Energy optimization control method and device for aircraft power system based on electro-thermal coupling

Abstract

The embodiment of the application discloses an aircraft power system energy optimization control method and device based on electrothermal coupling. The method comprises the following steps: according to the coupling architecture of the aircraft power system and the thermal management system, dynamic and steady-state models and a graph theory model are established respectively; and the power system and the thermal management system are controlled in a coordinated manner by using a model predictive control algorithm and a rule-based strategy based on the dynamic and steady-state models and the graph theory model. Through the application, the problem that the electrothermal management method based on a state machine cannot meet the temperature requirements of the thermal management system in the related art is solved, the working point efficiency of the system during the flight task of the aircraft is effectively improved and optimized, the power loss of the power system is reduced, and the technical effect of electrothermal dynamic energy optimization management of the aircraft power system is achieved.

Description

Technical Field

[0001] This invention relates to the fields of energy and power and aviation electrical systems, and in particular to an energy optimization control method and apparatus for aircraft electrical systems based on electrothermal coupling. Background Technology

[0002] In recent years, the development of more-electric / all-electric aircraft technology has brought severe challenges to thermal management. The coordinated control of electrical and thermal management systems has been identified as a key technology for the design of future advanced aircraft. The biggest problem in meeting the normal operating requirements of aircraft electrical systems is the lack of mature energy management technologies for the coordinated optimization of aircraft electrical energy allocation from both the supply and demand sides. Currently, research on modeling and analysis methods for energy flow and coupling mechanisms in aircraft electrical and thermal coupling systems is severely lacking, and there is a lack of accumulated collaborative design models for electrical and thermal management systems. In traditional design methods, the aircraft's electrical and thermal management systems are designed separately, without considering the coupling relationship between their energy sources or coordinated control. Each subsystem simply uses conventional linear control methods, such as PID control, to adjust the system's operating point.

[0003] While the electrification of aircraft electromechanical systems currently offers numerous performance and efficiency benefits, it also introduces significant challenges in thermal management. In commercial aviation, onboard thermal management capabilities are a major obstacle to the further development of high-power electric propulsion systems, and the coordinated control of electric and thermal systems has been emphasized as a necessary step in the design of hybrid and all-electric aircraft. Military aircraft are currently facing several challenges. Future military aircraft will need to be equipped with high-energy directed-energy weapons, providing a variety of offensive / defensive and lethal / non-lethal combat methods, including particle beams, microwaves, and lasers. Although these weapons have broad application prospects, their operating efficiency is very low, rapidly generating large amounts of heat load in a short period of time, resulting in high heat flux density. This necessitates a significant increase in the power generation capacity of the aircraft's electrical system, and the system's thermal management and limited heat sinks are insufficient to meet the system's requirements. Therefore, if future advanced fighter jets are to be equipped with directed-energy weapons, the thermal management problem must be addressed first.

[0004] There is no effective solution yet to address the problem that state machine-based electrothermal management methods alone cannot meet the temperature requirements of thermal management systems. Summary of the Invention

[0005] This invention provides an energy optimization control method and apparatus for an aircraft electrical system based on electrothermal coupling, which at least solves the technical problem that the state machine-based electrothermal management method alone cannot meet the temperature requirements of the thermal management system in related technologies.

[0006] According to one aspect of the present invention, an energy optimization control method for an aircraft electrical system based on electrothermal coupling is provided, comprising: establishing dynamic and steady-state models and a graph theory model respectively according to the coupled architecture of the aircraft's electrical system and thermal management system, wherein the coupled architecture of the electrical system and thermal management system includes at least an electrical system and a thermal management system; performing coordinated control of the electrical system and the thermal management system using a combination of model predictive control (MPC) algorithm and rule-based strategy based on the dynamic and steady-state models and the graph theory model, wherein the rule-based strategy controls the battery pack charging and discharging state, shieldable load, and converter operating state of the electrical system according to flight power requirements and electrical system efficiency; controlling the mass flow rate of the cooling oil tank outlet and the fuel-ram air heat exchange ratio of the thermal management system according to the temperature changes of electrical system components and cooling oil tank; setting a multi-objective optimization function and corresponding constraints based on the model predictive control algorithm, and using rolling optimization technology to control the battery pack charging and discharging state, the shieldable load, and the converter operating state of the electrical system.

[0007] Optionally, based on the coupled architecture of the aircraft's electrical system and thermal management system, a graph theory model is established, including: obtaining typical components in the coupled architecture of the electrical system and thermal management system, wherein the typical components include at least one of the following: generator, converter, battery pack, thermal management fuel pump, cold plate, radiator; and performing parametric modeling of optimization indicators based on the typical components to obtain the graph theory model, wherein the graph theory model includes at least one of the following: electrical system graph theory model, thermal management system graph theory model.

[0008] Optionally, before obtaining typical components in the coupled architecture of the power system and thermal management system, the method further includes: obtaining a power system architecture and flight power demand profile; and determining the optimization index, optimization variables, and optimization algorithm based on the power system architecture and the flight power demand profile.

[0009] Optionally, before establishing dynamic and steady-state models and graph theory models based on the coupled architecture of the aircraft's electrical and thermal management systems, the method further includes: establishing a functional relationship between energy consumption and weight of each device in the electrical system with respect to optimization variables based on the coupled architecture of the electrical and thermal management systems; and establishing a multi-objective optimization model of the electrical system based on the converter efficiency curve with the total energy consumption and total weight of the electrical system as the objective function, to obtain the number and specifications of each device.

