Automatic unmanned aerial vehicle transportation system based on solar power generation

Abstract

The invention relates to the field of unmanned aerial vehicles and technologies, in particular to an automatic unmanned aerial vehicle transportation system based on solar power generation. The automatic unmanned aerial vehicle transportation system based on solar power generation comprises a solar power generation module used for collecting and converting solar energy into electric energy; the power management module is connected with the solar power generation module and is used for adjusting and distributing electric energy; the energy storage module is connected to the power management module and is used for storing redundant electric energy; and the automatic driving control module is connected with the power management module and the navigation module and is used for realizing autonomous flight control of the unmanned aerial vehicle. The automatic unmanned aerial vehicle transportation system based on solar power generation can efficiently collect and utilize solar energy under different illumination conditions so as to prolong the endurance time of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles and the technical field, and in particular to an automatic driving unmanned aerial vehicle transportation system based on solar power generation. Background Art

[0002] With the continuous advancement of electronic technology and automatic control technology, unmanned aerial vehicles (UAVs) are playing an increasingly important role in logistics distribution, agricultural monitoring, emergency rescue, urban traffic monitoring, etc. However, most common commercial and industrial drones currently rely on traditional lithium batteries or fuel power as their main energy source. Limited by battery capacity or fuel weight, their endurance and load capacity are difficult to meet the needs of long-distance and long-term transportation at the same time.

[0003] In order to extend the flight time of drones and reduce their dependence on ground charging or refueling, the industry has gradually explored the application of solar power generation technology in the field of drones. For example, in high-altitude and long-flight drones, by laying solar cells on the surface of the fixed-wing fuselage, a certain amount of clean energy can be obtained during the day; in some multi-rotor drones, there are also attempts to integrate solar thin-film batteries on the fuselage or blades to supplement electricity. However, due to the changing flight posture, unstable lighting conditions and the limited available area of ​​the drone itself, these attempts often fail to achieve significant energy replenishment effects. In addition, the traditional solar power generation system and the drone flight control system have not yet established an effective coordination mechanism in power management and control strategies, which is prone to problems such as low power generation efficiency, insufficient energy storage utilization or inability to ensure flight safety.

[0004] On the other hand, autonomous driving technology is rapidly integrating with drone platforms. By equipping drones with high-precision sensors, navigation modules and intelligent algorithms, drones can achieve autonomous path planning, obstacle detection and avoidance, and multi-machine collaborative operations. However, when there is insufficient light or a sudden increase in flight load, if power generation and energy storage cannot be accurately managed and allocated, it will lead to insufficient power supply, which will in turn affect the stability of drone mission execution and flight safety. Summary of the invention

[0005] According to an embodiment of the present invention, an autonomous driving drone transportation system based on solar power generation is provided to solve the problems raised by the above background technology.

[0006] In a first aspect of the present invention, an autonomous driving drone transport system based on solar power generation is provided.

[0007] The autonomous drone transportation system based on solar power generation includes: a solar power generation module for collecting and converting solar energy into electrical energy;

[0008] A power management module, connected to the solar power generation module, for regulating and distributing electric energy;

[0009] an energy storage module, connected to the power management module, for storing excess electrical energy;

[0010] The autopilot control module is connected to the power management module and the navigation module to realize autonomous flight control of the UAV.

[0011] Preferably, the system further comprises:

[0012] A cargo loading module, connected to the autonomous driving control module, for loading and managing transport cargo;

[0013] A communication module, connected to the automatic driving control module, for realizing communication between modules in the system and with an external control center;

[0014] The monitoring and feedback module is connected to the communication module and is used to monitor the system status in real time and feedback control instructions.

[0015] Preferably, the power management module includes:

[0016] A data processing unit, used for collecting the working parameters of the solar power generation module and the energy storage module in real time, and converting the working parameters into a data format that can be called by a control algorithm;

[0017] a calculation unit, communicating with the data processing unit, configured to perform maximum power point tracking calculation according to the operating parameters and output a generated power estimation result;

[0018] The energy optimization unit is connected to the computing unit and is used to dynamically allocate energy based on the autonomous driving control and energy storage requirements after obtaining the maximum power point tracking result of the computing unit.

