A method for joint optimization of parameters in an aircraft turbine hybrid electric propulsion system

Abstract

This invention discloses a parameter co-optimization method for an aircraft turbine hybrid electric propulsion system, comprising: uniformly modeling the aerobatic characteristics of the turbine hybrid electric propulsion system, and providing a method based on... SOC This invention employs a rule-based energy management strategy based on threshold and turbine power regulation coefficients. It extracts structural and control parameters affecting system performance, combines them into a joint parameter vector, constructs a feasible region, and limits the range of selectable parameters. A multi-objective performance index is constructed and normalized to form a comprehensive evaluation function. A feasible region constraint model is established. The established multi-objective and constraint model, combined with feasible region projection and performance calculation, yields system structural parameters and an optimal energy management strategy that satisfies all constraints and achieves optimal overall performance. This invention incorporates multiple types of parameters into the same parameter set and utilizes an improved superspiral algorithm with global search capabilities for collaborative optimization to obtain a parameter combination that meets the overall operational requirements of the system.

Description

Technical Field

[0001] This invention belongs to the field of aerospace propulsion systems and energy management and control technology, and specifically relates to a parameter co-optimization method for an aircraft turbine hybrid electric propulsion system. Background Technology

[0002] With the continuous development of aviation electrification, decarbonization, and green propulsion technologies, hybrid electric propulsion systems based on multi-energy coupling have gradually become an important research direction in the field of aviation power. Turbine-hybrid electric propulsion systems combine turbine generator sets, electric drive propulsion units, and high-energy-density batteries within the same power system, enabling fuel and electricity to participate in energy supply at different flight phases. This forms a multi-path, multi-mode propulsion structure, combining the strong continuous power supply capability of fuel power, the fast response speed of electric drive propulsion, and the flexible allocation of battery energy storage. It can provide differentiated power output modes in various mission phases such as takeoff and landing, cruise, acceleration, and maneuvering. As the application scope of hybrid electric propulsion technology in platforms such as eVTOL, general aviation aircraft, and unmanned aerial vehicles continues to expand, the overall structure, energy flow logic, and mission adaptation modes of its power system are exhibiting more complex system characteristics, placing higher demands on unified dynamic modeling and multi-source energy collaborative management.

[0003] In a turbine-hybrid electric propulsion system, the turbine generator, electric propulsion unit, and battery energy storage device constitute a multi-path energy supply structure. Energy distribution behavior at different flight phases is simultaneously influenced by structural parameters and energy management control strategies, with a clear coupling relationship between the two types of parameters. Structural parameters describe the system's composition and capacity boundaries, while control strategies determine the power source's participation during the mission cycle. However, these are often modeled and defined independently during the design process, lacking a method to simultaneously reflect the coupling relationship between the two types of parameters within a unified dynamic framework. Furthermore, the system contains a large number and diverse range of parameters, forming a high-dimensional joint parameter space. Interactions between different parameters make effective searching within this space challenging, and global optimization is difficult to achieve, thus leading to high complexity in the parameter design of hybrid electric propulsion systems.

[0004] Therefore, as the energy link structure of the system becomes more complex, the types of parameters increase, and the operating conditions of the missions become more diverse, how to coordinate the structural parameters and control parameters under a unified dynamic model to maintain consistency at the overall level and achieve effective global optimization in a high-dimensional coupled joint parameter space has become a key technical problem for improving the overall performance and operational reliability of turbine hybrid electric propulsion systems. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, the present invention aims to provide a parameter co-optimization method for aircraft turbine hybrid electric propulsion systems. This method constructs a unified dynamic description at the system level, incorporates multiple types of parameters formed by structural configuration and energy management strategies into the same parameter set, and utilizes an improved superspiral algorithm with global search capabilities to co-optimize the parameters, thereby obtaining a parameter combination that meets the overall operational requirements of the system.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for coordinated parameter optimization of an aircraft turbine hybrid electric propulsion system includes the following steps:

[0008] 1) A unified model is developed for the aerodynamic characteristics of the turbine hybrid electric propulsion system, including the vertical and horizontal power requirements of the aircraft under typical airspace missions, the power output relationship between the propulsion motor and the turbine generator set, the SOC / SOH state change model of the battery, and the mass composition of the system. Based on this, the basic form of a regular energy management strategy based on the SOC threshold and the turbine power regulation coefficient is given.

