A hybrid power system parameter matching method

Abstract

The present application relates to a hybrid power system parameter matching method, which comprises: obtaining a flight mission profile and flying car parameters; the flight mission profile includes the flight phases included in the flight mission and the required propulsion power; from the interval [P min , P max ] select the engine rated power that minimizes the mission economy index as the optimal engine rated power; P min and P max They represent the minimum propulsion power and maximum propulsion power required for the flight mission respectively; with the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution; the engine power distribution includes the engine output power in each flight phase; the method of the present application optimizes the engine rated power and engine power distribution in the flight mission based on the optimal mission economy, which can improve the mission economy of the hybrid flying car.

Description

Technical Field

[0001] The present application relates to the field of hybrid power technology, and in particular to a hybrid power system parameter matching method. Background Art

[0002] Energy conservation and carbon reduction are inevitable trends in transportation technology development. Flying cars are emerging urban transportation vehicles that can leverage the three-dimensional space of cities to improve urban transportation efficiency. Compared to pure electric flying cars, hybrid flying cars offer the advantages of large payloads and long ranges. They can be mass-produced in the near future, reducing carbon emissions and better meeting the needs of future low-carbon and high-efficiency transportation. Hybrid flying cars are flying cars that utilize a hybrid powertrain. A hybrid powertrain is a vehicle propulsion system that uses two different power sources (fuel and electricity) and includes complex electrical components such as an engine and power batteries, electric motors, and electronic controls. Existing research primarily addresses energy management and power distribution between electrical components and the engine from the perspective of flight envelope and hybrid powertrain dynamics, aiming to reduce total engine fuel consumption to achieve optimal economic performance and energy conservation and emission reduction. However, for hybrid flying cars with transport capabilities, solely considering total engine fuel consumption does not reflect actual economic performance. Furthermore, current research is mostly based on a given powertrain configuration, leaving insufficient room for optimization in payload and electrical component configuration. Summary of the Invention

[0003] In view of this, the present application proposes a hybrid power system parameter matching method, device, electronic device and storage medium, which can optimize the engine rated power and engine power distribution of a flying car during a flight mission, thereby improving the mission economy of a hybrid flying car.

[0004] According to one aspect of the present application, a hybrid power system parameter matching method is provided, wherein the hybrid power system includes an engine and a power battery; the method comprises: obtaining a flight mission profile and flying car parameters; wherein the flight mission profile includes multiple flight phases included in the flight mission, the change of the flying car's flight speed and flight altitude over time during the flight mission, and the propulsion power required for each flight phase; the flying car parameters include a maximum takeoff weight and battery parameters of the power battery; from the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min The minimum propulsion power required in each flight phase; P maxThe maximum propulsion power required for each flight phase; and taking the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, and the engine power distribution includes the engine output power in each flight phase; wherein the value of the mission economy index is calculated according to the load weight of the flying car, the range of the flight mission and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the load weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the value of the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the value of the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

[0005] In a possible implementation, the multiple flight phases include a cruise phase; the process of calculating the payload weight and the mission cost includes: according to the design point flight speed, design point flight altitude and design point power of the engine, based on the engine design point calculation method, obtaining the fuel consumption rate when the engine operates at the design point; wherein, the design point flight speed is the flight speed of the flying car in the cruise phase; the design point flight altitude is the flight altitude of the flying car in the cruise phase; the design point power is the rated power of the engine; the flight speed and the flight altitude in the cruise phase are obtained according to the flight mission profile; according to the non-design point calculation method of the engine The non-design point flight speed, non-design point flight altitude and non-design point power are calculated based on the engine non-design point calculation method, and the engine non-design point table is obtained; wherein, the engine non-design point table includes the fuel consumption rate when the engine is working at the non-design point; the non-design point flight speed is the flight speed other than the design point flight speed; the non-design point flight altitude is the flight altitude other than the design point flight altitude; the non-design point power is the engine output power other than the design point power; according to the change of the flight speed and flight altitude of the flying car in the flight mission with time, the engine output power in each flight stage, the fuel consumption rate when the engine is working at the design point and the engine The non-design point table of the aircraft is used to obtain the change of the fuel consumption rate of the engine in the flight mission over time, and according to the change of the fuel consumption rate of the engine in the flight mission over time, the total fuel consumption of the engine in the flight mission is obtained; according to the battery output power and the battery parameters of the power battery in each flight phase, the change of the battery voltage and battery current of the power battery in the flight mission over time and the number of battery cells required for the flight mission are obtained, and according to the battery cell weight of the power battery and the number of battery cells required for the flight mission, the total weight of the power battery is obtained; wherein, the battery output power in each flight phase is determined according to the propulsion required in each flight phase. The mission cost is calculated based on the maximum take-off weight, the structural weight of the flying car, and the weight of the hybrid power system; the payload weight is calculated based on the weights of the hybrid power system components, the weight of the engine, the total fuel consumption, and the total weight of the power battery; the weight of the engine is obtained based on the rated power of the engine; the mission cost is calculated based on the total fuel consumption, the unit price of fuel, the total electric energy consumption in the flight mission, and the unit price of electric energy; the total electric energy consumption is obtained based on the changes in the battery voltage and battery current of the power battery over time during the flight mission.

[0006] In a possible implementation, according to the change of the flight speed and flight altitude of the flying car in the flight mission over time, the engine output power in each flight phase, the fuel consumption rate when the engine operates at the design point, and the engine non-design point table, the change of the fuel consumption rate of the engine in the flight mission over time is obtained, including: according to the change of the flight speed and flight altitude of the flying car in the flight mission over time and the engine output power in each flight phase, the flight speed, flight altitude, and engine output power at each moment in the flight mission are obtained; if the flight speed at the first moment in the flight mission is the design point flight speed, the flight altitude at the first moment is the design point flight altitude, and the flight altitude at the first moment is the design point flight altitude, The engine output power at the first moment is the design point power, and the fuel consumption rate of the engine at the first moment is the fuel consumption rate when the engine operates at the design point; wherein, the first moment is any moment in the flight mission; if the flight speed at the first moment is not the design point flight speed or the flight altitude at the first moment is not the design point flight altitude or the engine output power at the first moment is not the design point power, the fuel consumption rate of the engine at the first moment is obtained by interpolation calculation based on the flight speed, flight altitude, engine output power and the engine non-design point table at the first moment; according to the fuel consumption rates of the engine at each moment in the flight mission, the change of the fuel consumption rate of the engine over time in the flight mission is obtained.

[0007] In a possible implementation, the battery parameters include the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance of the power battery with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature and the discharge cut-off voltage of the power battery; according to the battery output power of the power battery and the battery parameters in each flight phase, the change of the battery voltage and battery current of the power battery with time in the flight mission and the number of battery cells required for the flight mission are obtained, including: according to the change of the battery voltage with the state of charge of the power battery, obtaining the linear relationship between the battery voltage and the state of charge of the power battery; according to the battery voltage The linear relationship between the voltage and the state of charge of the power battery, the battery output power in each flight phase, the energy constraint condition and the power constraint condition are iteratively calculated to obtain the initial number of battery cells; according to the initial number of battery cells, the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature, the battery output power in each flight phase, the energy constraint condition and the power constraint condition, the change of the battery voltage and the battery current with time in the flight mission and the number of battery cells required for the flight mission are iteratively calculated; wherein, the energy constraint condition is SOC ≥ SOC min ; The power constraint condition is u-I·R≥u d ; SOC represents the state of charge of the power battery; SOC min represents the preset minimum state of charge value; u represents the battery voltage; I represents the battery current; R represents the internal resistance; u d represents the discharge cut-off voltage.

[0008] In one possible implementation, the payload weight is calculated by subtracting the structure weight from the maximum takeoff weight and then subtracting the weight of the hybrid system; the mission cost is calculated by the following formula: C = w carbon ·C fuel ·M fuel +C e ·E e ; Wherein, C represents the task cost; M fuel Indicates the total fuel consumption; C fuel Indicates the unit price of fuel; E e Indicates the total amount of electric energy consumption; C e represents the unit price of electric energy; w carbon represents the penalty factor, w carbon >1.

[0009] In a possible implementation, the value of the task economy index is calculated by the following formula: ECO = C / (M p ·R A ); wherein ECO represents the economic index of the task; M p Indicates the load weight; R A Indicates the range of the flight mission.

[0010] In one possible implementation, with minimizing the value of the mission economy index as an optimization goal, the engine power distribution is optimized based on a particle swarm optimization algorithm to obtain an optimal engine power distribution, including: optimizing the engine power distribution vector based on the particle swarm optimization algorithm to obtain an optimal engine power distribution vector that minimizes the value of the mission economy index; wherein the size of the engine power distribution vector is M×1; M represents the number of flight phases included in the flight mission; each element in the engine power distribution vector is a ratio of the engine output power of each flight phase to the optimal value; and the optimal engine power distribution is obtained based on the optimal engine power distribution vector and the optimal value.

[0011] According to another aspect of the present application, a hybrid power system parameter matching device is provided, wherein the hybrid power system includes an engine and a power battery; the device includes: an acquisition module for acquiring a flight mission profile and flying car parameters; wherein the flight mission profile includes multiple flight phases included in the flight mission, the change of the flying car's flight speed and flight altitude over time during the flight mission, and the propulsion power required for each flight phase; the flying car parameters include a maximum takeoff weight and battery parameters of the power battery; an optimization module for obtaining a flight mission profile and a flying car parameter from the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min The minimum propulsion power required in each flight phase; P maxThe maximum propulsion power required for each flight phase; and taking the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, and the engine power distribution includes the engine output power in each flight phase; wherein the value of the mission economy index is calculated according to the load weight of the flying car, the range of the flight mission and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the load weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the value of the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the value of the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

[0012] According to another aspect of the present application, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-mentioned parameter matching method for the hybrid power system when executing the instructions stored in the memory.