[0019] Preferably, the calculation process of the calculation unit is as follows:

[0020] Get the sampling sequence consisting of instantaneous voltage V and instantaneous current I {(V t , I t )|t=1, 2, ...};

[0021] The incremental conductance calculation of the power change at adjacent moments is performed using the following formula:

[0022] ΔI=I t -I t-1 , ΔV=V t -V t-1

[0023] when When the solar power generation module is operating at or close to the maximum power point;

[0024] like Then increasing the operating voltage of the solar power generation module;

[0025] like then reducing the operating voltage of the solar power generation module;

[0026] Update the current maximum power estimate P according to the adjustment result mppt ;

[0027] The calculation formula is P mppt =V t ×I t ×η adj ; where η adj is the power correction factor that is adaptively adjusted according to transient conditions;

[0028] The energy optimization unit calculates the maximum power estimate output by the computing unit and the current system total power demand P demand and the energy storage module status SOC; construct the following optimization objectives:

[0029]

[0030] In meeting E c +E s ≤P mppt Solve under the constraints of ;

[0031] E c Indicates the electrical energy allocated to the autonomous driving control module; E s Indicates the charging energy allocated to the energy storage module at this moment; U c (·) is the utility function set for autonomous driving control, which is used to quantify the degree to which the UAV meets the demand for sufficient electrical energy during the mission execution; U s (·, SOC) is the energy storage utility function, which comprehensively considers the health of the energy storage module and the expected subsequent usage requirements; λ is the weight coefficient for balancing the autonomous driving control requirements and the importance of energy storage.

[0032] Preferably, the energy storage module comprises:

[0033] The composite battery unit adopts a structure combining multi-stage series-parallel connected lithium-ion power batteries and supercapacitors;

[0034] A temperature monitoring unit, used to monitor the temperature distribution of the composite battery unit in real time;

[0035] An adaptive battery management unit communicates with the power management module and is used to dynamically adjust the charging and discharging strategy and perform fault prediction according to the mission requirements of the autonomous driving control module and the state of charge and health state (SOH) parameters of the composite battery unit.

[0036] Preferably, the adaptive battery management unit comprises:

[0037] A Kalman filter SOC estimation module is used to estimate and dynamically correct the state of charge of the energy storage module by integrating the terminal voltage, discharge current, temperature and historical working data of the composite battery unit to reduce the estimation error caused by environmental changes or sensor noise;

[0038] The dynamic life prediction submodule communicates with the Kalman filter SOC estimation module and is used to evaluate the health status of the energy storage unit in real time based on internal resistance modeling and accumulated discharge data, and to send a warning signal to the power management module when capacity decay or failure risk is predicted.

[0039] Preferably, the dynamic life prediction submodule evaluates the health status of the energy storage unit in real time by establishing an internal resistance increment equation and a discharge capacity decay equation based on an equivalent circuit model, including:

[0040] R t =R0+α·Δt+β·f(discharge rate, temperature)

[0041] Among them, R t represents the equivalent internal resistance at the current moment, R0 is the initial internal resistance, α and β are coefficients related to the environment and load conditions, and f(·) is a correction function used to correct the effects of discharge rate and temperature on the growth of internal resistance.

[0042] C remaining =C0-∫0 t k·g(ΔDOD,ΔT,R t )dt

[0043] Among them, C remaining and C0 represent the current and initial available capacity, respectively; k is the decay rate coefficient; g(·) is the comprehensive discharge depth (ΔDOD), temperature change (ΔT) and internal resistance (R t )’s decay function.

