Power Management for Long-Range Drones

Distributed multi-propeller vertical take-off and landing aircraft hybrid power system for long range flight with improved endurance. The system has a parallel hybrid setup with multiple engines, generators, batteries, and motors. The engines, generators, and batteries are integrated and share cooling/lubrication. The system intelligently manages power allocation between engines, batteries, and motors based on flight mode and accelerator position. In vertical takeoff/landing, all engines start and provide peak power. In hover/cruise, batteries power the motors until needed, then engines ramp up. This allows smaller engines for cruising and avoids oversizing. In emergency, a failed engine is disconnected and batteries/adjacent engine take over.

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Hybrid power system for unmanned aerial vehicles (UAVs) that improves flight safety and stability by stabilizing the power output. The hybrid system combines an engine with a battery pack. A control system monitors the UAV's power demands and generates compensating signals. It adjusts the engine throttle and battery charging to match the power needs. This compensates for engine output lag and stabilizes the overall power supply. The control logic uses feedforward compensation based on UAV demand changes.

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A hybrid propulsion system and control method for lightweight fixed-wing UAVs weighing 60-70 kg that improves efficiency, flexibility, and noise reduction compared to traditional direct-drive engines. The hybrid system uses a gas engine and electric motors. It has four power modes: fastest, standard (most efficient), energy-saving, and silent. The standard and energy-saving modes use a learning algorithm to optimize power distribution between engines and motors based on flight conditions. This adaptive hybrid power system enables targeted performance optimization, improves fuel efficiency, and allows silent cruising for missions requiring stealth.

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Method for flying electric aircraft using contactless network wireless power supply to enable long-duration flights without carrying heavy batteries. The method involves using wireless charging pads on the ground power grid to charge the aircraft mid-air. The aircraft routes are optimized based on environmental factors to conserve energy. This allows the aircraft to fly for longer distances without needing to land and swap batteries. The wireless charging eliminates the need for onboard batteries and restrictive flight paths near power lines.

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Hybrid power system for unmanned aerial vehicles (UAVs) that improves efficiency and endurance compared to using just batteries or engines. The system uses a combination of a piston engine, generator, electric motor, clutch, and battery pack. It allows the UAV to fly in different modes optimized for specific conditions. The modes include engine power alone, motor power alone, engine and motor simultaneously, and engine with generator charging the battery. This enables the UAV to operate efficiently in various flight scenarios by matching the power sources to the demands.

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Hybrid unmanned aerial vehicle (UAV) that uses parallel high-power density and high-energy density power sources to enable vertical takeoff/landing and extended flight time. The UAV has a fuselage and wings, with the power system being a brushless motor driving a propeller. Inside the fuselage, there are separate sets of high-power density (lithium batteries) and high-energy density (combinations of lithium batteries, fuel cells, solar cells, and internal combustion engines) power sources. During vertical takeoff/landing, the high-power density batteries power the propeller. For extended flight, the high-energy density batteries provide power and can charge the high-power density batteries. This allows the UAV to balance high-power needs for vertical takeoff/landing with extended flight range using optimized power sources.

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Long-endurance unmanned aerial vehicle (UAV) hybrid power system that combines a lithium battery with a fuel engine and generator for efficient and lightweight power. The system uses a flight control system to optimize power usage based on altitude and load. It starts with the battery providing power during takeoff, then charges during flight. At high altitudes, the engine shuts off and the battery powers the UAV. This avoids inefficiencies of always using the engine. The engine restarts when descending to recharge the battery. It also uses high-altitude restart and short-term motor overload to save weight. The system switches between subsystems based on sensors like altitude and temperature.

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Energy efficient method for an unmanned aerial vehicle (UAV) to wake up and communicate with sensor nodes in a wireless network where the nodes can't be powered by wires or batteries. The method involves optimizing the UAV's flight path and the sensor nodes' wakeup times to minimize total energy consumption. It splits the communication process into two stages: setup and data transfer. By alternately optimizing the node schedules and UAV path, it finds a suboptimal solution to the mixed integer nonconvex optimization problem.

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Optimizing resource allocation for unmanned aerial vehicles (UAVs) in wireless power supply networks to improve system efficiency and prevent data loss. The method jointly optimizes UAV flight position, wireless energy transmission time, and wireless information transmission time for multiple ground nodes. It maximizes the sum of effective capacities, which represents the maximum constant arrival rate of data that can be supported with guaranteed quality of service. This reduces delay and prevents backlogging in node buffers. By considering both energy and information transmission, it improves overall resource utilization compared to separating them.

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Energy efficiency optimization method for unmanned aerial vehicle (UAV) assisted backscatter communication systems. The method involves finding the optimal UAV flight path and altitude to balance throughput and energy efficiency. It models the UAV-BN communication system, calculates energy consumption and throughput at different heights, and finds the height and path that maximizes the energy efficiency metric. This addresses the tradeoff between UAV coverage and energy efficiency in UAV-assisted backscatter communication systems.

