Electric aircraft propulsion system power-to-weight ratio sensitivity analysis and optimization design method

Abstract

The application belongs to the technical field of new energy power of aviation, and particularly discloses a power-weight ratio sensitivity analysis and optimization design method for an electric aircraft propulsion system, defines a power-energy constraint conversion coefficient, divides a power constraint region, an energy constraint region and a constraint conversion optimal interval, and establishes a battery parameter optimal matching criterion; local sensitivity coefficients and global sensitivity coefficients are calculated, a three-level sensitivity quantification system is constructed, and the corresponding research and development resource proportion of each parameter is quantitatively distributed; the global average interaction effect strength is defined, the parameters are divided into different interaction levels, and the corresponding design strategy is formulated; an optimization model is established with the maximum system power-weight ratio as the target, the power-weight ratios of all parameter combinations are calculated in combination with the battery parameter matching constraint, the technical feasibility constraint and the cost constraint, and the engineering optimal value interval of each parameter is output. The application solves the problem that the power-weight ratio optimization of the propulsion system lacks a systematic and quantitative design method.

Description

Technical Field

[0001] This invention belongs to the field of aviation new energy power technology, specifically relating to a method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system. Background Technology

[0002] With its significant advantages such as zero emissions, low noise, and low operating costs, electric aircraft have become the core development direction of next-generation aviation technology. Application scenarios such as urban air mobility (UAM), instant low-altitude logistics, emergency rescue, and medical transport are rapidly maturing. Electric vertical takeoff and landing (eVTOL) aircraft, as the core carrier of the low-altitude economy, have become a strategic high ground that global aviation industry and technology companies are vying to establish.

[0003] Power-to-weight ratio is a core indicator for evaluating the performance of an electric aircraft's propulsion system, directly determining its payload capacity, maximum range, climb performance, and flight safety. Unlike traditional fuel-powered aircraft, where the propulsion system accounts for only 20%–30% of the total empty weight, the propulsion system (including batteries, motors, controllers, and power distribution systems) of a pure electric aircraft accounts for as much as 40%–60% of its weight. Its performance optimization relies entirely on the precise matching of parameters for each component. Any unreasonable design of any parameter will be amplified through system coupling effects, ultimately leading to a significant decrease in overall aircraft performance.

[0004] However, the low power-to-weight ratio of current electric propulsion systems has become the biggest technical bottleneck restricting the commercialization of eVTOL. Limited by current battery technology, there is an inherent contradiction between battery energy density and power density. At the same time, improving the power-to-weight ratio of motors and controllers faces technical limitations in materials, heat dissipation, and integration. Against this backdrop, traditional sensitivity analysis methods based on experience and statistical sampling can no longer meet the demands of high-precision and high-efficiency engineering design. Traditional methods rely heavily on the engineering experience of designers for parameter trial and error, which not only results in long development cycles and high costs but also easily leads to problems such as over-design of parameters leading to weight redundancy or insufficient parameters leading to substandard performance, seriously hindering the industrialization process of electric aircraft.

[0005] The most similar implementation to this invention is: a hierarchical progressive design optimization method for the power system of an electric vertical takeoff and landing (eVTOL) aircraft (CN113704896B). This invention proposes a power-energy matching method based on mission profiles and a hierarchical parameter optimization process, dividing the power system design into three levels: system level, subsystem level, and component level. Through multidisciplinary design optimization, the performance of the power system is improved. A rapid optimization design method for the electric propulsion system of a vertical takeoff and landing fixed-wing aircraft (CN115964796A) establishes performance and mass models of the electric motor, power converter, and variable-pitch propeller, forming a non-convex optimization model of the electric propulsion system. Initial values ​​are quickly determined through a convex optimization analytical model, and then the gradient descent method is used to solve the problem, achieving a balance between the thrust-to-weight ratio and propulsion efficiency of the propulsion system.

[0006] Current power-to-weight ratio optimization design methods for electric aircraft propulsion systems can be mainly divided into two categories: "hierarchical progressive multidisciplinary optimization" and "single-parameter disturbance sensitivity analysis".

