Electric-thermal energy management optimization method for electric propulsion system of hybrid aircraft

Abstract

The invention discloses an electric-thermal energy management optimization method for an electric propulsion system of a hybrid aircraft, and belongs to the field of energy management. The method comprises the following steps: optimizing the electric propulsion system of the hybrid aircraft by adopting a multidisciplinary optimization design method; based on the hybrid aircraft electric propulsion system, an electric-thermal management system of the hybrid aircraft electric propulsion system is constructed; the hybrid aircraft electric propulsion system electric-thermal management system comprises a lithium ion battery electric-thermal coupling model and a motor thermal model, and thermal management of the hybrid aircraft electric propulsion system is achieved through a thermal management assembly. An electric-thermal energy comprehensive management and optimization strategy of the electric propulsion system of the hybrid aircraft is constructed based on the MPC, fuel economy and temperature are taken as optimization targets, the minimum value of a target function is solved by utilizing MPC rolling optimization control under each flight profile, and accurate distribution of power and control of temperature are realized. According to the invention, accurate distribution of power and effective control of key component temperature can be realized.

Description

Technical Field

[0001] The invention belongs to the field of energy management, and in particular relates to an electric-thermal energy management optimization method for an electric propulsion system of a hybrid power aircraft. Background Art

[0002] With the global shortage of petrochemical energy and increasingly severe environmental problems, the efficiency, pollution emissions, noise and other issues of the power system have attracted much attention. In recent years, countries around the world have looked forward to and formulated the future development needs of the aviation industry. The European Union launched the Clean Aviation program in 2021, focusing on the research and development of hybrid electric propulsion regional aircraft, ultra-efficient medium- and short-range aircraft, and hydrogen-powered aircraft, aiming to put them into use in 2035 and reduce fuel consumption by 50% and emissions by 90%. Multi / all-electric aircraft gradually unify the onboard secondary energy into electrical energy, effectively improving the economy, reliability, maintainability and safety of the aircraft, reducing pollutant emissions, and has become an important direction for the development of aviation technology. Therefore, multi-electrification has become an important way to improve the technical and tactical performance of aircraft and support the development of green aviation.

[0003] Common aircraft hybrid power system energy management strategies can be divided into four categories: rule-based, global optimization-based, instantaneous optimization-based, and artificial intelligence-based. Rule-based EMS is robust, computationally small, and easy to implement, but it is difficult to adapt to time-varying conditions under high reliability constraints. Global optimization-based EMS can effectively handle multi-objective and multi-constraint optimization problems, but the computational cost is high and cannot be applied online. Global optimization-based EMS includes dynamic programming (DP), Pontryagin minimum principle (PMP), and metaheuristic algorithms. Artificial intelligence-based EMS has good self-learning and adaptability, and can adjust the optimization strategy in real time in a dynamic environment, but it is limited by the aircraft's own examples and it is difficult to balance its data dependence and interpretability.

[0004] Therefore, the present invention is aimed at hybrid regional aircraft, and utilizes MPC to perform comprehensive management and optimization of electric-thermal energy of the hybrid aircraft electric propulsion system, thereby achieving precise power distribution and temperature control of key components. Summary of the invention

[0005] Technical issues to be solved:

[0006] In order to avoid the shortcomings of the prior art, the present invention provides an electric-thermal energy management optimization method for a hybrid aircraft electric propulsion system. Based on multidisciplinary strong constraint design, multi-objective optimization of the "aerodynamic-structure-propulsion" of a hybrid aircraft is performed to obtain the optimization parameters of key components of the electric propulsion system, and a hybrid distributed series propulsion system is designed. The heat transfer unit method (E-NTU) is used to analyze the heat dissipation and design a matching component thermal management system. The lumped electric-thermal coupling model of the lithium-ion battery, as well as the thermal model of the drive motor and generator are combined with the energy management strategy to construct a hybrid thermal management system. With fuel economy and key component temperature as the optimization goals, the MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile, thereby achieving accurate power distribution and effective control of key component temperature.

[0007] The technical solution of the present invention is: a method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system, the specific steps of which are as follows:

[0008] The electric propulsion system of hybrid aircraft is optimized by adopting multidisciplinary optimization design method. The optimization objective in the optimization problem is defined as minimizing fuel consumption and maximum take-off weight (MTOW). The optimization variables are maximum take-off weight (MTOW), battery weight, and rated power of electric propulsion system components. The constraint condition is the balance of "aerodynamics-structure-propulsion".

[0009] Based on the hybrid aircraft electric propulsion system, an electric-thermal management system for the hybrid aircraft electric propulsion system is constructed; the electric-thermal management system for the hybrid aircraft electric propulsion system includes a lithium-ion battery electric-thermal coupling model and a motor thermal model, and thermal management of the hybrid aircraft electric propulsion system is achieved through a thermal management component;

[0010] Based on MPC, a comprehensive management and optimization strategy for the electric-thermal energy of the hybrid aircraft electric propulsion system is constructed. With fuel economy and temperature as optimization targets, the MPC rolling optimization control is used to solve the minimum value of the objective function in each flight profile to achieve precise power distribution and temperature control.

