Multi-objective optimization configuration method, device and storage medium for aircraft power supply system

Abstract

The present invention discloses a method, device, and storage medium for optimizing the configuration of an aircraft power system. The method utilizes an optimized two-stage low-pass filtering energy management strategy to obtain three different power signals, which are respectively distributed to the generator, lithium battery system, and supercapacitor system of a high-voltage direct current power system. This method implements intelligent optimized configuration of a power system containing high-power pulse loads, can achieve optimized performance configuration of the power system with light weight, small size, and high efficiency, and provides guidance for capacity design of aircraft power systems containing high-power pulse loads.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and in particular relates to a configuration method for a high-voltage direct current power supply system of an aircraft power grid. Background Art

[0002] With increasing demands for reliability, maintainability, and fuel economy, more and all-electric aircraft have become a development trend in both civil and military aircraft, providing a crucial path to supporting green aviation and improving tactical performance. More Electric Aircraft (AEA) and All Electric Aircraft (AEA) gradually integrate secondary energy sources such as air pressure, hydraulic, and mechanical energy into electrical energy, simplifying system structure, improving reliability and maintainability, while also increasing overall system efficiency and reducing fuel consumption. The power of aircraft electrical loads is increasing.

[0003] High voltage direct current (HVDC) power supply systems have significant advantages in terms of reliability, maintainability, cost, weight and power supply quality.

[0004] Improving the efficiency of aircraft energy systems has become a crucial factor in enhancing flight platform performance. Due to the significant increase in power consumption by electronic equipment, the generated power of aircraft power systems has increased significantly from over 100 kW to 1000 kW. This large power requirement poses a significant challenge to generator development. By analyzing the load characteristics of future advanced aircraft, it is found that the base power demand of a 1000 kW load is approximately 400 kW, with the remainder being short-term or transient loads. By shifting from the traditional design concept based on peak load to using base power as the generator design point, and incorporating a new energy topology, the design capacity of the engine-driven generator can be reduced, thereby simplifying product development and improving system reliability. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-objective optimization configuration method for an aircraft high-voltage DC power supply system, so as to solve the problem of irrational configuration of an aircraft high-voltage DC power supply system in the prior art.

[0006] In order to achieve the above object, the present invention provides the following solutions.

[0007] A multi-objective optimization configuration method for an aircraft power supply system, characterized in that energy management of the aircraft high-voltage direct current power supply system is implemented using a two-stage low-pass filter; the multi-objective optimization configuration method comprises:

[0008] Input load demand power signal;

[0009] performing a first-stage low-pass filtering on the load demand power signal to obtain a first power signal having a frequency range of [0, f1], where f1 is a cutoff frequency of the first-stage low-pass filtering, and the power of the first power signal is the response power of the generator of the aircraft high-voltage direct current power system;

[0010] subtracting the load demand power signal from the first power signal to obtain a first difference signal;

[0011] Performing a second-stage low-pass filtering on the first difference signal to obtain a second power signal with a frequency range of (f1, f2], where f2 is the cutoff frequency of the second-stage low-pass filtering, and the power of the second power signal is the response power of the lithium battery system of the aircraft high-voltage direct current power system;

[0012] subtracting the first difference signal from the second power signal to obtain a third power signal, wherein the power of the third power signal is the response power of the supercapacitor system of the aircraft high-voltage DC power system;

[0013] Calculate the configuration parameters of the generator, lithium battery system, and supercapacitor system respectively according to the response power of the generator, lithium battery system, and supercapacitor system;

[0014] Calculate the multi-objective optimization configuration parameters of the aircraft high voltage DC power system, including the overall system weight, volume, and efficiency;

[0015] Configure the aircraft high voltage DC power supply system according to the optimized configuration parameter results.

[0016] The beneficial effects of the present invention are:

[0017] The present invention proposes a multi-objective optimization configuration method for an aircraft high-voltage direct current (HVDC) power supply system. This method utilizes an optimized two-stage low-pass filtering energy management strategy to obtain three different power signals, which are distributed to the HVDC system's generator, lithium battery system, and supercapacitor system, respectively. This method implements intelligent optimization configuration of the power supply system containing high-power pulse loads, enabling optimized performance configuration with light weight, small size, and high efficiency. This method provides guidance for the capacity design of aircraft power supply systems containing high-power pulse loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural block diagram of the aircraft high-voltage DC power supply system of the present invention.

