in the aerospace field, and are mostly solved using traditional thermodynamic and aerodynamic calculation methods. The electrical components encompass multiple subsystems, including the generator system, power conversion and transmission subsystem, battery subsystem, and motor subsystem. Traditionally, a sequential calculation logic is used, that is, modeling and solving each electrical subsystem in a fixed direction from the energy side to the load side.

[0004] However, traditional simulation methods have the following prominent problems in practical applications:

[0005] Low computational efficiency and high resource consumption: In the process of modeling the performance of electrical components, it is usually necessary to establish a balance between the output of the motor and the demand side of the aerodynamic system (such as propellers and ducted fans) and perform iterative solutions. In steady-state or transient simulations, in order to meet the overall convergence of the system, it is often necessary to perform repeated, full-process calculations on all subsystems, which leads to a significant increase in computational load and high memory consumption, severely restricting the simulation speed and scale, and making it difficult to meet the needs of rapid analysis and optimization design of complex systems and multiple operating conditions.

[0006] Insufficient system scalability and flexibility: Traditional modeling methods involve tight coupling between subsystems, with computational logic exhibiting a unidirectional serial characteristic. When the system structure changes (e.g., adding or deleting subsystems, adjusting topology connections) or when upgrading a local model, the highly rigid computational flow often necessitates rebuilding the overall simulation framework and data interfaces. This not only significantly increases the workload of secondary development but may also introduce risks such as model inconsistencies and interface errors, limiting the practical application of this method in iterative design, multi-scheme comparison, and functional expansion.

[0007] In summary, existing performance simulation methods for aerospace hybrid electric propulsion systems have significant limitations in terms of computational efficiency, resource consumption, and model scalability, making them unsuitable for the high-performance, multi-variable, and rapidly iterative development requirements of modern aerospace hybrid electric systems. Therefore, there is an urgent need for a performance simulation method that can significantly improve computational efficiency, enhance model flexibility and scalability while maintaining simulation accuracy, in order to support the design, analysis, and optimization of such complex systems. Summary of the Invention

[0008] This invention aims to overcome the problems of low computational efficiency, large resource consumption, and poor system scalability in existing performance simulation technologies for aerospace hybrid electric propulsion systems. It provides a performance simulation calculation method for aerospace hybrid electric propulsion systems. This simulation calculation method is based on reverse logic calculation and bus-side power balance solution. It can significantly improve the simulation speed and computational stability of complex hybrid electric propulsion systems under multiple operating conditions and multiple task profiles while ensuring simulation accuracy, and enhance the model's adaptability to different system architectures and component iterations.

[0009] The technical solution of this invention is as follows:

[0010] A method for performance simulation calculation of an aero-electric hybrid propulsion system includes the following steps:

[0011] Step S1. Construct computational models of sub-components of the aviation hybrid power system;

[0012] Step S2. Establish a reverse logic calculation framework, using the power demand or thrust demand at the bus end of the load side as input, to drive the reverse calculation process from the load side to the power source side.

[0013] Step S3. Establish a bus-side solution framework, establish power balance equations at the bus nodes and use the Newton-Raphson algorithm for iterative solution to achieve dynamic power balance allocation and energy management of the system.

[0014] Furthermore, in step S1, the sub-component calculation model includes:

[0015] A proxy model-based aerodynamic thermodynamic component calculation model is used to map the output engine speed and torque according to the input flight conditions and mechanical power requirements.

[0016] The electronic system calculation model includes the mechanical efficiency model of the reducer and the electrical efficiency model of the generator, which is used to calculate the output electric power based on the input mechanical parameters;

[0017] The calculation model for the power conversion and transmission subsystem includes the efficiency model of the converter and the resistance model of the cable;

[0018] The battery subsystem calculation model is established by using a second-order RC equivalent circuit and combining it with the open-circuit voltage-state-of-charge characteristic curve.

[0019] Aerodynamic system calculation model, used to solve for the rotational speed, torque and required shaft power of propeller or ducted fan based on thrust requirements, flight speed and air density;

[0020] The calculation model of the electric motor subsystem includes the electric efficiency model of the electric motor and the mechanical efficiency model of the shaft system.