[0007] The former, aiming to reduce the computational complexity of multidisciplinary coupled optimization, decomposes the power system design into three levels: system level, subsystem level, and component level. It improves system performance by iteratively matching the parameters of each component at each level. However, this design method cannot quantitatively distinguish between the power constraints and energy constraints of the system, which can easily lead to incorrect optimization direction, resulting in over-design of parameters or insufficient performance. Furthermore, it does not quantify the interactive synergistic effects between parameters, and optimizing a single parameter alone cannot maximize system performance. At the same time, the analysis results are heavily dependent on specific benchmark schemes, have poor versatility, and are difficult to guide the serial development of aircraft with different mission profiles and different tonnages.

[0008] The latter approach aims to select key parameters that significantly impact system performance. It calculates the impact of changing individual parameters one by one on the power-to-weight ratio and iteratively optimizes parameters based on sensitivity ranking. However, this design method can only provide qualitative conclusions and cannot offer quantitative criteria that can be directly used in engineering design. This results in a design that heavily relies on trial and error and cannot quantitatively allocate R&D resources based on the degree of parameter impact. Consequently, it is prone to serious resource misallocation problems, such as insufficient investment in highly sensitive parameters and excessive investment in low-sensitive parameters. Summary of the Invention

[0009] The purpose of this invention is to address the lack of a systematic quantitative design method for optimizing the power-to-weight ratio of propulsion systems, and to propose a method for sensitivity analysis and optimization design of the power-to-weight ratio of electric aircraft propulsion systems.

[0010] The technical solution of the present invention is as follows: Firstly, a method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system, comprising the following steps: Power-energy constraint conversion judgment and battery parameter matching: Calculate the peak mechanical power and total mechanical energy demand of the mission based on the mission profile of the aircraft, define the power-energy constraint conversion coefficient, quantitatively divide the power constraint zone, energy constraint zone and the optimal constraint conversion interval, establish the optimal matching criterion for battery parameters, and obtain the battery pack weight in the power constraint zone and energy constraint zone. Single-parameter sensitivity quantification and R&D resource allocation: Local sensitivity coefficient and global sensitivity coefficient are calculated separately to construct a three-level sensitivity quantification system, and the R&D resource ratio corresponding to each parameter is quantitatively allocated based on the global sensitivity coefficient. Two-parameter interaction effect quantification and collaborative design: Define the global average interaction effect strength, divide the parameters into different interaction levels, and formulate corresponding design strategies; Multi-parameter global optimization and engineering selection: Based on battery pack weight and design strategy, an optimization model is established with the goal of maximizing the system power-to-weight ratio. Combining battery parameter matching constraints, technical feasibility constraints and cost constraints, the power-to-weight ratio of all parameter combinations is calculated, combinations with a power-to-weight ratio greater than a preset threshold are selected, and the optimal engineering value range of each parameter is output.

[0011] As a preferred method, the quantitative division of the power constraint region, energy constraint region, and optimal constraint transition interval is as follows: when At that time, the propulsion system of the electric aircraft was in a power-constrained region, and the battery weight was determined by the power demand. Therefore, optimization focused on battery power density. Indicates the power-energy constraint conversion factor. Indicates battery power density. Indicates battery energy density; when At that time, the propulsion system of electric aircraft is in the energy-constrained region, the weight of the battery is determined by the energy demand, and the optimization direction focuses on the battery energy density; when At that time, the electric aircraft propulsion system is in the optimal range of constraint transition.

[0012] Preferably, the battery pack weight in the power-constrained region is:

[0013]

[0014] in, This refers to the weight of the battery pack. Indicates the peak mechanical power of the mission. For the overall system efficiency, , , These are the weight coefficients for battery structural components, cooling system, and management system, respectively. The battery pack weight in the energy-constrained region is: .

[0015] Preferably, the local sensitivity coefficient is used to guide the iterative optimization of parameters within a local range. for:

[0016] in, As the baseline power-to-weight ratio, For parameters The baseline value, This indicates the change in the baseline power-to-weight ratio. Indicates the parameter at the reference parameter point The change in; Global sensitivity coefficient for:

[0017] in, , , Parameters The maximum, minimum, and average values ​​of the system power-to-weight ratio when varying within the range of values.

