Patent Publication Number: US-2023158907-A1

Title: Recharging station for electric aircrafts and a method of its use

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of Non-Provisional application Ser. No. 17/678,337 filed on Feb. 23, 2022, and entitled “RECHARGING STATION FOR ELECTRIC AIRCRAFTS AND A METHOD OF ITS USE”, the entirety of which is incorporated herein by reference; which is a continuation-in-part of Non-provisional application Ser. No. 17/361,911 filed on Jun. 29, 2021 and entitled “RECHARGING STATION FOR ELECTRIC AIRCRAFTS AND A METHOD OF ITS USE,” the entirety of which is incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention generally relates to the field of charging systems for electric aircrafts. In particular, the present invention is directed to a recharging station for an electric aircraft and a method of its use. 
     BACKGROUND 
     Modern electric aircraft such as vertical takeoff and landing (eVTOL) aircrafts are limited in their range depending on their battery capacity. As such, electric aircrafts require charging stations to rapidly and reliably charge during trips. However modern electric vehicle charging stations put a great strain on electric power grids and cannot reliably and quickly receive power from these sources. 
     SUMMARY OF THE DISCLOSURE 
     In an aspect a recharging station for an electric aircraft includes an elevated landing pad disposed on top of a building, a recharging component coupled to the elevated landing pad, wherein the recharging component comprises a cable module, a power delivery unit configured to deliver stored power from a power supply unit to the recharging component, and a support component coupled to the bottom of the elevated landing pad. 
     In an aspect, a method of charging an electric aircraft using an elevated landing pad. In some embodiment, the method may include providing an elevated landing pad disposed on top of a building. In some embodiments, the elevated landing pad may be coupled to a rechargeable component. In some embodiments, the rechargeable component may be connected to a power delivery unit. In some embodiments, the method may include connecting an electric aircraft placed on the elevatable landing pad coupled to the rechargeable component. In some embodiments, the method may include charging the electric aircraft with power delivered by the rechargeable component using a cable module. 
     These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: 
         FIG.  1    is a side view of an exemplary embodiment of a recharging station; 
         FIG.  2    is a top view of an exemplary embodiment of a recharging station; 
         FIG.  3    is a block diagram of an exemplary embodiment of a recharging system; 
         FIG.  4    is a front view of an exemplary embodiment of a battery pack; 
         FIG.  5    is a block diagram of a thermal management system for a battery pack; 
         FIG.  6    is a block diagram of a battery charging management system; 
         FIG.  7    is a block diagram of an exemplary embodiment of a health and charge monitoring system; 
         FIG.  8    is a front view of an exemplary embodiment of an eVTOL; 
         FIG.  9    is a flowchart of illustrating an exemplary embodiment of a flight controller system; 
         FIG.  10    is a flowchart of an exemplary machine learning module; 
         FIG.  11    is a flow diagram illustrating an exemplary embodiment of a method of recharging an electric aircraft; 
         FIG.  12    is an exemplary embodiment of a cable reel module; 
         FIG.  13    is an exemplary embodiment of a connector for charging an electric aircraft 
         FIG.  14    is a block diagram of an exemplary embodiment of a computing system; and 
         FIG.  15    is a flow diagram illustrating an exemplary embodiment of a computing system; 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the art to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. 
     Described herein is a system for a recharging station for an electric aircraft. In one embodiment, a station for recharging an electric aircraft may include an elevated landing pad, a recharging component coupled to the elevated landing pad, a power delivery unit configured to deliver power from a power supply unit or a power storage unit to the recharging component, and a support component coupled to the bottom of the elevated landing pad. In some embodiments, recharging component may include a trickle charger. In other embodiments, support component may include a hydraulic lift system configured to move one or more persons and cargo to the elevated landing pad. In some embodiments, elevated landing pad may include an integrated lighting system. An integrated lighting system of elevated landing pad may include light-emitting diodes (LEDs) with night vision goggle compatibility. In some embodiments, elevated landing pad may comprise a deicing system. In some embodiments, power supply unit may include a solar inverter for on-site power generation. In some embodiments, support component may include a plurality of housing units. Plurality of housing units may include a freshwater storage unit. Plurality of housing units may also include a kitchen. In some embodiments, station may include a battery management system. In some embodiments, station may include a thermal management system. In another embodiment, recharging station may be configured to communicate data to and from an electric aircraft. In some embodiments, system may include a battery health monitoring system. In some embodiments, an electric aircraft to be used with station may include an eVTOL aircraft. 
     In some embodiments, recharging component may include a constant voltage charger, a constant current charger, a taper current charger, a pulsed current charger, a negative pulse charger, an IUI charger, a trickle charger and/or a float charger. In some embodiments, power delivery unit may be configured to deliver power stored from a power storage unit. In one embodiment, power storage unit may have a capacity of at least 500 kwh. In some embodiments, power delivery unit may be configured to connect to power storage unit through a DC to DC converter. In one embodiment, elevated landing pad may include an integrated lighting system. In some embodiments, integrated lighting system may include LEDs with night vision goggle compatibility. In one embodiment, elevated landing pad may include an integrated deicing system. In one embodiment, power delivery unit may be configured to connect to power storage unit through a DC to DC converter. In another embodiment, two or more electric aircrafts may be charged through the rechargeable component. 
     Referring now to  FIG.  1   , an exemplary embodiment of a recharging station  100  for recharging an electric aircraft is illustrated. As used in this disclosure an “aircraft” is a vehicle that may fly by gaining support from the air. As a non-limiting example, aircraft may include airplanes, helicopters, airships, blimps, gliders, paramotors, drones and the like thereof. In some embodiments, electric aircraft may be an electric vertical takeoff and landing (eVTOL) aircraft, for instance and without limitation as described in further detail below. 
     Continuing to refer to  FIG.  1   , an illustration of a recharging station  100  is shown. In some embodiments a recharging station  100  may be constructed from any of variety of suitable materials or any combination thereof. In some embodiments, recharging station  100  may be constructed from metal, concrete, polymers, or other durable materials. In one embodiment, recharging station  100  may be constructed from a lightweight metal alloy. In some embodiments, recharging station  100  may include a helideck or helipad. 
     In some embodiments, and with further reference to  FIG.  1   , recharging station  100  may be elevated above sea level. In one embodiment, recharging station  100  may be elevated at least 20 feet above sea level. In some embodiments, recharging station  100  may be elevated more than 20 feet above sea level. In one embodiment, recharging station  100  may have dimensions suitable for supporting various aircraft. In one embodiment, recharging station  100  may be at least 50 feet in area. In other embodiments recharging station  100  may have an area of greater or less than 50 feet. In another embodiment, two or more recharging stations  100  may combine together for greater surface area to support more aircrafts. 
     In some embodiments, and still referring to  FIG.  1   , recharging station  100  may have a support component  102  coupled to recharging station  100 . In one embodiment, support component  102  may include a support column  104 . Support column  104  may be made from a variety of suitable materials, which may include without limitation any materials described above as suitable for the recharging station  100 , to support one or more aircrafts on a recharging station  100 . In some embodiments the support column  104  may be made from a lightweight metal alloy. In some embodiments, a support component  102  may be coupled to the recharging station  100 . The support component  102  may be beneath the recharging station  100  to provide structural support and elevation. 
     In some embodiments, and continuing to refer to  FIG.  1   , the support component  102  may have a plurality of support columns  104 . The recharging pad  100  may also include supporting structures  110 . Supporting structures  110  may provide additional structural support to the recharging station  100 . Supporting structures  100  may have a net meshing  112 . Net meshing  112  may include a variety of suitable materials. In one embodiment, net meshing  112  may include, without limitation, polyester, nylon, polypropylene, polyethylene, PVC and PTFE. Net meshing  112  may provide additional support to recharging station  100 . Net meshing  112  may also act as a safety measure to prevent persons or cargo from falling off recharging station  100 . 
     In some embodiments, and with further reference to  FIG.  1   , support component  102  may comprise a plurality of modular housings  106 . Modular housings  106  may be configured based on the needs of a mission or location. For example, modular housings  106  may contain a hotel container for the pilot and flight crew to rest in. In one embodiment, a hotel container may include a bed, bathroom, shower, and integrated water heaters. In another embodiment, the modular housings  106  may have a control room for pilots and flight crew to relax, eat, study, and plan their next mission. 
     In another embodiment, and still referring to  FIG.  1   , a unit of a modular housings  106  may include an electrical power supply  108 . Electrical power supply may include an electrical storage unit such as a battery storage unit. The battery storage unit may contain batteries, a solar inverter, a power grid component, and power distribution panels. Any component of electrical power supply, including electrical storage may include, be included in, share components with, and/or be implemented according to any other electrical power supplies, storage units, or the like as described in this disclosure. In one embodiment, the plurality of modular housings of a support component  102  may enable quick construction and deconstruction of a recharging station  100 . In one embodiment, a support component  102  may be constructed on top of one or more buildings. In another embodiment, a support component  102  may be constructed in a remote location. In one embodiment, one of the modular housings of support component  102  may have a hotel container. The hotel container may include a bed, a bathroom, a shower, and a sink. In some embodiments, the hotel container may also serve as a storage unit for freshwater, gray water, and blackwater. In other embodiments, the hotel container may serve as a storage unit for a plumbing system. In some embodiments, a plumbing system may be integrated throughout support component  102 . In one embodiment, a plumbing system may include integrated water heaters. In some embodiments, the support component  102  may have a hydraulic lift system. In one embodiment, the hydraulic lift system may be configured to ascend or descend one or more persons and cargo to the recharging station  100 . In some embodiments, support component  102  may be configured to connect to a surrounding plumbing system. In some embodiments, support component  102  may be configured to connect to a surrounding sewage system. In other embodiments, support component  102  may be configured to connect to a septic tank system. 
