Series hybrid electric propulsion system sliding mode prediction energy management method

Abstract

The invention discloses a sliding mode prediction energy management method for a series hybrid electric propulsion system, and belongs to the field of energy management. According to the method, firstly, a series hybrid electric propulsion system power model is established, the power flow direction in the system is analyzed, an energy management system is designed, the working principle of all parts of the hybrid electric propulsion system and the mutual relation are clarified, and the hybrid electric propulsion system energy management optimization control method is provided. Optimal distribution of the output power of the engine and the battery is achieved, and it is guaranteed that the fuel consumption of the aircraft is minimized under various constraint conditions. Therefore, on the premise that all related parts of the hybrid electric propulsion system are normal and operate safely, reasonable distribution between the output power of the engine and the output power of the battery is ensured, the fuel consumption of an aircraft is minimized, the overall energy efficiency is improved, and energy optimization management of the hybrid electric propulsion system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a sliding mode prediction energy management method for a series hybrid electric propulsion system. Background Art

[0002] As a crucial bridge between traditional fuel-powered aircraft and pure electric aircraft, hybrid-electric propulsion technology is becoming a key path in driving the aviation industry's green transformation. Currently, the aviation industry faces the dual pressures of reducing greenhouse gas emissions and improving energy efficiency. While traditional fuel-powered aircraft offer mature technology, long range, and high payload, their high fuel consumption and emissions no longer meet the requirements of sustainable development. Pure electric aircraft, however, are limited by technical bottlenecks such as low battery energy density and insufficient endurance, making them difficult to meet the demands of large-scale commercialization. Hybrid-electric propulsion technology combines electric and traditional powertrains, intelligently switching between power modes during different flight phases. This technology not only effectively reduces fuel consumption and emissions, but also improves overall flight efficiency and system redundancy, making it one of the most viable solutions in this transitional period. With continuous breakthroughs in new materials, new energy sources, and intelligent control technologies, its development prospects are becoming increasingly promising.

[0003] The core components of hybrid-electric propulsion technology include the power generation system, energy storage device, and electric propulsion unit. Together, these three components form the foundation of the entire power system. However, with increasing system complexity and power density, efficiently managing the large amounts of heat generated during operation has become a key issue in ensuring system stability and safety. Furthermore, optimizing energy management is becoming increasingly important. This includes strategies for allocating fuel and electrical energy during different flight phases, controlling battery charge and discharge, and designing energy recovery mechanisms, all of which are key factors in improving system efficiency. Furthermore, the dynamic response characteristics of hybrid-electric propulsion systems are complex, and accurate modeling and simulation of their power flow patterns are also crucial for system design, performance prediction, and the development of control strategies. Breakthroughs in these technical issues will not only help improve the performance and reliability of hybrid-electric propulsion systems but will also directly drive the development of aviation propulsion systems towards greener, more efficient, and more intelligent systems. Summary of the Invention

[0004] The present invention provides a sliding mode predictive energy management method for a series hybrid electric propulsion system. By modeling the power of the series hybrid electric propulsion system, based on sliding mode predictive control and combined with the minimum principle, the output power of the engine and battery is optimized and distributed, ensuring that the aircraft fuel consumption is minimized under various constraints.

[0005] An embodiment of the present invention provides a sliding mode predictive energy management method for a series hybrid electric propulsion system, comprising the following steps:

[0006] Analyze the power flow in the series hybrid electric propulsion system and establish a power model for the series hybrid electric propulsion system;

[0007] By utilizing sliding mode predictive control and the minimum principle, the output power of the engine and battery of the series hybrid electric propulsion system is optimally distributed according to the power model of the series hybrid electric propulsion system, thereby minimizing the aircraft fuel consumption under various constraints.

[0008] Optionally, in one embodiment of the present invention, establishing a power model of a series hybrid electric propulsion system includes:

[0009] The aircraft is simplified into a mass model and the dynamic modeling of the aircraft is carried out to obtain the relationship between propulsion power and fuel consumption in different flight phases. The relationship between fuel consumption rate and aircraft propulsion power is expressed by the relationship between propulsion power and aircraft mass.

