Wing Aviation LLC

Matthew Nubbe

Jesse Blake

Scott Velez

Kaiwen Gu

Eric Teller

Andre Prager

James Schmalzried

Evan Twyford

Stephen Benson

Kevin Jenkins

Jim Schmalzried

Theran Cochran

Nick Renold

Dustin Sanders

Systems, devices, and techniques for active thermal control of energy storage units are described. In some embodiments, an unmanned aerial vehicle (UAV) includes a battery pack. The battery pack includes a plurality of battery cells and an enclosure coupled with the plurality of battery cells to physically retain the plurality of battery cells in an arrangement. The arrangement defines a void space between the plurality of battery cells. The UAV also includes a cooling system configured to cool the battery cells. The cooling system includes a source of forced convection fluidically coupled with the battery pack to drive a cooling fluid through the void space. The cooling system also includes a cooling controller electrically coupled with the source of forced convection to controllably activate the source of forced convection.

Active thermal control of UAV energy storage units

An example method involves determining an expected demand level for a first type of a plurality of types of transport tasks for unmanned aerial vehicles (UAVs), the first type of transport tasks associated with a first payload type. Each of the UAVs is physically reconfigurable between at least a first and a second configuration corresponding to the first payload type and a second payload type, respectively. The method also involves determining based on the expected demand level for the first type of transport tasks, (i) a first number of UAVs having the first configuration and (ii) a second number of UAVs having the second configuration. The method further involves, at or near a time corresponding to the expected demand level, providing one or more UAVs to perform the transport tasks, including at least the first number of UAVs.

Anticipatory dispatch of UAVs to pre-staging locations

A method includes determining a threshold capacity associated with at least a first unmanned aerial vehicle (UAV) and a second UAV. The method includes initially setting a target charge voltage of a first battery of the first UAV to less than a full charge voltage to limit a state of charge of the first battery based on the threshold capacity. The method includes, over a lifetime of the first battery of the first UAV, periodically comparing a full charge capacity of the first battery to the threshold capacity. The method includes, based on the comparing, periodically adjusting the target charge voltage of the first battery, such that, as the full charge capacity of the first battery decreases with age, the target charge voltage increases towards the full charge voltage of the first battery.

Systems and methods for battery capacity management in a fleet of UAVs

Aspects of the disclosure relate to identifying and responding to problem conditions for a fleet of aerial vehicles. This may include receiving at one or more processors of one or more server computing devices sensor feedback from an AV of the fleet. A problem condition may be identified using the sensor feedback. A mitigation response for the problem condition relating to a mission assigned to the aerial vehicle may be determined. The mitigation response may be sent to the AV in order to cause the aerial vehicle to maneuver according to the mitigation response and thereby automatically respond to the problem condition.

Multi-UAV management

Controlling a vehicle according to a trained neural network model capable of being used to generate an output from which one or more vehicle operating variables can be estimated. The neural network model can be used to process, as input, aggregated data corresponding to operational and/or environmental characteristics experienced by the vehicle during at least a portion of a voyage. The aggregated data can include a range of values collected over a period of time when the vehicle is traversing the portion of the voyage. The output generated by the neural network model, based on processing the input, can be further processed in order to determine, for example, an estimated state of charge and/or an estimated remaining flight time for the vehicle. Such estimated values can thereafter be used by a controller of the vehicle to maintain course or maneuver to a charging station.

Using machine learning techniques to estimate available energy for vehicles

Disclosed herein are methods and systems that can help an aerial transport service provider (ATSP) determine how to distribute and redistribute unmanned aerial vehicles (UAVs) amongst a plurality of UAV deployment stations located throughout a geographic area. In accordance with example embodiments, the ATSP system can take one or more performance metrics for item providers into account when determining how much UAV transport capacity to allocate to different item providers for a given time period. The ATSP can then determine how to distribute UAVs amongst different UAV nests in advance of and/or during the given time period, such that each item provider's allocated UAV transport capacity is available from the UAV nest or nest(s) that serve each item provider during the given time period.

Distribution of aerial vehicle transport capacity based on item-provider performance metrics

An example embodiment includes a landing pad having a housing and a power terminal configured to draw electric power from a power source. The landing pad further includes an electrically conductive landing terminal dorsal to the housing and configured such that, during a landing state of an aerial vehicle, the landing terminal makes contact with a plurality of electric contacts disposed ventrally to a fuselage of the aerial vehicle. The landing terminal is configured to transfer electric power drawn by the power terminal to the aerial vehicle via the electric contacts during the landing state of the aerial vehicle.

Systems for charging aerial vehicles

A technique for power an apparatus during a mission includes powering the apparatus with a first energy storage device during a first mission segment of the mission. The first energy storage device has a first energy density and a first peak power rating. The apparatus is powered with a second energy storage device, distinct from the first energy storage device, during a second mission segment of the mission. The second energy storage device has a second energy density lower than the first energy density and a second peak power rating that is greater than the first peak power rating.

Hybrid energy storage system with multiple energy and power densities

In an embodiment, an apparatus includes a plurality of electrical contacts, wherein first and second electrical contacts of the plurality of electrical contacts electrically couple with a charging device; one or more rechargeable batteries configured to be charged from power received, via the first and second electrical contacts, from the charging device; and circuitry configured to obtain battery state information associated with the one or more rechargeable batteries during charging of the one or more rechargeable batteries and generate battery charge rate data based on the battery state information. At least one of the first and second electrical contacts is configured to transmit the battery charge rate data to the charging device, and the battery charge rate data is configured to be used by the charging device to regulate charging of the one or more rechargeable batteries.

Growing community of inventors

Santa Clara, CA, United States of America

Matthew Nubbe