Direct current small capacity microgrid fast control prototype system for aviation power system

Abstract

This invention discloses a prototype system for rapid control of a small-capacity DC microgrid in aerospace propulsion systems, relating to the field of aerospace propulsion energy system control. The system includes a power supply module, a load module, and a control module. The power supply module comprises a turbine generator, a power battery pack, and an auxiliary power supply. The load module includes an electrically driven fuel pump, an electric actuator, impact loads, and a fan / propeller system. The control module adopts a distributed and centralized collaborative control architecture, integrating a shaft power extraction model, a stability analysis module, a robustness optimization module, and an energy efficiency optimization module. This invention achieves decoupling between the load and engine speed through electrical energy conversion, significantly improving energy utilization efficiency over a wide operating range compared to traditional mechanical transmission. Combined with hardware hardening and algorithm parameter correction, it can operate stably in electromagnetic interference environments. A supporting FPGA+DSP heterogeneous verification platform enables full-condition simulation, control law optimization, and fault verification, providing a scalable and reusable prototype verification environment for aerospace propulsion electrification.

Description

Technical Field

[0001] This invention relates to the field of energy system control technology for aviation power systems, and specifically to a prototype system for rapid control of a small-capacity DC microgrid for aviation power systems. Background Technology

[0002] In traditional aviation power systems' fuel and control systems, the fuel pump is typically driven by the engine's high-pressure rotor via mechanical components such as gearboxes and drive shafts, directly converting mechanical energy into hydraulic energy. This mechanical transmission method has significant drawbacks: Firstly, the mechanical transmission chain includes more than 20 components such as gears, bearings, and couplings, leading to a 15%-20% increase in system volume and a 10%-15% increase in weight. Furthermore, gear meshing wear requires regular maintenance, increasing annual maintenance costs by 8%-12%. Secondly, the fuel pump speed is rigidly coupled with the engine rotor speed (the speed ratio is fixed at 0.8-1.2). When the engine switches between idle (3000 r / min), cruise (8000 r / min), and afterburner (12000 r / min) operating conditions, the fuel pump flow rate passively changes, requiring pressure adjustment via a throttle valve, resulting in a 5%-8% energy loss. In extreme cases, the delayed flow response can cause engine thrust fluctuations of ±3%.

[0003] With the increasing demands of high-speed combat aircraft for thrust-to-weight ratio, range, and multi-mission payload, traditional mechanical and hydraulic drive methods can no longer meet the energy management requirements under wide operating conditions. In existing technologies, Pratt & Whitney in the US attempted to use an electric-driven fuel pump on the F135 engine, but this relied on a single lithium battery, resulting in insufficient power density (≤2kW / kg) and slow dynamic response (response time ≥50ms). A DC microgrid solution proposed by a domestic research institute did not consider virtual impedance compensation, leading to a power allocation error of 10%-15% across multiple power sources. Furthermore, existing technologies lack a rapid prototyping platform integrating power simulation, load simulation, and real-time control, resulting in a verification cycle of 6-12 months for new control strategies, thus hindering the technological iteration speed of aviation power electrification. Summary of the Invention

[0004] The purpose of this invention is to provide a prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system, in order to solve the problems of low energy utilization efficiency and lack of control prototypes in the existing technology.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A prototype system for rapid control of a small-capacity DC microgrid in an aerospace propulsion system includes a power supply module, a load module, a control module, and a rapid control prototype verification platform. Power supply module: Includes at least a turbine generator (TEG), a power battery pack, and auxiliary power; the turbine generator serves as the primary energy source, configured with a shaft power extraction model based on the engine's full operating envelope parameters (speed n, pressure ratio π, temperature T), and outputs power... in: It is the shaft power output by the turbine; turbine ); It is the output electrical power of the generator; The efficiency of the generator is as follows: the power battery pack adopts the lithium iron phosphate system, supports 1C charge and discharge rate, and the SOC estimation error is ≤2%; the auxiliary power supply is a 5kW DC switching power supply, which provides basic power supply during engine start-up and low power conditions.

