A power mode switching system for an electric aircraft

Abstract

This invention relates to the field of power control technology for electric aircraft and discloses a power mode switching system for electric aircraft, comprising: a power acquisition module for acquiring full-dimensional state parameters, flight condition parameters, and fault signals from multiple power sources; and a data preprocessing and fusion module for performing noise reduction, calibration, and multi-source fusion analysis on the parameters to construct a unified "power-condition" data model and predict load power changes and the upper limit of power output within the next 1-3 seconds. It possesses advantages such as proactive predictive switching of power modes, optimal energy efficiency under all operating conditions, and rapid fault redundancy replenishment, improving the flight safety, endurance, and power supply reliability of electric flight platforms. It solves the problems of passive switching, poor multi-source coordination, insufficient fault redundancy, and lack of cross-system coordination in existing electric flight power mode switching systems.

Description

Technical Field

[0001] This invention relates to the field of power control technology for electric aircraft, specifically a power mode switching system for electric aircraft. Background Technology

[0002] With the rapid development of aviation electrification technology, electric flight platforms (such as electric drones, electric light aircraft, and multi-electric aircraft) have been widely used in civilian and military fields due to their advantages of being environmentally friendly, efficient, and low-noise. These platforms typically employ a multi-source power supply mode, with common power sources including fuel cells, lithium batteries, photovoltaic auxiliary power supplies, and ground-based static variable power supplies. Different power sources have different performance characteristics: fuel cells have strong endurance but slow transient response; lithium batteries have fast transient response but limited endurance; photovoltaic auxiliary power supplies can provide energy replenishment but are greatly affected by the environment; and ground-based static variable power supplies are suitable for ground maintenance and energy replenishment scenarios.

[0003] The power mode switching system is a core component for the stable operation of an electric flight platform, and its performance directly determines flight safety, range, and power supply reliability. Currently, existing electric flight power mode switching technologies suffer from the following key challenges: The switching logic is passive and the response delay is high: Existing technologies mostly use "fault triggering" or "fixed threshold triggering" (such as switching to backup power when the battery SOC is lower than a certain fixed value), without combining dynamic changes in flight conditions for prediction. When the flight attitude and load demand change suddenly, switching delays are likely to occur, leading to power interruption or power overload failure, which seriously affects flight safety. Poor multi-source coordination and high energy consumption: Most switching systems only realize simple parallel connection or backup switching of dual power supplies, without considering the energy efficiency differences of each power supply, and cannot dynamically allocate the output power of each power supply. There are obvious switching losses and voltage surge losses during the switching process, resulting in low overall energy efficiency and limiting the range of electric flight platforms. Insufficient fault redundancy and poor reliability: Existing systems often separate the switching function from fault diagnosis and redundancy backup design, resulting in long fault detection response time and large redundancy switching delay. When a power supply or switching link fails, redundancy replacement cannot be quickly achieved, which can easily lead to power outages. Insufficient cross-system coordination and limited adaptability: The power switching system works independently from the avionics system and flight control system, only realizing simple power supply and data transmission. The power switching strategy is not deeply bound to the flight mission and flight conditions, and it cannot adapt to the power supply requirements of different flight scenarios, resulting in poor adaptability.

[0004] Therefore, we propose a power mode switching system for electric aircraft to solve the above problems. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a power mode switching system for electric aircraft, which features proactive predictive switching of power modes, optimal energy efficiency under all operating conditions, and rapid fault redundancy replacement. This system improves the flight safety, endurance, and power supply reliability of electric flight platforms, and solves the problems of passive switching, poor multi-source coordination, insufficient fault redundancy, and lack of cross-system coordination in existing electric flight power mode switching systems.