     In some embodiments, and continuing to refer to  FIG.  1   , recharging station  100  may include a power supply unit. The power supply unit may have electrical components that may be configured to receive electrical power, which may include alternating current (“AC”) and/or direct current (“DC”) power, and output DC and/or AC power in a useable voltage, current, and/or frequency. In one embodiment, the power supply unit may include a power storage unit  108 . The power storage unit  108  may be configured to store 500 kwh of electrical energy. In another embodiment, power storage unit  108  may be configured to store more than 500 kwh of electrical energy. Power storage unit  108  may house a variety of electrical components. In one embodiment, power storage unit  108  may contain a solar inverter. The solar inverter may be configured to produce on-site power generation. In one embodiment, the power generated from the solar inverter may be stored in power storage unit  108 . In some embodiments, power storage unit  108  may include a used electric aircraft battery pack no longer fit for flight. Battery pack may be implemented, without limitation, as described in further detail with regard to  FIG.  4    below. 
     Still referring to  FIG.  1   , recharging station  100  may be disposed on top of a building. For the purposes of this disclosure, a “building” is a manmade structure including at least walls and a roof. As a non-limiting example, a base portion of recharging station  100  may be in direct abutment with an outer surface of a roof of a building. In some instances, support components  102  may in direct abutment with an outer surface of a roof of a building, In some instances, support columns  104  may be fixed into openings in a roof of a building. As a non-limiting example, support columns  104  may be cylindrical with a diameter. Openings in the roof of a building may be slightly larger than the diameter. Openings may include a depth. As used in this disclosure “depth” is a distance measurement that describes how far down into a roof of a building an opening extends through. Depth may be at least a third of a length of support columns  104 . In some instances, support columns may be fixed into openings at a depth by using a substance to keep support columns  104  in place. In some instances, support columns  104  may be fixed in place using bolts that may extend through support columns  104 . 
     With continued reference to  FIG.  1   , recharging station  100  may be elevated above a base level. As used in this disclosure, “base level” is zero elevation with respect to a roof of a building. In one embodiment, recharging station  100  may be elevated at least 20 feet above base level. In some embodiments, recharging station  100  may be elevated more than 20 feet above base level. In one embodiment, recharging station  100  may have dimensions suitable for supporting various aircraft. In one embodiment, recharging station  100  may be at least 50 feet in area. In other embodiments recharging station  100  may have an area of greater or less than 50 feet. In another embodiment, two or more recharging stations  100  may combine together for greater surface area to support more aircrafts. 
     Still referring to  FIG.  1   , power storage unit  108  may be disposed in between an elevated landing pad and base level. Power storage unit  108  may include a power supply unit. In some instances, elevated landing pad may be at base level. Power storage unit  108  may be stored within a building and may include connections to recharging station  100  to facilitate charging of electric aircraft. In some embodiments, roof of a building may include one or more ports for power storage unit  108  to be physically connected and/or electrically connected thereto. In some instances, one or more ports may be disposed on a different portions of roof of a building. One or more ports being disposed on different portions of roof of a building may enable multiple configurations of recharging station  100 . In some instances, one or more ports may enable one or more recharging stations  100  to be disposed on a roof of a building. One or more recharging stations  100  may enable one or more electric aircrafts to land on a roof of a building. It should be noted that configurations of one or more recharging stations may change as a function of space limitations on roof of a building. 
       FIG.  2    illustrates a top view of an embodiment of a recharging station  200 . In one embodiment, recharging station  200  may include an integrated lighting system  204 . In one embodiment, the integrated lighting system may include a plurality of light sources  204 , such as fluorescent, OLED, incandescent, halogen, metal halide, neon, high intensity discharge, low pressure sodium, and LEDS  204 . In one embodiment, light sources  204  may be green. In one embodiment, light sources  204  of the integrated lighting system may include night vision compatibility. In one embodiment, light sources  204  may be able to change colors. In another embodiment, light sources  204  may be configured to switch on and off in a pattern to signal to aircraft various messages, such as a SOS message. 
     In some embodiments, recharging station  200  may have an integrated deicing system. The integrated deicing system may be configured to keep recharging pad  200  free of weather obstruction such as snow, ice, sleet, or hail. In one embodiment, recharging station  200  may have supporting structures  206 . Supporting structures  206  may be configured to support one or more electric aircrafts on recharging station  200 . In one embodiment, recharging pad  200  may have a supported base  202 . Supported base  202  may be configured to be wider than recharging station  200 . Support base  202  may also provide a foundation for other supporting components such as modular units. In another embodiment, support base  202  may have an integrated heating and lighting system. 
       FIG.  3    illustrates a block diagram of an electrical system for recharging an electric aircraft, which may, without limitation, be incorporated in station  100 . In one embodiment, an electric aircraft  300  may be electrically coupled to a rechargeable component  302  of station  100 . Electric aircraft  300  may be charged through a charging connector attached to the recharge component  302  by a cable module. In some instances, cable module may include a cable reel. In some instances, cable module may include a dangling cable. In some instances, cable module may include any cable capable of charging electric aircraft  300 . Rechargeable component  302  may have a plurality of connections to comply with various electric air vehicle needs. Connectors for charging an electric aircraft is discussed in further detail in  FIG.  13   . In one embodiment, rechargeable component  302  may connect to manned and unmanned electric aircrafts of various sizes, such as an EVTOL or a drone. In another embodiment, rechargeable component  302  may switch between power transfer standards such as the combined charging system standard (CCS) and CHAdeMO standards. In another embodiment, rechargeable component  302  may adapt to multiple demand response interfaces. In one embodiment, rechargeable component  302  may have ADR 2.0 as a demand response interface. 
     In some embodiments, and still referring to  FIG.  3   , rechargeable component  302  may have a continuous power rating of at least 350 kVA. In other embodiments, the rechargeable component  302  may have a continuous power rating of over 350 kVA. In some embodiments, rechargeable component  302  may have a battery charge range up to 950 Vdc. In other embodiments, rechargeable component  302  may have a battery charge range of over 950 Vdc. In some embodiments, rechargeable component  302  may have a continuous charge current of at least 350 amps. In other embodiments, rechargeable component  302  may have a continuous charge current of over 350 amps. In some embodiments, rechargeable component  302  may have a boost charge current of at least 500 amps. In other embodiments, rechargeable component  302  may have a boost charge current of over 500 amps. In some embodiments, rechargeable component  302  may include any component with the capability of recharging an energy source of the electric aircraft  300 . In some embodiments, rechargeable component  302  may include a constant voltage charger, a constant current charger, a taper current charger, a pulsed current charger, a negative pulse charger, an IUI charger, a trickle charger, and a float charger. 
     In some embodiments, rechargeable component  302  may receive power from a power supply unit  304 . Power supply unit  304  may have a DC to DC converter to convert power into a variety of voltages for rechargeable component  302 . Power supply unit  304  may actively switch between multiple power sources. In one embodiment, power supply unit  304  may switch between power from a power storage unit  306  and power from a solar inverter  308 . In one embodiment, solar inverter  308  may be configured to absorb solar energy and transform the solar energy into electrical energy. In one embodiment, solar inverter  308  may transform DC to AC. In some embodiments, solar inverter  308  may have a capacity of at least 250 kwh. In other embodiments, solar inverter  308  may have a capacity higher than 250 kwh. In some embodiments, solar inverter  308  may include a solar panel, electrical grade papers, films, coated cloths, laminates, insulation tape, lead pads, and phase separators. 
     In some embodiments, power supply unit  304  may receive power from the power storage unit  306 . Power storage unit  306  may include one or more batteries, capacitors, inductors, or other electrical power storing components. In one embodiment, power supply unit  304  may include repurposed electric aircraft batteries. In some embodiments, power storage unit  306  may have a capacity of at least 500 kwh. In another embodiment, power storage  306  may have a capacity of over 500 kwh. In some embodiments, power storage unit  306  may have a connection to grid power component  310 . Grid power component  310  may be connected to an external electrical power grid. In some embodiments, grid power component  310  may be configured to slowly charge one or more batteries in power storage unit  306  in order to reduce strain on nearby electrical power grids. In one embodiment, grid power component  310  may have an AC grid current of at least 450 amps. In some embodiments, grid power component  310  may have an AC grid current of more or less than 450 amps. In one embodiment, grid power component  310  may have an AC voltage connection of 480 Vac. In other embodiments, grid power component  310  may have an AC voltage connection of above or below 480 Vac. In some embodiments, power supply storage unit  306  may provide power to the grid power component  310 . In this configuration, power storage unit  306  may provide power to a surrounding electrical power grid. 