[0044] The dynamic life prediction submodule dynamically evaluates the health status of the energy storage unit based on the calculation results of the internal resistance increment equation and the discharge capacity attenuation equation, combined with the real-time charge state output by the Kalman filter SOC estimation module, and generates an alarm signal when a potential failure risk is detected, thereby improving the safety and continuous power supply capability of the drone system under multi-scenario operation.

[0045] Preferably, the energy storage module is provided with an environmental temperature adaptive control unit, and the environmental temperature adaptive control unit adjusts the heat dissipation or heat preservation measures of the energy storage unit in real time according to the temperature changes in the operating environment of the drone.

[0046] Preferably, the automatic driving control module further includes a fault diagnosis and redundancy decision unit, which is used to automatically switch to a backup control strategy or backup power supply through a redundant algorithm when an abnormality is detected in a key sensor or power output.

[0047] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0048] The solar-powered autonomous drone transportation system of the present invention can efficiently collect and utilize solar energy under different lighting conditions to extend the drone's flight time. Especially in long-distance or continuous operation scenarios, the system can provide stable and reliable power support for the autonomous driving control module through the intelligent scheduling of the energy storage module and the power management module, thereby realizing a flight transportation service with a high degree of autonomy, long working hours, and high task flexibility. The system has broad application prospects in the fields of logistics distribution, emergency rescue, geographic surveying and mapping, etc.

[0049] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0051] Figure 1 A system block diagram of an autonomous driving drone transportation system based on solar power generation according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0054] like Figure 1 As shown, the autonomous driving drone transportation system based on solar power generation includes:

[0055] A solar power generation module is used to collect and convert solar energy into electrical energy; the module is installed on the top of the drone fuselage or the surface of the wing to fully receive external light. Preferably, the solar cell uses a single crystal silicon or polycrystalline silicon cell with high conversion efficiency, and uses thin film packaging technology to reduce the weight burden. The solar power generation module can also be equipped with a light sensor to detect the ambient light intensity in real time, and improve the overall energy collection efficiency by automatically adjusting the power generation working point under different lighting conditions.

[0056] The power management module is connected to the solar power generation module and is used to regulate and distribute electric energy. Furthermore, the power management module is electrically connected to the solar power generation module and includes one or more regulating circuits and a control chip to execute maximum power point tracking (MPPT) or other energy optimization algorithms, so as to obtain better power generation efficiency under changing light conditions. The module is also responsible for distributing the electric energy generated by the solar power generation module to different power consumption units according to pre-set rules, and coordinating with the energy storage module described below, so that the UAV can maintain a stable power supply when performing flight missions.

[0057] The energy storage module is connected to the power management module and is used to store excess electrical energy to ensure the continuous operation of the system under insufficient light or nighttime conditions. The energy storage module is connected to the power management module and is used to store excess electrical energy and provide continuous operating power for the UAV under insufficient light or nighttime conditions. In this embodiment, the energy storage module may adopt a lithium-ion battery pack or a composite supercapacitor battery pack to take into account both high energy density and instantaneous high power output requirements. The energy storage module is internally configured with a temperature sensor and a battery management system (BMS), which can monitor the battery's state of charge (SOC) and state of health (SOH) in real time, and send an alarm signal to the power management module when an abnormal condition is detected, to ensure the flight safety of the UAV in various operating environments.

[0058] The autopilot control module is connected to the power management module and the navigation module to realize autonomous flight control of the UAV, including path planning, speed regulation and mission execution. In specific implementation, the autopilot control module can be equipped with an inertial measurement unit (IMU), a GPS / Beidou navigation module and an image recognition algorithm to obtain the attitude information and environmental perception data of the UAV; combined with pre-set mission parameters or ground station instructions, autonomous flight control is realized. When the UAV needs to perform a transportation task, the module can dynamically adjust the flight speed and flight altitude according to the real-time flight data and the remaining power information provided by the energy storage module to maximize the use of existing power resources and ensure transportation efficiency.