[0010] The fuel consumption rate is expressed as a function of engine power and speed, and an engine power model is established, where the constraints are that the engine speed and power are both within the lean flameout boundary and the surge boundary;

[0011] The motor power model is established by fitting the motor input power with the motor output power and speed as variables, where the constraints are that the motor output speed and output power are within the safety limit;

[0012] The generator output power and generator speed are used as independent variables to fit and calculate the generator input power and establish a generator power model;

[0013] The battery is modeled as a physical model of an ideal voltage source connected in series with a resistor. Based on the simplified circuit structure of the physical model, the relationship between battery power and battery effective power loss, as well as the relationship between battery state of charge and battery power, are established to obtain a battery power model. The constraint condition is that the battery state of charge is within the upper and lower bounds of the battery state of charge.

[0014] Optionally, in one embodiment of the present invention, the output power of the engine and the battery of the series hybrid electric propulsion system is optimally distributed according to the power model of the series hybrid electric propulsion system by using sliding mode predictive control and the minimum principle, including:

[0015] Set initial co-state variables and battery state of charge;

[0016] Calculate the required power of the series hybrid electric propulsion system at any time and determine the range of fuel consumption rate;

[0017] Discretize the fuel consumption rate;

[0018] Calculate state of charge error and sliding surface;

[0019] Calculate the Hamiltonian function corresponding to each discretized fuel consumption rate, and compare to obtain the smallest Hamiltonian function minimum. The control variable corresponding to the Hamiltonian function minimum is the optimal control variable at the current moment.

[0020] The state of charge value of the battery at the end of the time is calculated through the state equation and the co-state equation, and the error is compared with the initial state of charge value. When the error meets the requirement, the cycle ends; otherwise, the value of the co-state variable is readjusted until the state of charge error requirement is met, and the output power of the optimal engine and battery of the series hybrid electric propulsion system is obtained.

[0021] Optionally, in one embodiment of the present invention, the output power of the engine and battery of the series hybrid electric propulsion system is optimized according to the power model of the series hybrid electric propulsion system by using sliding mode predictive control and the minimum principle, specifically including:

[0022] discretizing the power model of the series hybrid electric propulsion system;

[0023] When performing energy management, the battery state of charge is selected as the state variable, and the function related to the turboshaft engine power and speed is selected as the control variable to establish the Hamiltonian function;

[0024] Calculate the value of the Hamiltonian function at any time. When the optimal control variable is reached, the Hamiltonian function value reaches its minimum value, and the state variables and co-state variables are updated.

[0025] Design of sliding surface:

[0026]

[0027] Among them, e(t) is the SOC tracking error, β>0 is the sliding mode parameter, SOC(t) is the battery state of charge, SOC ref (t) is the expected state of charge of the battery;

[0028]

[0029] in, is the derivative of the sliding surface function, is the derivative of the SOC tracking error, is the derivative of the battery state of charge, is the derivative of the battery's desired state of charge;

[0030] According to the battery power model, we can get:

[0031]

[0032] Among them, P b (t) is the battery power;

[0033] Design control law u s Make the sliding surface meet the approach condition Where η>0, we get:

[0034]

[0035] Where K>0 is the sliding mode gain, sign(s(t)) is the sliding mode sign function;

[0036] The sliding mode control law is used as a constraint or part of the objective function, and a sliding surface penalty term is added:

[0037]

[0038] Among them, J is the rolling optimization objective function, α is the SOC deviation weight coefficient, SOC f For the target terminal SOC, SOC k is the current SOC, t f is the optimization end time, t0 is the optimization start time, is the fuel consumption, u(t) is the control variable, and γ>0 is the sliding mode gain.

[0039] The sliding mode predictive energy management method for a series hybrid electric propulsion system according to an embodiment of the present invention has the following beneficial effects:

[0040] (1) Minimize the fuel consumption of the aircraft while meeting multiple constraints, ensure that the components of the hybrid electric propulsion system operate within a safe temperature range, achieve optimal distribution of the output power of the engine and battery, and improve the energy efficiency of the hybrid electric propulsion system.