[0013] According to another aspect of the present application, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above-mentioned parameter matching method of the hybrid power system when executed by a processor.

[0014] According to another aspect of the present application, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned parameter matching method for the hybrid system.

[0015] The hybrid power system parameter matching method disclosed herein optimizes the engine rated power and engine power distribution, given the maximum takeoff weight, battery parameters, and flight mission profile of a given flying vehicle, with the goal of minimizing the mission economy index. This method achieves the optimal engine rated power and optimal engine power distribution, thereby optimizing mission economy. This method addresses the existing problem of directly specifying hybrid power system parameter configurations for flying vehicles, resulting in insufficient optimization space for mission economy, thereby improving the economic performance of hybrid flying vehicles.

[0016] Other features and aspects of the present application will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the application and, together with the description, serve to explain the principles of the application.

[0018] Figure 1 A flow chart of a hybrid power system parameter matching method according to an embodiment of the present application is shown.

[0019] Figure 2 The flight phases included in a typical mission profile are shown.

[0020] Figure 3 A flow chart showing the engine design point performance calculation.

[0021] Figure 4 A flow chart showing the calculation of engine off-design point performance.

[0022] Figure 5 A flow chart showing the calculation of the actual engine operating point performance according to an embodiment of the present application is shown.

[0023] Figure 6 A flowchart of calculating a full mission profile of a power battery according to an embodiment of the present application is shown.

[0024] Figure 7 A flowchart of engine selection optimization according to an embodiment of the present application is shown.

[0025] Figure 8 A flow chart of obtaining the optimal engine power distribution based on a particle swarm optimization algorithm according to an embodiment of the present application is shown.

[0026] Figure 9 A flow chart of a hybrid power system parameter matching method according to an embodiment of the present application is shown.

[0027] Figure 10 A schematic diagram showing the power distribution results of performing parameter matching for a hybrid power system of a flying car according to a method according to an embodiment of the present application is shown.

[0028] Figure 11 A schematic diagram showing the optimization results of the mission economy index of the flying car hybrid power system parameter matching according to the method of one embodiment of the present application is shown.

[0029] Figure 12 A schematic diagram illustrating the optimization results of the total fuel consumption for parameter matching of a hybrid power system of a flying car according to a method of an embodiment of the present application is shown.

[0030] Figure 13 A schematic structural diagram of a hybrid power system parameter matching device according to an embodiment of the present application is shown.

[0031] Figure 14 A block diagram of an electronic device 1900 according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0033] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0034] In addition, numerous specific details are provided in the detailed description below to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0035] The engine and electrical components in a hybrid flying car's powertrain not only experience highly coupled dynamic performance during flight missions (i.e., the operating points of the engine and power battery influence each other), but also begin to influence each other during the initial parameter matching design phase. Taking a series hybrid system consisting of an engine and power battery as an example, parameters such as the battery's total energy and design power influence the engine's design point, which in turn influences engine selection. Furthermore, the battery's mass on the flying car is considered dead weight (i.e., its weight does not change with flight time). The battery design also affects the flying car's payload (i.e., the maximum cargo weight the flying car can carry during a flight mission). During the parameter matching design phase of a hybrid flying car's system, comprehensively optimizing the engine's operating points, electrical component configuration, and payload within the mission profile is key to achieving optimal hybrid system operation.

[0036] In view of this, the present application proposes a hybrid power system parameter matching method, which can optimize the engine rated power and engine power distribution of a flying car during a flight mission, thereby improving the mission economy of the hybrid flying car.

[0037] Figure 1 A flow chart showing a hybrid power system parameter matching method according to an embodiment of the present application is shown. The hybrid power system includes an engine and a power battery. Figure 1 As shown, the method may include:

[0038] S101. Obtain a flight mission profile and flying car parameters.

[0039] A mission profile is a graphical representation of an aircraft's flight path drawn to complete a specific mission. It includes the multiple flight phases included in the mission, the time-varying speed and altitude of the flying vehicle during the mission, and the propulsion power required for each phase. The propulsion power required for each phase of the mission is referred to as the propulsion power demand distribution. Flying vehicle parameters include maximum takeoff weight and battery parameters. The flying vehicle in this embodiment utilizes the aforementioned hybrid power system.

[0040] For example, the mission profile can be obtained from a publicly available dataset or configured by those skilled in the art based on actual needs. When obtaining the mission profile, one can directly input the time-varying flight speed and altitude during the mission, as well as the propulsion power demand distribution. Alternatively, one can input the flight phase, cruising altitude, range (i.e., the horizontal distance from the mission's starting point to its destination), horizontal / vertical speed, and horizontal / vertical acceleration. Based on the flying car's aerodynamic parameters (including but not limited to the flying car's wing area, lift-to-drag ratio at different angles of attack, and propeller / rotor thrust coefficient), a flying car parameter dynamics calculation method is used to obtain the time-varying flight speed and altitude during the mission, as well as the propulsion power demand distribution. The specific calculation process can refer to existing technologies.

[0041] For example, the propulsion power required for each flight phase can be the maximum propulsion power for each flight phase. For example, if the propulsion power of the flying car fluctuates between 100kW and 101kW during a certain flight phase, the propulsion power required for that flight phase can be set to 101kW. This provides a safety margin for the propulsion power requirement of the flying car.

[0042] Exemplarily, the multiple flight phases may include a vertical take-off phase, a vertical landing phase, a taxiing phase, a climbing phase, a descending phase, a horizontal flight phase, a cruising phase (i.e., a phase in which the flying car maintains a constant speed after reaching a stable flight altitude), and the like. Figure 2 The flight phases included in a typical mission profile are shown as follows: Figure 2 As shown, flight mission profile 1 includes three stages: 11-vertical takeoff, 12-cruise, and 13-vertical landing; flight mission profile 2 includes five stages: 21-vertical takeoff, 22-climb, 23-cruise, 24-descent, and 25-vertical landing; flight mission profile 3 includes seven stages: 31-vertical takeoff, 32-horizontal flight, 33-climb, 34-cruise, 35-descent, 36-horizontal flight, and 37-vertical landing; flight mission profile 4 includes five stages: 41-rolling, 42-climbing, 43-cruise, 44-descent, and 45-rolling.

[0043] For example, the maximum takeoff weight can be set by those skilled in the art based on actual needs. For example, the maximum takeoff weight can be set based on the flying car's structural weight. The structural weight of a flying car refers to the total weight of the flying car's body and frame without any other equipment installed. This weight is a fixed weight that does not change with flight time. It is fixed at the time the flying car leaves the factory and can be obtained from the flying car's factory data. The structural weight is typically 25%-40% of the maximum takeoff weight, and the maximum takeoff weight can be set accordingly.

[0044] For example, battery parameters may include changes in battery voltage with the state of charge (SOC) and temperature of the power battery, changes in battery internal resistance with SOC and temperature, battery energy density (i.e., the ratio of battery energy to its weight), battery capacity, battery energy, discharge cut-off voltage, etc. Battery parameters can be obtained through experiments or from public datasets.

[0045] S102, from the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min is the minimum propulsion power required in each flight phase, P max is the maximum propulsion power required in each flight phase; and with the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, which includes the engine output power in each flight phase.

[0046] The mission economy index is calculated based on the payload weight of the flying car, the range of the flight mission, and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the payload weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

[0047] In a possible implementation, the multiple flight phases include a cruise phase; and the process of calculating the payload weight and the mission cost may include:

[0048] Step 1: According to the design point flight speed, design point flight altitude and design point power of the engine, based on the engine design point calculation method, obtain the fuel consumption rate when the engine operates at the design point.

[0049] Among them, the design point flight speed is the flight speed of the flying car in the cruising phase; the design point flight altitude is the flight altitude of the flying car in the cruising phase; the design point power is the rated power of the engine; the flight speed in the cruising phase and the flight altitude in the cruising phase are obtained according to the flight mission profile.

[0050] The engine design point refers to the specific flight conditions and engine operating states corresponding to the aerodynamic and thermodynamic parameters and geometric dimensions of the engine and its components, as determined during engine design. For the purposes of this application, the engine design point defines the flight speed and altitude consistent with the cruise phase specified in the mission profile. The engine design point power is the rated power. The engine fuel consumption rate at the design point represents the fuel consumption rate when the flying car is in the cruise phase at the speed and altitude at which the flying car is operating, with the engine operating at rated power. The engine fuel consumption rate represents the weight of fuel consumed per 1 kWh of the engine.

[0051] For example, the design point parameters of the engine when operating at the design point can be obtained based on the engine design point thermodynamic calculation method, thereby obtaining the fuel consumption rate when the engine operates at the design point. The process of calculating the engine design point parameters can be referred to as engine design point performance calculation.