[0021] Furthermore, the aerodynamic and thermodynamic component calculation model based on the surrogate model directly obtains the output by calling the pre-trained surrogate model. The input of the surrogate model includes at least flight altitude, Mach number and mechanical power requirement, and the output includes at least engine speed and torque.

[0022] Furthermore, in the generator system calculation model, the generator's output power calculation satisfies the following relationship:

[0023]

[0024] in and These are the generator's input speed and torque, respectively. This represents the electrical efficiency of the generator.

[0025] Furthermore, the terminal voltage calculation of the battery subsystem calculation model satisfies the following relationship:

[0026]

[0027] in For battery output current, This is the battery open-circuit voltage. The internal resistance of the battery is in ohms. , The polarization voltages of the two parallel RC branches are described by differential equations.

[0028] Furthermore, the calculation of the input power of the motor in the motor subsystem calculation model satisfies the following relationship:

[0029]

[0030] in This refers to the output shaft speed of the electric motor. This refers to the output shaft torque of the electric motor. The electrical efficiency of the electric motor. This refers to the mechanical efficiency of the motor shaft system.

[0031] Furthermore, in step S2, the reverse logic computation framework specifically executes the following three reverse computation paths:

[0032] Aerodynamic system to bus terminal path: Based on the target thrust and flight conditions, the aerodynamic system operating parameters, motor input current, inverter input current and cable voltage drop are solved in reverse order to finally obtain the bus terminal current requirement of the load branch;

[0033] The path from the power generation system to the bus terminal: Based on the power demand of the bus terminal for the power generation branch, the operating parameters of the aerodynamic and thermodynamic components, the generator output current and the rectifier input current are solved in reverse order.

[0034] Battery subsystem to bus path: Based on the power demand of the bus to the battery branch, the output current and voltage of the battery and the input current of the converter are solved in reverse.

[0035] Furthermore, in step S3, the bus-side solution framework includes a bus power balancing module and an energy constraint module. The bus power balancing module achieves system input-output matching by establishing power balance equations at bus nodes. The energy constraint module applies constraints to the output parameters of the generator system and battery subsystem during the system solution process to meet the requirements of system safety, stability, and lifespan management.

[0036] Furthermore, in the bus power balancing module, a power balance equation based on Kirchhoff's current law is established at the bus node:

[0037]

[0038] in , , These are the output currents of the generator branch and the battery branch, and the input current of the load branch, respectively; the load branch current... As known quantities, the output power of the power generation branch and the battery branch are taken as variables to be solved; the Newton-Raphson algorithm is used to iteratively solve the power balance equation and dynamically allocate the output power of the power generation system and the battery subsystem.

[0039] Furthermore, the energy constraint module includes at least upper and lower limit constraints on the battery state of charge, amplitude constraints on the battery charging and discharging current, and upper and lower limit constraints on the generator output power; during the iterative solution process, if the solution result violates the constraints, the corresponding variables will be clamped to the constraint boundary.

[0040] Beneficial effects

[0041] The technical advantages of this invention are as follows:

[0042] 1. This invention adopts computational logic that is derived from the load side to the source side, and replaces complex component models (especially aerodynamic and thermodynamic components) with efficient proxy models. This fundamentally reduces the number of iterations and repetitive calculations required in the simulation process, thereby increasing the system-level simulation speed by orders of magnitude and reducing memory usage.

[0043] 2. Each sub-component model can be coupled with the reverse computation framework through standardized interfaces, resulting in a high degree of modularity. When the system topology changes (such as switching between series, parallel, and hybrid architectures) or when it is necessary to add, delete, or replace component models, only the connection relationships and interface parameters of the corresponding modules need to be adjusted, without having to reconstruct the entire simulation process. This greatly facilitates the iterative upgrade of the model and the comparative analysis of multiple schemes.

[0044] 3. The mature Newton-Raphson algorithm is used at the bus end to solve the core power balance equation, which has good numerical convergence and high solution accuracy. The introduction of the energy constraint module further ensures that the solution results conform to physical reality and system safety constraints, thereby improving the credibility of the simulation results.