[0006] (II) Technical Solution To achieve the goals of proactive predictive switching of power modes, optimal energy efficiency under all operating conditions, and rapid redundancy replacement in case of faults, thereby improving the flight safety, endurance, and power supply reliability of electric flight platforms, this invention provides the following technical solution: A power mode switching system for electric aircraft, comprising: The power acquisition module is used to collect full-dimensional status parameters, flight condition parameters, and fault signals from multiple power sources. The data preprocessing and fusion module is used to perform noise reduction, calibration and multi-source fusion analysis on the parameters, construct a unified "power supply-operating condition" data model, and predict the load power change and the upper limit of the power supply's output power in the next 1-3 seconds. The core decision-making module adopts a dual-redundancy architecture of a main controller and a redundant controller, and is equipped with a predictive switching decision algorithm and multi-source collaborative power allocation logic. The predictive switching decision algorithm is configured to: based on the "power supply-operating condition" data model, combined with the flight attitude, speed and load demand change trends, actively predict the timing of switching and trigger the switching command, rather than relying solely on faults or fixed thresholds; the multi-source collaborative power allocation logic is configured to: dynamically allocate the output power of each power supply according to the energy efficiency ratio, loss value and health status of each power supply, to achieve optimal energy consumption under all operating conditions. The mode switching execution module, including a multi-source power switching link built on SiCMOSFET power switching devices and a high-frequency isolation transformer, is configured to use voltage pre-synchronization and inrush current suppression technology to achieve seamless switching between the multi-source power sources. It also includes a monitoring and interaction module, used to realize visual monitoring of status, fault alarms, and linkage with airborne avionics and flight control systems; The core decision module also integrates fault redundancy decision logic, which is configured to: quickly determine the fault level when a power supply or switching link fault is detected, automatically trigger the redundancy switching strategy, and realize the integrated closed-loop control of "fault isolation-redundancy switching-filling". Furthermore, the system constructs a cross-system collaborative control architecture of "avionics-power supply" to deeply bind the power switching strategy with the flight mission and adaptively adjust the power mode according to the flight status.

[0007] As a further optimization of the present invention, the power acquisition module adopts a MEMS sensor array integrated design. The multi-source power sources include fuel cells, lithium batteries, photovoltaic auxiliary power sources, and ground-based static power sources. The full-dimensional state parameters include the output voltage, current, hydrogen utilization rate, and stack temperature of the fuel cells; the SOC, SOH, single cell voltage, charge / discharge current, and cell temperature of the lithium batteries; the output power and light intensity of the photovoltaic auxiliary power sources; and the input voltage, output power, and frequency of the ground-based static power sources.

[0008] As a further optimization of the present invention, the flight operating parameters include flight attitude, flight speed, flight altitude, load requirements, and environmental parameters. The flight attitude includes pitch angle and roll angle. The load requirements include propulsion motor power, avionics power consumption, and environmental control system load. The environmental parameters include temperature, humidity, and air pressure. The fault signals include overcurrent, overvoltage, overtemperature, undervoltage, and insulation fault signals. The power acquisition module integrates an FPGA parallel detection circuit, and the fault detection response time is ≤1μs.

[0009] As a further optimization of the present invention, the data preprocessing and fusion module uses the Kalman filter algorithm to eliminate sensor acquisition noise, uses the temperature compensation algorithm to calibrate the temperature drift of voltage and current parameters and remove abnormal data, and uses a multi-source data fusion algorithm to couple power state parameters and flight condition parameters to construct a power efficiency model, a load demand prediction model and a fault risk assessment model.

[0010] As a further optimization of the present invention, the predictive switching decision algorithm is based on the improved NSGA-II multi-objective genetic algorithm, with the three-dimensional optimization objectives of minimizing switching delay, minimizing energy consumption, and minimizing fault risk. Under normal operating conditions, the switching delay is ≤10ms, and under emergency operating conditions, the switching delay is ≤5ms. The multi-source collaborative power allocation logic is adapted to different flight conditions. During the cruise phase, photovoltaic auxiliary power and fuel cell collaborative power supply are given priority. During the takeoff and climb phases, lithium battery and fuel cell collaborative power supply are used. During the ground maintenance phase, the power supply is switched to ground static variable power supply and the energy storage power supply is replenished. The energy efficiency under all operating conditions is ≥94%.