[0041] (2) The power flow of the hybrid electric propulsion system was analyzed, an aircraft dynamics model was established, the relationship between fuel consumption and propulsion power was obtained, and a power model of the hybrid electric propulsion system including the engine, motor, generator and battery was established.

[0042] (3) Through specific examples, the energy management optimization control method of the SM-MPC-MP hybrid electric propulsion system proposed in the present invention is demonstrated. The propulsion system power curve, battery SOC curve, fuel consumption curve, etc. in each mission phase of the flight profile effectively verify the effectiveness of the proposed method.

[0043] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 This is a flow chart of a sliding mode predictive energy management method for a series hybrid electric propulsion system according to an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the power framework of a series hybrid electric propulsion according to an embodiment of the present invention;

[0047] Figure 3 Schematic diagram of the stress condition of an aircraft under typical working conditions according to an embodiment of the present invention;

[0048] Figure 4 Schematic diagram of a battery equivalent circuit according to an embodiment of the present invention;

[0049] Figure 5 This is a flow chart of SM-MPC-MP energy management according to an embodiment of the present invention;

[0050] Figure 6 A schematic cross-sectional diagram of a flight mission according to an embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of a propulsion system power curve according to an embodiment of the present invention;

[0052] Figure 8 Schematic diagram of a battery SOC curve according to an embodiment of the present invention;

[0053] Figure 9 is a schematic diagram of a battery power curve according to an embodiment of the present invention;

[0054] Figure 10 A schematic diagram of system power calibration according to an embodiment of the present invention;

[0055] Figure 11 Schematic diagram of equivalent fuel consumption according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0057] Figure 1 The present invention provides a flow chart of a sliding mode predictive energy management method for a series hybrid electric propulsion system according to an embodiment of the present invention.

[0058] like Figure 1 As shown, the sliding mode predictive energy management method of the series hybrid electric propulsion system includes the following steps:

[0059] Step 1: Analyze the power flow in the series hybrid electric propulsion system and establish a power model of the series hybrid electric propulsion system.

[0060] Step 2: Utilizing sliding mode predictive control and the principle of minimum, the output power of the engine and battery of the series hybrid electric propulsion system is optimally distributed according to the power model of the series hybrid electric propulsion system, thereby minimizing the fuel consumption of the aircraft under various constraints.

[0061] An aviation hybrid electric propulsion system combines traditional fuel engine and electric motor technology to improve aircraft efficiency, reduce emissions, and potentially lower operating costs. Using a turboshaft engine as the power source, a generator converts mechanical energy into electrical energy, which is then used by an electric motor to drive a propeller or other propulsion device. Depending on whether the engine directly provides propulsion for flight, hybrid electric propulsion systems can be categorized as either series or parallel.

[0062] The tandem structure is simple, reducing the complexity of mechanical and electrical interfaces and improving reliability and ease of maintenance. The engine does not directly drive the propeller or other loads. Instead, the generator converts mechanical energy into electrical energy, which is then provided by the electric motor on demand. This avoids direct power coupling between the engine and the electric motor, simplifies control system design, and enables more flexible energy management. It also facilitates modular design, adapting to different mission requirements or future technological upgrades. Therefore, this paper focuses on the tandem structure to conduct research on fuel consumption optimization of hybrid power systems.

[0063] The series hybrid electric propulsion system includes an engine, a generator, a battery, an electric motor, a rectifier, etc. In the series structure, the engine does not provide thrust and outputs all power to the generator. The generator inputs the electric energy into the aircraft power grid after rectification and inversion. The battery supplies power to or takes power from the grid according to the aircraft's operating status. The aircraft grid supplies power to the electric motor after rectification and inversion. The electric motor drives the propeller to generate thrust. When the aircraft slows down or lands, the system enters the windmill state. At this time, the propeller reverses, driving the electric motor to generate power for the grid, and the grid supplies power to the battery and other electronic loads. The framework of the series hybrid electric propulsion system is as follows: Figure 2 shown.