[0019] Figure 2 It is a schematic diagram of the energy management system of the aircraft high voltage DC power supply system of the present invention.

[0020] Figure 3 This is a schematic diagram of power distribution between the main power supply and energy storage device of the aircraft high-voltage DC power supply system of the present invention.

[0021] Figure 4 This is a multi-objective optimization configuration process for an aircraft high-voltage DC power supply system of the present invention.

[0022] Figure 5 This is a schematic diagram of the HESS device configuration calculation ideas of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It will be understood that the specific examples described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, only portions relevant to the present invention, rather than all structures, are shown in the accompanying drawings.

[0024] Example 1

[0025] This embodiment provides a multi-objective optimization configuration method for an aircraft power system. The aircraft power system is a high-voltage DC power system, and its structure is as follows: Figure 1 As shown, the system includes a generator, a battery pack, a supercapacitor pack, and a DC / DC converter connected to the battery and supercapacitor, which can meet the power needs of high-power and pulsating loads. The aircraft high-voltage DC power supply system of this embodiment adopts the following system energy management: Figure 2 The two-stage low-pass filtering implementation shown in FIG. 1 includes the following steps for multi-objective optimization configuration:

[0026] Step s1: inputting a load demand power signal.

[0027] Step S2: Perform a first-stage low-pass filter on the load demand power signal to obtain a first power signal with a frequency range of [0, f1], where f1 is the cutoff frequency of the first-stage low-pass filter. The power of the first power signal is the response power of the generator of the aircraft's high-voltage DC power system.

[0028] Step S3: Subtract the load demand power signal from the first power signal to obtain a first difference signal.

[0029] Step S4: Perform a second-stage low-pass filter on the first difference signal to obtain a second power signal with a frequency range of (f1, f2], where f2 is the cutoff frequency of the second-stage low-pass filter. The power of the second power signal is the response power of the lithium battery system of the aircraft high-voltage DC power system.

[0030] Step S5: Subtract the first difference signal from the second power signal to obtain a third power signal. The power of the third power signal is the response power of the supercapacitor system of the aircraft high-voltage DC power system.

[0031] Step s6: Calculate the configuration parameters of the generator, lithium battery system, and supercapacitor system based on their response powers, including the weight, volume, and efficiency of the generator, the weight, volume, and efficiency of the lithium battery pack and the connected converter, and the weight, volume, and efficiency of the supercapacitor pack and the connected converter.

[0032] Step S7: Calculate the multi-objective optimization configuration parameters of the aircraft high voltage DC power supply system, including the overall system weight, volume, and efficiency.

[0033] Step S8: Configure the aircraft high voltage DC power supply system according to the optimized configuration parameter results.

[0034] The aircraft high-voltage DC power supply system of this embodiment employs a two-stage low-pass filtering energy management strategy. This allows the power supply to respond to power levels based on its own operating characteristics, addressing the generator's need to respond to high-frequency power. The multi-objective optimization configuration method for the aircraft power supply system of this embodiment optimizes the overall system weight, volume, and efficiency, achieving a lightweight, compact, and highly efficient power supply system. This results in a rational and efficient system configuration.

[0035] Example 2

[0036] This embodiment provides a multi-objective optimization configuration method for an aircraft power system. The specific process is as follows: Figure 4 Shown, including:

[0037] Step 1: Input the load power requirement, power system parameters, and the equivalent model of the energy storage system.

[0038] Step 2: Establish a multi-objective optimization function for the aircraft high-voltage DC power supply system with light weight, small size and high efficiency, set constraints, and constrain the working conditions of the energy storage system and the operating conditions of the power supply system.

[0039] Step 3: Based on the input load demand power data, the particle swarm algorithm is used to solve the multi-objective optimization function to obtain the optimal configuration of the power supply system.