[0052] Figure 3 A flow chart showing the engine design point performance calculation is shown in FIG. Figure 3As shown, first input the design point flight conditions and thermodynamic parameters of the engine. The design point flight conditions include the design point flight speed of the engine (i.e., the flight speed in the cruise phase) and the design point flight altitude (i.e., the flight altitude in the cruise phase); the thermodynamic parameters include air flow, compressor pressure ratio, compressor efficiency, turbine inlet temperature, turbine expansion ratio, turbine efficiency, combustion efficiency, etc. Thermodynamic parameters can be selected according to the design point power of the engine (i.e., engine rated power) and actual needs. The design point parameter calculation process is as follows: (1) Calculate the compressor inlet total temperature and inlet total pressure (i.e., compressor inlet parameters) based on the design point flight speed and design point flight altitude; (2) Calculate the compressor power and the compressor outlet total temperature and outlet total pressure based on the air flow, compressor pressure ratio, and compressor efficiency; (3) Calculate the fuel flow based on the turbine inlet temperature and combustion efficiency, and then calculate the turbine inlet total temperature and inlet total pressure (i.e., turbine inlet parameters); (4) Calculate the turbine power and the turbine outlet total temperature and outlet total pressure (i.e., nozzle inlet total temperature and inlet total pressure) based on the turbine expansion ratio and turbine efficiency, and then calculate the nozzle thrust and nozzle area based on the nozzle inlet total temperature, inlet total pressure, and nozzle outlet pressure balance conditions; (5) Calculate the engine output power based on the turbine power and compressor power, and then obtain the fuel consumption rate when the engine is operating at the design point by dividing the fuel flow rate by the engine output power. The unit of the fuel consumption rate is kg / kWh. The specific calculation method can refer to the existing technology. The engine design point parameters obtained during the calculation process can be stored for subsequent analysis and viewing.

[0053] Step 2: Obtain an engine non-design point table based on the engine non-design point calculation method according to the engine's non-design point flight speed, non-design point flight altitude, and non-design point power; wherein the engine non-design point table includes the fuel consumption rate when the engine operates at a non-design point; the non-design point flight speed is the flight speed other than the design point flight speed; the non-design point flight altitude is the flight altitude other than the design point flight altitude; and the non-design point power is the engine output power other than the design point power.

[0054] An engine's off-design point refers to flight conditions and engine operating states that are not at the design point. For purposes of this application, the fuel consumption rate when the engine is operating at an off-design point refers to the fuel consumption rate when the flying car is not in the cruising phase at a certain speed and altitude, and when the engine output power is not at rated power.

[0055] For example, the engine's off-design point parameters can be obtained using an off-design point batch calculation method. This off-design point batch calculation method refers to the process of batch simulating and analyzing engine performance at multiple off-design points. The process of calculating engine off-design point parameters can be referred to as engine off-design point performance calculation.

[0056] For example, the calculated engine off-design point parameters can be stored as a lookup table structure to generate an engine off-design point table. The engine off-design point table includes the engine's fuel consumption rate at different flight speeds, flight altitudes, and output powers. Therefore, the engine off-design point table can reflect the relationship between the engine's fuel consumption rate and flight speed, flight altitude, and engine output power.

[0057] Figure 4 The flow chart showing the calculation of engine off-design performance is as follows: Figure 4As shown, first enter the off-design operating conditions, which include off-design flight speed, off-design flight altitude, and off-design power. If thrust is required, the off-design thrust can also be entered. As an example, the off-design flight speed can range from 0 to the design flight speed, the off-design flight altitude can range from 0 to the design flight altitude, and the off-design power can range from 0.1*design power to 1.1*design power. The compressor beta value β1, the turbine beta value β2, the speed (if power is required), and the turbine inlet temperature (if thrust is required) are used as the variables to be solved, X, in the equilibrium equation. The compressor beta and turbine beta values ​​are used to interpolate the performance map (MAP). The compressor / turbine MAP describes the relationship between the compressor / turbine's key performance parameters (compression ratio / expansion ratio) and the input parameter (flow rate). Due to experimental data limitations, the MAP may only include data points for a subset of operating conditions. MAP interpolation is required to predict performance between and beyond these data points. The beta value is an auxiliary quantity introduced in the MAP interpolation process to improve the interpolation accuracy. The calculation process of non-design point parameters is as follows: (1) Calculate the compressor inlet total temperature and inlet total pressure based on the non-design point flight speed and non-design point flight altitude; (2) Use the compressor beta value β1 and the speed to interpolate the compressor component MAP map to obtain the compressor air flow, pressure ratio, efficiency, and then calculate the compressor power and outlet total temperature; (3) Calculate the fuel flow based on the turbine pre-temperature, and then calculate the turbine inlet total temperature, inlet total pressure, and inlet gas flow (air flow + fuel flow); (4) Use the turbine beta value β2 and the speed to interpolate the turbine component MAP map to obtain the turbine gas flow, expansion ratio, efficiency, and then calculate the turbine power and outlet total temperature, and subtract the gas flow obtained by MAP interpolation from the inlet gas flow to obtain the flow residual ΔM; (5) According to the turbine power and compressor The engine output power is calculated by power. If there is a power requirement, the calculated power is subtracted from the power requirement to obtain the power residual ΔPW; (6) Based on the nozzle inlet total temperature, inlet total pressure, and nozzle outlet pressure balance conditions, the nozzle thrust and nozzle area are calculated, and the calculated nozzle area is subtracted from the nozzle design point area to obtain the area residual ΔAN. If there is a thrust requirement, the calculated nozzle thrust is subtracted from the thrust requirement to obtain the thrust residual ΔThrust; (7) The fuel consumption rate is calculated by dividing the fuel flow rate by the engine output power; (8) The balance equation, i.e., flow rate, power, nozzle area, and thrust balance, is solved iteratively until the residual is reduced to an acceptable range, and the compressor beta value, turbine beta value, speed, and turbine inlet temperature at the non-design point are obtained, thereby obtaining the fuel consumption rate at the non-design point. The specific calculation method can refer to the existing technology.In this way, the non-design point parameters (including fuel consumption rate, gas flow, compressor operating point parameters, turbine operating point parameters, etc.) of the engine under multiple different non-design point operating conditions (different flight speeds, different flight altitudes, different output powers) can be obtained and stored as an engine non-design point table.

[0058] Step 3: Based on the changes in the flight speed and flight altitude of the flying car over time during the flight mission, the engine output power in each flight phase, the fuel consumption rate when the engine operates at the design point, and the engine non-design point table, obtain the changes in the fuel consumption rate of the engine during the flight mission over time, and obtain the total fuel consumption of the engine during the flight mission based on the changes in the fuel consumption rate of the engine during the flight mission.

[0059] Exemplarily, obtaining the change in the fuel consumption rate of the engine during the flight mission over time based on the change in the flight speed and altitude of the flying car during the flight mission over time, the engine output power during each flight phase, the fuel consumption rate when the engine operates at the design point, and the engine off-design point table may include:

[0060] (1) According to the changes in the flight speed and flight altitude of the flying car during the flight mission over time and the engine output power in each flight phase, the flight speed, flight altitude, and engine output power at each moment in the flight mission are obtained.

[0061] Because aircraft engine power output doesn't fluctuate significantly during different flight phases, it can be considered constant. The mission profile provides the speed, altitude, and engine power at each moment in the mission.

[0062] (2) If the flight speed at the first moment in the flight mission is the design point flight speed, the flight altitude at the first moment is the design point flight altitude, and the engine output power at the first moment is the design point power, the fuel consumption rate of the engine at the first moment is the fuel consumption rate when the engine operates at the design point; wherein the first moment is any moment in the flight mission.

[0063] If the flight speed, flight altitude and engine output power at a certain moment in the flight mission are consistent with the design point, the fuel consumption rate of the engine at that moment is the fuel consumption rate calculated in step 1 when the engine is operating at the design point.

[0064] (3) If the flight speed at the first moment is not the design point flight speed, or the flight altitude at the first moment is not the design point flight altitude, or the engine output power at the first moment is not the design point power, the fuel consumption rate of the engine at the first moment is obtained by interpolation calculation based on the flight speed, flight altitude, engine output power at the first moment and the engine non-design point table.

[0065] If the flight speed, altitude, and engine output power at a particular moment during a mission deviate from the design points, an interpolation calculation can be performed based on the engine off-design point table to determine the engine's fuel consumption rate at that moment. Interpolation is a method of estimating unknown data points using known data points. The engine off-design point table contains multiple engine fuel consumption rates at different flight speeds, altitudes, and engine output powers. Based on these known data points and the flight speed, altitude, and engine output power at each moment during the mission, an interpolation calculation can be performed to determine the fuel consumption rate at each moment. Interpolation calculation methods can refer to existing technologies.

[0066] (4) According to the fuel consumption rate of the engine at each moment in the flight mission, the change of the fuel consumption rate of the engine in the flight mission over time is obtained.

[0067] After calculating the fuel consumption rate at each moment in the flight mission, the change of the engine's fuel consumption rate over time during the flight mission can be obtained.

[0068] For example, the total fuel consumption of the engine in the flight mission (i.e., the total weight of fuel required to be consumed by the engine to complete the entire flight mission) can be obtained by the time integration method based on the change in the engine's fuel consumption rate over time during the flight mission. The unit of the total fuel consumption is kg.

[0069] For example, interpolation calculations can also be performed based on the engine non-design point table to obtain the changes in other engine parameters (such as compressor operating point parameters, turbine operating point parameters, etc.) over time during the flight mission.