[0009] 2) Based on the dynamic model and rule-based energy management strategy obtained in step 1), extract the structural and control parameters affecting system performance, including turbine selection, number of parallel batteries, SOC threshold, and turbine power regulation coefficient, and combine them into a joint parameter vector. Simultaneously, construct a corresponding feasible region to limit the selectable range of parameters.

[0010] 3) Based on the parameter set in step 2), construct multi-objective performance indicators by combining system mass, fuel consumption, and battery degradation, and form a comprehensive evaluation function through normalization. At the same time, establish constraints for thrust satisfaction, motor / turbine operating boundaries, and battery SOC safety range, and unify various constraints into a feasible region constraint model;

[0011] 4) Based on the multi-objective and constraint model established in step 3), a multi-center spiral search method is used for optimization in the joint parameter space. By generating initial samples, constructing multi-center candidate points, performing spiral updates and adaptive gravity adjustment, and combining feasible region projection and performance calculation, the system structure parameters and energy management strategy that satisfy all constraints and have the best overall performance are obtained.

[0012] Further, step 1) specifically includes:

[0013] 11) Flight Dynamics Modeling: For the power requirements of the turbine-hybrid electric propulsion system in typical aerial missions such as vertical takeoff and landing, climb / descent, and cruise, establish the vertical force requirement F for each. v Horizontal force requirement F h With total thrust requirement F req Model:

[0014]

[0015] Where M is the total mass of the aircraft, g is the acceleration due to gravity, and a v Let A be the vertical acceleration, ρ be the air density, and A be the vertical acceleration. v C is the area subjected to vertical force. D v is the air drag coefficient. v Let a be the vertical relative airflow velocity. h For horizontal acceleration, A h v is the horizontal force-bearing area. h This represents the horizontal relative airflow velocity.

[0016] Further, the required power P in the corresponding spatial domain is obtained. req :

[0017]

[0018] Among them, v air The resultant velocity is relative to the air.

[0019] 12) Modeling of the propulsion motor and turbine generator: The propulsion unit consists of a propulsion motor and a turbine generator set. The output power P of the propulsion motor... m and the output power P of the turbine generator TGP Represented as:

[0020]

[0021] Among them, T m ω is the output torque of the motor. m η is the motor speed. m For motor efficiency, T e For the turbocharger output torque, ω e η is the turbine output speed. e This refers to the generator efficiency.

[0022] The fuel consumption of the turbo module is represented by a steady-state mapping:

[0023]

[0024] in, The instantaneous mass flow rate of fuel. This is the turbocharger steady-state fuel consumption characteristic function.

[0025] Total fuel consumption is expressed as:

[0026]

[0027] in, This represents the cumulative fuel consumption during the mission period. Let be the instantaneous mass flow rate of fuel at time t.

[0028] 13) Battery SOC and SOH Modeling: The charging and discharging behavior and degradation of the battery under long-term tasks are closely related. A port power model P for the battery needs to be established. b The SOC update model and SOH decay model are as follows:

[0029]

[0030] in, I is the open-circuit voltage related to the state of charge, and I is the current. Q is the internal resistance related to the state of charge, Ah is the battery's rated capacity, k and z are the cumulative discharge ampere-hours, and k and z are empirical decay coefficients.

[0031] 14) System Mass Model: The total system mass includes the mass of the machine structure, the turbine unit, and the battery, expressed as:

[0032]

[0033] Where M0 is the structural mass of the organism. For turbine specifications The determined unit quality, n s Let n be the number of batteries connected in series. p m is the number of batteries connected in parallel. cell This refers to the quality of a single battery cell.

