PREFLIGHT TEMPERATURE MANAGEMENT OF AN ELECTRIC AIRCRAFT ENERGY SOURCE

The present invention is systems and methods for preflight temperature management of an energy source of an electric aircraft. The system may include a sensor attached to an energy source of an electric aircraft, where the sensor is configured to detect a temperature of the energy source and generate a corresponding temperature datum. A computing device communicatively connected to the sensor may receive the temperature datum and determine the readiness of the electric aircraft for flight based on the temperature of the energy source. If the electric aircraft is not considered ready by the computing device, then an energy source modification may be initiated that changes the temperature of the energy source.

FIELD OF THE INVENTION

The present invention generally relates to the field of electric aircrafts. In particular, the present invention is directed to a systems and methods for preflight temperature management of an electric aircraft energy source.

BACKGROUND

Preliminary preparations and maintenance of an aircraft before flight are important. Failure to conduct appropriate preflight preparations can result in critical failure of the aircraft during operation.

SUMMARY OF THE DISCLOSURE

In an aspect, a preflight temperature management system for an energy source of an electric aircraft is provided. The system includes: a sensor configured to generate a temperature datum of an energy source of an electric aircraft; a computing device communicatively connected to the sensor, the computing device configured to: receive the temperature datum from the sensor; identify a current condition of the energy source as a function of the temperature datum; determine a temperature anomaly associated with the energy source as a function of the current condition and an optimal temperature parameter; and initiate an energy source modification as a function of the temperature anomaly.

In another aspect, a method for preflight temperature management of an energy source of an electric aircraft is provided. The method includes: generating, by a sensor communicatively connected to an energy source of an electric aircraft and a computing device, a temperature datum of the energy source; obtaining, by the computing device communicatively connected to the sensor, an optimal temperature parameter of the energy source; identifying, by the computing device, a current condition of the energy source as a function of the temperature datum; determining, by the computing device, a temperature anomaly as a function of the optimal temperature parameter and the current condition of the energy source; and initiating, by the computing device, an energy source modification.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to an apparatus and methods for preflight management of an aircraft. More specifically, the present disclosure can be used to prepare an energy source of an electric aircraft for flight. Often aircraft subsystems are manually prepared by flight crews. However, manual preflight preparations may limit the speed and accuracy at which aircraft subsystems are checked prior to flight. Thus, the present disclosure provides an apparatus and methods for rapidly and reliably determining preflight readiness of electric aircraft subsystems, such as an energy source used for storing electrical power.

Referring now toFIG.1, a block diagram illustrating an exemplary embodiment of a preflight management apparatus100is shown in accordance with one or more embodiments of the present disclosure. Apparatus100is configured to manage a temperature of energy source104of electric aircraft108prior to flight of electric aircraft108. For the purposes of this disclosure, an “energy source” may refer to a device and/or component used to store and provide electrical energy and/or power to an aircraft or aircraft subsystems. For example, and without limitation, energy source104may be a power source such as a battery and/or a battery pack. Battery may include one or more battery modules112a-nor battery cells, as discussed further in this disclosure. In one or more embodiments, energy source104may include one or more various types of batteries, such as a pouch cell battery, stack batteries, prismatic battery, lithium-ion cells, or the like. In one or more non-limiting embodiments, energy source104may include a battery, flywheel, rechargeable battery, flow battery, glass battery, lithium-ion battery, lithium-metal battery, lithium-air battery, ultrabattery, a combination thereof, and the like.

With continued reference toFIG.1, apparatus100includes a sensor116that is connected to energy source104and configured to detect a temperature of energy source104and/or a components thereof. As used in this disclosure, a “sensor” is a device that is configured to detect a physical characteristic and/or a phenomenon and convert the detection into a signal, such as an output sensor signal. In one or more embodiments, sensor116may be configured to transmit information, such as datum, related to a detection. For example, and without limitation, sensor may transduce a detected phenomenon, such as and without limitation, temperature, voltage, current, pressure, and the like, into an output sensor signal. Sensor116may detect a plurality of characteristics about energy source104to provide datum related to a temperature of energy source104to a computing device120. For instance, and without limitation, sensor116may detect a temperature of energy source104directly. In another instance, and without limitation, sensor116may detect various characteristics of energy source104and/or a surrounding environment of energy source104that allows for temperature of energy source104to be indirectly determined, such as by computing device120. For example, and without limitation, sensor116may determine a voltage, current, and thermal conditions of a surrounding environment of energy source104to determine temperature of energy source104.

In one or more embodiments, sensor116may include one or more sensors. For example, and without limitation, sensor116may include a plurality of sensors, such as for redundancy purposes or confidence level reassurance of each detection by a sensor of the plurality of sensors. Sensor116may be a contact or a non-contact sensor. For example, and without limitation, sensor116may be physically attached to energy source104. In other embodiments, sensor116may be remote to energy source104but physically attached to electric aircraft108.

In one or more embodiments, sensor116includes a temperature sensor or probe. A temperature sensor may include a thermocouple, thermometer, pyrometer, resistance temperature detector (RTD), platinum resistance temperature detector (PRTD), thermistor, negative temperature coefficient (NTC) thermistor, semiconductor based integrated circuit (IC), microbolometers, local temperature sensor, remote digital temperature sensor, infrared sensor, infrared or visible spectrum imaging device, thermophile infrared sensor, any combination thereof, and the like. Temperature, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of the heat energy of a system. Temperature, as measured by any number or combinations of sensors present within sensor116, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (° K), or another scale alone or in combination. The temperature measured by sensors may comprise electrical signals, such as output sensor signal, which are transmitted to their appropriate destination, such as computing device120, using a wireless and/or wired connection.

In other embodiments, sensor116may also include other electrical sensors to detect temperature of energy source104and/or components thereof. Electrical sensors may be configured to measure voltage across a component, electrical current through a component, and resistance of a component. For instance, and without limitation, sensor116may include a voltmeter, current sensor, hydrometer, photoelectric sensor, pressure sensor, radiation sensor, moisture sensor, electrical sensor, Hall sensor, and the like. In one or more embodiments, sensor116may include a plurality of independent sensors, where any number of the described sensors may be used to detect any number of physical quantities associated with energy source104or an electrical energy storage system of aircraft108. Independent sensors may include separate sensors measuring physical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability of sensor116to detect phenomenon, such as temperature of energy source104, may be maintained.

In some embodiments, sensor116may include a pressure sensor. “Pressure”, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of force required to stop a fluid from expanding and is usually stated in terms of force per unit area. The pressure sensor that may be included in sensor116may be configured to measure an atmospheric pressure and/or a change of atmospheric pressure. In some embodiments, the pressure sensor may include an absolute pressure sensor, a gauge pressure sensor, a vacuum pressure sensor, a differential pressure sensor, a sealed pressure sensor, and/or other unknown pressure sensors or alone or in a combination thereof. The pressor sensor may include a barometer. In some embodiments, the pressure sensor may be used to indirectly measure fluid flow, speed, water level, and altitude. In some embodiments, the pressure sensor may be configured to transform a pressure into an analogue electrical signal. In some embodiments, the pressure sensor may be configured to transform a pressure into a digital signal.