[0011] A further technical solution of the present invention is: the constraints include flight mechanics constraints, weight characteristic constraints and electric propulsion system component constraints;

[0012] The flight mechanics constraint condition is that the aircraft flight thrust is equal to the sum of the aircraft flight resistance and the component force of the aircraft weight in the flight direction;

[0013] The weight characteristic constraint condition is that the maximum take-off weight of the aircraft is equal to the sum of the empty weight of the aircraft, the payload weight, the fuel mass, and the battery pack weight, and the fuel mass is greater than or equal to the used fuel mass;

[0014] The electric propulsion system components include an engine, a generator, a drive motor and a battery pack, and the engine and the generator constitute a generator group; the constraints are that the sum of the generator output power and the battery pack output power is equal to the drive motor output power; the difference between the total energy capacity of the battery pack and the energy consumed by the battery pack is greater than or equal to 0; the minimum state of charge of the battery pack is less than or equal to the final state of charge of the battery pack.

[0015] A further technical solution of the present invention is that the electric propulsion system component meets the following constraints when the aircraft is in a climbing state:

[0016] The output power of the drive motor is less than or equal to 1.2 times the rated power of the drive motor;

[0017] The turbine engine output power is less than or equal to 1.2 times the turbine engine power;

[0018] The generator output power is less than or equal to 1.2 times the generator power;

[0019] The battery pack output power is greater than or equal to 0.6 times the product of the battery pack weight and the battery pack output power, and less than or equal to the product of the battery pack weight and the battery pack output power.

[0020] The electric propulsion system components meet the following constraints in the cruising state and the landing state:

[0021] The output power of the drive motor is less than or equal to the rated power of the drive motor;

[0022] The turbine engine output power is less than or equal to the turbine engine power;

[0023] The generator output power is less than or equal to the generator power;

[0024] The battery pack output power is less than or equal to the product of the battery pack weight and the battery pack output power, which is equal to the rated power of the battery pack.

[0025] A further technical solution of the present invention is that the lithium-ion battery electric-thermal coupling model includes a battery pack equivalent circuit model, a battery pack heat generation model and a battery pack heat dissipation model, and the specific expression is as follows:

[0026]

[0027] Where Vt is the battery terminal voltage; I L is the load current; R cell is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are the battery temperature and the battery heat generation respectively; Q coolant is the heat dissipation of battery liquid; C p,bat and mbat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,coolant and are the specific heat capacity and mass flow rate of the coolant respectively; T bat and T liq,in Respectively represent the battery module temperature and the coolant temperature at the cold plate inlet; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0028] A further technical solution of the present invention is: the motor thermal model includes a drive motor thermal model and a generator thermal model, and the thermal management components used include a coolant reservoir, a circulating pump, a heat exchanger and a liquid cooling jacket, wherein the drive motor thermal model is composed of the coolant reservoir, the circulating pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger, and the generator thermal model is composed of the fuel tank, the circulating pump, the liquid cooling jacket connected to the generator, and the heat exchanger;

[0029] The expression of the drive motor thermal model is as follows:

[0030]

[0031] Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot They represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly respectively; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel of the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0032] The expression of the generator thermal model is as follows:

[0033]

[0034] Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Respectively represent the constant pressure specific heat capacity and thermal mass of the generator assembly; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0035] A further technical solution of the present invention is: the specific steps of constructing the electric-thermal energy comprehensive management and optimization strategy of the hybrid aircraft electric propulsion system are:

[0036] Build a prediction model for energy management systems;

[0037] Build a prediction model for thermal management systems;

[0038] Construct the objective function;

[0039] Set system constraints;

[0040] The rolling optimization control seeks the minimum value of the objective function.

[0041] A further technical solution of the present invention is: the construction process of the energy management system prediction model is:

[0042] The output power of the battery group and the generator group at the current sampling time k is selected as the control variable:

[0043] u(k)=[P bat (k),P tg (k)]

[0044] Where P bat is the output power of the battery pack, P tg is the output power of the generator set;

[0045] Select the lithium battery state of charge SOC and fuel mass m fuel For state variables:

[0046] x(k)=[SOC(k),m fuel (k)]

[0047] Then the energy management prediction model expression is:

[0048]

[0049] Where: A and B are the state matrix and control matrix of the prediction model respectively;

[0050]

[0051] Where: PSFC is the engine specific fuel consumption; η bat E is the charging and discharging efficiency of lithium-ion batteries; bat,max is the total energy capacity of the battery; △t is the time difference between two adjacent sampling moments, that is, the sampling step.

[0052] A further technical solution of the present invention is: the construction process of the thermal management system prediction model is:

[0053] The speeds of the battery circulation pump, generator circulation pump and drive motor circulation pump at the current sampling time k are selected as control variables:

[0054] u(k)=[ω pump,bat (k),ω pump,gen (k),ω pump,mot (k)]

[0055] Where: pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulating pump speed; ω pump,mot The speed of the circulating pump for the driving motor;

[0056] The battery pack temperature, generator temperature and drive motor temperature are selected as state variables:

[0057] x(k)=[T bat (k),T gen (k),T mot (k)]

[0058] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature;

[0059] The thermal management prediction model expression is:

[0060]

[0061] Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term;

[0062]

[0063] Where: C bat , C mot and C gen are the constant pressure specific heat capacity of lithium-ion battery, drive motor and generator respectively; Q bat , Q mot and Q gen are the heat generation rates of lithium-ion batteries, drive motors and generators respectively; M bat 、M mot and M gen are the thermal masses of lithium-ion battery, drive motor and generator respectively; ω bat0 ,ω gmot0 and ω gen0 are the initial speeds of lithium-ion battery, drive motor and generator circulation pump respectively; T bat,liq , T mot,liq and T gen,liq are the coolant temperatures of lithium-ion battery, drive motor and generator respectively; Tbat0 , T mot0 and T gen0 are the initial temperatures of the lithium-ion battery, drive motor and generator respectively.