[0034] 15) Rule-based energy management strategy design: Based on battery SOC and power demand, a power allocation rule between the turbine generator set and the power battery is proposed. This utilizes SOC upper and lower limits. , and the turbine's adjustment coefficient in the high and low load power ranges. , Dividing the range determines the output power P of the turbine generator set. TGP and battery power P b P TGP,h P TGP,l The output power of the turbine generator set in different load areas is represented by the value k. TGP,h k TGP,l The product of P and the rated power b,min P b,max These are the minimum and maximum power outputs of the battery.

[0035] Further, step 2) includes:

[0036] 21) System parameter definition: System parameter X S Used to describe the hardware configuration of the propulsion system, including turbine unit model STGP Number of batteries connected in parallel n p :

[0037]

[0038] 22) Definition of control parameters: Control parameter X C Used to describe power allocation strategies, including SOC threshold and turbine power regulation coefficient:

[0039]

[0040] 23) Construction of two parameter vectors: Combine system parameters and control parameters into a unified vector X:

[0041]

[0042] in, The feasible region is defined by engineering constraints and physical boundaries.

[0043] Further, step 3) includes:

[0044] 31) Construction of multi-objective function: integrating three key performance indicators: system quality, fuel consumption, and battery degradation. , , Constructing multi-objective vectors :

[0045]

[0046] Normalize it into a comprehensive objective J:

[0047]

[0048] in, For the target weight, For each target's actual value, , These are the upper and lower limits of the reference range, respectively.

[0049] 32) Safety Constraints: To ensure mission feasibility and hardware safety, thrust requirements must be met, the motor and turbine must be within their operational range, and the battery must be maintained within a safe SOC range. The following constraints are established:

[0050]

[0051] in, , For time t, the demand thrust and supply thrust are given. , , , These are the upper and lower speed limits for the motor and generator, respectively. , , , These are the upper and lower limits of torque for the motor and generator, respectively.

[0052] 33) Establishing a feasible optimization problem: Express the objective function and constraints in a unified manner as follows:

[0053]

[0054] Further, step 4) includes:

[0055] 41) Initialization and Multicenter Construction: In the Feasible Domain The initial original is generated using a space-filling sequence:

[0056]

[0057] in, Let i be the i-th initial solution.

[0058] During the iteration process, multiple representative solutions are saved to form a set:

[0059]

[0060] in, This is the optimal solution. This is a suboptimal solution. The third best solution. The average value is calculated based on historical advantages. This set serves as the central reference point for spiral updates, enhancing the diversity and stability of the search.

[0061] 42) Spiral Renewal and Gravitational Factor Adjustment: Generating New Candidate Solutions Using Spiral Trajectories:

[0062]

[0063] Where r is the helix radius factor. For rotation matrix, For the current global optimum, As the central candidate point, It is the gravitational factor.

[0064] The gravity factor adaptively adjusts based on whether a better solution is obtained:

[0065]

[0066] in, This is the attenuation coefficient.

[0067] The obtained new solution is projected onto the feasible region. This is to ensure that all physical constraints are met.

[0068] 43) Output of the optimal solution:

[0069] When the algorithm reaches the iteration limit or meets the convergence condition, it outputs the final optimization result:

[0070]

[0071] As the optimal coordination parameter configuration for a turbine-battery hybrid propulsion system.

[0072] An aircraft turbine hybrid electric propulsion system that implements the above-mentioned parameter collaborative optimization method includes a turbine hybrid electric propulsion system comprising a fuel power unit, a battery power unit, an electric drive propulsion unit, and an energy management unit. Its structure includes a turbine engine module, a reducer and generator module, an AC / DC converter module, a power battery module, a DC / DC converter module, an electrical bus module, a multi-rotor motor propulsion module, and an energy management module.