[0064] like Figure 1 middle, is the fuel mass change rate, P eng is the engine power, P gen is the generator power, P b is the battery power, P be is the effective electric power of the battery, P me is the electric power input from the power grid to the motor, P em is the motor output power, P drvRequired propulsion power of a hybrid electric propulsion aircraft.

[0065] In a series hybrid electric propulsion system, the electric power of the motor is provided by the DC bus and the battery, which can be obtained:

[0066] P me =P gen +P be (1)

[0067] Optionally, in one embodiment of the present invention, establishing a power model of a series hybrid electric propulsion system includes:

[0068] The aircraft is simplified into a mass model and the dynamic modeling of the aircraft is carried out to obtain the relationship between propulsion power and fuel consumption in different flight phases. The relationship between fuel consumption rate and aircraft propulsion power is expressed by the relationship between propulsion power and aircraft mass.

[0069] The fuel consumption rate is expressed as a function of engine power and speed, and an engine power model is established, where the constraints are that the engine speed and power are both within the lean flameout boundary and the surge boundary;

[0070] The motor power model is established by fitting the motor input power with the motor output power and speed as variables, where the constraints are that the motor output speed and output power are within the safety limit;

[0071] The generator output power and generator speed are used as independent variables to fit and calculate the generator input power and establish a generator power model;

[0072] The battery is modeled as a physical model of an ideal voltage source connected in series with a resistor. Based on the simplified circuit structure of the physical model, the relationship between battery power and battery effective power loss, as well as the relationship between battery state of charge and battery power, are established to obtain a battery power model. The constraint condition is that the battery state of charge is within the upper and lower bounds of the battery state of charge.

[0073] Specifically, aircraft dynamics modeling: By building an accurate aircraft dynamics model, we can deeply understand and calculate the specific relationship between the aircraft's propulsion power and fuel consumption in different flight phases. In order to simplify calculations and analysis, the aircraft is simplified into a point mass model, focusing on studying the force conditions of the aircraft in typical flight phases. The force conditions in typical phases are as follows: Figure 3 As shown in the figure, the key forces acting on the aircraft, such as lift, drag, thrust and gravity, are depicted.

[0074]

[0075] Among them, x, y, h are the position information of the aircraft, T, L, and D are the pull, lift, and drag of the aircraft respectively, V is the flight speed of the aircraft, m is the total mass of the aircraft, ψ is the yaw angle, γ is the track angle, μ is the roll angle, and α is the angle of attack of the aircraft.

[0076] The lift of an aircraft is the vertical upward force on the aircraft, and the drag is the air resistance when the aircraft is flying, which is proportional to the square of the aircraft's speed. The formula for lift and drag is:

[0077]

[0078] according to Figure 3 From the force diagram of the aircraft shown, we can obtain:

[0079]

[0080] According to formula (5), the propulsion power of the aircraft can be written as:

[0081]

[0082] Formula (6) can describe the relationship between propulsion power and aircraft mass, and thus the relationship between fuel consumption rate and aircraft propulsion power can be obtained.

[0083] Establish an engine power model: The aircraft's mass change rate is the fuel mass consumption rate. The amount of fuel directly affects the turboshaft engine's output power and speed. Therefore, the fuel mass consumption rate is written as a function of engine power and speed:

[0084] The rate of change of an aircraft's mass during flight is determined by the mass rate of fuel consumption. For turboshaft engines in particular, fuel consumption directly affects engine output power and speed. Therefore, to accurately describe this relationship, the mass rate of fuel consumption can be expressed as a function of engine power and speed. This functional relationship allows for a better understanding and control of aircraft performance during different flight phases.

[0085]

[0086] Among them, n l is the engine low-pressure shaft speed. Based on the relationship between the fuel quantity, output torque and speed of the turboshaft engine, the relationship between them can be expressed in the following fitting form:

[0087]

[0088] Among them, β1(n l )>0,β2(n l)>0 are all coefficient functions related to the engine output speed. At the same time, the engine speed and power must be between the lean flameout boundary and the surge boundary, that is:

[0089]

[0090] Establishing a motor power model: The motor power model uses the same method as the engine power model. Since the motor output power and speed are closely related to the grid input power, the motor output power and speed are used as variables to fit the motor input power:

[0091]

[0092] Among them, ω em is the motor speed, κ1(ω em )>0,κ2(ω em )>0 is the relevant coefficient. The output speed and output power of the motor must be within the safety limit to ensure safe and reliable operation under various working conditions, that is:

[0093]

[0094] When studying the power model of a generator, the output power and speed of the generator are used as independent variables, and these variables are used to fit and calculate the input power of the generator:

[0095]

[0096] Among them, ω gen is the generator speed, ν1(ω gen ),ν2(ω gen ) is the correlation coefficient.