     In some embodiments, and still referring to  FIG.  3   , system  300  may include a computing device  312 . Computing device  312  may include 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. Computing device  312  may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device  312  may 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. Computing device  312  may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device  312  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. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Computing device  312  may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device  312  may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device  312  may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device  312  may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of system  100  and/or computing device. 
     With continued reference to  FIG.  3   , computing device  312  may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device  312  may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device  312  may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. 
     Further referring to  FIG.  3   , computing device  312  may monitor a power grid of the power recharging station and actively switch between charging electric aircraft  300  and charging one or more batteries in power storage unit  306 . In one embodiment, computing device  312  may monitor the power grid of the recharging station as well as an external power grid. Computing device  312  may route power from power storage unit  306  to an external power grid to power said external power grid. In some embodiments, computing device  312  may be connected to electric aircraft  300  physically or wirelessly. In some embodiments, computing device  312  may be connected to rechargeable component  302  and power supply unit  304 . Computing device  312  may monitor the power grid of the recharging station and its various electrical components. Computing device  312  may be configured to perform a variety of functions and procedures. 
     In some embodiments, the computing device  312  may assist with electric aircraft  300  by helping to guide electric aircraft  300  to a landing pad. In one embodiment, computing device  312  may make and send a landing plan to electric aircraft  300 . In another embodiment, computing device  312  may receive landing data from electric aircraft  300  and instruct rechargeable component  310  and power supply unit  304  to power up in anticipation of charging electric aircraft  300 . In some embodiments, computing device  312  may receive a battery status from electric aircraft  300 , either or both while charging the electric aircraft  300  and while electric aircraft  300  is in the air. Computing device  312  may also receive and report health and damage status of electric aircraft  300 . In some embodiments, computing device  312  may send estimated charge times and health and status of rechargeable component  302  to electric aircraft  300 . In some embodiments, computing device  312  may also include a temperature sensor. Computing device  312  may use the temperature data gathered from the temperature sensor to track the heating and cooling of rechargeable component  302  and electric aircraft  300 . In one embodiment, computing device  312  may coordinate the cooling of electric aircraft  300  to prevent it from overheating in various scenarios, such as being charged. In some embodiments, computing device  312  may be configured to monitor and track the state of health of the batteries, which is discussed in further detail with regards to  FIG.  7    below. 
     In some embodiments, computing device  312  may send flight plans to electric aircraft  300 . In some embodiments, this may occur while electric aircraft  300  is connected and charging through rechargeable component  302 . In other embodiments, computing device  312  may send flight plans to electric aircraft  300  while it is airborne. In some embodiments, the flight plans may be real-time and updated based on, but not limited to, battery status of electric aircraft  300 , battery and health status of rechargeable component  302 , charge times, weather conditions, and travel times. In some embodiments, computing device  312  may send flight plans and other flight information to another recharging station. Computing device  312  may communicate between two or more recharging stations to create an efficient flight plan and charging plan for electric aircraft  300 . In some embodiments, computing device  312  may send software and firmware updates to electric aircraft  300 . Electric aircraft  300  may similarly request software and firmware updates from computing device  312 . Computing device  312  may also update the software and firmware of rechargeable component  302 . In some embodiments, the status of the firmware and software updates of electric aircraft  300  and rechargeable component  302  may be reported by computing device  312 . In some embodiments, computing device  312  may update the software and firmware of individual components of electric aircraft  300 . 
     In some embodiments, computing device  312  may transfer many forms of data to and from electric aircraft  300 , either wired or wirelessly. These forms of data may include, but are not limited to, flight plan updates, software updates, firmware updates, flight records, charge data, weather data, traffic data, or other data, as described in detail below in  FIG.  9   . 
       FIG.  4    illustrates an exemplary embodiment of a battery pack  400  that may be housed in the power storage unit to store power. Battery pack  400  may be a power storing device that is configured to store electrical energy in the form of a plurality of battery modules, which themselves may be comprised of a plurality of electrochemical cells. These cells may utilize electrochemical cells, galvanic cells, electrolytic cells, fuel cells, flow cells, and/or voltaic cells. In general, an electrochemical cell is a device capable of generating electrical energy from chemical reactions or using electrical energy to cause chemical reactions. Voltaic or galvanic cells are electrochemical cells that generate electric current from chemical reactions, while electrolytic cells generate chemical reactions via electrolysis. In general, the term ‘battery’ is used as a collection of cells connected in series or parallel to each other. A battery cell may, when used in conjunction with other cells, be electrically connected in series, in parallel or a combination of series and parallel. Series connection comprises wiring a first terminal of a first cell to a second terminal of a second cell and further configured to comprise a single conductive path for electricity to flow while maintaining the same current (measured in Amperes) through any component in the circuit. A battery cell may use the term ‘wired’, but one of ordinary skill in the art would appreciate that this term is synonymous with ‘electrically connected’, and that there are many ways to couple electrical elements like battery cells together. An example of a connector that does not comprise wires may be prefabricated terminals of a first gender that mate with a second terminal with a second gender. Battery cells may be wired in parallel. Parallel connection comprises wiring a first and second terminal of a first battery cell to a first and second terminal of a second battery cell and further configured to comprise more than one conductive path for electricity to flow while maintaining the same voltage (measured in Volts) across any component in the circuit. Battery cells may be wired in a series-parallel circuit which combines characteristics of the constituent circuit types to this combination circuit. Battery cells may be electrically connected in a virtually unlimited arrangement which may confer onto the system the electrical advantages associated with that arrangement such as high-voltage applications, high-current applications, or the like. In an exemplary embodiment, battery pack  400  may include at least 196 battery cells in series and at least 18 battery cells in parallel. This is, as someone of ordinary skill in the art would appreciate, only an example and battery pack  400  may be configured to have a near limitless arrangement of battery cell configurations. 
     With continued reference to  FIG.  4   , battery pack  400  may include a plurality of battery modules  404 . The battery modules may be wired together in series and in parallel. Battery pack  400  may include a center sheet  408  which may include a thin barrier. The barrier may include a fuse connecting battery modules on either side of center sheet  408 . The fuse may be disposed in or on center sheet  408  and configured to connect to an electric circuit comprising a first battery module and therefore battery unit and cells. In general, and for the purposes of this disclosure, a fuse is an electrical safety device that operate to provide overcurrent protection of an electrical circuit. As a sacrificial device, its essential component is metal wire or strip that melts when too much current flows through it, thereby interrupting energy flow. The fuse may comprise a thermal fuse, mechanical fuse, blade fuse, expulsion fuse, spark gap surge arrestor, varistor, or a combination thereof. 
     Battery pack  400  may also include a side wall  412  which may include a laminate of a plurality of layers configured to thermally insulate the plurality of battery modules  404  from external components of battery pack  400 . Side wall  412  layers may include materials which possess characteristics suitable for thermal insulation such as fiberglass, air, iron fibers, polystyrene foam, and thin plastic films. Side wall  412  may additionally or alternatively electrically insulate the plurality of battery modules  404  from external components of battery pack  400  and the layers of which may include polyvinyl chloride (PVC), glass, asbestos, rigid laminate, varnish, resin, paper, Teflon, rubber, and mechanical lamina. Center sheet  408  may be mechanically coupled to side wall  412 . Side wall  412  may include a feature for alignment and coupling to center sheet  408 . This feature may comprise a cutout, slots, holes, bosses, ridges, channels, and/or other undisclosed mechanical features, alone or in combination. 
     Battery pack  400  may also include an end panel  416  having a plurality of electrical connectors and further configured to fix battery pack  400  in alignment with at least a side wall  412 . End panel  416  may include a plurality of electrical connectors of a first gender configured to electrically and mechanically couple to electrical connectors of a second gender. End panel  416  may be configured to convey electrical energy from battery cells to at least a portion of an eVTOL aircraft. Electrical energy may be configured to power at least a portion of an eVTOL aircraft or comprise signals to notify aircraft computers, personnel, users, pilots, and any others of information regarding battery health, emergencies, and/or electrical characteristics. The plurality of electrical connectors may comprise blind mate connectors, plug and socket connectors, screw terminals, ring and spade connectors, blade connectors, and/or an undisclosed type alone or in combination. The electrical connectors of which end panel  416  comprises may be configured for power and communication purposes. 
     A first end of end panel  416  may be configured to mechanically couple to a first end of a first side wall  412  by a snap attachment mechanism, similar to end cap and side panel configuration utilized in the battery module. To reiterate, a protrusion disposed in or on end panel  416  may be captured, at least in part, by a receptacle disposed in or on side wall  412 . A second end of end panel  416  may be mechanically coupled to a second end of a second side wall  412  in a similar or the same mechanism. 
       FIG.  5    illustrates an exemplary embodiment of a thermal management system  500  for a battery pack  508  that may be used to charge an electric aircraft  504 . Thermal management system  500  may be configured to facilitate the flow of media to cool battery pack  508  of electric aircraft  504 . Thermal management system  500  may include battery pack  508 , a plurality of battery modules  512 A-N, at least a portion of a battery unit  516 A-N, a plurality of first circuits  520 A-N, a plurality of second circuits  524 A-N, thermal management apparatus  528 , media feeder  532 , temperature controlling component  536 , distribution component  540 , or any combination thereof. 