[0073] The turbine engine module, reducer and generator module, and AC / DC converter module together constitute the system's fuel power link, which is used to complete intake, combustion and power output, converting the mechanical power output by the engine into DC electrical energy and transmitting it to the electrical system.

[0074] The power battery module and the DC / DC converter module form an energy storage and supply link, which is used to store and release energy under different flight conditions, and provide stable DC power to the system through bidirectional voltage regulation, while supporting the charging and discharging management of the power battery.

[0075] The electrical bus module is used to collect DC power from the fuel power link and the power storage link and distribute it to the multi-rotor motor propulsion module. The multi-rotor motor propulsion module generates lift and thrust according to control commands to meet the needs of aircraft attitude maintenance, trajectory tracking and various flight missions.

[0076] The energy management module is used to obtain the system operating status, coordinate the power distribution of each energy link according to the preset rule-based management strategy, and send control commands to the turbine engine module and the power battery module to realize the management of system energy.

[0077] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0078] 1. This invention performs joint modeling of system structural parameters and energy management control parameters under a unified dynamic model, which can simultaneously reflect the coupling relationship between multiple energy links, thereby avoiding the insufficient adaptability problem caused by "independent structural design and later control matching" in traditional methods, and making the overall dynamic system configuration more coordinated and consistent.

[0079] 2. This invention employs an improved superspiral algorithm to search the high-dimensional coupled parameter space. Compared with traditional methods that rely on local iteration or single-center search, it can more effectively escape local optima, improve global optimization capabilities, and thus obtain parameter combinations that better meet the overall performance requirements of the system.

[0080] 3. The collaborative optimization results of the present invention can simultaneously take into account multiple performance indicators such as mass, fuel consumption, battery degradation and power demand matching, so that the system can maintain more stable energy distribution and power response characteristics under different flight missions such as take-off and landing, cruise, climb and maneuver, thereby improving overall operating efficiency and reliability. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of the turbine hybrid propulsion system of the present invention;

[0082] Figure 2 This is a flowchart illustrating the principle of the method of the present invention. Detailed Implementation

[0083] The present invention will now be described in detail with reference to the accompanying drawings.

[0084] like Figure 1 As shown, an aircraft turbine-hybrid electric propulsion system includes a fuel power unit, a battery power unit, an electric drive propulsion unit, and an energy management unit 4. The fuel power unit includes a turbine engine module 11, a reduction gear module 12, a generator module 13, and an AC / DC converter module 14; the battery power unit includes a power battery module 21 and a DC / DC converter module 22; and the electric drive propulsion unit includes an electrical bus module 31 and a multi-rotor motor propulsion module 32.

[0085] The turbine engine module 11, reducer module 12, generator module 13 and AC / DC converter module 14 together constitute the fuel power link of the system, which is used to complete intake, combustion and power output, converting the mechanical power output by the engine into DC electrical energy and transmitting it to the electrical system.

[0086] The power battery module 21 and the DC / DC converter module 22 constitute an energy storage and supply link, which is used to store and release energy under different flight conditions, and provide stable DC power to the system through bidirectional voltage regulation, while supporting the charging and discharging management of the power battery.

[0087] The electrical bus module 31 is used to collect DC power from the fuel power link and the power storage link and distribute it to the multi-rotor motor propulsion module 32. The multi-rotor motor propulsion module 32 generates lift and thrust according to control commands to meet the needs of aircraft attitude maintenance, trajectory tracking and various flight missions.

[0088] The energy management module 4 is used to obtain the system operating status, coordinate the power allocation of each energy link according to the preset energy and power scheduling rules, and send control commands to the turbine engine module 11 and the power battery module 21 to realize the management of system energy.

[0089] like Figure 2 As shown, a parameter co-optimization method for a turbine-hybrid electric propulsion system includes the following steps:

[0090] S1) A unified model is constructed for the aerodynamic characteristics of the turbine hybrid electric propulsion system, including the vertical and horizontal power requirements of the aircraft under typical airspace missions, the power output relationship between the propulsion motor and the turbine generator set, the SOC / SOH state change model of the battery, and the mass composition of the system. Based on this, the basic form of a regular energy management strategy based on the SOC threshold and the turbine power regulation coefficient is given.