[0097] Establishing a battery power model: A common simplified method for battery modeling is to model the battery as an ideal voltage source in series with an internal resistor. The physical model can be simplified as follows: Figure 4 The circuit structure is shown.

[0098] According to the KVL equation, we can get:

[0099] U oc -R i I b =U b (13)

[0100] Among them, R i is the internal resistance of the battery; I b is the current flowing through the battery; U oc is a constant open circuit voltage.

[0101] Formula (13) can be expressed as:

[0102]

[0103] Among them, P bc It is the effective electrical power that the battery can deliver.

[0104] Therefore, the relationship between battery power and battery effective electric power is:

[0105]

[0106] The relationship between battery state of charge (SOC) and battery power is:

[0107]

[0108] At the same time, to ensure the battery's service life and safety, the battery's state of charge must be maintained within a reasonable range. The upper and lower limits of this range are determined by the battery's physical and chemical properties, and are also affected by the battery's operating environment and usage conditions:

[0109]

[0110] Among them, SOC is the lower limit of battery state of charge, is the upper bound of the state of charge.

[0111] Optionally, in one embodiment of the present invention, the output power of the engine and the battery of the series hybrid electric propulsion system is optimally distributed according to the power model of the series hybrid electric propulsion system by using sliding mode predictive control and the minimum principle, including:

[0112] Set initial co-state variables and battery state of charge;

[0113] Calculate the required power of the series hybrid electric propulsion system at any time and determine the range of fuel consumption rate;

[0114] Discretize the fuel consumption rate;

[0115] Calculate state of charge error and sliding surface;

[0116] Calculate the Hamiltonian function corresponding to each discretized fuel consumption rate, and compare to obtain the smallest Hamiltonian function minimum. The control variable corresponding to the Hamiltonian function minimum is the optimal control variable at the current moment.

[0117] The state of charge value of the battery at the end of the time is calculated through the state equation and the co-state equation, and the error is compared with the initial state of charge value. When the error meets the requirement, the cycle ends; otherwise, the value of the co-state variable is readjusted until the state of charge error requirement is met, and the output power of the optimal engine and battery of the series hybrid electric propulsion system is obtained.

[0118] In an embodiment of the present invention, a hybrid electric propulsion system energy management optimization control method (SM-MPC-MP) based on sliding mode predictive control and minimum principle is proposed.

[0119] Based on the characteristics of predictive control, the power model of the series hybrid electric propulsion system is first discretized.

[0120] Under discrete time conditions, the fuel mass consumption and engine power model can be rewritten as:

[0121] m(k+1)=m(k)-δf k (P eng (k),ω eng (k))(18)

[0122] f k (P eng (k),ω eng (k))=β2(ω eng (k))P eng (k) 2 +β1(ω eng (k))P eng (k)+β0(ω eng (k))(19)

[0123] The motor power discretization model can be rewritten as:

[0124]

[0125] The generator power discretization model can be rewritten as:

[0126]

[0127] The discretization model between battery state of charge and battery power can be rewritten as:

[0128] SOC(k+1)=SOC(k)-δP b (k)(22)

[0129] The mission objective of hybrid electric propulsion energy management is to ensure a reasonable distribution of power between the engine and battery output. Under the premise of ensuring the normal and safe operation of all relevant components, the aircraft's fuel consumption is minimized and the overall energy efficiency is improved. The performance index of the SM-MPC-MP energy management optimization control is shown in Equation (23):

[0130]

[0131] Among them, m e (u(t)) represents the fuel consumption, and l is the prediction time domain.