[0010] Optionally, before coordinating the control of the power system and the thermal management system using a combination of model predictive control algorithms and rule-based strategies based on the dynamic and steady-state models and the graph theory model, the method further includes: determining a typical load mission profile of the aircraft based on the aircraft's flight parameters, wherein the flight parameters include: cruise altitude, Mach number, ambient air temperature, and ram air temperature, and the typical load mission profile of the aircraft is used to represent the changes in non-critical load power, critical load power, and total system power over time.

[0011] Optionally, after coordinating the control of the power system and the thermal management system using a combination of model predictive control algorithms and rule-based strategies based on the dynamic and steady-state models and the graph theory model, the method further includes: comparing the differences in power system efficiency, temperature, and battery state of charge between the model predictive control algorithm and the rule-based strategy; and determining the optimal energy management strategy based on the comparison results.

[0012] Optionally, the constraints include at least one of the following: battery charging and discharging rate constraints, decision variable converter switching constraints, battery state of charge constraints, converter power and source point input power constraints, input variable inequalities and equality constraints.

[0013] According to another aspect of the present invention, an energy optimization control device for an aircraft electrical system based on electrothermal coupling is also provided, comprising: a first establishment module, configured to establish dynamic and steady-state models and a graph theory model respectively according to the coupled architecture of the aircraft's electrical system and thermal management system, wherein the coupled architecture of the electrical system and thermal management system includes at least: an electrical system and a thermal management system; a control module, configured to perform coordinated control of the electrical system and the thermal management system based on the dynamic and steady-state models and the graph theory model using a combination of model predictive control algorithms and rule-based strategies, wherein the rule-based strategy controls the battery pack charging and discharging state, shieldable load, and converter operating state of the electrical system according to flight power requirements and electrical system efficiency; the rule-based strategy controls the mass flow rate of the cooling oil tank outlet and the fuel-ram air heat exchange ratio of the thermal management system according to changes in the temperature of electrical system components and cooling oil tank; and sets a multi-objective optimization function and corresponding constraints based on the model predictive control algorithm, and uses rolling optimization technology to control the battery pack charging and discharging state, the shieldable load, and the converter operating state of the electrical system.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the energy optimization control method for an aircraft electrical system based on electrothermal coupling as described above.

[0015] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a program, wherein the program, when running, executes the energy optimization control method for an aircraft electrical system based on electrothermal coupling as described above.

[0016] In this embodiment of the invention, dynamic and steady-state models and graph theory models are established according to the coupled architecture of the aircraft's electrical and thermal management systems. Based on these models, a combination of model predictive control algorithms and rule-based strategies is used to coordinate the control of the electrical and thermal management systems. Specifically, the rule-based strategy controls the battery charging / discharging state, shieldable loads, and converter operating states of the electrical system based on flight power requirements and electrical system efficiency. It also controls the mass flow rate at the cooling tank outlet and the fuel-ram air heat exchange ratio of the thermal management system based on changes in the temperatures of electrical system components and the cooling tank. A multi-objective optimization function and corresponding constraints are set based on the model predictive control algorithm, and rolling optimization techniques are used to control the battery charging / discharging state, shieldable loads, and converter operating states. In other words, this embodiment of the invention abandons the traditional rule-based temperature control of the electrical system, thus solving the problem that state machine-based electrothermal management methods alone cannot meet the temperature requirements of the thermal management system. This effectively improves and optimizes the system's operating point efficiency during aircraft flight missions, reduces power losses in the electrical system, and achieves the technical effect of optimizing the dynamic energy management of the aircraft's electrical and thermal systems. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 A flowchart of an energy optimization control method for an aircraft electrical system based on electrothermal coupling, provided for an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a coupling architecture between an aircraft electrical system and a thermal management system provided in an embodiment of the present invention;

[0020] Figure 3A schematic diagram of the power demand spectrum for an aircraft's overall mission, provided as an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the efficiency curves of a common AC / DC converter provided in an embodiment of the present invention;

[0022] Figure 5 An aircraft electrical system architecture diagram provided for an embodiment of the present invention;

[0023] Figure 6 A graph theory model of an aircraft electrical system is provided for embodiments of the present invention;

[0024] Figure 7 A schematic diagram of an aircraft thermal management system provided in an embodiment of the present invention;

[0025] Figure 8 A structural diagram of an aircraft thermal management system provided in an embodiment of the present invention;

[0026] Figure 9 A graph theory model of an aircraft thermal management system is provided for embodiments of the present invention;

[0027] Figure 10 A modeling relationship diagram of an aircraft electrical and thermal coupling system provided in an embodiment of the present invention;

[0028] Figure 11 This is a schematic diagram of the principle structure of an aircraft electrothermal coupling MPC control system provided in an embodiment of the present invention;

[0029] Figure 12 A schematic diagram illustrating the control algorithm solution process provided in an embodiment of the present invention;

[0030] Figure 13 A model predictive control flowchart provided in an embodiment of the present invention;

[0031] Figure 14 The aircraft flight parameter diagram provided for embodiments of the present invention;

[0032] Figure 15 A typical aircraft payload cross-sectional view provided for an embodiment of the present invention;

[0033] Figure 16 This is a schematic diagram of the generator output power versus load demand power curve under the MPC control strategy provided in an embodiment of the present invention.