In one or more embodiments, sensor116may include a moisture sensor. “Moisture”, as used in this disclosure, is the presence of water, which may include vaporized water in air, condensation on the surfaces of objects, or concentrations of liquid water. Moisture may include humidity. “Humidity”, as used in this disclosure, is the property of a gaseous medium (almost always air) to hold water in the form of vapor.

In one or more embodiments, sensor116may include a sensor suite which may include an array of sensors that may detect similar or unique phenomena. For example, in a non-limiting embodiment, sensor suite may include a plurality of temperature sensors, such as, for example, thermometers or a mixture of thermistors and thermometers. Apparatus100may include a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. A sensor suite may include a plurality of independent sensors, as described in this disclosure, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with an energy source of an electric aircraft. Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as computing device120. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability to detect a phenomenon is maintained.

In one or more embodiments, sensor116may include a sense board. Sense board may have at least a portion of a circuit board that includes one or more sensors configured to, for example, measure a temperature of energy source104. In one or more embodiments, a sense board may be connected to one or more battery modules112a-nor cells of energy source104. In one or more embodiments, a sense board may include one or more circuits and/or circuit elements, including, for example, a printed circuit board component. Sense board may include, without limitation, a control circuit configured to perform and/or direct any actions performed by the sense board and/or any other component and/or element described in this disclosure. The control circuit may include any analog or digital control circuit, including without limitation a combinational and/or synchronous logic circuit, a processor, microprocessor, microcontroller, or the like.

In one or more embodiments, sensor116may be part of a battery management system of energy source104. The monitoring of a temperature of an energy source may be consistent with the battery monitoring and management disclosed in patent application Ser. No. 17/529,653, entitled “ELECTRIC AIRCRAFT BATTERY PACK AND METHOD OF USE”, which is incorporated in this disclosure in its entirety.

As previously mentioned, sensor116is communicatively connected to computing device120. Computing device120may include an crew-altering system (CAS), computer, processor124, pilot control, and/or a controller, such as a flight controller604(shown inFIG.6), so that sensor may transmit/receive signals to/from computing device120, respectively. Computing device120may include a processor124, a memory component128, a display132, and other components136, such as communication circuitry, sensors, or the like. Sensor116may be configured to generate an output sensor signal that includes temperature datum representing detected characteristics of energy source104and/or individual components thereof. Signals may include electrical, electromagnetic, visual, audio, radio waves, or another undisclosed signal type alone or in combination. Computing device120may include one or more processors, such as analog-to-digital converters. Processor124may be implemented as any appropriate processing device that may execute appropriate instructions, such as software instructions provided in memory128of computing device120. For example, and without limitation, processor124may include an application specific integrated circuit (ASIC), control circuit, microcontroller, logic device, such as a programmable logic device (PLD), processor, any combination thereof, and the like. Memory128may include one or more memory devices adapted to store data and information, such as temperature datum or temperature anomaly information. In one or more non-limiting embodiments, memory may include a volatile or nonvolatile memory. Computing device120may be a programmable computer that includes instruction recorded on a machine-readable memory, where the computer is operatively connected to one or more sensors, such as sensor116. Processor124may be configured to interface and communicate with various components of system100In one or more embodiments, computing device120may include display132. Display132may be directly attached to computing device120or may be remote to computing device120while communicatively connected to computing device120. Display132may be used to display captured and/or processed data from sensor116transmitted via an output sensor signal. Data may also include information processed by processor124, such as a temperature anomaly or alert. Data and/or information may be shown as an image. Data from sensor may also be shown on a graphic user interface (GUI) or other user application. Data from sensor116may also be displayed in conjunction with other information provided by and/or processed by computing device120. Display132ma include an image display device, such as a liquid crystal display (LCD), or any other types of display or monitors used to provide visual representation of datum and/or information. Other components136of computing device120may be used to implement any features of computing device120as may be desired for various applications. For example, and without limitation, other components136may include a clock, an independent power source, a visible light camera, communication circuitry, and the like. In one or more embodiments, computing device120may include a machine readable medium, which may be provided for storing non-transitory instructions for loading into memory128that may then be executed by processor124.

Still referring toFIG.1, sensor116is configured to measure a temperature of energy source104prior to flight and/or operation of electric aircraft108. For example, and without limitation, sensor116may monitor individual temperatures of battery modules112a-nand/or cells of energy source104. Sensor116may then transmit datum related to the detection to computing device120. For example, sensor116may transmit temperature datum related to a temperature of energy source104to an CAS, which may be, in non-limiting embodiments, within a cockpit of electric aircraft108. CAS may display, on for example display132, temperature information related to energy source104, or components thereof, indicating a preparedness of energy source104and electric aircraft108for flight. In one or more exemplary embodiments, battery cell failure may be characterized by a spike in temperature and sensor116may be configured to detect that increase in temperature and generate signals, which are discussed further below, to notify and/or alert users, support personnel, safety personnel, flight crew, maintainers, operators, emergency personnel, aircraft computers, a combination thereof, and the like.

Outputs, such as temperature datum or other output sensor signal, from sensor116or any other component present within system100may be analog or digital. Onboard or remotely located processors can convert those output signals from sensor116or sensor suite to a usable form by the destination of those signals, such as computing device120. The usable form of output signals from sensors through, for example, processor124may be either digital, analog, a combination thereof, or an otherwise unstated form. Processing may be configured to trim, offset, or otherwise compensate the outputs of sensor suite. Based on sensor output, processor124can determine the output to send to a downstream component. Processor124can include signal amplification, operational amplifier (OpAmp), filter, digital/analog conversion, linearization circuit, current-voltage change circuits, resistance change circuits such as Wheatstone Bridge, an error compensator circuit, a combination thereof or otherwise undisclosed components.

Still referring toFIG.1, as previously mentioned, sensor116may be configured to detect a temperature of energy source104, and/or components thereof, prior to use of electric aircraft108as part of a preflight routine. In one or more embodiments, sensor116may be configured to generate an output sensor signal representing the detected temperature. For instance, and without limitation, sensor116may generate temperature datum as a function of the temperature detected by sensor116. For purposes of this disclosure, a “temperature datum” is a signal representing at least a quantifiable element of data correlated to a temperature of an energy source. For instance, and without limitation, energy source may need to be a certain temperature to operate properly during flight; temperature datum may provide a numerical value, such as temperature in degrees, that indicates the current temperature of energy source104. For example, and without limitation, sensor116may be a thermistor that detects the temperature of energy source104to be at a numerical value of 70° F. and transmits the corresponding temperature datum to, for example, computing device120.