[0045] 4. This method can adapt to the simulation requirements of multi-stage flight mission profiles, and quickly calculate the system power allocation, energy consumption and state evolution under each stage, providing an efficient and reliable computing platform for the overall performance evaluation of the propulsion system and the optimization of energy management strategies.

[0046] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0047] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0048] Figure 1: Flowchart of the simulation calculation method for the performance of the aviation hybrid electric propulsion system of the present invention;

[0049] Figure 2: Power system framework diagram of a specific embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0051] This invention provides a method for simulating the performance of an aero-electric hybrid power system. Its core lies in achieving efficient and accurate system performance simulation through modular sub-component models, reverse-driven calculation logic, and a global solution framework based on bus power balance. A typical embodiment is described in detail below with reference to Figure 1 (the solution flowchart of this invention) and Figure 2 (the power system framework diagram of a specific implementation).

[0052] This embodiment uses a general aviation aircraft employing a parallel hybrid electric propulsion system as the simulation object, and the system architecture is shown in Figure 2. Its power source includes an aircraft engine (as an aerodynamic and thermodynamic component) driving a generator, and a lithium-ion battery pack. Electrical energy is collected via a bus and then drives an electric motor to power a propeller (as an aerodynamic system) to generate thrust. The simulation task is to perform performance calculations on a complete mission profile including takeoff, climb, cruise, descent, and landing.

[0053] Specifically, the following steps are included:

[0054] Step S1: Based on the system architecture, construct parameterized and pluggable sub-component calculation models, including aerodynamic and thermodynamic component calculation models, generator system calculation models, power conversion and transmission subsystem calculation models, battery subsystem calculation models, aerodynamic system calculation models, and electric motor subsystem calculation models.

[0055] An aerodynamic-thermal component computational model is used to map the output engine speed and torque based on input flight conditions and mechanical power requirements. A surrogate model-based aerodynamic-thermal component computational model extracts and analyzes simulation data of aerodynamic-thermal component performance under different operating conditions to construct a surrogate model encompassing multi-input and multi-output characteristics, achieving rapid mapping and prediction between high-dimensional input parameters and output performance indicators. During the overall system simulation calculation, this surrogate model is embedded into the system solution process. By calling this surrogate model, the mechanical power output of the aerodynamic-thermal component under the target operating condition is directly obtained, enabling real-time performance calculation and characteristic response prediction under various flight conditions and power requirement constraints, thus improving the system's solution efficiency and accuracy.

[0056] In this embodiment, for aero-piston engines, a dataset containing parameters such as intake conditions, fuel flow, engine speed, output torque, and power is generated by pre-running a large number of steady-state and transient high-fidelity models covering different altitudes, Mach numbers, and throttle positions. Based on this dataset, a multi-input model (such as flight altitude H, Mach number Ma, engine mechanical power requirements, etc.) is trained. Multiple outputs (such as engine output speed) Engine output torque A proxy model (such as the Kriging model or a neural network model) can be used to calculate engine fuel consumption rate (SFC). In system simulation, this proxy model can be called simply by inputting the current flight conditions and power requirements. This allows for the direct and rapid acquisition of the engine's mechanical power output. and torque This avoids the enormous computational overhead of solving complex thermodynamic cycles online.

[0057] The generator-electric system calculation model includes a mechanical efficiency model for the reducer and an electrical efficiency model for the generator, used to calculate the output electrical power based on input mechanical parameters. The mechanical transmission part of the generator-electric system calculation model establishes a detailed mapping relationship between mechanical loss models and mechanical efficiency for shaft components such as the reducer and gearbox, considering different mechanical input speeds. The effects of transmitted torque T and lubrication condition on transmission losses were investigated. Simultaneously, a model based on the generator input shaft torque was established for the generator section. Generator input shaft speed The electrical efficiency lookup table model with the independent variable enables rapid lookup and dynamic updating of generator output characteristics, providing accurate data support for subsequent energy allocation and scheduling in the electrical system.