[0064] A further technical solution of the present invention is: the expression of the objective function is:

[0065]

[0066] Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems is the energy management objective function; J tms is the thermal management objective function.

[0067] A further technical solution of the present invention is: the system constraints are:

[0068] System output power balance:

[0069] P load (k) = P bat (k)+P tg (k)+P tms (k)

[0070] Where: P load P is the propulsion power requirement reference value; tms is the output power of all components in the thermal management system;

[0071] Propulsion system power constraints:

[0072]

[0073] Where: P tg,max is the maximum continuous output power of the generator set, P bat,min , P bat,max are the maximum charge / discharge power of the battery pack respectively;

[0074] State of Charge Constraints:

[0075]

[0076] Where: SOC min and SOC max They are the minimum and maximum SOC values ​​of the battery pack respectively;

[0077] Key component temperature constraints:

[0078]

[0079] Circulation pump constraints:

[0080]

[0081] Thermal Management Cascade Coupling Constraints:

[0082] Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat

[0083] Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

[0084] Beneficial Effects

[0085] The beneficial effects of the present invention are as follows: the present invention performs multi-objective optimization of the "aerodynamics-structure-propulsion" of hybrid aircraft based on multidisciplinary strong constraint design, can determine the parameters of key components of hybrid aircraft, and play a supporting role in the modeling of hybrid propulsion systems; with the continuous improvement of the requirements for the maneuverability and cruising capabilities of multi / all-electric aircraft, the internal electric and thermal requirements are further intensified, and a lumped electric and thermal coupling model of lithium-ion batteries, as well as a thermal model of drive motors and generators are established, and a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system is designed; to ensure accurate power distribution and effective control of the temperature of key components, MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile, which improves the reliability of model predictive control and achieves accurate power distribution and effective control of the temperature of key components. The specific advantages are as follows:

[0086] (1) The present invention significantly reduces the fuel consumption of the aircraft, reduces operating costs and carbon emissions, and improves the overall performance and reliability of the aircraft by performing multidisciplinary optimization design on fuel consumption and maximum take-off weight (MTOW);

[0087] (2) The present invention constructs an electric-thermal coupling model of lithium-ion batteries and designs an efficient heat dissipation solution in combination with the thermal management system of the drive motor and generator to ensure that key components operate at an appropriate temperature, reduce the impact of overheating on system performance and safety, and improve the overall thermal management efficiency;

[0088] (3) By constructing a prediction model for the energy management system and the thermal management system and utilizing the MPC rolling optimization control strategy, the present invention can achieve precise energy distribution under different flight profiles, ensure that each component operates at the optimal working point, and optimize the overall energy efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1Distributed tandem propulsion architecture for a hybrid regional aircraft.

[0090] Figure 2 This is the electrical-thermal coupling model of lithium-ion batteries.

[0091] Figure 3 Thermal model for the drive motor.

[0092] Figure 4 Thermal model for the generator.

[0093] Figure 5 This is the architecture diagram of the thermal management system of a hybrid aircraft.

[0094] Figure 6 This is a flow chart of the comprehensive management strategy of electric and thermal energy based on MPC.

[0095] Figure 7 Parametric optimization process for hybrid aircraft.

[0096] Figure 8 Partial weight breakdown comparison of hybrid aircraft and reference aircraft.

[0097] Fig. 9 The flight mission profile.

[0098] Fig.10 are the ambient temperature and ram air temperature under the full flight profile.

[0099] Fig.11 The flowchart of the rule-based energy management strategy is shown in Figure 2.

[0100] Fig.12 Flowchart of rule-based thermal management strategy.

[0101] Fig.13 The power allocation simulation results of the rule-based integrated energy management strategy.

[0102] Fig.14 The power allocation simulation results of the integrated energy management strategy based on model prediction.

[0103] Fig.15 The result is fuel consumption.

[0104] Fig.16 The battery temperature control results of MPC and rule control under the full flight profile.

[0105] Fig.17 The temperature control results of the drive motor under MPC and rule control in the full flight profile.

[0106] Fig.18 The temperature control results of the generator under MPC and rule control in the full flight profile.

[0107] Explanation of the reference numerals: 1. drive motor, 2. inverter, 3. battery pack, 4. generator set, 41. turbine engine, 42. generator, 5. rectifier, 6. coolant reservoir, 7. heat exchanger, 8. liquid cooling jacket, 9. circulation pump, 10. fuel tank, 11. coolant-air heat exchanger, 12. liquid-liquid heat exchanger, 13. fuel-air heat exchanger. DETAILED DESCRIPTION

[0108] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0109] Based on the problem of optimization methods for energy management strategies of hybrid power systems of conventional aircraft, the present invention is aimed at hybrid regional aircraft, and uses MPC to conduct comprehensive management and optimization of the electric-thermal energy of the hybrid aircraft electric propulsion system, so as to achieve accurate power distribution and temperature control of key components. Among them, the instantaneous optimized EMS has good real-time performance and computational efficiency, can quickly respond to dynamic conditions and achieve local optimal control; the EMS based on instantaneous optimization is widely used in energy management of aircraft hybrid power systems, mainly including equivalent consumption minimization strategy (ECMS) and model predictive control (MPC). The technical solution adopted by the present invention mainly includes three parts: multidisciplinary optimization design of hybrid aircraft electric propulsion system, modeling of electric-thermal management system of hybrid aircraft electric propulsion system, and comprehensive management and optimization strategy of electric-thermal energy of hybrid aircraft electric propulsion system.

