Photovoltaic fuel cell hybrid power supply unmanned aerial vehicle system and energy management strategy

Abstract

The invention provides a photovoltaic fuel cell hybrid power supply unmanned aerial vehicle system and an energy management strategy, and the system comprises a photovoltaic power generation assembly, a high-temperature methanol fuel cell assembly, and a thermoelectric power generation unit. The hot end synergistically recovers photovoltaic waste heat QA, fuel cell waste heat QB, electronic equipment waste heat QC, a power management unit, an energy storage device, a sensing and monitoring module and an environment sensing and adapting system. The method is characterized in that a prediction model adopts a BP neural network to construct a TEG power prediction model, and the thermoelectric power generation efficiency is optimized in real time in combination with an improved particle swarm optimization (IPSO) algorithm; dynamic energy management: executing a hierarchical power supply strategy (photovoltaic priority-fuel cell / energy storage supplementation-TEG special energy storage charging); dynamically adjusting a power available threshold Pavailabl through triple correction coefficients (illumination intensity alpha light, environment temperature alpha temp and flight phase alpha phase), wherein alpha phase is associated with a climbing power difference and a landing residual fuel ratio; according to an emergency processing algorithm, when fuel is insufficient, a graded landing mechanism is triggered, the output landing power PLanding of the fuel cell is forcibly distributed, and energy storage responds to instantaneous fluctuation delta P; and when the maximum power of the fuel cell is insufficient, giving an alarm and performing limited-power operation. According to the design, through multi-source waste heat collaborative recovery and intelligent working condition adaptation, the cruising ability of the system is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hybrid power systems and heat management for light-hydrogen photovoltaic fuel cell unmanned aerial vehicles, and relates to a photovoltaic cell, a fuel cell heat recovery thermoelectric power generation management strategy. BACKGROUND

[0002] With the growing global demand for renewable and clean energy, the era of rapid technological development, the field of unmanned aerial vehicles is also undergoing profound changes, and light-hydrogen hybrid power unmanned aerial vehicles have emerged as the times require. The development of unmanned aerial vehicles and low-altitude economy is developing rapidly.

[0003] From the perspective of energy demand, traditional unmanned aerial vehicles rely on lithium batteries. However, as the application scenarios of unmanned aerial vehicles continue to expand, the demand for endurance is becoming higher and higher. The energy density of lithium batteries has certain limitations, which limits the operation time of unmanned aerial vehicles and requires frequent battery replacement. Hydrogen energy has the significant advantage of high energy density. The energy density of hydrogen is much higher than that of traditional lithium batteries, and the energy contained in a unit mass of hydrogen; photovoltaic cells rely on photovoltaic effect to generate pollution-free electricity. Combining photovoltaic cells and hydrogen fuel cells can improve the endurance of unmanned aerial vehicles. A large amount of heat is generated during the operation of light-hydrogen unmanned aerial vehicles. If this heat is not dissipated in time, it will affect the working efficiency of the components and reduce the stability of the flight and the service life of the fuel cell. However, if it is collected and reused for thermoelectric power generation, it will greatly improve the endurance of the unmanned aerial vehicle.

[0004] In terms of technology, many key technologies lay a solid foundation for its development. Flight control technology integrates advanced sensors and complex algorithms to give unmanned aerial vehicles stable flight capabilities, combined with high-precision satellite positioning and inertial navigation positioning technologies to achieve precise flight path planning. In the field of power systems, electric systems are attracting attention due to their cleanliness and low noise, while high-performance battery research and development and hybrid power technology exploration are constantly advancing, aiming to improve endurance and energy utilization efficiency. SUMMARY

[0005] In view of the technical problems in the technical field of heat not dissipating affecting the working efficiency of the components, reducing the stability of the flight, and the service life of the fuel cell, the present application relates to a photovoltaic fuel cell hybrid power unmanned aerial vehicle system and energy management strategy, aiming to significantly improve the endurance through multi-source waste heat cooperative recovery and intelligent working condition adaptation. The system includes a photovoltaic power generation component, a high-temperature methanol fuel cell, a thermoelectric power generation unit, an energy storage device, and an intelligent monitoring module.