[0006] The load module includes at least an electrically driven fuel pump, an electric actuator, an impact load, and a fan / propeller system. The electrically driven fuel pump is the core load, driven by a 30kW permanent magnet synchronous motor, supporting stepless speed regulation from 0-3000r / min and a flow rate adjustment range of 0-50L / min. The electric actuator is a 5kW servo motor with a position control accuracy of ≤0.1°. The impact load consists of a supercapacitor energy storage generator, which can output 10-100kW pulse power within 0.1-1s.

[0007] Control module: It adopts distributed and centralized collaborative control. The core controller is an FPGA+DSP heterogeneous architecture (DSP main frequency 200MHz, FPGA logic unit 160K). It is equipped with a multi-power supply power allocation algorithm (based on the improved strategy of droop control, the corrected droop coefficient k=k+Z / U), a system stability analysis module (small signal model Δx'=A·Δx+B·Δu), a robustness optimization module (sliding surface s=ΔU+c·∫ΔUdt), and an energy efficiency optimization module (power prediction model P(t)=α·P(t-τ)+β·a(t)+γ·h(t)), with a control cycle ≤1ms.

[0008] Rapid control prototype verification platform: adopts FPGA+DSP heterogeneous computing architecture, integrating high voltage DC power supply simulator, electric actuator load simulation, impact load generator and real-time simulator (step size ≤100μs).

[0009] Furthermore, the power supply module, load module, and control module adopt an environmentally adaptable design: at the hardware level, wide-temperature components (-55℃~125℃), vibration-damping brackets (damping coefficient 0.3), and electromagnetic shielding boxes (shielding effectiveness ≥40dB) are selected; at the algorithm level, temperature correction formulas are used. and electromagnetic interference filter coefficient adjustment formula Enhance robustness to ensure parameter error ≤1% under extreme conditions.

[0010] The present invention has the following beneficial effects: The technical effects of this invention are achieved through a logical link of "architecture-algorithm-adaptation-verification": First, a three-level microgrid architecture of "power supply-load-control" is constructed to break the speed coupling limitation of traditional mechanical transmission through power conversion, realizing flexible decoupling between load and engine operating conditions; Second, the control module integrates improved power distribution, stability analysis, and robustness optimization algorithms, and improves the multi-power supply coordination accuracy and system dynamic stability performance through parameter correction and control strategy optimization; Third, the dual environmental adaptability design of hardware hardening and algorithm parameter compensation ensures reliable operation of the system in extreme aviation environments; Finally, relying on the FPGA+DSP heterogeneous rapid control prototype verification platform, full-condition simulation and control strategy iterative verification are realized, providing efficient support for the engineering application of the technology and forming a complete closed loop from technological innovation to practical verification. Attached Figure Description

[0011] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0012] Figure 2 This is a flowchart of the power extraction model.

[0013] Figure 3 This is a flowchart of the power distribution control process. Detailed Implementation

[0014] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0015] refer to Figure 1-3In this embodiment, the system hardware adopts a modular integrated design, with each module connected via a standard aviation connector to ensure convenient installation and maintenance. The specific configuration is as follows: Power supply module: The turbine generator is a 50kW permanent magnet synchronous generator (model: YKS50-4), whose efficiency curve maintains ≥95% within the speed range of 3000-15000r / min, adapting to engine speed variations under all operating conditions; the power battery pack uses a 20Ah / 400V lithium iron phosphate power battery pack (200 cells in series, single cell capacity 20Ah), supporting 1C charge / discharge rate, and equipped with a battery management system (BMS) to achieve accurate SOC estimation (error ≤2%) and overcharge / overdischarge protection; the auxiliary power supply is a 5kW DC switching power supply (input AC220V, output DC400V±2%), providing basic power supply to the system during engine start-up and low-power operation. Load module: The electric fuel pump uses a 30kW permanent magnet synchronous motor (model: 1LE0001-1CB23-3AA4) with a vector controller (carrier frequency 10kHz), supporting stepless speed regulation from 0-3000r / min to meet the fuel pump flow rate adjustment requirements of 0-50L / min; the electric actuator uses a 5kW servo motor (model: MHMD052P1U) with a servo driver (MADHT1507), with a position control accuracy of ≤0.1°; the impact load generator adopts a supercapacitor energy storage design, consisting of a 200F / 450V supercapacitor bank and a DC / DC converter, which can output 10-100kW pulse power within 0.1-1s to simulate the short-term high power requirements of radar and electronic warfare equipment. Control Module: The core controller adopts a heterogeneous architecture of TI's TMS320F28377D DSP (200MHz main frequency, floating-point operation capability) and Xilinx Kintex-7 FPGA (160K logic units). The DSP is responsible for complex logic operations such as power allocation algorithm and energy efficiency optimization model, while the FPGA is responsible for high-speed signal acquisition (sampling frequency 10kHz), PWM waveform generation (output frequency 20kHz), and fast fault signal response (response time ≤1μs). The controller is equipped with 16 analog inputs (range ±10V), 32 digital I / Os and 2 CAN bus interfaces to realize real-time communication with each module.