[0070] The process of calculating how engine parameters change over time during a flight mission can be called engine actual operating point performance calculation. Figure 5 A flow chart showing the actual operating point performance calculation of an engine according to an embodiment of the present application is shown as follows: Figure 5As shown, t = 0 represents the time the mission begins. The mission profile is input. For the current time t, the flight speed and altitude at time t are calculated based on their temporal variations in the mission profile. The engine output power at time t is the engine output power during the flight phase at that time. By inputting a table of engine off-design points and performing interpolation calculations, the engine parameters at time t are obtained. The fuel consumption from the mission start to time t is updated based on the fuel consumption rate at time t. The mission is then determined to be complete at time t. If not, the calculation is continued by setting t = t + Δt. If the mission is complete, the dynamic temporal variations of the engine parameters during the mission are output.

[0071] The present embodiment generates an engine off-design point table through batch calculation of off-design points. The off-design point table contains engine parameters for different flight speeds, different flight altitudes, and different engine output powers. Interpolation calculations are performed based on the off-design point table to determine the dynamic changes in engine parameters over time during a flight mission. The calculation results better reflect the dynamic operating conditions of the engine. Existing methods for calculating actual engine operating point performance assume a constant fuel consumption rate or that the fuel consumption rate is a single-variable function that depends solely on engine output power. The calculation method of the present embodiment comprehensively considers the impact of flight speed, flight altitude, and engine output power on the engine operating point, resulting in higher accuracy than existing calculation methods. The calculation process only adds the step of batch calculation of off-design points to generate the off-design point table. The engine operating status at each moment during the entire mission can be determined based on the off-design point table, improving accuracy without significantly increasing calculation time.

[0072] The above-mentioned process of engine design point performance calculation, engine non-design point performance calculation and engine actual operating point performance calculation can be called aircraft engine full mission profile calculation.

[0073] Step 4: Based on the battery output power and the battery parameters of the power battery in each flight phase, obtain the changes in the battery voltage and battery current of the power battery over time during the flight mission and the number of battery cells required for the flight mission, and obtain the total weight of the power battery based on the battery cell weight of the power battery and the number of battery cells required for the flight mission; wherein the battery output power in each flight phase is obtained based on the propulsion power required in each flight phase and the engine output power in each flight phase.

[0074] In hybrid power systems, power batteries supplement or absorb engine output power. This application considers propulsion power = engine output power + battery output power. The propulsion power required for each flight phase, the engine output power within each flight phase, and the battery output power within each flight phase are all considered constant. By subtracting the engine output power within each flight phase from the propulsion power required for each flight phase, the battery output power within each flight phase can be calculated, thereby determining the battery output power at each moment during the mission. The battery output power within each flight phase can be referred to as the battery power profile.

[0075] Exemplarily, the battery parameters include changes in the battery voltage with the state of charge of the power battery, changes in the internal resistance of the power battery with the state of charge of the power battery, changes in the battery voltage with temperature, changes in the internal resistance with temperature, and the discharge cut-off voltage of the power battery. Obtaining changes in the battery voltage and battery current of the power battery over time during the flight mission and the number of battery cells required for the flight mission based on the battery output power and the battery parameters of the power battery in each flight phase may include:

[0076] (1) According to the change of the battery voltage along with the state of charge of the power battery, a linear relationship between the battery voltage and the state of charge of the power battery is obtained.

[0077] The linear relationship between battery voltage and the power battery's state of charge can be expressed as u = a·SOC + b. Here, u represents the battery voltage, SOC represents the power battery's state of charge, and a and b are constants. The coefficients a and b can be obtained through linear fitting based on the variation of battery voltage with SOC.

[0078] (2) An initial number of battery cells is obtained by performing iterative calculation based on the linear relationship between the battery voltage and the state of charge of the power battery, the battery output power in each flight phase, the energy constraint condition, and the power constraint condition.

[0079] Among them, the energy constraint condition is SOC ≥ SOC min ; The power constraint condition is u-I·R≥u d ; SOC represents the state of charge of the power battery; SOC min represents the preset minimum SOC; u represents the battery voltage; I represents the battery current; R represents the internal resistance; u d represents the discharge cut-off voltage.

[0080] Before calculating the number of battery cells required for the flight mission, an initial number of battery cells can be estimated. When subsequently iteratively calculating the number of battery cells required for the flight mission, this initial number of battery cells can be used as the initial value for the iteration, thereby saving calculation amount and improving calculation efficiency.

[0081] The process of obtaining the initial number of battery cells may include: setting the number of battery cells to 1, performing iterative calculations based on the linear relationship between the battery voltage and the state of charge of the power battery, the battery output power of the power battery at each moment in the flight mission, and the internal resistance and state of charge of the power battery at the start of the flight mission (i.e., t=0), to obtain the battery voltage and state of charge of the power battery at time t (t>0) in the flight mission; obtaining the battery voltage and state of charge of the power battery at time t in the flight mission based on the battery output power of the power battery at time t in the flight mission, the internal resistance of the power battery at the start of the flight mission, the current number of battery cells, a, b, SOC min and u d , determine whether the battery voltage and state of charge at time t satisfy the energy constraint conditions and the power constraint conditions; if the battery voltage and state of charge at time t do not satisfy the energy constraint conditions and the power constraint conditions, exit the iteration, increase the number of battery cells by 1, and return to execute the steps of obtaining the battery voltage and state of charge of the power battery at time t during the flight mission and subsequent steps; if the battery voltage and state of charge at time t satisfy the energy constraint conditions and the power constraint conditions, set t = t + Δt, and continue iterating until the battery voltage and state of charge of the power battery at the end of the flight mission are obtained, and when the battery voltage and state of charge at the end of the flight mission satisfy the energy constraint conditions and the power constraint conditions, stop the iteration, and use the current number of battery cells as the initial number of battery cells; Δt represents the time change between two adjacent moments in the flight mission.

[0082] (3) performing iterative calculations based on the initial number of battery cells, the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature, the battery output power in each flight phase, the energy constraint condition, and the power constraint condition to obtain the change of the battery voltage and the battery current with time in the flight mission and the number of battery cells required for the flight mission.

[0083] The process of obtaining the changes of battery voltage and battery current over time during the flight mission and the number of battery cells required for the flight mission may include: setting the number of battery cells to be equal to the initial number of battery cells, and obtaining the battery voltage and internal resistance of the power battery at time t (t>0) during the flight mission according to the changes of battery voltage with the state of charge of the power battery, the changes of internal resistance with the state of charge of the power battery, the changes of battery voltage with temperature, the changes of internal resistance with temperature, and the changes of the state of charge and temperature of the power battery over time during the flight mission; obtaining the battery current of the power battery at time t during the flight mission according to the battery voltage, internal resistance and battery output power of the power battery at time t during the flight mission and the current number of battery cells; judging whether the battery voltage, internal resistance, battery current and state of charge of the power battery at time t during the flight mission meet the energy constraint conditions and power constraint conditions; if the battery voltage, internal resistance, battery current and state of charge of the power battery at time t during the flight mission meet the energy constraint conditions and power constraint conditions; if the battery voltage, internal resistance, battery current and state of charge of the power battery at time t meet the energy constraint conditions and power constraint conditions, the battery voltage, internal resistance, internal resistance and internal resistance ... If the battery voltage, internal resistance, battery current, and state of charge do not satisfy the energy and power constraints, the iteration is exited, the number of battery cells is increased by 1, and the process returns to execute the steps to obtain the battery voltage and internal resistance of the power battery at time t during the flight mission and the subsequent steps. If the battery voltage, internal resistance, battery current, and state of charge at time t satisfy the energy and power constraints, set t = t + Δt, and continue iterating until the battery voltage, internal resistance, and battery current of the power battery at the end of the flight mission are obtained. If the battery voltage, internal resistance, battery current, and state of charge at the end of the flight mission satisfy the energy and power constraints, the iteration is stopped, and the current number of battery cells is used as the number of battery cells required for the flight mission. Based on the battery voltage and battery current of the power battery at each moment during the flight mission obtained from the last round of iterations, the changes in the battery voltage and battery current over time during the flight mission are obtained.

[0084] The estimated initial number of battery cells and the calculation of the number of battery cells required for the flight mission can be performed according to the formula and steps shown below.

[0085] The battery current I at each moment during the flight mission can be calculated using formula (1):

[0086]

[0087] Where, u represents the battery voltage of the power battery, P out Indicates the battery output power (negative when charging), R indicates the internal resistance of the power battery, and N indicates the number of battery cells.

[0088] The energy constraint and power constraint of the power battery are shown in formula (2) and formula (3) respectively:

[0089] uI·R≥u d (2)

[0090] SOC≥SOC min (3)

[0091] Among them, u d Indicates the discharge cut-off voltage of the power battery, SOC indicates the state of charge of the power battery, SOC min Indicates the minimum state of charge value, SOC min It can be set by those skilled in the art according to actual needs.

[0092] The battery voltage is related to the state of charge. Based on the linearization assumption, we can set u = a·SOC + b. By replacing u with the variable x, we can express formula (1) as formulas (4)-(6), and express formulas (2) and (3) as formulas (7)-(9):

[0093]

[0094] Among them, Q represents the battery capacity of the power battery, P out >0 means the power battery is in discharge state, P out <0 indicates that the power battery is in charging state.

[0095] By integrating formula (4) over time, we can obtain formulas (10) and (11):

[0096]

[0097] Among them, f1(x) is the function of variable x when the power battery is in the discharge state, f2(x) is the function of variable x when the power battery is in the charging state, df1(x) represents the change of f1(x), df2(x) represents the change of f2(x), and dt represents the change over time.