[0132] When performing energy management, the battery SOC is selected as the state variable:

[0133] x(t)=SOC(t)(24)

[0134] By taking the derivative of formula (24), we can get the derivative of the PMP energy management state quantity:

[0135]

[0136] Select the function related to the turboshaft engine power and speed as the control variable:

[0137] u(t)=f(P eng ,n eng )(26)

[0138] According to Equations (25) and (26), Hamiltonian can be written as:

[0139]

[0140] For any time t∈(t0,t f ), optimal control function u * (t) under the control of the Hamiltonian function to achieve the minimum value:

[0141] H(SOC(t),u * (t),λ(t))=H(SOC(t),u(t),λ(t))(28)

[0142] The update of state quantity SOC and co-state variables is:

[0143]

[0144] According to the battery power model, λ(t) can be written as:

[0145]

[0146] When studying the effect of SM-MPC-MP energy management optimization control, the co-state variable plays a vital role. In order to simplify the complexity of the optimal control problem, according to Equation (30), the changes of battery internal resistance and open circuit voltage with SOC are ignored:

[0147]

[0148] From formula (31), we can get: A comorphic variable can be thought of as a constant.

[0149] To ensure power balance during operation of the hybrid system, the battery exists in two states: charging and discharging. The hybrid electric propulsion system meets the following requirements under all operating conditions:

[0150]

[0151] The natural boundary conditions of the hybrid propulsion system are:

[0152]

[0153] The speed boundary condition of the hybrid propulsion system is:

[0154]

[0155] Among them, n eng,min and n eng,max Represent the lowest and highest engine speeds, n m,min and n m,max Represent the minimum and maximum speed of the motor respectively.

[0156] The torque boundary condition of the hybrid propulsion system is:

[0157]

[0158] Among them, T eng,min and T eng,max Represent the lowest and highest torque of the engine, T m,min and T m,max Represent the minimum and maximum torque of the motor respectively.

[0159] The battery boundary conditions of the hybrid propulsion system are:

[0160]

[0161] Among them, P b,min and P b,max Represent the minimum and maximum output power of the battery, I b,min and I b,max Represent the lowest and highest output current of the battery, SOC(t0) and SOC(t f) represent the initial and final charges of the battery, respectively.

[0162] The sliding surface s(t) is designed to enhance robustness by combining the SOC tracking error and its integral term:

[0163]

[0164] Where, e(t) is the SOC tracking error, SOC(t) is the battery state of charge, and SOC ref (t) is the expected state of charge of the battery, and β>0 is the sliding mode parameter that determines the error convergence rate.

[0165]

[0166] in, is the derivative of the sliding surface function, is the derivative of the SOC tracking error, is the derivative of the battery state of charge, is the derivative of the battery's desired state of charge;

[0167] According to the battery power model (22), we can get:

[0168]

[0169] Among them, P b (t) is the battery power;

[0170] Design control law u s Make the sliding surface meet the approach condition Where η>0, we can get:

[0171]

[0172] Where K>0 is the sliding mode gain, which is used to suppress disturbances, and sign(s(t)) is the sliding mode sign function.

[0173] In the rolling optimization of MPC, the sliding mode control law is used as a constraint or part of the objective function, and a sliding surface penalty term is added:

[0174]

[0175] Among them, J is the rolling optimization objective function, α is the SOC deviation weight coefficient, SOC f For the target terminal SOC, SOC k is the current SOC, t f is the optimization end time, t0 is the optimization start time, is the fuel consumption, u(t) is the control variable, γ>0, is the sliding mode gain.

[0176] When the initial value of λ is selected and the initial value of SOC is known, we can start to find the optimal control function u at any time under given working conditions. * (t), thereby obtaining the optimal control trajectory of the hybrid electric propulsion system under the entire working condition. The SM-MPC-MP energy management flow chart is as follows: Figure 5 shown.