[0034] Figure 17 A schematic diagram of the efficiency variation curve of the aircraft power system converter group provided in an embodiment of the present invention;

[0035] Figure 18A schematic diagram of the temperature profile of the aircraft power system converter group provided in an embodiment of the present invention;

[0036] Figure 19 A schematic diagram of the temperature change curve of the aircraft electronics compartment provided in an embodiment of the present invention;

[0037] Figure 20 This is a schematic diagram of the temperature change curve of fuel in an aircraft fuel tank as it passes through a cold plate, provided in an embodiment of the present invention.

[0038] Figure 21 A schematic diagram illustrating the change in fuel input temperature in an aircraft fuel tank, provided as an embodiment of the present invention;

[0039] Figure 22 This is a schematic diagram of an energy optimization control device for an aircraft electrical system based on electrothermal coupling, provided as an embodiment of the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish different objects, rather than to limit a specific order.

[0042] This invention provides an energy optimization control method for aircraft electrical systems based on electrothermal coupling, such as... Figure 1 As shown, the method includes the following steps:

[0043] Step S102: Based on the coupled architecture of the aircraft's electrical system and thermal management system, establish dynamic and steady-state models and graph theory models respectively. The coupled architecture of the electrical system and thermal management system includes at least the electrical system and the thermal management system.

[0044] Optionally, analytical parametric graph theory modeling is performed on the coupled architecture of the aircraft's electrical and thermal management systems. Based on the determined electro-thermal coupling architecture, energy flow analysis of the aircraft's electrical and thermal coupling systems is conducted. A graph theory-based method is used to model the multiphysics system, obtaining the system's dynamic formulas. The graph theory models of the electrical and thermal management systems are then verified and simulated in the Matlab environment. This coupling of the electrical and thermal management systems is also referred to as the electrical-thermal coupled system.

[0045] Step S104: Based on dynamic and steady-state models, graph theory models, and a combination of model predictive control algorithms and rule-based strategies, the power system and thermal management system are coordinated for control. Specifically, the rule-based strategy controls the battery pack charging / discharging state, shieldable loads, and converter operating state of the power system according to flight power requirements and power system efficiency. The rule-based strategy also controls the mass flow rate at the cooling oil tank outlet and the fuel-ram air heat exchange ratio of the thermal management system based on changes in the temperatures of power system components and the cooling oil tank. Finally, the model predictive control algorithm sets a multi-objective optimization function and corresponding constraints, and uses rolling optimization techniques to control the battery pack charging / discharging state, shieldable loads, and converter operating state of the power system.

[0046] Optionally, based on the energy management modeling technology of the aircraft's electrical and thermal coupling systems, the power system selects a model predictive control algorithm for optimized control. Simultaneously, the thermal management system employs a rule-based strategy for integrated and coordinated control. This rule-based strategy is also known as a rule-based state machine-based energy management strategy.

[0047] It should be noted that the above methods are applicable to scenarios including but not limited to multi-electric / all-electric aircraft. Multi-scale hierarchical model predictive control algorithms, in conjunction with rule-based policies, can achieve the energy-coordinated optimization control objective of multi-timescale and high-efficiency operation.

[0048] The aforementioned electrical system integrates the power supply system, power distribution system, and load electrical equipment. It also interacts with the airborne thermal management system to achieve efficient heat dissipation and cooling. Furthermore, the entire electrical system and thermal management system operate on different time scales, requiring a hierarchical real-time control method. A collaborative control method, combining model predictive control of the electrical system with rule-based strategies of the thermal management system, can optimize the charge / discharge rate of the power system's battery packs when carrying high-power electrical loads. Utilizing the peak-shaving and valley-filling effects of energy storage devices, it improves and optimizes the operating point efficiency of the electrical system during flight missions, reduces power losses, and simultaneously coordinates with the thermal management system's cooling devices to achieve dynamic energy optimization management of the aircraft's electrical and thermal systems.

[0049] Furthermore, with the development of integrated aircraft energy systems, the traditional independent design approach for each subsystem has been broken, replaced by the collaborative design of thermal, electrical, and mechanical systems. Increasingly stringent requirements for these systems mean that the coordinated control of electrical and thermal systems is crucial for managing aircraft power generation, distribution, and consumption. Coordinated control helps bridge the gap between the performance requirements and capabilities of current-generation aircraft and facilitates the design and sizing adjustments for next-generation aircraft. In future aircraft electrical systems, the design of integrated and optimized aircraft electrical and thermal coupling systems, along with comprehensive research into energy management optimization technologies for these systems, will achieve optimized allocation of power and energy from both the load demand and energy supply sides. This will meet the constraints of multi-electric / all-electric aircraft, such as load capacity, power generation, heat dissipation requirements, volume, and weight. Based on the temperatures of electrical system components and fuel tanks, the rational allocation and coordinated control of onboard fuel thermal management will be achieved, meeting the temperature requirements of the thermal management system.