[0016] 29.2 Implementation of Key Algorithms 29.2.1 Shaft Power Extraction Model The shaft power extraction model is implemented through two steps: "offline fitting - online correction". In the offline fitting stage, 200 sets of test data were collected from the engine at speeds of 3000-12000 r / min and pressure ratios of 1.5-5.0. The turbine efficiency function η = 0.85 - 0.002n / 10000 + 0.03π (where n is in r / min and π is the pressure ratio) was fitted using the least squares method. The goodness of fit R0 was...2 ≥0.98; Simultaneously, establish a correlation model between gas mass flow rate m and intake pressure P and temperature T: m=0.002P / T (P is in kPa, T is in K). Online correction stage: The control module collects engine speed n, pressure ratio π and intake parameters (P=1MPa, T=300K) in real time, and calculates m=0.002×1000 / 300≈0.5kg / s; Combined with the turbine inlet and outlet temperature sensors collecting T=800℃ and T=400℃, and looking up the table, we get h=1200kJ / kg and h=800kJ / kg, respectively. Substituting into the formula P=η·m·(hh)=0.85×0.5×(1200-800)=170kW; Considering the generator efficiency η=0.95, the final generator output power is P=170×0.95=161.5kW, with a calculation period ≤1ms.

[0017] 29.2.2 Power Distribution Control The power distribution control adopts the strategy of "initial parameter setting - real-time dynamic adjustment": Initial parameter setting: The DC bus reference voltage U is set to 400V. The droop coefficient is allocated based on the capacity ratio of the power supply module. The rated power of TEG is 50kW, and the droop coefficient k=U / (1.2×P)=400 / (1.2×50000)=0.05V / W; the rated charging and discharging power of the power battery is 20kW, and the droop coefficient k=U / (1.2×P)=400 / (1.2×20000)=0.08V / W. Virtual Impedance Correction: The measured line impedance from the TEG to the busbar is 0.05Ω, and the line impedance from the power battery to the busbar is 0.1Ω. To compensate for the impedance difference, virtual impedances Z=0.1Ω and Z=0.15Ω are set. Substituting these values ​​into the correction formula k=k+Z / U, we get k=0.05+0.1 / 400=0.05025V / W and k=0.08+0.15 / 400=0.080375V / W. Real-time Control Effect: When the system load power suddenly increases to 60kW, the TEG output power is 48kW, and the power battery outputs 12kW, with a power distribution ratio of 4:1, consistent with the design ratio. The distribution error is ≤3%, and the busbar voltage fluctuation range is 396-402V, with a fluctuation amplitude ≤±1.5%.