[0011] As a further optimization of the present invention, the main controller of the core decision module adopts an aerospace-grade ARM Cortex-A53 processor, and the redundant controller adopts an aerospace-grade Xilinx Spartan-6 FPGA chip; the redundancy design corresponding to the fault redundancy decision logic runs through all modules, the power acquisition module adopts dual sensor backup, the mode switching execution module adopts multi-switching link redundancy, the redundancy switching time is ≤10ms, and the switching success rate is ≥99.9%.

[0012] As a further optimization of the present invention, the switching time of the SiCMOSFET power switch module of the mode switching execution module is ≤100ns, which reduces the switching loss by more than 30% compared with the traditional IGBT switch module; the voltage pre-synchronization and inrush current suppression technology ensures that the output voltage fluctuation is ≤5% and the inrush current is ≤1.2 times the rated current during switching; the switching link adopts a modular redundancy design, with each power supply branch equipped with an independent switching module.

[0013] As a further optimization of the present invention, the monitoring and interaction module supports airborne local monitoring and ground remote monitoring, has audible and visual alarm and remote alarm functions, can record fault details for fault diagnosis, supports switching between manual and automatic switching modes, allows operators to set switching parameters, and is linked with the avionics system and flight control system, and can synchronously adjust the power switching strategy when flight conditions change suddenly.

[0014] As a further optimization of the present invention, the power acquisition module adopts an AD8418 voltage and current sensor and an ADT7320 temperature sensor, combined with an ADR4540 reference voltage source to achieve an accuracy calibration of ±0.5%, and a sampling frequency of ≥10kHz; the PMBus ring redundant communication bus supports 1kHz real-time communication and uses CRC-16 check to ensure data integrity.

[0015] As a further optimization of the present invention, the system is compatible with electric unmanned aerial vehicles, electric light aircraft, multi-electric aircraft and electric vertical take-off and landing aircraft, with an input voltage range of 24V-400V, a maximum output power of 0-100kW which can be expanded to the megawatt level, an operating temperature range of -55℃ to 200℃, meets the DO-160G aviation environmental standard, and has a mean time between failures (MTBF) ≥2000 hours.

[0016] (III) Beneficial Effects Compared with the prior art, the present invention provides a power mode switching system for electric aircraft, which has the following advantages: 1. Achieve predictive proactive switching to reduce flight safety risks: This invention uses a predictive switching decision algorithm to deeply couple avionics flight parameters with power status parameters, proactively predict load fluctuations caused by changes in flight operating conditions, and trigger power mode switching in advance. The switching delay for normal operating conditions is ≤10ms, and for emergency operating conditions it is ≤5ms, which completely solves the response delay and power interruption problems caused by traditional passive switching and improves flight safety. 2. Optimal energy efficiency through multi-source collaboration, extending the driving range: Through multi-source collaborative power allocation logic, the output power is dynamically allocated according to the energy efficiency ratio and loss value of each power source. Combined with switching loss compensation technology, the energy efficiency under all operating conditions is ≥94%, and the switching loss is reduced by more than 30% compared with traditional systems, effectively extending the driving range of the electric flight platform. 3. Integrated fault redundancy to improve power supply reliability: The full-module redundancy design is adopted, with a fault detection response time of ≤1μs, a redundancy switching time of ≤10ms, and a switching success rate of ≥99.9%. It realizes integrated closed-loop control of "fault isolation-redundancy switching-filling" to avoid power outages caused by faults and meet aviation-grade reliability requirements. 4. Deep cross-system collaboration and wide adaptability: Construct a cross-system collaborative architecture of "avionics-power supply", deeply bind the power switching strategy with flight mission and operating conditions, and can adapt to different scenarios such as takeoff, cruise, climb and ground maintenance, adapt to various electric flight platforms, and can be expanded to adapt to more power types. 5. High level of intelligence and low operation and maintenance cost: It realizes power mode self-adaptation, fault self-diagnosis and redundancy self-completion, supports local and remote monitoring, and does not require frequent manual intervention, reducing operation and maintenance costs. At the same time, it has a manual switching mode to deal with emergency scenarios and improve the system's practicality. Attached Figure Description

[0017] Figure 1 This is a block diagram of the overall architecture of the power mode switching system of the electric aircraft of the present invention; Figure 2 This is a schematic diagram of the power acquisition module of the present invention; Figure 3 This is a flowchart of the control logic of the core decision-making module of the present invention; Figure 4 This is a schematic diagram of the switching link of the mode switching execution module of the present invention; Figure 5 This is a flowchart illustrating the fault diagnosis and redundancy compensation process of this invention.