[0050] In this embodiment of the invention, dynamic and steady-state models and graph theory models are established according to the coupled architecture of the aircraft's electrical and thermal management systems. Based on these models, a combination of model predictive control algorithms and rule-based strategies is used to coordinate the control of the electrical and thermal management systems. Specifically, the rule-based strategy controls the battery charging / discharging state, shieldable loads, and converter operating states of the electrical system based on flight power requirements and electrical system efficiency. It also controls the mass flow rate at the cooling tank outlet and the fuel-ram air heat exchange ratio of the thermal management system based on changes in the temperatures of electrical system components and the cooling tank. A multi-objective optimization function and corresponding constraints are set based on the model predictive control algorithm, and rolling optimization techniques are used to control the battery charging / discharging state, shieldable loads, and converter operating states. In other words, this embodiment of the invention abandons the original rule-based temperature control of the electrical system, thus solving the problem that state machine-based electrothermal management methods alone cannot meet the temperature requirements of the thermal management system. This effectively improves and optimizes the system's operating point efficiency during aircraft flight missions, reduces power losses in the electrical system, and achieves the technical effect of optimizing the dynamic energy management of the aircraft's electrical and thermal systems.

[0051] In one optional implementation, a graph theory model is established based on the coupled architecture of the aircraft's electrical system and thermal management system, including: obtaining typical components in the coupled architecture of the electrical system and thermal management system, wherein the typical components include at least one of the following: generator, converter, battery pack, thermal management fuel pump, cold plate, radiator; and performing parametric modeling of optimization indicators based on the typical components to obtain a graph theory model, wherein the graph theory model includes at least one of the following: electrical system graph theory model, thermal management system graph theory model.

[0052] The aforementioned typical components are common parts of aircraft, including but not limited to generators, converters, battery packs, thermal management fuel pumps, cold plates, and radiators. Furthermore, the coupled architecture of the power system and thermal management system mainly includes the power system and the thermal management system. By parametrically modeling the optimization indicators of the typical components involved in each system, the corresponding graph theory models of the power system and / or thermal management system can be obtained.

[0053] It should be noted that the power system graph theory model includes each node and the power demand between each node, wherein the power demand includes, but is not limited to, mechanical power, electrical power and thermal power; the above-mentioned thermal management system graph theory model includes each node and the energy flow relationship between each node, wherein the energy flow relationship includes, but is not limited to, advection, bidirectional convection and input power.

[0054] The above implementation method allows for the creation of corresponding graph theory models for each typical component within its respective system.

[0055] In an alternative implementation, before obtaining typical components in the coupled architecture of the power system and thermal management system, the method further includes: obtaining a profile of the power system architecture and flight power demand; and determining optimization indices, optimization variables, and optimization algorithms based on the profile of the power system architecture and flight power demand.

[0056] The aforementioned flight power demand profile illustrates the changes in power demand for an aircraft during different missions, such as takeoff, climb, cruise, and descent. In practical implementation, optimization indices, matching optimization variables, and optimization algorithms can be selected based on the power system architecture and the flight power demand profile. Among these, the optimization indices serve as the primary basis for parametric modeling.

[0057] In an optional implementation, before establishing dynamic and steady-state models and graph theory models based on the coupled architecture of the aircraft's electrical and thermal management systems, the method further includes: establishing a functional relationship between energy consumption and weight of each device in the electrical system with respect to optimization variables based on the coupled architecture of the electrical and thermal management systems; and establishing a multi-objective optimization model of the electrical system based on the converter efficiency curve with the total energy consumption and total weight of the electrical system as objective functions to obtain the number and specifications of each device.

[0058] The aforementioned power system equipment mainly includes fuel generators, AC loads, critical electronic loads, non-critical electronic loads, and batteries. In the specific implementation process, functional relationships between energy consumption and weight of each piece of equipment in the power system can be established as optimization variables. Furthermore, based on the converter efficiency curve, a multi-objective optimization model of the power system can be established with the total energy consumption and total weight of the power system as objective functions, thus obtaining the quantity and specifications of each piece of equipment. It should be noted that the quantity and specifications of each piece of equipment include, but are not limited to, the number of converters and their corresponding load power.

[0059] In an alternative implementation, before coordinating the control of the power system and the thermal management system using a combination of model predictive control algorithms and rule-based strategies based on dynamic and steady-state models and graph theory models, the method further includes: determining a typical load profile of the aircraft based on the aircraft's flight parameters, wherein the flight parameters include: cruise altitude, Mach number, ambient air temperature, and ram air temperature, and the typical load profile is used to represent the changes in non-critical load power, critical load power, and total system power over time.

[0060] In the specific implementation process, a typical load mission profile of the aircraft can be drawn based on flight parameters such as cruising altitude, Mach number, ambient air temperature and ram air temperature. Through this typical load mission profile, the changes in non-critical load power, critical load power and total system power over time can be observed.

[0061] It should be noted that the above flight parameters include, but are not limited to, cruising altitude, Mach number, ambient air temperature, and ram air temperature.

[0062] In an alternative implementation, after coordinating the control of the power system and the thermal management system using a combination of model predictive control algorithms and rule-based strategies based on dynamic and steady-state models and graph theory models, the method further includes: comparing the differences between model predictive control algorithms and rule-based strategies in terms of power system efficiency, temperature, and battery state of charge; and determining the optimal energy management strategy based on the comparison results.