[0059] The solar-powered autonomous drone transportation system of the present invention can efficiently collect and utilize solar energy under different lighting conditions to extend the drone's flight time. Especially in long-distance or continuous operation scenarios, the system can provide stable and reliable power support for the autonomous driving control module through the intelligent scheduling of the energy storage module and the power management module, thereby realizing a flight transportation service with a high degree of autonomy, long working hours, and high task flexibility. The system has broad application prospects in the fields of logistics distribution, emergency rescue, geographic surveying and mapping, etc.

[0060] In this embodiment, the system further includes:

[0061] The cargo loading module is connected to the autonomous driving control module and is used to load and manage the transported cargo. In a specific implementation, a mechanical structure such as a clamp or an adjustable fixed bracket can be used to accommodate cargo of different sizes, shapes and weights, and load information can be measured and recorded in real time before and after cargo loading.

[0062] The cargo loading module can cooperate with the flight attitude adjustment function of the automatic driving control module to dynamically correct the center of gravity offset of the UAV according to changes in cargo weight and center of gravity distribution during takeoff, landing and flight, thereby improving flight stability and safety.

[0063] A communication module is connected to the autopilot control module and is used to realize communication between modules in the system and with an external control center. As an optional embodiment, the communication module may include a two-way wireless communication unit (such as Wi-Fi, 4G / 5G cellular network, dedicated narrowband radio) and a satellite communication unit to ensure that the drone can exchange real-time data with a ground station or a cloud server in different environments.

[0064] The communication module can simultaneously maintain data exchange with the solar power generation module, power management module, energy storage module and autonomous driving control module, timely update key information such as light intensity, remaining power, flight path, cargo weight, etc., and transmit it to the external control center for remote monitoring and task scheduling.

[0065] The monitoring and feedback module is connected to the communication module and is used to monitor the system status in real time and feedback control instructions. Specifically, during the execution of the UAV's flight mission, the module can collect data including the engine (or motor) working status, battery temperature, cargo load, flight attitude and GPS location information, and issue an alarm signal in time when any abnormality or failure risk is detected.

[0066] The monitoring and feedback module can send control instructions to the external control center or the automatic driving control module through the communication module, such as adjusting the route, reducing the flight speed, emergency return, etc., to deal with emergencies or complete new mission instructions.

[0067] In a preferred embodiment, the module can also be combined with intelligent data analysis algorithms to perform trend analysis and fault prediction on historical monitoring data, provide technical support for power management, energy optimization, and cargo loading safety, thereby enhancing the overall reliability and safety of the UAV system.

[0068] In this embodiment, the power management module includes:

[0069] A data processing unit, used for collecting the working parameters of the solar power generation module and the energy storage module in real time, and converting the working parameters into a data format that can be called by a control algorithm;

[0070] a calculation unit, communicating with the data processing unit, for performing maximum power point tracking calculation according to the operating parameters and outputting a power generation estimation result, so as to optimize the overall efficiency of the solar power generation module and the energy storage module;

[0071] The energy optimization unit is connected to the computing unit and is used to dynamically allocate energy based on the automatic driving control and energy storage requirements after obtaining the maximum power point tracking result of the computing unit, so as to improve the overall energy utilization efficiency and endurance of the UAV system.

[0072] In this embodiment, the calculation process of the calculation unit is as follows:

[0073] Get the sampling sequence consisting of instantaneous voltage V and instantaneous current I {(V t , I t )|t=1,2,...};

[0074] The incremental conductance calculation of the power change at adjacent moments is performed using the following formula:

[0075] ΔI=I t -I t-1 , ΔV=V t -V t-1

[0076] when When the solar power generation module is operating at or close to the maximum power point;

[0077] like Then increasing the operating voltage of the solar power generation module;

[0078] like then reducing the operating voltage of the solar power generation module;

[0079] Update the current maximum power estimate P according to the adjustment result mppt ;

[0080] The calculation formula is P mppt =V t ×I t ×η adj ; where η adj A power correction factor that is adaptively adjusted according to transient conditions (such as temperature and rate of change of light intensity);