[0110] (1) Multidisciplinary optimization design of hybrid aircraft electric propulsion system

[0111] In order to evaluate the configuration of the electric propulsion system at the aircraft level, a multidisciplinary analysis and semi-empirical model of aerodynamics, aircraft power model, propulsion characteristics, and mission analysis was constructed based on the OpenMDAO framework. The optimization objectives are fuel consumption and maximum take-off weight (MTOW), the optimization variables are MTOW, battery weight, and the rated power of propulsion system components (engine, generator, motor), and the constraint is the "aerodynamic-structure-propulsion" balance. Finally, the optimization parameters of the key components of the electric propulsion system are solved by the Newton-Raphson method.

[0112] (2) Modeling of the electric-thermal management system of hybrid aircraft electric propulsion system

[0113] An electric-thermal coupling model of the lithium-ion battery, as well as a thermal model of the drive motor and generator, was constructed. The heat transfer unit method (E-NTU) was used to analyze the heat dissipation and design a matching component thermal management system. A hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system was constructed.

[0114] (3) Comprehensive management and optimization strategy of electrical and thermal energy in hybrid aircraft electric propulsion system

[0115] A hybrid aircraft energy management system prediction model and a thermal management system prediction model were built. With fuel economy and key component temperature as optimization targets, MPC rolling optimization control was used to solve the minimum value of the objective function in each flight profile, thereby achieving precise power distribution and effective control of key component temperatures.

[0116] The above technical solution is further described below in conjunction with the accompanying drawings and specific embodiments:

[0117] In one embodiment, improvement and optimization are performed with reference to a 50-seat regional aircraft, and the specific steps are as follows:

[0118] Step 1: Multidisciplinary Optimization Design of Hybrid Aircraft Electric Propulsion System

[0119] Distributed tandem propulsion architecture for hybrid regional aircraft Figure 1 As shown, it specifically includes a turbine engine, a generator, a lithium-ion battery pack, a DC / DC converter, an AC / DC rectifier, an inverter and a drive motor.

[0120] In order to evaluate the configuration of the electric propulsion system at the aircraft level, a multidisciplinary analysis and semi-empirical model of aerodynamics, aircraft power model, propulsion characteristics, and mission analysis is constructed based on the OpenMDAO framework, and the optimization parameters of the key components of the electric propulsion system are solved by the Newton-Raphson method. The optimization objectives are fuel consumption and maximum take-off weight (MTOW), and the optimization variables are MTOW, battery weight, and rated power of propulsion system components (engine, generator, motor). The constraint condition is the "aerodynamic-structure-propulsion" balance. The aircraft-level model consists of the following three parts: hybrid propulsion system model, aerodynamic model, and whole-machine weight characteristic model. The optimization objectives are as follows:

[0121] min(W fuel +W MTOW )(1)

[0122] Where: W fuel is the fuel consumption; W MTOW is the maximum take-off weight. The optimization variable is: W MTOW (maximum take-off weight), W bat (battery pack weight), P mot,rate (rated power of driving motor), P ts,rate (Turbine engine power), P gen,rate (Generator power).

[0123] The constraints are as follows:

[0124] 1) Flight mechanics constraints:

[0125] TDW TO sin(γ)=0 (2)

[0126] Where: D is resistance; T is thrust; W TO is the aircraft weight; γ is the elevation angle.

[0127] 2) Weight characteristic constraints:

[0128] W MTOW -W OEW -W PL -W fuel -W bat =0 (3)

[0129] W fuel -W fuel,used ≥0 (4)

[0130] Where: W OEW W is the empty weight; PL is the effective load weight; W fuel is the fuel quality; W bat is the battery weight; W fuel，used The quality of fuel used.

[0131] 3) Electric propulsion component constraints:

[0132]

[0133] If the flight status is "climbing status" (6)

[0134] P mot ≤1.2P mot,rated

[0135] P ts ≤1.2P ts,rated

[0136] P gen ≤1.2P gen,rated

[0137] 0.6W bat ·p bat ≤P bat ≤W bat ·p bat

[0138] If the flight status is "cruise status" and "landing status"

[0139]

[0140] Where: P gen is the generator output power; P batis the battery output power; P mot Output power to drive the motor.