[0091] Specifically, it includes:

[0092] 11) Flight Dynamics Modeling: For the power requirements of the turbine-hybrid electric propulsion system in typical aerial missions such as vertical takeoff and landing, climb / descent, and cruise, establish the vertical force requirement F for each. v Horizontal force requirement F h With total thrust requirement F req Model:

[0093]

[0094] Where M is the total mass of the aircraft, g is the acceleration due to gravity, and a v Let A be the vertical acceleration, ρ be the air density, and A be the vertical acceleration. v C is the area subjected to vertical force. D v is the air drag coefficient. v Let a be the vertical relative airflow velocity. h For horizontal acceleration, A h v is the horizontal force-bearing area. h This represents the horizontal relative airflow velocity.

[0095] Further, the required power P in the corresponding spatial domain is obtained. req :

[0096]

[0097] Among them, v air The resultant velocity is relative to the air.

[0098] 12) Modeling of the propulsion motor and turbine generator: The propulsion unit consists of a propulsion motor and a turbine generator set. The output power P of the propulsion motor... m and the output power P of the turbine generatorTGP Represented as:

[0099]

[0100] Among them, T m ω is the output torque of the motor. m η is the motor speed. m For motor efficiency, T e For the turbocharger output torque, ω e η is the turbine output speed. e This refers to the generator efficiency.

[0101] The fuel consumption of the turbo module is represented by a steady-state mapping:

[0102]

[0103] in, The instantaneous mass flow rate of fuel. This is the turbocharger steady-state fuel consumption characteristic function.

[0104] Total fuel consumption is expressed as:

[0105]

[0106] in, This represents the cumulative fuel consumption during the mission period. Let be the instantaneous mass flow rate of fuel at time t.

[0107] 13) Battery SOC and SOH Modeling: The charging and discharging behavior and degradation of the battery under long-term tasks are closely related. A port power model P for the battery needs to be established. b The SOC update model and SOH decay model are as follows:

[0108]

[0109] in, I is the open-circuit voltage related to the state of charge, and I is the current. Q is the internal resistance related to the state of charge, Ah is the battery's rated capacity, k and z are the cumulative discharge ampere-hours, and k and z are empirical decay coefficients.

[0110] 14) System Mass Model: The total system mass includes the mass of the machine structure, the turbine unit, and the battery, expressed as:

[0111]

[0112] Where M0 is the structural mass of the organism. For turbine specifications The determined unit quality, n s Let n be the number of batteries connected in series.p m is the number of batteries connected in parallel. cell This refers to the quality of a single battery cell.

[0113] 15) Rule-based energy management strategy design: Based on battery SOC and power demand, the power allocation rules between the turbine generator set and the power battery are given, as shown in Table 1. Utilizing SOC upper and lower limits... , and the turbine's adjustment coefficient in the high and low load power ranges. , Dividing the range determines the output power P of the turbine generator set. TGP and battery power P b .

[0114] Table 1

[0115] Among them, P TGP,h P TGP,l The output power of the turbine generator set in different load areas is represented by the value k. TGP,h k TGP,l The product of P and the rated power b,min P b,max These are the minimum and maximum power outputs of the battery.

[0116] S2) Based on the dynamic model and rule-based energy management strategy obtained in step S1), extract the structural and control parameters that affect system performance, including turbine selection, number of parallel batteries, SOC threshold, and turbine power regulation coefficient, and combine them into a joint parameter vector. Simultaneously, construct a corresponding feasible region to limit the selectable range of parameters.