[0018] As a preferred option, the three-level sensitivity quantification system is as follows: Highly sensitive parameters: ; Medium-sensitive parameters: ; Low-sensitivity parameters: ; The formula for calculating the proportion of R&D resources allocated to each parameter based on the global sensitivity coefficient is as follows:

[0019] in, To assign to parameters The proportion of R&D resources Indicates parameters The global sensitivity coefficient, This indicates the total number of parameters.

[0020] As a preferred option, the global average interaction effect strength for:

[0021] in, Indicates parameters Take horizontal ,parameter Take horizontal The system power-to-weight ratio at that time , .

[0022] As a preferred method, the specific approach to dividing the parameters into different interaction levels is as follows: Extremely interactive: joint design is required, and parameter matching relationships take precedence over the absolute value of individual parameters; Strong interaction: Prioritize joint optimization, and determine one parameter before determining another. Medium-level interaction: Independently designed, with matching and verification conducted in the final stage; Weak interaction: Completely independent design, no need to consider parameter matching relationships.

[0023] As a preferred option, the objective function for optimizing the model is:

[0024] in, This indicates the maximum power-to-weight ratio of the system. Indicates the peak mechanical power of the mission. This refers to the weight of the battery pack. For the weight of the power distribution system, This represents the total weight of the motor and controller.

[0025] The beneficial effects of this invention are: 1. The power-to-weight ratio sensitivity analysis and optimization design method for electric aircraft propulsion systems proposed in this invention can quantitatively determine the system constraint type in advance based on the mission profile. At the same time, by combining single-parameter sensitivity quantification and two-parameter interaction effect evaluation, the priority of parameter optimization and collaborative design strategy can be clarified to maximize the improvement of the power-to-weight ratio of the propulsion system.

[0026] 2. The design method of this invention uses the peak power and total energy requirements of the mission profile as the core design inputs. While maximizing the system's power-to-weight ratio, it quantitatively determines the system constraint type using the power-energy constraint conversion coefficient. Through a combination of single-parameter sensitivity quantification and two-parameter interaction effect evaluation, it clarifies parameter optimization priorities and collaborative design strategies. This allows for a significant improvement in the propulsion system's power-to-weight ratio while also meeting the need for efficient utilization of R&D resources. Furthermore, the design method of this invention is based on analytical theoretical derivation, does not rely on a specific benchmark scheme, and is applicable to pure electric configuration aircraft of different mission profiles and tonnages, possessing good versatility and engineering practicality.

[0027] In a second aspect, there is a power-to-weight ratio sensitivity analysis and optimization design system for an electric aircraft propulsion system, comprising a processor for executing the power-to-weight ratio sensitivity analysis and optimization design method for an electric aircraft propulsion system as described in the first aspect.

[0028] Thirdly, a computer-readable storage medium stores computer instructions, and in response to a computer reading the computer instructions in the storage medium, the computer executes the power-to-weight ratio sensitivity analysis and optimization design method for an electric aircraft propulsion system as described in the first aspect. Attached Figure Description

[0029] Figure 1 The diagram shows a flowchart of a power-to-weight ratio sensitivity analysis and optimization design method for an electric aircraft propulsion system.

[0030] Figure 2 The figure shows a three-dimensional surface plot illustrating the combined effect of the battery's two parameters on the system's power-to-weight ratio.

[0031] Figure 3 The image shows a contour plot of the constraint transformation line and the location of the reference parameter points.

[0032] Figure 4 The figure shown is a sensitivity analysis diagram of the bus voltage.

[0033] Figure 5 The figure shown is a sensitivity analysis diagram of battery energy density.

[0034] Figure 6 The figure shown is a sensitivity analysis diagram of battery power density.

[0035] Figure 7 The figure shown is a sensitivity analysis diagram of the power density of a direct-drive motor.

[0036] Figure 8 The figure shown is a sensitivity analysis diagram of the controller's power density.