[0006] The specific implementation is as follows:

[0007] The power system structure is designed with two symmetrical motors distributed at both ends of the wings (10 kW per motor), two fuel cell stacks placed in the middle of the fuselage, and methanol fuel tanks placed on both sides of the fuselage to balance the center of gravity. The TEG array is integrated at the junction of the motor heat dissipation cabin and the fuel cell cabin to maximize the recovery of waste heat from the power system. The energy storage device is placed in the belly cabin, with a weight ratio of ≤25%, and is used to respond to instantaneous power fluctuations through supercapacitors (instantaneous discharge ≥100 kW).

[0008] The thermoelectric power generation system and the innovative design of the heat conduction structure have a hierarchical heat recovery architecture: six groups of Bi2Te3 modules (80mm×80mm) are connected in series in the high-temperature zone (fuel cell side ≥150℃), outputting 48V±5%; twelve groups of PbTe modules are connected in parallel in the medium and low-temperature zone (photovoltaic side ≥80℃, electronic equipment side ≥70℃) and are independently controlled by MPPT. The hot end is coupled with the fuel cell heat dissipation surface, the photovoltaic backplane, and the electronic equipment shell through a copper-based heat pipe (thermal conductivity ≥400W / m·K), combined with a double-fan supercharging system and a micro-channel liquid cooling plate to enhance heat dissipation, ensuring that the total heat recovery efficiency is ≥8% and the power output meets P TEG ≥0.08×(0.5Q B +0.3Q A +0.2Q C ).

[0009] The prediction model is based on a BP neural network to build a TEG power prediction model, inputting power system parameters and outputting waste heat distribution under a 20kW load, and combining an improved particle swarm optimization algorithm (IPSO) to optimize the thermoelectric generation efficiency in real time.

[0010] The energy management model implements a hierarchical power supply strategy: photovoltaic priority power supply → fuel cell / energy storage supplement → TEG dedicated power supply for energy storage charging. The dynamic adjustment of the power available threshold P available is adapted to the working conditions through three correction coefficients: the irradiance α light is calculated in segments to reflect the influence of irradiance, the ambient temperature α temp compensates for temperature changes, and the flight phase α phase is related to the climb power difference and the remaining fuel ratio, and the fuel cell power response delay ≤0.1s ensures transient demand.

[0011] The emergency handling algorithm includes fuel shortage response and fault tolerance: when the fuel amount drops to 15% of the threshold, the hierarchical landing mechanism is triggered, and the fuel cell is forced to output a fixed landing power P Landing =10kW, and the energy storage responds to instantaneous fluctuations ΔP; if the maximum power of the fuel cell is insufficient, an alarm is issued and the power is limited; when a single motor fails, the other motor automatically overclocks to 15kW to maintain emergency flight. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1is the installation layout diagram of each module on the unmanned aerial vehicle body in the embodiment of the application.

[0013] Figure 2 is the thermoelectric power generation principle diagram of the system in the embodiment of the application.

[0014] Figure 3 is the layout structure diagram of the unmanned aerial vehicle wing heat dissipation air duct and the TEG array in the embodiment of the application.

[0015] Figure 4 is the power system flow chart in the embodiment of the application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and embodiments.

[0017] The power system adopts a double-motor symmetrical layout: two 10kW motors are arranged at the two ends of the wing, a double stack (single stack 10kW, total 20kW) of high-temperature methanol fuel cell is installed in the middle of the fuselage, and methanol fuel tanks (20L capacity, energy density≥2000Wh / kg) are arranged on both sides of the belly to balance the center of gravity. The TEG array is integrated at the junction of the motor heat dissipation cabin and the fuel cell cabin, and directly recovers the waste heat of the power system. The energy storage device is composed of a super capacitor group (instantaneous discharge≥100kW) and a lithium battery group (capacity 10kWh), which is placed in the belly cabin (weight ratio≤25%), and is connected to the power assembly through a DC / DC converter to ensure the instantaneous power fluctuation response.

[0018] Thermoelectric power generation system design:

[0019] High-temperature area: for fuel cell waste heat (temperature≥150℃), 6 groups of Bi2Te3 modules (80mm×80mm) are connected in series, and the output voltage is stabilized at 48V±5%.

[0020] Medium and low temperature area: for photovoltaic waste heat (≥80℃) and electronic equipment waste heat (≥70℃), 12 groups of PbTe modules are connected in parallel, and each branch is independently controlled by MPPT.