     Continuing to refer to  FIG.  5   , thermal management system  500  may be designed and configured to include battery pack  508  mechanically coupled to electric aircraft  504 . Battery pack  508  may be designed and configured to power electric aircraft  504  to meet the demands of a flight mission, wherein the flight mission includes a takeoff and landing. Battery pack  508  may be designed and configured to include a plurality of battery modules  512 A-N. As an exemplary embodiment,  FIG.  5    illustrates 3 battery modules  512 A-N housed within battery pack  508 ; however, a person of ordinary skill in the art would understand that any number of battery modules  512 A-N may be housed within battery pack  508 . Each battery module of the plurality of battery modules  512 A-N may include one or more battery units  516 A-N. Each battery module of the plurality of battery modules  512 A-N may be configured to house and/or encase at least a portion of each battery unit  516 A-N. Battery units  516 A-N may be configured to be contained within each battery module of the plurality of battery modules  512 A-N, wherein each battery unit  516 A-N is disposed between each first circuit  520 A-N and each second circuit  524 A-N. As an exemplary embodiment,  FIG.  5    illustrates one battery unit  516 A-N housed within each battery module of the plurality of battery modules  512 A-N; however, a person of ordinary skill in the art would understand that any number of battery units  516 A-N may be housed within battery module  512 A-N. In embodiments, each battery unit  516 A-N is configured to provide power to at least a portion of electric aircraft  504 . 
     Still referring to  FIG.  5   , each battery unit  516 A-N may include a plurality of battery cells, wherein the plurality of battery cells may be aligned in a first row and a second row. A “battery cell” as described herein, is a single anode and cathode separated by electrolyte, wherein the cell produces voltage and current. Each battery cell of the plurality of battery cells may have a shape, such as a cylinder, and may include a radius. Each battery cell of the plurality of battery cells may comprise an electrochemical reaction configured to produce electrical energy. For example, and without limitation, the electrical energy produced by one or more battery cells of the plurality of battery cells may be sufficient to power at least a portion of electric aircraft  504 , such as an eVTOL aircraft. Each battery cell of the plurality of battery cells may comprise a primary battery or a secondary battery. Each battery cell of the plurality of battery cells may include electrochemical cells, galvanic cells, electrolytic cells, fuel cells, flow cells, voltaic cells, and/or any combination thereof. The electrolyte of each battery cell may include any material, such as a liquid electrolyte or a paste electrolyte. For example, and without limitation, the electrolyte of each battery cell of the plurality of battery cells may include molten salt or ammonium chloride. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various cells that may be used as the plurality of battery cells consistently with this disclosure. 
     Continuing to refer to  FIG.  5   , each battery unit  516 A-N further includes a thermal conduit. In embodiments, the thermal conduit may include a first side and a second opposite opposing side with a thickness between the first and second surfaces. Further, the thermal conduit may include a first and second opposite, opposing ends. A “thermal conduit” as described herein, is a thermally conductive component separating each row of the battery cells within a respective battery unit  516 . The thermal conduit is configured to include a first end and a second end, wherein the second end is opposite the first end of the thermal conduit. The thermal conduit may be configured to be coupled to each battery unit of the plurality of battery units  516 A-N. According to embodiments, the thermal conduit may have a height that is equal to or less than the height of one or more battery cells. In embodiments, the thermal conduit may be composed of any suitable material. In an embodiment, the thermal conduit may be composed utilizing aluminum. For example, and without limitation, the thermal conduit may be composed utilizing a plurality of manufacturing processes, such as extrusion, casting, subtractive manufacturing processes, and the like. As a further non-limiting example, the thermal conduit may be composed utilizing injection molding. Injection molding may comprise injecting a liquid material into a mold and letting the liquid material solidify, taking the shape of the mold in a hardened form, the liquid material may include liquid crystal polymer, polypropylene, polycarbonate, acrylonitrile butadiene styrene, polyethylene, nylon, polystyrene, polyether ether ketone, and the like, and/or any combination thereof. The thermal conduit may be configured to cool the plurality of battery cells of the respective battery unit  516 , wherein the battery cells are cooled by allowing a media to flow though the thermal conduit. The “media”, as used in this disclosure, is any fluid and/or gas that may transfer the heat generated by each battery unit of the plurality of battery units  516 A-N out of battery pack  508  and electric aircraft  504 . In an embodiment, for example and without limitation, the media may include a fluid, such as water, heat-transfer oil, molten salt, and the like. As a further example and without limitation, the media may include a gas, such as air, steam, compressed air, and the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various fluids and/or gases that may be used as the media consistently with this disclosure. Further, persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various thermally conductive components that may be used as the thermal conduit consistently with this disclosure. 
     With continued reference to  FIG.  5   , each battery module of the plurality of battery modules  512 A-N may further include first circuit  520 A-N mechanically coupled to the at least a portion of a battery unit  516 A-N. The “first circuit”, as described in this disclosure, is a circuit substantially aligned with the first end of the thermal conduit of each battery unit of the plurality of battery units  516 A-N. First circuit  520 A-N may include any component configured to facilitate the flow of media to battery pack  508  by utilizing an electrical current. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various circuits that may be used as the first circuit consistently with this disclosure. Further, each battery module of the plurality of battery modules  512 A-N may include second circuit  524 A-N mechanically coupled to the at least portion of a battery unit  516 A-N. The “second circuit” as described in this disclosure, is a circuit substantially aligned with the second end of the thermal conduit of each battery unit of the plurality of battery units  516 A-N. Second circuit  516 A-N may include any component configured to facilitate the flow of media out of battery pack  508  by utilizing an electrical current. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various circuits that may be used as the second circuit consistently with this disclosure. 
     Continuing to refer to  FIG.  5   , assembly  500  may be further designed and configured to include thermal management apparatus  528 . Thermal management apparatus  528  may be in any location such that it may be coupled to battery pack  508  on electric aircraft  504  when the aircraft is not in flight, such as when the aircraft is grounded, taxiing, parked, and the like. For example, and without limitation, the location may include on the ground, on a platform, any raised structure, coupled to a recharging pad infrastructure, and the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various locations that may be used location of the ground cooling apparatus consistently with this disclosure. 
     With continued reference to  FIG.  5   , thermal management apparatus  528  may be configured to include media feeder  532 . “Media feeder” as used in this disclosure, is a component that mechanically couples to first circuit  520 A-N of plurality of battery modules  512 A-N, wherein mechanically coupling creates an open passage for the media to transport into first circuit  520 -N of plurality of battery modules  512 A-N. Media feeder  532  may be configured to transport the media to plurality of battery modules  512 A-N when mechanically coupled to the respective first circuit  520 A-N. In an embodiment, media feeder  532  may be mechanically coupled to first circuit  520 A-N when electric aircraft  504  is not in flight, such as when taxiing, recharging, parked, and the like. In an embodiment, media feeder  532  may be coupled to a recharging infrastructure, the natural ground, any platform, and/or any structure, wherein media feeder  532  may be accessible to mechanically coupled to plurality of battery modules  512 A-N of electric aircraft  104 . For example, and without limitation, media feeder  532  may include any tubing, piping, hose, and/or any other hollow component capable of facilitating the transport of the media from ground cooling apparatus  528  to plurality of battery modules  512 A-N. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various components that may be used as the media feeder consistently with this disclosure. 
     Still referring to  FIG.  5   , thermal management apparatus  528  may be further configured to include temperature controlling component  536 . “Temperature controlling component”, as used in this disclosure, is a component that is capable of raising and/or lowering the temperature of the media. For example, and without limitation, temperature controlling component  536  may include a heater, cooler, thermolater, a temperature control unit, chiller, and/or the like. Temperature controlling component  536  may be configured to adjust the media to a temperature that will efficiently cool battery pack  508  including plurality of battery cells  512 A-N of electric aircraft  504 . In an embodiment, temperature controlling component  536  may be mechanically coupled to media feeder  532 . Further, in an embodiment, once the media reaches a threshold temperature, the media will be transported to plurality of battery modules  512 A-N of electric aircraft  504  through media feeder  532 . Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various components that may be used as the temperature controlling component consistently with this disclosure. 
     Continuing to refer to  FIG.  5   , thermal management apparatus  528  may be further configured to include distribution component  540 . “Distribution component”, as used in this disclosure, is a component that intakes the media into thermal management apparatus  528 . For example, and without limitation, distribution component  540  may include a compressor, a fan, a pump, and/or the like. In an embodiment, distribution component  540  may be mechanically coupled to temperature controlling component  536 . In a further embodiment, distribution component  540  may be configured to supply the media to battery pack  508  including plurality of battery modules  512 A-N of electric aircraft  504 , wherein distribution component  540  is configured to transfer the media to temperature controlling component  536 . Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various components that may be used as the distribution component consistently with this disclosure. 