[0081] The energy optimization unit calculates the maximum power estimate output by the computing unit and the current system total power demand P demand and the energy storage module status SOC; construct the following optimization objectives:

[0082]

[0083] In meeting E c +E s ≤P mppt Solve under the constraints of ;

[0084] E c Indicates the electrical energy allocated to the autopilot control module (including the basic energy consumption required for key flight control and navigation); E s Indicates the charging energy allocated to the energy storage module at this moment; U c (·) is the utility function set for autonomous driving control, which is used to quantify the degree to which the UAV meets the demand for sufficient electrical energy during the mission execution; U s (·, SOC) is the energy storage utility function, which comprehensively considers the health of the energy storage module (avoiding overcharging or over-discharging) and the expected subsequent usage needs; λ is the weight coefficient for balancing the control needs of autonomous driving and the importance of energy storage;

[0085] The above optimization problem is iteratively solved by Lagrange multipliers or numerical methods based on gradient descent to obtain the optimal allocation solution and will and The allocation results are sent to the corresponding execution control part to ensure the energy utilization efficiency and sustainability of the UAV in the current environment.

[0086] The following table only illustrates two consecutive sampling moments (t1, t2);

[0087]

[0088] ΔV=V t -V t-1 , ΔI=I t -I t-1 when When η is determined to be near the maximum power point; if it is greater than , the voltage is increased; if it is less than , the voltage is reduced. adj It is the power correction coefficient, which can be adjusted adaptively according to temperature, light intensity change rate, etc.

[0089] When the maximum power estimate output by the computing unit is P mppt When the energy optimization unit is based on the current system total power demand P demand and energy storage state SOC;, construct the following optimization objectives and solve them under constraints:

[0090]

[0091]

[0092] E c Indicates the electrical energy allocated to the autonomous driving control module, E s Indicates the charging distribution to the energy storage module. c (E c ) and U s (E s , SOC) are the corresponding utility functions respectively; λ is the coefficient for balancing the weights of the two; the values ​​in the table indicate that the utility and are optimized through an iterative algorithm (such as the Lagrange multiplier method). The utility of Scheme B is slightly higher than that of Scheme A, so Scheme B is considered to be the current better allocation scheme.

[0093] In this embodiment, the energy storage module includes:

[0094] The composite battery unit adopts a structure combining multi-stage series-parallel lithium-ion power batteries and supercapacitors to simultaneously meet the needs of instantaneous high current output and long battery life. Specifically, under rapid acceleration or strong wind conditions, the supercapacitor can provide instantaneous high power output, while the lithium-ion battery takes on the main battery life function, thereby effectively reducing the impact of high current discharge on the life of the lithium battery.

[0095] In order to improve the overall energy density of the system and reduce weight, a hybrid configuration of high-energy-density lithium-ion cells and small-volume supercapacitor packs can be adopted, and multi-layer protection circuits and safety valves can be used to ensure high-intensity discharge and charging safety.

[0096] The temperature monitoring unit is distributed and placed in the key temperature rise area of ​​the composite battery unit, such as near the lithium-ion battery pack, supercapacitor unit and connector. The unit can collect temperature data at each sampling point and send the collected temperature information to the adaptive battery management unit (BMS) or the control unit of the external heat dissipation device by wireless or wired means.

[0097] When an abnormal increase in the temperature inside the battery pack is detected, the temperature monitoring unit will trigger a signal and work in conjunction with an external heat dissipation device (such as an air cooling or liquid cooling system) to prevent overheating-induced battery cell expansion, performance degradation, or safety hazards by increasing the cooling air volume, adjusting the coolant flow, or taking other appropriate cooling measures. In addition, in extremely low temperature environments, the unit can also work in conjunction with corresponding insulation and heating measures to maintain the battery unit within a reasonable operating temperature range.