[0037] Figure 9 The image shows a multi-parameter sensitivity ranking chart.

[0038] Figure 10 The figure shown is a thermogram of the average power-to-weight ratio of bus voltage and battery energy.

[0039] Figure 11 The figure shown is a heat map of the average power-to-weight ratio of bus voltage and battery power.

[0040] Figure 12 The figure shown is a thermogram of the average power-to-weight ratio of bus voltage and motor density.

[0041] Figure 13 The figure shows the average power-to-weight ratio thermogram of bus voltage and controller density.

[0042] Figure 14 The figure shown is a thermogram of the average power-to-weight ratio of battery energy and battery power.

[0043] Figure 15 The figure shown is a thermogram of the average power-to-weight ratio of battery energy and motor density.

[0044] Figure 16 The figure shown is a thermogram of the average power-to-weight ratio of battery energy to controller density.

[0045] Figure 17 The figure shown is a heat map of the average power-to-weight ratio of battery power and motor density.

[0046] Figure 18 The figure shown is a heat map of the average power-to-weight ratio of battery power and controller density.

[0047] Figure 19 The figure shown is a heat map of the average power-to-weight ratio of motor density and controller density.

[0048] Figure 20 The figure shows the main effect curve of the bus voltage.

[0049] Figure 21 The figure shows the main effect curve of battery energy density.

[0050] Figure 22 The figure shows the main effect curve of battery power density.

[0051] Figure 23 The figure shows the density main effect curve of a direct drive motor.

[0052] Figure 24 The figure shows the main effect curve of the controller density.

[0053] Figure 25 The figure shows the histogram of the power-to-weight ratio distribution of the traversal parameter combinations. Detailed Implementation

[0054] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0055] Example 1: like Figure 1 As shown, a method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system includes the following steps: S1: Power-Energy Constraint Conversion Judgment and Battery Parameter Matching: Calculate the peak mechanical power and total mechanical energy demand of the mission based on the mission profile of the aircraft, define the power-energy constraint conversion coefficient, quantitatively divide the power constraint zone, energy constraint zone and the optimal constraint conversion interval, establish the optimal matching criterion for battery parameters, and obtain the battery pack weight in the power constraint zone and energy constraint zone. S2: Single-parameter sensitivity quantification and R&D resource allocation: Calculate the local sensitivity coefficient and the global sensitivity coefficient respectively, construct a three-level sensitivity quantification system, and quantitatively allocate the R&D resource ratio corresponding to each parameter based on the global sensitivity coefficient; S3: Two-parameter interaction effect quantification and collaborative design: Define the global average interaction effect strength, divide the parameters into different interaction levels, and formulate corresponding design strategies; S4: Multi-parameter global optimization and engineering selection: Based on the battery pack weight and design strategy, an optimization model is established with the goal of maximizing the system power-to-weight ratio. Combining battery parameter matching constraints, technical feasibility constraints and cost constraints, the power-to-weight ratio of all parameter combinations is calculated, combinations with a power-to-weight ratio greater than a preset threshold are selected, and the optimal engineering value range of each parameter is output.

[0056] In this embodiment, step S1 specifically includes: S11. Calculate the peak mechanical power of the mission based on the spacecraft mission profile. Total mechanical energy demand Define the power-energy constraint conversion coefficient. : .

[0057] The power-energy constraint conversion factor is only related to the mission profile and is not related to the component technical parameters.

[0058] S12. Quantitatively determine the type of system constraints: when At this point, the system is in a power-constrained region, and the battery weight is determined by the power demand; therefore, the optimization focus is on battery power density. Indicates battery power density. Indicates battery energy density; when At this time, the system is in the energy-constrained region, the battery weight is determined by the energy demand, and the optimization direction focuses on the battery energy density; when At this point, the system is in the optimal range for constraint transformation, and there is no waste of battery parameters.

[0059] S13. In engineering design, reserve a power safety margin of 5% to 10% and determine the optimal matching criteria for battery parameters: .