[0018] 29.2.3 Energy Efficiency Optimization The energy efficiency optimization module achieves its goal through a closed-loop "prediction-control-feedback" mechanism: The operating condition prediction unit, based on flight trajectory data (acceleration a, altitude h) provided by the Flight Management System (FMS) and combined with a historical power database (storing power curves from nearly 100 flight missions), constructs a prediction model P(t) = α·P(t-τ) + β·a(t) + γ·h(t) using a multiple linear regression algorithm. Cross-validation determines the fitting coefficients α = 0.6, β = 0.2, and γ = 0.2, with a time delay τ = 0.5s and a prediction error ≤ 8%. When the flight acceleration a(t) = 2 m / s², the prediction is performed correctly. 2 When the altitude h(t) = 10000m (h(t) = 10 after standardization) and the historical power P(t-0.5) = 100kW, the predicted power P(t) = 0.6×100 + 0.2×2 + 0.2×10 = 62.4kW. Motor control optimization unit: The electric fuel pump motor adopts a vector control strategy with i = 0. The three-phase stator currents i, i, i are converted into two-phase stationary coordinate system currents i, i through Clark transformation, and then the d / q axis currents i, i are obtained through Park transformation. The PI regulator controls i = 0, and only i regulates the motor torque. When the number of pole pairs p = 4, the excitation flux linkage ψ = 0.1Wb, and the q-axis current i = 50A, the torque T = 1.5×4×0.1×50 = 30N·m, and the motor operating efficiency is improved by 6%-8% compared to V / F control. Feedback and adjustment: Real-time collection of motor speed, current, and fuel pump pressure and flow data, comparison with predicted values, and dynamic correction of prediction model coefficients to ensure continuous and stable optimization results.

[0019] 29.2.4 Environmental Adaptability Modification Environmental adaptability correction is ensured through a dual approach of hardware selection and algorithm compensation: Temperature correction: Key components are selected for their wide temperature range (-55℃ to 125℃), such as the sampling resistor, which is an alloy resistor (model: MRS25000C1004FP) with a temperature coefficient α = ±50ppm / ℃. At the algorithm level, a resistance value temperature correction model is established: R(T) = R·[1 + α·(T-25)]. When the reference resistance R = 100Ω and T = -55℃, R(-55) = 100 × [1 + 50 × 10 × (-55-25)] = 99.6Ω, resulting in a corrected current sampling error ≤1%. Electromagnetic interference correction: The control module uses a metal shielded box (shielding effectiveness ≥40dB), and input / output signals are routed using twisted-pair cables with shielding. Adaptive filtering is introduced into the algorithm, with a filter coefficient k = k·(1 + κ·E), where the reference filter coefficient k is determined to be 0.5 through electromagnetic compatibility (EMC) testing, and the interference coefficient κ = 0.01. When the electromagnetic interference field strength E=10V / m, k=0.5×(1+0.01×10)=0.55, and the signal-to-noise ratio is improved by more than 15dB after filtering. Vibration protection: The hardware module is fixed with a shock-absorbing bracket (damping coefficient 0.3), and the circuit board is treated with conformal coating. Under random vibration at 102000Hz (acceleration 20g), there is no component solder joint detachment and no packet loss in communication.

[0020] 29.3 System Testing and Verification A hardware-in-the-loop (HIL) test environment was built on a rapid control prototype verification platform. The dSPACE SCALEXIO real-time simulation system (simulation step size 50μs) was used as the host computer, and an NI PXI data acquisition card (sampling rate 1MS / s) was used to record test data. The following test items were carried out: Full-condition testing: Simulation models were used to simulate three typical operating conditions: engine idle (n=3000r / min, pressure ratio 1.8), cruising (n=8000r / min, pressure ratio 3.5), and afterburner (n=12000r / min, pressure ratio 5.0), each lasting 30 minutes. Test results showed that the TEG output power was 35kW, 80kW, and 120kW respectively, and the power battery charging and discharging power dynamically adjusted within the range of -5kW to 15kW, with a power distribution error ≤3%, meeting the design requirements.

[0021] Dynamic response test: Apply a step input to the flight control command (0→100% load power, a sudden increase from 30kW to 60kW) to test the dynamic characteristics of the bus voltage. The results show that the bus voltage drops from 400V to 392V, with a maximum overshoot of 2%, a convergence time of 45ms, no oscillation, and a dynamic response speed that is 40% faster than that of the traditional mechanical transmission system.