[0063] Optionally, the thermal power control of the power system is based on the model predictive control algorithm and coordinated with the thermal management system based on the rule-based strategy. The advantages and disadvantages of the two energy management methods in terms of power system efficiency, temperature and battery SOC are compared and analyzed. Finally, the design optimization results and final effects are given as the optimal energy management strategy.

[0064] In one alternative implementation, the above constraints include at least one of the following: battery charging and discharging rate constraints, decision variable converter switching constraints, battery state of charge constraints, converter power and source point input power constraints, and input variable inequalities and equality constraints.

[0065] It should be noted that the above constraints include, but are not limited to, battery charging and discharging rate constraints, decision variable converter switching constraints, battery state of charge constraints, converter power and source point input power constraints, and input variable inequalities and equality constraints.

[0066] The following is a detailed description of an optional embodiment of the present invention.

[0067] To achieve interactive operation and multi-scale control of the aircraft's electrical and thermal coupling systems, a graph theory model is used for analysis, and dynamic and steady-state models of the multi-electric aircraft's electrical and thermal coupling systems are developed. Mathematical simulations are performed on the system, and a simulation model of the aircraft's electrical and thermal coupling systems is built in the Matlab / Simulink environment. For the established graph theory model of the aircraft's electrical system, a rolling optimization is performed using a combination of multi-timescale MPC algorithm and rule-based strategies, and the solution is obtained using the Yalmip toolbox and Gurobi optimizer in the Matlab environment. Thermal power control of the electrical system based on the MPC algorithm is implemented, and coordinated control is achieved with a thermal management system under a state machine control strategy. The advantages and disadvantages of the two energy management strategies in terms of electrical system efficiency, temperature, and battery SOC are compared and analyzed. Finally, the design optimization results and final effects are presented.

[0068] The established optimized aircraft electrical and thermal coupling system architecture, such as Figure 2 As shown, the aircraft electrical and thermal management system comprises two main parts: the power supply and distribution system and the cooling and thermal management system. The fuel system is part of the thermal management system. Multiple devices are shown in the diagram. The aircraft's electrical and thermal coupling system consists of a generator system, secondary power supply, energy storage system, power distribution system, load systems (including shielded and non-shielded components), and the thermal management system. Generators mounted on the aircraft engines provide electrical energy, which is converted to voltage by power converters and then distributed to AC and DC buses. It can also supply electrical energy to the energy storage system. When the aircraft load suddenly increases, the energy storage system and generator system can jointly supply power to the load. Simultaneously, the redundant energy from the generators can be fed back into the energy storage system for storage. The thermal management system components can be divided into two main categories: storage and transmission. Fuel tanks, air compartments, and phase change materials act as thermal energy storage elements, while heat exchangers, pumps, fans, pipes, and flow valves are used to transfer heat dissipation fluid and thermal energy throughout the aircraft.

[0069] In electrical systems, electrical losses generated by various components are converted into heat. These losses are considered additional heat loads in thermal management systems. However, thermal management systems require electricity to operate various control components, such as fans and pumps, which are used to transfer the generated heat throughout the system.

[0070] The energy optimization management of aircraft electrical and thermal coupling systems is essentially a multi-objective optimization problem. The overall performance indicators of the power system include system efficiency, weight, reliability, DC bus voltage, and power quality. The optimization approach for energy management of electro-thermal coupling systems is as follows:

[0071] 1) According to Figure 2 Establish the function relating energy consumption and weight of each device in the aircraft's electrical system to the optimization parameters;

[0072] 2) According to Figure 4 A multi-objective optimization model of the power system is established with the energy consumption and total weight of the entire aircraft power system as the objective function, and information such as the quantity and specifications of each device is obtained.

[0073] 3) Based on the data on changes in the power demand of the aircraft during flight missions, such as... Figure 3 As shown, the possible power transmission methods are analyzed, and then a graph theory model of the entire aircraft electrical system is determined based on the aircraft electrical system architecture, such as... Figure 5 , 6 As shown. Based on power balance and various constraints, a model predictive control system control model is designed; then, the architecture of the thermal management system is determined according to the thermal management system, as shown. Figure 7 , 8 As shown, a corresponding system graph theory model is established based on this, such as... Figure 9 As shown, the energy transfer process in a specific thermal management system architecture is presented.

[0074] 4) Establish the dynamic equations of the aircraft electric system based on the graph theory model, the objective function of the aircraft dynamic energy model predictive control, and the mathematical expression of the constraints as follows:

[0075]

[0076] The only continuous input to the power system controller is the battery charging and discharging rate, limited to ±100kW.

[0077] -100≤u(k)≤100

[0078] The decision variable transformer switch is constrained to binary:

[0079] u B ∈{0,1}

[0080] Battery SOC is a dynamic variable in the power system controller, and its state constraints are:

[0081] 0≤x(k)≤100

[0082] Converter power and source point input power constraints:

[0083] P1+P2+P3+P4+P5≤P in

[0084] Other state variables, input variable inequalities, and equality constraints:

[0085] g(k|t)≥0,k=0,...,N p -1

[0086] h(k|t)=0,k=0,...,N p -1

[0087] 5) The input state variables and output decision variables in the model predictive controller were defined through optimization model establishment. The optimization objective and cost function of the controlled object were also given. For the dynamic energy control process of an aircraft, the basic flow of the control algorithm was designed as follows: Figure 12 As shown, during rolling optimization, variables are defined using Yalmip, and objective functions and constraints are established based on these variables. The Gurobi optimization solver is then used to solve the problem, outputting control signals before proceeding to the next iteration for rolling optimization.