[0040] In step 2 above, the multi-objective optimization function and its constraints are established as follows:

[0041] min V sys =V G +V bat +V bat_dc +V sc +V sc_dc

[0042] min M sys =M G +M bat +M bat_dc +Msc +M sc_dc

[0043]

[0044] Where V sys 、V G 、V bat 、V bat_de 、V sc 、V sc_dc Respectively represent the total volume of the aircraft high-voltage DC power system, the generator volume, the lithium battery pack volume, the volume of the DC / DC connected to the lithium battery pack, the supercapacitor pack volume, and the volume of the DC / DC connected to the supercapacitor pack, M sys 、M G 、M bat 、M bat_dc 、M sc 、M sc_dc Respectively represent the total weight of the aircraft high-voltage DC power system, the weight of the generator, the weight of the lithium battery pack, the weight of the DC / DC connected to the lithium battery pack, the weight of the supercapacitor pack, and the weight of the DC / DC connected to the supercapacitor pack. Δt represents the sampling time; Δ s Indicates the energy change of the energy storage device system during the entire working cycle; P loss Represents system losses, including generator losses, energy storage medium losses, and losses in connected converters.

[0045] The constraints are:

[0046] P bat (t)+P sc (t)+P G (t)-P loss (t)≥P re (t)

[0047] SOC bat_min ≤SOC bat ≤SOC bat_max

[0048] SOC sc_min ≤SOC sc ≤SOC sc_max

[0049] U min ≤U bus ≤U max

[0050] ΔU≤ΔU max

[0051] Among them, P bat (t) represents the output power of the lithium battery pack at time t; P sc(t) represents the output power of the supercapacitor group at time t; P G (t) represents the generator output power at time t; P loss (t) represents the system power loss at time t; P re (t) represents the load power demand at time t; SOC bat Indicates the state of charge of the lithium battery; SOC bat_min Indicates the lower limit of the state of charge of the lithium battery; SOC bat_max Indicates the upper limit of the state of charge of the lithium battery; SOC sc Indicates the state of charge of the supercapacitor; SOC sc_min Indicates the lower limit of the supercapacitor's state of charge; SOC sc_max Indicates the upper limit of the supercapacitor's state of charge; U bus Indicates the bus voltage of the aircraft power system; U min Indicates the lower limit of bus voltage when the system is working, U max Indicates the upper limit of the bus voltage when the system is working, and its value is set according to the requirements of GJB81B; ΔU indicates the fluctuation value of the bus voltage in steady state; ΔU max Indicates the upper limit of the bus voltage fluctuation allowed in steady state, and its value is set according to the requirements of GJB81B.

[0052] In step 3 above, the method of using particle swarm optimization to solve the multi-objective optimization function is as follows:

[0053] 1) Initialize the particle swarm algorithm parameters, including population size, maximum number of iterations, search space dimension, variable space, external storage space, weight and learning factor.

[0054] 2) Set the cutoff frequencies f1 and f2 and perform multi-objective optimization function calculation, that is, calculate the three objectives of the overall volume, weight, and efficiency of the aircraft high-voltage DC power supply system.

[0055] 3) Update the particle swarm algorithm population and update the non-inferior solution.

[0056] 4) End condition judgment, determine whether the maximum number of iterations is satisfied, and proceed to the next step if satisfied; if not, proceed to step 2).

[0057] 5) Output the optimal frontier solution for the overall volume, weight, and efficiency of the aircraft high-voltage DC power supply system.

[0058] The fitness function calculation process is as follows:

[0059] The goal of the filter configuration is to have the generator respond to the first low-pass filtered power, and the hybrid energy storage compensates for other power requirements, where the lithium battery compensates for the medium frequency and the supercapacitor compensates for the high frequency. The power distribution diagram is as follows: Figure 3 shown.

[0060] The configuration calculation steps using this method are described as follows:

[0061] Step 1: Input the load power requirement P re ;

[0062] Step 2: Set the low-pass filter cutoff frequencies f1 and f2; the relationship between them and the filter time constant is:

[0063] Step 3: Calculate the weight, volume and loss of the main power supply;

[0064] The power of the main power supply response is the power at the frequency [0, f1] (the frequency unit in this article is Hz), that is, the power obtained after the first low-pass filtering, as shown in formula (1):

[0065]

[0066] Where, P lfl [n] represents the power at time n after the first low-pass filter, T f1 represents the first low-pass filter coefficient, and Δt represents the sampling time. The weight and volume of the main power supply are calculated as shown in formula (2).

[0067]

[0068] Where, P G_N is the rated power of the generator, and p is the specific power of the generator.

[0069] The loss calculation of the main power supply is shown in formula (3).

[0070] P G_loss =P G / η G *(1-η G ) (3)

[0071] Where, P G represents the generator output power, η G Indicates the efficiency of the generator.