[0177] It is not difficult to find from the flowchart that the specific steps of SM-MPC-MP energy management are as follows:

[0178] (1) Set the initial co-state variables and battery SOC, and predict the relevant parameters of the model;

[0179] (2) Calculate the required power of the hybrid electric propulsion system at any time, thereby determining the range of the fuel consumption rate u(t);

[0180] (3) Discretize the fuel consumption rate u(t): u i =m i+1 -m i =-δf i (P eng,i ,ω eng,i ),i=1,2,3,…,n+1;

[0181] (4) Calculation error and sliding surface:

[0182] (5) Calculate each u i The corresponding H i , compare and get the smallest H i , H i_min The corresponding control variable u is the optimal control variable at the current moment, that is, u * =argmin(H(u));

[0183] (6) The SOC value of the battery at the end of the time can be calculated by the state equation and the co-state equation, and the error is compared with the initial SOC value. If the error meets the requirements, the cycle ends; otherwise, the value of λ0 will be readjusted until |2α(SOC f -SOC0)|≤ε.

[0184] The sliding mode predictive energy management method for a series hybrid electric propulsion system of the present invention is described in detail below through a specific embodiment.

[0185] The model parameters of the hybrid electric propulsion system are shown in Table 1.

[0186] Table 1 Hybrid electric propulsion system model parameters

[0187]

[0188] This paper uses MATLAB software to solve. Perform a complete aircraft mission: taxiing, takeoff, climb, cruise, descent and landing. The mission time is one hour, and the speed and altitude mission profile is defined as follows Figure 6 shown.

[0189] Within 500 seconds after the aircraft takes off, the aircraft speed and altitude continue to increase, reaching the maximum cruising speed; within 500-1000 seconds, the aircraft altitude remains unchanged at 2000 meters; within 1000-1500 seconds, the aircraft begins to climb further, reaching 3000 meters; within 1500-2500 seconds, the aircraft enters cruising state; after 2500 seconds, the aircraft begins to descend and the speed decreases until it lands.

[0190] Under a given flight speed and altitude mission profile, the power flow and distribution based on the SM-MPC-MP energy management method are as follows: Figure 7 As shown in the figure, the power demand during the takeoff and climb phases is relatively high, the power demand is relatively low during the cruise phase, and the power demand is the lowest during the descent and landing phase. Furthermore, after entering the windmill state, the motor power becomes negative, and the propulsion motor transforms into a generator to supply power to the grid. The power variation pattern of the propulsion system meets the power requirements of the mission profile.

[0191] During the mission, the battery SOC and battery power change curves are as follows: Figure 8 and Figure 9 As shown, the upper and lower limits of SOC are given constraints. The battery continuously discharges for 0.2 and then enters the windmill mode. The battery enters the charging mode, the power becomes negative, and the SOC increases.

[0192] The power model of the hybrid electric propulsion system is calibrated, and the battery power, engine power and propulsion power curves are as follows: Figure 10 As shown, the sum of the generator output power and the battery output power is the propulsion power.

[0193] like Figure 11 As shown in the figure, the equivalent fuel consumption of the two models is compared. The initial fuel calorific value is equivalent to 90kg of fuel oil. The fuel consumption rate is highest during the climb phase, followed by the cruise phase, and lowest during the descent phase. After 2800 seconds, it enters the windmill state and no longer consumes fuel.

[0194] According to the sliding mode predictive energy management method for a series hybrid electric propulsion system proposed in an embodiment of the present invention, a power model of the series hybrid electric propulsion system is first established, the power flow in the system is analyzed, an energy management system is designed, the working principles of the various components of the hybrid electric propulsion system and the relationship between them are clarified, and a hybrid electric propulsion system energy management optimization control method is proposed. Combining sliding mode predictive control with the minimum value principle, the output power of the engine and battery is optimized, ensuring that the fuel consumption of the aircraft is minimized under various constraints. Therefore, the present invention ensures a reasonable distribution between the output power of the engine and the battery, minimizing the fuel consumption of the aircraft, improving the overall energy efficiency, and achieving energy optimization management of the hybrid electric propulsion system, while ensuring the normal and safe operation of all relevant components of the hybrid electric propulsion system.