[0098] An adaptive battery management unit (BMS) communicates with the power management module and is used to dynamically adjust the charging and discharging strategy and perform fault prediction according to the mission requirements of the autopilot control module and the state of charge (SOC) and state of health (SOH) parameters of the composite battery unit to ensure the safe and efficient flight of the UAV in a changing environment.

[0099] The adaptive battery management unit communicates with the power management module and dynamically adjusts the charging and discharging strategy of the energy storage module according to the flight mission requirements (such as flight duration, flight speed and mission type) formulated by the autopilot control module and the state of charge (SOC) and health state (SOH) of the composite battery unit acquired in real time.

[0100] When it detects that parameters such as the operating voltage, current or temperature of the energy storage module exceed the safety threshold, the adaptive battery management unit will promptly send a warning signal to the power management module, and decide whether to limit the discharge current, reduce the charging power, or execute an emergency load reduction / return strategy based on the specific type of abnormality, so as to ensure the safety and efficient flight of the drone in a changing environment.

[0101] In this embodiment, the adaptive battery management unit (BMS) includes:

[0102] The module integrates the terminal voltage, discharge current, temperature and historical working data of the composite battery unit, and establishes a mathematical model based on the Kalman filter algorithm to estimate and dynamically correct the state of charge (SOC) of the energy storage module in real time.

[0103] In a specific implementation, an extended Kalman filter (EKF) or an unscented Kalman filter (UKF) may be used to fuse temperature sensing data and historical charge and discharge curves to reduce errors caused by environmental changes or sensor noise.

[0104] On the one hand, this method can greatly improve the accuracy of SOC estimation, allowing the UAV control system to plan its flight life based on more realistic remaining power data; on the other hand, in scenarios with rapid power changes (such as high-power ramping or sudden braking), this module can also promptly adapt to transient voltage and current fluctuations, providing reliable basic data for subsequent life prediction and charge and discharge control.

[0105] The dynamic life prediction submodule communicates with the Kalman filter SOC estimation module to perform real-time evaluation of the health state (SOH) of the composite battery cell based on internal resistance modeling and accumulated discharge data.

[0106] In a preferred embodiment, the submodule can establish a comprehensive model that includes the lithium-ion battery internal resistance growth equation, the supercapacitor durability decay equation, and the effect of ambient temperature on the decay rate, so as to timely predict possible failure risks after detecting the capacity decay threshold or multiple abnormal discharge behaviors.

[0107] When the prediction results show that the energy storage unit is about to experience a sharp decline in performance or cannot meet the requirements of future flight missions, the dynamic life prediction submodule will send a fault warning signal to the power management module, triggering the corresponding safety strategy or backup power switching mechanism, thereby ensuring the reliable operation of the UAV in long-flight, multi-tasking or extreme climate conditions.

[0108] In this embodiment, the dynamic life prediction submodule evaluates the health status of the energy storage unit in real time by establishing an internal resistance increment equation and a discharge capacity decay equation based on an equivalent circuit model, including:

[0109] R t =R0+α·Δt+β·f(discharge rate, temperature)

[0110] Among them, R t represents the equivalent internal resistance at the current moment, R0 is the initial internal resistance, α and β are coefficients related to the environment and load conditions, and f(·) is a correction function used to correct the effects of discharge rate and temperature on the growth of internal resistance.

[0111] C remaining =C0-∫0 t k·g(ΔDOD,ΔT,R t )dt

[0112] Among them, C remainingand C0 represent the current and initial available capacity, respectively; k is the decay rate coefficient; g(·) is the comprehensive discharge depth (ΔDOD), temperature change (ΔT) and internal resistance (R t )’s decay function.

[0113] The dynamic life prediction submodule dynamically evaluates the health state (SOH) of the energy storage unit based on the calculation results of the above two equations, combined with the real-time state of charge (SOC) output by the Kalman filter SOC estimation module. When a potential failure risk or capacity decay reaches a preset threshold, the submodule will immediately generate an alarm signal and transmit it to the power management module or the autopilot control module, providing a decision-making basis for further adjusting the flight strategy or switching the backup power supply, thereby improving the safety and continuous power supply capability of the drone system under multi-scenario operation.