[0018] In the diagram: 1-Power Acquisition Module, 2-Data Preprocessing and Fusion Module, 3-Core Decision Module, 4-Mode Switching Execution Module, 5-Monitoring and Interaction Module, 6-PMBus Ring Redundant Communication Bus, 11-MEMS Sensor Array, 12-FPGA Parallel Detection Circuit, 31-Main Controller, 32-Redundant Controller, 33-Predictive Switching Decision Algorithm Module, 34-Multi-Source Cooperative Power Allocation Logic Module, 35-Fault Redundancy Decision Logic Module, 41-SiC MOSFET Power Switch Module, 42-High Frequency Isolation Transformer, 43-Switching Link Redundancy Unit. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the power mode switching system of the electric aircraft of the present invention includes a power acquisition module 1, a data preprocessing and fusion module 2, a core decision module 3, a mode switching execution module 4, and a monitoring and interaction module 5. Each module realizes data interaction and command transmission through a PMBus ring redundant communication bus 6 to ensure the real-time performance and integrity of communication. The PMBus ring redundant communication bus supports 1kHz real-time communication and adopts CRC-16 check mode to avoid data loss or misjudgment during data transmission.

[0021] like Figure 2 As shown, the power acquisition module 1 adopts an integrated design of MEMS sensor array 11 to adapt to the needs of multi-source power acquisition. The specific acquisition objects include fuel cells, lithium batteries, photovoltaic auxiliary power sources and ground static power sources. The acquired parameters cover the full-dimensional state parameters, flight condition parameters and fault signals of each power source.

[0022] Specifically, the power status acquisition uses AD8418 voltage and current sensors and ADT7320 temperature sensors, combined with an ADR4540 reference voltage source to achieve ±0.5% accuracy calibration. The sampling frequency is set to 10kHz. Among them, the fuel cell acquisition parameters include output voltage (0-500V), current (0-50A), hydrogen utilization rate (0-100%), and stack temperature (-55℃ to 200℃); the lithium battery acquisition parameters include SOC (0-100%), SOH (0-100%), single cell voltage (2.5V-4.2V), charge and discharge current (0-30A), and cell temperature (-55℃ to 200℃); the photovoltaic auxiliary power acquisition parameters include output power (0-10kW) and illuminance (0-1000W / m²); and the ground static power acquisition parameters include input voltage (220V / 380V), output power (0-50kW), and frequency (50Hz±1Hz).

[0023] Flight condition data is collected in conjunction with the airborne avionics and flight control systems. Using BeiDou navigation and positioning and an IMU attitude sensing module, the system collects flight attitude (pitch angle -90° to 90°, roll angle -180° to 180°), flight speed (0-200km / h), flight altitude (0-10000m), load requirements (propulsion motor power 0-100kW, avionics power consumption 0-5kW, environmental control system load 0-2kW), and environmental parameters (temperature -55℃ to 200℃, humidity 0-100%RH, air pressure 50kPa-150kPa). The data update cycle is set to 1ms to ensure the real-time performance of the data.

[0024] The fault signal acquisition integrates an FPGA parallel detection circuit 12 to monitor fault states such as overcurrent (≥60A), overvoltage (≥550V), overtemperature (≥210℃), undervoltage (≤20V), and insulation fault (insulation resistance ≤1MΩ) in real time. The fault detection response time is ≤1μs. Once a fault signal is detected, it is immediately transmitted to the data preprocessing and fusion module, and a local preliminary alarm is triggered at the same time.