[0060] S14. Derive the formulas for calculating the total weight of the battery pack in the power-constrained region and the energy-constrained region respectively: Power-constrained region:

[0061] in, This refers to the weight of the battery pack. For the overall system efficiency, , , These are the weight coefficients for battery structural components, cooling system, and management system, respectively.

[0062] Energy-constrained region: .

[0063] In this embodiment, step S2 specifically includes: S21. Calculate the local sensitivity coefficient , indicating the parameter at the reference parameter point The percentage change in system power-to-weight ratio when the change is 1%:

[0064] in, As the baseline power-to-weight ratio, For parameters The baseline value, This indicates the change in the baseline power-to-weight ratio. Indicates the parameter at the reference parameter point The local sensitivity coefficient represents the magnitude of performance change resulting from fine-tuning a certain parameter based on the current design. As a dimensionless variable, the local sensitivity coefficient enables cross-parameter comparisons and can guide iterative optimization of parameters within a local range.

[0065] S22. Calculate the global sensitivity coefficient. This reflects the overall impact of the parameter on the system's power-to-weight ratio across its entire value range.

[0066] in, , , Parameters The maximum, minimum, and average values ​​of the system power-to-weight ratio when varying within the range of values.

[0067] S23. Divide the parameters into three sensitivity levels: Highly sensitive parameters: ; Medium-sensitive parameters: ; Low-sensitivity parameters: .

[0068] Sensitivity level is an engineered simplification of the global sensitivity coefficient. Essentially, it divides the global sensitivity coefficient into several levels according to intervals.

[0069] S24. Quantitative allocation of R&D resources based on global sensitivity coefficient:

[0070] in, To assign to parameters The proportion of R&D resources Indicates parameters Global sensitivity system This indicates the total number of parameters.

[0071] In this embodiment, step S3 specifically includes: S31. Define the global average interaction strength The difference between the performance gain when two parameters change simultaneously and the sum of the gains when they change individually is quantified.

[0072] in, Indicates parameters Take horizontal ,parameter Take horizontal The system power-to-weight ratio at that time , .

[0073] S32. Classify the interaction effects based on their intensity and formulate design strategies: Highly interactive ): Joint design is required, and parameter matching takes precedence over the absolute value of individual parameters; Strong Interaction ): Prioritize joint optimization, determining one parameter before determining the next; Medium interaction ( ): Can be designed independently, with matching and verification performed in the final stage; Weak interaction ( ): Completely independent design, no need to consider parameter matching relationships.

[0074] In this embodiment, step S4 specifically includes: S41. Establish an optimization objective function with the goal of maximizing the system's power-to-weight ratio:

[0075] in, This refers to the weight of the battery pack. For the weight of the power distribution system, This represents the total weight of the motor and controller.

[0076] S42. Set project constraints: Battery parameter matching constraints:

[0077] Technical feasibility constraints: All parameters are within the current or foreseeable technological range; Cost constraint: The values ​​of key parameters should not lead to a sharp increase in costs.

[0078] S43. The power-to-weight ratio of all parameter combinations is calculated using the parameter traversal method. Combinations with a power-to-weight ratio greater than 95% of the global optimal value are selected, and the optimal range of engineering values ​​for each parameter is statistically obtained.

[0079] In this embodiment, the invention can be designed to adjust constraint conversion coefficients and battery parameter matching criteria for different mission profiles (such as 30-minute short-distance commuting, 90-minute intercity flights, and 120-minute logistics delivery); for electric aircraft of different tonnages (500kg, 1-ton, and 2-ton classes), the optimal design range can be obtained by adjusting the parameter value range; the parameter traversal method can be replaced with intelligent optimization algorithms such as genetic algorithms and particle swarm optimization algorithms to further improve the optimization efficiency under large parameter spaces; it can be extended to the design of hybrid electric aircraft propulsion systems by adding fuel engine power parameters to establish a constraint conversion and sensitivity analysis system for hybrid power systems. Subsequently, this invention can be extended to the propulsion system design of other electric configuration aircraft such as electric fixed-wing aircraft and electric helicopters; it can be used as a technical evaluation tool for electric aircraft propulsion system R&D projects to evaluate the performance potential of different technical routes; and it can be integrated into aircraft design simulation software to form a standardized propulsion system design module.