Still referring toFIG.1, computing device120is communicatively connected to sensor116and configured to determine a temperature anomaly of energy source104as a function of received temperature datum from sensor116. In one or more embodiments, computing device120may include a processor that executes instructions provided by for example, a user input or memory128, and receives sensor output such as, for example, temperature datum, to output a temperature anomaly.

In one or more embodiments, computing device120may be configured to obtain an optimal temperature parameter of energy source104of electric aircraft108, where the optimal temperature range is provided by, for example, a user input or a database. For purposes of this disclosure, an “optimal temperature parameter” is an element of information regarding a maximized and/or a most effective operating temperature of an energy source. In one or more embodiments, an optimal temperature may include one or more desired operating temperatures of energy source104for takeoff of electric aircraft108. For example, and without limitation, an optimal temperature parameter may include a range of optimal temperatures with, for example, an upper threshold and a lower threshold of operational temperatures for energy source104. In a non-limiting embodiment, an optimal temperature parameter of energy source104prior to takeoff may be a range between 75° F. to 80° F., where 75° F. is the lower threshold and 80° F. is the upper threshold. Optimal temperature parameter may be obtained by computing device120in various ways. For example, in non-limiting embodiments, an optimal temperature parameter may be obtained from a prior use element, where a past optimal temperature parameter of energy source104may be stored in a memory128of computing device120for future reference. For the purposes of this disclosure, a “prior use element” is data and/or information obtained from previous experiences related to a temperature of a power source that may be stored in a memory of a computing device. In other non-limiting embodiments, an optimal performance condition is obtained from a user input. For example, and without limitation, a user, such as maintenance personnel, may manually input an optimal temperature parameter using, for example, a graphic user interface of computing device120or of a remote device that may interface with system100. In other non-limiting embodiments, an optimal temperature parameter is obtained from an energy source database, as shown inFIG.2, as discussed further below in this disclosure.

Still referring toFIG.1, computing device120may identify a current condition of an energy source104as a function of temperature datum. For purposes of this disclosure, an “current condition” is an element of information regarding a present-time temperature of an energy source and/or a component thereof. Current condition of energy source104may be determined based on temperature datum provided by sensor116. For example, and without limitation, a current condition for a temperature of energy source104may be 80° F. In yet another example, and without limitation, a current condition for a state of temperature of energy source104may be 60° F. due to cool ambient temperatures caused by, for example, environmental weather. Current condition of energy source104may be displayed on a display of computing device120for a user to view. For example, current condition may be displayed on a CAS of electric aircraft108. CAS may also store information related to the temperature of energy source104, such as temperature datum, current condition of energy source104, temperature anomaly, optimal temperature parameter, magnitude of divergence, and the like. Such information may also be stored on a fleet management system, remote device, and the like.

Still referring toFIG.1, computing device120may determine a temperature anomaly as a function of optimal temperature parameter and current condition of energy source104. For the purposes of this disclosure, a “temperature anomaly” is qualitative and/or quantitative information describing a deviation of a current condition from an optimal temperature parameter of an energy source. In one or more embodiments, temperature anomaly may indicate energy source104is operating outside of a preconfigured threshold (also referred to herein as a “threshold”) of optimal temperature parameter, as previously mentioned in this disclosure. For the purposes of this disclosure, a “threshold” is a set desired range and/or value that, when current condition is outside of set desired range and/or value, a specific reaction of computing device120is initiated. A specific reaction may be, for example, activation of a thermal management component140, such as a cooling or heating system of electric aircraft108, as discussed further below in this disclosure. In one or more embodiments, temperature anomaly may include a divergence magnitude, which indicates a quantity that current condition is outside of threshold. Threshold may be set by, for example, a user based on, for example, prior use or input. In one or more embodiments, if current condition of energy source104is determined to be outside of threshold of optimal temperature parameter, then temperature anomaly and divergence magnitude may be determined by computing device120. For example, and without limitation, if electric aircraft108has just landed and parked, then energy source104may need to be cooled to a lower temperature than the current measured temperature of energy source104prior to taking off again. An optimal temperature parameter for a temperature of energy source104may be 75° F. A threshold related to optimal temperature parameter may, thus, be set at 75° F. If a current condition is determined to be 90° F., the temperature anomaly is detected with a divergence magnitude of 15° F., indicating the amount that current condition is above threshold. Current condition being below threshold indicates that energy source104is a temperature considered too high to for takeoff of electric aircraft108.

In one or more embodiments, determining temperature anomaly may include one or more thresholds that denote a magnitude and/or level of divergence. For instance, and without limitation, a magnitude of divergence may include a quantitative value, as described previously in this disclosure. In another instance, and without limitation, magnitude of divergence may include a qualitative value. For example, and without limitation, a magnitude of divergence may include “low” divergence, a “medium” divergence, and/or a “high” divergence. In one or more exemplary embodiments, a “low” magnitude of divergence may result in notification of a user via, for example, an indicator or graphic user interface but energy source104may still be considered in operational condition and, thus, prepared for takeoff. In another example, a user may choose to takeoff despite the determined temperature anomaly or may decide to initiate an energy source modification. For the purposes of this disclosure, an “energy source modification” is a signal transmitted to an aircraft and/or energy source subsystem providing a command and/or instruction to perform an operation that adjusts a current temperature of an energy source to an optimal temperature parameter of the power source and/or adjust the current condition by the magnitude of divergence. In one or more exemplary embodiments, a “medium” magnitude of divergence may result in a notification or alert of a user and a required energy source modification. For example, and without limitation, if energy source104is considered too cold to operate, an energy source modification of heating energy source104must be initiated and completed prior to takeoff. In one or more exemplary embodiments, a “high” magnitude of divergence may result in computing device120determining that energy source requires maintenance and/or replacement prior to takeoff. As understood by one skilled in the art, a temperature anomaly may be determined for energy source104and/or for each battery module112a-nof energy source104.

Still referring toFIG.1, computing device120may initiate energy source modification as a function of determined temperature anomaly to regulate a temperature of energy source104. In one or more embodiments, energy source modification instructions may include an adjustment of a temperature of energy source104using a thermal management component140, such as a cooling component or a heating component. For instance, and without limitation, energy source modification may include computing device120providing a command signal to a cooling component200(shown inFIG.2) to perform a modification action. For the purposes of this disclosure, a “modification action” is an act and/or process performed by an electric aircraft subsystem in response to a received energy source modification from computing device120. In one or more non-limiting embodiments, energy source modification may include a temperature adjustment of energy source104using a cooling system or a heating system. For example, and without limitation, energy source modification may be sent to a cooling component200of system100. As a result, cooling component may produce a modification action, which includes providing cooled air to energy source104via, for example, a cooling component200. In one or more non-limiting exemplary embodiments, a thermal management component140may include a cooling component, heating component, liquid cooling system, battery ventilation, where ambient air is drawn about batteries then vented outboard (using an air conditioning duct), a heat pump, a heat sink, a puller fan, a compressor (used to supply bleed-air, which can be utilized in, for example, deicing and anti-icing of energy source104and pneumatic starting of engines), a condenser, a humidifier, an extract fan, a ground cooling unit, a blower fan, or the like. For example, and without limitation, thermal management component140may include a block heater that may be commanded to perform a modification action including heating energy source104to an optimal temperature parameter. Thermal management component may be consistent with the cooling circuit disclosed in patent application Ser. No. 17/405,840, entitled “CONNECTOR AND METHODS OF USE FOR CHARGING AN ELECTRIC VEHICLE”, which is incorporated in this disclosure in its entirety. Furthermore, thermal management component may be consistent with the venting system disclosed in patent application Ser. No. 17/702,195, entitled “A SYSTEM AND METHODS FOR VENTING OF A POWER SOURCE OF AN ELECTRIC VEHICLE”, which is incorporated in this disclosure in its entirety. Furthermore, thermal management component may be consistent with the thermal management system disclosed in patent application Ser. No. 17/515,441, entitled “SYSTEM AND METHODS FOR PRECONDITIONING A POWER SOURCE OF AN ELECTRIC AIRCRAFT”, which is incorporated in this disclosure in its entirety.