[0058] In this embodiment, the mechanical transmission part establishes the mechanical efficiency of the reduction gearbox. The efficiency of the mapping model is the input rotational speed. The function of the transmitted torque T is obtained by looking up a table, i.e. This function enables the calculation of losses in the input mechanical power.

[0059] The efficiency of establishing a permanent magnet synchronous generator in the generator section Lookup table model, based on rotational speed and torque The independent variable is, i.e. Its electrical power output is:

[0060]

[0061] The power conversion and transmission subsystem calculation model includes efficiency models for converters and resistance models for cables. This model maps the voltage, current, and efficiency characteristics of converters such as transformers, inverters, and rectifiers, and establishes conversion characteristic curves of components under different input and output conditions using interpolation methods. Simultaneously, it models the power loss of transmission cables, considering the effects of factors such as length and resistivity, providing a computational basis for overall power transmission loss analysis and ensuring the predictability and stability of the system's energy transmission path.

[0062] In this embodiment, efficiency is established for the transformer, rectifier, or inverter respectively. Characteristic model, efficiency is input voltage Input current The function is obtained through two-dimensional interpolation, i.e. Its input-output relationship is uniformly represented as:

[0063]

[0064] in and This refers to the input and output power of a transformer, rectifier, or inverter.

[0065] For transmission cables, establish their resistance model and calculate voltage drop and power loss:

[0066]

[0067]

[0068] in This is the equivalent resistance of the cable. For the voltage drop on the cable, This refers to power loss on the cable.

[0069] Battery Subsystem Calculation Model: The battery subsystem calculation model is based on a second-order RC equivalent circuit structure and incorporates an open-circuit voltage-state-of-charge characteristic curve to establish a dynamic equivalent model. The model automatically determines the operating mode based on input power, operating current, and external load conditions, as well as key parameters such as output voltage, SOC, available power, remaining capacity, and rate of change of state of charge. Simultaneously, the model incorporates built-in charge / discharge protection logic and power scheduling strategies to avoid abnormal operating conditions such as overcharging and over-discharging, thereby improving system operational safety and lifespan prediction capabilities.

[0070] In this embodiment, the battery terminal voltage for:

[0071]

[0072] in This represents the battery's output current; positive for discharging and negative for charging. This is the battery open-circuit voltage, a function of the battery's state of charge (SOC). The internal resistance of the battery is in ohms. , The polarization voltages of the two parallel RC branches are described by a differential equation:

[0073] ,

[0074] in , The resistances of the two RC branches are... , Given the time constants of the two RC branches, this model can be based on the current. Calculate the terminal voltage in real time based on the current SOC. And changes in SOC.

[0075] An aerodynamic system computational model is used to solve for the rotational speed, torque, and required shaft power of the aerodynamic system based on thrust requirements, flight speed, and air density. The aerodynamic component is a propeller system as the research object. Under given flight conditions and propulsion requirements, key performance parameters such as steady-state rotational speed, shaft torque, thrust coefficient, and power coefficient are solved by coupling aerodynamic equations and momentum theory. Internally, the model employs an adaptive iterative interpolation algorithm to continuously solve and extrapolate predictions across different aerodynamic boundary conditions and discrete data points, providing high-precision input support for propulsion performance calculations.

[0076] In this embodiment, an aerodynamic performance database (including advance ratio J and propeller power coefficient) is established for a fixed-pitch propeller. propeller thrust coefficient Given flight speed air density and target thrust requirements The model uses the thrust formula or power coefficient relationship to iteratively solve for the propeller speed that meets the thrust requirements. and required shaft power And further obtain the required shaft torque of the propeller. The thrust formula and power balance relationship are as follows:

[0077]

[0078]

[0079] in This is the diameter of the propeller.

[0080] The electric motor subsystem calculation model establishes a shaft transmission mechanical efficiency model and an electric motor electrical efficiency model, and corrects them based on electromagnetic losses and temperature rise characteristics under different operating conditions. The model can rapidly calculate the motor output performance under different speeds, torques, and voltages, providing accurate input data support for system power demand response and control strategy optimization.