     Referring now to  FIG.  6   , an embodiment of battery management system  600  is presented. Battery management system  600  may be integrated in a battery pack configured for use in an electric aircraft. The battery management system  600  may be integrated in a portion of the battery pack or subassembly thereof. Battery management system  600  includes first battery management component  604  disposed on a first end of the battery pack. One of ordinary skill in the art will appreciate that there are various areas in and on a battery pack and/or subassemblies thereof that may include first battery management component  604 . First battery management component  604  may take any suitable form. In a non-limiting embodiment, first battery management component  604  may include a circuit board, such as a printed circuit board and/or integrated circuit board, a subassembly mechanically coupled to at least a portion of the battery pack, standalone components communicatively coupled together, or another undisclosed arrangement of components; for instance, and without limitation, a number of components of first battery management component  604  may be soldered or otherwise electrically connected to a circuit board. First battery management component may be disposed directly over, adjacent to, facing, and/or near a battery module and specifically at least a portion of a battery cell. First battery management component  604  includes first sensor suite  608 . First sensor suite  608  is configured to measure, detect, sense, and transmit first plurality of battery pack data  628  to data storage system  620 , which will be disclosed in further detail with reference to  FIG.  6   . 
     Referring again to  FIG.  6   , battery management system  600  includes second battery management component  612 . Second battery management component  612  is disposed in or on a second end of battery pack  634 . Second battery management component  612  includes second sensor suite  616 . Second sensor suite  616  may be consistent with the description of any sensor suite disclosed herein. Second sensor suite  616  is configured to measure second plurality of battery pack data  632 . Second plurality of battery pack data  632  may be consistent with the description of any battery pack data disclosed herein. Second plurality of battery pack data  632  may additionally or alternatively include data not measured or recorded in another section of battery management system  600 . Second plurality of battery pack data  632  may be communicated to additional or alternate systems to which it is communicatively coupled. Second sensor suite  616  includes a humidity sensor consistent with any humidity sensor disclosed herein. 
     With continued reference to  FIG.  6   , first battery management component  604  disposed in or on battery pack  634  may be physically isolated from second battery management component  612  also disposed on or in battery pack  634 . “Physical isolation”, for the purposes of this disclosure, refer to a first system&#39;s components, communicative coupling, and any other constituent parts, whether software or hardware, are separated from a second system&#39;s components, communicative coupling, and any other constituent parts, whether software or hardware, respectively. First battery management component  604  and second battery management component  608  may perform the same or different functions in battery management system  600 . In a non-limiting embodiment, the first and second battery management components perform the same, and therefore redundant functions. If, for example, first battery management component  604  malfunctions, in whole or in part, second battery management component  608  may still be operating properly and therefore battery management system  600  may still operate and function properly for electric aircraft in which it is installed. Additionally, or alternatively, second battery management component  608  may power on while first battery management component  604  is malfunctioning. One of ordinary skill in the art would understand that the terms “first” and “second” do not refer to either “battery management components” as primary or secondary. In non-limiting embodiments, first battery management component  604  and second battery management component  608  may be powered on and operate through the same ground operations of an electric aircraft and through the same flight envelope of an electric aircraft. This does not preclude one battery management component, first battery management component  604 , from taking over for second battery management component  608  if it were to malfunction. In non-limiting embodiments, the first and second battery management components, due to their physical isolation, may be configured to withstand malfunctions or failures in the other system and survive and operate. Provisions may be made to shield first battery management component  604  from second battery management component  608  other than physical location such as structures and circuit fuses. In non-limiting embodiments, first battery management component  604 , second battery management component  608 , or subcomponents thereof may be disposed on an internal component or set of components within battery pack  634 . 
     Referring again to  FIG.  6   , first battery management component  604  is electrically isolated from second battery management component  608 . “Electrical isolation”, for the purposes of this disclosure, refer to a first system&#39;s separation of components carrying electrical signals or electrical energy from a second system&#39;s components. First battery management component  604  may suffer an electrical catastrophe, rendering it inoperable, and due to electrical isolation, second battery management component  608  may still continue to operate and function normally, managing the battery pack of an electric aircraft. Shielding such as structural components, material selection, a combination thereof, or another undisclosed method of electrical isolation and insulation may be used, in non-limiting embodiments. For example, a rubber or other electrically insulating material component may be disposed between the electrical components of the first and second battery management components preventing electrical energy to be conducted through it, isolating the first and second battery management components from each other. 
     With continued reference to  FIG.  6   , battery management system  600  includes data storage system  620 . Data storage system  620  is configured to store first plurality of battery pack data  628  and second plurality of battery pack data  632 . Data storage system  620  may include a database. Data storage system  620  may include a solid-state memory or tape hard drive. Data storage system  620  is communicatively coupled to first battery management component  604  and second battery management component  612  and configured to receive electrical signals related to physical or electrical phenomenon measured and store those electrical signals as first battery pack data  628  and second battery pack data  632 , respectively. Alternatively, data storage system  620  may include more than one discrete data storage systems that are physically and electrically isolated from each other. In this non-limiting embodiment, each of first battery management component  604  and second battery management component  612  may store first battery pack data  628  and second battery pack data  632  separately. One of ordinary skill in the art would understand the virtually limitless arrangements of data stores with which battery management system  600  could employ to store the first and second plurality of battery pack data. 
     Referring again to  FIG.  6   , data storage system  620  may store first plurality of battery pack data  628  and second plurality of battery pack data  632 . First plurality of battery pack data  628  and second plurality of battery pack data  632  may include total flight hours battery pack  634  and or electric aircraft have been operating. The first and second plurality of battery pack data may include total energy flowed through battery pack  634 . Data storage system  620  may be communicatively coupled to sensors that detect, measure and store energy in a plurality of measurements which may include current, voltage, resistance, impedance, coulombs, watts, temperature, or a combination thereof. Additionally or alternatively, data storage system  620  may be communicatively coupled to a sensor suite consistent with this disclosure to measure physical and/or electrical characteristics. Data storage system  620  may be configured to store first battery pack data  628  and second battery pack data  632  wherein at least a portion of the data includes battery pack maintenance history. Battery pack maintenance history may include mechanical failures and technician resolutions thereof, electrical failures and technician resolutions thereof. Additionally, battery pack maintenance history may include component failures such that the overall system still functions. Data storage system  620  may store the first and second battery pack data that includes an upper voltage threshold and lower voltage threshold consistent with this disclosure. First battery pack data  628  and second battery pack data  632  may include a moisture level threshold. The moisture level threshold may include an absolute, relative, and/or specific moisture level threshold. 
     Referring now to  FIG.  7   , an exemplary embodiment of a system  700  for state determination of a battery module configured for use in an electric vehicle is illustrated. System  700  may communicate with a battery management system as described above. System  700  may send and receive data to the recharging station. In some embodiments, system  700  may send and receive data from a battery management system to optimize recharging of an electric aircraft via the recharging station. In some embodiments, system  700  may include a computing device. The computing device may include any computing device as described in this disclosure. The computing device could include, be included in, and/or share any component with any other computing device and/or system described in this disclosure. System  700  and any one or more computing devices may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, system  700  may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. System  700  may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. 
     With continued reference to  FIG.  7   , system  700  for state determination of a battery module configured for use in an electric vehicle is presented in block diagram form. System  700  may include a battery module  704 . Battery module  704  may include a battery cell  708 . System  700  may include sensor  712 . Sensor  712  may include proximity sensor  716 . System  700  may include processor  720 . Processor  720  may include status datum  724 . Status datum  724  may be configured to communicate with charge datum  728  and health datum  732 . Processor  720  may be configured to output data on display  736 . Additional disclosure related to systems for state determination of a battery module may be found in co-owned U.S. patent application entitled “SYSTEM AND METHOD FOR STATE DETERMINATION OF A BATTERY MODULE CONFIGURED FOR USED IN AN ELECTRIC VEHICLE”, having U.S. patent application Ser. No. 17/241,396, the entirety of which is incorporated herein by reference 
     Referring now to  FIG.  8   , an embodiment of an electric aircraft  800  is presented. Electric aircraft  800  may be configured to be positioned on the recharging station. In some embodiments, electric aircraft  800  may be configured to receive power and be charged by the recharging station. Electric aircraft  800  may include a vertical takeoff and landing aircraft (eVTOL). As used herein, a vertical take-off and landing (eVTOL) aircraft is one that may hover, take off, and land vertically. An eVTOL, as used herein, is an electrically powered aircraft typically using an energy source, of a plurality of energy sources to power the aircraft. In order to optimize the power and energy necessary to propel the aircraft. 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, as described herein, is where the aircraft generated lift and propulsion by way of one or more powered rotors coupled with an engine, such as a “quad copter,” multi-rotor helicopter, or other vehicle that maintains its lift primarily using downward thrusting propulsors. Fixed-wing flight, as described herein, is where the aircraft is capable of flight using wings and/or foils that generate life caused by the aircraft&#39;s forward airspeed and the shape of the wings and/or foils, such as airplane-style flight. 