[0098] The initial number of battery cells can be estimated based on formulas (4)-(11). Since the changes of R and u with temperature and R with SOC are not obvious, the changes of R and u with temperature and R with SOC can be ignored when estimating the initial number of battery cells. That is, R in formulas (4)-(11) is regarded as a constant value (R can be the internal resistance of the power battery at the beginning of the flight mission), u is regarded as a value that only changes with SOC, and it is assumed that there is a linear relationship between u and SOC, which can save calculation amount and time and improve calculation efficiency.

[0099] The process of estimating the initial number of battery cells is as follows: first, let N = 1, and obtain the battery output power P at each moment during the flight mission. outAs well as R, SOC and u at time t = 0 (i.e., the start time of the flight mission), x and f(x) at time t = 0 can be calculated according to formulas (4)-(11) (if P out >0, the calculated value is f1(x), if P out <0, the calculated value is f2(x)); According to formulas (10) and (11), the change of f1(x) and f2(x) is the time change dt multiplied by a linear factor -a / (2Q·R), a, Q, and R are known, and dt is defined as the time change between two adjacent moments in the flight mission. Based on this, the change of f1(x) and f2(x) can be calculated. The f(x) at time t plus the change of f(x) can be obtained to obtain f(x) at time t+Δt. According to f(x) at time t+Δt, x at time t+Δt can be obtained; According to x at time t+Δt and P at time t+Δt, out , R and N, the u at time t+Δt can be calculated by formula (5) or (6), and the SOC at time t+Δt can be calculated according to u=a·SOC+b; thus, the SOC and P at the current moment are known. out , we can calculate x and f(x) at the current moment, and then get f(x) and x at the next moment, and then get the SOC at the next moment. Through the above iterative calculation, we can get the SOC at each moment in the flight mission, and thus get u and x at each moment; in the iterative process, for each x calculated at a certain moment, we need to judge whether it meets the energy constraint and power constraint of formulas (7)-(9); if it does not meet the constraint, we exit the iteration, let N=N+1, and then start the iterative calculation again from time t=0; if it meets the constraint, we continue to calculate x at the next moment until we get x at the end of the flight mission and x at the end of the flight mission meets the energy constraint and power constraint of formulas (7)-(9). At this time, x at each moment in the flight mission meets the energy constraint and power constraint of formulas (7)-(9), and N at this time is used as the estimated initial number of battery cells.

[0100] This embodiment of the present application uses a time integration method to estimate the initial number of battery cells. Based on the linearized relationship between battery voltage and SOC, as well as the current charge and discharge state of the power battery, battery output power, power duration (i.e., the duration of the flight phase in which the battery output power is located), and SOC, the SOC at the next moment is quickly calculated in a single step, eliminating the need for multiple calculations. If the constraints are not met, the current iteration is exited, the number of battery cells is updated, and the iterative calculation is repeated. Because the battery output power within each flight phase is considered constant in this application, this method can reduce the number of time steps to the number of power intervals, reducing the number of iterations required for the calculation and allowing for a rapid estimation of the required number of battery cells.

[0101] Because the changes in R and u with temperature and R with SOC are not considered when estimating the initial number of battery cells, the estimated initial number of battery cells is not the exact number of battery cells required for the flight mission. It is necessary to perform iterative calculations while considering the changes in R and u with temperature and SOC. According to formulas (1)-(3), the number of battery cells required for the flight mission and the changes in battery voltage and battery current over time during the flight mission are obtained. The process of calculating the number of battery cells required for the flight mission and obtaining the changes in battery voltage and battery current over time during the flight mission can be called the power battery full mission profile calculation.

[0102] Figure 6 A flowchart of calculating the full mission profile of a power battery according to an embodiment of the present application is shown as follows: Figure 6 As shown in the figure, the estimated initial number of battery cells is used as the initial value of iteration (that is, the initial value of N is the initial number of battery cells), and P at each moment in the flight mission is obtained. out As well as u, R, SOC and temperature of the power battery at time t = 0, I at time t = 0 can be calculated according to formula (1). The change of SOC and temperature of the power battery over time during the flight mission can be obtained through existing technology. For example, the change of SOC over time during the flight mission can be obtained by using the ampere-hour integration method, and the change of temperature of the power battery over time can be obtained by using the thermal capacity of the power battery, so as to update the SOC and temperature at the next moment. Given the SOC and temperature at time t (t>0), u and R at time t can be obtained according to the change of u with SOC, the change of R with SOC, the change of u with temperature, and the change of R with temperature. According to u, R, P at time t outand N can be used to calculate I at time t using formula (1). In this way, the SOC, temperature, u, R, and I at each moment in the flight mission can be obtained. After obtaining u, R, I and SOC at each moment, it is necessary to determine whether u, R, I and SOC at that moment meet the energy and power constraints shown in formulas (2) and (3); if the constraints are not met, exit the iteration, set N = N + 1, and restart the iterative calculation from time t = 0; if the constraints are met, set t = t + Δt, and continue to calculate u, R, I and SOC at the next moment until u, R, I and SOC at the end of the flight mission are calculated and meet the energy and power constraints shown in formulas (2) and (3). At this time, the calculated u, R, I and SOC at each moment in the flight mission meet the energy and power constraints shown in formulas (2) and (3), and there is no energy redundancy when the flight mission is completed. The current N is used as the number of battery cells required for the final flight mission, and the u, R, I, SOC and temperature at each moment in the flight mission obtained in the last round of iteration are output.

[0103] The embodiment of the present application adopts a time difference method, using the initial number of battery cells estimated by the time integration method as the initial value, substituting it into the full mission profile of the power battery for iterative calculation, and continuously updating the number of battery cells. Finally, the number of battery cells that just meets the power and energy requirements of the entire flight mission can be obtained, as well as the dynamic changes in the power battery parameters during the flight mission (i.e., the changes in the battery voltage, battery current, internal resistance, SOC, and temperature of the power battery). This method reduces the number of time steps to the number of power intervals, reducing the number of iterations required for a full mission profile calculation. It also uses the estimated initial number of battery cells as the iterative initial value, reducing the number of required iterations and improving computational efficiency, thereby quickly and accurately calculating the number of battery cells required for the flight mission.

[0104] After determining the number of battery cells required for the mission, the total weight of the power battery can be calculated by multiplying the cell weight by the number of cells required for the mission. The cell weight can be determined based on the power battery's energy density and battery energy. By varying the power battery's energy density, the cell weight and total weight of the power battery can be calculated for different energy densities.

[0105] Step 5: Calculate the load weight of the flying car based on the maximum takeoff weight, the structural weight of the flying car, and the weight of the hybrid power system.

[0106] This application defines maximum takeoff weight as the sum of the flying car's structural weight, payload weight (i.e., effective load), and the weight of the hybrid powertrain. The flying car's payload weight is equal to the maximum takeoff weight minus the flying car's structural weight minus the weight of the hybrid powertrain.

[0107] The weight of the hybrid system can be calculated based on the weight of each hybrid system component (such as the motor, inverter, etc.), the engine weight, the total fuel consumption, and the total weight of the power battery. The weight of each hybrid system component can be obtained from the flying car data. If the weight of each hybrid system component is unknown, the output power of each hybrid system component can be calculated based on the flying car's maximum propulsion power. The weight of each hybrid system component can then be calculated based on a specified power density (i.e., the ratio of the output power of each hybrid system component to its weight). The specific calculation method can be found in related art. The total fuel consumption can be calculated in step 3. The total weight of the power battery can be calculated in step 4. The engine weight can be calculated based on engine parameters using existing engine weight estimation methods. For example, the engine weight can be estimated based on the engine rated power. The specific calculation method can be found in related art. The weight of the hybrid system is the sum of the weight of each hybrid system component, the engine weight, the total fuel consumption, and the total weight of the power battery.

[0108] Step 6: Calculate the mission cost based on the total fuel consumption, the unit price of fuel, the total electricity consumption during the flight mission, and the unit price of electricity; wherein the total electricity consumption is obtained based on the changes in the battery voltage and battery current of the power battery during the flight mission over time.

[0109] The task cost can be calculated using the following formula:

[0110] C=w carbon ·C fuel ·M fuel +C e ·E e (12)

[0111] Among them, C represents the task cost (in yuan); M fuel Indicates the total fuel consumption (in kg); C fuel Indicates the unit price of fuel (in yuan / kg); E e Indicates the total amount of electrical energy consumed during the flight mission (in kWh), E e It can be obtained by time-integrating the product of the battery voltage and battery current of the power battery at each moment in the flight mission; C e Indicates the unit price of electricity (in yuan / kWh); w carbon represents the penalty factor considering environmental costs, wcarbon >1,w carbon It can be set by those skilled in the art according to actual needs. carbon The larger the value, the more it means that the impact of environmental costs is considered in the task cost.

[0112] Since the total cost of fuel consumption and electricity per unit payload can reflect the actual economic performance, this application defines the mission economic index based on the payload weight, flight range and mission cost. The mission economic index can be calculated using the following formula:

[0113]

[0114] Among them, ECO represents the mission economy index (unit: yuan / kg / km), M p Indicates the load weight (in kg), R A Indicates the range of the flight mission (in km).

[0115] In a possible implementation, a multi-point method can be used to select the engine rated power range [P min , P max ], with the goal of minimizing the ECO value. The core of the multi-partitioning method is to divide the engine rated power selection interval into multiple subintervals and update the engine rated power selection interval with the optimal power value calculated each time until the length of the engine rated power selection interval converges to a certain value.