[0088] Furthermore, the power system achieves higher output efficiency of the converter group under the model predictive control strategy. After the thermal management system is controlled by the rule-based control, the temperature of the power system converter group, the electronic compartment, and the fuel input temperature of the two cooling oil tanks are all lower than the temperature of the power system under simple rule-based control. This proves that the thermal environment of the power system under the MPC strategy control helps to extend the life of electronic equipment.

[0089] The solution results of the collaborative optimization of the aircraft electrothermal coupling system based on the combination of MPC algorithm and rule-based strategy control proposed in this invention are as follows: Figures 16 to 21 As shown in the figure, the results demonstrate that the collaborative optimization energy management control strategy based on the MPC algorithm is significantly superior to the traditional state machine-based electrothermal management method.

[0090] Example

[0091] 1. First, based on the architecture and equipment configuration relationships of the aircraft's electrical and thermal management systems, such as... Figure 2 As shown. The system's energy, including electrical and thermal power, is determined through transmission pathways and mechanisms.

[0092] 2. Determine the power system architecture and flight power demand profile, such as... Figure 3 As shown, power system optimization indicators are selected, and appropriate optimization variables and optimization algorithms are chosen.

[0093] 3. Perform parametric modeling of relevant indicators for typical components such as generators, converters, battery packs, thermal management fuel pumps, cold plates, and radiators. Figure 4As shown, a corresponding graph theory system model is established as follows. Figure 6 , 9 As shown, the system power requirements and typical component-related performance constraints are met.

[0094] 4. Based on Figure 7 The aircraft fuel thermal management system shown is designed with an airborne thermal management system control architecture and loop.

[0095] 5. For example Figure 10 As shown, typical components are cascaded to establish analytical relationships among optimization indices for the entire power and thermal management coupled system architecture, satisfying system-related constraints such as energy flow relationships and power requirements.

[0096] 6. Based on the above analysis, a model predictive control method is established for the aircraft's electrical system. This method combines graph theory modeling with steady-state and dynamic models of thermal management, such as... Figure 11 , 12 As shown in Figure 13.

[0097] 7. Based on this, design, and combine Figure 14 Given the aircraft flight parameters, the results are obtained through calculation and analysis. Figure 15 The diagram shows the aircraft power variation profile.

[0098] 8. A collaborative control approach combining model predictive control and rule-based control is employed to achieve control of the entire power and thermal management system, resulting in the following: Figures 16 to 21 The rendered image.

[0099] According to another aspect of the present invention, an energy optimization control device for an aircraft electrical system based on electrothermal coupling is also provided, such as... Figure 22 As shown, the aircraft electrical system energy optimization control device based on electrothermal coupling includes a first establishment module 2202 and a control module 2204. The following is a detailed description of this aircraft electrical system energy optimization control device based on electrothermal coupling.

[0100] The first establishment module 2202 is used to establish dynamic and steady-state models and graph theory models respectively based on the coupled architecture of the aircraft's electrical system and thermal management system. The coupled architecture of the electrical system and thermal management system includes at least: an electrical system and a thermal management system. The control module 2204 is connected to the first establishment module 2202 and is used to perform coordinated control of the electrical system and thermal management system based on the dynamic and steady-state models and graph theory models, using a combination of model predictive control algorithms and rule-based strategies. The rule-based strategy controls the charging and discharging state of the battery pack, the shieldable load, and the converter operating state of the electrical system according to the flight power requirements and electrical system efficiency. The rule-based strategy controls the mass flow rate of the cooling oil tank outlet and the fuel-ram air heat exchange ratio of the thermal management system according to the temperature changes of electrical system components and cooling oil tank. The model predictive control algorithm sets a multi-objective optimization function and corresponding constraints, and uses rolling optimization technology to control the charging and discharging state of the battery pack, the shieldable load, and the converter operating state of the electrical system.

[0101] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, they can be implemented in the following ways: the above modules can be located in the same processor; and / or, the above modules can be located in different processors in any combination.

[0102] In this embodiment of the invention, the aircraft electrical system energy optimization control device based on electrothermal coupling can establish dynamic and steady-state models and graph theory models respectively according to the coupled architecture of the aircraft's electrical system and thermal management system. Based on the dynamic and steady-state models and graph theory models, a combination of model predictive control algorithms and rule-based strategies is used to perform coordinated control of the electrical system and thermal management system. Specifically, the rule-based strategy controls the battery pack charging and discharging state, shieldable loads, and converter operating state of the electrical system according to flight power requirements and electrical system efficiency. The rule-based strategy controls the mass flow rate of the cooling oil tank outlet and the fuel-ram air heat exchange ratio of the thermal management system according to the temperature changes of electrical system components and cooling oil tanks. Based on the model predictive control algorithm, a multi-objective optimization function and corresponding constraints are set, and rolling optimization technology is used to control the battery pack charging and discharging state, shieldable loads, and converter operating state of the electrical system. In other words, the embodiments of the present invention abandon the original power system temperature control based solely on rules, thereby solving the problem that the state machine-based electrothermal management method alone cannot meet the temperature requirements of the thermal management system in related technologies. This achieves the technical effect of effectively improving and optimizing the system's operating point efficiency during aircraft flight missions, reducing the power loss of the power system, and realizing the technical effect of optimizing the dynamic energy management of the aircraft's electrical and thermal systems.