[0081] In this embodiment, similar to the generator part of the electronic system calculation model, the efficiency of the permanent magnet synchronous motor is established. Lookup table model It also includes the mechanical efficiency of the motor shaft system. Its electrical power input for:

[0082]

[0083] in This refers to the output shaft speed of the electric motor. This refers to the output shaft torque of the electric motor.

[0084] Step S2: In the power system architecture of this embodiment, the energy supply path includes two main branches: "engine-generator-rectifier-bus" and "battery-transformer-bus", as well as a load branch: "bus-inverter-motor-propeller". The three branches converge and distribute energy through the bus terminal, forming a complete electric propulsion power supply and transmission system. Therefore, a reverse logic calculation framework is established. This framework is driven by the power demand or thrust demand of the bus terminal on the load side, and performs reverse calculations along the three paths respectively:

[0085] Aerodynamic system to bus terminal path: Based on the target thrust and flight conditions, the aerodynamic system operating conditions, motor input current, inverter output current, and cable voltage drop are solved in reverse order to obtain the bus terminal current requirement; specifically, the calculation framework for the propeller aerodynamic system to bus terminal is based on the target thrust requirement. and flight operating parameters and Using the input conditions, the aerodynamic system calculation model is used to solve for the target thrust requirement. The propeller speed Torque and required shaft power and increase the propeller speed Torque It is input to the motor subsystem as a load in reverse direction, and simultaneously receives an estimated signal of the bus voltage from downstream (inverter direction). The motor subsystem operates according to the rotational speed. Torque and bus voltage estimate Based on the motor efficiency characteristic diagram, interpolation calculations are performed to inversely solve for the input current required from the inverter side to output the mechanical power. Current The input current is passed in reverse to the inverter model, which then obtains the input current. and voltage signals from downstream (bus direction) Then, combined with efficiency characteristic data Complete the reverse derivation of input current and output voltage, and calculate the current on the input side (bus side) in reverse. The cable model receives current. With bus voltage Then calculate the pressure drop. It also transmits the corrected voltage signal forward (in the direction of the motor). To obtain more accurate Estimate the accuracy of the solution in subsequent modules; and convert the current signal... As load demand and other branch currents converge at the bus end, a complete reverse power transfer link is formed from the load end to the energy end. This process is continuously corrected during iteration. The estimated value is obtained until convergence.

[0086] The path from the generator electronic system to the bus terminal: Based on the power demand at the bus terminal, the operating conditions of the aerodynamic and thermodynamic components, the generator output current, and the rectifier output current are solved in reverse order; specifically: the calculation framework for the path from the generator electronic system to the bus terminal is based on the power demand of the system bus terminal for this path. As the initial input condition, the power requirement is first calculated and output by the surrogate model of the aerodynamic and thermodynamic components. The engine output speed at the following speed With torque This parameter is then input into the generator subsystem model. The inputs to the generator subsystem include the voltage signal transmitted forward from the rectifier. and the mechanical rotation speed output by the pneumatic and thermodynamic components Torque data The model is based on the electrical efficiency characteristic diagram. Perform interpolation calculations to obtain the corresponding output current. The rectifier model receives the current parameters output by the generator. and the voltage signal at the bus end Through efficiency characteristics Interpolation to solve for its input current It transmits the current signal to the bus terminal, thereby realizing the step-by-step reverse derivation and matching of the power demand from the power supply to the energy end of the power generation system.

[0087] Battery subsystem to bus path: Based on the bus power requirements, the battery output current, voltage, and transformer input current are calculated in reverse order. Specifically, the battery subsystem to bus calculation framework uses the bus power requirements for this path. As the input signal, the battery subsystem computational model is based on Using the current SOC and its equivalent circuit model, the output voltage is calculated in reverse. Output current The output signal is input to the transformer model, and the current is calculated based on the relationship between winding losses and turns ratio. It connects directly to the bus terminal to realize reverse energy supply calculation from the battery side to the bus. The battery module can be dynamically adjusted internally based on SOC, remaining capacity, and power limit constraints to ensure safety and energy management coordination under simulation conditions.

[0088] Step S3: Establish a bus-side solution framework, including a bus power balancing module and an energy constraint module, to achieve dynamic calculation of power allocation and global energy management, ensuring the output coordination of each subsystem under different operating conditions and the stability of system operation.