[0116] As an example, 11 power calculation points can be selected within the engine rated power selection interval, and the engine rated power selection interval can be divided into 10 power sub-intervals. For each power calculation point, the power value of the power calculation point is used as the engine rated power and it is assumed that the engine always outputs at rated power throughout the flight mission, and the ECO value corresponding to the power calculation point is calculated. The optimal power calculation point that minimizes the ECO value among the 11 power calculation points is determined, and the power interval between the power calculation point before and after the optimal power calculation point is used as the new engine rated power selection interval. The steps of selecting 11 power calculation points and subsequent steps within the engine rated power selection interval are repeated until the length of the engine rated power selection interval finally obtained converges to a preset value (for example, converges to 0.1kW). The power value of the optimal power calculation point at this time is the optimal engine rated power.

[0117] The process of selecting the optimal engine power rating can be referred to as engine selection optimization. Figure 7 A flow chart of engine selection optimization according to an embodiment of the present application is shown as follows: Figure 7As shown, the initial engine rated power selection range is [P min , P max ], select n power calculation points within the engine rated power selection interval, for each power calculation point, use its power value as the engine rated power and assume that the engine always outputs at rated power throughout the entire flight mission, perform aviation engine full mission profile calculation and power battery full mission profile calculation through the above steps 1 to step 6, obtain the payload weight and mission cost, and then calculate the ECO value corresponding to each power calculation point; use the power calculation point that minimizes the ECO value as the optimal power calculation point, use the optimal power calculation point to update the engine rated power selection interval, and use the previous power calculation point and the next power calculation point of the optimal power calculation point as the two endpoints of the new engine rated power selection interval; repeat the above steps to update the engine rated power selection interval until the length of the obtained engine rated power selection interval converges to the preset value, and output the power value of the optimal power calculation point at this time as the optimal engine rated power.

[0118] Aircraft engines do not experience significant power fluctuations between flight phases, so the engine control strategy can be simplified to represent the engine's power output during each phase. For a mission consisting of M flight phases, the engine control strategy is a vector X of length M, where each element in X corresponds to the engine's output power during a flight phase. The value of each element in X is the ratio of the engine's output power to the rated power during that phase. For example, if a mission consists of five flight phases (takeoff, climb, cruise, descent, and landing), then X = [1, 1, 1, 0.5], indicating that the engine's power output during takeoff, climb, cruise, descent, and landing is 1, 1, 1, 1, and 0.5 times the rated power, respectively. Vector X can be called the engine power distribution vector.

[0119] The engine control strategy optimization problem can be simplified as a vector optimization problem. The objective function is the mission economy index (ECO), and the variable to be optimized is the engine power distribution vector X. This means that the engine control strategy optimization problem can be viewed as optimizing the engine power distribution. The particle swarm optimization (PSO) algorithm can be used to find the optimal engine power distribution, thereby achieving the optimal control strategy for the hybrid power system. The core of the PSO algorithm is to simulate the natural behavior of bird flocks seeking optimal roosting spots, updating the solution vector through group optimization and individual optimization.

[0120] In one possible implementation, the engine power distribution is optimized based on a particle swarm optimization algorithm with the minimum value of the mission economy index as the optimization goal to obtain the optimal engine power distribution, including:

[0121] (1) The engine power distribution vector is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution vector that minimizes the value of the mission economy index; wherein the size of the engine power distribution vector is M×1; M represents the number of flight phases included in the flight mission; and each element in the engine power distribution vector is the ratio of the engine output power of each flight phase to the optimal engine rated power.

[0122] Assume that the optimal engine rated power is P M , engine power distribution vector X=[X1,X2,……,X M ], indicating that the engine output power in the M flight phases is X1·P M 、X2·P M 、……、X M ·P M By calculating the ECO value corresponding to the engine operating at the output power corresponding to X in each flight phase and the engine rated power being the optimal rated power, X is optimized based on the particle swarm optimization algorithm to obtain the X that minimizes the ECO value as the optimal engine power distribution vector.

[0123] (2) Obtaining the optimal engine power distribution according to the optimal engine power distribution vector and the optimal engine rated power.

[0124] The engine output power in each flight phase obtained by multiplying each element in the optimal engine power distribution vector by the optimal engine rated power is the optimal engine power distribution.

[0125] Figure 8 A flow chart showing how to obtain the optimal engine power distribution based on a particle swarm optimization algorithm according to an embodiment of the present application is shown in FIG. Figure 8 As shown, first generate an initialized particle swarm X ARRAY =P1+rand(N m ,N p )·(P2-P1), where P1 represents the ratio of the minimum output power of the engine when it is not operating at the design point to the optimal rated power of the engine, P2 represents the ratio of the maximum output power of the engine when it is not operating at the design point to the optimal rated power of the engine, and N m Indicates the number of flight phases included in the mission, N p Indicates the number of particles, N p It can be set by those skilled in the art according to actual needs, rand(N m ,N p ) represents an N m Row N pA matrix with columns, each element in the matrix is ​​a random number between 0 and 1. ARRAY Each column vector in XARRAY can be called a particle, and each particle represents an engine power distribution vector. Simulation results show that setting all elements in the first column of XARRAY (i.e., the first particle) to 0.9 significantly improves optimization results. Traversing the entire particle swarm, calculating the ECO value corresponding to each particle, updating each particle's historical optimal vector (i.e., calculating the individual optimality) and the particle swarm's group optimal vector (i.e., calculating the group optimality), the optimization goal is to minimize the ECO value. The particle swarm is updated based on the following formula:

[0126] V=W·V+C1·(X p -X ARRAY )+C2·(X g -X ARRAY ) (14)

[0127] X ARRAY =X ARRAY +V (15)

[0128] Among them, V represents the change of particle swarm, W represents the inertia parameter, C1 represents the learning parameter based on the individual optimality, C2 represents the learning parameter based on the group optimality, X p represents the optimal choice of an individual, X p Each column in represents the historical optimal vector of a particle, X g represents the optimal choice of the group, X ARRAY Indicates the current selection of the group. V, W, C1, and C2 can be set by those skilled in the art according to actual needs. Update the particle swarm until the preset number of iterations is reached or the particle swarm converges, then stop updating and set X at this time. g As the optimal engine power distribution vector, the optimal engine power distribution can be obtained according to the optimal engine power distribution vector and the optimal engine rated power.

[0129] After obtaining the optimal engine rated power and optimal engine power distribution, based on the propulsion power requirement and optimal engine power distribution of the flight mission, the battery power distribution when the engine is operating at the optimal engine rated power and optimal engine power distribution can be obtained, and then the number of battery cells required for the flight mission when the engine is operating at the optimal engine rated power and optimal engine power distribution can be calculated.

[0130] The present embodiment proposes a hybrid power system parameter matching method based on optimal mission economy. Given a flying car's maximum takeoff weight and flight mission profile, the method achieves high-precision and efficient hybrid power system parameter matching by calculating the full mission profile of the aircraft engine (including engine design point performance calculation, engine off-design point performance calculation, and engine actual operating point performance calculation) and the full mission profile of the power battery. The method can analyze the full mission profile operation of the hybrid power system under different oil-electric configurations, define the unit payload fuel consumption and total electricity cost as the flying car's mission economy index, and optimize the engine rated power and engine power distribution with the goal of minimizing the mission economy index. The optimal engine rated power and optimal engine power distribution are obtained, thereby achieving optimal flight mission economy. The method of the present embodiment solves the problem of the prior art of directly specifying the hybrid power system parameter configuration for flying cars, resulting in insufficient space for flight mission economy optimization.

[0131] Figure 9 A flow chart showing a hybrid system parameter matching method according to an embodiment of the present application is shown as follows: Figure 9 As shown, after inputting the flight mission profile and flying car parameters, the full mission profile of the aircraft engine and the full mission profile of the power battery are calculated, and the total fuel consumption and the total weight of the power battery can be obtained. Then, the payload of the flying car and the mission cost of the flight mission can be calculated, and then the mission economy index can be calculated. With the minimum value of the mission economy index as the optimization goal, the engine rated power and engine power distribution are iteratively optimized to obtain the optimal engine rated power and optimal engine power distribution. The dynamic changes in the overall performance of the hybrid power system (including the dynamic changes in the engine parameters, the dynamic changes in the battery parameters, the changes in the mission economy index, the changes in the payload, etc.) can be output.

[0132] The method of the embodiment of the present application is a hybrid power system parameter matching method based on optimal mission economy. After given the maximum takeoff weight and flight mission profile of the flying car, this method achieves high-precision and efficient calculation of power system parameter matching based on the dynamic performance model of the engine and power battery during the hybrid power system selection and matching stage. It can analyze the full mission profile operation of the hybrid power system under different oil-electric configurations, and can calculate the engine's off-design point operating performance, the dynamic changes in power battery parameters, and key performance parameters such as the total cost of the flight mission and payload. By optimizing the engine output power distribution and battery output power distribution in the hybrid power system, the optimal parameter configuration of the flying car hybrid power system (i.e., the optimal engine rated power and the optimal engine power distribution) can be obtained, thereby optimizing the flying car's mission economy.

[0133] It should be noted that although this application introduces the hybrid power system parameter matching method of this application by taking the parameter matching of the hybrid power system of a flying car as an example, those skilled in the art should understand that this application should not be limited to this. The hybrid power system parameter matching method of this application can be applied to other means of transportation using a hybrid power system, such as aircraft and automobiles using a hybrid power system. This application does not limit this, as long as the requirements are met.