[0072] Step 4: Calculation of weight, volume and loss of hybrid energy storage device

[0073] The calculation idea of ​​its energy storage device is as follows Figure 5 shown.

[0074] (1) Calculation of lithium battery weight and matching DC / DC weight, volume, and loss

[0075] The weight of the hybrid energy storage system primarily includes the weight of the lithium battery, supercapacitor, and the DC / DC converter connected to it. The hybrid energy storage system needs to respond to power with a frequency exceeding f1, which is divided into the lithium battery's response power within the frequency range (f1, f2], and the supercapacitor's response power with a frequency range exceeding f2. The power required to respond to the DC / DC high-voltage terminal connected to the lithium battery is shown in Equation (4).

[0076]

[0077] Where: P lf2 [n] represents the power at time n after the second low-pass filter, T f2 Represents the second low-pass filter coefficient, P bat_s Indicates the lithium battery pack system response power.

[0078] The power that the lithium battery pack needs to respond to is shown in formula (5).

[0079]

[0080] Where: η DC / DC Indicates the efficiency of the DC / DC converter, P bat_req Indicates that the lithium battery pack needs response power.

[0081] The total number of lithium batteries required to meet the power requirements is N ba1 The calculation is shown in formula (6).

[0082]

[0083] Where u bat1 、i bat1 、R batl They represent the lithium battery cell terminal voltage, output current and cell resistance respectively.

[0084] The number of lithium-ion batteries configured must not only meet the response power requirement but also the energy requirement. The energy requirement of a lithium-ion battery throughout its entire operating cycle is calculated as shown in Equation (7). The calculation method used is the average value of the power integral.

[0085]

[0086] Where T0 represents the working cycle of the lithium battery, and Δt represents the sampling time.

[0087] Therefore, to meet the energy demand, the total number of lithium batteries required is N ba2 The calculation is shown in formula (8).

[0088]

[0089] Where λ1 represents the allowable variation range of SOC when the lithium battery is working normally.

[0090] Based on the above analysis, the number of lithium batteries configured is the maximum value that meets the power demand and energy demand, as shown in formula (9).

[0091] N bat =max(N ba1 , Nb a2 ) (9)

[0092] Then, the weight of lithium battery and matching DC / DC are calculated as shown in equations (10) and (11).

[0093]

[0094]

[0095] In the formula, M and V represent weight and volume respectively, m bat 、v bat Respectively represent the weight and volume of lithium battery cells. dc Indicates the selected DC / DC power density.

[0096] The loss calculation of lithium battery pack is shown in formula (12).

[0097]

[0098] Where, P bat_loss Indicates the loss of lithium battery pack. Respectively represent the charging and discharging efficiency of lithium battery pack.

[0099] The loss calculation of the lithium battery pack connected to the converter is shown in formula (13).

[0100]

[0101] Where, P bat_dc_loss Indicates the loss of lithium battery pack connection conversion.

[0102] (2) Calculation of supercapacitor pack weight and matching DC / DC weight, volume, and loss

[0103] The power of the supercapacitor system response frequency range exceeding f2 (unit Hz) is the high-frequency power, which is calculated by subtracting the power of the lithium battery system response from the power of the hybrid energy storage response, as shown in formula (14).

[0104] P sc_s =P re -P G -P 1f2 (14)

[0105] Where, P scs Indicates the response power required by the supercapacitor system, that is, the supercapacitor and DC / DC system.

[0106] The power that the supercapacitor needs to respond to is shown in formula (15).

[0107]

[0108] Where, P sc_req Indicates the response power of the supercapacitor group.

[0109] When the power requirement is met, the total number of supercapacitors required is N scl The calculation is shown in formula (16).

[0110]

[0111] Where u sc1 、i sc1 、R sc1 Represent the SC monomer terminal voltage, output current and monomer resistance respectively.

[0112] Similarly, the number of supercapacitors configured must meet not only the power requirement but also the energy requirement. Supercapacitors respond to high-frequency power and require less energy. Therefore, the energy requirement can be calculated based on the amplitude-frequency characteristics of the power response of the supercapacitor, as shown in Equation (17).

[0113]

[0114] Where, E sc_req Indicates the energy required by the supercapacitor bank to respond.