[0089] The bus power balancing module achieves system input-output matching by establishing power balance equations at the bus nodes. The power demand calculated from the aerodynamic system to the bus end is defined as a known quantity and used as a fixed boundary condition, participating in the overall power solution process as input to the sub-solution module. The output power of the generator and battery subsystems are set as variables to be solved, represented as their respective output functions in the balance equations. The Newton-Raphson iterative algorithm is used to numerically solve the power balance equations, obtaining the overall system's power input and output parameters through iterative approximation, and dynamically allocating the output power of the generator and battery subsystems to achieve dynamic power balance at the bus end.

[0090] Specifically, at the DC bus node, the balance equations are established based on Kirchhoff's current law:

[0091]

[0092] in, The current of the input bus to the power generation branch (i.e. ), The current of the battery branch input bus (i.e. ), The current drawn from the bus by the load branch (i.e. ). The known quantity is determined by the reverse calculation in step S2. and These are their respective branch power setpoints. and The function, and can be expressed as, through their respective reverse computation links, is and .

[0093] In the solution process, the first step is to select a control strategy. For example, in this embodiment, a "constant engine speed + battery assistance" strategy is adopted during the cruise phase. Set to a fixed value, determined by the engine's optimal operating point. x is the control variable. Define the residual function.

[0094]

[0095] The goal is to find x such that The residual function is solved using the Newton-Raphson iteration:

[0096]

[0097] Where the derivative This can be approximated using the finite difference method. Each iteration calls the reverse computation framework of the power generation branch and the battery branch, based on the current... and bus voltage renew and Until If the power is less than the preset tolerance, the bus power reaches a balance, resulting in generator power that meets load requirements and conforms to the control strategy. and battery power .

[0098] The energy constraint module imposes constraints on the output parameters of the generator and battery subsystems during system solution to meet the requirements of system safety, stability, and lifespan management. The module limits battery discharge rate, state of charge (SOC), and generator maximum output capacity based on real-time operating conditions to prevent overload operation of any single subsystem. Simultaneously, the energy constraint module dynamically adjusts the output strategies of the two power sources based on real-time bus power demand, enabling the scheduling of energy allocation ratios and ensuring the stability and continuity of the overall system power supply under different task profiles and load demands. This design not only improves the system's safety margin and energy utilization efficiency but also provides interface support for subsequent expansion of energy management strategies and multi-source collaborative optimization.

[0099] Specifically, in each solution step and throughout the entire task profile simulation, energy constraints are achieved by applying inequality constraints to power and state variables:

[0100]

[0101] For the battery subsystem, its SOC is constrained to always remain within a preset safety window. The charging and discharging current is constrained to not exceed the maximum multiplier. ,Right now During the solution process, if the result obtained by Newton's method is... If it causes an out-of-bounds movement, clamp it to the boundary. and readjust Alternatively, the labeling strategy may not be feasible. Furthermore, for electronic systems, the output power must be constrained to not exceed the maximum continuous power. .

[0102] By advancing the time step, repeating step S2 for each stage of the task profile, it is possible to output the power distribution curve, battery SOC change trajectory, fuel consumption, system total efficiency and other performance indicators for the entire task cycle, thus completing a comprehensive simulation evaluation of the hybrid power system performance.

[0103] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for performance simulation calculation of an aero-electric hybrid propulsion system, characterized in that: The process includes the following steps: Step S1. Constructing a computational model for the sub-components of the aviation hybrid electric propulsion system; Step S2. Establishing a reverse logic computation framework, using the power demand or thrust demand at the bus end on the load side as input to drive the reverse computation process from the load side to the power source side; Step S3. Establishing a bus end solution framework, establishing power balance equations at the bus nodes and using the Newton-Raphson algorithm for iterative solution to achieve dynamic power balance allocation and energy management of the system.