[0022] Abnormal operating condition test: Two fault scenarios were set: ① TEG power drops by 50% (from 80kW to 40kW), the system switches to power battery for charging within 8ms, the bus voltage drops to a minimum of 385V, and then recovers to 395V within 100ms; ② Load short circuit fault (short circuit current 200A), the controller triggers overcurrent protection within 1.5ms, disconnects the faulty load, and the bus voltage does not collapse.

[0023] Environmental testing: The system was placed in a high and low temperature test chamber (model: GDW-1000) and a vibration test bench (model: SD-100) and operated continuously for 4 hours under high temperature conditions of -55℃ and 125℃ and random vibration of 1000Hz (acceleration 15g). During the test, communication between all units was normal, the BMS SOC estimation error was ≤3%, the motor speed fluctuation was ≤2%, and there were no hardware failures.

[0024] Energy efficiency comparison test: Compared with the traditional mechanical transmission fuel system, under the same flight mission profile (takeoff-cruise-landing), the energy utilization efficiency of this system reaches 88%, which is 13% higher than the traditional system (75%), and fuel consumption is reduced by 12%.

[0025] Full-condition test: Power distribution error ≤3% under engine idle (n=3000r / min), cruise (n=8000r / min), and afterburner (n=12000r / min) conditions.

[0026] Dynamic response test: When the flight control command is stepped (0→100%), the bus voltage fluctuation range is 392-408V (≤±2%), and the convergence time is ≤50ms.

[0027] Abnormal operating condition test: Simulate TEG fault (power drop of 50%), the system switches to power battery, switching time is 8ms; simulate load short circuit, protection response time ≤2ms.

[0028] Environmental testing: The system ran continuously for 4 hours under conditions of -55℃, 125℃ and 1000Hz vibration, and no abnormalities were found in any of the performance indicators.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system, characterized in that, The system includes a power module comprising at least a turbine generator, a power battery pack, and an auxiliary power supply. The turbine generator serves as the primary energy source, with its output power dynamically varying according to engine operating conditions. The power battery pack is used for energy storage, buffering, smoothing power fluctuations, and providing emergency power. The auxiliary power supply is used for supplementary power supply under low-power conditions and for system startup. The load module includes at least an electrically driven fuel pump, an electric actuator, an impact mission payload, and a fan / propeller system. The electrically driven fuel pump is the core load, and its power requirements are strongly correlated with flight conditions. The electric actuator is an intermittent load. The impact mission payload is a short-duration high-power load. Control module: Adopting a distributed and centralized collaborative control mode, it connects the power supply module and the load module to achieve dynamic power balance and stable system operation under all operating conditions.

2. The prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system according to claim 1, characterized in that, The turbine generator is equipped with a shaft power extraction model, which is constructed based on the engine's full operating envelope parameters, including speed n, pressure ratio π, and temperature T. In the shaft power extraction model, the expression for the turbine output shaft power P is: Where η is the turbine efficiency, and η = f(n, π), f(n, π) is a function obtained by fitting engine test data, m is the gas mass flow rate, which is calculated from the engine intake parameters, h1 is the turbine inlet gas specific enthalpy, and h2 is the turbine outlet gas specific enthalpy, which is obtained from a table based on the temperature T.

3. The prototype system for rapid control of aero-engine systems using a small-capacity DC microgrid as described in claim 2, characterized in that, The generator output power P satisfies: in: It is the shaft power output by the turbine; It is the output electrical power of the generator; It refers to the generator's efficiency.

4. The prototype system for rapid control of aerospace power systems using a small-capacity DC microgrids according to claim 1, characterized in that, The control module is equipped with a multi-power supply power distribution control algorithm, which is an improved strategy based on droop control, including a basic droop control formula and a virtual impedance correction strategy. The basic droop control formula is as follows: For turbine generators: For power battery packs: in, It is the DC bus reference voltage. It is the droop factor of the TEG cell. It is the sag coefficient of the power battery cell. It is the output power of TEG. It refers to the charging and discharging power of the power battery, with discharging being positive and charging being negative.