[0195] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0196] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0197] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

Claims

1. A sliding mode predictive energy management method for a series hybrid electric propulsion system, characterized in that: The following steps are involved: Analyze the power flow in the series hybrid electric propulsion system and establish a power model for the series hybrid electric propulsion system; By utilizing sliding mode predictive control and the minimum principle, the output power of the engine and battery of the series hybrid electric propulsion system is optimally distributed according to the power model of the series hybrid electric propulsion system, thereby minimizing the aircraft fuel consumption under various constraints.

2. The method according to claim 1, characterized in that Establishing the power model of the series hybrid electric propulsion system includes: The aircraft is simplified into a mass model and the dynamic modeling of the aircraft is carried out to obtain the relationship between propulsion power and fuel consumption in different flight phases. The relationship between fuel consumption rate and aircraft propulsion power is expressed by the relationship between propulsion power and aircraft mass. The fuel consumption rate is expressed as a function of engine power and speed, and an engine power model is established, where the constraints are that the engine speed and power are both within the lean flameout boundary and the surge boundary; The motor power model is established by fitting the motor input power with the motor output power and speed as variables, where the constraints are that the motor output speed and output power are within the safety limit; The generator output power and generator speed are used as independent variables to fit and calculate the generator input power and establish a generator power model; The battery is modeled as a physical model of an ideal voltage source connected in series with a resistor. Based on the simplified circuit structure of the physical model, the relationship between battery power and battery effective power loss, as well as the relationship between battery state of charge and battery power, are established to obtain a battery power model. The constraint condition is that the battery state of charge is within the upper and lower bounds of the battery state of charge.

3. The method according to claim 2, characterized in that Utilizing sliding mode predictive control and the minimum principle, and based on the power model of the series hybrid electric propulsion system, the output power of the engine and the battery of the series hybrid electric propulsion system is optimally distributed, including: Set initial co-state variables and battery state of charge; Calculate the required power of the series hybrid electric propulsion system at any time and determine the range of fuel consumption rate; Discretize the fuel consumption rate; Calculate state of charge error and sliding surface; Calculate the Hamiltonian function corresponding to each discretized fuel consumption rate, and compare to obtain the smallest Hamiltonian function minimum. The control variable corresponding to the Hamiltonian function minimum is the optimal control variable at the current moment. The state of charge value of the battery at the end of the time is calculated through the state equation and the co-state equation, and the error is compared with the initial state of charge value. When the error meets the requirement, the cycle ends; otherwise, the value of the co-state variable is readjusted until the state of charge error requirement is met, and the output power of the optimal engine and battery of the series hybrid electric propulsion system is obtained.

4. The method according to claim 3, characterized in that Utilizing sliding mode predictive control and the minimum principle, and based on the power model of the series hybrid electric propulsion system, the output power of the engine and battery of the series hybrid electric propulsion system is optimally distributed, specifically including: discretizing the power model of the series hybrid electric propulsion system; When performing energy management, the battery state of charge is selected as the state variable, and the function related to the turboshaft engine power and speed is selected as the control variable to establish the Hamiltonian function; Calculate the value of the Hamiltonian function at any time. When the optimal control variable is reached, the Hamiltonian function value reaches its minimum value, and the state variables and co-state variables are updated. Design of sliding surface: Among them, e(t) is the SOC tracking error, β>0 is the sliding mode parameter, SOC(t) is the battery state of charge, SOC ref (t) is the expected state of charge of the battery; in, is the derivative of the sliding surface function, is the derivative of the SOC tracking error, is the derivative of the battery state of charge, is the derivative of the battery's desired state of charge; According to the battery power model, we can get: Among them, P b (t) is the battery power; Design control law u s Make the sliding surface meet the approach condition Where η>0, we get: Where K>0 is the sliding mode gain, sign(s(t)) is the sliding mode sign function; The sliding mode control law is used as a constraint or part of the objective function, and a sliding surface penalty term is added: Among them, J is the rolling optimization objective function, α is the SOC deviation weight coefficient, SOC f For the target terminal SOC, SOC k is the current SOC, t f is the optimization end time, t0 is the optimization start time, is the fuel consumption, u(t) is the control variable, and γ>0 is the sliding mode gain.

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