[0080] This invention addresses the engineering pain points of traditional sensitivity analysis methods in the design of electric aircraft propulsion systems. It solves the core technical problem of the lack of a systematic quantitative design method for optimizing the power-to-weight ratio of propulsion systems. Specifically, in the scheme demonstration stage, traditional methods can only provide qualitative conclusions and cannot provide quantitative criteria that can be directly used for engineering design, resulting in a high dependence on experience-based trial and error. They cannot accurately distinguish between the power constraints and energy constraints of the system, often leading to incorrect optimization directions, resulting in over-design of parameters or insufficient performance. They do not quantify the interactive synergistic effects between parameters, and optimizing a single parameter alone cannot maximize system performance, resulting in serious waste of R&D resources. Moreover, the analysis results are heavily dependent on specific benchmark schemes, with poor universality, making it difficult to guide the serial development of aircraft with different mission profiles and tonnages. Therefore, this invention can take into account both the need to improve the power-to-weight ratio of propulsion systems and the need for efficient use of R&D resources, achieving a dual comprehensive benefit of design efficiency and system performance. At the same time, the method system established by this invention is based on analytical theoretical derivation, does not depend on specific benchmark schemes, has good universality and scalability, and can be directly applied to the propulsion system design of various pure electric configuration aircraft, demonstrating good engineering practicality.

[0081] In the overall design of an aircraft, the power-to-weight ratio requirement of the propulsion system is a core design indicator, which should be clearly defined and used as a design input during the feasibility study phase. Therefore, the design method of this invention uses the peak power and total energy requirements of the mission profile as core design inputs. While ensuring the maximization of the system's power-to-weight ratio, it quantitatively determines the system constraint type using the power-energy constraint conversion coefficient. Through a combination of single-parameter sensitivity quantification and two-parameter interaction effect evaluation, it clarifies parameter optimization priorities and collaborative design strategies, thereby achieving a significant improvement in the propulsion system's power-to-weight ratio while also considering the need for efficient utilization of R&D resources.

[0082] Example 2: Based on Example 1, this embodiment of the invention uses a typical 60-minute intercity commute pure electric direct drive eVTOL as a verification case to achieve high-confidence full-factor numerical simulation verification. The verification results are as follows.

[0083] Constraint type determination: Calculate the constraint transformation coefficients. The baseline battery power density of 1250W / kg is slightly higher than the critical value of 1134W / kg, which is in the optimal range at the edge of the energy constraint, with no parameter waste.

[0084] Local sensitivity analysis: Based on the local sensitivity analysis, battery energy density is a locally highly sensitive parameter, battery power density and direct drive motor power density are locally moderately sensitive parameters, and bus voltage and controller power density are locally low-sensitive parameters. The calculation results of the local sensitivity coefficients are shown in Table 1, which lists the local sensitivity coefficients and sensitivity levels of each core parameter.

[0085] Table 1 Calculation results of local sensitivity coefficient

[0086] Global sensitivity analysis: Battery power density and battery energy density are globally highly sensitive parameters, accounting for approximately 40.3% and 26.9% of R&D resources, respectively; bus voltage and direct drive motor power density are globally moderately sensitive parameters, accounting for approximately 19.4% and 8.2% of R&D resources, respectively; controller power density is a globally low-sensitive parameter, accounting for approximately 5.2% of R&D resources. Table 2 shows the global sensitivity coefficients and corresponding R&D resource allocations for each core parameter.

[0087] Table 2 Global Sensitivity Coefficient and R&D Resource Allocation

[0088] Interaction effect analysis: The interaction strength between battery energy density and battery power density reaches 3.07, which is an extremely strong interaction, requiring joint design; the interaction between battery parameters and bus voltage is strong, requiring collaborative optimization. The intensity classification of the two-parameter interaction effects is shown in Table 3, which lists the interaction effect intensity, level, and design strategy for all parameter pairs.