[0025] The data preprocessing and fusion module 2 receives the raw data transmitted from the power acquisition module 1. First, it uses a Kalman filter algorithm to eliminate sensor acquisition noise, with the filter coefficient set to 0.01, effectively filtering out high-frequency interference signals. Then, it uses a temperature compensation algorithm to calibrate the temperature drift of voltage and current parameters. The compensation formula is: U compensation = U acquisition × [1 + α × (T actual - T standard)], where α is the temperature coefficient (taken as 0.001 / ℃), and T standard is 25℃. At the same time, abnormal data (such as parameter values ​​that suddenly exceed the normal range of ±10%) are removed to ensure data accuracy.

[0026] A multi-source data fusion algorithm is employed to couple and analyze power state parameters with flight condition parameters, constructing three major models: a power efficiency model (efficiency ratio = output power / input power), a load demand prediction model (based on an LSTM neural network, predicting load power changes in the next 1-3 seconds), and a fault risk assessment model (assessing fault levels based on fault signal strength and parameter deviation). For example, by using flight speed and pitch angle change rate, it predicts that the load power will increase from 15kW to 35kW within the next 2 seconds. Simultaneously, by combining lithium battery SOC (85%) and fuel cell stack temperature (80℃), it predicts that the upper limit of output power for both the lithium battery and the fuel cell is 20kW, providing accurate data support for the core decision-making module.

[0027] Data storage uses an onboard solid-state storage module with a storage cycle of 72 hours. It stores key data such as power status, switching records, and fault information in real time, and feeds back the pre-processed data to the core decision-making module, forming a data closed loop of "acquisition-processing-feedback".

[0028] like Figure 3 As shown, the core decision module 3 adopts a dual-redundancy architecture with a main controller 31 and a redundant controller 32. The main controller uses an aerospace-grade ARM Cortex-A53 processor (1.2GHz, supporting multi-threaded processing), and the redundant controller uses an aerospace-grade Xilinx Spartan-6 FPGA chip (strong parallel processing capability and fast response speed). The two synchronize data in real time. When the main controller fails, the redundant controller takes over the decision-making function within 10ms to ensure that the decision-making is not interrupted.

[0029] The predictive switching decision algorithm module 33 adopts an improved NSGA-II multi-objective genetic algorithm, with the three-dimensional optimization objectives of "minimum switching delay, minimum energy consumption, and minimum fault risk". The algorithm iteration count is set to 100 times, with a crossover probability of 0.8 and a mutation probability of 0.05. Through the "power supply-operating condition" data model output by the fusion layer, it actively predicts the switching timing. For example, when the flight control system reports "approaching the climb phase", the algorithm predicts that the load power will increase from 15kW to 35kW based on parameters such as flight speed (80km / h) and pitch angle (15°). At this time, it triggers the "lithium battery + fuel cell" collaborative power supply mode in advance to avoid overload of a single power source, and the switching delay is controlled within 8ms.

[0030] The multi-source collaborative power allocation logic module 34 dynamically allocates output power based on the energy efficiency ratio, loss value, and health status of each power source: During the cruise phase (flight speed 60-100km / h, load power 10-20kW), photovoltaic auxiliary power (energy efficiency ratio ≥80%) and fuel cell (energy efficiency ratio ≥90%) are prioritized for collaborative power supply, while the lithium battery is in float charging state (SOC maintained at 80%-90%); During takeoff and climb phases (load power 25-100kW), lithium battery (fast transient response) and fuel cell are used for collaborative power supply, with lithium battery output power accounting for 50%-60% and fuel cell output power accounting for 40%-50%, maximizing output power; During ground maintenance phases, the power supply is switched to ground static power supply, while simultaneously replenishing energy for lithium battery and fuel cell, with lithium battery charging current controlled at 5A and fuel cell hydrogen replenishment rate controlled at 0.5L / min, maintaining an overall energy efficiency of over 94%.

[0031] The fault redundancy decision logic module 35 integrates a fault level determination unit, classifying faults into general faults, severe faults, and emergency faults: For general faults (such as a single sensor failure), the backup sensor is activated, and parameters are compensated through data fusion algorithms; for severe faults (such as a power supply branch failure), the faulty branch is immediately isolated, the backup power supply is switched, and the power distribution strategy is adjusted; for emergency faults (such as a main controller failure), the lithium battery emergency power supply mode is switched to prioritize the power supply to the avionics and flight control systems, while triggering a ground alarm. The redundancy switching time is ≤10ms, and the switching success rate is ≥99.9%.