[0117] Specifically, it includes:

[0118] 21) System parameter definition: System parameter X S Used to describe the hardware configuration of the propulsion system, including turbine unit model S TGP Number of batteries connected in parallel n p :

[0119]

[0120] 22) Definition of control parameters: Control parameter X C Used to describe power allocation strategies, including SOC threshold and turbine power regulation coefficient:

[0121]

[0122] 23) Construction of two parameter vectors: Combine system parameters and control parameters into a unified vector X:

[0123]

[0124] in, The feasible region is defined by engineering constraints and physical boundaries.

[0125] S3) Based on the parameter set in step S2), construct multi-objective performance indicators by combining system mass, fuel consumption, and battery degradation, and form a comprehensive evaluation function through normalization. At the same time, establish constraints for thrust satisfaction, motor / turbine operating boundaries, and battery SOC safety range, and unify various constraints into a feasible domain constraint model;

[0126] Specifically, it includes:

[0127] 31) Construction of multi-objective function: integrating three key performance indicators: system quality, fuel consumption, and battery degradation. , , Constructing multi-objective vectors :

[0128]

[0129] Normalize it into a comprehensive objective J:

[0130]

[0131] in, For the target weight, For each target's actual value, , These are the upper and lower limits of the reference range, respectively.

[0132] 32) Safety Constraints: To ensure mission feasibility and hardware safety, thrust requirements must be met, the motor and turbine must be within their operational range, and the battery must be maintained within a safe SOC range. The following constraints are established:

[0133]

[0134] in, , For time t, the demand thrust and supply thrust are given. , , , These are the upper and lower speed limits for the motor and generator, respectively. , , , These are the upper and lower limits of torque for the motor and generator, respectively.

[0135] 33) Establishing a feasible optimization problem: Express the objective function and constraints in a unified manner as follows:

[0136] .

[0137] S4) Based on the multi-objective and constraint model established in step S3), a multi-center spiral search method is used for optimization in the joint parameter space. By generating initial samples, constructing multi-center candidate points, performing spiral updates and adaptive gravity adjustment, and combining feasible region projection and performance calculation, the system structure parameters and energy management strategy that satisfy all constraints and have the best overall performance are obtained.

[0138] Specifically, it includes:

[0139] 41) Initialization and Multicenter Construction: In the Feasible Domain The initial original is generated using a space-filling sequence:

[0140]

[0141] in, Let i be the i-th initial solution.

[0142] During the iteration process, multiple representative solutions are saved to form a set:

[0143]

[0144] in, This is the optimal solution. This is a suboptimal solution. The third best solution. The average value is calculated based on historical advantages. This set serves as the central reference point for spiral updates, enhancing the diversity and stability of the search.

[0145] 42) Spiral Renewal and Gravitational Factor Adjustment: Generating New Candidate Solutions Using Spiral Trajectories:

[0146]

[0147] Where r is the helix radius factor. For rotation matrix, For the current global optimum, As the central candidate point, It is the gravitational factor.

[0148] The gravity factor adaptively adjusts based on whether a better solution is obtained:

[0149]

[0150] in, This is the attenuation coefficient.

[0151] The obtained new solution is projected onto the feasible region. This is to ensure that all physical constraints are met.

[0152] 43) Output of the optimal solution:

[0153] When the algorithm reaches the iteration limit or meets the convergence condition, it outputs the final optimization result:

[0154]

[0155] As the optimal coordination parameter configuration for a turbine-battery hybrid propulsion system.