[0089] Table 3. Grading of the intensity of the two-parameter interaction effect

[0090] Global optimization results: The optimal parameter range for the project was selected from 243 parameter combinations, and the globally optimal power-to-weight ratio reached 0.818 kW / kg, an improvement of 73.4% compared to the baseline scheme of 0.472 kW / kg. The optimal parameter range for the project is shown in Table 4, which lists the optimal value range and optimization explanation for each core parameter.

[0091] Table 4 Optimal Parameter Ranges for the Project

[0092] Figure 2 and Figure 3 The results show the combined effects of the two battery parameters. Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 The system intuitively displays the order of how much each core parameter affects the system's power-to-weight ratio. Figure 10 , Figure 11 , Figure 12 , Figure 13 , Figure 14 , Figure 15 , Figure 16 , Figure 17 , Figure 18 and Figure 19 This is a heat map of the average power-to-weight ratio for a two-parameter combination. The shades of the color indicate the level of the average power-to-weight ratio of the system under the corresponding parameter combination. Figure 20 , Figure 21 , Figure 22 , Figure 23 and Figure 24 The main effect curves of all factors show the trend of the influence of changes in the level of each parameter on the average power-to-weight ratio of the system. Figure 25 It is a histogram of the power-to-weight ratio distribution of traversing parameter combinations, showing the power-to-weight ratio distribution characteristics and global optimal values ​​of 243 parameter combinations.

[0093] This invention employs a full-process quantitative design approach, encompassing power-energy constraint conversion determination, single-parameter sensitivity quantification, dual-parameter interaction effect evaluation, and multi-parameter global optimization. This enables the system to accurately distinguish between power and energy constraints, clarify parameter optimization priorities, quantify the synergistic effects between parameters, and quickly output engineering-feasible parameter ranges. This balances the need to improve the power-to-weight ratio of the propulsion system with the need for efficient utilization of R&D resources, achieving a dual comprehensive benefit of design efficiency and system performance. Furthermore, the methodology established in this invention does not rely on a specific benchmark scheme and can be directly applied to pure electric configuration aircraft with different mission profiles and tonnages. High-confidence full-factor numerical simulation results demonstrate that, using a typical 60-minute intercity commuting pure electric direct-drive eVTOL as a verification case, the optimal parameter combination can improve the system's power-to-weight ratio by 73.4% compared to the benchmark scheme.

[0094] Example 3: Based on Example 1, this embodiment of the invention provides a power-to-weight ratio sensitivity analysis and optimization design system for an electric aircraft propulsion system, which can be used to implement the power-to-weight ratio sensitivity analysis and optimization design method for an electric aircraft propulsion system as described in the foregoing embodiments.

[0095] In this embodiment, the system may be an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement some or all of the steps of the method described in Embodiment 1.

[0096] In this embodiment, the electronic device may include: a processor, a memory, a bus, and a communication interface. The processor, the communication interface, and the memory are connected via the bus. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it performs some or all of the steps of the method provided in the foregoing embodiment 1 of this application.

[0097] The system in this embodiment of the invention may also be a computer-readable storage medium storing a computer program that, when executed, implements some or all of the steps of the method described in Embodiment 1.

[0098] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0099] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0100] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0101] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0102] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0103] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system, characterized in that, Includes the following steps: Power-energy constraint conversion judgment and battery parameter matching: Calculate the peak mechanical power and total mechanical energy demand of the mission based on the mission profile of the aircraft, define the power-energy constraint conversion coefficient, quantitatively divide the power constraint zone, energy constraint zone and the optimal constraint conversion interval, establish the optimal matching criterion for battery parameters, and obtain the battery pack weight in the power constraint zone and energy constraint zone. Single-parameter sensitivity quantification and R&D resource allocation: Calculate local sensitivity coefficient and global sensitivity coefficient, construct a three-level sensitivity quantification system, and quantitatively allocate the R&D resource ratio corresponding to each parameter based on the global sensitivity coefficient; Two-parameter interaction effect quantification and collaborative design: Define the global average interaction effect strength, divide the parameters into different interaction levels, and formulate corresponding design strategies; Multi-parameter global optimization and engineering selection: Based on battery pack weight and design strategy, an optimization model is established with the goal of maximizing the system power-to-weight ratio. Combining battery parameter matching constraints, technical feasibility constraints and cost constraints, the power-to-weight ratio of all parameter combinations is calculated, combinations with a power-to-weight ratio greater than a preset threshold are selected, and the optimal engineering value range of each parameter is output.

