Parameter matching method for hybrid power system

Abstract

The invention relates to a hybrid power system parameter matching method. The method comprises the steps that a flight mission profile and hovercar parameters are acquired; the flight mission profile comprises flight stages included in the flight mission and required propulsion power; the engine rated power enabling the value of the task economy index to be the minimum is selected from the interval [Pmin, Pmax] to serve as the optimal engine rated power; pmin and Pmax respectively represent the minimum propulsion power and the maximum propulsion power required by the flight task; taking the minimum value of the task economy index as an optimization target, optimizing engine power distribution based on a particle swarm optimization algorithm, and obtaining optimal engine power distribution; the engine power distribution comprises engine output power in each flight stage; according to the method, the engine rated power and the engine power distribution in the flight task are optimized based on the optimal task economy, and the task economy of the hybrid power hovercar can be improved.

Description

Technical Field

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

[0002] Energy conservation and carbon reduction are the inevitable trends in the development of transportation technology. Flying cars are emerging urban transportation tools that can use the three-dimensional space of the city to improve urban transportation efficiency. Compared with pure electric flying cars, hybrid flying cars have the advantages of large payload and long range. They can be mass-produced in a short period of time to reduce carbon emissions, so as to better meet the needs of low-carbon and high-efficiency development of future transportation. Hybrid flying cars are flying cars that use hybrid power systems. A hybrid power system refers to a vehicle power system that uses two different power sources (fuel and electricity), including complex electrical components such as engines and power batteries, electric motors, and electronic controls. Existing research mainly solves the energy management and power distribution problems of electrical components and engines from the perspective of flight envelope and dynamic performance of hybrid power systems, and reduces the total fuel consumption of the engine to achieve optimal economic performance and energy conservation and emission reduction. However, for hybrid flying cars with transportation functions, only considering the total fuel consumption of the engine cannot reflect the actual economic performance. In addition, most current research is based on a given power system configuration, and there is insufficient room for optimization in terms of 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 in a flight mission, thereby improving the mission economy of the 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 includes: obtaining a flight mission profile and flying car parameters; wherein the flight mission profile includes a plurality of flight phases included in the flight mission, the change of the flight speed and the 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 a maximum take-off 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 maxis the maximum propulsion power required in 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 load 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, and based on the engine non-design point calculation method, an engine non-design point table is obtained; wherein, the engine non-design point table includes the fuel consumption rate when the engine works 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 works 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 the total fuel consumption of the engine in the flight mission is obtained according to the change of the fuel consumption rate of the engine in the flight mission over time; according to the battery output power of the power battery and the battery parameters in each flight stage, 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 the total weight of the power battery is obtained according to 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 stage is calculated according to the propulsion required in each flight stage. The load weight is calculated 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 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; the weight of the engine is obtained according to the rated power of the engine; the mission cost is calculated 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; wherein the total electric energy consumption is obtained according to the changes of the battery voltage and the battery current of the power battery over time in the flight mission.

[0006] In a possible implementation, according to the change of the flight speed and the flight altitude of the flying car in the flight mission over time, the engine output power in each flight stage, the fuel consumption rate when the engine works 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 the flight altitude of the flying car in the flight mission over time and the engine output power in each flight stage, the flight speed, the flight altitude, and the 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 first time is 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 works 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 according to the flight speed, flight altitude, engine output power and the engine non-design point table at the first moment; 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.

[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 in the flight mission with time 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, the linear relationship between the battery voltage and the state of charge of the power battery 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 with time and the number of battery cells required for the flight mission are obtained. 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 a possible implementation, the load weight is calculated by subtracting the structure weight from the maximum takeoff weight and then subtracting the weight of the hybrid power 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 the 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 efficiency index of the task; M p Indicates the load weight; R A Indicates the range of the flight mission.

[0010] In a possible implementation, 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: optimizing the engine power distribution vector 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; each element in the engine power distribution vector is the ratio of the engine output power of each flight phase to the optimal value; and the optimal engine power distribution is obtained according to 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 a plurality of flight phases included in the flight mission, the change of the flight speed and the 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 a maximum take-off weight and battery parameters of the power battery; an optimization module for obtaining the parameters of the flying car 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 maxis the maximum propulsion power required in 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 hybrid power system parameter matching method 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 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, including 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 parameter matching method of the above-mentioned hybrid power system.