[0114] In this embodiment, an ambient temperature adaptive control unit is provided in the energy storage module, and the ambient temperature adaptive control unit is arranged in the energy storage module, and is used for adjusting the heat dissipation and insulation measures of the composite battery unit or other energy storage components in real time under different ambient temperature conditions when the UAV performs tasks.

[0115] In specific implementation, precise temperature control of the energy storage unit can be achieved by adjusting the speed of the cooling fan, the flow of the cooling liquid, or activating the battery heating film according to the temperature value fed back by the temperature monitoring unit, thereby maintaining the performance stability of the energy storage module and reducing energy loss in extremely high or low temperature environments.

[0116] For example, if the battery temperature is detected to be too high, the system can increase the fan speed or increase the cooling fluid circulation rate; if it is in a cold environment, the battery cells can be preheated through low-power heating to keep them within the appropriate operating temperature range, thereby delaying battery aging and ensuring charging and discharging efficiency.

[0117] In this embodiment, the automatic driving control module further includes a fault diagnosis and redundancy decision unit, which is used to quickly switch to a backup control strategy or backup power supply to deal with sudden failures when an abnormality is detected in a key sensor, power output or control algorithm.

[0118] The fault diagnosis and redundancy decision unit can use multi-sensor fusion and neural network analysis technology to monitor key data such as attitude sensor (IMU), GPS / Beidou positioning module, motor drive signal, etc. When there is a significant deviation from the normal threshold, the unit will first determine whether it is a transient disturbance or hardware failure, and make corresponding decisions:

[0119] Dispatch backup sensors or backup control algorithms: If only a single path or a single sensor is abnormal, redundant sensors or algorithms can be enabled to maintain flight control stability;

[0120] Switch to backup power supply or emergency return strategy: If it is determined that the energy storage module is abnormal or the core power system is faulty, the drone can be instructed to switch to safe mode or emergency return procedure to ensure the safety of the cargo and the aircraft as much as possible.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An autonomous driving drone transportation system based on solar power generation, characterized in that: include: Solar power generation module, used to collect and convert solar energy into electrical energy; A power management module, connected to the solar power generation module, for regulating and distributing electric energy; an energy storage module, connected to the power management module, for storing excess electrical energy; The autopilot control module is connected to the power management module and the navigation module to realize autonomous flight control of the UAV.

2. The autonomous driving drone transportation system based on solar power generation according to claim 1 is characterized in that: The system also includes: A cargo loading module, connected to the autonomous driving control module, for loading and managing transport cargo; A communication module, connected to the automatic driving control module, for realizing communication between modules in the system and with an external control center; The monitoring and feedback module is connected to the communication module and is used to monitor the system status in real time and feedback control instructions.

3. The autonomous driving drone transportation system based on solar power generation according to claim 1 is characterized in that: The power management module comprises: A data processing unit, used for collecting the working parameters of the solar power generation module and the energy storage module in real time, and converting the working parameters into a data format that can be called by a control algorithm; a calculation unit, communicating with the data processing unit, configured to perform maximum power point tracking calculation according to the operating parameters and output a generated power estimation result; The energy optimization unit is connected to the computing unit and is used to dynamically allocate energy based on the autonomous driving control and energy storage requirements after obtaining the maximum power point tracking result of the computing unit.