[0115] Therefore, to meet the energy demand, the total number of supercapacitors required is N sc2 The calculation is shown in formula (18).

[0116]

[0117] Where λ2 represents the allowable variation range of SOC when the supercapacitor pack is working.

[0118] Based on the above analysis, the number of lithium batteries configured is the maximum value that meets the power demand and energy demand, as shown in formula (19).

[0119] N sc =max(N sc1 , N sc2 ) (19)

[0120] Then, the weight of the supercapacitor group and the matching DC / DC are calculated as shown in equations (20) and (21).

[0121]

[0122]

[0123] In the formula, M and V represent weight and volume respectively, m sc 、v sc Respectively represent the weight and volume of the supercapacitor monomer. dc Indicates the selected DC / DC power density.

[0124] The supercapacitor group loss calculation is shown in formula (22).

[0125]

[0126] Where, P sc_loss Indicates the loss of supercapacitor group, Respectively represent the charging and discharging efficiency of the supercapacitor group.

[0127] The loss calculation of the supercapacitor group connected to the converter is shown in formula (23).

[0128]

[0129] Where, P sc_dc_loss Indicates the loss of supercapacitor bank connection conversion.

[0130] Step 5: Calculate the total weight, volume, and efficiency of the main power supply and energy storage device;

[0131] The process is to calculate the total weight, volume, and efficiency of the configured main power supply and energy storage device as shown in Equations (24) to (26).

[0132] V sys =V G +V bat +V bat_dc +V sc +V sc_dc (twenty four)

[0133] M sys =M G +M bat +M bat_dc +M sc +M sc_dc (25)

[0134]

[0135] The multi-objective optimization configuration method for an aircraft power system of this embodiment utilizes a two-stage low-pass filtering energy management strategy optimized by a particle swarm algorithm to obtain three different power signals, which are respectively distributed to the generator, lithium battery system, and supercapacitor system of the high-voltage DC power system. This implements intelligent optimization configuration of the power system containing high-power pulse loads, and can achieve the performance goals of light weight, small size, and high efficiency of the power system.

[0136] Example 3

[0137] This embodiment provides an electronic device, which includes a memory and a processor. The memory stores a program that can be executed on the processor. When the program is executed by the processor, it implements the steps of the multi-objective optimization configuration method of the aircraft power system in the above embodiment.

[0138] Example 4

[0139] This embodiment provides a memory storing at least one program. The at least one program can be executed by at least one processor. When executed by the at least one processor, the at least one program implements the steps of the multi-objective optimization configuration method for the aircraft power system in the above embodiment.

Claims

1. A multi-objective optimization configuration method for an aircraft power system, characterized in that: The aircraft power supply system is a high-voltage direct current (HVDC) power supply system, and energy management of the aircraft high-voltage direct current (HVDC) power supply system is implemented using a two-stage low-pass filter. The multi-objective optimization configuration method includes: Input load demand power signal; performing a first-stage low-pass filtering on the load demand power signal to obtain a first power signal having a frequency range of [0, f1], where f1 is a cutoff frequency of the first-stage low-pass filtering, and the power of the first power signal is the response power of the generator of the aircraft high-voltage direct current power system; subtracting the load demand power signal from the first power signal to obtain a first difference signal; Performing a second-stage low-pass filtering on the first difference signal to obtain a second power signal with a frequency range of (f1, f2], where f2 is the cutoff frequency of the second-stage low-pass filtering, and the power of the second power signal is the response power of the lithium battery system of the aircraft high-voltage direct current power system; subtracting the first difference signal from the second power signal to obtain a third power signal, wherein the power of the third power signal is the response power of the supercapacitor system of the aircraft high-voltage DC power system; Calculate the configuration parameters of the generator, lithium battery system, and supercapacitor system respectively according to the response power of the generator, lithium battery system, and supercapacitor system; including establishing a multi-objective optimization function for the weight, volume, and efficiency of the aircraft high-voltage DC power supply system, wherein the multi-objective optimization function is: min V sys =V G +V bat +V bat_dc +V sc +V sc_dc ; min M sys =M G +M bat +M bat_dc +M sc +M sc_dc ; Among them, V sys 、V G 、V bat 、V bat_dc 、V sc 、V sc_dc Respectively represent the total volume of the aircraft high-voltage DC power system, the volume of the generator, the volume of the lithium battery pack, the volume of the DC / DC connected to the lithium battery pack, the volume of the supercapacitor pack, and the volume of the DC / DC connected to the supercapacitor pack; M sys 、M G 、M bat 、M bat_dc 、M sc 、M sc_dc They represent the total weight of the aircraft's high-voltage DC power system, the weight of the generator, the weight of the lithium battery pack, the weight of the DC / DC connected to the lithium battery pack, the weight of the supercapacitor pack, and the weight of the DC / DC connected to the supercapacitor pack; Δt represents the sampling time; Δ s Indicates the energy change of the energy storage device system during the entire working cycle; P loss Indicates system loss; P re Indicates the load demand power; η sys Indicates the efficiency of the aircraft power system; Calculate the multi-objective optimization configuration parameters of the aircraft high voltage DC power system, including the overall system weight, volume, and efficiency; Configure the aircraft high voltage DC power supply system according to the optimized configuration parameter results.