[0103] It should be noted here that the first establishment module 2202 and control module 2204 mentioned above correspond to steps S102 to S104 in the method embodiment. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment.

[0104] Optionally, the first establishment module 2202 includes: an acquisition unit for acquiring typical components in the coupled architecture of the power system and the thermal management system, wherein the typical components include at least one of the following: generator, converter, battery pack, thermal management fuel pump, cold plate, radiator; and a modeling unit for parametric modeling of optimization indicators based on the typical components to obtain a graph theory model, wherein the graph theory model includes at least one of the following: power system graph theory model, thermal management system graph theory model.

[0105] Optionally, the above apparatus further includes: an acquisition module, used to acquire a profile of the power system architecture and flight power demand before acquiring typical components in the coupled architecture of the power system and thermal management system; and a first determination module, used to determine optimization indicators, optimization variables, and optimization algorithms based on the profile of the power system architecture and flight power demand.

[0106] Optionally, the above-mentioned device further includes: a second establishment module, used to establish the functional relationship between energy consumption and weight of each device in the power system with respect to optimization variables based on the coupled architecture of the power system and thermal management system of the aircraft, before establishing dynamic and steady-state models and graph theory models respectively based on the coupled architecture of the power system and thermal management system; and a third establishment module, used to establish a multi-objective optimization model of the power system based on the converter efficiency curve with the total energy consumption and total weight of the power system as objective functions, and obtain the number and specifications of each device.

[0107] Optionally, the above-mentioned device further includes: a second determining module, used to determine a typical load mission profile of the aircraft based on the aircraft's flight parameters before coordinating the control of the power system and the thermal management system using a combination of model predictive control algorithms and rule-based strategies based on dynamic and steady-state models and graph theory models. The flight parameters include: cruise altitude, Mach number, ambient air temperature and ram air temperature. The typical load mission profile of the aircraft is used to represent the changes in non-critical load power, critical load power and total system power over time.

[0108] Optionally, the above-mentioned device further includes: a comparison module, used to compare the differences in power system efficiency, temperature and battery state of charge between the model predictive control algorithm and the rule-based strategy after the power system and thermal management system are coordinated and controlled using a combination of model predictive control algorithm and rule-based strategy based on dynamic and steady-state models and graph theory models; and a third determination module, used to determine the optimal energy management strategy based on the comparison results.

[0109] Optionally, the above constraints include at least one of the following: battery charging and discharging rate constraints, decision variable converter switching constraints, battery state of charge constraints, converter power and source point input power constraints, and input variable inequalities and equality constraints.

[0110] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, the device where the computer-readable storage medium is located executes any of the above-described electrothermal coupling-based aircraft electrical system energy optimization control methods.

[0111] It should be noted that the aforementioned computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, and / or in any mobile terminal in a group of mobile terminals, and the aforementioned computer-readable storage medium includes stored programs.

[0112] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program executes any of the above-described electrothermal coupling-based aircraft electrical system energy optimization control methods.

[0113] According to another aspect of the present invention, an apparatus is also provided, the apparatus including a processor, a memory, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described electrothermal coupling-based aircraft electrical system energy optimization control method.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. An electric-thermal coupling-based energy optimization control method for an aircraft power system, characterized in that, The method comprises the following steps: According to the coupling architecture of the aircraft's power system and thermal management system, dynamic and steady-state models and graph theory models are established, wherein the coupling architecture of the power system and thermal management system at least includes the power system and the thermal management system; Based on the dynamic and steady-state models and the graph theory models, the power system and the thermal management system are cooperatively controlled by using a model predictive control algorithm and a rule-based strategy, wherein the rule-based strategy is used to control the battery pack charging and discharging state, the shieldable load, and the converter operating state of the power system according to flight power demand and power system efficiency; the rule-based strategy is used to control the mass flow rate of the cooling oil tank outlet of the thermal management system and the fuel and ram air heat exchange ratio according to the changes of the power system components and the cooling oil tank temperature; the model predictive control algorithm is used to set a multi-objective optimization function and corresponding constraints, and the battery pack charging and discharging state, the shieldable load, and the converter operating state of the power system are controlled by using a rolling optimization technique; The model predictive control algorithm is used to set a multi-objective optimization function and corresponding constraints; The mathematical expression of the objective function is as follows: in, J Let N represent the multi-objective cost function of model predictive control, where N represents the prediction time domain length of model predictive control, and k represents the index of the prediction step. The mathematical expression of the constraints is as follows: 1 represents the weight coefficient of the state tracking term; ||*||2 represents the L2 norm. The only continuous input of the power system controller is the battery charging and discharging rate, which ranges from -100 to 100: 2 indicates the weighting coefficient of the control input tracking item; -100≤u(k)≤100 4 represents the weighting coefficient of the converter power term; Indicates the prediction time domain; The decision variable converter switch is constrained to be binary: uB∈{0,1} The battery SOC is a dynamic variable in the power system controller, and its state constraint is: 0≤x(k)≤100 The constraint of the converter power and the source input power Pin is: P1+P2+P3+P4+P5≤Pin P1, P2, P3, P4, and P5 represent the input powers of the five converters, respectively; The input variable inequality and equality constraints are: g(k|t)≥0, k=0,...,Np-1 h(k|t)=0, k=0,...,Np-1. According to the coupling architecture of the aircraft's power system and thermal management system, a graph theory model is established, comprising: Typical components in the coupling architecture of the power system and thermal management system are obtained, wherein the typical components include at least one of the following: a generator, a converter, a battery pack, a thermal management fuel pump, a cold plate, and a radiator; Parameterized modeling of optimization indicators is performed according to the typical components to obtain the graph theory model, wherein the graph theory model includes at least one of the following: a power system graph theory model and a thermal management system graph theory model.