2. The method for performance simulation calculation of an aero-electric hybrid power system according to claim 1, characterized in that: In step S1, the sub-component calculation models include: an aerodynamic-thermal component calculation model based on a proxy model, used to map the output engine speed and torque according to the input flight conditions and mechanical power requirements; a generator-electronic system calculation model, including the mechanical efficiency model of the reducer and the electrical efficiency model of the generator, used to calculate the output electrical power according to the input mechanical parameters; a power conversion and transmission subsystem calculation model, including the efficiency model of the converter and the resistance model of the cable; a battery subsystem calculation model, established using a second-order RC equivalent circuit combined with the open-circuit voltage-state-of-charge characteristic curve; an aerodynamic system calculation model, used to solve for the propeller or ducted fan speed, torque, and required shaft power according to thrust requirements, flight speed, and air density; and a motor subsystem calculation model, including the electric efficiency model of the motor and the mechanical efficiency model of the shaft system.

3. The performance simulation calculation method for an aerospace hybrid power system according to claim 2, characterized in that: The aerodynamic and thermodynamic component calculation model based on the surrogate model obtains its output directly by calling the pre-trained surrogate model. The input of the surrogate model includes at least flight altitude, Mach number, and mechanical power requirements, and the output includes at least engine speed and torque.

4. The performance simulation calculation method for an aerospace hybrid power system according to claim 2, characterized in that: In the calculation model of the generator system, the output power of the generator is calculated according to the following relationship: in and These are the generator's input speed and torque, respectively. This represents the electrical efficiency of the generator.

5. The performance simulation calculation method for an aerospace hybrid power system according to claim 2, characterized in that: The terminal voltage calculation of the battery subsystem calculation model satisfies the following relationship: in For battery output current, This is the battery open-circuit voltage. The internal resistance of the battery is in ohms. 、 The polarization voltages of the two parallel RC branches are described by differential equations.

6. The performance simulation calculation method for an aerospace hybrid power system according to claim 2, characterized in that: The calculation of the input power of the motor in the motor subsystem calculation model satisfies the following relationship: in This refers to the output shaft speed of the electric motor. This refers to the output shaft torque of the electric motor. The electrical efficiency of the electric motor. This refers to the mechanical efficiency of the motor shaft system.

7. The performance simulation calculation method for an aerospace hybrid power system according to claim 1, characterized in that: In step S2, the reverse logic calculation framework specifically executes the following three reverse calculation paths: Aerodynamic system to bus terminal path: Based on the target thrust and flight conditions, the aerodynamic system operating parameters, motor input current, inverter input current and cable voltage drop are solved in reverse order to finally obtain the bus terminal current requirement of the load branch. The path from the power generation system to the bus terminal: Based on the power demand of the bus terminal for the power generation branch, the operating parameters of the aerodynamic and thermodynamic components, the generator output current, and the rectifier input current are solved in reverse order; The path from the battery subsystem to the bus terminal: Based on the power demand of the bus terminal for the battery branch, the output current and voltage of the battery and the input current of the converter are solved in reverse order.

8. The method for performance simulation calculation of an aero-electric hybrid power system according to claim 1, characterized in that: In step S3, the bus-side solution framework includes a bus power balancing module and an energy constraint module. The bus power balancing module achieves system input-output matching by establishing power balance equations at bus nodes. The energy constraint module applies constraints to the output parameters of the generator system and battery subsystem during the system solution process to meet the requirements of system safety, stability and lifespan management.

9. The performance simulation calculation method for an aerospace hybrid power system according to claim 8, characterized in that: In the bus power balancing module, a power balance equation based on Kirchhoff's current law is established at the bus node: in 、 、 These are the output currents of the generator branch and the battery branch, and the input current of the load branch, respectively; the load branch current... As known quantities, the output power of the power generation branch and the battery branch are taken as variables to be solved; the Newton-Raphson algorithm is used to iteratively solve the power balance equation and dynamically allocate the output power of the power generation system and the battery subsystem.

10. The performance simulation calculation method for an aerospace hybrid power system according to claim 8, characterized in that: The energy constraint module includes at least upper and lower limit constraints on the battery state of charge, amplitude constraints on the battery charging and discharging current, and upper and lower limit constraints on the generator output power. During the iterative solution process, if the solution result violates the constraints, the corresponding variables will be clamped to the constraint boundary.

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