[0134] Figure 10 A schematic diagram showing the power distribution results of the hybrid power system parameter matching of a flying car according to an embodiment of the present application is shown. The flying car is a NASAX-57 taxiing takeoff and landing flying car with a maximum takeoff weight of 1400 kg. Figure 2 The flight mission profile 4 in the figure is a flight mission profile, that is, the flight mission includes five stages: taxiing-climbing-cruising-descent-taxiing. The take-off altitude is set to 0m above sea level, the cruising altitude is set to 2000m above sea level, the cruising speed is set to 62m / s (this value is calculated by the optimal aerodynamic efficiency and may vary for different aerodynamic configurations), and the range is 100km-300km. The method of the embodiment of the present application is used to match the parameters of the hybrid power system, optimize the engine rated power and engine power distribution, and obtain the optimal engine rated power of 47kW. Then, the power distribution of the engine and the power battery is adjusted to optimize the mission economy index, and the following is obtained: Figure 10 From the power distribution results shown, we can see that the engine always operates around 47kW, while the power battery plays the role of peak shaving and valley filling, providing most of the power output during the climbing and cruising phases, and absorbing the engine power to replenish itself when the propulsion power demand is small during the deceleration and descent phases.

[0135] Figure 11 A schematic diagram showing the optimization results of the mission economy index of the flying car hybrid system parameter matching according to the method of one embodiment of the present application is shown. Figure 11 The figure shows a comparison of the economic efficiency index of the missions without optimization and after optimization using the method of the embodiment of the present application at different flight ranges. Figure 12 A schematic diagram showing the optimization results of the total fuel consumption of a flying car hybrid power system parameter matching according to a method of an embodiment of the present application is shown. Figure 12 The figure shows the comparison of the total fuel consumption under different ranges without optimization and after optimization using the method of the embodiment of the present application. Figure 11 and Figure 12 It can be seen that optimizing the hybrid power system parameter matching design using the method of the embodiment of the present application can improve the mission economy of the hybrid power system, which is specifically manifested in reduced flight mission costs (i.e., a reduction in the mission economy index) and reduced fuel consumption rate (i.e., energy conservation and emission reduction). The greater the range, the more obvious the optimization effect.

[0136] Based on the same inventive concept of the above method embodiment, the present application also proposes a hybrid power system parameter matching device.

[0137] Figure 13 A schematic diagram of the structure of a hybrid system parameter matching device according to an embodiment of the present application is shown. Figure 13 As shown, the device includes: an acquisition module 1301, which is used to obtain a flight mission profile and flying car parameters; wherein the flight mission profile includes multiple flight phases included in the flight mission, the change of the flight speed and flight altitude of the flying car in the flight mission over time, and the propulsion power required for each flight phase; the flying car parameters include the maximum take-off weight and the battery parameters of the power battery; an optimization module 1302, which is used to obtain the flight mission profile and flying car parameters from the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min is the minimum propulsion power required in each flight phase, P max The maximum propulsion power required for each flight phase; and taking the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, and the engine power distribution includes the engine output power in each flight phase; wherein the value of the mission economy index is calculated according to the load weight of the flying car, the range of the flight mission and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the load weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the value of the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the value of the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

[0138] In a possible implementation, the multiple flight phases include a cruise phase; the device further includes: an engine design point calculation module, configured to obtain the fuel consumption rate of the engine when operating at the design point based on the engine design point calculation method according to the design point flight speed, design point flight altitude and design point power of the engine; wherein the design point flight speed is the flight speed of the flying car in the cruise phase; the design point flight altitude is the flight altitude of the flying car in the cruise phase; the design point power is the rated power of the engine; the flight speed and the flight altitude in the cruise phase are obtained according to the flight mission profile; an engine non-design point calculation module is configured to obtain the fuel consumption rate of the engine when operating at the design point according to the design point flight speed, design point flight altitude and design point power of the engine. The non-design point flight speed, non-design point flight altitude and non-design point power of the engine are obtained based on the engine non-design point calculation method; wherein, the engine non-design point table includes the fuel consumption rate when the engine is working at the non-design point; the non-design point flight speed is the flight speed other than the design point flight speed; the non-design point flight altitude is the flight altitude other than the design point flight altitude; the non-design point power is the engine output power other than the design point power; the engine actual operating point calculation module is used to calculate the actual operating point of the engine according to the change of the flight speed and flight altitude of the flying car in the flight mission over time, the engine output power in each flight stage, the engine The fuel consumption rate when the engine is working at the design point and the non-design point table of the engine are used to obtain the change of the fuel consumption rate of the engine in the flight mission over time, and according to the change of the fuel consumption rate of the engine in the flight mission over time, the total fuel consumption of the engine in the flight mission is obtained; a power battery full mission profile calculation module is used to obtain the change of the battery voltage and battery current of the power battery in the flight mission over time and the number of battery cells required for the flight mission according to the battery output power and the battery parameters of the power battery in each flight phase, and obtain the total weight of the power battery according to the weight of the battery cells of the power battery and the number of battery cells required for the flight mission. wherein the battery output power in each flight phase is obtained according to the propulsion power required in each flight phase and the engine output power in each flight phase; a payload weight calculation module is configured to calculate the payload weight according to the maximum take-off weight, the structural weight of the flying car, and the weight of the hybrid power system; wherein the weight of the hybrid power system is obtained according to the weight of each component of the hybrid power system, the weight of the engine, the total fuel consumption, and the total weight of the power battery; the weight of the engine is obtained according to the rated power of the engine; a mission cost calculation module is configured to calculate the mission cost according to the total fuel consumption, the unit price of fuel, the total electric energy consumption in the flight mission, and the unit price of electric energy;The total amount of electric energy consumption is obtained based on the changes in the battery voltage and battery current of the power battery over time during the flight mission.

[0139] In one possible implementation, the engine actual operating point calculation module is further configured to: obtain the flight speed, flight altitude, and engine output power at each moment in the flight mission based on the temporal changes in the flight speed and flight altitude of the flying vehicle during the flight mission and the engine output power during each flight phase; if the flight speed at a first moment in the flight mission is the design point flight speed, the flight altitude at the first moment is the design point flight altitude, and the engine output power at the first moment is the design point power, the fuel consumption rate of the engine at the first moment is the fuel consumption rate when the engine operates at the design point; wherein the first moment is any moment in the flight mission; if the flight speed at the first moment is not the design point flight speed, the flight altitude at the first moment is not the design point flight altitude, or the engine output power at the first moment is not the design point power, the fuel consumption rate of the engine at the first moment is obtained by interpolation calculation based on the flight speed, flight altitude, engine output power at the first moment, and the engine non-design point table; and obtain the temporal changes in the fuel consumption rate of the engine in the flight mission based on the fuel consumption rates of the engine at each moment in the flight mission.

[0140] In a possible implementation, the battery parameters include the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance of the power battery with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature and the discharge cut-off voltage of the power battery; the power battery full mission profile calculation module is further used to: obtain the linear relationship between the battery voltage and the state of charge of the power battery according to the change of the battery voltage with the state of charge of the power battery; obtain the linear relationship between the battery voltage and the state of charge of the power battery according to the linear relationship between the battery voltage and the state of charge of the power battery, the battery output power in each flight phase Iterative calculation is performed based on the rate, energy constraint condition and power constraint condition to obtain the initial number of battery cells; iterative calculation is performed based on the initial number of battery cells, the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature, the battery output power in each flight phase, the energy constraint condition and the power constraint condition to obtain the change of the battery voltage and the battery current with time in the flight mission and the number of battery cells required for the flight mission; wherein the energy constraint condition is SOC ≥ SOC min; The power constraint condition is u-I·R≥u d ; SOC represents the state of charge of the power battery; SOC min represents the preset minimum state of charge value; u represents the battery voltage; I represents the battery current; R represents the internal resistance; u d represents the discharge cut-off voltage.

[0141] In one possible implementation, the payload weight is calculated by subtracting the structure weight from the maximum takeoff weight and then subtracting the weight of the hybrid system; the mission cost is calculated by the following formula: C = w carbon ·C fuel ·M fuel +C e ·E e ; Wherein, C represents the task cost; M fuel Indicates the total fuel consumption; C fuel Indicates the unit price of fuel; E e Indicates the total amount of electric energy consumption; C e represents the unit price of electric energy; w carbon represents the penalty factor, w carbon >1.

[0142] In a possible implementation, the device further includes: a task economy index calculation module, configured to calculate the value of the task economy index using the following formula: ECO = C / (M p ·R A ); wherein ECO represents the economic index of the task; M p Indicates the load weight; R A Indicates the range of the flight mission.

[0143] In one possible implementation, the optimization module 1302 is further used to: optimize the engine power distribution vector based on a particle swarm optimization algorithm to obtain an optimal engine power distribution vector that minimizes the value of the mission economy index; wherein the size of the engine power distribution vector is M×1; M represents the number of flight phases included in the flight mission; each element in the engine power distribution vector is a ratio of the engine output power of each flight phase to the optimal value; and obtain the optimal engine power distribution based on the optimal engine power distribution vector and the optimal value.

[0144] The hybrid power system parameter matching device of the embodiment of the present application can achieve high-precision and efficient calculation of hybrid power system parameter matching, can analyze the full mission profile working conditions of the hybrid power system under different oil-electric configurations, and define the unit payload fuel consumption and total electric energy cost as the mission economy index. The engine rated power and engine power distribution are optimized with the minimum value of the mission economy index as the optimization goal, and the optimal engine rated power and optimal engine power distribution are obtained to optimize the mission economy.