[0021] Performance guarantee: the total thermoelectric conversion power≥1kW, and the heat recovery efficiency η satisfies:

[0022] The power output needs to satisfy the inequality: P TEG ≥0.08×(0.5Q B +0.3Q A +0.2Q C )

[0023] Among them, the high-temperature waste heat accounts for 50%, which ensures efficient recovery.

[0024] Thermal structure design: The hot end is coupled with three heat sources (fuel cell heat sink, photovoltaic backplane, and electronic device shell) through a copper-based heat pipe.

[0025] Heat dissipation enhancement measures: Lightweight aluminum alloy partition wind duct is installed inside the wing, and a double-vortex fan pressurization system is configured at the end to increase the heat dissipation power to ≥3kW. The fuel cell heat sink is embedded in a micro-channel liquid cooling plate, and the cooling liquid flow rate is ≥5L / min to maintain the temperature difference power generation efficiency.

[0026] Prediction model (BP neural network + IPSO algorithm):

[0027] Input layer: motor speed, torque, and other power system parameters, real-time monitoring of waste heat distribution under a 20kW load.

[0028] Output layer: predicted Q A , Q B , and Q C heat values, with an error of ≤5%.

[0029] Optimization algorithm: Improved particle swarm optimization (IPSO) is used, with a population size of 100 and 200 iterations to search for the global optimal working point.

[0030] Waste heat calculation formula:

[0031] Photovoltaic heat:

[0032] (k is the thermal conductivity of the backplane, A is the area, and ΔT is the temperature difference)

[0033] Fuel cell heat: QB=ΔH⋅n(1−η) (ΔH is the reaction enthalpy change, and n is the methanol molar flow rate)

[0034] Electronic device heat: QC=∑ i=1 n P i t (P i is the component thermal power)

[0035] Energy management model: hierarchical power supply strategy: photovoltaic priority power supply → fuel cell / energy storage supplement → TEG dedicated power supply for energy storage charging.

[0036] Dynamic power threshold adjustment: reference value Pavailable=2kW, dynamically adjusted by three correction coefficients:

[0037] Illuminance correction coefficient α light According to the solar irradiance G, it is divided into three intervals for calculation: α light =0.8+0.2×(G−800) / 200, G≥800 α light= 1.0 + 0.2 x (800 - G) / 500, 300 < G < 800 α light = 1.2 + 0.3 x (300 - G) / 300, G < 300

[0038] Temperature correction factor α temp According to the temperature T, it is divided into three intervals to calculate: α temp = 1 + 0.001 x (T - 25), T < 25 α temp = 1 + 0.003 x (T - 25), 25 < T < 50 α temp = 1 + 0.005 x (T - 25) + 0.002 x (T - 50), T > 50

[0039] Flight phase correction factor α phase According to the flight phase to calculate:

[0040] Take-off / climb phase:

[0041] Cruise phase:

[0042] Landing phase:

[0043] Fuel cell power response delay < 0.1 s, adapt transient demand.

[0044] When the solar cell does not output, judge whether the current state of charge (SOC) of the lithium battery is greater than the alarm value (SOC warning ), that is, only the amount of electricity that can be used for landing emergency. If SOC < SOC warning , an "SOP_warning" alarm is issued, that is, the SOC value is too low, and the lithium battery cannot be used. At the same time, judge whether the fuel cell is available, that is, whether the current methanol fuel pressure PH2 is greater than the alarm pressure P warning (the amount of methanol used for landing), it is considered to be available.

[0045] If P MeOH < P warning , an "SOP_warning" alarm is issued, that is, the current methanol pressure is too low, and the fuel cell cannot be used. At this time, the energy system will no longer be able to continue to maintain flight and must land, and the power distribution is as follows:

[0046] Where θ is the pitch angle, which is obviously less than zero: P Langing is the specified landing power, provided by the fuel cell. That is, P F = P Langding: And ΔP is the instantaneous power fluctuation value of the emergency event provided by lithium battery, that is, P L = ΔP, at this time the solar energy still does not output, that is, P s = 0, and the total demand power becomes P d = P Landing + ΔP.