     With continued reference to  FIG.  8   , a number of aerodynamic forces may act upon the electric aircraft  800  during flight. Forces acting on an electric aircraft  800  during flight may include, without limitation, thrust, the forward force produced by the rotating element of the electric aircraft  800  and acts parallel to the longitudinal axis. Another force acting upon electric aircraft  800  may be, without limitation, drag, which may be defined as a rearward retarding force which is caused by disruption of airflow by any protruding surface of the electric aircraft  800  such as, without limitation, the wing, rotor, and fuselage. Drag may oppose thrust and acts rearward parallel to the relative wind. A further force acting upon electric aircraft  800  may include, without limitation, weight, which may include a combined load of the electric aircraft  800  itself, crew, baggage, and/or fuel. Weight may pull electric aircraft  800  downward due to the force of gravity. An additional force acting on electric aircraft  800  may include, without limitation, lift, which may act to oppose the downward force of weight and may be produced by the dynamic effect of air acting on the airfoil and/or downward thrust from the propulsor of the electric aircraft. Lift generated by the airfoil may depend on speed of airflow, density of air, total area of an airfoil and/or segment thereof, and/or an angle of attack between air and the airfoil. For example, and without limitation, electric aircraft  800  are designed to be as lightweight as possible. Reducing the weight of the aircraft and designing to reduce the number of components is essential to optimize the weight. To save energy, it may be useful to reduce weight of components of an electric aircraft  800 , including without limitation propulsors and/or propulsion assemblies. In an embodiment, the motor may eliminate need for many external structural features that otherwise might be needed to join one component to another component. The motor may also increase energy efficiency by enabling a lower physical propulsor profile, reducing drag and/or wind resistance. This may also increase durability by lessening the extent to which drag and/or wind resistance add to forces acting on electric aircraft  800  and/or propulsors. 
     Referring still to  FIG.  8   , Aircraft may include at least a vertical propulsor  804  and at least a forward propulsor  808 . A forward propulsor is a propulsor that propels the aircraft in a forward direction. Forward in this context is not an indication of the propulsor position on the aircraft; one or more propulsors mounted on the front, on the wings, at the rear, etc. A vertical propulsor is a propulsor that propels the aircraft in an upward direction; one of more vertical propulsors may be mounted on the front, on the wings, at the rear, and/or any suitable location. A propulsor, as used herein, is a component or device used to propel a craft by exerting force on a fluid medium, which may include a gaseous medium such as air or a liquid medium such as water. At least a vertical propulsor  804  is a propulsor that generates a substantially downward thrust, tending to propel an aircraft in a vertical direction providing thrust for maneuvers such as without limitation, vertical take-off, vertical landing, hovering, and/or rotor-based flight such as “quadcopter” or similar styles of flight. 
     With continued reference to  FIG.  8   , at least a forward propulsor  808  as used in this disclosure is a propulsor positioned for propelling an aircraft in a “forward” direction; at least a forward propulsor may include one or more propulsors mounted on the front, on the wings, at the rear, or a combination of any such positions. At least a forward propulsor may propel an aircraft forward for fixed-wing and/or “airplane”-style flight, takeoff, and/or landing, and/or may propel the aircraft forward or backward on the ground. At least a vertical propulsor  804  and at least a forward propulsor  808  includes a thrust element. At least a thrust element may include any device or component that converts the mechanical energy of a motor, for instance in the form of rotational motion of a shaft, into thrust in a fluid medium. At least a thrust element may include, without limitation, a device using moving or rotating foils, including without limitation one or more rotors, an airscrew or propeller, a set of airscrews or propellers such as contrarotating propellers, a moving or flapping wing, or the like. At least a thrust element may include without limitation a marine propeller or screw, an impeller, a turbine, a pump-jet, a paddle or paddle-based device, or the like. As another non-limiting example, at least a thrust element may include an eight-bladed pusher propeller, such as an eight-bladed propeller mounted behind the engine to ensure the drive shaft is in compression. Propulsors may include at least a motor mechanically coupled to the at least a first propulsor as a source of thrust. A motor may include without limitation, any electric motor, where an electric motor is a device that converts electrical energy into mechanical energy, for instance by causing a shaft to rotate. At least a motor may be driven by direct current (DC) electric power; for instance, at least a first motor may include a brushed DC at least a first motor, or the like. At least a first motor may be driven by electric power having varying or reversing voltage levels, such as alternating current (AC) power as produced by an alternating current generator and/or inverter, or otherwise varying power, such as produced by a switching power source. At least a first motor may include, without limitation, brushless DC electric motors, permanent magnet synchronous at least a first motor, switched reluctance motors, or induction motors. In addition to inverter and/or a switching power source, a circuit driving at least a first motor may include electronic speed controllers or other components for regulating motor speed, rotation direction, and/or dynamic braking. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices that may be used as at least a thrust element. 
     With continued reference to  FIG.  8   , during flight, a number of forces may act upon the electric aircraft. Forces acting on an aircraft  800  during flight may include thrust, the forward force produced by the rotating element of the aircraft  800  and acts parallel to the longitudinal axis. Drag may be defined as a rearward retarding force which is caused by disruption of airflow by any protruding surface of the aircraft  800  such as, without limitation, the wing, rotor, and fuselage. Drag may oppose thrust and acts rearward parallel to the relative wind. Another force acting on aircraft  800  may include weight, which may include a combined load of the aircraft  800  itself, crew, baggage and fuel. Weight may pull aircraft  800  downward due to the force of gravity. An additional force acting on aircraft  800  may include lift, which may act to oppose the downward force of weight and may be produced by the dynamic effect of air acting on the airfoil and/or downward thrust from at least a propulsor. Lift generated by the airfoil may depends on speed of airflow, density of air, total area of an airfoil and/or segment thereof, and/or an angle of attack between air and the airfoil. 
     Now referring to  FIG.  9   , an exemplary embodiment  900  of a flight controller  904  is illustrated. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller  904  may 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 controller  904  may 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 controller  904  may 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 to  FIG.  9   , flight controller  904  may include a signal transformation component  908 . 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 component  908  may 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 component  908  may 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 component  908  may 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 component  908  may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component  908  may 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 to  FIG.  9   , signal transformation component  908  may be configured to optimize an intermediate representation  912 . As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component  908  may 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 component  908  may optimize intermediate representation  912  as 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 component  908  may 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 component  908  may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller  904 . 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 component  908  may 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 to  FIG.  9   , flight controller  904  may include a reconfigurable hardware platform  916 . 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 platform  916  may 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 to  FIG.  9   , reconfigurable hardware platform  916  may include a logic component  920 . 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 component  920  may 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 component  920  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component  920  may 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 component  920  may 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 component  920  may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation  912 . Logic component  920  may 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 controller  904 . Logic component  920  may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component  920  may be configured to execute the instruction on intermediate representation  912  and/or output language. For example, and without limitation, logic component  920  may be configured to execute an addition operation on intermediate representation  912  and/or output language. 
     In an embodiment, and without limitation, logic component  920  may be configured to calculate a flight element  924 . 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 element  924  may 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 element  924  may 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 element  924  may denote that aircraft is following a flight path accurately and/or sufficiently. 
     Still referring to  FIG.  9   , flight controller  904  may include a chipset component  928 . As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component  928  may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component  920  to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component  928  may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component  920  to 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 component  928  may manage data flow between logic component  920 , memory cache, and a flight component  932 . As used in this disclosure a “flight component” is a portion of an aircraft that may be moved or adjusted to affect one or more flight elements. For example, flight component  932  may include a component used to affect the aircrafts&#39; roll and pitch which may comprise one or more ailerons. As a further example, flight component  932  may include a rudder to control yaw of an aircraft. In an embodiment, chipset component  928  may be configured to communicate with a plurality of flight components as a function of flight element  924 . For example, and without limitation, chipset component  928  may 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 to  FIG.  9   , flight controller  904  may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller  904  that 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 element  924 . 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 controller  904  will 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 controller  904  will 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 to  FIG.  9   , flight controller  904  may 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 element  924  and a pilot signal  936  as 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 signal  936  may 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 signal  936  may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal  936  may 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 signal  936  may include an explicit signal directing flight controller  904  to 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 signal  936  may include an implicit signal, wherein flight controller  904  detects 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 signal  936  may 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 signal  936  may include one or more local and/or global signals. For example, and without limitation, pilot signal  936  may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal  936  may 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 signal  936  may 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 to  FIG.  9   , autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller  904  and/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 controller  904 . 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 to  FIG.  9   , 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 controller  904  may 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 to  FIG.  9   , flight controller  904  may 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 controller  904 . Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller  904  that 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 controller  904  as a software update, firmware update, or corrected habit 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 to  FIG.  9   , flight controller  904  may 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 may employ a wired and/or a wireless mode of communication. 
     In an embodiment, and still referring to  FIG.  9   , flight controller  904  may 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 controller  904  may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller  904  may 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 controller  904  may 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, Mass., 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&#39;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 to  FIG.  9   , 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 component  932 . 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 to  FIG.  9   , 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 controller  904 . 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 representation  912  and/or output language from logic component  920 , 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 to  FIG.  9   , 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 to  FIG.  9   , 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 to  FIG.  9   , flight controller  904  may 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 controller  904  may 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 to  FIG.  9   , a node may include, without limitation a plurality of inputs x i  that 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 w i  that are multiplied by respective inputs x i . 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 w i  applied to an input x i  may 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 or more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights w i  may 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 w i  that are derived using machine-learning processes as described in this disclosure. 