Now referring toFIG.2, an exemplary embodiment of a thermal management component, cooling component200, is shown in accordance with one or more embodiments of the present disclosure. In one or more embodiments, cooling component200may be used for regulating a temperature of an electric aircraft energy source. Cooling component200may perform a modification action based on a received energy source modification. Cooling component200may be integrated into electric aircraft108or be a separate unit. In one or more embodiments, cooling component200includes a channel204extending throughout energy source104of electric aircraft108. Channel204may be configured to contain a coolant that absorbs heat from energy source during preflight management of energy source104. In one or more embodiments, channel204extends from and/or through energy source104to, for example, an electric port208of energy source104. As used in this disclosure, a “channel” is a component that is substantially impermeable to a coolant and contains and/or directs a coolant flow, such as along a coolant flow path. In or more embodiments, a coolant may include various types of liquids, such as glycol or water. Coolant may traverse along a flow path212within channel204. For example, and without limitation, coolant may flow parallel to a longitudinal axis of channel204. In one or more embodiments, channel may include a duct, passage, tube, pipe, conduit, and the like. In one or more embodiments, channel204may be various shapes and sizes, for example, channel204may have a circular, triangular, rectangular, or any other shaped cross-section. Channel204may be composed of a rigid material or a flexible material. For example, and without limitation, channel204may be composed of polypropylene, polycarbonate, acrylonitrile butadiene styrene, polyethylene, nylon, polystyrene, polyether ether ketone, and the like. In one or more embodiments, channel204may be arranged in a loop. For example, and without limitation, liquid traversing through channel204may repeatedly circulate through channel204and be reused as well as liquid may be traversed through channel204to a distal end of power supply then return to energy source104at the proximal end of power supply. For example, and without limitation, a coolant may be circulated unidirectionally through channel204. In other embodiments, channel204may be a singular path. For example, and without limitation, channel204may include a path that allows for a liquid to move bidirectionally through channel204. In one or more embodiments, channel204may include a plurality of channels (as shown InFIG.2). For example, and without limitation, one channel may bifurcate into two channels that are configured to be positioned at different locations along energy source112and/or components thereof. In another example, channel204may be a singular channel that, for example, forms a loop.

In one or more embodiments, channel204may include a passage that contains a coolant and allows the coolant to traverse therethrough. As used in this disclosure, “coolant” is any flowable heat transfer medium. Coolant may include a liquid. For example, and without limitation, coolant may be glycol or water, as previously mentioned. Coolant may include a compressible fluid and/or a non-compressible fluid. Coolant may include a non-electrically conductive liquid such as a fluorocarbon-based fluid, such as without limitation Fluorinert™ from3M of Saint Paul, Minnesota, USA. As used in this disclosure, a “flow of coolant” is a fluid motion of a coolant, such as a stream of coolant. In one or more embodiments, channel204may be in fluidic communication with a coolant source144and/or a heat exchanger216, as discussed further below. In one or more embodiments, channel204may abut energy source104so that coolant within channel204may absorb heat from energy source104. For example, and without limitation, channel204may abut battery modules112a-nof energy source104, which may be, for example, a battery and/or battery pack.

Still referring toFIG.2, coolant component200includes a heat exchanger216configured to dissipate heat absorbed by a coolant. For the purposes of this disclosure, a “heat exchanger” is a component and/or system used to transfer thermal energy, such as heat, from one medium to another. For example, and without limitation, a heat exchanger may be a radiator. In one or more embodiments, heat exchanger216may be configured to transfer heat between a coolant and ambient air. As used in this disclosure, “ambient air” is air which is proximal a system and/or subsystem, for instance the air in an environment which a system and/or sub-system is operating. In one or more embodiments, heat exchanger216may include a cross-flow, parallel-flow, or counter-flow heat exchanger. In one or more embodiments, heat exchanger216may include a finned tube heat exchanger, a plate fin heat exchanger, a plate heat exchanger, a helical-coil heat exchanger, and the like. In other embodiments, heat exchanger216may include chillers, Peltier junctions, heat pumps, refrigeration, air conditioning, expansion or throttle valves, and the like, vapor-compression cycle system, vapor absorption cycle system, gas cycle system, Stirling engine, reverse Carnot cycle system, and the like.

In one or more embodiments, computing device120may be configured to control a temperature of coolant using means previously described in this disclosure. For instance, in some cases, a cooling unit sensor220may be located within thermal communication with coolant, such that sensor is able to detect, measure, or otherwise quantify a temperature of coolant. In some cases, sensor may include a thermometer. Exemplary thermometers include without limitation, pyrometers, infrared non-contacting thermometers, thermistors, thermocouples, and the like. In some cases, coolant flow may have a rate within a specified range. A non-limiting exemplary coolant flow range may be about 0.1 CFM to about 100 CFM. In some cases, rate of coolant flow may be considered as a volumetric flow rate. Alternatively or additionally, rate of coolant flow may be considered as a velocity or flux. In one or more embodiments, heat exchanger216may cool, or lower the temperature, of coolant. For example, heat exchanger216may cool coolant to below an ambient air temperature. In some cases, coolant source224and heat exchanger216may be powered by electricity, such as by way of one or more electric motors. Alternatively or additionally, coolant source224and heat exchanger216may be powered by a combustion engine, for example a gasoline powered internal combustion engine. In one or more embodiments, coolant flow may be configured, such that heat transfer is facilitated between coolant and an energy source, by any methods known and/or described in this disclosure. In some cases, coolant flow may be configured to facilitate heat transfer between the coolant flow and at least a conductor of electric aircraft, including, and without limitation, electrical busses connected to energy source104.