[0015] The hybrid power system parameter matching method of the present application optimizes the engine rated power and engine power distribution with the minimum value of the mission economy index as the optimization goal after the maximum take-off weight, battery parameters and flight mission profile of the flying car are given, and obtains the optimal engine rated power and optimal engine power distribution, so that the flight mission economy is optimal. The method of the present application can solve the problem that the prior art directly specifies the hybrid power system parameter configuration of the flying car, resulting in insufficient optimization space for the flight mission economy, and improve the economic performance of the hybrid flying car.

[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 present application and, together with the description, serve to explain the principles of the present 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 Flowchart showing engine design point performance calculations.

[0021] Figure 4 Flowchart showing off-design performance calculations for an engine.

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

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

[0024] Figure 7 A flow chart 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] Fig. 9 A flow chart of a hybrid power system parameter matching method according to an embodiment of the present application is shown.

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

[0028] Fig.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] Fig.12 A schematic diagram showing the optimization result 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.

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

[0031] Fig.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 specified.

[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, in order to better illustrate the present application, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0035] The engine and electrical components in the power system of a hybrid flying car are not only highly coupled in dynamic performance when performing flight missions (i.e., the operating points of the engine and power battery will affect each other), but the two have already begun to affect each other in the early parameter matching design stage. Taking the series hybrid system composed of the engine and power battery as an example, the total energy, design power and other parameters of the power battery will affect the selection of the engine design point, and then affect the engine selection. In addition, the mass of the battery on the flying car is dead weight (i.e., the battery weight does not change with the flight time). The design of the battery will also affect the payload of the flying car (i.e., the maximum cargo weight that the flying car can carry when performing a flight mission). In the parameter matching design stage of the flying car hybrid system, the key to achieving the optimal operation of the hybrid system is to comprehensively optimize the engine's operating condition point, electrical component configuration and payload within the flight mission profile.

[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 in 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, wherein the hybrid power system includes an engine and a power battery, such as Figure 1 As shown, the method may include:

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

[0039] Among them, the flight mission profile is an aircraft track diagram drawn to complete a specific flight mission. The flight mission profile includes multiple flight phases included in the flight mission, the changes in 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 propulsion power required by the flying car in each flight phase of the flight mission can be called the propulsion power demand distribution; the flying car parameters include the maximum take-off weight and the battery parameters of the power battery. The flying car of the embodiment of the present application is a flying car using the above-mentioned hybrid power system.

[0040] Exemplarily, the flight mission profile can be obtained from a public data set, or can be set by those skilled in the art according to actual needs. When obtaining the flight mission profile, the flight speed and flight altitude changes over time in the flight mission and the propulsion power demand distribution can be directly input, or the flight stage, cruising altitude, range (i.e., the horizontal distance from the start point to the end point of the flight mission), horizontal / vertical speed, horizontal / vertical acceleration can be input, and according to the aerodynamic parameters of the flying car (including but not limited to the wing area of ​​the flying car, the lift-to-drag ratio at different angles of attack, the propeller / rotor thrust coefficient, etc.), the flying car parameter dynamics calculation method is used to obtain the flight speed and flight altitude changes over time in the flight mission and the propulsion power demand distribution. The specific calculation process can refer to the prior art.

[0041] Exemplarily, the propulsion power required for each flight phase may be the maximum value of the propulsion power for each flight phase. For example, if the propulsion power of the flying car fluctuates between 100 kW and 101 kW in a certain flight phase, the propulsion power required for the flight phase is set to 101 kW, which can leave a safety margin for the propulsion power demand 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), etc. 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 take-off, 12-cruise, and 13-vertical landing; flight mission profile 2 includes five stages: 21-vertical take-off, 22-climb, 23-cruise, 24-descent, and 25-vertical landing; flight mission profile 3 includes seven stages: 31-vertical take-off, 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-cruising, 44-descent, and 45-rolling.

[0043] For example, the maximum take-off weight can be set by those skilled in the art according to actual needs, for example, the maximum take-off weight can be set according to the structural weight of the flying car. The structural weight of the flying car refers to the total weight of the body frame of the flying car when no other equipment is installed. The structural weight is a dead weight that does not change with the flight time. It is fixed when the flying car leaves the factory and can be obtained from the factory data of the flying car. The structural weight is usually 25%-40% of the maximum take-off weight, and the maximum take-off weight can be set accordingly.

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

[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 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.

[0046] Among them, the value of the mission economy index is calculated based on 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 stage 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.

[0047] In a possible implementation, the multiple flight phases include a cruise phase; and the process of calculating the load 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, the fuel consumption rate when the engine operates at the design point is obtained.