[0032] like Figure 4As shown, the mode switching execution module 4 includes a SiC MOSFET power switch module 41, a high-frequency isolation transformer 42, and a switching link redundancy unit 43. The SiC MOSFET power switch module uses a device with model number C2M0025120D (rated voltage 1200V, rated current 25A), with a switching time ≤100ns. Compared with traditional IGBT switch modules, the switching loss is reduced by 35%. Each power supply branch is equipped with an independent switching module, and the failure of a single module does not affect the overall switching function.

[0033] The high-frequency isolation transformer 42 operates at a frequency of 300kHz, with an isolation voltage ≥2kV. It is small in size and has low losses, and is used to achieve electrical isolation between multiple power sources to avoid mutual interference. Employing voltage pre-synchronization technology and inrush current suppression algorithms, it synchronizes the voltage, frequency, and phase of the power source to be switched in real time during the switching process, with a synchronization error ≤0.5%. This ensures that the output voltage fluctuation during switching is ≤5%, the inrush current is ≤1.2 times the rated current, and the switching delay is ≤10ms, meeting the stringent requirements for power supply continuity in electric flight propulsion systems and avionics systems.

[0034] The switching link redundancy unit 43 sets up a backup switching link, which works in parallel with the main switching link and monitors the status of the main link in real time. When the main link fails, it automatically switches to the backup link with a switching time of ≤5ms. At the same time, it feeds back the switching status to the core decision module to realize closed-loop control of the switching action.

[0035] The monitoring and interaction module 5 includes an airborne monitoring unit and a ground remote monitoring unit. The airborne monitoring unit uses an OLED visual display screen to display the status of multiple power sources, switching modes, load power distribution, fault information, etc. in real time, and supports touch operation. The ground remote monitoring unit is linked with the airborne system through a wireless communication module (communication distance ≥10km) to realize remote status monitoring and command issuance.

[0036] The fault alarm function combines airborne audible and visual alarms (alarm sound level ≥85dB, alarm light flashing red) with ground remote alarms (SMS + platform pop-up). When an anomaly is detected, the alarm is triggered immediately, and fault details (fault type, occurrence time, parameter status) are recorded to support fault troubleshooting.

[0037] It supports switching between manual and automatic switching modes. Manual switching mode is used in emergency scenarios (such as in case of automatic switching failure), where operators can issue switching commands via onboard buttons or a ground control platform. Automatic switching mode is the default mode; the system automatically switches based on flight conditions and power status, while also allowing operators to set switching parameters (such as lithium battery SOC threshold and power allocation ratio). Furthermore, it integrates with the avionics and flight control systems to synchronously adjust the power switching strategy when sudden changes in flight conditions occur (such as sudden turbulence causing abnormal attitude), ensuring stable power supply.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power mode switching system for an electric aircraft, characterized in that, include: The power acquisition module is used to collect full-dimensional status parameters, flight condition parameters, and fault signals from multiple power sources. The data preprocessing and fusion module is used to perform noise reduction, calibration and multi-source fusion analysis on the parameters, construct a unified "power supply-operating condition" data model, and predict the load power change and the upper limit of the power supply's output power in the next 1-3 seconds. The core decision-making module adopts a dual-redundancy architecture of a main controller and a redundant controller, and is equipped with a predictive switching decision algorithm and multi-source collaborative power allocation logic. The predictive switching decision algorithm is configured to: based on the "power supply-operating condition" data model, combined with the flight attitude, speed and load demand change trends, actively predict the timing of switching and trigger the switching command, rather than relying solely on faults or fixed thresholds; the multi-source collaborative power allocation logic is configured to: dynamically allocate the output power of each power supply according to the energy efficiency ratio, loss value and health status of each power supply, to achieve optimal energy consumption under all operating conditions. The mode switching execution module, including a multi-source power switching link built on SiCMOSFET power switching devices and a high-frequency isolation transformer, is configured to use voltage pre-synchronization and inrush current suppression technology to achieve seamless switching between the multi-source power sources. It also includes a monitoring and interaction module, used to realize visual monitoring of status, fault alarms, and linkage with airborne avionics and flight control systems; The core decision module also integrates fault redundancy decision logic, which is configured to: quickly determine the fault level when a power supply or switching link fault is detected, automatically trigger the redundancy switching strategy, and realize the integrated closed-loop control of "fault isolation-redundancy switching-filling". Furthermore, the system constructs a cross-system collaborative control architecture of "avionics-power supply" to deeply bind the power switching strategy with the flight mission and adaptively adjust the power mode according to the flight status.