In one or more embodiments, coolant component may include coolant source224, which may be actuated to circulate coolant through channel204, such as along flow path212. As used in this disclosure, a “coolant source” is an origin, generator, reservoir, or flow of coolant producer. In one or more embodiments, coolant source224may include a flow producer, such as a pump or valve, that is configured to displace a coolant within channel204. In one or more embodiments, coolant source224may also include a reservoir, such as a tank or container, that may be configured to store a coolant until the coolant is moved into channel204or receive coolant, such as receive coolant returning from absorbing heat from energy source104. Coolant source224may include a flow producer, such as a fan and/or a pump. Coolant source224may include any of following non-limiting examples, air conditioner, refrigerator, heat exchanger, pump, fan, expansion valve, and the like. In one or more embodiments, coolant source144is configured to displace coolant through channel204during charging of energy source104of electric aircraft108. In some embodiments, channel204may facilitate fluidic and/or thermal communication with heat exchanger216and, for example, energy source104prior to takeoff of electric aircraft108. In some cases, a plurality of channels, coolant sources, and/or connectors may be used to connect to multiple components of electric aircraft108. In one or more embodiments, cooling of energy source104may be feedback controlled, by way of at least a sensor, such as sensor116, and occur until or for a predetermined time after a certain condition has been met, such as, and without limitation, when at least energy source104is within optimal temperature parameter. In some non-limiting cases, computing device120may use a machine-learning process to optimize cooling time relative of current charging metrics, for example energy source parameters and/or sensor signals. Coolant source224may include any computing device described in this disclosure. Coolant source224and computing device120may utilize any machine-learning process described in this disclosure.

As understood by one skilled in the art, various other thermal management components may be used to alter the temperature of energy source104so that energy source may achieve a temperature within a desired optimal temperature parameter, as previously discussed in this disclosure. For example, a temperature of energy source104may be regulated using a passive ventilation system that allows ambient air from an external environment of electric aircraft108to circulated within an area around energy source104. In other embodiments, a ground cooling unit may be used, where a charging station may provide a coolant during charging to reduce a temperature of energy source104.

Now referring toFIG.3, flow chart of an exemplary method300of preconditioning energy source104of electric aircraft108is shown in accordance with one or more embodiments of the present disclosure. As shown in block305, method300may include detecting, by sensor116attached to energy source104of electric aircraft108, condition datum132of energy source104.

As shown in block310, method300may include obtaining, by flight controller communicatively connected to sensor116, an optimal performance condition of energy source104of electric aircraft108. As previously mentioned, an optimal performance condition may include a maximized function of energy source104.

As shown in block315, method300may include identifying an operating condition of the power source as a function of the condition datum.

As shown in block320, method300may include determining a temperature anomaly as a function of the optimal performance condition and the operating condition of the power source. In one or more embodiments, the temperature anomaly may indicate the power source is operating outside of the optimal performance condition. If a temperature anomaly is determined, method300includes initiating an energy source modification as a function of the temperature anomaly, as shown in block325. Initiating an energy source modification includes thermal management component140adjusting a temperature of energy source104. In one or more embodiments, determining a temperature anomaly may include training a divergence machine-learning model using a training data, where the training data includes optimal performance condition and operating condition; and generating temperature anomaly as a function of divergence machine-learning model, as discussed further below in this disclosure. In one or more embodiments, initiating an energy source modification may include commanding thermal management component140of electric aircraft108to perform a modification action.

In one or more embodiments, method300may also include displaying a temperature anomaly on a display of, for example, flight controller; and receiving a user input, by flight controller, for energy source modification.

In one or more embodiments, and without limitation, a temperature anomaly may be determined as a function of optimal performance condition and operating condition. For example, and without limitation, computing device120may be configured to train a divergence machine-learning model using condition training data, where the condition training data includes a plurality of optimal performance condition elements correlated with operating condition elements. Computing device120may then be configured to generate temperature anomaly as a function of the divergence machine-learning model. For example, and without limitation, divergence machine-learning model may relate optimal performance condition with one or more operating conditions to determine a corresponding temperature anomaly and magnitude of divergence.

In one or more non-limiting embodiments, an anomaly machine-learning model may be used to identify a temperature anomaly of energy source104by computing device120. An energy source database may include one or more optimal temperature parameters for one or more types of energy sources. Energy source database may be programmed into computing device120, inputted by a user, acquired by a third-party application, any combination thereof, and the like. Energy source database may also change based on a prior use element. For example, and without limitation, a previous identification of one or more optimized temperatures of energy source104may be considered prior use elements. As understood by one skilled in the art, optimal temperature parameter may include a plurality of optimal temperature parameters that each maximize one or more functions, states, and/or outputs of energy source104.

In one or more embodiments, determining temperature anomaly may include using one or more machine-learning models, such as an exemplary anomaly machine-learning model. As previously mentioned, a machine-learning model may include one or more supervised machine-learning models, unsupervised machine-learning models, and the like thereof. For example, and without limitation, computing device120may be configured to train an anomaly machine-learning model using training data, where the training data includes a plurality of optimal temperature elements correlated with current condition elements. In one or more non-limiting exemplary embodiments, anomaly machine-learning model may include various algorithms and/or functions used to relate current condition and optimal temperature parameter to determine if there is a temperature anomaly of energy source104. If there is a temperature anomaly as determined by anomaly machine-learning module, then energy source modification may be initiated, as discussed above in this disclosure.

In one or more embodiments, and without limitation, temperature anomaly may be determined as a function of optimal temperature parameter and current condition of energy source104. For example, and without limitation, computing device120may be configured to train anomaly machine-learning model using condition training data, which includes a plurality of optimal temperature parameter elements correlated with current condition elements. Computing device120may then be configured to generate temperature anomaly and a magnitude of divergence as a function of anomaly machine-learning model. For example, and without limitation, anomaly machine-learning model may relate optimal temperature parameter with one or more current conditions to determine a corresponding temperature anomaly and magnitude of divergence.

After determining temperature anomaly and/or magnitude of divergence, computing device120may be configured to display temperature anomaly and receive a user input for energy source modification or automatically initiate energy source modification. In one or more embodiments, graphic user interface may notify a user of how much time is required to remedy a determined temperature anomaly based on thermal management component140used to change the temperature of energy source104and the magnitude of divergence. For example, and without limitation, a battery temperature status of energy source104may be provided on a display of an CAS of aircraft108or via an indicator, such as an LED indicator.