[0145] In some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to execute the parameter matching method of the hybrid system described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0146] The present application also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the hybrid power system parameter matching method. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0147] An embodiment of the present application further proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned parameter matching method of the hybrid power system when executing the instructions stored in the memory.

[0148] An embodiment of the present application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned hybrid system parameter matching method.

[0149] Figure 14 FIG1 shows a block diagram of an electronic device 1900 according to an embodiment of the present application. For example, the electronic device 1900 can be provided as a server or a terminal device. Figure 14 Electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions executable by processing component 1922, such as applications. The applications stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 1922 is configured to execute the instructions to perform the hybrid powertrain parameter matching method described above.

[0150] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , Mac OS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0151] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions. The computer program instructions can be executed by the processing component 1922 of the electronic device 1900 to perform the hybrid power system parameter matching method.

[0152] The present application may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0153] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0154] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0155] The computer program instructions for performing the operation of the present application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data or source code or object code written in any combination of one or more programming languages, wherein the programming language includes object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or executed completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as by using an Internet service provider to connect to the Internet). In certain embodiments, by utilizing the state information of computer-readable program instructions to personalize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs), the electronic circuits can execute computer-readable program instructions, thereby realizing various aspects of the present application.

[0156] Various aspects of the present application are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0157] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0158] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0159] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a special hardware-based system that performs the function or action of the specification, or can be implemented by a combination of special hardware and computer instructions.

[0160] While various embodiments of the present application have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A hybrid power system parameter matching method, characterized in that: The hybrid system includes an engine and a power battery; the method includes: Obtaining a flight mission profile and flying car parameters; wherein the flight mission profile includes multiple flight phases included in the flight mission, the changes in the flying car's flight speed and flight altitude over time during the flight mission, and the propulsion power required for each flight phase; the flying car parameters include a maximum takeoff weight and battery parameters of the power battery; From the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min is the minimum propulsion power required in each flight phase, P max is the maximum propulsion power required in each flight phase; and with the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, wherein the engine power distribution includes the engine output power in each flight phase; The mission economy index is calculated based on the payload weight of the flying car, the range of the flight mission, and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the payload weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

2. The method according to claim 1, characterized in that The plurality of flight phases include a cruise phase; and the process of calculating the payload weight and the mission cost includes: According to the design point flight speed, design point flight altitude, and design point power of the engine, based on the engine design point calculation method, a fuel consumption rate when the engine operates at the design point is obtained; wherein the design point flight speed is the flight speed of the flying car during the cruising phase; the design point flight altitude is the flight altitude of the flying car during the cruising phase; the design point power is the rated power of the engine; and the flight speed and flight altitude during the cruising phase are obtained according to the flight mission profile; An engine non-design point table is obtained based on the engine non-design point calculation method according to the non-design point flight speed, non-design point flight altitude, and non-design point power of the engine; wherein the engine non-design point table includes the fuel consumption rate when the engine operates at the non-design point; the non-design point flight speed is the flight speed other than the design point flight speed; the non-design point flight altitude is the flight altitude other than the design point flight altitude; and the non-design point power is the engine output power other than the design point power; Obtaining, based on the time-varying flight speed and altitude of the flying vehicle during the flight mission, the engine output power during each flight phase, the fuel consumption rate when the engine operates at a design point, and a table of engine non-design points, how the fuel consumption rate of the engine during the flight mission has changed over time; and obtaining, based on the time-varying fuel consumption rate of the engine during the flight mission, the total fuel consumption of the engine during the flight mission; According to the battery output power and battery parameters of the power battery in each flight phase, the changes in the battery voltage and battery current of the power battery in the flight mission over time and the number of battery cells required for the flight mission are obtained, and according to the battery cell weight of the power battery and the number of battery cells required for the flight mission, the total weight of the power battery is obtained; wherein the battery output power in each flight phase is obtained based on the propulsion power required in each flight phase and the engine output power in each flight phase; The payload weight is calculated based on the maximum takeoff weight, the structural weight of the flying car, and the weight of the hybrid power system; wherein the weight of the hybrid power system is obtained based on the weights of the components of the hybrid power system, the weight of the engine, the total fuel consumption, and the total weight of the power battery; and the weight of the engine is obtained based on the rated power of the engine; The mission cost is calculated based on the total fuel consumption, the unit price of fuel, the total electricity consumption in the flight mission, and the unit price of electricity; wherein the total electricity consumption is obtained based on the changes in the battery voltage and battery current of the power battery in the flight mission over time.

3. The method according to claim 2, characterized in that According to the changes in the flight speed and flight altitude of the flying car during the flight mission over time, the engine output power in each flight phase, the fuel consumption rate when the engine operates at the design point, and the table of the engine non-design points, the changes in the fuel consumption rate of the engine during the flight mission over time are obtained, including: Obtaining the flight speed, flight altitude, and engine output power at each moment in the flight mission based on changes in the flight speed and flight altitude of the flying car over time during the flight mission and the engine output power during each flight phase; If the flight speed at the first moment in the flight mission is the design point flight speed, the flight altitude at the first moment is the design point flight altitude, and the engine output power at the first moment is the design point power, the fuel consumption rate of the engine at the first moment is the fuel consumption rate when the engine operates at the design point; wherein the first moment is any moment in the flight mission; If the flight speed at the first moment is not the design point flight speed, the flight altitude at the first moment is not the design point flight altitude, or the engine output power at the first moment is not the design point power, calculate the fuel consumption rate of the engine at the first moment by interpolation based on the flight speed, flight altitude, engine output power at the first moment, and the engine non-design point table; According to the fuel consumption rate of the engine at each moment in the flight mission, the change of the fuel consumption rate of the engine in the flight mission over time is obtained.

4. The method according to claim 2, characterized in that The battery parameters include the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance of the power battery with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature and the discharge cut-off voltage of the power battery; According to the battery output power and the battery parameters of the power battery in each flight phase, the changes of the battery voltage and battery current of the power battery in the flight mission over time and the number of battery cells required for the flight mission are obtained, including: Obtaining a linear relationship between the battery voltage and the state of charge of the power battery according to a change in the battery voltage as the state of charge of the power battery changes; An initial number of battery cells is obtained by performing iterative calculation based on a linear relationship between the battery voltage and the state of charge of the power battery, the battery output power in each flight phase, energy constraints, and power constraints; performing an iterative calculation based on the initial number of battery cells, the change of the battery voltage with the state of charge of the power battery, the change of the internal resistance with the state of charge of the power battery, the change of the battery voltage with temperature, the change of the internal resistance with temperature, the battery output power in each flight phase, the energy constraint condition, and the power constraint condition, to obtain the change of the battery voltage and the battery current with time in the flight mission and the number of battery cells required for the flight mission; Among them, the energy constraint condition is SOC ≥ SOC min ; The power constraint condition is u-I·R≥u d ; SOC represents the state of charge of the power battery; SOC min represents the preset minimum state of charge value; u represents the battery voltage; I represents the battery current; R represents the internal resistance; u d represents the discharge cut-off voltage.

5. The method according to claim 2, characterized in that The payload weight is calculated by subtracting the structure weight from the maximum takeoff weight and then subtracting the weight of the hybrid power system; The task cost is calculated using the following formula: C=w carbon ·C fuel ·M fuel +C e ·E e Wherein, C represents the cost of the task; M fuel Indicates the total fuel consumption; C fuel Indicates the unit price of fuel; E e Indicates the total amount of electric energy consumption; C e represents the unit price of electric energy; w carbon represents the penalty factor, w carbon >1.

6. The method according to claim 5, characterized in that The value of the task economy index is calculated by the following formula: Wherein, ECO represents the economic index of the task; M p Indicates the load weight; R A Indicates the range of the flight mission.

7. The method according to claim 1, characterized in that Taking the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, including: The engine power distribution vector is optimized based on a particle swarm optimization algorithm to obtain an optimal engine power distribution vector that minimizes the mission economy index; wherein the size of the engine power distribution vector is M×1, where M represents the number of flight phases included in the flight mission; and each element in the engine power distribution vector is a ratio of the engine output power in each flight phase to the optimal value; The optimal engine power distribution is obtained according to the optimal engine power distribution vector and the optimal value.

8. A hybrid power system parameter matching device, characterized in that: The hybrid power system includes an engine and a power battery; the device includes: an acquisition module for acquiring a flight mission profile and flying car parameters; wherein the flight mission profile includes multiple flight phases included in the flight mission, the changes in the flying car's flight speed and flight altitude over time during the flight mission, and the propulsion power required for each flight phase; and the flying car parameters include a maximum takeoff weight and battery parameters of the power battery; Optimization module for the interval [P min , P max ] select the power that minimizes the mission economy index as the optimal value of the engine rated power, P min is the minimum propulsion power required in each flight phase, P max is the maximum propulsion power required in each flight phase; and with the minimum value of the mission economy index as the optimization goal, the engine power distribution is optimized based on the particle swarm optimization algorithm to obtain the optimal engine power distribution, wherein the engine power distribution includes the engine output power in each flight phase; The mission economy index is calculated based on the payload weight of the flying car, the range of the flight mission, and the mission cost of the flight mission; the range of the flight mission represents the horizontal distance from the starting point to the end point of the flight mission; the payload weight and the mission cost are both related to the engine rated power and the engine power distribution; in the process of calculating the mission economy index to select the optimal value, the engine output power in each flight phase is the engine rated power; in the process of calculating the mission economy index to obtain the optimal engine power distribution, the engine rated power is the optimal value.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method according to any one of claims 1 to 7 when executing the instructions stored in the memory.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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