2. The system according to claim 1, characterized in that, The power acquisition module adopts a MEMS sensor array integrated design. The multi-source power sources include fuel cells, lithium batteries, photovoltaic auxiliary power sources, and ground-based static power sources. The full-dimensional state parameters include the output voltage, current, hydrogen utilization rate, and stack temperature of the fuel cells; the SOC, SOH, single cell voltage, charge and discharge current, and cell temperature of the lithium batteries; the output power and light intensity of the photovoltaic auxiliary power sources; and the input voltage, output power, and frequency of the ground-based static power sources.

3. The system according to claim 1, characterized in that, The flight operating parameters include flight attitude, flight speed, flight altitude, load requirements, and environmental parameters. The flight attitude includes pitch angle and roll angle. The load requirements include propulsion motor power, avionics power consumption, and environmental control system load. The environmental parameters include temperature, humidity, and air pressure. The fault signals include overcurrent, overvoltage, overtemperature, undervoltage, and insulation fault signals.

4. The system according to claim 1, characterized in that, The data preprocessing and fusion module uses the Kalman filter algorithm to eliminate sensor acquisition noise, uses the temperature compensation algorithm to calibrate the temperature drift of voltage and current parameters and remove abnormal data, and uses a multi-source data fusion algorithm to couple power state parameters and flight condition parameters to construct a power efficiency model, a load demand prediction model and a fault risk assessment model.

5. The system according to claim 1, characterized in that, The predictive switching decision algorithm is based on the improved NSGA-II multi-objective genetic algorithm, which optimizes switching delay, energy consumption, and fault risk. The multi-source collaborative power allocation logic adapts to different flight conditions. During the cruise phase, photovoltaic auxiliary power and fuel cell power are used in combination. During the takeoff and climb phases, lithium battery and fuel cell power are used in combination. During the ground maintenance phase, the power is switched to ground static variable power supply and the energy storage power supply is replenished.

6. The system according to claim 1, characterized in that, The main controller of the core decision module uses an aerospace-grade ARM Cortex-A53 processor, and the redundant controller uses an aerospace-grade Xilinx Spartan-6 FPGA chip. The redundancy design corresponding to the fault redundancy decision logic runs through all modules. The power acquisition module adopts dual sensor backup, and the mode switching execution module adopts multi-switching link redundancy.

7. The system according to claim 1, characterized in that, The switching time of the SiCMOSFET power switch module of the mode switching execution module is ≤100ns; the voltage pre-synchronization and inrush current suppression technology ensure that the output voltage fluctuation is ≤5% and the inrush current is ≤1.2 times the rated current during switching; the switching link adopts a modular redundancy design, and each power supply branch is equipped with an independent switching module.

8. The system according to claim 1, characterized in that, The monitoring and interaction module is used for airborne local monitoring and ground remote monitoring. It can record fault details for fault diagnosis through audible and visual alarms and remote alarms. It can also switch between manual and automatic modes, allowing operators to set switching parameters. It is also linked with the avionics system and flight control system, and can synchronously adjust the power switching strategy when flight conditions change suddenly.

9. The system according to claim 1, characterized in that, The power acquisition module uses an AD8418 voltage and current sensor, an ADT7320 temperature sensor, and an ADR4540 reference voltage source; the PMBus ring redundant communication bus uses CRC-16 verification.

10. The system according to claim 1, characterized in that, The system is compatible with electric unmanned aerial vehicles, electric light aircraft, multi-electric aircraft, and electric vertical take-off and landing aircraft.

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