2. The multi-objective optimization configuration method for an aircraft power system according to claim 1, wherein: The constraints of the multi-objective optimization function include: P bat (t)+P sc (t)+P G (t)-P loss (t)≥P re (t); SOC bat_min ≤SOC bat ≤SOC bat_max SOC sc_min ≤SOC sc ≤SOC sc_max IN min ≤U bus ≤U max ; ΔU≤ΔU max ; Among them, P bat (t) represents the output power of the lithium battery pack at time t; P sc (t) represents the output power of the supercapacitor group at time t; P G (t) represents the generator output power at time t; P loss (t) represents the system power loss at time t; P re (t) represents the load power demand at time t; SOC bat Indicates the state of charge of the lithium battery; SOC bat_min Indicates the lower limit of the state of charge of the lithium battery; SOC bat_max Indicates the upper limit of the state of charge of the lithium battery; SOC sc Indicates the state of charge of the supercapacitor; SOC sc_min Indicates the lower limit of the supercapacitor's state of charge; SOC sc_max Indicates the upper limit of the supercapacitor's state of charge; U bus Indicates the bus voltage of the aircraft power system; U min Indicates the lower limit of the bus voltage when the system is working; U max Indicates the upper limit of the bus voltage when the system is working; ΔU indicates the fluctuation value of the bus voltage in steady state; ΔU max Indicates the upper limit of the bus voltage fluctuation allowed in steady state.

3. The multi-objective optimization configuration method for an aircraft power system according to claim 1, wherein: The particle swarm algorithm is used to solve the multi-objective optimization function and obtain the multi-objective optimization configuration parameters of the aircraft high-voltage DC power supply system.

4. The multi-objective optimization configuration method for an aircraft power supply system according to claim 3, wherein: The particle swarm algorithm is used to solve the multi-objective optimization function, and the multi-objective optimization configuration parameters of the aircraft high-voltage DC power supply system are obtained, including: Step 1, initialize the particle swarm algorithm parameters; Step 2: Set the cutoff frequencies f1 and f2 and calculate the overall volume, weight, and efficiency targets of the aircraft's high-voltage DC power system. Step 3: Update the particle swarm algorithm population and non-inferior solutions; Step 4: Determine whether the maximum number of iterations is met. If so, proceed to the next step. If not, return to step 2. Step 5: Output the optimal frontier solution for the overall volume, weight, and efficiency of the aircraft high-voltage DC power system.

5. The multi-objective optimization configuration method for an aircraft power system according to claim 1, wherein: Calculating the configuration parameters of the generator, lithium battery system, and supercapacitor system respectively according to the response power of the generator, lithium battery system, and supercapacitor system includes: Calculate the weight, volume and efficiency of the generator based on the generator response power; Calculate the weight, volume, and efficiency of the lithium battery pack and the connected converter based on the lithium battery system response power; The weight, volume and efficiency of the supercapacitor group and the connected converter are calculated based on the supercapacitor system response power.

6. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a program that can be run on the processor, and when the program is executed by the processor, the steps of the multi-objective optimization configuration method of the aircraft power supply system according to any one of claims 1 to 5 are implemented.

7. A memory, characterized in that: The memory stores at least one program, which can be executed by at least one processor. When executed by the at least one processor, the at least one program implements the steps of the multi-objective optimization configuration method for an aircraft power supply system according to any one of claims 1 to 5.

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