[0141] Step 2: Modeling the Electric-Thermal Management System of the Hybrid Aircraft Electric Propulsion System

[0142] Based on the hybrid aircraft electric propulsion system constructed in step 1, the electric-thermal management system of the hybrid aircraft electric propulsion system is constructed, in which the lithium-ion battery is constructed with a two-state lumped electric-thermal coupling model based on experimental data, and the drive motor and generator are constructed with a classical electric model based on factory data, so as to realize the electric-thermal characteristics analysis and optimization design of the system under multiple working conditions. Combining the lithium-ion battery electric-thermal coupling model with the motor thermal model, the heat transfer unit method (E-NTU) is used to analyze the heat dissipation process, and a thermal management system matching the battery and motor components is designed. Finally, through the basic thermal management components such as liquid cooling plate, circulation pump, heat exchanger, and liquid reservoir, the battery, drive motor, and generator electric-thermal management system are combined to construct an efficient thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system. The motor thermal model includes a drive motor thermal model and a generator thermal model, and the thermal management components used include a coolant reservoir, a circulating pump, a heat exchanger and a liquid cooling jacket, wherein the drive motor thermal model is composed of the coolant reservoir, the circulating pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger, and the generator thermal model is composed of the fuel tank, the circulating pump, the liquid cooling jacket connected to the generator, and the heat exchanger.

[0143] 1)Reference Figure 2 As shown, the lithium-ion battery electrical-thermal coupling model

[0144]

[0145]

[0146] Where: V t is the battery terminal voltage; I L is the load current; R cell is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are the battery temperature and the battery heat generation respectively; Q coolant is the heat dissipation of battery liquid; C p,bat and m bat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,bat and are the specific heat capacity and mass flow rate of the coolant respectively; T bat and T liq,in Respectively represent the battery module temperature and the coolant temperature at the cold plate inlet; h liq is the convective heat transfer coefficient; A liqis the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0147] 2)Reference Figure 3 The thermal model of the drive motor is shown, and the calculation formula is as follows:

[0148]

[0149] Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot They represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly respectively; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel of the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0150] Reference Figure 4 The thermal model of the generator is shown, and the calculation formula is as follows:

[0151]

[0152] Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Respectively represent the constant pressure specific heat capacity and thermal mass of the generator assembly; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0153] Reference Figure 5 As shown, the thermal management architecture uses a modular loop to form a hybrid thermal management architecture, which is divided into two subsystems: a fuel cooling system and a closed-loop liquid cooling system. 1) In the fuel cooling system, part of the heat absorbed by the fuel is discharged into the environment through a heat exchanger by the ram air bypassing the engine, and the retained heat can be used to improve the engine's operating performance. 2) Cooling loop 1 is responsible for providing cooling for the drive motor. Cooling loop 2 is responsible for cooling the battery assembly separately, and the two loops are cascaded through a liquid-liquid heat exchanger. Since the ambient temperature greatly affects the external characteristics of the lithium-ion battery pack, cooling loop 2 is set at the thermal management entrance.

[0154] Step 3: Comprehensive management and optimization strategy of electric-thermal energy of hybrid aircraft electric propulsion system;

[0155] Based on the hybrid aircraft electric propulsion system constructed in step 1 and the hybrid aircraft electric-thermal management system established in step 2, an electric-thermal energy comprehensive management strategy is proposed to achieve the optimal allocation and management of system energy utilization efficiency. By comprehensively considering the electric-thermal characteristics of the electric propulsion system components (lithium-ion batteries, drive motors, generators) and the power requirements of the aircraft mission profile, an energy comprehensive management model that adapts to different flight conditions is established. The specific steps are as follows:

[0156] (1) Energy management system prediction model

[0157] The output power of the lithium battery group and the generator group at the current sampling time k is selected as the control variable:

[0158] u(k)=[P bat (k), P tg (k)] (15)

[0159] Select the lithium battery state of charge SOC and fuel quality as state variables:

[0160] x(k)=[SOC(k), m fuel (k)] (16)

[0161] The energy management prediction model is as follows:

[0162]

[0163] Where A and B are the state matrix and control matrix of the prediction model respectively.

[0164]

[0165] (2) Thermal management system prediction model

[0166] The speeds of the battery circulation pump, generator circulation pump and motor circulation pump at the current sampling time k are selected as control variables:

[0167] u(k)=[ω pump,bat (k),ω pump,gen (k),ω pump,mot (k)] (19)

[0168] Where: pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulating pump speed; ω pump,mot is the motor circulation pump speed.

[0169] Select battery temperature, generator temperature and drive motor temperature as state variables:

[0170] x(k)=[T bat(k), T gen (k), T mot (k)] (20)

[0171] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature.

[0172] The thermal management prediction model is as follows:

[0173]

[0174] Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term.

[0175]

[0176] (3) Objective function construction

[0177] The energy management system prediction model and the thermal management system prediction model were built, with fuel economy and key component temperature as the optimization targets, and MPC rolling optimization control was used to solve the minimum value of the objective function under each flight profile, so as to achieve accurate power distribution and effective control of key component temperature. The objective function is as follows:

[0178]

[0179] Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems and J tms They are energy management objective function and thermal management objective function respectively.

[0180] (4) Setting system constraints

[0181] System output power balance:

[0182] P load (k) = P bat (k)+P tg (k)+P tms (k) (24)

[0183] Where: P load P is the propulsion power requirement reference value; bat and P tg are the output power of lithium battery pack and generator set respectively; P tms Represents the output power of all components in the thermal management system.

[0184] Propulsion system power constraints:

[0185]

[0186] Where: P tg,max is the maximum continuous output power of the generator set, P bat,min , P bat,max They are the maximum charge / discharge power of lithium batteries respectively.

[0187] State of Charge Constraints:

[0188]

[0189] Where: SOC min and SOC max They are the minimum and maximum SOC values ​​of the lithium battery respectively.