     Still referring to  FIG.  9   , flight controller may include a sub-controller  940 . 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 controller  904  may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller  940  may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller  940  may include any component of any flight controller as described above. Sub-controller  940  may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller  940  may 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-controller  940  may 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 to  FIG.  9   , flight controller may include a co-controller  944 . As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller  904  as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller  944  may include one or more controllers and/or components that are similar to flight controller  904 . As a further non-limiting example, co-controller  944  may include any controller and/or component that joins flight controller  904  to distributer flight controller. As a further non-limiting example, co-controller  944  may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller  904  to distributed flight control system. Co-controller  944  may include any component of any flight controller as described above. Co-controller  944  may be implemented in any manner suitable for implementation of a flight controller as described above. 
     In an embodiment, and with continued reference to  FIG.  9   , flight controller  904  may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, flight controller  904  may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Flight controller may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. 
     In some embodiments, and with continued reference to  FIG.  9   , any data, software, and/or firmware that may be usable/storable by flight controller  905  may be exchanged with the recharging station. In some embodiments, the data exchanged may include flight plans, flight records, current navigational status, and/or commands from the recharging station. In some embodiments, the commands from the recharging station may include commands to alter, steer, or navigate an electric aircraft. In other embodiments, the data exchanged between flight controller  905  and the recharging station may include a battery state of an electric aircraft. In some embodiments, the battery state may be tracked by any battery management system as described above. 
     Referring now to  FIG.  10   , an exemplary embodiment of a machine-learning module  1000  that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data  1004  to generate an algorithm that will be performed by a computing device/module to produce outputs  1008  given data provided as inputs  1012 ; 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. 
     Still referring to  FIG.  10   , “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data  1004  may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data  1004  may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data  1004  according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data  1004  may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data  1004  may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data  1004  may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data  1004  may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. 
     Alternatively or additionally, and continuing to refer to  FIG.  10   , training data  1004  may include one or more elements that are not categorized; that is, training data  1004  may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data  1004  according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person&#39;s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data  1004  to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data  1004  used by machine-learning module  1000  may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example flight elements and/or pilot signals may be inputs, wherein an output may be an autonomous function. 
     Further referring to  FIG.  10   , training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier  1016 . Training data classifier  1016  may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module  1000  may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data  1004 . Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher&#39;s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier  1016  may classify elements of training data to sub-categories of flight elements such as torques, forces, thrusts, directions, and the like thereof. 
     Still referring to  FIG.  10   , machine-learning module  1000  may be configured to perform a lazy-learning process  1020  and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data  1004 . Heuristic may include selecting some number of highest-ranking associations and/or training data  1004  elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below. 
     Alternatively or additionally, and with continued reference to  FIG.  10   , machine-learning processes as described in this disclosure may be used to generate machine-learning models  1024 . A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model  1024  once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model  1024  may be generated by creating an artificial neural network, such as a convolutional neural network comprising 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 data  1004  set 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 to  FIG.  10   , machine-learning algorithms may include at least a supervised machine-learning process  1028 . At least a supervised machine-learning process  1028 , as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include flight elements and/or pilot signals as described above as inputs, autonomous functions as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data  1004 . Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process  1028  that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above. 
     Further referring to  FIG.  10   , machine learning processes may include at least an unsupervised machine-learning processes  1032 . An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. 
     Still referring to  FIG.  10   , machine-learning module  1000  may be designed and configured to create a machine-learning model  1024  using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure. 
     Continuing to refer to  FIG.  10   , machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. 
       FIG.  11    illustrates a flowchart  1100  for a method of recharging an electric aircraft. At step  1102 , an elevated landing pad coupled to a rechargeable component is provided. In one embodiment, the elevated landing pad may include a helideck or helipad. The elevated landing pad may include an integrated deicing system. In one embodiment, the integrated deicing system may clear obstruction from snow, ice, sleet, hail, or other forms of precipitation. The elevated landing pad may also include an integrated lighting system. The integrated lighting system may provide night vision goggle compatibility. In some embodiments, providing an elevated landing pad may include the rapid construction of modular housing units to provide an elevated support for the elevated landing pad. In some embodiments, the elevated landing pad may be provided in densely populated cities. The elevated landing pad may be constructed on top of a pre-existing building to clear the landing zone of any obstruction. In other embodiments, the elevated landing pad may be constructed in a rural area isolated from cities and buildings. In some embodiments, the elevated landing pad may be provided in a location along a flight path of an electric vehicle. 
     At step  1104 , an electric aircraft placed on the elevated landing pad is connected to the rechargeable component. In one embodiment, the connection between the rechargeable component and the electric aircraft may be wired. In another embodiment, this connection may be wireless. In some embodiments, the connection between the electric aircraft and the rechargeable component may be automated. 
     At step  1106 , the electric aircraft is charged with the power delivered by the rechargeable component. The electric aircraft may be rapidly charged to full capacity as soon as possible. In other embodiments, the electric aircraft may have a scheduled charge that adaptably increases or decreases the rate at which the electric aircraft is charged. In one embodiment, the electric aircraft may be charged at a slow and steady rate overnight. In one embodiment, the electric aircraft may trickle charge so as to maintain the health of the electric aircraft&#39;s battery. 
     Now referring to  FIG.  12   , an exemplary embodiment of a cable reel module  1200 . Cable reel module is included in the recharging component  302 . Cable reel module may facilitate the power transfer from the power delivery unit to the electric aircraft through a charging connector  1212 , discussed in  FIG.  13   . Power delivery unit may be configured to deliver power stored from a power storage unit  306  or a solar inverter  308 . Power storage unit  306  and solar inverter  308  may supply power to a power supply unit  304  wherein the power supply unit  304  may supply power to the aircraft  300  through the charging connector  1212  connected to a cable reel module  1200 . The cable reel module  1200  may include a reel  1220 . For the purposes of this disclosure, “a cable reel module” is the portion of a charging system containing a reel, that houses a charging cable  1208  when charging cable  1208  is stowed. For the purposes of this disclosure, a “reel” is a rotary device around which an object may be wrapped. Reel  1220  is rotatably mounted to cable reel module  1200 . For the purposes of this disclosure, “rotatably mounted” means mounted such that the mounted object may rotate with respect to the object that the mounted object is mounted on. Additionally, when charging cable  1208  is in a stowed configuration, the charging cable  1208  is wound around reel  1220 . As a non-limiting example, charging cable  1208  is in the stowed configuration in  FIG.  12   . In the stowed configuration, charging cable  1208  need not be completely wound around reel  1220 . As a non-limiting example, a portion of charging cable  1208  may hang free from reel  1220  even when charging cable  1208  is in the stowed configuration. 
     With continued reference to  FIG.  12   , cable reel module  1200  includes a rotation mechanism  1224 . A “rotation mechanism,” for the purposes of this disclosure is a mechanism that is configured to cause another object to undergo rotary motion. As a non-limiting example, rotation mechanism may include a rotary actuator. As a non-limiting example, rotation mechanism  1224  may include an electric motor. As another non-limiting example, rotation mechanism  1224  may include a servomotor. As yet another non-limiting example, rotation mechanism  1224  may include a stepper motor. In some embodiments, rotation mechanism  1224  may include a compliant element. For the purposes of this disclosure, a “compliant element” is an element that creates force through elastic deformation. As a non-limiting example, rotation mechanism  1224  may include a torsional spring, wherein the torsional spring may elastically deform when reel  1220  is rotated in, for example, the forward direction; this would cause the torsional spring to exert torque on reel  1220 , causing reel  1220  to rotate in a reverse direction when it has been released. Rotation mechanism  1224  is configured to rotate reel  1220  in a forward direction and a reverse direction. Forward direction and reverse direction are opposite directions of rotation. As a non-limiting example, the forward direction may be clockwise, whereas the reverse direction may be counterclockwise, or vice versa. As a non-limiting example, rotating in the forward direction may cause charging cable  1208  to extend, whereas rotating in the reverse direction may cause charging cable  1208  to stow, or vice versa. In some embodiments, rotation mechanism  1224  may continually rotate reel  1220  when rotation mechanism  1224  is enabled. In some embodiments, rotation mechanism  1224  may be configured to rotate reel  1220  by a specific number of degrees. In some embodiments, rotation mechanism  1224  may be configured to output a specific torque to reel  1220 . As a non-limiting example, this may be the case, wherein rotation mechanism  1224  is a torque motor. Rotation mechanism  1224  may be electrically connected to energy source  12204 . Energy source  12204  may be connected to power supply unit  304  such that the energy source  12204  may draw energy from the power supply unit  304 . Rotation mechanism  1224  may be activated by a toggle  1232  located on the cable reel module  1200 . A toggle may be a button that a user may press to activate the rotation of the rotation mechanism  1224 . In some embodiments, the reel may be configured to unspool the charging cable  1208  in response to tension on the charging cable  1208 . For example, the charging cable  1208  may unspool when a user pulls on the charging cable  1208 . 