Now referring toFIG.5, an exemplary embodiment of electric aircraft108is illustrated in accordance with one or more embodiments of the present disclosure. An “aircraft”, as described herein, is a vehicle that travels through the air. As a non-limiting example, aircraft may include airplanes, helicopters, airships, blimps, gliders, paramotors, drones, and the like. Additionally or alternatively, an aircraft may include one or more electric aircrafts and/or hybrid electric aircrafts. For example, and without limitation, electric aircraft108may include an electric vertical takeoff and landing (eVTOL) aircraft, as shown inFIG.5. As used herein, a vertical takeoff and landing (eVTOL) aircraft is an electrically powered aircraft that can take off and land vertically. An eVTOL aircraft may be capable of hovering. In order, without limitation, to optimize power and energy necessary to propel an eVTOL or to increase maneuverability, the eVTOL may be capable of rotor-based cruising flight, rotor-based takeoff, rotor-based landing, fixed-wing cruising flight, airplane-style takeoff, airplane-style landing, and/or any combination thereof. Rotor-based flight is where the aircraft generates lift and propulsion by way of one or more powered rotors coupled with an engine, such as a “quad copter,” helicopter, or other vehicle that maintains its lift primarily using downward thrusting propulsors. Fixed-wing flight, as described herein, flight using wings and/or foils that generate life caused by an aircraft's forward airspeed and the shape of the wings and/or foils, such as in airplane-style flight.

Now referring toFIG.6, an exemplary embodiment600of a flight controller604is illustrated in accordance with one or more embodiments or the present disclosure. As used in this disclosure a “flight controller” is a computing device or a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller604may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller604may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller604may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith.

In an embodiment, and still referring toFIG.6, flight controller604may include a signal transformation component608. As used in this disclosure a “signal transformation component” is a component that transforms and/or converts a first signal to a second signal, wherein a signal may include one or more digital and/or analog signals. For example, and without limitation, signal transformation component608may be configured to perform one or more operations such as preprocessing, lexical analysis, parsing, semantic analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component608may include one or more analog-to-digital convertors that transform a first signal of an analog signal to a second signal of a digital signal. For example, and without limitation, an analog-to-digital converter may convert an analog input signal to a 10-bit binary digital representation of that signal. In another embodiment, signal transformation component608may include transforming one or more low-level languages such as, but not limited to, machine languages and/or assembly languages. For example, and without limitation, signal transformation component608may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component608may include transforming one or more high-level languages and/or formal languages such as but not limited to alphabets, strings, and/or languages. For example, and without limitation, high-level languages may include one or more system languages, scripting languages, domain-specific languages, visual languages, esoteric languages, and the like thereof. As a further non-limiting example, high-level languages may include one or more algebraic formula languages, business data languages, string and list languages, object-oriented languages, and the like thereof.

Still referring toFIG.6, signal transformation component608may be configured to optimize an intermediate representation612. As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component608may optimize intermediate representation as a function of a data-flow analysis, dependence analysis, alias analysis, pointer analysis, escape analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component608may optimize intermediate representation612as a function of one or more inline expansions, dead code eliminations, constant propagation, loop transformations, and/or automatic parallelization functions. In another embodiment, signal transformation component608may optimize intermediate representation as a function of a machine dependent optimization such as a peephole optimization, wherein a peephole optimization may rewrite short sequences of code into more efficient sequences of code. Signal transformation component608may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller604. For example, and without limitation, native machine language may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformation component608may include transform one or more inputs and outputs as a function of an error correction code. An error correction code, also known as error correcting code (ECC), is an encoding of a message or lot of data using redundant information, permitting recovery of corrupted data. An ECC may include a block code, in which information is encoded on fixed-size packets and/or blocks of data elements such as symbols of predetermined size, bits, or the like. Reed-Solomon coding, in which message symbols within a symbol set having q symbols are encoded as coefficients of a polynomial of degree less than or equal to a natural number k, over a finite field F with q elements; strings so encoded have a minimum hamming distance of k+1, and permit correction of (q−k−1)/2 erroneous symbols. Block code may alternatively or additionally be implemented using Golay coding, also known as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-check coding, and/or Hamming codes. An ECC may alternatively or additionally be based on a convolutional code.

In an embodiment, and still referring toFIG.6, flight controller604may include a reconfigurable hardware platform616. A “reconfigurable hardware platform,” as used herein, is a component and/or unit of hardware that may be reprogrammed, such that, for instance, a data path between elements such as logic gates or other digital circuit elements may be modified to change an algorithm, state, logical sequence, or the like of the component and/or unit. This may be accomplished with such flexible high-speed computing fabrics as field-programmable gate arrays (FPGAs), which may include a grid of interconnected logic gates, connections between which may be severed and/or restored to program in modified logic. Reconfigurable hardware platform616may be reconfigured to enact any algorithm and/or algorithm selection process received from another computing device and/or created using machine-learning processes.

Still referring toFIG.6, reconfigurable hardware platform616may include a logic component620. As used in this disclosure a “logic component” is a component that executes instructions on output language. For example, and without limitation, logic component may perform basic arithmetic, logic, controlling, input/output operations, and the like thereof. Logic component620may include any suitable processor, such as without limitation a component incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; logic component620may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component620may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC). In an embodiment, logic component620may include one or more integrated circuit microprocessors, which may contain one or more central processing units, central processors, and/or main processors, on a single metal-oxide-semiconductor chip. Logic component620may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation612. Logic component620may be configured to fetch and/or retrieve the instruction from a memory cache, wherein a “memory cache,” as used in this disclosure, is a stored instruction set on flight controller604. Logic component620may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component620may be configured to execute the instruction on intermediate representation612and/or output language. For example, and without limitation, logic component620may be configured to execute an addition operation on intermediate representation612and/or output language.

In an embodiment, and without limitation, logic component620may be configured to calculate a flight element624. As used in this disclosure a “flight element” is an element of datum denoting a relative status of aircraft. For example, and without limitation, flight element624may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, forces, orientations, and the like thereof. For example, and without limitation, flight element624may denote that aircraft is cruising at an altitude and/or with a sufficient magnitude of forward thrust. As a further non-limiting example, flight status may denote that is building thrust and/or groundspeed velocity in preparation for a takeoff. As a further non-limiting example, flight element624may denote that aircraft is following a flight path accurately and/or sufficiently.

Still referring toFIG.6, flight controller604may include a chipset component628. As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component628may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component620to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component628may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component620to lower-speed peripheral buses, such as a peripheral component interconnect (PCI), industry standard architecture (ICA), and the like thereof. In an embodiment, and without limitation, southbridge data flow path may include managing data flow between peripheral connections such as ethernet, USB, audio devices, and the like thereof. Additionally or alternatively, chipset component628may manage data flow between logic component620, memory cache, and a flight component632. As used in this disclosure a “flight component” is a portion of an aircraft that can be moved or adjusted to affect one or more flight elements. For example, flight component632may include a component used to affect the aircrafts' roll and pitch which may comprise one or more ailerons. As a further example, flight component632may include a rudder to control yaw of an aircraft. In an embodiment, chipset component628may be configured to communicate with a plurality of flight components as a function of flight element624. For example, and without limitation, chipset component628may transmit to an aircraft rotor to reduce torque of a first lift propulsor and increase the forward thrust produced by a pusher component to perform a flight maneuver.