2. The method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system according to claim 1, characterized in that, The method for quantitatively dividing the power constraint region, energy constraint region, and optimal constraint transition region is as follows: when At that time, the propulsion system of the electric aircraft was in a power-constrained region, and the battery weight was determined by the power demand. Therefore, optimization focused on battery power density. Indicates the power-energy constraint conversion factor. Indicates battery power density. Indicates battery energy density; when At that time, the propulsion system of electric aircraft is in the energy-constrained region, the weight of the battery is determined by the energy demand, and the optimization direction focuses on the battery energy density; when At that time, the electric aircraft propulsion system is in the optimal range of constraint transition.

3. The method for sensitivity analysis and optimization design of power-to-weight ratio of electric aircraft propulsion system according to claim 2, characterized in that, The battery pack weight in the power-constrained region is: in, This refers to the weight of the battery pack. Indicates the peak mechanical power of the mission. For the overall system efficiency, , , These are the weight coefficients for battery structural components, cooling system, and management system, respectively. The battery pack weight in the energy-constrained region is: 。 4. The method for sensitivity analysis and optimization design of power-to-weight ratio of electric aircraft propulsion system according to claim 1, characterized in that, The local sensitivity coefficient is used to guide the iterative optimization of parameters within a local range. for: in, As the baseline power-to-weight ratio, For parameters The baseline value, This indicates the change in the baseline power-to-weight ratio. Indicates the parameter at the reference parameter point The change in; Global sensitivity coefficient for: in, , , Parameters The maximum, minimum, and average values ​​of the system power-to-weight ratio when varying within the range of values.

5. The method for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system according to claim 4, characterized in that, The three-level sensitivity quantification system is as follows: Highly sensitive parameters: ; Medium-sensitive parameters: ; Low-sensitivity parameters: ; The formula for calculating the proportion of R&D resources allocated to each parameter based on the global sensitivity coefficient is as follows: in, To assign to parameters The proportion of R&D resources Indicates parameters The global sensitivity coefficient, This indicates the total number of parameters.

6. The method for sensitivity analysis and optimization design of power-to-weight ratio of electric aircraft propulsion system according to claim 1, characterized in that, Global average interaction strength for: in, Indicates parameters Take horizontal ,parameter Take horizontal The system power-to-weight ratio at that time , .

7. The method for sensitivity analysis and optimization design of power-to-weight ratio of electric aircraft propulsion system according to claim 6, characterized in that, The specific method for dividing parameters into different interaction levels is as follows: Extremely interactive: joint design is required, and parameter matching relationships take precedence over the absolute value of individual parameters; Strong interaction: Prioritize joint optimization, and determine one parameter before determining another. Medium-level interaction: Independently designed, with matching and verification conducted in the final stage; Weak interaction: Completely independent design, no need to consider parameter matching relationships.

8. The method for sensitivity analysis and optimization design of power-to-weight ratio of electric aircraft propulsion system according to claim 6, characterized in that, The objective function for optimizing the model is: in, This indicates the maximum power-to-weight ratio of the system. Indicates the peak mechanical power of the mission. This refers to the weight of the battery pack. For the weight of the power distribution system, This represents the total weight of the motor and controller.

9. A system for sensitivity analysis and optimization design of the power-to-weight ratio of an electric aircraft propulsion system, characterized in that, Includes a processor, said processor being used to execute the power-to-weight ratio sensitivity analysis and optimization design method for the electric aircraft propulsion system according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. In response to the computer reading the computer instructions in the storage medium, the computer executes the power-to-weight ratio sensitivity analysis and optimization design method for the electric aircraft propulsion system as described in any one of claims 1-8.

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