2. The method of claim 1, wherein, Before obtaining the typical components in the coupling architecture of the power system and thermal management system, the method further comprises: An electrical power system architecture and a flight power demand profile are obtained; Based on the electrical power system architecture and the flight power demand profile, optimization indicators, optimization variables, and an optimization algorithm are determined.

3. The method of claim 2, wherein, Before establishing dynamic and steady-state models and a graph theory model according to the coupling architecture of the aircraft's power system and thermal management system, the method further comprises: According to the coupling architecture of the power system and thermal management system, a functional relationship between energy consumption and weight of each device in the power system and optimization variables is established; ​ 4. The method of claim 1, wherein, ​ ​ According to the transformer efficiency curve, the total energy consumption and the total weight of the power system are taken as the objective functions to establish a multi-objective optimization model of the power system, and the number and specifications of each device are obtained.

5. The method of claim 1, wherein, Before the collaborative control of the power system and the thermal management system is performed in a manner of combining a model predictive control algorithm and a rule-based strategy based on the dynamic and steady-state models and the graph theory model, the method further includes: According to the flight parameters of the aircraft, an aircraft typical load mission profile is determined, wherein the flight parameters include a cruising altitude, a Mach number, an ambient air temperature, and a ram air temperature, and the aircraft typical load mission profile is used to represent changes in non-critical load power, critical load power, and total system power over time.

6. The method of claim 1, wherein, After the collaborative control of the power system and the thermal management system is performed in a manner of combining a model predictive control algorithm and a rule-based strategy based on the dynamic and steady-state models and the graph theory model, the method further includes: The differences in power system efficiency, temperature, and battery state of charge based on the model predictive control algorithm and the rule-based strategy are compared; According to the comparison result, an optimal energy management strategy is determined.

7. An electric-thermal coupling based energy optimization control device for an aircraft power system, characterized in that, The application of the aircraft power system energy optimization control method based on electro-thermal coupling as claimed in claim 1 comprises: A first establishment module is configured to establish dynamic and steady-state models and a graph theory model based on a coupling architecture of an aircraft power system and a thermal management system, wherein the coupling architecture of the aircraft power system and the thermal management system at least includes the power system and the thermal management system. A control module is configured to perform collaborative control of the power system and the thermal management system in a manner of combining a model predictive control algorithm and a rule-based strategy based on the dynamic and steady-state models and the graph theory model, wherein the rule-based strategy controls battery pack charging and discharging states, shieldable loads, and transformer operating states of the power system according to flight power requirements and power system efficiency, controls mass flow rates of cooling oil tank outlets and fuel and ram air heat exchange ratios of the thermal management system according to changes in power system components and cooling oil tank temperatures, and sets a multi-objective optimization function and corresponding constraint conditions based on the model predictive control algorithm and controls the battery pack charging and discharging states, the shieldable loads, and the transformer operating states of the power system by using a rolling optimization technique. The model predictive control algorithm is used to set a multi-objective optimization function and corresponding constraint conditions. A mathematical expression of the objective function is as follows: A mathematical expression of the constraint condition is as follows: in, J Let N represent the multi-objective cost function of model predictive control, where N represents the prediction time domain length of model predictive control, and k represents the index of the prediction step. The only continuous input of the power system controller is the battery charging and discharging rate, which ranges from -100 to 100. 1 represents the weight coefficient of the state tracking term; ||*||2 represents the L2 norm. -100≤u(k)≤100 2 indicates the weighting coefficient of the control input tracking item; The transformer switch is constrained to be binary: 4 represents the weighting coefficient of the converter power term; Indicates the prediction time domain; uB∈{0,1} The battery SOC is a dynamic variable in the power system controller, and its state constraint is as follows: 0≤x(k)≤100 The constraint of the transformer power and the source input power Pin is as follows: P1+P2+P3+P4+P5≤Pin ​ ​ ​ ​ P1, P2, P3, P4 and P5 represent the input power of five transformers respectively; Input variable inequality and equality constraints: g(k|t) ≥ 0, k = 0,..., Np-1 h(k|t) = 0, k = 0,..., Np-1.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program, when running, controls the device where the computer readable storage medium is located to execute the electric-thermal coupling based energy optimization control method of the aircraft power system according to any one of claims 1 to 6.

9. A processor, comprising: The processor is configured to run a program, wherein the program, when running, executes the electric-thermal coupling based energy optimization control method of the aircraft power system according to any one of claims 1 to 6.

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