In an embodiment, and still referring toFIG.6, flight controller604may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller604that controls aircraft automatically. For example, and without limitation, autonomous function may perform one or more aircraft maneuvers, take offs, landings, altitude adjustments, flight leveling adjustments, turns, climbs, and/or descents. As a further non-limiting example, autonomous function may adjust one or more airspeed velocities, thrusts, torques, and/or groundspeed velocities. As a further non-limiting example, autonomous function may perform one or more flight path corrections and/or flight path modifications as a function of flight element624. In an embodiment, autonomous function may include one or more modes of autonomy such as, but not limited to, autonomous mode, semi-autonomous mode, and/or non-autonomous mode. As used in this disclosure “autonomous mode” is a mode that automatically adjusts and/or controls aircraft and/or the maneuvers of aircraft in its entirety. For example, autonomous mode may denote that flight controller604will adjust the aircraft. As used in this disclosure a “semi-autonomous mode” is a mode that automatically adjusts and/or controls a portion and/or section of aircraft. For example, and without limitation, semi-autonomous mode may denote that a pilot will control the propulsors, wherein flight controller604will control the ailerons and/or rudders. As used in this disclosure “non-autonomous mode” is a mode that denotes a pilot will control aircraft and/or maneuvers of aircraft in its entirety.

In an embodiment, and still referring toFIG.6, flight controller604may generate autonomous function as a function of an autonomous machine-learning model. As used in this disclosure an “autonomous machine-learning model” is a machine-learning model to produce an autonomous function output given flight element624and a pilot signal636as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. As used in this disclosure a “pilot signal” is an element of datum representing one or more functions a pilot is controlling and/or adjusting. For example, pilot signal636may denote that a pilot is controlling and/or maneuvering ailerons, wherein the pilot is not in control of the rudders and/or propulsors. In an embodiment, pilot signal636may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal636may include an explicit signal, wherein the pilot explicitly states there is a lack of control and/or desire for autonomous function. As a further non-limiting example, pilot signal636may include an explicit signal directing flight controller604to control and/or maintain a portion of aircraft, a portion of the flight plan, the entire aircraft, and/or the entire flight plan. As a further non-limiting example, pilot signal636may include an implicit signal, wherein flight controller604detects a lack of control such as by a malfunction, torque alteration, flight path deviation, and the like thereof. In an embodiment, and without limitation, pilot signal636may include one or more explicit signals to reduce torque, and/or one or more implicit signals that torque may be reduced due to reduction of airspeed velocity. In an embodiment, and without limitation, pilot signal636may include one or more local and/or global signals. For example, and without limitation, pilot signal636may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal636may include a global signal that is transmitted by air traffic control and/or one or more remote users that are in communication with the pilot of aircraft. In an embodiment, pilot signal636may be received as a function of a tri-state bus and/or multiplexor that denotes an explicit pilot signal should be transmitted prior to any implicit or global pilot signal.

Still referring toFIG.6, autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller604and/or a remote device may or may not use in the generation of autonomous function. As used in this disclosure “remote device” is an external device to flight controller604. Additionally or alternatively, autonomous machine-learning model may include one or more autonomous machine-learning processes that a field-programmable gate array (FPGA) may or may not use in the generation of autonomous function. Autonomous machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

In an embodiment, and still referring toFIG.6, autonomous machine learning model may be trained as a function of autonomous training data, wherein autonomous training data may correlate a flight element, pilot signal, and/or simulation data to an autonomous function. For example, and without limitation, a flight element of an airspeed velocity, a pilot signal of limited and/or no control of propulsors, and a simulation data of required airspeed velocity to reach the destination may result in an autonomous function that includes a semi-autonomous mode to increase thrust of the propulsors. Autonomous training data may be received as a function of user-entered valuations of flight elements, pilot signals, simulation data, and/or autonomous functions. Flight controller604may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function. Autonomous training data may be received by one or more remote devices and/or FPGAs that at least correlate a flight element, pilot signal, and/or simulation data to an autonomous function. Autonomous training data may be received in the form of one or more user-entered correlations of a flight element, pilot signal, and/or simulation data to an autonomous function.

Still referring toFIG.6, flight controller604may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes, wherein a remote device and an FPGA is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, FPGA, microprocessor and the like thereof. Remote device and/or FPGA may perform the autonomous machine-learning process using autonomous training data to generate autonomous function and transmit the output to flight controller604. Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller604that at least relates to autonomous function. Additionally or alternatively, the remote device and/or FPGA may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, an autonomous machine-learning process correction, and the like thereof. As a non-limiting example, a software update may incorporate a new simulation data that relates to a modified flight element. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device and/or FPGA, wherein the remote device and/or FPGA may replace the autonomous machine-learning model with the updated machine-learning model and generate the autonomous function as a function of the flight element, pilot signal, and/or simulation data using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and/or FPGA and received by flight controller604as a software update, firmware update, or corrected autonomous machine-learning model. For example, and without limitation autonomous machine learning model may utilize a neural net machine-learning process, wherein the updated machine-learning model may incorporate a gradient boosting machine-learning process.

Still referring toFIG.6, flight controller604may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Further, flight controller may communicate with one or more additional devices as described below in further detail via a network interface device. The network interface device may be utilized for commutatively connecting a flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. The network may include any network topology and can may employ a wired and/or a wireless mode of communication.

In an embodiment, and still referring toFIG.6, flight controller604may include, but is not limited to, for example, a cluster of flight controllers in a first location and a second flight controller or cluster of flight controllers in a second location. Flight controller604may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller604may be configured to distribute one or more computing tasks as described below across a plurality of flight controllers, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. For example, and without limitation, flight controller604may implement a control algorithm to distribute and/or command the plurality of flight controllers. As used in this disclosure a “control algorithm” is a finite sequence of well-defined computer implementable instructions that may determine the flight component of the plurality of flight components to be adjusted. For example, and without limitation, control algorithm may include one or more algorithms that reduce and/or prevent aviation asymmetry. As a further non-limiting example, control algorithms may include one or more models generated as a function of a software including, but not limited to Simulink by MathWorks, Natick, Massachusetts, USA. In an embodiment, and without limitation, control algorithm may be configured to generate an auto-code, wherein an “auto-code,” is used herein, is a code and/or algorithm that is generated as a function of the one or more models and/or software's. In another embodiment, control algorithm may be configured to produce a segmented control algorithm. As used in this disclosure a “segmented control algorithm” is control algorithm that has been separated and/or parsed into discrete sections. For example, and without limitation, segmented control algorithm may parse control algorithm into two or more segments, wherein each segment of control algorithm may be performed by one or more flight controllers operating on distinct flight components.