[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 design point of the engine refers to the specific flight conditions and engine operating conditions corresponding to the aerodynamic and thermal parameters and geometric dimensions of the engine and its components determined when designing the engine. In this application, it is defined that when the engine is operating at the design point, the flight speed and flight altitude are consistent with the flight speed and flight altitude in the cruise phase specified by the flight mission profile, the engine design point power is the engine rated power, and the fuel consumption rate when the engine is operating at the design point represents the fuel consumption rate when the flying car is in the cruise phase. The flight speed and flight altitude and the engine are operating at rated power. The engine fuel consumption rate represents the weight of fuel consumed by the engine for every 1kWh.

[0051] For example, the design point parameters of the engine when it is 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 is operating at the design point. The process of calculating the engine design point parameters can be called 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. the rated power of the engine) 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 the 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 pre-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 working at the design point through the fuel flow / engine output power, and the unit of the fuel consumption rate is kg / kWh. The specific calculation method can refer to the prior art. The engine design point parameters obtained during the calculation process can be stored for subsequent analysis and viewing.

[0053] Step 2: According to the non-design point flight speed, non-design point flight altitude and non-design point power of the engine, based on the engine non-design point calculation method, an engine non-design point table is obtained; 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; the non-design point power is the engine output power other than the design point power.

[0054] The non-design point of the engine refers to the flight conditions and engine operating conditions that are not at the design point. In this application, the fuel consumption rate when the engine is operating at the non-design point means the fuel consumption rate when the flying car is not at the flight speed and altitude in the cruising phase and the engine output power is not the rated power.

[0055] Exemplarily, the non-design point parameters of the engine can be obtained by the engine non-design point batch calculation method. The engine non-design point batch calculation refers to the process of batch simulation and analysis of engine performance for multiple non-design points. The process of calculating the engine non-design point parameters can be called engine non-design point performance calculation.

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

[0057] Figure 4 A flow chart showing the calculation of engine off-design performance is shown in FIG. Figure 4As shown, first input the non-design point working conditions, which include non-design point flight speed, non-design point flight altitude and non-design point power. If there is a requirement for thrust, the non-design point thrust can also be input. As an example, the range of non-design point flight speed can be 0 to design point flight speed, the range of non-design point flight altitude can be 0 to design point flight altitude, and the range of non-design point power can be 0.1*design point power to 1.1*design point power. The compressor beta value β1, the turbine beta value β2, the speed (if there is a requirement for power), and the turbine front temperature (if there is a requirement for thrust) are used as the quantity X to be solved in the equilibrium equation. The compressor beta value and the turbine beta value are used for interpolation of the characteristic map (MAP map). The MAP map of the compressor / turbine describes the relationship between the key performance parameters (compression ratio / expansion ratio) of the compressor / turbine and the input parameters (flow rate). Due to the limitation of experimental data, the MAP map may only contain data points under some working conditions. In order to predict performance between these data points and outside the data points, MAP map interpolation is required. The beta value is an auxiliary quantity introduced in the process of MAP interpolation 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 diagram to obtain the compressor air flow, pressure ratio, and 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 diagram to obtain the turbine gas flow, expansion ratio, and efficiency, and then calculate the turbine power and outlet total temperature, and subtract the gas flow obtained by MAP diagram interpolation from the inlet gas flow to obtain the flow residual ΔM; (5) According to the turbine power and compressor The power is used to calculate the engine output power. If there is a requirement for power, the calculated power is subtracted from the power demand to obtain the power residual ΔPW; (6) According to 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 requirement for thrust, the calculated nozzle thrust is subtracted from the thrust demand to obtain the thrust residual ΔThrust; (7) The fuel consumption rate is calculated by the fuel flow rate / engine output power; (8) The balance equation, i.e., flow rate, power, nozzle area, and thrust balance, is iteratively solved until the residual is reduced to an acceptable range, and the compressor beta value, turbine beta value, speed, and turbine inlet temperature of the non-design point are obtained, thereby obtaining the non-design point fuel consumption rate. The specific calculation method can refer to the prior art.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: According to the changes in 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 fuel consumption rate when the engine works at the design point, and the engine non-design point table, the changes in the fuel consumption rate of the engine in the flight mission over time are obtained, and according to the changes in 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.

[0059] Exemplarily, according to the change of the flight speed and the flight altitude of the flying car in the flight mission over time, the engine output power in each flight stage, the fuel consumption rate when the engine works at the design point, and the engine non-design point table, obtaining the change of the fuel consumption rate of the engine in the flight mission over time may include:

[0060] (1) 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 stage, the flight speed, flight altitude and engine output power at each moment in the flight mission are obtained.

[0061] Since the output power of aircraft engines will not fluctuate greatly in different flight phases, the engine output power in each flight phase can be regarded as constant. According to the flight mission profile, the flight speed, flight altitude and engine output power at each moment in the flight mission can be obtained.