Claims

1. A method for coordinated optimization of parameters in an aircraft turbine hybrid electric propulsion system, characterized in that, Includes the following steps: 1) A unified model is developed for the aerodynamic characteristics of the turbine hybrid electric propulsion system, including the vertical and horizontal power requirements of the aircraft under typical airspace missions, the power output relationship between the propulsion motor and the turbine generator set, the SOC / SOH state change model of the battery, and the mass composition of the system. A regular energy management strategy based on the SOC threshold and the turbine power regulation coefficient is also given. 2) Based on the dynamic model and regular energy management strategy obtained in step 1), extract the structural parameters and control parameters that affect the system performance, including turbine selection, number of parallel batteries, SOC threshold and turbine power regulation coefficient, and combine them into a joint parameter vector to construct the feasible region and limit the range of optional parameters. 3) Based on the parameters in step 2), construct multi-objective performance indicators by combining system mass, fuel consumption and battery degradation, and form a comprehensive evaluation function by normalization; establish constraints for thrust satisfaction, motor / turbine operating boundary and battery SOC safety range, and unify various constraints into a feasible domain constraint model; 4) Based on the multi-objective and constraint model established in step 3), the multi-center spiral search method is used for optimization in the joint parameter space. By generating initial samples, constructing multi-center candidate points, performing spiral updates and adaptive gravity adjustment, and combining feasible region projection and performance calculation, the system structure parameters and energy management strategy that satisfy all constraints and have the best overall performance are obtained.

2. The parameter co-optimization method for an aircraft turbine hybrid electric propulsion system according to claim 1, characterized in that, Step 1) specifically includes: 11) Flight Dynamics Modeling: For the power requirements of the turbine-hybrid electric propulsion system in typical aerial missions such as vertical takeoff and landing, climb / descent, and cruise, establish the vertical force requirement F for each. v Horizontal force requirement F h With total thrust requirement F req Model: ； Where M is the total mass of the aircraft, g is the acceleration due to gravity, and a v Let A be the vertical acceleration, ρ be the air density, and A be the vertical acceleration. v C is the area subjected to vertical force. D v is the air drag coefficient. v Let a be the vertical relative airflow velocity. h For horizontal acceleration, A h v is the horizontal force-bearing area. h This refers to the horizontal relative airflow velocity; To obtain the required power P in the corresponding spatial domain req : ； Among them, v air The resultant velocity relative to the air; 12) Modeling of the propulsion motor and turbine generator: The propulsion unit consists of a propulsion motor and a turbine generator set. The output power P of the propulsion motor is... m and the output electrical power P of the turbine generator TGP Represented as: ； Among them, T m ω is the output torque of the motor. m η is the motor speed. m For motor efficiency, T e For the turbocharger output torque, ω e η is the turbine output speed. e For generator efficiency; The fuel consumption of the turbo module is represented by a steady-state mapping: ； in, The instantaneous mass flow rate of fuel. This is the turbocharger steady-state fuel consumption characteristic function; Total fuel consumption is expressed as: ； in, This represents the cumulative fuel consumption during the mission period. Let be the instantaneous mass flow rate of fuel at time t; 13) Battery SOC and SOH Modeling: The charging and discharging behavior and degradation of the battery under long-term tasks are closely related. A port power model P for the battery needs to be established. b The SOC update model and SOH decay model are as follows: ； in, I is the open-circuit voltage related to the state of charge, and I is the current. Q is the internal resistance related to the state of charge, Ah is the rated capacity of the battery, k and z are the cumulative discharge ampere-hours, and k and z are the empirical decay coefficients. 14) System Mass Model: The total system mass includes the mass of the machine structure, the turbine generator set, and the battery, expressed as: ； Where M0 is the structural mass of the organism. For turbine specifications The determined unit quality, n s Let n be the number of batteries connected in series. p m is the number of batteries connected in parallel. cell For the quality of a single battery cell; 15) Rule-based energy management strategy design: Based on battery SOC and power demand, a power allocation rule between the turbine generator set and the power battery is proposed, utilizing the upper and lower limits of SOC. , and the turbine's adjustment coefficient in the high and low load power ranges. , Dividing the range determines the output power P of the turbine generator set. TGP and battery power P b P TGP,h P TGP,l The output power of the turbine generator set in different load areas is represented by the value k. TGP,h k TGP,l The product of P and the rated power b,min P b,max These are the minimum and maximum power outputs of the battery.