5. The prototype system for rapid control of aero-engine systems using a small-capacity DC microgrid as described in claim 4, characterized in that, In the virtual impedance correction strategy, the corrected droop coefficient k satisfies in: It is the initial droop factor of the power module, the reference value before virtual impedance correction; It is the droop factor after virtual impedance correction; Z is the virtual impedance of the i-th power supply module; U is the reference voltage of the DC bus, used to compensate for line impedance differences and ensure that the power distribution accuracy of different power modules is ≤5%.

6. The prototype system for rapid control of aero-engine systems using DC small-capacity microgrids according to claim 1, characterized in that, The control module is also equipped with a system stability analysis module, which determines stability by establishing a system small-signal model. The state equation of the small-signal model of the system is: Δx' = A·Δx + B·Δu; Δy = C·Δx + D·Δu; Wherein, Δx is the increment of the state variable, which includes the DC bus voltage U, the SOC of the power battery, and the output current of each power supply; A is the system matrix, and the system stability is determined by judging whether the real part of the eigenvalue λ(A) is less than 0; B is the input matrix, and Δu is the increment of the input variable; C is the output matrix, and Δy is the increment of the output variable; D is the direct transfer matrix.

7. The prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system according to claim 1, characterized in that, The control module is also equipped with a robustness optimization module, which adopts sliding mode variable structure control. The sliding surface s satisfies: s = ΔU + c·∫ΔUdt, where c is a positive feedback coefficient, which is used to ensure that the system converges to a stable state quickly under parameter perturbation.

8. The prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system according to claim 1, characterized in that, It also includes a rapid control prototype verification platform, which adopts an FPGA+DSP heterogeneous computing architecture and integrates a high-voltage DC power supply simulator, an electric actuator load simulator, an impact load generator, and a real-time simulator. The high-voltage DC power supply simulator outputs an adjustable voltage of 0-500V to simulate the characteristics of TEG and power batteries; The electric actuator load simulation uses a permanent magnet synchronous motor and supports torque / speed mode switching; The impact load generator outputs 10-100kW pulse power for a short time to simulate mission load; The real-time simulator has a step size of ≤100μs and is used to run the system dynamics model.

9. The prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system according to claim 1, characterized in that, The control module is also equipped with an energy efficiency optimization module, which includes a condition prediction unit and a motor control optimization unit. The condition prediction unit constructs a power demand prediction model based on flight trajectory parameters, expressed as follows: P(t) = α·P(t-τ) + β·a(t) + γ·h(t); Where P(t) is the predicted power at time t; P(t-τ) represents the historical power at time τ; a(t) is the flight acceleration; h(t) is the flight altitude, and α, β, and γ are the fitting coefficients; The motor control optimization unit adopts a vector control strategy, and the stator current decomposition expression is as follows: ； in It is the stator current, a complex vector. It is the d-axis current. It is the q-axis current; Torque formula: ; in, This is the q-axis current, and p is the number of pole pairs of the motor. For excitation flux linkage.

10. The prototype system for rapid control of a small-capacity DC microgrid in an aerospace power system according to claim 1, characterized in that, The power module, load module, and control module are all designed to be environmentally adaptable, including hardware hardening measures and algorithm robustness enhancement. The hardware hardening measures employ wide-temperature components, vibration-resistant structural design, and electromagnetic shielding packaging. The robustness enhancement of the algorithm is achieved through an environmental parameter correction algorithm, with the temperature correction formula being: It is the reference resistor at 25℃. It is the resistance value at temperature T; Electromagnetic interference filter coefficient adjustment formula: in, : Reference filter coefficients under interference-free operating conditions at 25℃; k: Adjusted filter coefficient under electromagnetic interference environment; Interference coefficient; E: Interference field strength; and verified by system performance testing under high and low temperature (-55℃~125℃), random vibration (102000Hz), and electromagnetic interference (10kHz~1GHz) scenarios.

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