[0047] If P MeOH > P warning , it is judged whether the fuel cell can meet the current demand power

[0048] If P Fmax < P d , an alarm of "Pd_warning" is sent, that is, the demand power is too high, the fuel cell cannot provide the original demand power flight, but in order to approach the original demand power as much as possible, it is made to output at the maximum power to enable the unmanned aerial vehicle to continue to fly, at this time the power distribution is as follows:

[0049] If P Fmax ≥ P d , the fuel cell can meet the demand power, then the fuel cell outputs, at this time the power distribution is as follows: .

Claims

1. A photovoltaic fuel cell hybrid power unmanned aerial vehicle system and energy management strategy, comprising a photovoltaic power generation component: rigid GaAs triple-junction solar cell, monolithic size 500mm x 800mm, total amount ≥12 pieces, total laying area ≥4.8m², conversion efficiency ≥28%; high-temperature methanol fuel cell component: double-stack parallel architecture, single-stack power 10kW, total output 20kW, size 400mm x 300mm x 200mm, methanol fuel tank capacity 20L (energy density ≥2000Wh / kg); thermoelectric generator unit (TEG array): using a hierarchical heat recovery architecture, Bi2Te3 modules (80mm x 80mm) are used in the high-temperature area (fuel cell side), PbTe modules are used in the medium-low temperature area (photovoltaic / electronic side), and the total thermoelectric conversion power is ≥1kW; heat conduction structure: the hot end of the TEG is coupled to the fuel cell heat dissipation surface (temperature ≥150℃), the photovoltaic backplane (temperature ≥80℃), and the power electronic equipment shell (temperature ≥70℃) through a copper-based heat pipe (thermal conductivity coefficient ≥400W / m·K).

2. The system of claim 1, wherein: The cavity inside the wing constitutes a heat dissipation air duct for cooling the photovoltaic power generation component, the air outlet of the air duct points to the hot end of the thermoelectric generator unit, and a fan is provided in the air duct to guide the airflow and conduct the waste heat generated by the photovoltaic power generation component to the thermoelectric generator unit.

3. The system of claim 1, wherein: Power system structure: dual-motor symmetrically distributed at both ends of the wing, single-motor power 10kW; fuel cell double-stack placed in the middle of the fuselage, methanol fuel tank divided and placed on both sides of the belly to balance the center of gravity; TEG array integrated at the junction of the motor heat dissipation cabin and the fuel cell cabin; energy storage device: combination of supercapacitor group and lithium battery, placed in the belly cabin, weight ratio ≤25%.

4. The system of claim 1, wherein: Thermoelectric system series-parallel rule: hierarchical heat recovery architecture: high temperature zone (≥ 150℃) configuration of fuel cell waste heat source Q B Exclusive channel, 6 groups of Bi2Te3 thermoelectric modules in series; low temperature zone (70-80℃) integration of photovoltaic waste heat Q A Parallel with electronic equipment waste heat QC 12 groups of PbTe thermoelectric modules; circuit control mechanism: high temperature branch output 48V±5% stable voltage, each module of the low temperature branch is independently MPPT controlled to match the non-uniform heat source distribution; total heat recovery efficiency ≥ 8% (η=P TEG / (Q A +Q B +Q C )×100%).

5. The system of claim 1, wherein: Prediction model: BP neural network input layer expanded to power system parameters (motor speed, torque), output waste heat distribution prediction under 20kW load, error ≤5%; IPSO algorithm population size increased to 100, iterated 200 times to search for the global optimal working point.

6. The system of claim 1, wherein: Dynamic Energy Management Model: Power Threshold P available Reference value set to 2 kW, correction factor range extended: a phase Climbing phase associated power difference (P total −P cruise ≤ 5 kW); Fuel cell power response delay ≤ 0.1 s, adapted to motor transient demand.

7. The system of claim 1, wherein: Emergency handling algorithm: fuel warning threshold P warning = 15% full fuel Emergency landing power P landing = 10 kW; the other motor automatically over-speeds to 15 kW in case of single motor failure.

8. The system of claim 3, wherein: TEG array power output satisfies: P TEG ≥ 0.08 x (0.5Q B + 0.3Q A + 0.2Q C ) (high-temperature waste heat weight increased to 50%).

9. The system of claim 1 or 2, wherein: Thermal management reinforcement design: wing air duct upgraded to dual-fan pressurization system, wind speed ≥10m / s, heat dissipation power ≥3kW; fuel cell heat dissipation surface embedded in micro-channel liquid cooling plate, flow rate ≥5L / min.

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