3. The parameter co-optimization method for an aircraft turbine hybrid electric propulsion system according to claim 2, characterized in that, Step 2) includes: 21) System parameter definition: System parameter X S Used to describe the hardware configuration of the propulsion system, including turbine unit model S TGP Number of batteries connected in parallel n p : ； 22) Definition of control parameters: Control parameter X C Used to describe power allocation strategies, including SOC threshold and turbine power regulation coefficient: ； 23) Construction of two parameter vectors: Combine system parameters and control parameters into a unified vector X: ； in, The feasible region is defined by engineering constraints and physical boundaries.

4. The parameter co-optimization method for an aircraft turbine hybrid electric propulsion system according to claim 3, characterized in that, Step 3) includes: 31) Construction of multi-objective function: integrating three key performance indicators: system quality, fuel consumption, and battery degradation. , , Constructing multi-objective vectors : ； Normalize it into a comprehensive objective J: ； in, For the target weight, For each target's actual value, , These are the upper and lower limits of the reference interval, respectively. 32) Safety Constraints: To ensure mission feasibility and hardware safety, thrust requirements must be met, the motor and turbine must be within their operational range, and the battery must be maintained within a safe SOC range. The following constraints are established: ； in, , For time t, the demand thrust and supply thrust are given. , , , These are the upper and lower speed limits for the motor and generator, respectively. , , , These are the upper and lower limits of torque for the motor and generator, respectively. 33) Establishing a feasible optimization problem: Express the objective function and constraints in a unified manner as follows: 。 5. The parameter co-optimization method for an aircraft turbine hybrid electric propulsion system according to claim 4, characterized in that, Step 4) includes: 41) Initialization and Multicenter Construction: In the Feasible Domain The initial original is generated using a space-filling sequence: ； in, This is the i-th initial solution; During the iteration process, multiple representative solutions are saved to form a set: ； in, This is the optimal solution. This is a suboptimal solution. The third best solution. The average value of the historical advantage solution is used as the central reference point for spiral updates, which enhances the diversity and stability of the search. 42) Spiral Renewal and Gravitational Factor Adjustment: Generating New Candidate Solutions Using Spiral Trajectories: ； Where r is the helix radius factor. Let be a rotation matrix. For the current global optimum, As the central candidate point, It is the gravitational factor; The gravity factor adaptively adjusts based on whether a better solution is obtained: ； in, The attenuation coefficient; The obtained new solution is projected onto the feasible region. This ensures that all physical constraints are met. 43) Optimal solution output: When the algorithm reaches the iteration limit or meets the convergence condition, it outputs the final optimization result: ； As the optimal coordination parameter configuration for a turbine-battery hybrid propulsion system.

6. The parameter co-optimization method for an aircraft turbine hybrid electric propulsion system according to claim 1, characterized in that, The electric propulsion system includes a fuel power unit, a battery power unit, an electric drive propulsion unit, and an energy management unit; The fuel-powered unit includes: a turbine engine module, a gearbox and generator module, and an AC / DC converter module; the battery-powered unit includes: a power battery module and a DC / DC converter module; the electric drive propulsion unit includes: an electrical bus module and a multi-rotor motor propulsion module. The turbine engine module, reducer and generator module, and AC / DC converter module together constitute the system's fuel power link, which is used to complete intake, combustion and power output, converting the mechanical power output by the engine into DC electrical energy and transmitting it to the electrical bus module. The power battery module and the DC / DC converter module form an energy storage and supply link, which is used to store and release energy under different flight conditions, and provide stable DC power to the system through bidirectional voltage regulation, while supporting the charging and discharging management of the power battery. The electrical bus module is used to collect DC power from the fuel power link and the power storage link and distribute it to the multi-rotor motor propulsion module. The multi-rotor motor propulsion module generates lift and thrust according to control commands to meet the needs of aircraft attitude maintenance, trajectory tracking and various flight missions. The energy management module is used to acquire the system operating status, coordinate the power distribution of each energy link according to the preset rule-based management strategy, and send control commands to the turbine engine module and the power battery module to realize the system energy management.

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