[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 operates at the design point.

[0064] (3) 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.

[0065] If the flight speed, flight altitude and engine output power at a certain moment in the flight mission deviate from the design point, interpolation calculation can be performed based on the engine non-design point table to obtain the fuel consumption rate of the engine at that moment. Interpolation calculation is a method of estimating unknown data points using known data points. The engine non-design point table contains multiple engine fuel consumption rates under different flight speeds, different flight altitudes and different engine output powers. The fuel consumption rate at each moment can be obtained by interpolation calculation based on these known data points and the flight speed, flight altitude and engine output power at each moment in the flight mission. The interpolation calculation method can refer to the existing technology.

[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 fuel consumption rate of the engine over time in 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] Exemplarily, interpolation calculations may also be performed based on the engine non-design point table to obtain 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 the engine actual operating point performance calculation. Figure 5 A flowchart showing the actual operating point performance calculation of the engine according to an embodiment of the present application is shown as follows: Figure 5As shown, t = 0 indicates the time when the flight mission starts. The flight mission profile is input. For the current time t, the flight speed and flight altitude at time t can be obtained according to the changes of the flight speed and flight altitude in the flight mission profile over time. The engine output power at time t is the engine output power in the flight phase at time t. The engine parameters at time t can be obtained by inputting the engine non-design point table and performing interpolation calculation. The fuel consumption from the start of the mission to time t is updated according to the fuel consumption rate at time t; it is judged whether the flight mission at time t is completed. If the mission is not completed, t = t + Δt is set and the calculation is continued; if the mission is completed, the dynamic changes of the engine parameters in the flight mission over time are output.

[0071] The embodiment of the present application generates an engine non-design point table through batch calculation of non-design points. The engine non-design point table contains engine parameters under different flight speeds, different flight altitudes, and different engine output powers. The dynamic changes of engine parameters over time in the flight mission are obtained by interpolation calculation based on the engine non-design point table. The calculation result can better reflect the dynamic working condition of the engine. The existing method for calculating the actual working point performance of the engine assumes that the fuel consumption rate is constant or that the fuel consumption rate is a single variable function that only depends on the engine output power. The calculation method of the embodiment of the present application comprehensively considers the influence of flight speed, flight altitude, and engine output power on the engine working point. Compared with the existing calculation method, it has higher accuracy, and only adds the link of batch calculation of non-design points to generate non-design point tables in the calculation process. In the whole mission, the engine working state at each moment can be obtained according to the non-design point table, which not only improves the accuracy, but also does not significantly increase the 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: According to the battery output power and the battery parameters of the power battery in each flight phase, the changes in the battery voltage and the 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 according to the propulsion power required in each flight phase and the engine output power in each flight phase.

[0074] The power battery plays the role of supplementing or absorbing the engine output power in the hybrid power system. In this application, it is considered that propulsion power = engine output power + battery output power. In this application, the propulsion power required for each flight stage, the engine output power in each flight stage, and the battery output power are all considered constant. By using the propulsion power required for each flight stage minus the engine output power in each flight stage, the battery output power in each flight stage can be obtained, thereby obtaining the battery output power at each moment in the flight mission. The battery output power in each flight stage can be called the battery power distribution.

[0075] Exemplarily, 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 in each flight phase and the battery parameters, the change of the battery voltage and battery current of the power battery in the flight mission with time and the number of battery cells required for the flight mission can be obtained, which may include:

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

[0077] The linear relationship between the battery voltage and the state of charge of the power battery can be u=a·SOC+b. Among them, u represents the battery voltage, SOC represents the state of charge of the power battery, and a and b are constants. The coefficients a and b can be obtained by linear fitting according to the change of the battery voltage with SOC.

[0078] (2) 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.