In an embodiment, and still referring toFIG.6, control algorithm may be configured to determine a segmentation boundary as a function of segmented control algorithm. As used in this disclosure a “segmentation boundary” is a limit and/or delineation associated with the segments of the segmented control algorithm. For example, and without limitation, segmentation boundary may denote that a segment in the control algorithm has a first starting section and/or a first ending section. As a further non-limiting example, segmentation boundary may include one or more boundaries associated with an ability of flight component632. In an embodiment, control algorithm may be configured to create an optimized signal communication as a function of segmentation boundary. For example, and without limitation, optimized signal communication may include identifying the discrete timing required to transmit and/or receive the one or more segmentation boundaries. In an embodiment, and without limitation, creating optimized signal communication further comprises separating a plurality of signal codes across the plurality of flight controllers. For example, and without limitation the plurality of flight controllers may include one or more formal networks, wherein formal networks transmit data along an authority chain and/or are limited to task-related communications. As a further non-limiting example, communication network may include informal networks, wherein informal networks transmit data in any direction. In an embodiment, and without limitation, the plurality of flight controllers may include a chain path, wherein a “chain path,” as used herein, is a linear communication path comprising a hierarchy that data may flow through. In an embodiment, and without limitation, the plurality of flight controllers may include an all-channel path, wherein an “all-channel path,” as used herein, is a communication path that is not restricted to a particular direction. For example, and without limitation, data may be transmitted upward, downward, laterally, and the like thereof. In an embodiment, and without limitation, the plurality of flight controllers may include one or more neural networks that assign a weighted value to a transmitted datum. For example, and without limitation, a weighted value may be assigned as a function of one or more signals denoting that a flight component is malfunctioning and/or in a failure state.

Still referring toFIG.6, the plurality of flight controllers may include a master bus controller. As used in this disclosure a “master bus controller” is one or more devices and/or components that are connected to a bus to initiate a direct memory access transaction, wherein a bus is one or more terminals in a bus architecture. Master bus controller may communicate using synchronous and/or asynchronous bus control protocols. In an embodiment, master bus controller may include flight controller604. In another embodiment, master bus controller may include one or more universal asynchronous receiver-transmitters (UART). For example, and without limitation, master bus controller may include one or more bus architectures that allow a bus to initiate a direct memory access transaction from one or more buses in the bus architectures. As a further non-limiting example, master bus controller may include one or more peripheral devices and/or components to communicate with another peripheral device and/or component and/or the master bus controller. In an embodiment, master bus controller may be configured to perform bus arbitration. As used in this disclosure “bus arbitration” is method and/or scheme to prevent multiple buses from attempting to communicate with and/or connect to master bus controller. For example and without limitation, bus arbitration may include one or more schemes such as a small computer interface system, wherein a small computer interface system is a set of standards for physical connecting and transferring data between peripheral devices and master bus controller by defining commands, protocols, electrical, optical, and/or logical interfaces. In an embodiment, master bus controller may receive intermediate representation612and/or output language from logic component620, wherein output language may include one or more analog-to-digital conversions, low bit rate transmissions, message encryptions, digital signals, binary signals, logic signals, analog signals, and the like thereof described above in detail.

Still referring toFIG.6, master bus controller may communicate with a slave bus. As used in this disclosure a “slave bus” is one or more peripheral devices and/or components that initiate a bus transfer. For example, and without limitation, slave bus may receive one or more controls and/or asymmetric communications from master bus controller, wherein slave bus transfers data stored to master bus controller. In an embodiment, and without limitation, slave bus may include one or more internal buses, such as but not limited to a/an internal data bus, memory bus, system bus, front-side bus, and the like thereof. In another embodiment, and without limitation, slave bus may include one or more external buses such as external flight controllers, external computers, remote devices, printers, aircraft computer systems, flight control systems, and the like thereof.

In an embodiment, and still referring toFIG.6, control algorithm may optimize signal communication as a function of determining one or more discrete timings. For example, and without limitation master bus controller may synchronize timing of the segmented control algorithm by injecting high priority timing signals on a bus of the master bus control. As used in this disclosure a “high priority timing signal” is information denoting that the information is important. For example, and without limitation, high priority timing signal may denote that a section of control algorithm is of high priority and should be analyzed and/or transmitted prior to any other sections being analyzed and/or transmitted. In an embodiment, high priority timing signal may include one or more priority packets. As used in this disclosure a “priority packet” is a formatted unit of data that is communicated between the plurality of flight controllers. For example, and without limitation, priority packet may denote that a section of control algorithm should be used and/or is of greater priority than other sections.

Still referring toFIG.6, flight controller604may also be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of aircraft and/or computing device. Flight controller604may include a distributer flight controller. As used in this disclosure a “distributer flight controller” is a component that adjusts and/or controls a plurality of flight components as a function of a plurality of flight controllers. For example, distributer flight controller may include a flight controller that communicates with a plurality of additional flight controllers and/or clusters of flight controllers. In an embodiment, distributed flight control may include one or more neural networks. For example, neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring toFIG.6, a node may include, without limitation a plurality of inputs xithat may receive numerical values from inputs to a neural network containing the node and/or from other nodes. Node may perform a weighted sum of inputs using weights withat are multiplied by respective inputs xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight wiapplied to an input ximay indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights wimay be determined by training a neural network using training data, which may be performed using any suitable process as described above. In an embodiment, and without limitation, a neural network may receive semantic units as inputs and output vectors representing such semantic units according to weights withat are derived using machine-learning processes as described in this disclosure.

Still referring toFIG.6, flight controller may include a sub-controller640. As used in this disclosure a “sub-controller” is a controller and/or component that is part of a distributed controller as described above; for instance, flight controller604may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller640may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller640may include any component of any flight controller as described above. Sub-controller640may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller640may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data across the distributed flight controller as described above. As a further non-limiting example, sub-controller640may include a controller that receives a signal from a first flight controller and/or first distributed flight controller component and transmits the signal to a plurality of additional sub-controllers and/or flight components.

Still referring toFIG.6, flight controller may include a co-controller644. As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller604as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller644may include one or more controllers and/or components that are similar to flight controller604. As a further non-limiting example, co-controller644may include any controller and/or component that joins flight controller604to distributer flight controller. As a further non-limiting example, co-controller644may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller604to distributed flight control system. Co-controller644may include any component of any flight controller as described above. Co-controller644may be implemented in any manner suitable for implementation of a flight controller as described above.

Computer system700may also include a storage device724. Examples of a storage device (e.g., storage device724) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device724may be connected to bus712by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device724(or one or more components thereof) may be removably interfaced with computer system700(e.g., via an external port connector (not shown)). Particularly, storage device724and an associated machine-readable medium728may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system700. In one example, software720may reside, completely or partially, within machine-readable medium728. In another example, software720may reside, completely or partially, within processor704.

Computer system700may also include an input device732. In one example, a user of computer system700may enter commands and/or other information into computer system700via input device732. Examples of an input device732include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device732may be interfaced to bus712via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus712, and any combinations thereof. Input device732may include a touch screen interface that may be a part of or separate from display736, discussed further below. Input device732may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system700via storage device724(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device740. A network interface device, such as network interface device740, may be utilized for connecting computer system700to one or more of a variety of networks, such as network744, and one or more remote devices748connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network744, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software720, etc.) may be communicated to and/or from computer system700via network interface device740.