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Energy efficiency optimization method for unmanned aerial vehicle (UAV) assisted wireless power supply networks to improve overall system efficiency by jointly optimizing wireless power and data transmission parameters. The optimization involves finding the UAV transmitting power, node powers, and transmission times that maximize system energy efficiency while meeting service quality constraints. This improves UAV-assisted wireless power supply networks compared to ignoring circuit power consumption in UAV-assisted sensor networks. The optimization balances uplink throughput and total energy consumption to reduce overheads.

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Downlink power control for unmanned aerial vehicle (UAV) data links that improves efficiency and range by adjusting transmission power based on UAV distance. The power control method involves the UAV and ground station exchanging distance measurements over the uplink. The ground station provides the UAV with a lookup table of transmit powers based on distance segments. The UAV then uses the segmented table to automatically adjust its downlink power as it flies. This reduces power consumption compared to fixed power or open loop methods while maintaining reliable communication over long distances.

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Low-power consumption optimization method for unmanned aerial vehicle (UAV) assisted backscatter edge computing networks. The method involves using UAVs to provide energy and computation offloading for devices with limited power and shielding between them and base stations. The UAVs transmit RF signals to power the devices, which communicate with the UAVs using backscatter. Devices offload computationally intensive tasks to the UAVs, which return results. This allows devices to conserve power compared to active transmission. Devices store remaining UAV-provided energy for later use. The UAV trajectory is optimized to balance energy consumption vs QoS.

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Method for optimizing energy utilization and network coverage in unmanned aerial vehicle (UAV) assisted wireless sensor networks. The method involves two phases: node registration and energy carrying communication. In the registration phase, sensor nodes inform the UAV of their energy levels. In the communication phase, the UAV hovers over nodes with low energy to charge them. The UAV balances charging time vs flight distance to maximize coverage. It also maintains a minimum received power threshold to ensure efficient charging. By registering node energy and balancing charging vs flight, the UAV can optimize energy utilization and distribute charge evenly among nodes.

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Energy-efficient unmanned aerial vehicle (UAV) relay cooperative communication system for expanding network coverage with optimal power consumption. The method involves UAV relays collecting energy from previous relays instead of batteries. It optimizes relay node placement, signal coding, and power allocation to maximize information rate while minimizing energy consumption. The UAV relays cooperatively transmit data by relaying signals through multiple hops. The method uses signal-to-noise ratio approximations to convert the optimization problem into convex form for efficient solution.

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An automated energy management system for unmanned aerial vehicles (UAVs) operating in near space that enables extended flight times. The system uses both solar power and fuel engines to balance energy generation and storage. It intelligently switches between solar and fuel power based on real-time consumption and availability. This allows the UAV to fly continuously for 24 hours at high altitudes by leveraging both power sources optimally. The system monitors energy needs and dynamically adjusts power generation and storage to maintain flight capability.

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Method and apparatus for controlling transmission power of unmanned vehicles in a wireless communication system to balance link reliability and interference mitigation. It involves checking the margin and required power of a non-mission data link, comparing it to the maximum power, and determining the optimal transmission power based on the margin. This prevents overpowering that causes interference while ensuring adequate link quality. The ground station can also send power control commands to adjust the unmanned vehicle's transmit power.

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Hybrid power supply system for unmanned aerial vehicles that allows the UAV to fly longer by efficiently managing power sources. The system has a power generation unit like solar panels or fuel cells, and a charging unit like batteries. A distribution unit intermittently switches power between the generation and charging units based on their states. This ensures the charging unit stays full while avoiding over-discharging the gen unit. A control module optimizes power allocation. If the gen unit fails, an emergency control supplies power from the charged unit. If an emergency is detected, it lands the UAV. The gen and charging units report power levels to a UAV controller.

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Powertrain control system and method for vertical take-off and landing air vehicles like drones to maintain battery charge level during flight. The system has a hybrid powertrain with engine, generator, motor, and battery. The control system charges the battery during flight by driving the engine based on motor power needs and battery charge level. This keeps the battery at a minimum charge rather than depleting it. This allows consistent battery charge for vertical takeoff/landing drones without needing higher capacity batteries.

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Energy collection method for unmanned aerial vehicle (UAV) edge computing systems using simultaneous wireless information and power transfer (SWIPT) to extend UAV endurance. The method involves optimizing energy efficiency of the UAV swarm by coordinating power allocation between base station and relays. Steps include: building a UAV swarm working model, calculating energy consumption and efficiency, determining optimal power allocation between base station and relays, and implementing the allocation in the swarm. This balances energy between base station and relays to maximize overall system endurance.

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