[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 first. When subsequently iterating and calculating the number of battery cells required for the flight mission, the initial number of battery cells can be used as the initial value of 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 equal 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., time 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 meet the energy constraint conditions and the power constraint conditions; if the battery voltage and state of charge at time t do not meet 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 in the flight mission and the subsequent steps; if the battery voltage and state of charge at time t meet the energy constraint conditions and the power constraint conditions, let t=t+Δt, continue to iterate 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 meet 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 in a flight mission and the number of battery cells required for the flight mission may include: setting the number of battery cells 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) in 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 in the flight mission; obtaining the battery current of the power battery at time t in the flight mission according to the battery voltage, internal resistance and battery output power of the power battery at time t in 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 in the flight mission meet the energy constraint condition and the power constraint condition; if the battery voltage, internal resistance, battery current and state of charge of the power battery at time t in the flight mission meet the energy constraint condition and the power constraint condition; if the battery voltage, internal resistance, battery current and state of charge of the power battery at time t meet the energy constraint condition and the power constraint condition, the battery voltage, internal resistance, internal resistance and battery output power of the power battery at time t in the flight mission meet the energy constraint condition and the power constraint condition, the battery voltage, internal resistance, internal resistance and battery current of the power battery at time t ... If the battery voltage, internal resistance, battery current and state of charge do not meet the energy constraint conditions and the power constraint conditions, the iteration is exited, the number of battery cells is increased by 1, and the execution is returned 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 meet the energy constraint conditions and the power constraint conditions, let t = t + Δt, and continue to iterate until the battery voltage, internal resistance and battery current of the power battery at the end of the flight mission are obtained, and the battery voltage, internal resistance, battery current and state of charge at the end of the flight mission meet the energy constraint conditions and the power constraint conditions, then 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 in the flight mission obtained from the last round of iterations, the change of the battery voltage and battery current over time in the flight mission is obtained.

[0084] Estimating the initial number of battery cells and then calculating the number of battery cells required for the flight mission can be done according to the formula and steps shown below.

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

[0086]

[0087] Where, u represents the battery voltage of the power battery, P out It represents the battery output power (negative when charging), R represents the internal resistance of the power battery, and N represents 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 a 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 means the power battery is in charging state.

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

[0096]

[0097] Where 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 in time.

[0098] The initial number of battery cells can be estimated according to 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 value 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 in 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. Based on f(x) at time t+Δt, x at time t+Δt can be obtained; Based on x at time t+Δt and P at time t+Δt, out , R and N, u at time t+Δt can be calculated by formula (5) or (6), and SOC at time t+Δt can be calculated according to u=a·SOC+b; thus, the SOC and P at the current time are known. out , you 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, the SOC at each moment in the flight mission can be obtained, thereby obtaining u and x at each moment; in the iterative process, for each calculated x at a moment, it is necessary to determine whether it meets the energy constraint and power constraint of formulas (7)-(9); if it does not meet the constraint, exit the iteration, set N=N+1, and then start the iterative calculation again from t=0; if it meets the constraint, continue to calculate x at the next moment until the x at the end of the flight mission is calculated and the 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] The embodiment of the present application estimates the initial number of battery cells by using a time integration method. Based on the linearized relationship between battery voltage and SOC, the charge and discharge state of the power battery at the current moment, the battery output power, the power duration (i.e., the duration of the flight phase in which the battery output power is located), and the SOC, the SOC at the next moment is quickly obtained through a one-step calculation without the need for multiple steps of calculation. If the constraint condition is not met, the current iteration is exited, the number of battery cells is updated, and the iterative calculation is performed again. Since the battery output power in each flight phase in the present application is regarded as constant, 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 can quickly estimate the number of battery cells required.

[0101] Since the changes of 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 accurate number of battery cells required for the flight mission. It is necessary to perform iterative calculations while considering the changes of R and u with temperature and SOC, and obtain the number of battery cells required for the flight mission and the changes of battery voltage and battery current over time during the flight mission according to formulas (1)-(3). The process of calculating the number of battery cells required for the flight mission and obtaining the changes of 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 technologies. 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. The SOC and temperature at time t (t>0) are known. 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, u and R at time t can be obtained. According to u, R, P at time t outand N can be used to calculate I at time t through formula (1). In this way, 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 constraint conditions and power constraint conditions shown in formulas (2) and (3); if the constraint conditions are not met, the iteration is exited, N is set to N+1, and the iterative calculation is restarted from time t=0; if the constraint conditions are met, t is set to t+Δt, and the calculation of u, R, I and SOC at the next moment is continued until the u, R, I and SOC at the end of the flight mission are calculated and meet the energy constraint conditions and power constraint conditions 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 constraint conditions and power constraint conditions 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 by the last round of iteration are output.

[0103] The embodiment of the present application adopts the time difference method, and uses the initial number of battery cells estimated by the time integration method as the initial value, substitutes it into the full mission profile of the power battery for iterative calculation, and continuously updates the number of battery cells. Finally, the number of battery cells that just meet the power and energy requirements of the entire flight mission and the dynamic changes of 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) can be obtained. This method reduces the number of time steps to the number of power intervals, reduces the number of iterations required for a full mission profile calculation, and uses the estimated initial number of battery cells as the iteration initial value, reducing the number of required iterations and improving the calculation efficiency, so that the number of battery cells required for the flight mission can be quickly and accurately calculated.