[0190] Key component temperature constraints:

[0191]

[0192] Circulation pump constraints:

[0193]

[0194] Thermal Management Cascade Coupling Constraints

[0195] Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat (29)

[0196] Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

[0197] With fuel economy and key component temperature as optimization targets, MPC performs rolling optimization control to solve the minimum value of the objective function based on the real-time collected electrical-thermal coupling information, the set energy management prediction model, thermal management prediction model, constraints and objective function, so as to achieve accurate power distribution and effective control of key component temperature.

[0198] In one embodiment, an MPC-based electric-thermal energy integrated management and optimization strategy for a hybrid aircraft electric propulsion system:

[0199] (1) Multidisciplinary optimization design of hybrid aircraft electric propulsion system

[0200] By reading references and referring to the flight parameters of a regional aircraft, the aircraft mission profile parameters are set as shown in Table 1. The hybrid aircraft parameter optimization process is as follows Figure 7shown.

[0201] Table 1 Typical flight profiles for the case with a range of 1000 km

[0202]

[0203] Reference Figure 7 As shown in Figure 1, the optimization process is as follows: (1) Generate a flight state vector to represent the flight state at each time point during the mission; (2) Calculate the flight atmospheric conditions at different altitudes to obtain multi-scale parameters such as fuel flow, battery state of charge (SOC), thrust and heat generation; (3) Calculate lift and drag based on the aerodynamic model, and iterate according to the difference between thrust and drag. The Newton-Raphson convergence criteria include three aspects: flight power balance, thrust and drag balance, and lift matching weight; energy balance, turbine engine shaft power and battery output power meet the power load requirements; weight balance, takeoff weight is consistent with operating weight, payload weight and battery weight. Considering the continuous improvement of lithium-ion battery energy density, this study evaluates the impact of battery energy density on hybrid aircraft performance by changing the battery energy density to 600Wh / kg, 800Wh / kg and 1000Wh / kg. Table 2 shows the comparison of decomposed weight and component power under different battery energy densities.

[0204] Table 2 Comparison of decomposed weight and component power at different battery energy densities in the case

[0205]

[0206] Note: The above engine, generator and motor power / weight are total power / weight.

[0207] As battery energy density increases, the MTOW of hybrid aircraft decreases. This is due to the additional weight of the electric propulsion system caused by using batteries as a propulsion energy source, such as Figure 8 The figure shows a weight comparison analysis of the battery weight, fuel consumption, payload and OEW of hybrid aircraft with three energy densities and the reference aircraft. Under the premise of ensuring that the aircraft payload remains unchanged, as the battery energy density increases, the battery weight, fuel consumption and empty weight (OEW) gradually decrease. At 600Wh / kg specific energy, the MTOW of the hybrid aircraft is 15% higher, at 800Wh / kg specific energy, the MTOW of the hybrid aircraft is 12.9% higher, and at 1000Wh / kg specific energy, the MTOW of the hybrid aircraft is 11% higher. According to the technical level in 2030, the optimized propulsion system parameters of the battery pack with an energy density of 600Wh / kg as shown in Table 3 are selected as the propulsion system of the hybrid regional aircraft in this study.

[0208] Table 3 Parameter selection of hybrid regional aircraft propulsion system in the case

[0209]

[0210] (2) Modeling of the electric-thermal management system of hybrid aircraft electric propulsion system

[0211] Build a lithium-ion battery electro-thermal coupling model, as well as a drive motor and generator thermal model, such as Figure 2 , 3 and 4. The heat transfer unit method (E-NTU) is used to analyze the heat dissipation and design a matching component thermal management system, and a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system is constructed, as shown in Figure 5 shown.

[0212] (3) Comprehensive management and optimization strategy of electrical and thermal energy in hybrid aircraft electric propulsion system

[0213] Set as Fig. 9 The physical parameters of the hybrid propulsion system are shown in Table 4. The temperature at the inlet of the ram air heat exchanger is calculated based on the standard atmosphere model and the flight profile, and the ambient temperature and ram air temperature under the flight profile are obtained as follows: Fig.10 shown.

[0214] T env =T env0 -0.0065h (30)

[0215]

[0216] Where: T ram represents the ram air temperature, T env represents the ambient temperature, v represents the flight speed, h represents the flight altitude, and γ represents the air adiabatic index.

[0217] Fig.10 are the ambient temperature and ram air temperature under the full flight profile.

[0218] Table 4 Physical parameters of hybrid propulsion system in the case

[0219]

[0220]

[0221] The temperature state limits and optimal operating temperature points of each key component are shown in Table 5.

[0222] Table 5 Temperature state constraints and optimal operating temperatures of key components in the case

[0223]

[0224] In order to better verify the feasibility and accuracy of the model, the energy distribution of each power source and the temperature of key components during the flight of the hybrid aircraft are respectively obtained through the energy management strategy based on the state machine and the energy management strategy based on the MPC, and the energy distribution methods under different methods are compared.

[0225] The energy management strategy based on the state machine takes the total load power of the hybrid system and the current SOC value of the lithium battery as input parameters, and formulates the rules of the state machine in combination with the maximum charge and discharge power limit of each power source, the working range of the lithium battery SOC and other constraints, such as Fig.11 shown. Fig.12 A rules-based thermal management process.