4. The autonomous driving drone transportation system based on solar power generation according to claim 3 is characterized in that: The calculation process of the calculation unit is as follows: Get the sampling sequence consisting of instantaneous voltage V and instantaneous current I {(V t , I t )|t=1, 2, ...}; The incremental conductance calculation of the power change at adjacent moments is performed using the following formula: ΔI=I t -I t-1 ,ΔV=V t -V t-1 when When the solar power generation module is operating at or close to the maximum power point; like Then increasing the operating voltage of the solar power generation module; like then reducing the operating voltage of the solar power generation module; Update the current maximum power estimate P according to the adjustment result mppt ; The calculation formula is P mppt =V t ×I t ×η adj ; where η adj is the power correction factor that is adaptively adjusted according to transient conditions; The energy optimization unit calculates the maximum power estimate output by the computing unit and the current system total power demand P demand and energy storage module status SOC; Construct the following optimization objectives: In meeting E c +E s ≤P mppt Solve under the constraints of ; E c Indicates the electrical energy allocated to the autonomous driving control module; E s Indicates the charging energy allocated to the energy storage module at this moment; U c (·) is the utility function set for autonomous driving control, which is used to quantify the degree to which the UAV meets the demand for sufficient electrical energy during the mission execution; U s (·, SOC) is the energy storage utility function, which comprehensively considers the health of the energy storage module and the expected subsequent usage requirements; λ is the weight coefficient for balancing the autonomous driving control requirements and the importance of energy storage.

5. The solar-powered autonomous driving drone transportation system according to claim 1, characterized in that: The energy storage module comprises: The composite battery unit adopts a structure combining multi-stage series-parallel connected lithium-ion power batteries and supercapacitors; A temperature monitoring unit, used to monitor the temperature distribution of the composite battery unit in real time; An adaptive battery management unit communicates with the power management module and is used to dynamically adjust the charging and discharging strategy and perform fault prediction according to the task requirements of the autonomous driving control module and the charge state and health state parameters of the composite battery unit.

6. The solar-powered autonomous drone transportation system according to claim 5, characterized in that: The adaptive battery management unit comprises: A Kalman filter SOC estimation module is used to estimate and dynamically correct the state of charge of the energy storage module by integrating the terminal voltage, discharge current, temperature and historical working data of the composite battery unit to reduce the estimation error caused by environmental changes or sensor noise; The dynamic life prediction submodule communicates with the Kalman filter SOC estimation module and is used to evaluate the health status of the energy storage unit in real time based on internal resistance modeling and accumulated discharge data, and to send a warning signal to the power management module when capacity decay or failure risk is predicted.

7. The solar-powered autonomous drone transportation system according to claim 6, characterized in that: The dynamic life prediction submodule evaluates the health status of the energy storage unit in real time by establishing an internal resistance increment equation and a discharge capacity decay equation based on an equivalent circuit model, including: R t =R0+α·Δt+β·f(discharge rate, temperature) Among them, R t represents the equivalent internal resistance at the current moment, R0 is the initial internal resistance, α and β are coefficients related to the environment and load conditions, and f(·) is a correction function used to correct the effects of discharge rate and temperature on the growth of internal resistance. C remaining =C0-∫0 t k·g(ΔDOD,ΔT,R t )dt Among them, C remaining and C0 represent the current and initial available capacity, respectively; k is the decay rate coefficient; g(·) is the comprehensive discharge depth (ΔDOD), temperature change (ΔT) and internal resistance (R t )’s decay function. The dynamic life prediction submodule dynamically evaluates the health status of the energy storage unit based on the calculation results of the internal resistance increment equation and the discharge capacity attenuation equation, combined with the real-time charge state output by the Kalman filter SOC estimation module, and generates an alarm signal when a potential failure risk is detected, thereby improving the safety and continuous power supply capability of the drone system under multi-scenario operation.

8. The solar-powered autonomous drone transportation system according to claim 1, characterized in that: The energy storage module is provided with an environmental temperature adaptive control unit, which adjusts the heat dissipation or heat preservation measures of the energy storage unit in real time according to the temperature changes in the operating environment of the drone.

9. The solar-powered autonomous drone transportation system according to claim 1, characterized in that: The automatic driving control module further includes a fault diagnosis and redundancy decision unit, which is used to automatically switch to a backup control strategy or backup power supply through a redundant algorithm when an abnormality is detected in a key sensor or power output.

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