[0104] After obtaining the number of battery cells required for the flight mission, the total weight of the power battery can be obtained by multiplying the weight of the battery cells of the power battery by the number of battery cells required for the flight mission. The weight of the battery cells can be determined based on the energy density and battery energy of the power battery. By changing the energy density of the power battery, the weight of the battery cells and the total weight of the power battery under different energy densities can be calculated.

[0105] Step 5: Calculate the load weight of the flying car according to the maximum take-off weight, the structural weight of the flying car and the weight of the hybrid power system.

[0106] The maximum takeoff weight is defined in this application as the sum of the structural weight of the flying car, the load weight (i.e., the effective load) and the weight of the hybrid power system. The load weight of the flying car is equal to the maximum takeoff weight minus the structural weight of the flying car minus the weight of the hybrid power system.

[0107] The weight of the hybrid power system can be obtained based on the weight of each component of the hybrid power system (such as motors, inverters, etc.), the weight of the engine, the total fuel consumption and the total weight of the power battery. The weight of each component of the hybrid power system can be obtained from the flying car data. If the weight of each component of the hybrid power system is unknown, the output power of each component of the hybrid power system can be calculated based on the maximum propulsion power of the flying car, and then the weight of each component of the hybrid power system can be obtained based on the specified power density (i.e., the ratio of the output power of each component of the hybrid power system to its weight). The specific calculation method can refer to the relevant technology. The total fuel consumption can be calculated by step 3. The total weight of the power battery can be calculated by step 4. The engine weight can be calculated based on the existing engine weight estimation method according to the engine parameters. For example, the engine weight can be estimated according to the engine rated power. The specific calculation method can refer to the existing technology. The weight of the hybrid power system is the sum of the weight of each component of the hybrid power system, 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 electric energy consumption in the flight mission, and the unit price of electric energy; wherein the total electric energy consumption is obtained based on the changes in the battery voltage and battery current of the power battery in 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] Where C represents the task cost (in RMB); 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 of the flight mission based on the payload weight, flight mission 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 (in 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 interval [P min , P max ] to find the optimal value of the engine rated power (i.e., the optimal engine rated power), and the optimization goal is to minimize the value of ECO. The core of the multi-division method is to divide the engine rated power selection interval into multiple sub-intervals, 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 the rated power throughout the flight mission, and the value of ECO corresponding to the power calculation point is calculated. The optimal power calculation point that minimizes the value of ECO is determined among the 11 power calculation points, and the power interval between the previous power calculation point and the next power calculation point of the optimal power calculation point is used as the new engine rated power selection interval. The steps of selecting 11 power calculation points within the engine rated power selection interval and the subsequent steps are repeated until the length of the engine rated power selection interval obtained finally converges to a preset value (for example, converges to 0.1kW), and the power value of the optimal power calculation point at this time is the optimal engine rated power.

[0117] The process of selecting the optimum 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 interval 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 the rated power throughout the flight mission, perform the full mission profile calculation of the aircraft engine and the full mission profile calculation of the power battery through the above steps 1 to 6, obtain the load 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 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 have large power fluctuations in different flight phases, so the engine control strategy can be simplified to the power output of the engine in different flight phases. For a flight mission that includes M flight phases, its engine control strategy is a vector X of length M, where each element in X corresponds to the engine output power of a flight phase, and the value of each element in X is the ratio of the engine output power of the flight phase corresponding to the element to the engine rated power. For example, a flight mission includes five flight phases: takeoff, climb, cruise, descent, and landing. X = [1,1,1,1,0.5], which means that the engine output power in the takeoff, climb, cruise, descent, and landing flight phases is 1 times, 1 times, 1 times, 1 times, and 0.5 times the engine 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 of the flight mission, and the quantity to be optimized is the engine power distribution vector X. That is, the engine control strategy optimization problem can be regarded as the engine power distribution optimization. The particle swarm optimization algorithm (PSO) can be used to find the optimal engine power distribution, thereby realizing the optimal control strategy of the hybrid power system. The core of the particle swarm optimization algorithm is to simulate the behavior of bird flocks in nature to find the optimal habitat, and update the solution vector through group optimality and individual optimality.