[0226] MPC-based integrated management and optimization of electrical and thermal energy Figure 6 As shown in the figure, based on multidisciplinary strong constraint design, multi-objective optimization of the hybrid aircraft "aerodynamic-structure-propulsion" was carried out to obtain the optimization parameters of the key components of the electric propulsion system; the lumped electric-thermal coupling model of the lithium-ion battery, as well as the thermal model of the drive motor and generator were established, and the heat transfer unit method (E-NTU) was used to analyze the heat dissipation and design a matching component thermal management system. At the same time, the multi-energy flow coupling characteristics of the aircraft were analyzed, and a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system was constructed; the energy management system prediction model and the thermal management system prediction model were built, and the fuel economy and key component temperature were used as the optimization targets. The MPC rolling optimization control was used to solve the minimum value of the objective function under each flight profile, so as to achieve accurate power distribution and effective control of the temperature of key components.

[0227] The simulation sets the initial SOC values ​​of the lithium-ion battery and supercapacitor to 0.5 and 0.7 respectively, and runs for 3600 seconds. The power allocation simulation results based on the rule-based energy integrated management strategy are shown in Figure 2. Fig.13 The power allocation simulation results of the energy comprehensive management strategy based on model prediction are shown in Fig.14 shown.

[0228] like Fig.13 As shown in the figure, during the climbing phase, the power of the generator set gradually increases linearly until it reaches the maximum power; during the cruising phase, the generator set operates at the optimal power. In the rule-based energy management strategy, the rapid change of propulsion power directly affects the generator set, especially in the climbing phase (about 150 seconds) and the descending phase (about 3000 seconds). There are inherent defects in the rule-based energy management strategy when dealing with rapid load power changes. Fig.14As shown in Figure 1, the model predicts that the energy management strategy considering TMS maintains a relatively constant turbine generator power level throughout the flight, operating at the optimal power point to maintain optimal fuel consumption. The shaded area shows that the generator power fluctuation is smaller than that of STM. The results of fuel consumption are shown in Figure 1. Fig.15 shown.

[0229] like Fig.15 As shown, the model predictive controller consumes 7.9% less fuel than the rule-based controller, and the model predictive controller keeps the generator set close to the optimal operating PSFC value during most of the flight. Fig.16 The battery temperature control results of MPC and rule control under the full flight profile. Fig.17 The temperature control results of the drive motor under MPC and rule control in the full flight profile. Fig.18 The temperature control results of the generator under MPC and rule control in the full flight profile.

[0230] like Fig.16 As shown in the figure, the rule-based comprehensive energy management strategy has a throttling effect between the two loops due to the cascade of liquid-liquid heat exchangers between cooling loops 1 and 2, which limits the heat dissipation of loop 2. Therefore, the battery temperature will rise. However, the MPC-based comprehensive energy management strategy has no obvious increase in temperature and always remains at 32°C. Fig.17 As shown in the figure, the rule-based comprehensive energy management strategy frequently turns on the circulating pump when the drive motor is running at high power, resulting in certain temperature fluctuations ranging from 48.5℃ to 52℃, while the MPC-based comprehensive energy management strategy keeps the drive motor temperature at 50℃. Fig.18 As shown, the temperature fluctuation of the rule-based integrated energy management strategy is between 49.5°C and 51.5°C, while the MPC-based strategy relies on the generator temperature prediction model to give a control command before the temperature exceeds the optimal operating point, so that the generator temperature reaches the range of 50°C near the optimal operating point.

[0231] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for optimizing the electric-thermal energy management of a hybrid aircraft electric propulsion system, characterized in that The specific steps are as follows: The electric propulsion system of hybrid aircraft is optimized by adopting multidisciplinary optimization design method. The optimization objective in the optimization problem is defined as minimizing fuel consumption and maximum take-off weight (MTOW). The optimization variables are maximum take-off weight (MTOW), battery weight, and rated power of electric propulsion system components. The constraint condition is "aerodynamic-structure-propulsion" balance. Based on the hybrid aircraft electric propulsion system, an electric-thermal management system for the hybrid aircraft electric propulsion system is constructed; the electric-thermal management system for the hybrid aircraft electric propulsion system includes a lithium-ion battery electric-thermal coupling model and a motor thermal model, and thermal management of the hybrid aircraft electric propulsion system is achieved through a thermal management component; Based on MPC, a comprehensive management and optimization strategy for the electric-thermal energy of the hybrid aircraft electric propulsion system is constructed. With fuel economy and temperature as optimization targets, the MPC rolling optimization control is used to solve the minimum value of the objective function in each flight profile to achieve precise power distribution and temperature control.

2. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 1, characterized in that: The constraints include flight mechanics constraints, weight characteristics constraints, and electric propulsion system component constraints; The flight mechanics constraint condition is that the aircraft flight thrust is equal to the sum of the aircraft flight resistance and the component force of the aircraft weight in the flight direction; The weight characteristic constraint condition is that the maximum take-off weight of the aircraft is equal to the sum of the empty weight of the aircraft, the payload weight, the fuel mass, and the battery pack weight, and the fuel mass is greater than or equal to the used fuel mass; The electric propulsion system assembly includes an engine, a generator, a drive motor and a battery pack, and the engine and the generator constitute a generator set; The constraints are: the sum of the generator output power and the battery pack output power is equal to the drive motor output power; the difference between the total energy capacity of the battery pack and the energy consumed by the battery pack is greater than or equal to 0; the minimum state of charge of the battery pack is less than or equal to the final state of charge of the battery pack.

3. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 2, characterized in that: The electric propulsion system components meet the following constraints when the aircraft is in a climbing state: The output power of the drive motor is less than or equal to 1.2 times the rated power of the drive motor; The turbine engine output power is less than or equal to 1.2 times the turbine engine power; The generator output power is less than or equal to 1.2 times the generator power; The battery pack output power is greater than or equal to 0.6 times the product of the battery pack weight and the battery pack output power, and less than or equal to the product of the battery pack weight and the battery pack output power; The electric propulsion system components meet the following constraints in the cruising state and the landing state: The output power of the drive motor is less than or equal to the rated power of the drive motor; The turbine engine output power is less than or equal to the turbine engine power; The generator output power is less than or equal to the generator power; The battery pack output power is less than or equal to the product of the battery pack weight and the battery pack output power, which is equal to the rated power of the battery pack.

4. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 3, characterized in that: The lithium-ion battery electric-thermal coupling model includes a battery pack equivalent circuit model, a battery pack heat generation model and a battery pack heat dissipation model. The specific expression is as follows: Where Vt is the battery terminal voltage; I L is the load current; R cell is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are the battery temperature and the battery heat generation respectively; Q coolant is the heat dissipation of battery liquid; C p,bat and m bat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,coolant and are the specific heat capacity and mass flow rate of coolant respectively; T bat and T liq,in Respectively represent the battery assembly temperature and the coolant temperature at the cold plate inlet; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

5. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 4, characterized in that: The motor thermal model includes a drive motor thermal model and a generator thermal model, and the thermal management components used include a coolant reservoir, a circulating pump, a heat exchanger and a liquid cooling jacket, wherein the coolant reservoir, the circulating pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger constitute the drive motor thermal model, and the fuel tank, the circulating pump, the liquid cooling jacket connected to the generator, and the heat exchanger constitute the generator thermal model; The expression of the drive motor thermal model is as follows: Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot They represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly respectively; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant; The expression of the generator thermal model is as follows: Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Respectively represent the constant pressure specific heat capacity and thermal mass of the generator assembly; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

6. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 5, characterized in that: The specific steps of constructing the electric-thermal energy comprehensive management and optimization strategy of the hybrid aircraft electric propulsion system are as follows: Build a prediction model for energy management systems; Build a prediction model for thermal management systems; Construct the objective function; Set system constraints; The rolling optimization control seeks the minimum value of the objective function.

7. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 6, characterized in that: The construction process of the energy management system prediction model is as follows: The output power of the battery group and the generator group at the current sampling time k is selected as the control variable: u(k)=[P bat (k),P tg (k)] Where P bat is the output power of the battery pack, P tg is the output power of the generator set; Select the lithium battery state of charge SOC and fuel mass m fuel For state variables: x(k)=[SOC(k),m fuel (k)] Then the energy management prediction model expression is: Where: A and B are the state matrix and control matrix of the prediction model respectively; Where: PSFC is the engine specific fuel consumption; η bat E is the charging and discharging efficiency of lithium-ion batteries; bat,max is the total energy capacity of the battery; △t is the time difference between two adjacent sampling moments, that is, the sampling step.

8. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 7, characterized in that: The construction process of the thermal management system prediction model is as follows: The speeds of the battery circulation pump, generator circulation pump and drive motor circulation pump at the current sampling time k are selected as control variables: u(k)=[ω pump,bat (k),ω pump,gen (k),ω pump,mot (k)] Where: pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulating pump speed; ω pump,mot The speed of the circulating pump for the driving motor; The battery pack temperature, generator temperature and drive motor temperature are selected as state variables: x(k)=[T bat (k),T gen (k),T mot (k)] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature; The thermal management prediction model expression is: Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term; Where: C bat , C mot and C gen are the constant pressure specific heat capacity of lithium-ion battery, drive motor and generator respectively; Q bat , Q mot and Q gen are the heat generation rates of lithium-ion batteries, drive motors and generators respectively; M bat 、M mot and M gen They are the thermal masses of lithium-ion battery, drive motor and generator respectively; ω bat0 ,ω gmot0 and ω gen0 are the initial speeds of the lithium-ion battery, drive motor, and generator circulation pump, respectively; T bat,liq , T mot,liq and T gen,liq They are the coolant temperatures of the lithium-ion battery, drive motor, and generator; T bat0 , T mot0 and T gen0 are the initial temperatures of the lithium-ion battery, drive motor and generator respectively.

9. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 8, characterized in that: The expression of the objective function is: Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems is the energy management objective function; J tms is the thermal management objective function.

10. The method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system according to claim 9, characterized in that: The system constraints are: System output power balance: P load (k)=P bat (k)+P tg (k)+P tms (k) Where: P load P is the propulsion power requirement reference value; tms is the output power of all components in the thermal management system; Propulsion system power constraints: Where: P tg,max is the maximum continuous output power of the generator set, P bat,min , P bat,max are the maximum charge / discharge power of the battery pack respectively; State of Charge Constraints: Where: SOC min and SOC max They are the minimum and maximum SOC values ​​of the battery pack respectively; Key component temperature constraints: Circulation pump constraints: Thermal Management Cascade Coupling Constraints: Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

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