     With continued reference to  FIG.  12   , cable reel module  1200  may include an outer case  1228 . Outer case  1228  may enclose reel  1220  and rotation mechanism  1224 . In some embodiments, outer case  1228  may enclose charging cable  1208  and possibly charging connector  1212  when the charging cable  1208  is in its stowed configuration. 
     With continued reference to  FIG.  12   , cable reel module  1200  may include a slip ring  1216 . Slip ring  1216  may be attached to the charging cable  1208  and the charging connector  1212 , both of which are discussed in further detail below. Slip ring  1216  may be positioned in between the charging cable  1208  and the charging connector  1212  such that the charging connector  1212  may rotate freely relative to the charging cable  1208 . As used in this disclosure, a “slip ring” is an electromechanical device that carries a current from a stationary wire into a rotating device. As used in this disclosure, a “current” is any movement of particles within a substance. For example, a current may be a flow of electrons or a flow of a liquid through a pipe. In an embodiment, slip ring  1216  may allow the flow of a coolant and a flow of electrons from the free moving charging connector  1212  to the stationary charging cable  1208 . Slip ring  1216  may allow a user to position the charging connector  1212  in any position to charge the electric aircraft  300 . In another embodiment, slip ring  1216  may also hold wires that allow for low-power communication signals between the charging connector  1212  and the charging cable  1208 . In another embodiment, slip ring  1216  may hold a low-voltage conductor configured to potential no greater than 100 V. Low-voltage conductor may be used to power auxiliary equipment while aircraft is on the ground. 
     With continued reference to  FIG.  12   , cable reel module  1200  may include a charging cable  1208 . A “charging cable,” for the purposes of this disclosure is a conductor or conductors adapted to carry power for the purpose of charging an electronic device. Charging cable  1208  may also include an embedded coolant tube  1304 , shown in  FIG.  13   . In an embodiment, cable reel module may run a coolant through the coolant tube in the charging cable  1208  to charge the electric aircraft more quickly. Coolant tube may facilitate the flow of coolant through a coolant flow path. Coolant may assist in rapid charging by cooling down the electrical components within the aircraft  300  and the charging cable  1208 . Charging cable  1208  may be configured to carry electricity. Charging cable  1208  is electrically connected to the power supply unit  304 . “Electrically connected,” for the purposes of this disclosure, means a connection such that electricity can be transferred over the connection. In some embodiments, charging cable  1208  may carry AC and/or DC power to a charging connector  1212 . The charging cable may include a coating, wherein the coating surrounds the conductor or conductors of charging cable  1208 . One of ordinary skill in the art, after having reviewed the entirety of this disclosure, would appreciate that a variety of coatings are suitable for use in charging cable  1208 . As a non-limiting example, the coating of charging cable  1208  may comprise rubber. As another non-limiting example, the coating of charging cable  1208  may comprise nylon. Charging cable  1208  may be a variety of lengths depending on the length required by the specific implementation. As a non-limiting example, charging cable  1208  may be 10 feet. As another non-limiting example, charging cable  1208  may be 25 feet. As yet another non-limiting example, charging cable  1208  may be 50 feet. 
     Now referring to  FIG.  13   , an exemplary embodiment of a charging connector  1212 . Charging cable  1208  may be electrically connected to charging connector  1212 . Charging connector  1212  may be disposed at one end of charging cable  1208 . Reel toggle  1232  and reel locking toggle  1236  may be disposed on the surface of charging connector  1212 . In some embodiments, charging connector may have a handle portion on which reel toggle  1232  and reel locking toggle may be disposed. In some embodiments, reel toggle  1232  and cable reel toggle  1232  may be disposed on charging connector  1212  such that a user that is holding charging connector is able to easily reach and use reel toggle  1232  and cable reel toggle  1232 . Charging connector  1212  may be configured to couple with a corresponding charging port on an electric aircraft. For the purposes of this disclosure, a “charging connector” is a device adapted to electrically connect a device to be charged with an energy source. For the purposes of this disclosure, a “charging port” is a section on a device to be charged, arranged to receive a charging connector. charging connector  1212  may include a variety of pins adapted to mate with a charging port disposed on an electric aircraft. The variety of pins included on charging connector  1212  may include, as non-limiting examples, a set of pins chosen from an alternating current (AC) pin, a direct current (DC) pin, a ground pin, a communication pin, a sensor pin, a proximity pin, and the like. In some embodiments, charging connector  1212  may include more than one of one of the types of pins mentioned above. 
     With continued reference to  FIG.  13   , for the purposes of this disclosure, a “pin” may be any type of electrical connector. An electrical connector is a device used to join electrical conductors to create a circuit. As a non-limiting example, in some embodiments, any pin of charging connector  1212  may be the male component of a pin and socket connector. In other embodiments, any pin of charging connector  1212  may be the female component of a pin and socket connector. As a further example of an embodiment, a pin may have a keying component. A keying component is a part of an electrical connector that prevents the electrical connector components from mating in an incorrect orientation. As a non-limiting example, this can be accomplished by making the male and female components of an electrical connector asymmetrical. Additionally, in some embodiments, a pin, or multiple pins, of charging connector  1212  may include a locking mechanism. For instance, as a non-limiting example, any pin of charging connector  1212  may include a locking mechanism to lock the pins in place. The pin or pins of charging connector  1212  may each be any type of the various types of electrical connectors disclosed above, or they could all be the same type of electrical connector. One of ordinary skill in the art, after reviewing the entirety of this disclosure, would understand that a wide variety of electrical connectors may be suitable for this application. 
     Additional details on electric aircraft charging with a cable reel can be found in non-provisional application Ser. No. ______ filed on ______ and entitled “A SYSTEM FOR AN ELECTRIC AIRCRAFT CHARGING WITH A CABLE REEL”, the entirety of which is incorporated herein by reference. 
     Referring now to  FIG.  14   , an exemplary embodiment of a system  1400  for monitoring and transferring data to and from an electric aircraft is illustrated. System  1400  includes a computing device  1402 . The computing device  1402  may include 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. The computing device  1402  may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. The computing device  1402  may 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. The computing device  1402  may interface or communicate with one or more additional devices as described below in further detail via a network interface device  1406 . Network interface device  1406  may be utilized for connecting the computing device  1402  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. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. The computing device may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. The computing device  1402  may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. The computing device  1402  may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. the computing device may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of system  1400  and/or computing device  1402 . 
     With continued reference to  FIG.  14   , the computing device  1402  may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, the computing device  1402  may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. The computing device  1402  may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. 
     The computing device  1402  may include a display screen  1404 . The display screen  1404  may be of any width, height, thickness, and brightness. In some embodiments, the display screen  1404  may be an LED or OLED screen. The computing device  1402  may be configured to have a camera  1408 . Camera  1408  may receive optical data and report it to the computing device  1402 , thereby providing facial recognition and security. In some embodiments, the computing device  1402  may be connected to a database  1410 . 
     It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding may readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module. 
     Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission. 
     Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein. 
     Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk. 
       FIG.  15    shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system  1500  within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system  1500  includes a processor  1504  and a memory  1508  that communicate with each other, and with other components, via a bus  1512 . Bus  1512  may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. 
     Processor  1504  may include any suitable processor, such as without limitation a processor 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; processor  1504  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor  1504  may 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). 
     Memory  1508  may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system  1516  (BIOS), including basic routines that help to transfer information between elements within computer system  1500 , such as during start-up, may be stored in memory  1508 . Memory  1508  may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)  1520  embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory  1508  may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. 
     Computer system  1500  may also include a storage device  1524 . Examples of a storage device (e.g., storage device  1524 ) 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 device  1524  may be connected to bus  1512  by 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 1594 (FIREWIRE), and any combinations thereof. In one example, storage device  1524  (or one or more components thereof) may be removably interfaced with computer system  1500  (e.g., via an external port connector (not shown)). Particularly, storage device  1524  and an associated machine-readable medium  1528  may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system  1500 . In one example, software  1520  may reside, completely or partially, within machine-readable medium  1528 . In another example, software  1520  may reside, completely or partially, within processor  1504 . 
     Computer system  1500  may also include an input device  1532 . In one example, a user of computer system  1500  may enter commands and/or other information into computer system  1500  via input device  1532 . Examples of an input device  1532  include, 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 device  1532  may be interfaced to bus  1512  via 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 bus  1512 , and any combinations thereof. Input device  1532  may include a touch screen interface that may be a part of or separate from display  1536 , discussed further below. Input device  1532  may 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 system  1500  via storage device  1524  (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device  1540 . A network interface device, such as network interface device  1540 , may be utilized for connecting computer system  1500  to one or more of a variety of networks, such as network  1544 , and one or more remote devices  1548  connected 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 network  1544 , may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software  1520 , etc.) may be communicated to and/or from computer system  1500  via network interface device  1540 . 
     Computer system  1500  may further include a video display adapter  1552  for communicating a displayable image to a display device, such as display device  1536 . Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter  1552  and display device  1536  may be utilized in combination with processor  1504  to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system  1500  may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus  1512  via a peripheral interface  1556 . Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof. 
     The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions may be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. 
     Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.