[0120] In a possible implementation, 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:

[0121] (1) 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 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 a ratio of the engine output power in 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, and X with the minimum ECO value is obtained 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 of obtaining the optimal engine power distribution based on a particle swarm optimization algorithm according to an embodiment of the present application is shown as follows: 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 working at a non-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 working at a non-design point to the optimal rated power of the engine, N m Indicates the number of flight phases included in the flight mission, N p Represents 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 can be called a particle, and each particle represents an engine power distribution vector. The simulation results show that setting all the first column elements (i.e., the first particle) of XARRAY to 0.9 can greatly improve the optimization effect. Traverse the entire particle swarm, calculate the value of ECO corresponding to each particle, update the historical optimal vector of each particle (i.e., calculate the individual optimal) and the group optimal vector of the particle swarm (i.e., calculate the group optimal), and the optimization goal is to minimize the value of ECO. Update the particle swarm 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 optimal learning parameter based on individual, C2 represents the optimal learning parameter based on group, 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 the optimal engine power distribution, based on the propulsion power requirement and the optimal engine power distribution of the flight mission, the battery power distribution when the engine is running at the optimal engine rated power and the optimal engine power distribution can be obtained, and then the number of battery cells required for the flight mission when the engine is running at the optimal engine rated power and the optimal engine power distribution can be calculated.

[0130] The embodiment of the present application proposes a hybrid power system parameter matching method based on optimal mission economy. After the maximum take-off weight and flight mission profile of the flying car are given, the high-precision and efficient calculation of hybrid power system parameter matching is achieved through the calculation of the full mission profile of the aviation engine (including the engine design point performance calculation, the engine non-design point performance calculation and the engine actual working point performance calculation) and the full mission profile calculation of the power battery. It can analyze the full mission profile working conditions of the hybrid power system under different oil-electric configurations, and define the unit effective load fuel consumption and the total cost of electric energy as the mission economy index of the flying car. 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, so that the flight mission economy is optimal. The method of the embodiment of the present application solves the problem that the prior art directly specifies the parameter configuration of the hybrid power system of the flying car, resulting in insufficient optimization space for the flight mission economy.

[0131] Fig. 9 A flow chart showing a hybrid power system parameter matching method according to an embodiment of the present application is shown as follows: Fig. 9 As shown, after inputting the flight mission profile and the 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 effective load of the flying car and the mission cost of the flight mission can be calculated, and then the mission economy index can be calculated. The engine rated power and the engine power distribution are iteratively optimized with the minimum value of the mission economy index as the optimization goal, and the optimal engine rated power and the optimal engine power distribution are obtained. The dynamic changes of the overall performance of the hybrid power system (including the dynamic changes of the engine parameters, the dynamic changes of the battery parameters, the changes of the mission economy index, the changes of the effective load, 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 the maximum take-off weight and flight mission profile of the flying car are given, the method realizes high-precision and efficient calculation of power system parameter matching based on the dynamic performance model of the engine and the power battery in the hybrid power system selection and matching stage. It can analyze the full mission profile working conditions of the hybrid power system under different oil-electric configurations, and can calculate the non-design point working performance of the engine, the dynamic changes of power battery parameters, and the total cost of the flight mission, payload and other key performance parameters. By optimizing the engine output power distribution and the battery output power distribution in the hybrid power system, the optimal parameter configuration of the hybrid power system of the flying car (i.e., the optimal engine rated power and the optimal engine power distribution) can be obtained, so that the flying car mission economy is optimized.

[0133] It should be noted that, although the present application introduces the hybrid power system parameter matching method of the present 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 the present application should not be limited to this, and the hybrid power system parameter matching method of the present application can be applied to other means of transportation using a hybrid power system, such as aircraft and automobiles using a hybrid power system, and the present application does not limit this, as long as the requirements are met.

[0134] Fig.10 A schematic diagram showing the power distribution result 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 take-off and landing flying car, and the maximum take-off weight is set to 1400 kg. Given the above Figure 2 The flight mission profile 4 in the figure is the 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 through the optimal aerodynamic efficiency, and different aerodynamic configurations may have differences), 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 adjust the power distribution of the engine and the power battery to optimize the mission economy index, and obtain the following: Fig.10 From the power distribution results shown, we can see that the engine always operates around 47kW, and 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] Fig.11 A schematic diagram showing the optimization results of the mission economy index of the hybrid power system of a flying car according to the method of one embodiment of the present application is shown, Fig.11 The figure shows the 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. Fig.12 A schematic diagram showing the optimization result of the total fuel consumption of the hybrid power system of a flying car according to the method of an embodiment of the present application is shown, Fig.12 The figure shows the comparison of the total fuel consumption under different flight ranges without optimization and after optimization using the method of the embodiment of the present application. Fig.11 and Fig.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 reduced 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] Fig.13 A schematic diagram of the structure of a hybrid power system parameter matching device according to an embodiment of the present application is shown. Fig.13 As shown, the device includes: an acquisition module 1301, which is used to acquire 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 the 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 is the maximum propulsion power required in 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 cruising phase; the device further includes: an engine design point calculation module, which is used to obtain the fuel consumption rate of the engine when it operates 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 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; an engine non-design point calculation module is used to obtain the fuel consumption rate of the engine when it operates 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 works 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 working point calculation module is used to calculate the actual working 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 works 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 the total fuel consumption of the engine in the flight mission is obtained according to the change of the fuel consumption rate of the engine in the flight mission over time; the 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 the total weight of the power battery is obtained according to 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 according to the propulsion power required in each flight phase and the engine output power in each flight phase; a load weight calculation module is used to calculate the load 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 used 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;Wherein, the total amount of electric energy consumption is obtained according to the changes of the battery voltage and battery current of the power battery over time during the flight mission. ;

[0139] In a possible implementation, the engine actual operating point calculation module is further used to: obtain the flight speed, flight altitude, and engine output power at each moment in the flight mission 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 stage; 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 works 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 according to 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.

[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 also 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 stage The rate, energy constraint condition and 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 stage, 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.

[0141] In a possible implementation, the load weight is calculated by subtracting the structure weight from the maximum takeoff weight and then subtracting the weight of the hybrid power 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 the 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 by the following formula: ECO = C / (M p ·R A ); wherein ECO represents the economic efficiency 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 the optimal engine power distribution is obtained 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, and optimize the engine rated power and engine power distribution with the minimum value of the mission economy index as the optimization goal, to obtain the optimal engine rated power and the optimal engine power distribution, so as 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 embodiment of the present application also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the hybrid power system parameter matching method described above is implemented. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

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

[0149] Fig.14 1 is a block diagram of an electronic device 1900 according to an embodiment of the present application. For example, the electronic device 1900 may be provided as a server or a terminal device. Fig.14 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions that can be executed by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the hybrid power system parameter matching method described above.

[0150] The electronic device 1900 may also 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, which can be executed by the processing component 1922 of the electronic device 1900 to complete the hybrid power system parameter matching method.

[0152] The present application may be a system, a method and / or a 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 may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may 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 of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) 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 disk 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 of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted 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 a 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, optical fiber transmissions, wireless transmissions, 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 for storage 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 assembler 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, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions can be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or 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 through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of a computer-readable program instruction to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), the electronic circuit can execute a computer-readable program instruction, thereby realizing various aspects of the present application.

[0156] Various aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes 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 operating 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 boxes in the flowchart and / or block diagram.

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

[0160] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A hybrid power system parameter matching method, characterized in that: The hybrid power system includes an engine and a power battery; the method includes: Acquire 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 speed and flying 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; 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 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, wherein the engine power distribution includes the engine output power in each flight phase; Among them, the value of the mission economy index is calculated based on 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 stage 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.

2. The method according to claim 1, characterized in that The multiple flight phases include a cruise phase; and the process of calculating the load 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, the fuel consumption rate when the engine works at the design point is obtained; wherein the design point flight speed is the flight speed of the flying car in the cruising stage; the design point flight altitude is the flight altitude of the flying car in the cruising stage; the design point power is the rated power of the engine; the flight speed in the cruising stage and the flight altitude in the cruising stage are obtained according to the flight mission profile; According to the non-design point flight speed, non-design point flight altitude and non-design point power of the engine, based on the engine non-design point calculation method, an engine non-design point table is obtained; wherein the engine non-design point table includes the fuel consumption rate when the engine works 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 changes in 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 fuel consumption rate when the engine works at the design point, and the table of the engine non-design point, the changes in the fuel consumption rate of the engine in the flight mission over time are obtained, and according to the changes in 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 of the power battery in each flight phase and the battery parameters, 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, 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 according to the propulsion power required in each flight phase and the engine output power in each flight phase; The load weight is calculated 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; 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; wherein the total electric energy 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 change of the flight speed and the flight altitude of the flying car in the flight mission over time, the engine output power in each flight stage, the fuel consumption rate when the engine works at the design point, and the non-design point table of the engine, 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 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 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 according to the flight speed at the first moment, the flight altitude, the engine output power 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 change of the battery voltage and the 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; Iteratively calculate 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, the energy constraint condition and the power constraint condition to obtain the initial number of battery cells; Iterative calculation is performed 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, 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 load 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 by 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 the 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 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; each element in the engine power distribution vector is the ratio of the engine output power of 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 a plurality of 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 a maximum take-off 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 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, wherein the engine power distribution includes the engine output power in each flight phase; Among them, the value of the mission economy index is calculated based on 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 stage 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.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in 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.

Images